def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_units_lines = parameters.charts_units_lines
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for lines
lines_file = files_root + 'stats_units_lines_onice.csv'
# create dataframe objects that read in info from the .csv files
lines_df = pd.read_csv(lines_file)
max_toi = lines_df['TOI'].max()
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
away_cmap = clr.LinearSegmentedColormap.from_list('custom away',
[(0, '#ffffff'),
(1, away_color)],
N=256)
home_cmap = clr.LinearSegmentedColormap.from_list('custom home',
[(0, '#ffffff'),
(1, home_color)],
N=256)
###
### 5v5
###
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
opponent_color = team_colors[1]
team_color_map = plt.cm.get_cmap(away_cmap)
opponent_color_map = plt.cm.get_cmap(home_cmap)
if team == home:
team_color = team_colors[1]
opponent_color = team_colors[0]
team_color_map = plt.cm.get_cmap(home_cmap)
opponent_color_map = plt.cm.get_cmap(away_cmap)
# create a lines dataframe; filter for team; sort by time on ice; keep the lines with the 8 highest totals; rank and then invert the rankings
team_lines_df = lines_df.copy()
team_lines_df = team_lines_df[(team_lines_df['TEAM'] == team)]
team_lines_df = team_lines_df.sort_values(by=['TOI'], ascending=True)
team_lines_df = team_lines_df.iloc[-8:]
team_lines_df['RANK'] = team_lines_df['TOI'].rank(method='first')
team_lines_df = team_lines_df.sort_values(by=['RANK'], ascending=True)
team_lines_df['RANK'] -= 1
# remove zeros from the goals for and against columns
team_lines_df['GF'] = team_lines_df['GF'].replace(0, np.NaN)
team_lines_df['GA'] = team_lines_df['GA'].replace(0, np.NaN)
# remove zeros from the differential column
team_lines_df['SD'] = team_lines_df['SD'].replace(0, np.NaN)
# make shots against negative values
team_lines_df['GA'] *= -1
team_lines_df['SA'] *= -1
# create another lines dataframe with just the time on ice column; set a max value; scale each line's time on ice relative to the max value
lines_toi = team_lines_df['TOI']
max_lines_toi = lines_toi.max()
lines_toi_color = lines_toi / float(max_lines_toi)
# connect team and opponent color map colors to each line's scaled time on ice
lines_toi_color_map_for = team_color_map(lines_toi_color)
lines_toi_color_map_against = opponent_color_map(lines_toi_color)
# create a figure with two subplots sharing the y-axis
fig = plt.figure(figsize=(8, 8))
grid = plt.GridSpec(1, 8, hspace=0.75, wspace=0.50)
ax_lines_shots = fig.add_subplot(grid[0, 0:-2])
ax_lines_toi = fig.add_subplot(grid[0, -1])
# set the plot title
fig.suptitle(date + ' Forward Lines On-Ice Shots\n\n')
# set the axes titles
ax_lines_shots.set_title('5v5 S', fontsize=10)
ax_lines_toi.set_title('5v5 TOI', fontsize=10)
# create bars for shots for and against as well as markers (to note the shot differential) for each line
try:
lines_SF_plot = team_lines_df.plot.barh(
x='LINE',
y='SF',
stacked=True,
color=lines_toi_color_map_for,
width=0.25,
legend=None,
label='',
ax=ax_lines_shots)
except:
pass
try:
lines_SA_plot = team_lines_df.plot.barh(
x='LINE',
y='SA',
stacked=True,
color=lines_toi_color_map_against,
width=0.25,
legend=None,
label='',
ax=ax_lines_shots)
except:
pass
try:
lines_GF_marker = team_lines_df.plot(x='GF',
y='RANK',
marker='D',
markersize=5,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_lines_shots)
except:
pass
try:
lines_GA_marker = team_lines_df.plot(x='GA',
y='RANK',
marker='D',
markersize=5,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_lines_shots)
except:
pass
try:
lines_SD_plot = team_lines_df.plot(x='SD',
y='RANK',
marker='|',
markersize=15,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend=None,
label='',
ax=ax_lines_shots)
except:
pass
# plot the bars for time on ice
try:
toi_lines = team_lines_df.plot.barh(x='LINE',
y='TOI',
color='white',
edgecolor=team_color,
width=0.25,
legend=None,
label='',
ax=ax_lines_toi)
except:
pass
# remove the labels for each subplot
ax_lines_shots.set_xlabel('')
ax_lines_shots.set_ylabel('')
ax_lines_toi.set_xlabel('')
ax_lines_toi.set_ylabel('')
# set vertical indicator for break-even shot differential
ax_lines_shots.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
linestyle=':',
color='black')
# change the tick parameters
ax_lines_shots.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=True, # labels along the left edge are on
labelbottom=True)
ax_lines_toi.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=False, # labels along the left edge are off
labelbottom=True)
# change the y-axis label colors
ax_lines_shots.tick_params(axis='y',
which='both',
labelcolor=team_color)
# create a list of x-axis tick values contingent on the max values for shots for and against
SF_max = lines_df['SF']
SF_max = SF_max.max()
SA_max = lines_df['SA']
SA_max = SA_max.max()
S_tickmax = int()
if SF_max >= SA_max:
S_tickmax = SF_max
if SF_max < SA_max:
S_tickmax = SA_max
S_ticklabels = []
if S_tickmax <= 5:
S_ticklabels = [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]
if S_tickmax > 5 and S_tickmax <= 10:
S_ticklabels = [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10]
if S_tickmax > 10 and S_tickmax <= 15:
S_ticklabels = [-15, -12, -9, -6, -3, 0, 3, 6, 9, 12, 15]
if S_tickmax > 15 and S_tickmax <= 20:
S_ticklabels = [-20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20]
if S_tickmax > 20 and S_tickmax <= 25:
S_ticklabels = [-25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25]
toi_tickmax = max_toi
toi_ticklabels = []
if toi_tickmax <= 2:
toi_ticklabels = [0, 2]
if toi_tickmax > 2 and toi_tickmax <= 4:
toi_ticklabels = [0, 4]
if toi_tickmax > 4 and toi_tickmax <= 6:
toi_ticklabels = [0, 6]
if toi_tickmax > 6 and toi_tickmax <= 8:
toi_ticklabels = [0, 8]
if toi_tickmax > 8 and toi_tickmax <= 10:
toi_ticklabels = [0, 10]
if toi_tickmax > 10 and toi_tickmax <= 12:
toi_ticklabels = [0, 12]
if toi_tickmax > 12 and toi_tickmax <= 14:
toi_ticklabels = [0, 14]
if toi_tickmax > 14 and toi_tickmax <= 16:
toi_ticklabels = [0, 16]
if toi_tickmax > 16 and toi_tickmax <= 18:
toi_ticklabels = [0, 18]
if toi_tickmax > 18 and toi_tickmax <= 20:
toi_ticklabels = [0, 20]
# set vertical indicator for midpoint of time on ice max
ax_lines_toi.axvspan(toi_ticklabels[1] / 2,
toi_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_lines_toi.axvspan(toi_ticklabels[1],
toi_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_lines_shots.set_xticks(S_ticklabels, minor=False)
ax_lines_toi.set_xticks(toi_ticklabels, minor=False)
# remove the borders to each subplot
ax_lines_shots.spines["top"].set_visible(False)
ax_lines_shots.spines["bottom"].set_visible(False)
ax_lines_shots.spines["right"].set_visible(False)
ax_lines_shots.spines["left"].set_visible(False)
ax_lines_toi.spines["top"].set_visible(False)
ax_lines_toi.spines["bottom"].set_visible(False)
ax_lines_toi.spines["right"].set_visible(False)
ax_lines_toi.spines["left"].set_visible(False)
# add a legend for the shot type markers
from matplotlib.lines import Line2D
elements = [
Line2D([0], [0],
marker='D',
markersize=5,
markerfacecolor='None',
markeredgecolor='black',
linewidth=0,
alpha=1,
label='Scored'),
Line2D([0], [0],
marker='|',
markersize=13,
markerfacecolor='None',
markeredgecolor='black',
linewidth=0,
alpha=1,
label='Differential')
]
ax_lines_shots.legend(handles=elements,
loc='center',
bbox_to_anchor=(.5, -.1),
ncol=2).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425,
0.936,
' ' + away + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525,
0.936,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490,
0.936,
'@',
color='black',
fontsize='12',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_units_lines + 'onice_shots_away_lines.png',
bbox_inches='tight',
pad_inches=0.2)
elif team == home:
plt.savefig(charts_units_lines + 'onice_shots_home_lines.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' lines 5v5 on-ice shots.')
# status update
print('Finished plotting 5v5 on-ice shots for lines.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_players_onice_situation = parameters.charts_players_onice_situation
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for the team and players
team_file = files_root + 'stats_teams_situation.csv'
players_file = files_root + 'stats_players_onice_situation.csv'
# create a dataframe object that reads in info from the .csv files
team_stats_df = pd.read_csv(team_file)
players_df = pd.read_csv(players_file)
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
###
### 5v5, PP, SH
###
situations = ['Leading', 'Tied', 'Trailing']
# loop through each situation
for situation in situations:
if situation == 'Leading':
home_situation = 'Leading'
away_situation = 'Trailing'
if situation == 'Tied':
home_situation = 'Tied'
away_situation = 'Tied'
if situation == 'Trailing':
home_situation = 'Trailing'
away_situation = 'Leading'
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
opponent_color = team_colors[1]
situation = away_situation
if team == home:
team_color = team_colors[1]
opponent_color = team_colors[0]
situation = home_situation
# create a dataframe from the team stats file for generating toi values for the different game states
team_toi_df = team_stats_df.copy()
team_all_toi = team_toi_df[(team_toi_df['TEAM'] == team) & (team_toi_df['STATE'] == 'ALL') & (team_toi_df['SITUATION'] == situation)]
team_all_toi = team_all_toi['TOI'].item()
team_5v5_toi = team_toi_df[(team_toi_df['TEAM'] == team) & (team_toi_df['STATE'] == '5v5') & (team_toi_df['SITUATION'] == situation)]
team_5v5_toi = team_5v5_toi['TOI'].item()
team_PP_toi = team_toi_df[(team_toi_df['TEAM'] == team) & (team_toi_df['STATE'] == 'PP') & (team_toi_df['SITUATION'] == situation)]
team_PP_toi = team_PP_toi['TOI'].item()
team_SH_toi = team_toi_df[(team_toi_df['TEAM'] == team) & (team_toi_df['STATE'] == 'SH') & (team_toi_df['SITUATION'] == situation)]
team_SH_toi = team_SH_toi['TOI'].item()
# create a dataframe; filter for team; sort by team, game state and position; rank by time on ice and then invert the rankings
team_df = players_df.copy()
team_df = team_df[(team_df['TEAM'] == team) & (team_df['POS'] != 'G')]
# remove zeros from the differential column
team_df['xGD'] = team_df['xGD'].replace(0, np.NaN)
# make expected goals against negative
team_df['xGA'] *= -1
# create a filtered dataframe for each game state; sort by team, game state and position; rank by time on ice and then invert the rankings
team_5v5_df = team_df.copy()
team_5v5_df = team_5v5_df[(team_5v5_df['STATE'] == '5v5') & (team_5v5_df['SITUATION'] == situation)]
team_5v5_df = team_5v5_df.sort_values(by=['TOI'], ascending = True)
team_5v5_df['RANK'] = team_5v5_df['TOI'].rank(method='first')
team_5v5_df['RANK'] -= 1
team_PP_df = team_df.copy()
team_PP_df = team_PP_df[(team_PP_df['STATE'] == 'PP') & (team_PP_df['TOI'] > 0) & (team_PP_df['SITUATION'] == situation)]
team_PP_df = team_PP_df.sort_values(by=['TOI'], ascending = True)
team_PP_df['RANK'] = team_PP_df['TOI'].rank(method='first')
team_PP_df['RANK'] -= 1
team_SH_df = team_df.copy()
team_SH_df = team_SH_df[(team_SH_df['STATE'] == 'SH') & (team_SH_df['TOI'] > 0) & (team_SH_df['SITUATION'] == situation)]
team_SH_df = team_SH_df.sort_values(by=['TOI'], ascending = True)
team_SH_df['RANK'] = team_SH_df['TOI'].rank(method='first')
team_SH_df['RANK'] -= 1
# for each game state, create a dataframe with just the time on ice column; set a max value; scale each player's time on ice relative to the max
toi_5v5 = team_5v5_df['TOI']
max_toi_5v5 = toi_5v5.max()
toi_PP = team_PP_df['TOI']
max_toi_PP = toi_PP.max()
toi_SH = team_SH_df['TOI']
max_toi_SH = toi_SH.max()
# create a figure with six subplots arrangled complexly using a grid structure
fig = plt.figure(figsize=(8,8))
grid = plt.GridSpec(5, 8, hspace=0.75, wspace=0.75)
ax_5v5_xG = fig.add_subplot(grid[0:-2, :-1])
ax_5v5_toi = fig.add_subplot(grid[0:-2, 7])
ax_PP_xG = fig.add_subplot(grid[3:, :2])
ax_PP_toi = fig.add_subplot(grid[3:, 2])
ax_SH_xG = fig.add_subplot(grid[3:, 5:-1])
ax_SH_toi = fig.add_subplot(grid[3:, 7])
# set the plot title
if team == away:
fig.suptitle(date + ' Skaters On-Ice Expected Goals (Away ' + situation + ')\n\n')
if team == home:
fig.suptitle(date + ' Skaters On-Ice Expected Goals (Home ' + situation + ')\n\n')
# set the axes titles
ax_5v5_xG.set_title('5v5 xG', fontsize=10)
ax_5v5_toi.set_title('5v5 TOI', fontsize=10)
ax_PP_xG.set_title('PP xG', fontsize=10)
ax_PP_toi.set_title('PP TOI', fontsize=10)
ax_SH_xG.set_title('SH xG', fontsize=10)
ax_SH_toi.set_title('SH TOI', fontsize=10)
# for each state, plot the bars for expected goals and a marker expected goal differential
try:
xGF_5v5_plot = team_5v5_df.plot.barh(x='PLAYER', y='xGF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_5v5_xG);
except:
pass
try:
xGA_5v5_plot = team_5v5_df.plot.barh(x='PLAYER', y='xGA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_5v5_xG);
except:
pass
try:
xGD_5v5_marker = team_5v5_df.plot(x='xGD', y='RANK', marker='|', markersize=11, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend=None, label='', ax=ax_5v5_xG);
except:
pass
if team_PP_toi != 0:
try:
xGF_PP_plot = team_PP_df.plot.barh(x='PLAYER', y='xGF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PP_xG);
except:
pass
try:
xGA_PP_plot = team_PP_df.plot.barh(x='PLAYER', y='xGA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PP_xG);
except:
pass
try:
xGD_PP_marker = team_PP_df.plot(x='xGD', y='RANK', marker='|', markersize=11, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend=None, label='', ax=ax_PP_xG);
except:
pass
if team_SH_toi != 0:
try:
xGF_SH_plot = team_SH_df.plot.barh(x='PLAYER', y='xGF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_SH_xG);
except:
pass
try:
xGA_SH_plot = team_SH_df.plot.barh(x='PLAYER', y='xGA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_SH_xG);
except:
pass
try:
xGD_SH_marker = team_SH_df.plot(x='xGD', y='RANK', marker='|', markersize=11, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend=None, label='', ax=ax_SH_xG);
except:
pass
# for each state, plot the bars for time on ice
try:
toi_5v5_plot = team_5v5_df.plot.barh(x='PLAYER', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_5v5_toi);
except:
pass
if team_PP_toi != 0:
try:
toi_PP_plot = team_PP_df.plot.barh(x='PLAYER', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_PP_toi);
except:
pass
if team_SH_toi != 0:
try:
toi_SH_plot = team_SH_df.plot.barh(x='PLAYER', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_SH_toi);
except:
pass
# set / remove the y-labels for the subplots
ax_5v5_xG.set_xlabel('')
ax_5v5_xG.set_ylabel('', fontsize=10)
ax_5v5_toi.set_xlabel('')
ax_5v5_toi.set_ylabel('')
ax_PP_xG.set_xlabel('')
ax_PP_xG.set_ylabel('', fontsize=10)
ax_PP_toi.set_xlabel('')
ax_PP_toi.set_ylabel('')
ax_SH_xG.set_xlabel('')
ax_SH_xG.set_ylabel('', fontsize=10)
ax_SH_toi.set_xlabel('')
ax_SH_toi.set_ylabel('')
# set vertical indicators for zero shots
ax_5v5_xG.axvspan(0, 0, ymin=0, ymax=1, alpha=.25, linestyle=':', color='black')
ax_PP_xG.axvspan(0, 0, ymin=0, ymax=1, alpha=.25, linestyle=':', color='black')
ax_SH_xG.axvspan(0, 0, ymin=0, ymax=1, alpha=.25, linestyle=':', color='black')
# change the tick parameters for each axes
ax_5v5_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
# create a list of x-axis tick values contingent on the max values for shots
team_5v5_df2 = team_5v5_df.copy()
team_PP_df2 = team_PP_df.copy()
team_SH_df2 = team_SH_df.copy()
team_5v5_df2['xGA'] *= -1
team_PP_df2['xGA'] *= -1
team_SH_df2['xGA'] *= -1
xGF_5v5_max = team_5v5_df2['xGF']
xGF_5v5_max = xGF_5v5_max.max()
xGA_5v5_max = team_5v5_df2['xGA']
xGA_5v5_max = xGA_5v5_max.max()
xG_5v5_tickmax = float()
if xGF_5v5_max >= xGA_5v5_max:
xG_5v5_tickmax = xGF_5v5_max
if xGF_5v5_max < xGA_5v5_max:
xG_5v5_tickmax = xGA_5v5_max
xG_5v5_ticklabels = []
if xG_5v5_tickmax >= 0 and xG_5v5_tickmax <= 0.5:
xG_5v5_ticklabels = [-0.50, -0.40, -0.30, -0.20, -0.10, 0.00, 0.10, 0.20, 0.30, 0.40, 0.50]
if xG_5v5_tickmax > 0.5 and xG_5v5_tickmax <= 1:
xG_5v5_ticklabels = [-1.00, -0.80, -0.60, -0.40, -0.20, 0.00, 0.20, 0.40, 0.60, 0.80, 1.00]
if xG_5v5_tickmax > 1 and xG_5v5_tickmax <= 1.5:
xG_5v5_ticklabels = [-1.50, -1.20, -0.90, -0.60, -0.30, 0.00, 0.30, 0.60, 0.90, 1.20, 1.50]
if xG_5v5_tickmax > 1.5 and xG_5v5_tickmax <= 2:
xG_5v5_ticklabels = [-2.00, -1.60, -1.20, -0.80, -0.40, 0.00, 0.40, 0.80, 1.20, 1.60, 2.00]
if xG_5v5_tickmax > 2 and xG_5v5_tickmax <= 2.5:
xG_5v5_ticklabels = [-2.50, -2.00, -1.50, -1.00, -0.50, 0.00, 0.50, 1.00, 1.50, 2.00, 2.50]
if xG_5v5_tickmax > 2.5 and xG_5v5_tickmax <= 3:
xG_5v5_ticklabels = [-3.00, -2.40, -1.80, -1.20, -0.60, 0.00, 0.60, 1.20, 1.80, 2.40, 3.00]
if xG_5v5_tickmax > 3 and xG_5v5_tickmax <= 3.5:
xG_5v5_ticklabels = [-3.50, -2.80, -2.10, -1.40, -0.70, 0.00, 0.70, 1.40, 2.10, 2.80, 3.50]
if xG_5v5_tickmax > 3.5 and xG_5v5_tickmax <= 4:
xG_5v5_ticklabels = [-4.00, -3.20, -2.40, -1.60, -0.80, 0.00, 0.80, 1.60, 2.40, 3.20, 4.00]
toi_5v5_tickmax = max_toi_5v5
toi_5v5_ticklabels = []
if toi_5v5_tickmax <= 10:
toi_5v5_ticklabels = [0, 10]
if toi_5v5_tickmax > 10 and toi_5v5_tickmax <= 15:
toi_5v5_ticklabels = [0, 15]
if toi_5v5_tickmax > 15 and toi_5v5_tickmax <= 20:
toi_5v5_ticklabels = [0, 20]
if toi_5v5_tickmax > 20 and toi_5v5_tickmax <= 25:
toi_5v5_ticklabels = [0, 25]
if toi_5v5_tickmax > 25 and toi_5v5_tickmax <= 30:
toi_5v5_ticklabels = [0, 30]
xGF_PP_max = team_PP_df2['xGF']
xGF_PP_max = xGF_PP_max.max()
xGA_PP_max = team_PP_df2['xGA']
xGA_PP_max = xGA_PP_max.max()
xG_PP_tickmax = float()
if xGF_PP_max >= xGA_PP_max:
xG_PP_tickmax = xGF_PP_max
if xGF_PP_max < xGA_PP_max:
xG_PP_tickmax = xGA_PP_max
xG_PP_ticklabels = []
if xG_PP_tickmax <= 0.5:
xG_PP_ticklabels = [-0.50, 0.00, 0.50]
if xG_PP_tickmax > 0.5 and xG_PP_tickmax <= 1:
xG_PP_ticklabels = [-1.00, 0.00, 1.00]
if xG_PP_tickmax > 1 and xG_PP_tickmax <= 1.5:
xG_PP_ticklabels = [-1.50, 0.00, 1.50]
if xG_PP_tickmax > 1.5 and xG_PP_tickmax <= 2:
xG_PP_ticklabels = [-2.00, 0.00, 2.00]
if xG_PP_tickmax > 2 and xG_PP_tickmax <= 2.5:
xG_PP_ticklabels = [-2.50, 0.00, 2.50]
if xG_PP_tickmax > 2.5 and xG_PP_tickmax <= 3:
xG_PP_ticklabels = [-3.00, 0.00, 3.00]
if xG_PP_tickmax > 3 and xG_PP_tickmax <= 3.5:
xG_PP_ticklabels = [-3.50, 0.00, 3.50]
if xG_PP_tickmax > 3 and xG_PP_tickmax <= 4:
xG_PP_ticklabels = [-4.00, 0.00, 4.00]
xGF_SH_max = team_SH_df2['xGF']
xGF_SH_max = xGF_SH_max.max()
xGA_SH_max = team_SH_df2['xGA']
xGA_SH_max = xGA_SH_max.max()
xG_SH_tickmax = float()
if xGF_SH_max >= xGA_SH_max:
xG_SH_tickmax = xGF_SH_max
if xGF_SH_max < xGA_SH_max:
xG_SH_tickmax = xGA_SH_max
xG_SH_ticklabels = []
if xG_SH_tickmax <= 0.5:
xG_SH_ticklabels = [-0.50, 0.00, 0.50]
if xG_SH_tickmax > 0.5 and xG_SH_tickmax <= 1:
xG_SH_ticklabels = [-1.00, 0.00, 1.00]
if xG_SH_tickmax > 1 and xG_SH_tickmax <= 1.5:
xG_SH_ticklabels = [-1.50, 0.00, 1.50]
if xG_SH_tickmax > 1.5 and xG_SH_tickmax <= 2:
xG_SH_ticklabels = [-2.00, 0.00, 2.00]
if xG_SH_tickmax > 2 and xG_SH_tickmax <= 2.5:
xG_SH_ticklabels = [-2.50, 0.00, 2.50]
if xG_SH_tickmax > 2.5 and xG_SH_tickmax <= 3:
xG_SH_ticklabels = [-3.00, 0.00, 3.00]
if xG_SH_tickmax > 3 and xG_SH_tickmax <= 3.5:
xG_SH_ticklabels = [-3.50, 0.00, 3.50]
if xG_SH_tickmax > 3 and xG_SH_tickmax <= 4:
xG_SH_ticklabels = [-4.00, 0.00, 4.00]
toi_PP_tickmax = max_toi_PP
toi_SH_tickmax = max_toi_SH
if toi_PP_tickmax >= toi_SH_tickmax:
toi_specialteams_tickmax = toi_PP_tickmax
if toi_PP_tickmax < toi_SH_tickmax:
toi_specialteams_tickmax = toi_SH_tickmax
toi_specialteams_ticklabels = []
if toi_specialteams_tickmax <= 2:
toi_specialteams_ticklabels = [0, 2]
if toi_specialteams_tickmax > 2 and toi_specialteams_tickmax <= 4:
toi_specialteams_ticklabels = [0, 4]
if toi_specialteams_tickmax > 4 and toi_specialteams_tickmax <= 6:
toi_specialteams_ticklabels = [0, 6]
if toi_specialteams_tickmax > 6 and toi_specialteams_tickmax <= 8:
toi_specialteams_ticklabels = [0, 8]
if toi_specialteams_tickmax > 8 and toi_specialteams_tickmax <= 10:
toi_specialteams_ticklabels = [0, 10]
if toi_specialteams_tickmax > 10 and toi_specialteams_tickmax <= 12:
toi_specialteams_ticklabels = [0, 12]
# set vertical indicator for midpoint of time on ice max
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1] / 2, toi_5v5_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1], toi_5v5_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1] / 2, toi_specialteams_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1], toi_specialteams_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1] / 2, toi_specialteams_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1], toi_specialteams_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_5v5_xG.set_xticks(xG_5v5_ticklabels, minor=False)
ax_5v5_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_PP_xG.set_xticks(xG_PP_ticklabels, minor=False)
ax_PP_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
ax_SH_xG.set_xticks(xG_SH_ticklabels, minor=False)
ax_SH_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
# remove axes ticks for instances where there is no special teams play
if team_PP_toi == 0:
ax_PP_xG.set_xticks([], minor=False)
ax_PP_xG.set_yticks([], minor=False)
ax_PP_toi.set_xticks([], minor=False)
ax_PP_toi.set_yticks([], minor=False)
if team_SH_toi == 0:
ax_SH_xG.set_xticks([], minor=False)
ax_SH_xG.set_yticks([], minor=False)
ax_SH_toi.set_xticks([], minor=False)
ax_SH_toi.set_yticks([], minor=False)
# remove the borders to each subplot
ax_5v5_xG.spines["top"].set_visible(False)
ax_5v5_xG.spines["bottom"].set_visible(False)
ax_5v5_xG.spines["right"].set_visible(False)
ax_5v5_xG.spines["left"].set_visible(False)
ax_5v5_toi.spines["top"].set_visible(False)
ax_5v5_toi.spines["bottom"].set_visible(False)
ax_5v5_toi.spines["right"].set_visible(False)
ax_5v5_toi.spines["left"].set_visible(False)
ax_PP_xG.spines["top"].set_visible(False)
ax_PP_xG.spines["bottom"].set_visible(False)
ax_PP_xG.spines["right"].set_visible(False)
ax_PP_xG.spines["left"].set_visible(False)
ax_PP_toi.spines["top"].set_visible(False)
ax_PP_toi.spines["bottom"].set_visible(False)
ax_PP_toi.spines["right"].set_visible(False)
ax_PP_toi.spines["left"].set_visible(False)
ax_SH_xG.spines["top"].set_visible(False)
ax_SH_xG.spines["bottom"].set_visible(False)
ax_SH_xG.spines["right"].set_visible(False)
ax_SH_xG.spines["left"].set_visible(False)
ax_SH_toi.spines["top"].set_visible(False)
ax_SH_toi.spines["bottom"].set_visible(False)
ax_SH_toi.spines["right"].set_visible(False)
ax_SH_toi.spines["left"].set_visible(False)
# add a legend for the shot type markers
from matplotlib.lines import Line2D
elements = [Line2D([0], [0], marker='|', markersize=11, markerfacecolor='None', markeredgecolor='black', linewidth=0, alpha=1, label='Differential')]
ax_5v5_xG.legend(handles=elements, loc='center', bbox_to_anchor=(.55, -.925), ncol=1).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425, 0.936, ' ' + away + ' ', color='white', fontsize='12', bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525, 0.936, ' ' + home + ' ', fontsize='12', color='white', bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490, 0.936, '@', color='black', fontsize='12', bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_players_onice_situation + 'skaters_onice_xG_away_' + situation.lower() + '.png', bbox_inches='tight', pad_inches=0.2)
elif team == home:
plt.savefig(charts_players_onice_situation + 'skaters_onice_xG_home_' + situation.lower() + '.png', bbox_inches='tight', pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' skaters on-ice xG while ' + situation.lower() + '.')
# status update
print('Finished plotting the on-ice xG for skaters by situation.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_teams_period = parameters.charts_teams_period
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
# create variables that point to the .csv team stats file and play-by-play file
stats_teams_file = files_root + 'stats_teams_period.csv'
pbp_file = files_root + 'pbp.csv'
# create a dataframe object that reads in the team stats file
stats_teams_df = pd.read_csv(stats_teams_file)
# create a dataframe object that reads in the play-by-file info; invert the X and Y coordinates
pbp_df = pd.read_csv(pbp_file)
pbp_df = pbp_df[(pbp_df['PERIOD'] <= 4)]
pbp_df['X_1'] *= -1
pbp_df['Y_1'] *= -1
pbp_df['X_2'] *= -1
pbp_df['Y_2'] *= -1
# make copies of the pbp file for later generation of team-specific subsets
away_df = pbp_df.copy()
home_df = pbp_df.copy()
# choose colors for each team; set them in a list; generate a custom colormap for each team
legend_color = 'black'
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
colors = [away_color, home_color, legend_color]
away_cmap = clr.LinearSegmentedColormap.from_list('custom away', [(0, '#ffffff'), (1, away_color)], N=256)
home_cmap = clr.LinearSegmentedColormap.from_list('custom home', [(0, '#ffffff'), (1, home_color)], N=256)
periods = [1, 2, 3, 4]
for period in periods:
if period == 1:
period_name = '1st'
if period == 2:
period_name = '2nd'
if period == 3:
period_name = '3rd'
if period == 4:
period_name = 'OT'
states = ['ALL', '5v5', 'PP', 'SH']
for state in states:
# pull the time on ice recorded for the home team from the team stats file
if state == 'ALL':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'ALL') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == '5v5' and period < 4 or state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == '5v5') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == '4v4') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == '3v3') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == 'PP':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'PP') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == 'SH':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'SH') & (stats_teams_df['PERIOD'] == period_name)]
toi = toi_df['TOI'].item()
except:
toi = 0.0
# get a count of the number of unblocked shots for each team
if state == 'ALL':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period)].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period)].count()[1])
if state == '5v5' and period < 4:
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')].count()[1])
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')].count()[1])
if state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
if state == 'PP':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
if state == 'SH':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
# create subsets of the distinct types of unblocked shots for each team from the play-by-play file
if state == 'ALL':
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period)]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period)]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period)]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period)]
if state == '5v5' and period < 4:
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '4v4') & (pbp_df['AWAY_STRENGTH'] == '4v4')]
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STRENGTH'] == '3v3') & (pbp_df['AWAY_STRENGTH'] == '3v3')]
if state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] >= period) & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
if state == 'PP':
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
if state == 'SH':
away_shots = away_df[(away_df['TEAM'] == away) & (away_df['EVENT'] == 'Shot') & (away_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
away_shots = away_shots.dropna(subset=['X_1', 'Y_1'])
away_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
home_shots = home_df[(home_df['TEAM'] == home) & (home_df['EVENT'] == 'Shot') & (home_df['EVENT_TYPE'] != 'Block') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
home_shots = home_shots.dropna(subset=['X_1', 'Y_1'])
home_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (away_df['PERIOD'] == period) & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
# create a figure to plot
fig, ax = plt.subplots(figsize=(8,6))
# style the plot using seaborn
sns.set_style("white")
# make 2D density plots for each team with their unblocked shots
try:
away_plot = sns.kdeplot(away_shots.X_1, away_shots.Y_1, shade=True, bw=5, clip=((-88,0),(-42,42)), shade_lowest=False, alpha=0.5, cmap=away_cmap, ax=ax)
except:
pass
try:
home_plot = sns.kdeplot(home_shots.X_1, home_shots.Y_1, shade=True, bw=5, clip=((0,88),(-42,42)), shade_lowest=False, alpha=0.5, cmap=home_cmap, ax=ax)
except:
pass
# create the marker that will be used for the legend (note the zorder)
teams_df_scored = pd.concat([away_scored, home_scored])
try:
scored_marker = teams_df_scored.plot.scatter(x='X_1', y='Y_1', zorder=0, marker=u'$\u2609$', s=120, c=colors[2], edgecolors=colors[2], linewidth='1', alpha=1, label='Scored', ax=ax);
except:
pass
# plot the goals for each team
try:
away_scored_plot = away_scored.plot.scatter(x='X_1', y='Y_1', zorder=3, marker=u'$\u2609$', s=100, c=colors[2], edgecolors=colors[0], linewidth='1', alpha=1, ax=ax);
except:
pass
try:
home_scored_plot = home_scored.plot.scatter(x='X_1', y='Y_1', zorder=3, marker=u'$\u2609$', s=100, c=colors[2], edgecolors=colors[1], linewidth='1', alpha=1, ax=ax);
except:
pass
# set the background image of what will be the plot to the 'rink_image.png' file
rink_img = plt.imread("rink_image.png")
plt.imshow(rink_img, extent=[-100,100,-42.5,42.5], zorder=0)
# eliminate the axis labels
plt.axis('off')
# add text boxes to indicate home and away sides
plt.text(58, 45, home_count + ' USF', color=colors[1], fontsize=12, ha='center')
plt.text(-58, 45, away_count + ' USF', color=colors[0], fontsize=12, ha='center')
# add text boxes with team names in white and with the team's color in the background
fig.text(.435, 0.744, ' ' + away + ' ', fontsize='12', color='white', bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.535, 0.744, ' ' + home + ' ', fontsize='12', color='white', bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.500, 0.744, '@', fontsize='12', color='black', bbox=dict(facecolor='white', edgecolor='None'))
# set the plot title
if state == 'ALL':
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' Minutes\n\n')
if state == '5v5' and period < 4:
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' 5v5 Minutes\n\n')
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' 4v4 Minutes\n\n')
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' 3v3 Minutes\n\n')
if state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' 5v5 Minutes\n\n')
if state == 'PP':
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' Home PP Minutes\n\n')
if state == 'SH':
plt.title(date + ' ' + period_name + ' Period Unblocked Shots\n' + str(toi) + ' Away PP Minutes\n\n')
# set the location of the plot legend
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.00), scatterpoints=1, ncol=3).get_frame().set_linewidth(0.0)
# set plot axis limits
plt.xlim(-100,100)
plt.ylim(-42.5,42.5)
# eliminate unnecessary whitespace
away_plot.axes.get_xaxis().set_visible(False)
away_plot.get_yaxis().set_visible(False)
###
### SAVE TO FILE
###
if state == 'ALL':
plt.savefig(charts_teams_period + 'shots_density_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
if state == '5v5' and period < 4 or state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
plt.savefig(charts_teams_period + 'shots_density_5v5_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
plt.savefig(charts_teams_period + 'shots_density_4v4_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
plt.savefig(charts_teams_period + 'shots_density_3v3_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
if state == 'PP':
plt.savefig(charts_teams_period + 'shots_density_pp_home_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
if state == 'SH':
plt.savefig(charts_teams_period + 'shots_density_pp_away_' + period_name + '.png', bbox_inches='tight', pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
if state == 'ALL':
print('Finished density plot for ' + period_name + ' period unblocked shots.')
if state == '5v5' and period < 4 or state == '5v5' and int(season_id) != 20192020 and period == 4 and int(game_id) >= 30000 or state == '5v5' and int(season_id) == 20192020 and period == 4 and int(game_id) >= 30021:
print('Finished density plot for ' + period_name + ' period unblocked 5v5 shots.')
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) <= 20142015:
print('Finished density plot for ' + period_name + ' period unblocked 4v4 shots.')
if state == '5v5' and period == 4 and int(game_id) < 30000 and int(season_id) >= 20152016 or state == '5v5' and season_id == 20192020 and period == 4 and int(game_id) > 30000 and int(game_id) <= 30020:
print('Finished density plot for ' + period_name + ' period unblocked 3v3 shots.')
if state == 'PP':
print('Finished density plot for ' + period_name + ' period unblocked shots during a home PP.')
if state == 'SH':
print('Finished density plot for ' + period_name + ' period unblocked shots during an away PP.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_units_pairings_matchups = parameters.charts_units_pairings_matchups
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats file for lines
pairings_file = files_root + 'stats_units_pairings_onice.csv'
pairings_matchups_lines_file = files_root + 'stats_units_pairings_onice_matchups_lines.csv'
# create dataframe objects that read in info from the .csv files
pairings_df = pd.read_csv(pairings_file)
pairings_matchups_lines_df = pd.read_csv(pairings_matchups_lines_file)
max_toi = pairings_matchups_lines_df['TOI'].max()
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
away_cmap = clr.LinearSegmentedColormap.from_list('custom away',
[(0, '#ffffff'),
(1, away_color)],
N=256)
home_cmap = clr.LinearSegmentedColormap.from_list('custom home',
[(0, '#ffffff'),
(1, home_color)],
N=256)
###
### 5v5
###
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
opponent_color = team_colors[1]
team_color_map = plt.cm.get_cmap(away_cmap)
opponent_color_map = plt.cm.get_cmap(home_cmap)
if team == home:
team_color = team_colors[1]
opponent_color = team_colors[0]
team_color_map = plt.cm.get_cmap(home_cmap)
opponent_color_map = plt.cm.get_cmap(away_cmap)
# create a lines dataframe; filter for team; sort by time on ice; keep the lines with the 4 highest totals; rank and then invert the rankings
team_pairings_df = pairings_df.copy()
team_pairings_df = team_pairings_df[(team_pairings_df['TEAM'] == team)]
team_pairings_df = team_pairings_df.sort_values(by=['TOI'],
ascending=True)
team_pairings_df = team_pairings_df.iloc[-3:]
team_pairings_df['RANK'] = team_pairings_df['TOI'].rank(method='first')
team_pairings_df = team_pairings_df.sort_values(by=['RANK'],
ascending=True)
team_pairings_df['RANK'] -= 1
team_pairings_list = team_pairings_df.PAIRING.unique()
# create a lines dataframe; filter for team
team_pairings_matchups_lines_df = pairings_matchups_lines_df.copy()
team_pairings_matchups_lines_df = team_pairings_matchups_lines_df[(
team_pairings_matchups_lines_df['TEAM'] == team)]
max_pairings_toi = team_pairings_matchups_lines_df['TOI'].max()
# remove zeros from the differential column
team_pairings_matchups_lines_df[
'xGD'] = team_pairings_matchups_lines_df['xGD'].replace(0, np.NaN)
# make expected goals against negative values
team_pairings_matchups_lines_df['xGA'] *= -1
# create dataframes for specific lines; sort by time on ice; rank and then invert the rankings
team_pairing1_matchups_lines_df = team_pairings_matchups_lines_df.copy(
)
team_pairing1_matchups_lines_df = team_pairing1_matchups_lines_df[(
team_pairing1_matchups_lines_df['PAIRING'] == team_pairings_list[2]
)]
team_pairing1_matchups_lines_df.sort_values(by=['TOI'], inplace=True)
team_pairing1_matchups_lines_df[
'RANK'] = team_pairing1_matchups_lines_df['TOI'].rank(
method='first')
team_pairing1_matchups_lines_df['RANK'] -= 1
team_pairing2_matchups_lines_df = team_pairings_matchups_lines_df.copy(
)
team_pairing2_matchups_lines_df = team_pairing2_matchups_lines_df[(
team_pairing2_matchups_lines_df['PAIRING'] == team_pairings_list[1]
)]
team_pairing2_matchups_lines_df.sort_values(by=['TOI'], inplace=True)
team_pairing2_matchups_lines_df[
'RANK'] = team_pairing2_matchups_lines_df['TOI'].rank(
method='first')
team_pairing2_matchups_lines_df['RANK'] -= 1
team_pairing3_matchups_lines_df = team_pairings_matchups_lines_df.copy(
)
team_pairing3_matchups_lines_df = team_pairing3_matchups_lines_df[(
team_pairing3_matchups_lines_df['PAIRING'] == team_pairings_list[0]
)]
team_pairing3_matchups_lines_df.sort_values(by=['TOI'], inplace=True)
team_pairing3_matchups_lines_df[
'RANK'] = team_pairing3_matchups_lines_df['TOI'].rank(
method='first')
team_pairing3_matchups_lines_df['RANK'] -= 1
# create lists for use in determining the differential marker size
team_pairing1_matchups_lines_list = team_pairing1_matchups_lines_df.MATCHUP.unique(
)
team_pairing2_matchups_lines_list = team_pairing2_matchups_lines_df.MATCHUP.unique(
)
team_pairing3_matchups_lines_list = team_pairing3_matchups_lines_df.MATCHUP.unique(
)
if len(team_pairing1_matchups_lines_list) >= 7:
pairing1_width = .8
elif len(team_pairing1_matchups_lines_list) == 6:
pairing1_width = .7
elif len(team_pairing1_matchups_lines_list) == 5:
pairing1_width = .6
elif len(team_pairing1_matchups_lines_list) == 4:
pairing1_width = .5
elif len(team_pairing1_matchups_lines_list) == 3:
pairing1_width = .4
elif len(team_pairing1_matchups_lines_list) == 2:
pairing1_width = .3
elif len(team_pairing1_matchups_lines_list) == 1:
pairing1_width = .2
if len(team_pairing2_matchups_lines_list) >= 7:
pairing2_width = .8
elif len(team_pairing2_matchups_lines_list) == 6:
pairing2_width = .7
elif len(team_pairing2_matchups_lines_list) == 5:
pairing2_width = .6
elif len(team_pairing2_matchups_lines_list) == 4:
pairing2_width = .5
elif len(team_pairing2_matchups_lines_list) == 3:
pairing2_width = .4
elif len(team_pairing2_matchups_lines_list) == 2:
pairing2_width = .3
elif len(team_pairing2_matchups_lines_list) == 1:
pairing2_width = .2
if len(team_pairing3_matchups_lines_list) >= 7:
pairing3_width = .8
elif len(team_pairing3_matchups_lines_list) == 6:
pairing3_width = .7
elif len(team_pairing3_matchups_lines_list) == 5:
pairing3_width = .6
elif len(team_pairing3_matchups_lines_list) == 4:
pairing3_width = .5
elif len(team_pairing3_matchups_lines_list) == 3:
pairing3_width = .4
elif len(team_pairing3_matchups_lines_list) == 2:
pairing3_width = .3
elif len(team_pairing3_matchups_lines_list) == 1:
pairing3_width = .2
# create more pairings dataframes with just the time on ice column; set a max value; scale each line's time on ice relative to the max
try:
pairing1_matchups_lines_toi = team_pairing1_matchups_lines_df[
'TOI']
max_pairing1_matchups_lines_toi = pairing1_matchups_lines_toi.max()
pairing1_matchups_lines_toi_color = pairing1_matchups_lines_toi / float(
max_pairing1_matchups_lines_toi)
except:
pass
try:
pairing2_matchups_lines_toi = team_pairing2_matchups_lines_df[
'TOI']
max_pairing2_matchups_lines_toi = pairing2_matchups_lines_toi.max()
pairing2_matchups_lines_toi_color = pairing2_matchups_lines_toi / float(
max_pairing2_matchups_lines_toi)
except:
pass
try:
pairing3_matchups_lines_toi = team_pairing3_matchups_lines_df[
'TOI']
max_pairing3_matchups_lines_toi = pairing3_matchups_lines_toi.max()
pairing3_matchups_lines_toi_color = pairing3_matchups_lines_toi / float(
max_pairing3_matchups_lines_toi)
except:
pass
# connect team and opponent color map colors to each line's scaled time on ice
try:
pairing1_matchups_lines_toi_color_map_for = team_color_map(
pairing1_matchups_lines_toi_color)
pairing1_matchups_lines_toi_color_map_against = opponent_color_map(
pairing1_matchups_lines_toi_color)
except:
pass
try:
pairing2_matchups_lines_toi_color_map_for = team_color_map(
pairing2_matchups_lines_toi_color)
pairing2_matchups_lines_toi_color_map_against = opponent_color_map(
pairing2_matchups_lines_toi_color)
except:
pass
try:
pairing3_matchups_lines_toi_color_map_for = team_color_map(
pairing3_matchups_lines_toi_color)
pairing3_matchups_lines_toi_color_map_against = opponent_color_map(
pairing3_matchups_lines_toi_color)
except:
pass
# create a figure with two subplots sharing the y-axis
fig = plt.figure(figsize=(8, 8))
grid = plt.GridSpec(4, 8, hspace=.40, wspace=0.50)
ax_pairing1_xG = fig.add_subplot(grid[0, 0:-2])
ax_pairing1_toi = fig.add_subplot(grid[0, -1])
ax_pairing2_xG = fig.add_subplot(grid[1, 0:-2])
ax_pairing2_toi = fig.add_subplot(grid[1, -1])
ax_pairing3_xG = fig.add_subplot(grid[2, 0:-2])
ax_pairing3_toi = fig.add_subplot(grid[2, -1])
# set the plot title
fig.suptitle(date + ' Pairings vs. Lines On-Ice Expected Goals\n\n')
fig.text(.396,
0.910,
'5v5 S',
color='black',
fontsize='10',
bbox=dict(facecolor='None', edgecolor='None'))
# set the axes titles
ax_pairing1_xG.set_title(team_pairings_list[2],
fontsize=10,
color=team_color)
ax_pairing1_toi.set_title('5v5 TOI\n', fontsize=10)
ax_pairing2_xG.set_title(team_pairings_list[1],
fontsize=10,
color=team_color)
ax_pairing2_toi.set_title('', fontsize=10)
ax_pairing3_xG.set_title(team_pairings_list[0],
fontsize=10,
color=team_color)
ax_pairing3_toi.set_title('', fontsize=10)
# create bars for expected goals for and against as well as line markers (to note the expected goals differential) for each line
try:
pairing1_xGF_plot = team_pairing1_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGF',
stacked=True,
color=pairing1_matchups_lines_toi_color_map_for,
width=pairing1_width,
legend=None,
label='',
ax=ax_pairing1_xG)
except:
pass
try:
pairing1_xGA_plot = team_pairing1_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGA',
stacked=True,
color=pairing1_matchups_lines_toi_color_map_against,
width=pairing1_width,
legend=None,
label='',
ax=ax_pairing1_xG)
except:
pass
try:
pairing1_xGD_plot = team_pairing1_matchups_lines_df.plot(
x='xGD',
y='RANK',
marker='|',
markersize=15,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend=None,
label='',
ax=ax_pairing1_xG)
except:
pass
try:
pairing2_xGF_plot = team_pairing2_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGF',
stacked=True,
color=pairing2_matchups_lines_toi_color_map_for,
width=pairing2_width,
legend=None,
label='',
ax=ax_pairing2_xG)
except:
pass
try:
pairing2_xGA_plot = team_pairing2_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGA',
stacked=True,
color=pairing2_matchups_lines_toi_color_map_against,
width=pairing2_width,
legend=None,
label='',
ax=ax_pairing2_xG)
except:
pass
try:
pairing2_xGD_plot = team_pairing2_matchups_lines_df.plot(
x='xGD',
y='RANK',
marker='|',
markersize=15,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend=None,
label='',
ax=ax_pairing2_xG)
except:
pass
try:
pairing3_xGF_plot = team_pairing3_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGF',
stacked=True,
color=pairing3_matchups_lines_toi_color_map_for,
width=pairing3_width,
legend=None,
label='',
ax=ax_pairing3_xG)
except:
pass
try:
pairing3_xGA_plot = team_pairing3_matchups_lines_df.plot.barh(
x='MATCHUP',
y='xGA',
stacked=True,
color=pairing3_matchups_lines_toi_color_map_against,
width=pairing3_width,
legend=None,
label='',
ax=ax_pairing3_xG)
except:
pass
try:
pairing3_xGD_plot = team_pairing3_matchups_lines_df.plot(
x='xGD',
y='RANK',
marker='|',
markersize=15,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend=None,
label='',
ax=ax_pairing3_xG)
except:
pass
# plot the bars for time on ice
try:
toi_pairing1 = team_pairing1_matchups_lines_df.plot.barh(
x='MATCHUP',
y='TOI',
color='white',
edgecolor=team_color,
width=pairing1_width,
legend=None,
label='',
ax=ax_pairing1_toi)
except:
pass
try:
toi_pairing2 = team_pairing2_matchups_lines_df.plot.barh(
x='MATCHUP',
y='TOI',
color='white',
edgecolor=team_color,
width=pairing2_width,
legend=None,
label='',
ax=ax_pairing2_toi)
except:
pass
try:
toi_pairing3 = team_pairing3_matchups_lines_df.plot.barh(
x='MATCHUP',
y='TOI',
color='white',
edgecolor=team_color,
width=pairing3_width,
legend=None,
label='',
ax=ax_pairing3_toi)
except:
pass
# remove the labels for each subplot
ax_pairing1_xG.set_xlabel('')
ax_pairing1_xG.set_ylabel('')
ax_pairing1_toi.set_xlabel('')
ax_pairing1_toi.set_ylabel('')
ax_pairing2_xG.set_xlabel('')
ax_pairing2_xG.set_ylabel('')
ax_pairing2_toi.set_xlabel('')
ax_pairing2_toi.set_ylabel('')
ax_pairing3_xG.set_xlabel('')
ax_pairing3_xG.set_ylabel('')
ax_pairing3_toi.set_xlabel('')
ax_pairing3_toi.set_ylabel('')
# set vertical indicators for break-even expected goals differential
ax_pairing1_xG.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
linestyle=':',
color='black')
ax_pairing2_xG.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
linestyle=':',
color='black')
ax_pairing3_xG.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
linestyle=':',
color='black')
# change the tick parameters
ax_pairing1_xG.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=True, # labels along the left edge are on
labelbottom=False)
ax_pairing1_toi.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=False, # labels along the left edge are off
labelbottom=False)
ax_pairing2_xG.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=True, # labels along the left edge are on
labelbottom=False)
ax_pairing2_toi.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=False, # labels along the left edge are off
labelbottom=False)
ax_pairing3_xG.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=True, # labels along the left edge are on
labelbottom=True)
ax_pairing3_toi.tick_params(
axis='both',
which='both',
bottom=False,
top=False,
left=False,
labelleft=False, # labels along the left edge are off
labelbottom=True)
# change the y-axis label colors
ax_pairing1_xG.tick_params(axis='y',
which='both',
labelcolor=opponent_color)
ax_pairing2_xG.tick_params(axis='y',
which='both',
labelcolor=opponent_color)
ax_pairing3_xG.tick_params(axis='y',
which='both',
labelcolor=opponent_color)
# create a list of x-axis tick values contingent on the max values for expected goals for and against
xGF_max = pairings_matchups_lines_df['xGF']
xGF_max = xGF_max.max()
xGA_max = pairings_matchups_lines_df['xGA']
xGA_max = xGA_max.max()
xG_tickmax = int()
if xGF_max >= xGA_max:
xG_tickmax = xGF_max
elif xGF_max < xGA_max:
xG_tickmax = xGA_max
xG_ticklabels = []
if xG_tickmax > 0 and xG_tickmax <= 0.5:
xG_ticklabels = [
-0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4, 0.5
]
if xG_tickmax > 0.5 and xG_tickmax <= 1:
xG_ticklabels = [
-1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2, 0.4, 0.6, 0.8, 1.0
]
if xG_tickmax > 1 and xG_tickmax <= 1.5:
xG_ticklabels = [
-1.5, -1.2, -0.9, -0.6, -0.3, 0.0, 0.3, 0.6, 0.9, 1.2, 1.5
]
if xG_tickmax > 1.5 and xG_tickmax <= 2:
xG_ticklabels = [
-2.0, -1.6, -1.2, -0.8, -0.4, 0.0, 0.4, 0.8, 1.2, 1.6, 2.0
]
if xG_tickmax > 2 and xG_tickmax <= 2.5:
xG_ticklabels = [
-2.5, -2.0, -1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 2.5
]
if xG_tickmax > 2.5 and xG_tickmax <= 3:
xG_ticklabels = [
-3.0, -2.4, -1.8, -1.2, -0.6, 0.0, 0.6, 1.2, 1.8, 2.4, 3.0
]
if xG_tickmax > 3 and xG_tickmax <= 3.5:
xG_ticklabels = [
-3.5, -2.8, -2.1, -1.4, -0.7, 0.0, 0.7, 1.4, 2.1, 2.8, 3.5
]
if xG_tickmax > 3.5 and xG_tickmax <= 4:
xG_ticklabels = [
-4.0, -3.2, -2.4, -1.6, -0.8, 0.0, 0.8, 1.6, 2.4, 3.2, 4.0
]
toi_tickmax = max_toi
toi_ticklabels = []
if toi_tickmax <= 2:
toi_ticklabels = [0, 2]
if toi_tickmax > 2 and toi_tickmax <= 4:
toi_ticklabels = [0, 4]
if toi_tickmax > 4 and toi_tickmax <= 6:
toi_ticklabels = [0, 6]
if toi_tickmax > 6 and toi_tickmax <= 8:
toi_ticklabels = [0, 8]
if toi_tickmax > 8 and toi_tickmax <= 10:
toi_ticklabels = [0, 10]
if toi_tickmax > 10 and toi_tickmax <= 12:
toi_ticklabels = [0, 12]
if toi_tickmax > 12 and toi_tickmax <= 14:
toi_ticklabels = [0, 14]
if toi_tickmax > 14 and toi_tickmax <= 16:
toi_ticklabels = [0, 16]
if toi_tickmax > 16 and toi_tickmax <= 18:
toi_ticklabels = [0, 18]
if toi_tickmax > 18 and toi_tickmax <= 20:
toi_ticklabels = [0, 20]
# set vertical indicator for midpoint of time on ice max
ax_pairing1_toi.axvspan(toi_ticklabels[1] / 2,
toi_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_pairing1_toi.axvspan(toi_ticklabels[1],
toi_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_pairing2_toi.axvspan(toi_ticklabels[1] / 2,
toi_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_pairing2_toi.axvspan(toi_ticklabels[1],
toi_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_pairing3_toi.axvspan(toi_ticklabels[1] / 2,
toi_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_pairing3_toi.axvspan(toi_ticklabels[1],
toi_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_pairing1_xG.set_xticks(xG_ticklabels, minor=False)
ax_pairing1_toi.set_xticks(toi_ticklabels, minor=False)
ax_pairing2_xG.set_xticks(xG_ticklabels, minor=False)
ax_pairing2_toi.set_xticks(toi_ticklabels, minor=False)
ax_pairing3_xG.set_xticks(xG_ticklabels, minor=False)
ax_pairing3_toi.set_xticks(toi_ticklabels, minor=False)
# remove the borders to each subplot
ax_pairing1_xG.spines["top"].set_visible(False)
ax_pairing1_xG.spines["bottom"].set_visible(False)
ax_pairing1_xG.spines["right"].set_visible(False)
ax_pairing1_xG.spines["left"].set_visible(False)
ax_pairing1_toi.spines["top"].set_visible(False)
ax_pairing1_toi.spines["bottom"].set_visible(False)
ax_pairing1_toi.spines["right"].set_visible(False)
ax_pairing1_toi.spines["left"].set_visible(False)
ax_pairing2_xG.spines["top"].set_visible(False)
ax_pairing2_xG.spines["bottom"].set_visible(False)
ax_pairing2_xG.spines["right"].set_visible(False)
ax_pairing2_xG.spines["left"].set_visible(False)
ax_pairing2_toi.spines["top"].set_visible(False)
ax_pairing2_toi.spines["bottom"].set_visible(False)
ax_pairing2_toi.spines["right"].set_visible(False)
ax_pairing2_toi.spines["left"].set_visible(False)
ax_pairing3_xG.spines["top"].set_visible(False)
ax_pairing3_xG.spines["bottom"].set_visible(False)
ax_pairing3_xG.spines["right"].set_visible(False)
ax_pairing3_xG.spines["left"].set_visible(False)
ax_pairing3_toi.spines["top"].set_visible(False)
ax_pairing3_toi.spines["bottom"].set_visible(False)
ax_pairing3_toi.spines["right"].set_visible(False)
ax_pairing3_toi.spines["left"].set_visible(False)
# add a legend for the shot type markers
from matplotlib.lines import Line2D
elements = [
Line2D([0], [0],
marker='|',
markersize=13,
markerfacecolor='None',
markeredgecolor='black',
linewidth=0,
alpha=1,
label='Differential')
]
ax_pairing3_xG.legend(handles=elements,
loc='center',
bbox_to_anchor=(.5, -.50),
ncol=2).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425,
0.936,
' ' + away + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525,
0.936,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490,
0.936,
'@',
color='black',
fontsize='12',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_units_pairings_matchups +
'onice_xg_away_pairings_matchups_lines.png',
bbox_inches='tight',
pad_inches=0.2)
elif team == home:
plt.savefig(charts_units_pairings_matchups +
'onice_xg_home_pairings_matchups_lines.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' pairings vs. lines 5v5 on-ice xG.')
# status update
print('Finished plotting the 5v5 on-ice xG for pairings vs. lines.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_teams = parameters.charts_teams
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
# create variables that point to the .csv livefeed, play-by-play and time-on-ice files
livefeed_file = files_root + 'livefeed.json'
pbp_file = files_root + 'pbp.csv'
toi_file = files_root + 'TOI_matrix.csv'
# opens the game's livefeed (JSON) file to create a few shared variables
with open(livefeed_file) as livefeed_json:
livefeed_data = json.load(livefeed_json)
livefeed_parsed = livefeed_data
try:
game_status = livefeed_parsed["liveData"]["linescore"][
"currentPeriodTimeRemaining"]
currentperiod = livefeed_parsed["liveData"]["linescore"][
"currentPeriod"]
if game_status != 'Final' and currentperiod < 4:
game_type = 'Regulation'
elif game_status != 'Final' and currentperiod > 3:
game_type = 'Overtime'
elif game_status == 'Final' and currentperiod < 4:
game_type = 'Regulation'
elif game_status == 'Final' and currentperiod > 3:
game_type = 'Overtime'
except:
game_type = ''
# create a dataframe object that reads in the play-by-play file
pbp_df = pd.read_csv(pbp_file)
# create a dataframe object that reads in the time on ice file; filter a copy for seconds of special teams play (for later generation of shaded bars)
toi_df = pd.read_csv(toi_file)
pp_toi = toi_df.copy()
pp_toi_df = pp_toi[(pp_toi['HOME_STATE'] == 'PP') |
(pp_toi['AWAY_STATE'] == 'PP')]
home_pp = pp_toi_df[(pp_toi_df['HOME_STATE'] == 'PP')]
away_pp = pp_toi_df[(pp_toi_df['AWAY_STATE'] == 'PP')]
# choose colors for each team and the legend; set them in a list
legend_color = 'black'
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
colors = [away_color, home_color, legend_color]
###
### ALL
###
# create a copy of the play-by-play dataframe; filter for shots; remove extraneous columns
gameflow = pbp_df.copy()
gameflow_df = gameflow[(gameflow['EVENT'] == 'Shot')
& (gameflow['EVENT_TYPE'] != 'Block')]
gameflow_df = gameflow_df.drop(columns=[
'HOME_RESULT', 'AWAY_RESULT', 'HOME_GOALS', 'AWAY_GOALS',
'HOME_SCOREDIFF', 'AWAY_SCOREDIFF', 'EVENT_DETAIL', 'HOME_ZONE',
'AWAY_ZONE', 'PLAYER_A', 'PLAYER_B', 'PLAYER_C', 'X_1', 'Y_1', 'X_2',
'Y_2', 'HOMEON_1', 'HOMEON_2', 'HOMEON_3', 'HOMEON_4', 'HOMEON_5',
'HOMEON_6', 'AWAYON_1', 'AWAYON_2', 'AWAYON_3', 'AWAYON_4', 'AWAYON_5',
'AWAYON_6'
])
# get the xG value for each instance; add a dataframe column with the cumulative number
home_event_xG = (np.where(
(gameflow_df['TEAM'] == home) & (gameflow_df['EVENT_TYPE'] != 'Block'),
gameflow_df['xG'], 0))
gameflow_df['HOME_xG'] = home_event_xG.cumsum()
away_event_xG = (np.where(
(gameflow_df['TEAM'] == away) & (gameflow_df['EVENT_TYPE'] != 'Block'),
gameflow_df['xG'], 0))
gameflow_df['AWAY_xG'] = away_event_xG.cumsum()
# for each instance of an unblocked shot, find the difference in xG for each team up to that point; add a dataframe column with the cumulative differential
home_xG_diff = home_event_xG - away_event_xG
gameflow_df['HOME_xG_DIFF'] = home_xG_diff.cumsum()
away_xG_diff = away_event_xG - home_event_xG
gameflow_df['AWAY_xG_DIFF'] = away_xG_diff.cumsum()
# duplicate the xG differential columns; set negative values for each team to zero (in order to prettify the step plots)
gameflow_df['HOME_xG_DIFF_COPY_1'] = gameflow_df['HOME_xG_DIFF']
gameflow_df.loc[(gameflow_df.HOME_xG_DIFF < 0),
['HOME_xG_DIFF_COPY_1']] = 0
gameflow_df
gameflow_df['HOME_xG_DIFF_COPY_2'] = gameflow_df['HOME_xG_DIFF']
gameflow_df.loc[(gameflow_df.HOME_xG_DIFF > 0),
['HOME_xG_DIFF_COPY_2']] = 0
gameflow_df
# copy the gameflow dataframe; filter for goals
goals = gameflow_df.copy()
goals_df = goals[(goals['EVENT_TYPE'] == 'Goal')]
home_goals = goals_df[(goals_df['TEAM'] == home)]
away_goals = goals_df[(goals_df['TEAM'] == away)]
# create a figure to plot
fig, ax = plt.subplots(figsize=(8, 6))
# create a subset for each team, contingent on the home team's cumulative xG differential, of the amended gameflow dataframe
try:
home_advantage = gameflow_df[(gameflow_df['HOME_xG_DIFF_COPY_1'] >= 0)]
except:
pass
try:
away_advantage = gameflow_df[(gameflow_df['HOME_xG_DIFF_COPY_2'] <= 0)]
except:
pass
# plot each team's cumulative xG differential; make these lines transparent as they are only needed to create the y-axis scale
try:
home_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=2,
alpha=0,
legend=False,
label='',
ax=ax)
except:
pass
try:
away_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='AWAY_xG_DIFF',
color=colors[0],
linewidth=2,
alpha=0,
legend=False,
label='',
ax=ax)
except:
pass
# plot stepped lines for each team's subset
try:
home_advantage_plot_step = plt.step(
x=home_advantage['SECONDS_GONE'],
y=home_advantage['HOME_xG_DIFF_COPY_1'],
linewidth=1,
alpha=1,
color=colors[1],
label='')
except:
pass
try:
away_advantage_plot_step = plt.step(
x=away_advantage['SECONDS_GONE'],
y=away_advantage['HOME_xG_DIFF_COPY_2'],
linewidth=1,
alpha=1,
color=colors[0],
label='')
except:
pass
# plot goal events in such a way that they will render exclusively as markers overlaying the stepped line plots
try:
home_goals_plot = home_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=0,
alpha=1,
legend=False,
label='',
marker=u'$\u2609$',
markersize=12,
ax=ax)
except:
pass
try:
away_goals_plot = away_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[0],
linewidth=0,
alpha=1,
legend=False,
label='',
marker=u'$\u2609$',
markersize=12,
ax=ax)
except:
pass
# create a contingency for situations in which one team has yet to or, if the game is final, did not score
if home_goals.empty == False:
goals_label = home_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
zorder=0,
color=colors[2],
linewidth=0,
alpha=1,
marker=u'$\u2609$',
markersize=12,
label='Goal',
ax=ax)
elif home_goals.empty == True and away_goals.empty == False:
goals_label = away_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
zorder=0,
color=colors[2],
linewidth=0,
alpha=1,
marker=u'$\u2609$',
markersize=12,
label='Goal',
ax=ax)
# set the break-even line
if game_type == 'Regulation':
breakeven_x, breakeven_y = [-0, 3600], [0, 0]
elif game_type == 'Overtime':
breakeven_x, breakeven_y = [-0, 3900], [0, 0]
clear_breakeven = plt.plot(breakeven_x,
breakeven_y,
linewidth=1,
color='white',
alpha=1)
breakeven = plt.plot(breakeven_x,
breakeven_y,
linewidth=1,
color=colors[2],
alpha=0.15)
# set vertical indicators for the end of each period
ax.axvspan(1200,
1201,
ymin=0,
ymax=1,
alpha=1,
color=colors[2],
linewidth=0,
label='Power Play')
ax.axvspan(1200, 1201, ymin=0, ymax=1, alpha=0.15, color=colors[2])
ax.axvspan(2400, 2401, ymin=0, ymax=1, alpha=0.15, color=colors[2])
ax.axvspan(3600, 3601, ymin=0, ymax=1, alpha=0.15, color=colors[2])
if game_type == 'Overtime':
ax.axvspan(3900, 3901, ymin=0, ymax=1, alpha=0.15, color=colors[2])
# set vertical shaded areas indcating power play situations
for second in home_pp['SECONDS_GONE']:
ax.axvspan(second,
second + 1,
ymin=0,
ymax=1,
alpha=0.025,
color=colors[1])
for second in away_pp['SECONDS_GONE']:
ax.axvspan(second,
second + 1,
ymin=0,
ymax=1,
alpha=0.025,
color=colors[0])
# eliminate unnecessary whitespace on the first axes
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(True)
# adjust the visibility of the first axes spines
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# remove ticks belonging to the first axes
ax.tick_params(axis='y', which="both", left=False, right=False, length=0)
# create a second axes that shares the same x-axis
ax2 = ax.twinx()
# plot each team's cumulative xG differential; make these lines transparent as they are only needed to create the second y-axis scale
home_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=2,
alpha=0,
legend=False,
ax=ax2)
away_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='AWAY_xG_DIFF',
color=colors[0],
linewidth=2,
alpha=0,
legend=False,
ax=ax2)
# eliminate unnecessary whitespace on the second axes
ax2.axes.get_xaxis().set_visible(False)
ax2.axes.get_yaxis().set_visible(True)
# adjust the visibility of the second axes spines
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.spines["left"].set_visible(False)
# get the maximum and minimum values in the xG differential column in order to set the y-values for the period labels
max_home_xG_diff = gameflow_df['HOME_xG_DIFF'].max()
min_home_xG_diff = gameflow_df['HOME_xG_DIFF'].min()
max_away_xG_diff = gameflow_df['AWAY_xG_DIFF'].max()
min_away_xG_diff = gameflow_df['AWAY_xG_DIFF'].min()
# remove ticks to the second axes
ax2.tick_params(axis='y', which="both", left=False, right=False, length=0)
# invert the second y-axis
ax2.invert_yaxis()
# set the team label
if max_home_xG_diff > max_away_xG_diff and game_type == 'Regulation':
plt.text(-660,
min_away_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4060,
max_home_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
elif max_home_xG_diff < max_away_xG_diff and game_type == 'Regulation':
plt.text(-660,
min_home_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4060,
max_away_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
if max_home_xG_diff > max_away_xG_diff and game_type == 'Overtime':
plt.text(-660,
min_away_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4350,
max_home_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
elif max_home_xG_diff < max_away_xG_diff and game_type == 'Overtime':
plt.text(-660,
min_home_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4350,
max_away_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
# set the period names as labels
if game_type == 'Regulation':
plt.text(0.33,
-0.05,
'1st',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.63,
-0.05,
'2nd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.93,
-0.05,
'3rd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
if game_type == 'Overtime':
plt.text(0.31,
-0.05,
'1st',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.58,
-0.05,
'2nd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.86,
-0.05,
'3rd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.94,
-0.05,
'OT',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
# set the plot title
plt.title(date + ' Gameflow: Expected Goals\n\n')
# set the location of the plot legend
ax.legend(loc='center', bbox_to_anchor=(.5, -0.10),
ncol=2).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.435,
0.916,
' ' + away + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.535,
0.916,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.501,
0.916,
'@',
fontsize='12',
color='black',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
plt.savefig(charts_teams + 'shots_gameflow_xg.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print(
'Finished plotting the xG differential game flow for all situations.')
###
### 5v5
###
# create a copy of the play-by-play dataframe; filter for shots and game state; remove extraneous columns
gameflow = pbp_df.copy()
gameflow_df = gameflow[(gameflow['EVENT'] == 'Shot')
& (gameflow['EVENT_TYPE'] != 'Block') &
(gameflow['HOME_STRENGTH'] == '5v5')]
gameflow_df = gameflow_df.drop(columns=[
'HOME_RESULT', 'AWAY_RESULT', 'HOME_GOALS', 'AWAY_GOALS',
'HOME_SCOREDIFF', 'AWAY_SCOREDIFF', 'EVENT_DETAIL', 'HOME_ZONE',
'AWAY_ZONE', 'PLAYER_A', 'PLAYER_B', 'PLAYER_C', 'X_1', 'Y_1', 'X_2',
'Y_2', 'HOMEON_1', 'HOMEON_2', 'HOMEON_3', 'HOMEON_4', 'HOMEON_5',
'HOMEON_6', 'AWAYON_1', 'AWAYON_2', 'AWAYON_3', 'AWAYON_4', 'AWAYON_5',
'AWAYON_6'
])
# get the xG value for each instance; add a dataframe column with the cumulative number
home_event_xG = (np.where(
(gameflow_df['TEAM'] == home) & (gameflow_df['EVENT_TYPE'] != 'Block'),
gameflow_df['xG'], 0))
gameflow_df['HOME_xG'] = home_event_xG.cumsum()
away_event_xG = (np.where(
(gameflow_df['TEAM'] == away) & (gameflow_df['EVENT_TYPE'] != 'Block'),
gameflow_df['xG'], 0))
gameflow_df['AWAY_xG'] = away_event_xG.cumsum()
# for each instance of an unblocked shot, find the difference in xG for each team up to that point; add a dataframe column with the cumulative differential
home_xG_diff = home_event_xG - away_event_xG
gameflow_df['HOME_xG_DIFF'] = home_xG_diff.cumsum()
away_xG_diff = away_event_xG - home_event_xG
gameflow_df['AWAY_xG_DIFF'] = away_xG_diff.cumsum()
# duplicate the xG differential columns; set negative values for each team to zero (in order to prettify the step plots)
gameflow_df['HOME_xG_DIFF_COPY_1'] = gameflow_df['HOME_xG_DIFF']
gameflow_df.loc[(gameflow_df.HOME_xG_DIFF < 0),
['HOME_xG_DIFF_COPY_1']] = 0
gameflow_df
gameflow_df['HOME_xG_DIFF_COPY_2'] = gameflow_df['HOME_xG_DIFF']
gameflow_df.loc[(gameflow_df.HOME_xG_DIFF > 0),
['HOME_xG_DIFF_COPY_2']] = 0
gameflow_df
# copy the gameflow dataframe; filter for goals
goals = gameflow_df.copy()
goals_df = goals[(goals['EVENT_TYPE'] == 'Goal')]
home_goals = goals_df[(goals_df['TEAM'] == home)]
away_goals = goals_df[(goals_df['TEAM'] == away)]
# create a figure to plot
fig, ax = plt.subplots(figsize=(8, 6))
# create a subset for each team, contingent on the home team's cumulative xG differential, of the amended gameflow dataframe
try:
home_advantage = gameflow_df[(gameflow_df['HOME_xG_DIFF_COPY_1'] >= 0)]
except:
pass
try:
away_advantage = gameflow_df[(gameflow_df['HOME_xG_DIFF_COPY_2'] <= 0)]
except:
pass
# plot each team's cumulative xG differential; make these lines transparent as they are only needed to create the y-axis scale
try:
home_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=2,
alpha=0,
legend=False,
label='',
ax=ax)
except:
pass
try:
away_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='AWAY_xG_DIFF',
color=colors[0],
linewidth=2,
alpha=0,
legend=False,
label='',
ax=ax)
except:
pass
# plot stepped lines for each team's subset
try:
home_advantage_plot_step = plt.step(
x=home_advantage['SECONDS_GONE'],
y=home_advantage['HOME_xG_DIFF_COPY_1'],
linewidth=1,
alpha=1,
color=colors[1],
label='')
except:
pass
try:
away_advantage_plot_step = plt.step(
x=away_advantage['SECONDS_GONE'],
y=away_advantage['HOME_xG_DIFF_COPY_2'],
linewidth=1,
alpha=1,
color=colors[0],
label='')
except:
pass
# plot goal events in such a way that they will render exclusively as markers overlaying the stepped line plots
try:
home_goals_plot = home_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=0,
alpha=1,
legend=False,
label='',
marker=u'$\u2609$',
markersize=12,
ax=ax)
except:
pass
try:
away_goals_plot = away_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[0],
linewidth=0,
alpha=1,
legend=False,
label='',
marker=u'$\u2609$',
markersize=12,
ax=ax)
except:
pass
# create a contingency for situations in which one team has yet to or, if the game is final, did not score
if home_goals.empty == False:
goals_label = home_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
zorder=0,
color=colors[2],
linewidth=0,
alpha=1,
marker=u'$\u2609$',
markersize=12,
label='Goal',
ax=ax)
elif home_goals.empty == True and away_goals.empty == False:
goals_label = away_goals.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
zorder=0,
color=colors[2],
linewidth=0,
alpha=1,
marker=u'$\u2609$',
markersize=12,
label='Goal',
ax=ax)
# set the break-even line
breakeven_x, breakeven_y = [-0, 3600], [0, 0]
clear_breakeven = plt.plot(breakeven_x,
breakeven_y,
linewidth=1,
color='white',
alpha=1)
breakeven = plt.plot(breakeven_x,
breakeven_y,
linewidth=1,
color=colors[2],
alpha=0.15)
# set vertical indicators for the end of each period
ax.axvspan(1200,
1201,
ymin=0,
ymax=1,
alpha=1,
color=colors[2],
linewidth=0,
label='Power Play')
ax.axvspan(1200, 1201, ymin=0, ymax=1, alpha=0.15, color=colors[2])
ax.axvspan(2400, 2401, ymin=0, ymax=1, alpha=0.15, color=colors[2])
ax.axvspan(3600, 3601, ymin=0, ymax=1, alpha=0.15, color=colors[2])
# set vertical shaded areas indcating power play and empty-net situations
for second in home_pp['SECONDS_GONE']:
ax.axvspan(second,
second + 1,
ymin=0,
ymax=1,
alpha=0.025,
color=colors[1])
for second in away_pp['SECONDS_GONE']:
ax.axvspan(second,
second + 1,
ymin=0,
ymax=1,
alpha=0.025,
color=colors[0])
# eliminate unnecessary whitespace on the first axes
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(True)
# adjust the visibility of the first axes spines
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
# remove ticks to the first axes
ax.tick_params(axis='y', which="both", left=False, right=False, length=0)
# create a second axes that shares the same x-axis
ax2 = ax.twinx()
# plot each team's cumulative shot differential; make these lines transparent as they are only needed to create the second y-axis scale
try:
home_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='HOME_xG_DIFF',
color=colors[1],
linewidth=2,
alpha=0,
legend=False,
ax=ax2)
except:
pass
try:
away_xG_diff_plot = gameflow_df.plot(x='SECONDS_GONE',
y='AWAY_xG_DIFF',
color=colors[0],
linewidth=2,
alpha=0,
legend=False,
ax=ax2)
except:
pass
# eliminate unnecessary whitespace on the second axes
ax2.axes.get_xaxis().set_visible(False)
ax2.axes.get_yaxis().set_visible(True)
# adjust the visibility of the second axes spines
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.spines["left"].set_visible(False)
# get the maximum and minimum values in the xG differential column in order to set the y-values for the period labels
max_home_xG_diff = gameflow_df['HOME_xG_DIFF'].max()
min_home_xG_diff = gameflow_df['HOME_xG_DIFF'].min()
max_away_xG_diff = gameflow_df['AWAY_xG_DIFF'].max()
min_away_xG_diff = gameflow_df['AWAY_xG_DIFF'].min()
# remove ticks to the second axes
ax2.tick_params(axis='y', which="both", left=False, right=False, length=0)
# invert the second y-axis
ax2.invert_yaxis()
# set the team labels
if max_home_xG_diff > max_away_xG_diff:
plt.text(-660,
min_away_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4060,
max_home_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
elif max_home_xG_diff < max_away_xG_diff:
plt.text(-660,
min_home_xG_diff,
home,
fontsize=12,
color=colors[1],
alpha=1)
plt.text(4060,
max_away_xG_diff,
away,
fontsize=12,
color=colors[0],
alpha=1)
# set the period names as labels
plt.text(0.33,
-0.05,
'1st',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.63,
-0.05,
'2nd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
plt.text(0.93,
-0.05,
'3rd',
fontsize=12,
color=colors[2],
alpha=1,
transform=ax.transAxes)
# set the plot title
plt.title(date + ' 5v5 Gameflow: Expected Goals\n\n')
# set the location of the plot legend
ax.legend(loc='center', bbox_to_anchor=(.5, -0.10),
ncol=2).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.435,
0.916,
' ' + away + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.535,
0.916,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.501,
0.916,
'@',
fontsize='12',
color='black',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
plt.savefig(charts_teams + 'shots_gameflow_xg_5v5.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Finished plotting the 5v5 xG differential game flow.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_teams = parameters.charts_teams
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
# create variables that point to the .csv processed teams stats file and play-by-play file
stats_teams_file = files_root + 'stats_teams.csv'
pbp_file = files_root + 'pbp.csv'
# create a dataframe object that reads in the team stats info
stats_teams_df = pd.read_csv(stats_teams_file)
# create a dataframe object that reads in the shots with expected goals info
pbp_df = pd.read_csv(pbp_file)
pbp_df['xG'] = round(pbp_df.xG.astype(float) * 100, 2)
if int(season_id) >= 20102011:
pbp_df['X_1'] *= -1
pbp_df['Y_1'] *= -1
# choose colors for each team and the legend; set them in a list
legend_color = 'black'
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
colors = [away_color, home_color, legend_color]
states = ['ALL', '5v5', 'PP', 'SH']
for state in states:
# pull the time on ice recorded for the home team from the team stats file
if state == 'ALL':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'ALL')]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == '5v5':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == '5v5')]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == 'PP':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'PP')]
toi = toi_df['TOI'].item()
except:
toi = 0.0
if state == 'SH':
try:
toi_df = stats_teams_df[(stats_teams_df['TEAM'] == home) & (stats_teams_df['STATE'] == 'SH')]
toi = toi_df['TOI'].item()
except:
toi = 0.0
# get a count of the number of unblocked shots for each team
if state == 'ALL':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block')].count()[1])
if state == '5v5':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
if state == 'PP':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
if state == 'SH':
away_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
home_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
# get the sum xG value of unblocked shots for each team
if state == 'ALL':
away_xG = str(round((np.where((pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
home_xG = str(round((np.where((pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
if state == '5v5':
away_xG = str(round((np.where((pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
home_xG = str(round((np.where((pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
if state == 'PP':
away_xG = str(round((np.where((pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
home_xG = str(round((np.where((pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
if state == 'SH':
away_xG = str(round((np.where((pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
home_xG = str(round((np.where((pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] != 'Block') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP'), pbp_df['xG'], 0).sum(), 1)[0] / 100, 2))
# get a count of the number of unblocked shots for each team
if state == 'ALL':
away_G_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal')].count()[1])
home_G_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal')].count()[1])
if state == '5v5':
away_G_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
home_G_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')].count()[1])
if state == 'PP':
away_G_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
home_G_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')].count()[1])
if state == 'SH':
away_G_count = str(pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
home_G_count = str(pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')].count()[1])
# create subsets of the distinct types of unblocked shots for each teeam from the play-by-play file
if state == 'ALL':
away_df_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal')]
away_df_saved = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save')]
away_df_missed = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss')]
home_df_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal')]
home_df_saved = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save')]
home_df_missed = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss')]
if state == '5v5':
away_df_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
away_df_saved = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
away_df_missed = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_df_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_df_saved = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
home_df_missed = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STRENGTH'] == '5v5') & (pbp_df['AWAY_STRENGTH'] == '5v5')]
if state == 'PP':
away_df_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
away_df_saved = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
away_df_missed = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
home_df_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
home_df_saved = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
home_df_missed = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STATE'] == 'PP') & (pbp_df['AWAY_STATE'] == 'SH')]
if state == 'SH':
away_df_scored = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
away_df_saved = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
away_df_missed = pbp_df[(pbp_df['TEAM'] == away) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
home_df_scored = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Goal') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
home_df_saved = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Save') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
home_df_missed = pbp_df[(pbp_df['TEAM'] == home) & (pbp_df['EVENT'] == 'Shot') & (pbp_df['EVENT_TYPE'] == 'Miss') & (pbp_df['HOME_STATE'] == 'SH') & (pbp_df['AWAY_STATE'] == 'PP')]
# create lists of the expected goals values for each type of unblocked shot
away_scored_list = away_df_scored['xG'].tolist()
away_saved_list = away_df_saved['xG'].tolist()
away_missed_list = away_df_missed['xG'].tolist()
home_scored_list = home_df_scored['xG'].tolist()
home_saved_list = home_df_saved['xG'].tolist()
home_missed_list = home_df_missed['xG'].tolist()
# create a figure to plot
fig, ax = plt.subplots(figsize=(8,6))
# plot the distinct types of unblocked shot events for each team in order to create labels
teams_df_scored = pd.concat([away_df_scored, home_df_scored])
teams_df_saved = pd.concat([away_df_saved, home_df_saved])
teams_df_missed = pd.concat([away_df_missed, home_df_missed])
try:
scored_marker = teams_df_scored.plot.scatter(x='X_1', y='Y_1', zorder=0, marker='D', s=60, c='None', edgecolors='black', linewidth=2, alpha=1, label='Scored', ax=ax);
except:
pass
try:
saved_marker = teams_df_saved.plot.scatter(x='X_1', y='Y_1', zorder=0, marker='o', s=40, c='None', edgecolors='black', linewidth=1, alpha=1, label='Saved', ax=ax);
except:
pass
try:
missed_marker = teams_df_missed.plot.scatter(x='X_1', y='Y_1', zorder=0, marker='x', s=40, c='black', edgecolors='None', alpha=1, label='Missed', ax=ax);
except:
pass
# plot the distinct types of unblocked shot events for each team
try:
away_scored_plot = away_df_scored.plot.scatter(x='X_1', y='Y_1', zorder=3, marker='D', s=[5*i for i in away_scored_list], c='None', edgecolors=colors[0], linewidth=2, alpha=1, ax=ax);
except:
pass
try:
away_saved_plot = away_df_saved.plot.scatter(x='X_1', y='Y_1', zorder=2, marker='o', s=[5*i for i in away_saved_list], c='None', edgecolors=colors[0], linewidth=1, alpha=0.5, ax=ax);
except:
pass
try:
away_missed_plot = away_df_missed.plot.scatter(x='X_1', y='Y_1', zorder=2, marker='x', s=[5*i for i in away_missed_list], c=colors[0], edgecolors='None', alpha=0.5, ax=ax);
except:
pass
try:
home_scored_plot = home_df_scored.plot.scatter(x='X_1', y='Y_1', zorder=3, marker='D', s=[5*i for i in home_scored_list], c='None', edgecolors=colors[1], linewidth=2, alpha=1, ax=ax);
except:
pass
try:
home_saved_plot = home_df_saved.plot.scatter(x='X_1', y='Y_1', zorder=2, marker='o', s=[5*i for i in home_saved_list], c='None', edgecolors=colors[1], linewidth=1, alpha=0.5, ax=ax);
except:
pass
try:
home_missed_plot = home_df_missed.plot.scatter(x='X_1', y='Y_1', zorder=2, marker='x', s=[5*i for i in home_missed_list], c=colors[1], edgecolors='none', alpha=0.5, ax=ax);
except:
pass
# set the background image of what will be the plot to the 'rink_image.png' file
rink_img = plt.imread("rink_image.png")
plt.imshow(rink_img, extent=[-100,100,-42.5,42.5], zorder=0)
# eliminate the axis labels
plt.axis('off')
# add text boxes to indicate home and away sides
plt.text(58, 45, home_count + ' USF\n' + home_xG + ' xGF, ' + home_G_count + ' GF', color=colors[1], fontsize=12, ha='center')
plt.text(-58, 45, away_count + ' USF\n' + away_xG + ' xGF, ' + away_G_count + ' GF', color=colors[0], fontsize=12, ha='center')
# set the plot title
if state == 'ALL':
plt.title(date + ' Unblocked Shots\n' + str(toi) + ' Minutes\n\n')
if state == '5v5':
plt.title(date + ' Unblocked Shots\n' + str(toi) + ' 5v5 Minutes\n\n')
if state == 'PP':
plt.title(date + ' Unblocked Shots\n' + str(toi) + ' Home PP Minutes\n\n')
if state == 'SH':
plt.title(date + ' Unblocked Shots\n' + str(toi) + ' Away PP Minutes\n\n')
# set the location of the plot legend
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.00), scatterpoints=1, ncol=3).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.435, 0.744, ' ' + away + ' ', fontsize='12', color='white', bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.535, 0.744, ' ' + home + ' ', fontsize='12', color='white', bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.501, 0.744, '@', fontsize='12', color='black', bbox=dict(facecolor='white', edgecolor='None'))
# eliminate unnecessary whitespace
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
###
### SAVE TO FILE
###
if state == 'ALL':
plt.savefig(charts_teams + 'shots_scatter.png', bbox_inches='tight', pad_inches=0.2)
if state == '5v5':
plt.savefig(charts_teams + 'shots_scatter_5v5.png', bbox_inches='tight', pad_inches=0.2)
if state == 'PP':
plt.savefig(charts_teams + 'shots_scatter_pp_home.png', bbox_inches='tight', pad_inches=0.2)
if state == 'SH':
plt.savefig(charts_teams + 'shots_scatter_pp_away.png', bbox_inches='tight', pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
if state == 'ALL':
print('Finished scatter plot for all unblocked shots sized by xG.')
if state == '5v5':
print('Finished scatter plot for unblocked 5v5 shots sized by xG.')
if state == 'PP':
print('Finished scatter plot for unblocked shots sized by xG during a home PP.')
if state == 'SH':
print('Finished scatter plot for unblocked shots sized by xG during an away PP.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_units = parameters.charts_units
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for the team and players
lines_file = files_root + 'stats_units_lines_onice.csv'
pairings_file = files_root + 'stats_units_pairings_onice.csv'
pk_file = files_root + 'stats_units_pk_onice.csv'
pp_file = files_root + 'stats_units_pp_onice.csv'
# create a dataframe object that reads in info from the .csv files
lines_df = pd.read_csv(lines_file)
pairings_df = pd.read_csv(pairings_file)
PP_df = pd.read_csv(pp_file)
PK_df = pd.read_csv(pk_file)
max_lines_toi = lines_df['TOI'].max()
max_pairings_toi = pairings_df['TOI'].max()
max_PP_toi = PP_df['TOI'].max()
max_PK_toi = PK_df['TOI'].max()
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
###
### 5v5, PP, PK
###
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
opponent_color = team_colors[1]
if team == home:
team_color = team_colors[1]
opponent_color = team_colors[0]
# create a dataframe; filter for team; sort by team, game state and position; rank by time on ice and then invert the rankings
team_lines_df = lines_df.copy()
team_lines_df = team_lines_df[(team_lines_df['TEAM'] == team)]
team_lines_df = team_lines_df.sort_values(by=['TOI'], ascending = True)
team_lines_df = team_lines_df.iloc[-4:]
team_lines_df['RANK'] = team_lines_df['TOI'].rank(method='first')
team_lines_df = team_lines_df.sort_values(by=['RANK'], ascending = True)
team_lines_df['RANK'] -= 1
# remove zeros from the goals for and against columns
team_lines_df['GF'] = team_lines_df['GF'].replace(0, np.NaN)
team_lines_df['GA'] = team_lines_df['GA'].replace(0, np.NaN)
# remove zeros from the differential column
team_lines_df['SD'] = team_lines_df['SD'].replace(0, np.NaN)
# make goals against and shots against negative values
team_lines_df['GA'] *= -1
team_lines_df['SA'] *= -1
# find the max toi value
team_max_lines_toi = team_lines_df['TOI'].max()
# create a pairings dataframe; filter for team; sort by time on ice; keep the pairs with the 3 highest totals; rank and then invert the rankings
team_pairings_df = pairings_df.copy()
team_pairings_df = team_pairings_df[(team_pairings_df['TEAM'] == team)]
team_pairings_df = team_pairings_df.sort_values(by=['TOI'], ascending = True)
team_pairings_df = team_pairings_df.iloc[-3:]
team_pairings_df['RANK'] = team_pairings_df['TOI'].rank(method='first')
team_pairings_df['RANK'] -= 1
# remove zeros from the goals for and against columns
team_pairings_df['GF'] = team_pairings_df['GF'].replace(0, np.NaN)
team_pairings_df['GA'] = team_pairings_df['GA'].replace(0, np.NaN)
# remove zeros from the differential column
team_pairings_df['SD'] = team_pairings_df['SD'].replace(0, np.NaN)
# make goals against and shots against negative values
team_pairings_df['GA'] *= -1
team_pairings_df['SA'] *= -1
# find the max toi value
team_max_pairings_toi = team_pairings_df['TOI'].max()
# create a power play units dataframe; filter for team; sort by time on ice; keep the units with the 4 highest totals; rank and then invert the rankings
team_PP_df = PP_df.copy()
team_PP_df = team_PP_df[(team_PP_df['TEAM'] == team)]
team_PP_df = team_PP_df.sort_values(by=['TOI'], ascending = True)
team_PP_df = team_PP_df.iloc[-4:]
team_PP_df['RANK'] = team_PP_df['TOI'].rank(method='first')
team_PP_df['RANK'] -= 1
# remove zeros from the goals for and against columns
team_PP_df['GF'] = team_PP_df['GF'].replace(0, np.NaN)
team_PP_df['GA'] = team_PP_df['GA'].replace(0, np.NaN)
# remove zeros from the differential column
team_PP_df['SD'] = team_PP_df['SD'].replace(0, np.NaN)
# make goals against and shots against negative values
team_PP_df['GA'] *= -1
team_PP_df['SA'] *= -1
# find the max toi value
team_max_PP_toi = team_PP_df['TOI'].max()
# create a penalty kill units dataframe; filter for team; sort by time on ice; keep the units with the 4 highest totals; rank and then invert the rankings
team_PK_df = PK_df.copy()
team_PK_df = team_PK_df[(team_PK_df['TEAM'] == team)]
team_PK_df = team_PK_df.sort_values(by=['TOI'], ascending = True)
team_PK_df = team_PK_df.iloc[-4:]
team_PK_df['RANK'] = team_PK_df['TOI'].rank(method='first')
team_PK_df['RANK'] -= 1
# remove zeros from the goals for and against columns
team_PK_df['GF'] = team_PK_df['GF'].replace(0, np.NaN)
team_PK_df['GA'] = team_PK_df['GA'].replace(0, np.NaN)
# remove zeros from the differential column
team_PK_df['SD'] = team_PK_df['SD'].replace(0, np.NaN)
# make goals against and shots against negative values
team_PK_df['GA'] *= -1
team_PK_df['SA'] *= -1
# find the max toi value
team_max_PK_toi = team_PK_df['TOI'].max()
# create a figure with six subplots arrangled complexly using a grid structure
fig = plt.figure(figsize=(8,8))
grid = plt.GridSpec(4, 8, hspace=0.75, wspace=0.50)
ax_5v5_lines_shots = fig.add_subplot(grid[0, 0:-2])
ax_5v5_lines_toi = fig.add_subplot(grid[0, -1])
ax_5v5_pairings_shots = fig.add_subplot(grid[1, 0:-2])
ax_5v5_pairings_toi = fig.add_subplot(grid[1, -1])
ax_PP_shots = fig.add_subplot(grid[2, 0:-2])
ax_PP_toi = fig.add_subplot(grid[2, -1])
ax_PK_shots = fig.add_subplot(grid[3, 0:-2])
ax_PK_toi = fig.add_subplot(grid[3, -1])
# set the plot title
fig.suptitle(date + ' Unit On-Ice Shots\n\n')
# set the axes titles
ax_5v5_lines_shots.set_title('5v5 S', fontsize=10)
ax_5v5_lines_toi.set_title('5v5 TOI', fontsize=10)
ax_PP_shots.set_title('PP S', fontsize=10)
ax_PP_toi.set_title('PP TOI', fontsize=10)
ax_PK_shots.set_title('SH S', fontsize=10)
ax_PK_toi.set_title('SH TOI', fontsize=10)
# for each state, plot the bars for total shots and markers for saved, missed and blocked shots markers
try:
SF_5v5_lines_plot = team_lines_df.plot.barh(x='LINE', y='SF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_5v5_lines_shots);
except:
pass
try:
SA_5v5_lines_plot = team_lines_df.plot.barh(x='LINE', y='SA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_5v5_lines_shots);
except:
pass
try:
GF_5v5_lines_marker = team_lines_df.plot(x='GF', y='RANK', marker='D', markersize=5, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_lines_shots);
except:
pass
try:
GA_5v5_lines_marker = team_lines_df.plot(x='GA', y='RANK', marker='D', markersize=5, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_lines_shots);
except:
pass
try:
SD_5v5_lines_plot = team_lines_df.plot(x='SD', y='RANK', marker='|', markersize=13, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_lines_shots);
except:
pass
try:
SF_5v5_pairings_plot = team_pairings_df.plot.barh(x='PAIRING', y='SF', color=team_color, edgecolor='None', width=0.55, legend=None, label='', ax=ax_5v5_pairings_shots);
except:
pass
try:
SA_5v5_pairings_plot = team_pairings_df.plot.barh(x='PAIRING', y='SA', color=opponent_color, edgecolor='None', width=0.55, legend=None, label='', ax=ax_5v5_pairings_shots);
except:
pass
try:
GF_5v5_pairings_marker = team_lines_df.plot(x='GF', y='RANK', marker='D', markersize=5, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_pairings_shots);
except:
pass
try:
GA_5v5_pairings_marker = team_lines_df.plot(x='GA', y='RANK', marker='D', markersize=5, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_pairings_shots);
except:
pass
try:
SD_5v5_pairings_plot = team_pairings_df.plot(x='SD', y='RANK', marker='|', markersize=13, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_5v5_pairings_shots);
except:
pass
try:
SF_PP_plot = team_PP_df.plot.barh(x='UNIT', y='SF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PP_shots);
except:
pass
try:
SA_PP_plot = team_PP_df.plot.barh(x='UNIT', y='SA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PP_shots);
except:
pass
try:
SD_PP_marker = team_PP_df.plot(x='SD', y='RANK', marker='|', markersize=13, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_PP_shots);
except:
pass
try:
SF_PK_plot = team_PK_df.plot.barh(x='UNIT', y='SF', color=team_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PK_shots);
except:
pass
try:
SA_PK_plot = team_PK_df.plot.barh(x='UNIT', y='SA', color=opponent_color, edgecolor='None', width=0.75, legend=None, label='', ax=ax_PK_shots);
except:
pass
try:
SD_PK_marker = team_PK_df.plot(x='SD', y='RANK', marker='|', markersize=13, markeredgewidth=1, markerfacecolor='None', markeredgecolor='white', linewidth=0, alpha=1, legend='', label='', ax=ax_PK_shots);
except:
pass
# for each state, plot the bars for time on ice
try:
toi_5v5_lines_plot = team_lines_df.plot.barh(x='LINE', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_5v5_lines_toi);
except:
pass
try:
toi_5v5_pairings_plot = team_pairings_df.plot.barh(x='PAIRING', y='TOI', color='white', edgecolor=team_color, width=0.55, legend=None, label='', ax=ax_5v5_pairings_toi);
except:
pass
try:
toi_PP_plot = team_PP_df.plot.barh(x='UNIT', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_PP_toi);
except:
pass
try:
toi_PK_plot = team_PK_df.plot.barh(x='UNIT', y='TOI', color='white', edgecolor=team_color, width=0.75, legend=None, label='', ax=ax_PK_toi);
except:
pass
# set / remove the y-labels for the subplots
ax_5v5_lines_shots.set_xlabel('')
ax_5v5_lines_shots.set_ylabel('')
ax_5v5_lines_toi.set_xlabel('')
ax_5v5_lines_toi.set_ylabel('')
ax_5v5_pairings_shots.set_xlabel('')
ax_5v5_pairings_shots.set_ylabel('')
ax_5v5_pairings_toi.set_xlabel('')
ax_5v5_pairings_toi.set_ylabel('')
ax_PP_shots.set_xlabel('')
ax_PP_shots.set_ylabel('')
ax_PP_toi.set_xlabel('')
ax_PP_toi.set_ylabel('')
ax_PK_shots.set_xlabel('')
ax_PK_shots.set_ylabel('')
ax_PK_toi.set_xlabel('')
ax_PK_toi.set_ylabel('')
# set vertical indicators for zero shots
ax_5v5_lines_shots.axvspan(0, 0, ymin=0, ymax=1, alpha=0.25, linestyle=':', color='black')
ax_5v5_pairings_shots.axvspan(0, 0, ymin=0, ymax=1, alpha=0.25, linestyle=':', color='black')
ax_PP_shots.axvspan(0, 0, ymin=0, ymax=1, alpha=0.25, linestyle=':', color='black')
ax_PK_shots.axvspan(0, 0, ymin=0, ymax=1, alpha=0.25, linestyle=':', color='black')
# change the tick parameters for each axes
ax_5v5_lines_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_lines_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_pairings_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_pairings_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PK_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PK_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
# change the y-axis label colors
ax_5v5_lines_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
ax_5v5_pairings_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
ax_PP_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
ax_PP_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
ax_PK_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
ax_PK_shots.tick_params(
axis='y',
which='both',
labelcolor=team_color)
# create a list of x-axis tick values contingent on the max values for shots
team_5v5_lines_df2 = team_lines_df.copy()
team_5v5_pairings_df2 = team_pairings_df.copy()
team_PP_df2 = team_PP_df.copy()
team_PK_df2 = team_PK_df.copy()
team_5v5_lines_df2['SA'] *= -1
team_5v5_pairings_df2['SA'] *= -1
team_PP_df2['SA'] *= -1
team_PK_df2['SA'] *= -1
SF_5v5_lines_max = team_5v5_lines_df2['SF']
SF_5v5_lines_max = SF_5v5_lines_max.max()
SA_5v5_lines_max = team_5v5_lines_df2['SA']
SA_5v5_lines_max = SA_5v5_lines_max.max()
S_5v5_lines_max = int()
if SF_5v5_lines_max >= SA_5v5_lines_max:
S_5v5_lines_max = SF_5v5_lines_max
if SF_5v5_lines_max < SA_5v5_lines_max:
S_5v5_lines_max = SA_5v5_lines_max
SF_5v5_pairings_max = team_5v5_pairings_df2['SF']
SF_5v5_pairings_max = SF_5v5_pairings_max.max()
SA_5v5_pairings_max = team_5v5_pairings_df2['SA']
SA_5v5_pairings_max = SA_5v5_pairings_max.max()
S_5v5_pairings_max = int()
if SF_5v5_pairings_max >= SA_5v5_pairings_max:
S_5v5_pairings_max = SF_5v5_pairings_max
if SF_5v5_pairings_max < SA_5v5_pairings_max:
S_5v5_pairings_max = SA_5v5_pairings_max
S_5v5_tickmax = int()
if S_5v5_lines_max >= S_5v5_pairings_max:
S_5v5_tickmax = S_5v5_lines_max
if S_5v5_lines_max < S_5v5_pairings_max:
S_5v5_tickmax = S_5v5_pairings_max
S_5v5_ticklabels = []
if S_5v5_tickmax <= 10:
S_5v5_ticklabels = [-10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10]
if S_5v5_tickmax > 10 and S_5v5_tickmax <= 20:
S_5v5_ticklabels = [-20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20]
if S_5v5_tickmax > 20 and S_5v5_tickmax <= 30:
S_5v5_ticklabels = [-30, -24, -18, -12, -6, 0, 6, 12, 18, 24, 30]
if S_5v5_tickmax > 30 and S_5v5_tickmax <= 40:
S_5v5_ticklabels = [-40, -32, -24, -16, -8, 0, 8, 16, 24, 32, 40]
toi_5v5_lines_tickmax = max_lines_toi
toi_5v5_pairings_tickmax = max_pairings_toi
toi_5v5_tickmax = int()
if toi_5v5_lines_tickmax >= toi_5v5_pairings_tickmax:
toi_5v5_tickmax = toi_5v5_lines_tickmax
if toi_5v5_lines_tickmax < toi_5v5_pairings_tickmax:
toi_5v5_tickmax = toi_5v5_pairings_tickmax
toi_5v5_ticklabels = []
if toi_5v5_tickmax <= 2:
toi_5v5_ticklabels = [0, 2]
if toi_5v5_tickmax > 2 and toi_5v5_tickmax <= 4:
toi_5v5_ticklabels = [0, 4]
if toi_5v5_tickmax > 4 and toi_5v5_tickmax <= 6:
toi_5v5_ticklabels = [0, 6]
if toi_5v5_tickmax > 6 and toi_5v5_tickmax <= 8:
toi_5v5_ticklabels = [0, 8]
if toi_5v5_tickmax > 8 and toi_5v5_tickmax <= 10:
toi_5v5_ticklabels = [0, 10]
if toi_5v5_tickmax > 10 and toi_5v5_tickmax <= 12:
toi_5v5_ticklabels = [0, 12]
if toi_5v5_tickmax > 12 and toi_5v5_tickmax <= 14:
toi_5v5_ticklabels = [0, 14]
if toi_5v5_tickmax > 14 and toi_5v5_tickmax <= 16:
toi_5v5_ticklabels = [0, 16]
if toi_5v5_tickmax > 16 and toi_5v5_tickmax <= 18:
toi_5v5_ticklabels = [0, 18]
if toi_5v5_tickmax > 18 and toi_5v5_tickmax <= 20:
toi_5v5_ticklabels = [0, 20]
SF_PP_max = team_PP_df2['SF']
SF_PP_max = SF_PP_max.max()
SA_PP_max = team_PP_df2['SA']
SA_PP_max = SA_PP_max.max()
S_PP_tickmax = int()
if SF_PP_max >= SA_PP_max:
S_PP_tickmax = SF_PP_max
if SF_PP_max < SA_PP_max:
S_PP_tickmax = SA_PP_max
SF_PK_max = team_PK_df2['SF']
SF_PK_max = SF_PK_max.max()
SA_PK_max = team_PK_df2['SA']
SA_PK_max = SA_PK_max.max()
S_PK_tickmax = int()
if SF_PK_max >= SA_PK_max:
S_PK_tickmax = SF_PK_max
if SF_PK_max < SA_PK_max:
S_PK_tickmax = SA_PK_max
if S_PP_tickmax >= S_PK_tickmax:
S_specialteams_tickmax = S_PP_tickmax
if S_PP_tickmax < S_PK_tickmax:
S_specialteams_tickmax = S_PK_tickmax
S_specialteams_ticklabels = []
if S_specialteams_tickmax <= 4:
S_specialteams_ticklabels = [-4, -2, 0, 2, 4]
if S_specialteams_tickmax > 4 and S_specialteams_tickmax <= 6:
S_specialteams_ticklabels = [-6, -3, 0, 3, 6]
if S_specialteams_tickmax > 6 and S_specialteams_tickmax <= 8:
S_specialteams_ticklabels = [-8, -4, 0, 4, 8]
if S_specialteams_tickmax > 8 and S_specialteams_tickmax <= 10:
S_specialteams_ticklabels = [-10, -5, 0, 5, 10]
if S_specialteams_tickmax > 10 and S_specialteams_tickmax <= 12:
S_specialteams_ticklabels = [-12, -6, 0, 6, 12]
if S_specialteams_tickmax > 12 and S_specialteams_tickmax <= 14:
S_specialteams_ticklabels = [-14, -7, 0, 7, 14]
if S_specialteams_tickmax > 14 and S_specialteams_tickmax <= 16:
S_specialteams_ticklabels = [-16, -8, 0, 8, 16]
if S_specialteams_tickmax > 16 and S_specialteams_tickmax <= 18:
S_specialteams_ticklabels = [-18, -9, 0, 9, 18]
if S_specialteams_tickmax > 18 and S_specialteams_tickmax <= 20:
S_specialteams_ticklabels = [-20, -10, 0, 10, 20]
toi_PP_tickmax = max_PP_toi
toi_SH_tickmax = max_PK_toi
toi_specialteams_tickmax = int()
if toi_PP_tickmax >= toi_SH_tickmax:
toi_specialteams_tickmax = toi_PP_tickmax
if toi_PP_tickmax < toi_SH_tickmax:
toi_specialteams_tickmax = toi_SH_tickmax
toi_specialteams_ticklabels = []
if toi_specialteams_tickmax <= 2:
toi_specialteams_ticklabels = [0, 2]
if toi_specialteams_tickmax > 2 and toi_specialteams_tickmax <= 4:
toi_specialteams_ticklabels = [0, 4]
if toi_specialteams_tickmax > 4 and toi_specialteams_tickmax <= 6:
toi_specialteams_ticklabels = [0, 6]
if toi_specialteams_tickmax > 6 and toi_specialteams_tickmax <= 8:
toi_specialteams_ticklabels = [0, 8]
if toi_specialteams_tickmax > 8 and toi_specialteams_tickmax <= 10:
toi_specialteams_ticklabels = [0, 10]
if toi_specialteams_tickmax > 10 and toi_specialteams_tickmax <= 12:
toi_specialteams_ticklabels = [0, 12]
# set vertical indicator for the midpoint and max of time on ice max
ax_5v5_lines_toi.axvspan(toi_5v5_ticklabels[1] / 2, toi_5v5_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_5v5_lines_toi.axvspan(toi_5v5_ticklabels[1], toi_5v5_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_5v5_pairings_toi.axvspan(toi_5v5_ticklabels[1] / 2, toi_5v5_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_5v5_pairings_toi.axvspan(toi_5v5_ticklabels[1], toi_5v5_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1] / 2, toi_specialteams_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1], toi_specialteams_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PK_toi.axvspan(toi_specialteams_ticklabels[1] / 2, toi_specialteams_ticklabels[1] / 2, ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
ax_PK_toi.axvspan(toi_specialteams_ticklabels[1], toi_specialteams_ticklabels[1], ymin=0, ymax=1, zorder=0, alpha=0.25, linestyle=':', color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_5v5_lines_shots.set_xticks(S_5v5_ticklabels, minor=False)
ax_5v5_lines_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_5v5_pairings_shots.set_xticks(S_5v5_ticklabels, minor=False)
ax_5v5_pairings_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_PP_shots.set_xticks(S_specialteams_ticklabels, minor=False)
ax_PP_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
ax_PK_shots.set_xticks(S_specialteams_ticklabels, minor=False)
ax_PK_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
# remove the borders to each subplot
ax_5v5_lines_shots.spines["top"].set_visible(False)
ax_5v5_lines_shots.spines["bottom"].set_visible(False)
ax_5v5_lines_shots.spines["right"].set_visible(False)
ax_5v5_lines_shots.spines["left"].set_visible(False)
ax_5v5_lines_toi.spines["top"].set_visible(False)
ax_5v5_lines_toi.spines["bottom"].set_visible(False)
ax_5v5_lines_toi.spines["right"].set_visible(False)
ax_5v5_lines_toi.spines["left"].set_visible(False)
ax_5v5_pairings_shots.spines["top"].set_visible(False)
ax_5v5_pairings_shots.spines["bottom"].set_visible(False)
ax_5v5_pairings_shots.spines["right"].set_visible(False)
ax_5v5_pairings_shots.spines["left"].set_visible(False)
ax_5v5_pairings_toi.spines["top"].set_visible(False)
ax_5v5_pairings_toi.spines["bottom"].set_visible(False)
ax_5v5_pairings_toi.spines["right"].set_visible(False)
ax_5v5_pairings_toi.spines["left"].set_visible(False)
ax_PP_shots.spines["top"].set_visible(False)
ax_PP_shots.spines["bottom"].set_visible(False)
ax_PP_shots.spines["right"].set_visible(False)
ax_PP_shots.spines["left"].set_visible(False)
ax_PP_toi.spines["top"].set_visible(False)
ax_PP_toi.spines["bottom"].set_visible(False)
ax_PP_toi.spines["right"].set_visible(False)
ax_PP_toi.spines["left"].set_visible(False)
ax_PK_shots.spines["top"].set_visible(False)
ax_PK_shots.spines["bottom"].set_visible(False)
ax_PK_shots.spines["right"].set_visible(False)
ax_PK_shots.spines["left"].set_visible(False)
ax_PK_toi.spines["top"].set_visible(False)
ax_PK_toi.spines["bottom"].set_visible(False)
ax_PK_toi.spines["right"].set_visible(False)
ax_PK_toi.spines["left"].set_visible(False)
# add a legend for the shot type markers
from matplotlib.lines import Line2D
elements = [Line2D([0], [0], marker='D', markersize=5, markerfacecolor='None', markeredgecolor='black', linewidth=0, alpha=1, label='Scored'), Line2D([0], [0], marker='|', markersize=13, markerfacecolor='None', markeredgecolor='black', linewidth=0, alpha=1, label='Differential')]
ax_PK_shots.legend(handles=elements, loc='center', bbox_to_anchor=(.5, -.6), ncol=2).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425, 0.936, ' ' + away + ' ', color='white', fontsize='12', bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525, 0.936, ' ' + home + ' ', fontsize='12', color='white', bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490, 0.936, '@', color='black', fontsize='12', bbox=dict(facecolor='white', edgecolor='None'))
# add a text box in the event a team does not have a power play
if team_max_PP_toi == 0:
fig.text(0.35, 0.375, 'No PP situations', color='black', fontsize='10', bbox=dict(facecolor='None', edgecolor='None'))
# add a text box in the event a team's opponent does not have a power play
if team_max_PK_toi == 0:
fig.text(0.35, 0.175, 'No PK situations', color='black', fontsize='10', bbox=dict(facecolor='None', edgecolor='None'))
# for games where one (or both!) teams spend no time on a powerplay or shorthanded, turn all indicators for the respective axes off
if team_max_PP_toi == 0:
ax_PP_shots.axis('off')
ax_PP_toi.axis('off')
if team_max_PK_toi == 0:
ax_PK_shots.axis('off')
ax_PK_toi.axis('off')
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_units + 'onice_shots_away.png', bbox_inches='tight', pad_inches=0.2)
elif team == home:
plt.savefig(charts_units + 'onice_shots_home.png', bbox_inches='tight', pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' lines, pairings and special teams units on-ice shots.')
# status update
print('Finished plotting the on-ice shots for lines, pairings and special teams units.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_players_individual = parameters.charts_players_individual
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for the team and players
players_file = files_root + 'stats_players_individual.csv'
# create a dataframe object that reads in info from the .csv files
players_df = pd.read_csv(players_file)
skaters_5v5_df = players_df.copy()
skaters_5v5_df = skaters_5v5_df[(skaters_5v5_df['STATE'] == '5v5')
& (skaters_5v5_df['POS'] != 'G')]
max_5v5_toi = skaters_5v5_df['TOI'].max()
skaters_PP_df = players_df.copy()
skaters_PP_df = skaters_PP_df[(skaters_PP_df['STATE'] == 'PP')
& (skaters_PP_df['POS'] != 'G')]
max_PP_toi = skaters_PP_df['TOI'].max()
skaters_SH_df = players_df.copy()
skaters_SH_df = skaters_SH_df[(skaters_SH_df['STATE'] == 'PP')
& (skaters_SH_df['POS'] != 'G')]
max_SH_toi = skaters_SH_df['TOI'].max()
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
###
### 5v5, PP, SH
###
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
if team == home:
team_color = team_colors[1]
# create a dataframe; filter for team; sort by team, game state and position; rank by time on ice and then invert the rankings
team_df = players_df.copy()
team_df = team_df[(team_df['TEAM'] == team) & (team_df['POS'] != 'G')]
# create columns for saved, missed and blocked shots; remove zeros from all the columns intended for plotting
team_df['SS'] = team_df['ONS'] - team_df['G']
team_df['MS'] = team_df['US'] - team_df['ONS']
team_df['BS'] = team_df['S'] - team_df['US']
team_df['G'] = team_df['G'].replace(0, np.NaN)
team_df['SS'] = team_df['SS'].replace(0, np.NaN)
team_df['MS'] = team_df['MS'].replace(0, np.NaN)
team_df['BS'] = team_df['BS'].replace(0, np.NaN)
# create a filtered dataframe for each game state; sort by team, game state and position; rank by time on ice and then invert the rankings
team_5v5_df = team_df.copy()
team_5v5_df = team_5v5_df[(team_5v5_df['STATE'] == '5v5')]
team_5v5_df = team_5v5_df.sort_values(by=['TOI'], ascending=True)
team_5v5_df['RANK'] = team_5v5_df['TOI'].rank(method='first')
team_5v5_df['RANK'] -= 1
team_PP_df = team_df.copy()
team_PP_df = team_PP_df[(team_PP_df['STATE'] == 'PP')
& (team_PP_df['TOI'] > 0)]
team_PP_df = team_PP_df.sort_values(by=['TOI'], ascending=True)
team_PP_df = team_PP_df.iloc[-10:]
team_PP_df['RANK'] = team_PP_df['TOI'].rank(method='first')
team_PP_df['RANK'] -= 1
team_SH_df = team_df.copy()
team_SH_df = team_SH_df[(team_SH_df['STATE'] == 'SH')
& (team_SH_df['TOI'] > 0)]
team_SH_df = team_SH_df.sort_values(by=['TOI'], ascending=True)
team_SH_df = team_SH_df.iloc[-10:]
team_SH_df['RANK'] = team_SH_df['TOI'].rank(method='first')
team_SH_df['RANK'] -= 1
# for each game state, create a dataframe with just the time on ice column; set a max value; scale each player's time on ice relative to the max
toi_5v5 = team_5v5_df['TOI']
max_toi_5v5 = toi_5v5.max()
toi_PP = team_PP_df['TOI']
max_toi_PP = toi_PP.max()
team_PP_toi = max_toi_PP
toi_SH = team_SH_df['TOI']
max_toi_SH = toi_SH.max()
team_SH_toi = max_toi_SH
# create a figure with six subplots arrangled complexly using a grid structure
fig = plt.figure(figsize=(8, 8))
grid = plt.GridSpec(5, 8, hspace=0.75, wspace=0.75)
ax_5v5_shots = fig.add_subplot(grid[0:-2, :-1])
ax_5v5_toi = fig.add_subplot(grid[0:-2, 7])
ax_PP_shots = fig.add_subplot(grid[3:, :2])
ax_PP_toi = fig.add_subplot(grid[3:, 2])
ax_SH_shots = fig.add_subplot(grid[3:, 5:-1])
ax_SH_toi = fig.add_subplot(grid[3:, 7])
# set the plot title
fig.suptitle(date + ' Skaters Individual Shots\n\n')
# set the axes titles
ax_5v5_shots.set_title('5v5 S', fontsize=10)
ax_5v5_toi.set_title('5v5 TOI', fontsize=10)
ax_PP_shots.set_title('PP S', fontsize=10)
ax_PP_toi.set_title('PP TOI', fontsize=10)
ax_SH_shots.set_title('SH S', fontsize=10)
ax_SH_toi.set_title('SH TOI', fontsize=10)
# create markers for the plot legend
try:
G_marker = team_5v5_df.plot(x='G',
y='RANK',
marker='D',
markersize=11,
markerfacecolor='None',
markeredgecolor='black',
linewidth=0,
alpha=1,
legend='Scored',
label='Scored',
ax=ax_5v5_shots)
except:
pass
try:
SS_marker = team_5v5_df.plot(x='SS',
y='RANK',
marker='o',
markersize=7,
markerfacecolor='None',
markeredgecolor='black',
linewidth=0,
alpha=1,
legend='Saved',
label='Saved',
ax=ax_5v5_shots)
except:
pass
try:
MS_marker = team_5v5_df.plot(x='MS',
y='RANK',
marker='x',
markersize=7,
markeredgewidth=1,
color='black',
linewidth=0,
alpha=1,
legend='Missed',
label='Missed',
ax=ax_5v5_shots)
except:
pass
try:
BS_marker = team_5v5_df.plot(x='BS',
y='RANK',
marker='_',
markersize=13,
markeredgewidth=1,
color='black',
linewidth=0,
alpha=1,
legend='Blocked',
label='Blocked',
ax=ax_5v5_shots)
except:
pass
# for each state, plot the bars for total shots and markers for saved, missed and blocked shots markers
try:
S_5v5_plot = team_5v5_df.plot.barh(x='PLAYER',
y='S',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_5v5_shots)
except:
pass
try:
G_5v5_marker = team_5v5_df.plot(x='G',
y='RANK',
marker='D',
markersize=11,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_5v5_shots)
except:
pass
try:
SS_5v5_marker = team_5v5_df.plot(x='SS',
y='RANK',
marker='o',
markersize=7,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_5v5_shots)
except:
pass
try:
MS_5v5_marker = team_5v5_df.plot(x='MS',
y='RANK',
marker='x',
markersize=7,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_5v5_shots)
except:
pass
try:
BS_5v5_marker = team_5v5_df.plot(x='BS',
y='RANK',
marker='_',
markersize=13,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_5v5_shots)
except:
pass
if team_PP_toi != 0:
try:
S_PP_plot = team_PP_df.plot.barh(x='PLAYER',
y='S',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_PP_shots)
except:
pass
try:
G_PP_marker = team_PP_df.plot(x='G',
y='RANK',
marker='D',
markersize=11,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_PP_shots)
except:
pass
try:
SS_PP_marker = team_PP_df.plot(x='SS',
y='RANK',
marker='o',
markersize=7,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_PP_shots)
except:
pass
try:
MS_PP_marker = team_PP_df.plot(x='MS',
y='RANK',
marker='x',
markersize=7,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_PP_shots)
except:
pass
try:
BS_PP_marker = team_PP_df.plot(x='BS',
y='RANK',
marker='_',
markersize=13,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_PP_shots)
except:
pass
if team_SH_toi != 0:
try:
S_SH_plot = team_SH_df.plot.barh(x='PLAYER',
y='S',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_SH_shots)
except:
pass
try:
G_SH_marker = team_SH_df.plot(x='G',
y='RANK',
marker='D',
markersize=11,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_SH_shots)
except:
pass
try:
SS_SH_marker = team_SH_df.plot(x='SS',
y='RANK',
marker='o',
markersize=7,
markerfacecolor='None',
markeredgecolor='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_SH_shots)
except:
pass
try:
MS_SH_marker = team_SH_df.plot(x='MS',
y='RANK',
marker='x',
markersize=7,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_SH_shots)
except:
pass
try:
BS_SH_marker = team_SH_df.plot(x='BS',
y='RANK',
marker='_',
markersize=11,
markeredgewidth=1,
color='white',
linewidth=0,
alpha=1,
legend='',
label='',
ax=ax_SH_shots)
except:
pass
# for each state, plot the bars for time on ice
try:
toi_5v5_plot = team_5v5_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_5v5_toi)
except:
pass
if team_PP_toi != 0:
try:
toi_PP_plot = team_PP_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_PP_toi)
except:
pass
if team_SH_toi != 0:
try:
toi_SH_plot = team_SH_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_SH_toi)
except:
pass
# set / remove the y-labels for the subplots
ax_5v5_shots.set_xlabel('')
ax_5v5_shots.set_ylabel('', fontsize=10)
ax_5v5_toi.set_xlabel('')
ax_5v5_toi.set_ylabel('')
ax_PP_shots.set_xlabel('')
ax_PP_shots.set_ylabel('', fontsize=10)
ax_PP_toi.set_xlabel('')
ax_PP_toi.set_ylabel('')
ax_SH_shots.set_xlabel('')
ax_SH_shots.set_ylabel('', fontsize=10)
ax_SH_toi.set_xlabel('')
ax_SH_toi.set_ylabel('')
# change the tick parameters for each axes
ax_5v5_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_shots.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
# change the y-axis label colors
ax_5v5_shots.tick_params(axis='y', which='both', labelcolor=team_color)
ax_PP_shots.tick_params(axis='y', which='both', labelcolor=team_color)
ax_SH_shots.tick_params(axis='y', which='both', labelcolor=team_color)
# create a list of x-axis tick values contingent on the max values for shots
S_5v5_max = team_5v5_df['S']
S_5v5_tickmax = S_5v5_max.max()
S_5v5_ticklabels = []
if S_5v5_tickmax <= 10:
S_5v5_ticklabels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
if S_5v5_tickmax > 10 and S_5v5_tickmax <= 20:
S_5v5_ticklabels = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
toi_5v5_tickmax = max_5v5_toi
toi_5v5_ticklabels = []
if toi_5v5_tickmax <= 10:
toi_5v5_ticklabels = [0, 10]
if toi_5v5_tickmax > 10 and toi_5v5_tickmax <= 15:
toi_5v5_ticklabels = [0, 15]
if toi_5v5_tickmax > 15 and toi_5v5_tickmax <= 20:
toi_5v5_ticklabels = [0, 20]
if toi_5v5_tickmax > 20 and toi_5v5_tickmax <= 25:
toi_5v5_ticklabels = [0, 25]
if toi_5v5_tickmax > 25 and toi_5v5_tickmax <= 30:
toi_5v5_ticklabels = [0, 30]
if toi_5v5_tickmax > 30 and toi_5v5_tickmax <= 35:
toi_5v5_ticklabels = [0, 35]
if toi_5v5_tickmax > 35 and toi_5v5_tickmax <= 40:
toi_5v5_ticklabels = [0, 40]
if toi_5v5_tickmax > 40 and toi_5v5_tickmax <= 45:
toi_5v5_ticklabels = [0, 45]
if toi_5v5_tickmax > 45 and toi_5v5_tickmax <= 50:
toi_5v5_ticklabels = [0, 50]
if toi_5v5_tickmax > 50 and toi_5v5_tickmax <= 55:
toi_5v5_ticklabels = [0, 55]
if toi_5v5_tickmax > 55 and toi_5v5_tickmax <= 60:
toi_5v5_ticklabels = [0, 60]
S_PP_max = team_PP_df['S']
S_PP_tickmax = S_PP_max.max()
S_PP_ticklabels = []
if S_PP_tickmax <= 5:
S_PP_ticklabels = [0, 1, 2, 3, 4, 5]
if S_PP_tickmax > 5 and S_PP_tickmax <= 10:
S_PP_ticklabels = [0, 2, 4, 6, 8, 10]
if S_PP_tickmax > 10 and S_PP_tickmax <= 15:
S_PP_ticklabels = [0, 3, 6, 9, 12, 15]
S_SH_max = team_SH_df['S']
S_SH_tickmax = S_SH_max.max()
S_SH_ticklabels = []
if S_SH_tickmax <= 5:
S_SH_ticklabels = [0, 1, 2, 3, 4, 5]
if S_SH_tickmax > 5 and S_SH_tickmax <= 10:
S_SH_ticklabels = [0, 2, 4, 6, 8, 10]
if S_SH_tickmax > 10 and S_SH_tickmax <= 15:
S_SH_ticklabels = [0, 3, 6, 9, 12, 15]
toi_PP_tickmax = max_PP_toi
toi_SH_tickmax = max_SH_toi
toi_specialteams_tickmax = float()
if toi_PP_tickmax >= toi_SH_tickmax:
toi_specialteams_tickmax = toi_PP_tickmax
if toi_PP_tickmax < toi_SH_tickmax:
toi_specialteams_tickmax = toi_SH_tickmax
toi_specialteams_ticklabels = []
if toi_specialteams_tickmax <= 2:
toi_specialteams_ticklabels = [0, 2]
if toi_specialteams_tickmax > 2 and toi_specialteams_tickmax <= 4:
toi_specialteams_ticklabels = [0, 4]
if toi_specialteams_tickmax > 4 and toi_specialteams_tickmax <= 6:
toi_specialteams_ticklabels = [0, 6]
if toi_specialteams_tickmax > 6 and toi_specialteams_tickmax <= 8:
toi_specialteams_ticklabels = [0, 8]
if toi_specialteams_tickmax > 8 and toi_specialteams_tickmax <= 10:
toi_specialteams_ticklabels = [0, 10]
if toi_specialteams_tickmax > 10 and toi_specialteams_tickmax <= 12:
toi_specialteams_ticklabels = [0, 12]
if toi_specialteams_tickmax > 12 and toi_specialteams_tickmax <= 14:
toi_specialteams_ticklabels = [0, 14]
if toi_specialteams_tickmax > 14 and toi_specialteams_tickmax <= 16:
toi_specialteams_ticklabels = [0, 16]
if toi_specialteams_tickmax > 16 and toi_specialteams_tickmax <= 18:
toi_specialteams_ticklabels = [0, 18]
if toi_specialteams_tickmax > 18 and toi_specialteams_tickmax <= 20:
toi_specialteams_ticklabels = [0, 20]
# set vertical indicator for midpoint of time on ice max
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1] / 2,
toi_5v5_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1],
toi_5v5_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_5v5_shots.set_xticks(S_5v5_ticklabels, minor=False)
ax_5v5_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_PP_shots.set_xticks(S_PP_ticklabels, minor=False)
ax_PP_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
ax_SH_shots.set_xticks(S_SH_ticklabels, minor=False)
ax_SH_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
# remove axes ticks for instances where there is no special teams play
if team_PP_toi == 0:
ax_PP_shots.set_xticks([], minor=False)
ax_PP_shots.set_yticks([], minor=False)
ax_PP_toi.set_xticks([], minor=False)
ax_PP_toi.set_yticks([], minor=False)
if team_SH_toi == 0:
ax_SH_shots.set_xticks([], minor=False)
ax_SH_shots.set_yticks([], minor=False)
ax_SH_toi.set_xticks([], minor=False)
ax_SH_toi.set_yticks([], minor=False)
# remove the borders to each subplot
ax_5v5_shots.spines["top"].set_visible(False)
ax_5v5_shots.spines["bottom"].set_visible(False)
ax_5v5_shots.spines["right"].set_visible(False)
ax_5v5_shots.spines["left"].set_visible(False)
ax_5v5_toi.spines["top"].set_visible(False)
ax_5v5_toi.spines["bottom"].set_visible(False)
ax_5v5_toi.spines["right"].set_visible(False)
ax_5v5_toi.spines["left"].set_visible(False)
ax_PP_shots.spines["top"].set_visible(False)
ax_PP_shots.spines["bottom"].set_visible(False)
ax_PP_shots.spines["right"].set_visible(False)
ax_PP_shots.spines["left"].set_visible(False)
ax_PP_toi.spines["top"].set_visible(False)
ax_PP_toi.spines["bottom"].set_visible(False)
ax_PP_toi.spines["right"].set_visible(False)
ax_PP_toi.spines["left"].set_visible(False)
ax_SH_shots.spines["top"].set_visible(False)
ax_SH_shots.spines["bottom"].set_visible(False)
ax_SH_shots.spines["right"].set_visible(False)
ax_SH_shots.spines["left"].set_visible(False)
ax_SH_toi.spines["top"].set_visible(False)
ax_SH_toi.spines["bottom"].set_visible(False)
ax_SH_toi.spines["right"].set_visible(False)
ax_SH_toi.spines["left"].set_visible(False)
# add a legend for the shot type markers
ax_5v5_shots.legend(loc='center', bbox_to_anchor=(.55, -.9),
ncol=4).get_frame().set_linewidth(0.0)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425,
0.936,
' ' + away + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525,
0.936,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490,
0.936,
'@',
color='black',
fontsize='12',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_players_individual +
'skaters_individual_shots_away.png',
bbox_inches='tight',
pad_inches=0.2)
elif team == home:
plt.savefig(charts_players_individual +
'skaters_individual_shots_home.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' skaters individual shots.')
# status update
print('Finished plotting the individual shots for skaters.')
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_players_composite_situation = parameters.charts_players_composite_situation
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for the team and players
team_file = files_root + 'stats_teams_situation.csv'
players_individual_file = files_root + 'stats_players_individual_situation.csv'
players_onice_file = files_root + 'stats_players_onice_situation.csv'
# create dataframe objects that read in info from the .csv files
team_stats_df = pd.read_csv(team_file)
players_individual_df = pd.read_csv(players_individual_file)
players_individual_df = players_individual_df.rename(columns={'GS': 'iGS'})
players_individual_df = players_individual_df[[
'TEAM', 'PLAYER', 'POS', 'STATE', 'SITUATION', 'TOI', 'iGS'
]]
players_onice_df = pd.read_csv(players_onice_file)
players_onice_df = players_onice_df.rename(columns={'GS': 'onGS'})
players_onice_df = players_onice_df[[
'TEAM', 'PLAYER', 'POS', 'STATE', 'SITUATION', 'TOI', 'onGS'
]]
players_df = pd.merge(
players_individual_df,
players_onice_df,
on=['TEAM', 'PLAYER', 'POS', 'STATE', 'SITUATION', 'TOI'],
how='inner')
players_df['GS'] = players_df['iGS'] + players_df['onGS']
# create dataframes filtered for game state, then player type
teams_all_df = players_df.copy()
teams_all_df = teams_all_df[(teams_all_df['STATE'] == 'ALL')]
teams_all_skaters_df = teams_all_df.copy()
teams_all_skaters_df = teams_all_skaters_df[(teams_all_skaters_df['POS'] !=
'G')]
teams_all_goalies_df = teams_all_df.copy()
teams_all_goalies_df = teams_all_goalies_df[(
teams_all_goalies_df['POS'] == 'G')]
teams_5v5_df = players_df.copy()
teams_5v5_df = teams_5v5_df[(teams_5v5_df['STATE'] == '5v5')]
teams_5v5_skaters_df = teams_5v5_df.copy()
teams_5v5_skaters_df = teams_5v5_skaters_df[(teams_5v5_skaters_df['POS'] !=
'G')]
teams_5v5_goalies_df = teams_5v5_df.copy()
teams_5v5_goalies_df = teams_5v5_goalies_df[(
teams_5v5_goalies_df['POS'] == 'G')]
teams_3v3_df = players_df.copy()
teams_3v3_df = teams_3v3_df[(teams_3v3_df['STATE'] == '3v3')]
teams_3v3_skaters_df = teams_3v3_df.copy()
teams_3v3_skaters_df = teams_3v3_skaters_df[(teams_3v3_skaters_df['POS'] !=
'G')]
teams_3v3_goalies_df = teams_3v3_df.copy()
teams_3v3_goalies_df = teams_3v3_goalies_df[(
teams_3v3_goalies_df['POS'] == 'G')]
teams_PP_df = players_df.copy()
teams_PP_df = teams_PP_df[(teams_PP_df['STATE'] == 'PP')]
teams_PP_skaters_df = teams_PP_df.copy()
teams_PP_skaters_df = teams_PP_skaters_df[(teams_PP_skaters_df['POS'] !=
'G')]
teams_PP_goalies_df = teams_PP_df.copy()
teams_PP_goalies_df = teams_PP_goalies_df[(
teams_PP_goalies_df['POS'] == 'G')]
teams_SH_df = players_df.copy()
teams_SH_df = teams_SH_df[(teams_SH_df['STATE'] == 'SH')]
teams_SH_skaters_df = teams_SH_df.copy()
teams_SH_skaters_df = teams_SH_skaters_df[(teams_SH_skaters_df['POS'] !=
'G')]
teams_SH_goalies_df = teams_SH_df.copy()
teams_SH_goalies_df = teams_SH_goalies_df[(
teams_SH_goalies_df['POS'] == 'G')]
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
###
### 5v5, PP, SH
###
situations = ['Leading', 'Tied', 'Trailing']
# loop through each situation
for situation in situations:
if situation == 'Leading':
home_situation = 'Leading'
away_situation = 'Trailing'
if situation == 'Tied':
home_situation = 'Tied'
away_situation = 'Tied'
if situation == 'Trailing':
home_situation = 'Trailing'
away_situation = 'Leading'
# add a loop for each team
for team in teams:
if team == away:
team_color = team_colors[0]
situation = away_situation
if team == home:
team_color = team_colors[1]
situation = home_situation
# create a dataframe from the team stats file for generating toi values for the different game states
team_toi_df = team_stats_df.copy()
team_all_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'ALL') &
(team_toi_df['SITUATION'] == situation)]
team_all_toi = team_all_toi['TOI'].item()
team_5v5_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == '5v5') &
(team_toi_df['SITUATION'] == situation)]
team_5v5_toi = team_5v5_toi['TOI'].item()
team_PP_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'PP') &
(team_toi_df['SITUATION'] == situation)]
team_PP_toi = team_PP_toi['TOI'].item()
team_SH_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'SH') &
(team_toi_df['SITUATION'] == situation)]
team_SH_toi = team_SH_toi['TOI'].item()
# create a dataframe; filter for team; sort by team, game state and position; rank by time on ice and then invert the rankings
team_df = players_df.copy()
team_df = team_df[(team_df['TEAM'] == team)
& (team_df['SITUATION'] == situation)]
# remove zeros from the individual gamescore, onice gamescore and overall gamescore columns
team_df['iGS'] = team_df['iGS'].replace(0, np.NaN)
team_df['onGS'] = team_df['onGS'].replace(0, np.NaN)
team_df['GS'] = team_df['GS'].replace(0, np.NaN)
# create a filtered dataframe for each game state; sort by team, game state and position; rank by time on ice and then invert the rankings
team_5v5_skaters_df = teams_5v5_skaters_df.copy()
team_5v5_skaters_df = team_5v5_skaters_df[
(team_5v5_skaters_df['TEAM'] == team)
& (team_5v5_skaters_df['SITUATION'] == situation)]
team_5v5_skaters_df = team_5v5_skaters_df.sort_values(
by=['TOI'], ascending=True)
team_5v5_skaters_df['RANK'] = team_5v5_skaters_df['TOI'].rank(
method='first')
team_5v5_skaters_df['RANK'] -= 1
team_5v5_goalies_df = teams_5v5_goalies_df.copy()
team_5v5_goalies_df = team_5v5_goalies_df[
(team_5v5_goalies_df['TEAM'] == team)
& (team_5v5_goalies_df['SITUATION'] == situation)]
team_5v5_goalies_df = team_5v5_goalies_df.sort_values(
by=['TOI'], ascending=True)
team_5v5_goalies_df['RANK'] = team_5v5_goalies_df['TOI'].rank(
method='first')
team_5v5_goalies_df['RANK'] -= 1
team_PP_skaters_df = teams_PP_skaters_df.copy()
team_PP_skaters_df = team_PP_skaters_df[
(team_PP_skaters_df['TEAM'] == team)
& (team_PP_skaters_df['SITUATION'] == situation)]
team_PP_skaters_df = team_PP_skaters_df[(team_PP_skaters_df['TOI']
> 0)]
team_PP_skaters_df = team_PP_skaters_df.sort_values(by=['TOI'],
ascending=True)
team_PP_skaters_df = team_PP_skaters_df.iloc[-10:]
team_PP_skaters_df['RANK'] = team_PP_skaters_df['TOI'].rank(
method='first')
team_PP_skaters_df['RANK'] -= 1
team_PP_goalies_df = teams_PP_goalies_df.copy()
team_PP_goalies_df = team_PP_goalies_df[
(team_PP_goalies_df['TEAM'] == team)
& (team_PP_goalies_df['SITUATION'] == situation)]
team_PP_goalies_df = team_PP_goalies_df[(team_PP_goalies_df['TOI']
> 0)]
team_PP_goalies_df = team_PP_goalies_df.sort_values(by=['TOI'],
ascending=True)
team_PP_goalies_df['RANK'] = team_PP_goalies_df['TOI'].rank(
method='first')
team_PP_goalies_df['RANK'] -= 1
team_SH_skaters_df = teams_SH_skaters_df.copy()
team_SH_skaters_df = team_SH_skaters_df[
(team_SH_skaters_df['TEAM'] == team)
& (team_SH_skaters_df['SITUATION'] == situation)]
team_SH_skaters_df = team_SH_skaters_df[(team_SH_skaters_df['TOI']
> 0)]
team_SH_skaters_df = team_SH_skaters_df.sort_values(by=['TOI'],
ascending=True)
team_SH_skaters_df = team_SH_skaters_df.iloc[-10:]
team_SH_skaters_df['RANK'] = team_SH_skaters_df['TOI'].rank(
method='first')
team_SH_skaters_df['RANK'] -= 1
team_SH_goalies_df = teams_SH_goalies_df.copy()
team_SH_goalies_df = team_SH_goalies_df[
(team_SH_goalies_df['TEAM'] == team)
& (team_SH_goalies_df['SITUATION'] == situation)]
team_SH_goalies_df = team_SH_goalies_df[(team_SH_goalies_df['TOI']
> 0)]
team_SH_goalies_df = team_SH_goalies_df.sort_values(by=['TOI'],
ascending=True)
team_SH_goalies_df['RANK'] = team_SH_goalies_df['TOI'].rank(
method='first')
team_SH_goalies_df['RANK'] -= 1
# for each game state, create a dataframe with just the time on ice column; set a max value; scale each player's time on ice relative to the max
toi_5v5_skaters = team_5v5_skaters_df['TOI']
max_toi_5v5_skaters = toi_5v5_skaters.max()
toi_5v5_goalies = team_5v5_goalies_df['TOI']
max_toi_5v5_goalies = toi_5v5_goalies.max()
toi_PP_skaters = team_PP_skaters_df['TOI']
max_toi_PP_skaters = toi_PP_skaters.max()
toi_PP_goalies = team_PP_goalies_df['TOI']
max_toi_PP_goalies = toi_PP_goalies.max()
toi_SH_skaters = team_SH_skaters_df['TOI']
max_toi_SH_skaters = toi_SH_skaters.max()
toi_SH_goalies = team_SH_goalies_df['TOI']
max_toi_SH_goalies = toi_SH_goalies.max()
# create a figure with six subplots arrangled complexly using a grid structure
fig = plt.figure(figsize=(9, 9))
grid = plt.GridSpec(11, 8, hspace=0.25, wspace=0.75)
ax_5v5_skaters_gamescore = fig.add_subplot(grid[0:5, :-1])
ax_5v5_skaters_toi = fig.add_subplot(grid[0:5, 7])
ax_5v5_goalies_gamescore = fig.add_subplot(grid[5:6, :-1])
ax_5v5_goalies_toi = fig.add_subplot(grid[5:6, 7])
ax_gap = fig.add_subplot(grid[6:7, :])
ax_PP_skaters_gamescore = fig.add_subplot(grid[7:10, :2])
ax_PP_skaters_toi = fig.add_subplot(grid[7:10, 2])
ax_PP_goalies_gamescore = fig.add_subplot(grid[10:, :2])
ax_PP_goalies_toi = fig.add_subplot(grid[10:, 2])
ax_SH_skaters_gamescore = fig.add_subplot(grid[7:10, 5:-1])
ax_SH_skaters_toi = fig.add_subplot(grid[7:10, 7])
ax_SH_goalies_gamescore = fig.add_subplot(grid[10:, 5:-1])
ax_SH_goalies_toi = fig.add_subplot(grid[10:, 7])
# set the plot title
if team == away:
fig.suptitle(date + ' GameScores (Away ' + situation + ')\n\n')
if team == home:
fig.suptitle(date + ' GameScores (Home ' + situation + ')\n\n')
# set the axes titles
ax_5v5_skaters_gamescore.set_title('5v5 GS', fontsize=10)
ax_5v5_skaters_toi.set_title('5v5 TOI', fontsize=10)
ax_5v5_goalies_gamescore.set_title('', fontsize=10)
ax_5v5_goalies_toi.set_title('', fontsize=10)
ax_PP_skaters_gamescore.set_title('PP GS', fontsize=10)
ax_PP_skaters_toi.set_title('PP TOI', fontsize=10)
ax_PP_goalies_gamescore.set_title('', fontsize=10)
ax_PP_goalies_toi.set_title('', fontsize=10)
ax_SH_skaters_gamescore.set_title('SH GS', fontsize=10)
ax_SH_skaters_toi.set_title('SH TOI', fontsize=10)
ax_SH_goalies_gamescore.set_title('', fontsize=10)
ax_SH_goalies_toi.set_title('', fontsize=10)
# ensure the gap axis is transparent
ax_gap.set_facecolor('None')
# for each state, plot the bars for total gamescore
try:
GS_5v5_skaters_plot = team_5v5_skaters_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.75,
legend=None,
label='',
ax=ax_5v5_skaters_gamescore)
except:
pass
try:
GS_5v5_goalies_plot = team_5v5_goalies_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.25,
legend=None,
label='',
ax=ax_5v5_goalies_gamescore)
except:
pass
if team_PP_toi != 0:
try:
GS_PP_skaters_plot = team_PP_skaters_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.75,
legend=None,
label='',
ax=ax_PP_skaters_gamescore)
except:
pass
try:
GS_PP_goalies_plot = team_PP_goalies_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.25,
legend=None,
label='',
ax=ax_PP_goalies_gamescore)
except:
pass
if team_SH_toi != 0:
try:
GS_SH_skaters_plot = team_SH_skaters_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.75,
legend=None,
label='',
ax=ax_SH_skaters_gamescore)
except:
pass
try:
GS_SH_goalies_plot = team_SH_goalies_df.plot.barh(
x='PLAYER',
y='GS',
color=team_color,
edgecolor='None',
width=0.25,
legend=None,
label='',
ax=ax_SH_goalies_gamescore)
except:
pass
# for each state, plot the bars for time on ice
try:
toi_5v5_skaters_plot = team_5v5_skaters_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_5v5_skaters_toi)
except:
pass
try:
toi_5v5_goalies_plot = team_5v5_goalies_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.25,
legend=None,
label='',
ax=ax_5v5_goalies_toi)
except:
pass
if team_PP_toi != 0:
try:
toi_PP_skaters_plot = team_PP_skaters_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_PP_skaters_toi)
except:
pass
try:
toi_PP_goalies_plot = team_PP_goalies_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.25,
legend=None,
label='',
ax=ax_PP_goalies_toi)
except:
pass
if team_SH_toi != 0:
try:
toi_SH_skaters_plot = team_SH_skaters_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_SH_skaters_toi)
except:
pass
try:
toi_SH_goalies_plot = team_SH_goalies_df.plot.barh(
x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.25,
legend=None,
label='',
ax=ax_SH_goalies_toi)
except:
pass
# set / remove the y-labels for the subplots
ax_5v5_skaters_gamescore.set_xlabel('')
ax_5v5_skaters_gamescore.set_ylabel('', fontsize=10)
ax_5v5_skaters_toi.set_xlabel('')
ax_5v5_skaters_toi.set_ylabel('')
ax_5v5_goalies_gamescore.set_xlabel('')
ax_5v5_goalies_gamescore.set_ylabel('', fontsize=10)
ax_5v5_goalies_toi.set_xlabel('')
ax_5v5_goalies_toi.set_ylabel('')
ax_gap.set_xlabel('')
ax_gap.set_ylabel('', fontsize=10)
ax_PP_skaters_gamescore.set_xlabel('')
ax_PP_skaters_gamescore.set_ylabel('', fontsize=10)
ax_PP_skaters_toi.set_xlabel('')
ax_PP_skaters_toi.set_ylabel('')
ax_PP_goalies_gamescore.set_xlabel('')
ax_PP_goalies_gamescore.set_ylabel('', fontsize=10)
ax_PP_goalies_toi.set_xlabel('')
ax_PP_goalies_toi.set_ylabel('')
ax_SH_skaters_gamescore.set_xlabel('')
ax_SH_skaters_gamescore.set_ylabel('', fontsize=10)
ax_SH_skaters_toi.set_xlabel('')
ax_SH_skaters_toi.set_ylabel('')
ax_SH_goalies_gamescore.set_xlabel('')
ax_SH_goalies_gamescore.set_ylabel('', fontsize=10)
ax_SH_goalies_toi.set_xlabel('')
ax_SH_goalies_toi.set_ylabel('')
# set vertical indicators for break-even gamescore
ax_5v5_skaters_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
ax_5v5_goalies_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
ax_PP_skaters_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
ax_PP_goalies_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
ax_SH_skaters_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
ax_SH_goalies_gamescore.axvspan(0,
0,
ymin=0,
ymax=1,
alpha=.25,
zorder=0,
linestyle=':',
color='black')
# change the tick parameters for each axes
ax_5v5_skaters_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_5v5_skaters_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_5v5_goalies_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are off
ax_5v5_goalies_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are off
ax_gap.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_gap.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_PP_skaters_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_PP_skaters_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=
False, # labels along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_PP_goalies_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_goalies_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=
False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_skaters_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_SH_skaters_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=False) # labels along the bottom edge are off
ax_SH_goalies_gamescore.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_goalies_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
# change the y-axis label colors
ax_5v5_skaters_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_5v5_goalies_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_PP_skaters_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_PP_goalies_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_SH_skaters_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_SH_goalies_gamescore.tick_params(axis='y',
which='both',
labelcolor=team_color)
# create a list of x-axis tick values contingent on the max values for gamescore
GS_5v5_maxmin = teams_5v5_df['GS']
GS_5v5_max = round(GS_5v5_maxmin.max(), 1)
GS_5v5_min = abs(round(GS_5v5_maxmin.min(), 1))
GS_5v5_tickmax = int()
GS_5v5_tickmin = int()
if GS_5v5_max >= GS_5v5_min:
GS_5v5_tickmax = GS_5v5_max
GS_5v5_tickmin = GS_5v5_max * -1
if GS_5v5_max < GS_5v5_min:
GS_5v5_tickmax = GS_5v5_min
GS_5v5_tickmin = GS_5v5_min * -1
GS_5v5_ticklabels = [
GS_5v5_tickmin,
round((GS_5v5_tickmin / 2), 1), 0,
round((GS_5v5_tickmax / 2), 1), GS_5v5_tickmax
]
toi_5v5_tickmax = max_toi_5v5_goalies
toi_5v5_ticklabels = []
if toi_5v5_tickmax <= 5:
toi_5v5_ticklabels = [0, 5]
if toi_5v5_tickmax > 5 and toi_5v5_tickmax <= 10:
toi_5v5_ticklabels = [0, 10]
if toi_5v5_tickmax > 10 and toi_5v5_tickmax <= 15:
toi_5v5_ticklabels = [0, 15]
if toi_5v5_tickmax > 15 and toi_5v5_tickmax <= 20:
toi_5v5_ticklabels = [0, 20]
if toi_5v5_tickmax > 20 and toi_5v5_tickmax <= 25:
toi_5v5_ticklabels = [0, 25]
if toi_5v5_tickmax > 25 and toi_5v5_tickmax <= 30:
toi_5v5_ticklabels = [0, 30]
if toi_5v5_tickmax > 30 and toi_5v5_tickmax <= 35:
toi_5v5_ticklabels = [0, 35]
if toi_5v5_tickmax > 35 and toi_5v5_tickmax <= 40:
toi_5v5_ticklabels = [0, 40]
if toi_5v5_tickmax > 40 and toi_5v5_tickmax <= 45:
toi_5v5_ticklabels = [0, 45]
if toi_5v5_tickmax > 45 and toi_5v5_tickmax <= 50:
toi_5v5_ticklabels = [0, 50]
if toi_5v5_tickmax > 50 and toi_5v5_tickmax <= 55:
toi_5v5_ticklabels = [0, 55]
if toi_5v5_tickmax > 55 and toi_5v5_tickmax <= 60:
toi_5v5_ticklabels = [0, 60]
GS_PP_maxmin = teams_PP_df['GS']
GS_PP_max = round(GS_PP_maxmin.max(), 1)
GS_PP_min = abs(round(GS_PP_maxmin.min(), 1))
GS_PP_tickmax = int()
GS_PP_tickmin = int()
if GS_PP_max >= GS_PP_min:
GS_PP_tickmax = GS_PP_max
GS_PP_tickmin = GS_PP_max * -1
if GS_PP_max < GS_PP_min:
GS_PP_tickmax = GS_PP_min
GS_PP_tickmin = GS_PP_min * -1
GS_PP_ticklabels = [
GS_PP_tickmin,
round((GS_PP_tickmin / 2), 1), 0,
round((GS_PP_tickmax / 2), 1), GS_PP_tickmax
]
GS_SH_maxmin = teams_SH_df['GS']
GS_SH_max = round(GS_SH_maxmin.max(), 1)
GS_SH_min = abs(round(GS_SH_maxmin.min(), 1))
GS_SH_tickmax = int()
GS_SH_tickmin = int()
if GS_SH_max >= GS_SH_min:
GS_SH_tickmax = GS_SH_max
GS_SH_tickmin = GS_SH_max * -1
if GS_SH_max < GS_SH_min:
GS_SH_tickmax = GS_SH_min
GS_SH_tickmin = GS_SH_min * -1
GS_SH_ticklabels = [
GS_SH_tickmin,
round((GS_SH_tickmin / 2), 1), 0,
round((GS_SH_tickmax / 2), 1), GS_SH_tickmax
]
toi_PP_skaters_tickmax = max_toi_PP_goalies
toi_SH_skaters_tickmax = max_toi_SH_goalies
toi_specialteams_tickmax = int()
if toi_PP_skaters_tickmax >= toi_SH_skaters_tickmax:
toi_specialteams_tickmax = toi_PP_skaters_tickmax
if toi_PP_skaters_tickmax < toi_SH_skaters_tickmax:
toi_specialteams_tickmax = toi_SH_skaters_tickmax
toi_specialteams_ticklabels = []
if toi_specialteams_tickmax <= 2:
toi_specialteams_ticklabels = [0, 2]
if toi_specialteams_tickmax > 2 and toi_specialteams_tickmax <= 4:
toi_specialteams_ticklabels = [0, 4]
if toi_specialteams_tickmax > 4 and toi_specialteams_tickmax <= 6:
toi_specialteams_ticklabels = [0, 6]
if toi_specialteams_tickmax > 6 and toi_specialteams_tickmax <= 8:
toi_specialteams_ticklabels = [0, 8]
if toi_specialteams_tickmax > 8 and toi_specialteams_tickmax <= 10:
toi_specialteams_ticklabels = [0, 10]
if toi_specialteams_tickmax > 10 and toi_specialteams_tickmax <= 12:
toi_specialteams_ticklabels = [0, 12]
if toi_specialteams_tickmax > 12 and toi_specialteams_tickmax <= 14:
toi_specialteams_ticklabels = [0, 14]
if toi_specialteams_tickmax > 14 and toi_specialteams_tickmax <= 16:
toi_specialteams_ticklabels = [0, 16]
if toi_specialteams_tickmax > 16 and toi_specialteams_tickmax <= 18:
toi_specialteams_ticklabels = [0, 18]
if toi_specialteams_tickmax > 18 and toi_specialteams_tickmax <= 20:
toi_specialteams_ticklabels = [0, 20]
# set vertical indicator for midpoint of time on ice max
ax_5v5_skaters_toi.axvspan(toi_5v5_ticklabels[1] / 2,
toi_5v5_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_5v5_skaters_toi.axvspan(toi_5v5_ticklabels[1],
toi_5v5_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_5v5_goalies_toi.axvspan(toi_5v5_ticklabels[1] / 2,
toi_5v5_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_5v5_goalies_toi.axvspan(toi_5v5_ticklabels[1],
toi_5v5_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_skaters_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_skaters_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_goalies_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_goalies_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_skaters_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_skaters_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_goalies_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_goalies_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
ax_5v5_skaters_gamescore.set_xticks(GS_5v5_ticklabels, minor=False)
ax_5v5_skaters_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_5v5_goalies_gamescore.set_xticks(GS_5v5_ticklabels, minor=False)
ax_5v5_goalies_toi.set_xticks(toi_5v5_ticklabels, minor=False)
ax_PP_skaters_gamescore.set_xticks(GS_PP_ticklabels, minor=False)
ax_PP_skaters_toi.set_xticks(toi_specialteams_ticklabels,
minor=False)
ax_PP_goalies_gamescore.set_xticks(GS_PP_ticklabels, minor=False)
ax_PP_goalies_toi.set_xticks(toi_specialteams_ticklabels,
minor=False)
ax_SH_skaters_gamescore.set_xticks(GS_SH_ticklabels, minor=False)
ax_SH_skaters_toi.set_xticks(toi_specialteams_ticklabels,
minor=False)
ax_SH_goalies_gamescore.set_xticks(GS_SH_ticklabels, minor=False)
ax_SH_goalies_toi.set_xticks(toi_specialteams_ticklabels,
minor=False)
# remove axes ticks for instances where there is no special teams play
if team_PP_toi == 0:
ax_PP_skaters_gamescore.set_xticks([], minor=False)
ax_PP_skaters_gamescore.set_yticks([], minor=False)
ax_PP_skaters_toi.set_xticks([], minor=False)
ax_PP_skaters_toi.set_yticks([], minor=False)
ax_PP_goalies_gamescore.set_xticks([], minor=False)
ax_PP_goalies_gamescore.set_yticks([], minor=False)
ax_PP_goalies_toi.set_xticks([], minor=False)
ax_PP_goalies_toi.set_yticks([], minor=False)
if team_SH_toi == 0:
ax_SH_skaters_gamescore.set_xticks([], minor=False)
ax_SH_skaters_gamescore.set_yticks([], minor=False)
ax_SH_skaters_toi.set_xticks([], minor=False)
ax_SH_skaters_toi.set_yticks([], minor=False)
ax_SH_goalies_gamescore.set_xticks([], minor=False)
ax_SH_goalies_gamescore.set_yticks([], minor=False)
ax_SH_goalies_toi.set_xticks([], minor=False)
ax_SH_goalies_toi.set_yticks([], minor=False)
# remove the borders to each subplot
ax_5v5_skaters_gamescore.spines["top"].set_visible(False)
ax_5v5_skaters_gamescore.spines["bottom"].set_visible(False)
ax_5v5_skaters_gamescore.spines["right"].set_visible(False)
ax_5v5_skaters_gamescore.spines["left"].set_visible(False)
ax_5v5_skaters_toi.spines["top"].set_visible(False)
ax_5v5_skaters_toi.spines["bottom"].set_visible(False)
ax_5v5_skaters_toi.spines["right"].set_visible(False)
ax_5v5_skaters_toi.spines["left"].set_visible(False)
ax_5v5_goalies_gamescore.spines["top"].set_visible(False)
ax_5v5_goalies_gamescore.spines["bottom"].set_visible(False)
ax_5v5_goalies_gamescore.spines["right"].set_visible(False)
ax_5v5_goalies_gamescore.spines["left"].set_visible(False)
ax_5v5_goalies_toi.spines["top"].set_visible(False)
ax_5v5_goalies_toi.spines["bottom"].set_visible(False)
ax_5v5_goalies_toi.spines["right"].set_visible(False)
ax_5v5_goalies_toi.spines["left"].set_visible(False)
ax_gap.spines["top"].set_visible(False)
ax_gap.spines["bottom"].set_visible(False)
ax_gap.spines["right"].set_visible(False)
ax_gap.spines["left"].set_visible(False)
ax_PP_skaters_gamescore.spines["top"].set_visible(False)
ax_PP_skaters_gamescore.spines["bottom"].set_visible(False)
ax_PP_skaters_gamescore.spines["right"].set_visible(False)
ax_PP_skaters_gamescore.spines["left"].set_visible(False)
ax_PP_skaters_toi.spines["top"].set_visible(False)
ax_PP_skaters_toi.spines["bottom"].set_visible(False)
ax_PP_skaters_toi.spines["right"].set_visible(False)
ax_PP_skaters_toi.spines["left"].set_visible(False)
ax_PP_goalies_gamescore.spines["top"].set_visible(False)
ax_PP_goalies_gamescore.spines["bottom"].set_visible(False)
ax_PP_goalies_gamescore.spines["right"].set_visible(False)
ax_PP_goalies_gamescore.spines["left"].set_visible(False)
ax_PP_goalies_toi.spines["top"].set_visible(False)
ax_PP_goalies_toi.spines["bottom"].set_visible(False)
ax_PP_goalies_toi.spines["right"].set_visible(False)
ax_PP_goalies_toi.spines["left"].set_visible(False)
ax_SH_skaters_gamescore.spines["top"].set_visible(False)
ax_SH_skaters_gamescore.spines["bottom"].set_visible(False)
ax_SH_skaters_gamescore.spines["right"].set_visible(False)
ax_SH_skaters_gamescore.spines["left"].set_visible(False)
ax_SH_skaters_toi.spines["top"].set_visible(False)
ax_SH_skaters_toi.spines["bottom"].set_visible(False)
ax_SH_skaters_toi.spines["right"].set_visible(False)
ax_SH_skaters_toi.spines["left"].set_visible(False)
ax_SH_goalies_gamescore.spines["top"].set_visible(False)
ax_SH_goalies_gamescore.spines["bottom"].set_visible(False)
ax_SH_goalies_gamescore.spines["right"].set_visible(False)
ax_SH_goalies_gamescore.spines["left"].set_visible(False)
ax_SH_goalies_toi.spines["top"].set_visible(False)
ax_SH_goalies_toi.spines["bottom"].set_visible(False)
ax_SH_goalies_toi.spines["right"].set_visible(False)
ax_SH_goalies_toi.spines["left"].set_visible(False)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425,
0.936,
' ' + away + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525,
0.936,
' ' + home + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490,
0.936,
'@',
color='black',
fontsize='12',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_players_composite_situation +
'gamescores_away_' + situation.lower() + '.png',
bbox_inches='tight',
pad_inches=0.2)
elif team == home:
plt.savefig(charts_players_composite_situation +
'gamescores_home_' + situation.lower() + '.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' ' + situation.lower() +
' gamescores for players.')
# status update
print('Finished plotting player gamescores by situation for ' + season_id +
' ' + game_id)
def parse_ids(season_id, game_id, images):
# pull common variables from the parameters file
charts_players_individual_period = parameters.charts_players_individual_period
files_root = parameters.files_root
# generate date and team information
schedule_csv = files_root + season_id + "_schedule.csv"
schedule_df = pd.read_csv(schedule_csv)
schedule_date = schedule_df[(schedule_df['GAME_ID'] == int(game_id))]
date = schedule_date['DATE'].item()
home = schedule_date['HOME'].item()
away = schedule_date['AWAY'].item()
teams = [away, home]
# create variables that point to the .csv processed stats files for the team and players
team_file = files_root + 'stats_teams_period.csv'
players_file = files_root + 'stats_players_individual_period.csv'
# create a dataframe object that reads in info from the .csv files
team_stats_df = pd.read_csv(team_file)
players_df = pd.read_csv(players_file)
# choose colors for each team; set them in a list; generate a custom colormap for each team
away_color = dict_team_colors.team_color_1st[away]
home_color = dict_team_colors.team_color_1st[home]
# change one team's color from its primary option to, depending on the opponent, either a second, third or fourth option
try:
away_color = mod_switch_colors.switch_team_colors(away, home)[0]
home_color = mod_switch_colors.switch_team_colors(away, home)[1]
except:
pass
team_colors = [away_color, home_color]
###
### 5v5, PP, SH
###
periods = [1, 2, 3, 4]
# loop through each period
for period in periods:
if period == 1:
period_name = '1st'
if period == 2:
period_name = '2nd'
if period == 3:
period_name = '3rd'
if period == 4:
period_name = 'OT'
skaters_PP_df = players_df.copy()
skaters_PP_df = skaters_PP_df[(skaters_PP_df['STATE'] == 'PP') &
(skaters_PP_df['PERIOD'] == period_name)
& (skaters_PP_df['POS'] != 'G')]
max_PP_toi = skaters_PP_df['TOI'].max()
skaters_SH_df = players_df.copy()
skaters_SH_df = skaters_SH_df[(skaters_SH_df['STATE'] == 'SH') &
(skaters_SH_df['PERIOD'] == period_name)
& (skaters_PP_df['POS'] != 'G')]
max_SH_toi = skaters_SH_df['TOI'].max()
# loop through each team
for team in teams:
if team == away:
team_color = team_colors[0]
if team == home:
team_color = team_colors[1]
# create a dataframe from the team stats file for generating toi values for the different game states
team_toi_df = team_stats_df.copy()
team_all_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'ALL') &
(team_toi_df['PERIOD'] == period_name)]
try:
team_all_toi = team_all_toi['TOI'].item()
except:
continue
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
team_5v5_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == '5v5') &
(team_toi_df['PERIOD']
== period_name)]
team_5v5_toi = team_5v5_toi['TOI'].item()
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
team_4v4_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == '4v4') &
(team_toi_df['PERIOD']
== period_name)]
team_4v4_toi = team_4v4_toi['TOI'].item()
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
team_3v3_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == '3v3') &
(team_toi_df['PERIOD']
== period_name)]
team_3v3_toi = team_3v3_toi['TOI'].item()
team_PP_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'PP') &
(team_toi_df['PERIOD'] == period_name)]
team_PP_toi = team_PP_toi['TOI'].item()
team_SH_toi = team_toi_df[(team_toi_df['TEAM'] == team)
& (team_toi_df['STATE'] == 'SH') &
(team_toi_df['PERIOD'] == period_name)]
team_SH_toi = team_SH_toi['TOI'].item()
# create a dataframe; filter for team; sort by team, game state and position; rank by time on ice and then invert the rankings
team_df = players_df.copy()
team_df = team_df[(team_df['TEAM'] == team)
& (team_df['POS'] != 'G') &
(team_df['PERIOD'] == period_name)]
# create a filtered dataframe for each game state; sort by team, game state and position; rank by time on ice and then invert the rankings
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
team_5v5_df = team_df.copy()
team_5v5_df = team_5v5_df[(team_5v5_df['STATE'] == '5v5')]
team_5v5_df = team_5v5_df.sort_values(by=['TOI'],
ascending=True)
team_5v5_df['RANK'] = team_5v5_df['TOI'].rank(method='first')
team_5v5_df['RANK'] -= 1
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
team_4v4_df = team_df.copy()
team_4v4_df = team_4v4_df[(team_4v4_df['STATE'] == '4v4')]
team_4v4_df = team_4v4_df.sort_values(by=['TOI'],
ascending=True)
team_4v4_df['RANK'] = team_4v4_df['TOI'].rank(method='first')
team_4v4_df['RANK'] -= 1
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
team_3v3_df = team_df.copy()
team_3v3_df = team_3v3_df[(team_3v3_df['STATE'] == '3v3')]
team_3v3_df = team_3v3_df.sort_values(by=['TOI'],
ascending=True)
team_3v3_df['RANK'] = team_3v3_df['TOI'].rank(method='first')
team_3v3_df['RANK'] -= 1
team_PP_df = team_df.copy()
team_PP_df = team_PP_df[(team_PP_df['STATE'] == 'PP')
& (team_PP_df['TOI'] > 0)]
team_PP_df = team_PP_df.sort_values(by=['TOI'], ascending=True)
team_PP_df = team_PP_df.iloc[-10:]
team_PP_df['RANK'] = team_PP_df['TOI'].rank(method='first')
team_PP_df['RANK'] -= 1
team_SH_df = team_df.copy()
team_SH_df = team_SH_df[(team_SH_df['STATE'] == 'SH')
& (team_SH_df['TOI'] > 0)]
team_SH_df = team_SH_df.sort_values(by=['TOI'], ascending=True)
team_SH_df = team_SH_df.iloc[-10:]
team_SH_df['RANK'] = team_SH_df['TOI'].rank(method='first')
team_SH_df['RANK'] -= 1
# for each game state, create a dataframe with just the time on ice column; set a max value; scale each player's time on ice relative to the max
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
toi_5v5 = team_5v5_df['TOI']
max_toi_5v5 = toi_5v5.max()
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
toi_4v4 = team_4v4_df['TOI']
max_toi_4v4 = toi_4v4.max()
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
toi_3v3 = team_3v3_df['TOI']
max_toi_3v3 = toi_3v3.max()
toi_PP = team_PP_df['TOI']
max_toi_PP = toi_PP.max()
toi_SH = team_SH_df['TOI']
max_toi_SH = toi_SH.max()
# create a figure with six subplots arrangled complexly using a grid structure
fig = plt.figure(figsize=(8, 8))
grid = plt.GridSpec(5, 8, hspace=0.75, wspace=0.75)
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG = fig.add_subplot(grid[0:-2, :-1])
ax_5v5_toi = fig.add_subplot(grid[0:-2, 7])
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG = fig.add_subplot(grid[0:-2, :-1])
ax_4v4_toi = fig.add_subplot(grid[0:-2, 7])
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG = fig.add_subplot(grid[0:-2, :-1])
ax_3v3_toi = fig.add_subplot(grid[0:-2, 7])
ax_PP_xG = fig.add_subplot(grid[3:, :2])
ax_PP_toi = fig.add_subplot(grid[3:, 2])
ax_SH_xG = fig.add_subplot(grid[3:, 5:-1])
ax_SH_toi = fig.add_subplot(grid[3:, 7])
# set the plot title
fig.suptitle(date + ' Skaters Individual Expected Goals (' +
period_name + ' Period)\n\n')
# set the axes titles
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.set_title('5v5 xG', fontsize=10)
ax_5v5_toi.set_title('5v5 TOI', fontsize=10)
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.set_title('4v4 xG', fontsize=10)
ax_4v4_toi.set_title('4v4 TOI', fontsize=10)
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.set_title('3v3 xG', fontsize=10)
ax_3v3_toi.set_title('3v3 TOI', fontsize=10)
ax_PP_xG.set_title('PP xG', fontsize=10)
ax_PP_toi.set_title('PP TOI', fontsize=10)
ax_SH_xG.set_title('SH xG', fontsize=10)
ax_SH_toi.set_title('SH TOI', fontsize=10)
# for each state, plot the bars for total shots and markers for saved, missed and blocked shots markers
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
try:
xG_5v5_plot = team_5v5_df.plot.barh(x='PLAYER',
y='xG',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_5v5_xG)
except:
pass
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
try:
xG_4v4_plot = team_4v4_df.plot.barh(x='PLAYER',
y='xG',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_4v4_xG)
except:
pass
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
try:
xG_3v3_plot = team_3v3_df.plot.barh(x='PLAYER',
y='xG',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_3v3_xG)
except:
pass
if team_PP_toi != 0:
try:
xG_PP_plot = team_PP_df.plot.barh(x='PLAYER',
y='xG',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_PP_xG)
except:
pass
if team_SH_toi != 0:
try:
xG_SH_plot = team_SH_df.plot.barh(x='PLAYER',
y='xG',
color=team_color,
edgecolor=None,
width=0.75,
legend=None,
label='',
ax=ax_SH_xG)
except:
pass
# for each state, plot the bars for time on ice
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
try:
toi_5v5_plot = team_5v5_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_5v5_toi)
except:
pass
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
try:
toi_4v4_plot = team_4v4_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_4v4_toi)
except:
pass
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
try:
toi_3v3_plot = team_3v3_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_3v3_toi)
except:
pass
if team_PP_toi != 0:
try:
toi_PP_plot = team_PP_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_PP_toi)
except:
pass
if team_SH_toi != 0:
try:
toi_SH_plot = team_SH_df.plot.barh(x='PLAYER',
y='TOI',
color='white',
edgecolor=team_color,
width=0.75,
legend=None,
label='',
ax=ax_SH_toi)
except:
pass
# set / remove the y-labels for the subplots
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.set_xlabel('')
ax_5v5_xG.set_ylabel('', fontsize=10)
ax_5v5_toi.set_xlabel('')
ax_5v5_toi.set_ylabel('')
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.set_xlabel('')
ax_4v4_xG.set_ylabel('', fontsize=10)
ax_4v4_toi.set_xlabel('')
ax_4v4_toi.set_ylabel('')
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.set_xlabel('')
ax_3v3_xG.set_ylabel('', fontsize=10)
ax_3v3_toi.set_xlabel('')
ax_3v3_toi.set_ylabel('')
ax_PP_xG.set_xlabel('')
ax_PP_xG.set_ylabel('', fontsize=10)
ax_PP_toi.set_xlabel('')
ax_PP_toi.set_ylabel('')
ax_SH_xG.set_xlabel('')
ax_SH_xG.set_ylabel('', fontsize=10)
ax_SH_toi.set_xlabel('')
ax_SH_toi.set_ylabel('')
# change the tick parameters for each axes
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_5v5_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_4v4_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_3v3_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_PP_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=
False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_xG.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelbottom=True) # labels along the bottom edge are on
ax_SH_toi.tick_params(
axis='both', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=False, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
left=False, # ticks along the left edge are off
labelleft=False, # labels along the left edge are off
labelbottom=True) # labels along the bottom edge are on
# change the y-axis label colors
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.tick_params(axis='y',
which='both',
labelcolor=team_color)
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.tick_params(axis='y',
which='both',
labelcolor=team_color)
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.tick_params(axis='y',
which='both',
labelcolor=team_color)
ax_PP_xG.tick_params(axis='y', which='both', labelcolor=team_color)
ax_SH_xG.tick_params(axis='y', which='both', labelcolor=team_color)
# create a list of x-axis tick values contingent on the max values for shots
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
xG_5v5_max = team_5v5_df['xG']
xG_5v5_tickmax = xG_5v5_max.max()
xG_5v5_ticklabels = []
if xG_5v5_tickmax >= 0 and xG_5v5_tickmax <= 0.5:
xG_5v5_ticklabels = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
if xG_5v5_tickmax > 5 and xG_5v5_tickmax <= 1.0:
xG_5v5_ticklabels = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
if xG_5v5_tickmax > 1 and xG_5v5_tickmax <= 1.5:
xG_5v5_ticklabels = [0.0, 0.3, 0.6, 0.9, 1.2, 1.5]
if xG_5v5_tickmax > 1.5 and xG_5v5_tickmax <= 2:
xG_5v5_ticklabels = [0.0, 0.4, 0.8, 1.2, 1.6, 2.0]
if xG_5v5_tickmax > 2 and xG_5v5_tickmax <= 2.5:
xG_5v5_ticklabels = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5]
toi_5v5_ticklabels = [0, 20]
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
xG_4v4_max = team_4v4_df['xG']
xG_4v4_tickmax = xG_4v4_max.max()
xG_4v4_ticklabels = []
if xG_4v4_tickmax >= 0 and xG_4v4_tickmax <= 0.5:
xG_4v4_ticklabels = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
if xG_4v4_tickmax > 5 and xG_4v4_tickmax <= 1.0:
xG_3v3_ticklabels = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
if xG_4v4_tickmax > 1 and xG_4v4_tickmax <= 1.5:
xG_4v4_ticklabels = [0.0, 0.3, 0.6, 0.9, 1.2, 1.5]
if xG_4v4_tickmax > 1.5 and xG_4v4_tickmax <= 2:
xG_4v4_ticklabels = [0.0, 0.4, 0.8, 1.2, 1.6, 2.0]
if xG_4v4_tickmax > 2 and xG_4v4_tickmax <= 2.5:
xG_4v4_ticklabels = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5]
toi_4v4_ticklabels = [0, 5]
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
xG_3v3_max = team_3v3_df['xG']
xG_3v3_tickmax = xG_3v3_max.max()
xG_3v3_ticklabels = []
if xG_3v3_tickmax >= 0 and xG_3v3_tickmax <= 0.5:
xG_3v3_ticklabels = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
if xG_3v3_tickmax > 5 and xG_3v3_tickmax <= 1.0:
xG_3v3_ticklabels = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
if xG_3v3_tickmax > 1 and xG_3v3_tickmax <= 1.5:
xG_3v3_ticklabels = [0.0, 0.3, 0.6, 0.9, 1.2, 1.5]
if xG_3v3_tickmax > 1.5 and xG_3v3_tickmax <= 2:
xG_3v3_ticklabels = [0.0, 0.4, 0.8, 1.2, 1.6, 2.0]
if xG_3v3_tickmax > 2 and xG_3v3_tickmax <= 2.5:
xG_3v3_ticklabels = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5]
toi_3v3_ticklabels = [0, 5]
xG_PP_max = team_PP_df['xG']
xG_PP_tickmax = xG_PP_max.max()
xG_PP_ticklabels = []
if xG_PP_tickmax <= 1:
xG_PP_ticklabels = [0.0, 0.5, 1.0]
if xG_PP_tickmax > 1 and xG_PP_tickmax <= 2:
xG_PP_ticklabels = [0.0, 1.0, 2.0]
if xG_PP_tickmax > 2 and xG_PP_tickmax <= 3:
xG_PP_ticklabels = [0.0, 1.5, 3.0]
if xG_PP_tickmax > 3 and xG_PP_tickmax <= 4:
xG_PP_ticklabels = [0.0, 3.0, 4.0]
xG_SH_max = team_SH_df['xG']
xG_SH_tickmax = xG_SH_max.max()
xG_SH_ticklabels = []
if xG_SH_tickmax <= 1:
xG_SH_ticklabels = [0.0, 0.5, 1.0]
if xG_SH_tickmax > 1 and xG_SH_tickmax <= 2:
xG_SH_ticklabels = [0.0, 1.0, 2.0]
if xG_SH_tickmax > 2 and xG_SH_tickmax <= 3:
xG_SH_ticklabels = [0.0, 1.5, 3.0]
if xG_SH_tickmax > 3 and xG_SH_tickmax <= 4:
xG_SH_ticklabels = [0.0, 2.0, 4.0]
toi_PP_tickmax = max_PP_toi
toi_SH_tickmax = max_SH_toi
toi_specialteams_tickmax = float()
if toi_PP_tickmax >= toi_SH_tickmax:
toi_specialteams_tickmax = toi_PP_tickmax
if toi_PP_tickmax < toi_SH_tickmax:
toi_specialteams_tickmax = toi_SH_tickmax
toi_specialteams_ticklabels = []
if toi_specialteams_tickmax <= 2:
toi_specialteams_ticklabels = [0, 2]
if toi_specialteams_tickmax > 2 and toi_specialteams_tickmax <= 4:
toi_specialteams_ticklabels = [0, 4]
if toi_specialteams_tickmax > 4 and toi_specialteams_tickmax <= 6:
toi_specialteams_ticklabels = [0, 6]
if toi_specialteams_tickmax > 6 and toi_specialteams_tickmax <= 8:
toi_specialteams_ticklabels = [0, 8]
if toi_specialteams_tickmax > 8 and toi_specialteams_tickmax <= 10:
toi_specialteams_ticklabels = [0, 10]
if toi_specialteams_tickmax > 10 and toi_specialteams_tickmax <= 12:
toi_specialteams_ticklabels = [0, 12]
if toi_specialteams_tickmax > 12 and toi_specialteams_tickmax <= 14:
toi_specialteams_ticklabels = [0, 14]
if toi_specialteams_tickmax > 14 and toi_specialteams_tickmax <= 16:
toi_specialteams_ticklabels = [0, 16]
if toi_specialteams_tickmax > 16 and toi_specialteams_tickmax <= 18:
toi_specialteams_ticklabels = [0, 18]
if toi_specialteams_tickmax > 18 and toi_specialteams_tickmax <= 20:
toi_specialteams_ticklabels = [0, 20]
# set vertical indicator for midpoint of time on ice max
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1] / 2,
toi_5v5_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_5v5_toi.axvspan(toi_5v5_ticklabels[1],
toi_5v5_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_toi.axvspan(toi_4v4_ticklabels[1] / 2,
toi_4v4_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_4v4_toi.axvspan(toi_4v4_ticklabels[1],
toi_4v4_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_toi.axvspan(toi_3v3_ticklabels[1] / 2,
toi_3v3_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_3v3_toi.axvspan(toi_3v3_ticklabels[1],
toi_3v3_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_PP_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1] / 2,
toi_specialteams_ticklabels[1] / 2,
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
ax_SH_toi.axvspan(toi_specialteams_ticklabels[1],
toi_specialteams_ticklabels[1],
ymin=0,
ymax=1,
zorder=0,
alpha=0.25,
linestyle=':',
color='black')
# use the newly-minted x-ticklabels to ensure the x-axis labels will always display as integers
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.set_xticks(xG_5v5_ticklabels, minor=False)
ax_5v5_toi.set_xticks(toi_5v5_ticklabels, minor=False)
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.set_xticks(xG_4v4_ticklabels, minor=False)
ax_4v4_toi.set_xticks(toi_4v4_ticklabels, minor=False)
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.set_xticks(xG_3v3_ticklabels, minor=False)
ax_3v3_toi.set_xticks(toi_3v3_ticklabels, minor=False)
ax_PP_xG.set_xticks(xG_PP_ticklabels, minor=False)
ax_PP_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
ax_SH_xG.set_xticks(xG_SH_ticklabels, minor=False)
ax_SH_toi.set_xticks(toi_specialteams_ticklabels, minor=False)
# remove axes ticks for instances where there is no special teams play
if team_PP_toi == 0:
ax_PP_xG.set_xticks([], minor=False)
ax_PP_xG.set_yticks([], minor=False)
ax_PP_toi.set_xticks([], minor=False)
ax_PP_toi.set_yticks([], minor=False)
if team_SH_toi == 0:
ax_SH_xG.set_xticks([], minor=False)
ax_SH_xG.set_yticks([], minor=False)
ax_SH_toi.set_xticks([], minor=False)
ax_SH_toi.set_yticks([], minor=False)
# remove the borders to each subplot
if period < 4 or season_id != 20192020 and period == 4 and int(
game_id
) >= 30000 or season_id == 20192020 and period == 4 and int(
game_id) >= 30021:
ax_5v5_xG.spines["top"].set_visible(False)
ax_5v5_xG.spines["bottom"].set_visible(False)
ax_5v5_xG.spines["right"].set_visible(False)
ax_5v5_xG.spines["left"].set_visible(False)
ax_5v5_toi.spines["top"].set_visible(False)
ax_5v5_toi.spines["bottom"].set_visible(False)
ax_5v5_toi.spines["right"].set_visible(False)
ax_5v5_toi.spines["left"].set_visible(False)
if period == 4 and int(game_id) < 30000 and int(
season_id) <= 20142015:
ax_4v4_xG.spines["top"].set_visible(False)
ax_4v4_xG.spines["bottom"].set_visible(False)
ax_4v4_xG.spines["right"].set_visible(False)
ax_4v4_xG.spines["left"].set_visible(False)
ax_4v4_toi.spines["top"].set_visible(False)
ax_4v4_toi.spines["bottom"].set_visible(False)
ax_4v4_toi.spines["right"].set_visible(False)
ax_4v4_toi.spines["left"].set_visible(False)
if period == 4 and int(game_id) < 30000 and int(
season_id
) >= 20152016 or season_id == 20192020 and period == 4 and int(
game_id) > 30000 and int(game_id) <= 30020:
ax_3v3_xG.spines["top"].set_visible(False)
ax_3v3_xG.spines["bottom"].set_visible(False)
ax_3v3_xG.spines["right"].set_visible(False)
ax_3v3_xG.spines["left"].set_visible(False)
ax_3v3_toi.spines["top"].set_visible(False)
ax_3v3_toi.spines["bottom"].set_visible(False)
ax_3v3_toi.spines["right"].set_visible(False)
ax_3v3_toi.spines["left"].set_visible(False)
ax_PP_xG.spines["top"].set_visible(False)
ax_PP_xG.spines["bottom"].set_visible(False)
ax_PP_xG.spines["right"].set_visible(False)
ax_PP_xG.spines["left"].set_visible(False)
ax_PP_toi.spines["top"].set_visible(False)
ax_PP_toi.spines["bottom"].set_visible(False)
ax_PP_toi.spines["right"].set_visible(False)
ax_PP_toi.spines["left"].set_visible(False)
ax_SH_xG.spines["top"].set_visible(False)
ax_SH_xG.spines["bottom"].set_visible(False)
ax_SH_xG.spines["right"].set_visible(False)
ax_SH_xG.spines["left"].set_visible(False)
ax_SH_toi.spines["top"].set_visible(False)
ax_SH_toi.spines["bottom"].set_visible(False)
ax_SH_toi.spines["right"].set_visible(False)
ax_SH_toi.spines["left"].set_visible(False)
# add text boxes with team names in white and with the team's color in the background
fig.text(.425,
0.936,
' ' + away + ' ',
color='white',
fontsize='12',
bbox=dict(facecolor=away_color, edgecolor='None'))
fig.text(.525,
0.936,
' ' + home + ' ',
fontsize='12',
color='white',
bbox=dict(facecolor=home_color, edgecolor='None'))
fig.text(.490,
0.936,
'@',
color='black',
fontsize='12',
bbox=dict(facecolor='white', edgecolor='None'))
###
### SAVE TO FILE
###
if team == away:
plt.savefig(charts_players_individual_period +
'skaters_individual_xG_away_' + period_name +
'.png',
bbox_inches='tight',
pad_inches=0.2)
elif team == home:
plt.savefig(charts_players_individual_period +
'skaters_individual_xG_home_' + period_name +
'.png',
bbox_inches='tight',
pad_inches=0.2)
# exercise a command-line option to show the current figure
if images == 'show':
plt.show()
###
### CLOSE
###
plt.close(fig)
# status update
print('Plotting ' + team + ' skaters individual xG for the ' +
period_name + ' period.')
# status update
print('Finished plotting the individual xG for skaters by period.')
