# get data for a Sevilla versus Barcelona match with a high amount of shots
kwargs = {
'related_event_df': False,
'shot_freeze_frame_df': False,
'tactics_lineup_df': False,
'warn': False
}
df = read_event(f'{EVENT_SLUG}/9860.json', **kwargs)['event']
# setup the mplsoccer StatsBomb Pitches
# note not much padding around the pitch so the marginal axis are tight to the pitch
# if you are using a different goal type you will need to increase the padding to see the goals
pitch = Pitch(pad_top=0.05,
pad_right=0.05,
pad_bottom=0.05,
pad_left=0.05,
line_zorder=2)
vertical_pitch = VerticalPitch(half=True,
pad_top=0.05,
pad_right=0.05,
pad_bottom=0.05,
pad_left=0.05,
line_zorder=2)
# setup a mplsoccer FontManager to download google fonts (Roboto-Regular / SigmarOne-Regular)
fm = FontManager()
fm_rubik = FontManager(
('https://github.com/google/fonts/blob/main/ofl/rubikmonoone/'
'RubikMonoOne-Regular.ttf?raw=true'))
##############################################################################
# Boolean mask for filtering the dataset by team
team1, team2 = df.team_name.unique()
mask_team1 = (df.type_name == 'Pass') & (df.team_name == team1)
##############################################################################
# Filter dataset to only include one teams passes and get boolean mask for the completed passes
df_pass = df.loc[mask_team1, ['x', 'y', 'end_x', 'end_y', 'outcome_name']]
mask_complete = df_pass.outcome_name.isnull()
##############################################################################
# Setup the pitch and number of bins
pitch = Pitch(pitch_type='statsbomb',
line_zorder=2,
line_color='#c7d5cc',
pitch_color='#22312b')
bins = (6, 4)
##############################################################################
# Plotting using a single color and length
fig, ax = pitch.draw(figsize=(16, 11),
constrained_layout=True,
tight_layout=False)
# plot the heatmap - darker colors = more passes originating from that square
bs_heatmap = pitch.bin_statistic(df_pass.x,
df_pass.y,
statistic='count',
bins=bins)
hm = pitch.heatmap(bs_heatmap, ax=ax, cmap='Blues')
# plot the pass flow map with a single color ('black') and length of the arrow (5)
##############################################################################
# Rotated markers
# ---------------
# I also included a method for rotating markers in mplsoccer.
#
# Warning: The rotation angle is in degrees and assumes the original marker is pointing upwards ↑.
# If it's not you will have to modify the rotation degrees.
# Rotates the marker in degrees, clockwise. 0 degrees is facing the
# direction of play (left to right).
# In a horizontal pitch, 0 degrees is this way →, in a vertical pitch, 0 degrees is this way ↑
#
# We are going to plot pass data as an arrowhead marker with the
# arrow facing in the direction of the pass
# The marker size is going to relate to the pass distance,
# so larger markers mean the pass was longer.
pitch = Pitch()
fig, ax = pitch.draw(figsize=(14, 12))
angle, distance = pitch.calculate_angle_and_distance(df_pass_barca.x,
df_pass_barca.y,
df_pass_barca.end_x,
df_pass_barca.end_y,
standardized=False,
degrees=True)
sc = pitch.scatter(
df_pass_barca.x,
df_pass_barca.y,
rotation_degrees=angle,
c='#b94b75', # color for scatter in hex format
edgecolors='#383838',
alpha=0.9,
s=(distance / distance.max()) * 900,
# Get the StatsBomb logo and Fonts
LOGO_URL = 'https://raw.githubusercontent.com/statsbomb/open-data/master/img/statsbomb-logo.jpg'
sb_logo = Image.open(urlopen(LOGO_URL))
# a FontManager object for using a google font (default Robotto)
fm = FontManager()
# path effects
path_eff = [path_effects.Stroke(linewidth=3, foreground='black'),
path_effects.Normal()]
##############################################################################
# Plot the percentages
# setup a mplsoccer pitch
pitch = Pitch(line_zorder=2, line_color='black', pad_top=20)
# mplsoccer calculates the binned statistics usually from raw locations, such as pressure events
# for this example we will create a binned statistic dividing
# the pitch into thirds for one point (0, 0)
# we will fill this in a loop later with each team's statistics from the dataframe
bin_statistic = pitch.bin_statistic([0], [0], statistic='count', bins=(3, 1))
GRID_HEIGHT = 0.8
CBAR_WIDTH = 0.03
fig, axs = pitch.grid(nrows=4, ncols=5, figheight=20,
# leaves some space on the right hand side for the colorbar
grid_width=0.88, left=0.025,
endnote_height=0.06, endnote_space=0,
# Turn off the endnote/title axis. I usually do this after
# I am happy with the chart layout and text placement
df_total = pd.DataFrame(df[pressure_cols].sum())
df_total.columns = ['total']
df_total = df_total.T
df_total = df_total.divide(df_total.sum(axis=1), axis=0) * 100
##############################################################################
# Calculate the percentages for each team and sort so that the teams which press higher are last
df[pressure_cols] = df[pressure_cols].divide(df[pressure_cols].sum(axis=1),
axis=0) * 100.
df.sort_values(['Att 3rd', 'Def 3rd'], ascending=[True, False], inplace=True)
##############################################################################
# Plot the percentages
# setup a mplsoccer pitch
pitch = Pitch(line_zorder=2, line_color='black')
# mplsoccer calculates the binned statistics usually from raw locations, such as pressure events
# for this example we will create a binned statistic dividing
# the pitch into thirds for one point (0, 0)
# we will fill this in a loop later with each team's statistics from the dataframe
bin_statistic = pitch.bin_statistic([0], [0], statistic='count', bins=(3, 1))
# Plot
fig, axes = pitch.draw(figsize=(16, 9),
ncols=5,
nrows=4,
tight_layout=False,
constrained_layout=True)
axes = axes.ravel()
teams = df['Squad'].values
'related_event_df': False,
'shot_freeze_frame_df': False,
'tactics_lineup_df': False,
'warn': False
}
df = pd.concat([
read_event(f'{EVENT_SLUG}/{file}', **kwargs)['event']
for file in match_files
])
# filter chelsea pressure events
mask_chelsea_pressure = (df.team_name == 'Chelsea FCW') & (df.type_name
== 'Pressure')
df = df.loc[mask_chelsea_pressure, ['x', 'y']]
##############################################################################
# Plot the heatmaps
# setup pitch
pitch = Pitch(pitch_type='statsbomb', line_zorder=2, line_color='white')
# draw
fig, ax = pitch.draw(figsize=(16, 9))
bin_statistic = pitch.bin_statistic(df.x,
df.y,
statistic='count',
bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax, cmap='hot', edgecolors='#22312b')
cbar = fig.colorbar(pcm, ax=ax)
TITLE_STR = 'Location of pressure events - 3 home games for Chelsea FC Women'
title = fig.suptitle(TITLE_STR, x=0.4, y=0.98, fontsize=23)
kwargs = {
'related_event_df': False,
'shot_freeze_frame_df': False,
'tactics_lineup_df': False,
'warn': False
}
df_false9 = read_event(f'{EVENT_SLUG}/69249.json', **kwargs)['event']
df_before_false9 = read_event(f'{EVENT_SLUG}/69251.json', **kwargs)['event']
# filter messi's actions (starting positions)
df_false9 = df_false9.loc[df_false9.player_id == 5503, ['x', 'y']]
df_before_false9 = df_before_false9.loc[df_before_false9.player_id == 5503,
['x', 'y']]
##############################################################################
# plotting
pitch = Pitch(pitch_type='statsbomb',
pitch_color='#22312b',
stripe=False,
line_zorder=2)
fig, ax = pitch.draw(
figsize=(16, 9),
nrows=1,
ncols=2,
)
pitch.hexbin(df_before_false9.x, df_before_false9.y, ax=ax[0], cmap='Blues')
pitch.hexbin(df_false9.x, df_false9.y, ax=ax[1], cmap='Blues')
TITLE_STR1 = 'Messi in the game directly before \n playing in the false 9 role'
TITLE_STR2 = 'The first Game Messi \nplayed in the false 9 role'
title1 = ax[0].set_title(TITLE_STR1, fontsize=25, pad=20)
title2 = ax[1].set_title(TITLE_STR2, fontsize=25, pad=20)
fig = plt.figure(figsize=FIGSIZE)
fm_rubik = FontManager(('https://github.com/google/fonts/blob/main/ofl/rubikmonoone/'
'RubikMonoOne-Regular.ttf?raw=true'))
# layout specifications
PAD = 1
pitch_spec = {'pad_left': PAD, 'pad_right': PAD,
'pad_bottom': PAD, 'pad_top': PAD, 'pitch_color': 'None'}
pitch_width, pitch_length = 80, 105
pitch_width3, pitch_length3 = 60, 105
pitch_length4, pitch_width4 = 120, 68
pitch_length6, pitch_width6 = 85, 68
# define pitches (top left, top middle, top right, bottom left, bottom middle, bottom right)
pitch1 = Pitch(pitch_type='custom', pitch_width=pitch_width, pitch_length=pitch_length,
line_color='#b94e45', **pitch_spec)
pitch2 = Pitch(pitch_type='statsbomb', **pitch_spec)
pitch3 = Pitch(pitch_type='custom', pitch_width=pitch_width3, pitch_length=pitch_length3,
line_color='#56ae6c', **pitch_spec)
pitch4 = VerticalPitch(pitch_type='custom', pitch_length=pitch_length4, pitch_width=pitch_width4,
line_color='#bc7d39', **pitch_spec)
pitch5 = VerticalPitch(pitch_type='statsbomb', **pitch_spec)
pitch6 = VerticalPitch(pitch_type='custom', pitch_length=pitch_length6, pitch_width=pitch_width6,
line_color='#677ad1', **pitch_spec)
TITLE_HEIGHT = 0.1 # title axes are 10% of the figure height
# width of pitch axes as percent of the figure width
TOP_WIDTH = 0.27
BOTTOM_WIDTH = 0.18
import matplotlib.pyplot as plt
# read data
df = read_event(f'{EVENT_SLUG}/7478.json',
related_event_df=False,
shot_freeze_frame_df=False,
tactics_lineup_df=False)['event']
##############################################################################
# Filter passes by Jodie Taylor
df = df[(df.player_name == 'Jodie Taylor') & (df.type_name == 'Pass')].copy()
##############################################################################
# Plotting
pitch = Pitch()
fig, ax = pitch.draw(figsize=(8, 6))
hull = pitch.convexhull(df.x, df.y)
poly = pitch.polygon(hull,
ax=ax,
edgecolor='cornflowerblue',
facecolor='cornflowerblue',
alpha=0.3)
scatter = pitch.scatter(df.x,
df.y,
ax=ax,
edgecolor='black',
facecolor='cornflowerblue')
plt.show(
) # if you are not using a Jupyter notebook this is necessary to show the plot
'connectionstyle': 'angle3,angleA=0,angleB=-90',
'color': FONTCOLOR
}
font_kwargs = {
'fontsize': 14,
'ha': 'center',
'va': 'bottom',
'fontweight': 'bold',
'fontstyle': 'italic',
'c': FONTCOLOR
}
for idx, pt in enumerate(pitch_types):
if pt in ['tracab', 'metricasports', 'custom', 'skillcorner']:
pitch = Pitch(pitch_type=pt,
pitch_length=105,
pitch_width=68,
**pitch_kwargs)
else:
pitch = Pitch(pitch_type=pt, **pitch_kwargs)
pitch.draw(axes[idx])
xmin, xmax, ymin, ymax = pitch.extent
if pitch.dim.aspect != 1:
TEXT = 'data coordinates \n are square (1:1) \n scale up to a real-pitch size'
axes[idx].annotate(TEXT,
xy=(xmin, ymin),
xytext=(0 + (xmax - xmin) / 2, ymin),
**font_kwargs)
axes[idx].xaxis.set_ticks([xmin, xmax])
axes[idx].yaxis.set_ticks([ymin, ymax])
axes[idx].tick_params(labelsize=15)
if pt == 'skillcorner':
# I created a function to calculate the maximum dimensions you can get away with while
# having a set figure size. Let's use this to create the largest pitch possible
# with a 16:9 figure aspect ratio.
FIGWIDTH = 16
FIGHEIGHT = 9
NROWS = 1
NCOLS = 1
# here we want the maximum side in proportion to the figure dimensions
# (height in this case) to take up all of the image
MAX_GRID = 1
# pitch with minimal padding (2 each side)
pitch = Pitch(pad_top=2,
pad_bottom=2,
pad_left=2,
pad_right=2,
pitch_color='#22312b')
# calculate the maximum grid_height/ width
GRID_WIDTH, GRID_HEIGHT = pitch.calculate_grid_dimensions(figwidth=FIGWIDTH,
figheight=FIGHEIGHT,
nrows=NROWS,
ncols=NCOLS,
max_grid=MAX_GRID,
space=0)
# plot using the mplsoccer grid function
fig, ax = pitch.grid(figheight=FIGHEIGHT,
grid_width=GRID_WIDTH,
grid_height=GRID_HEIGHT,
# Filter dataset to only include one teams passes and get boolean mask for the completed passes
df_pass = df.loc[mask_team1, ['x', 'y', 'end_x', 'end_y', 'outcome_name']]
mask_complete = df_pass.outcome_name.isnull()
##############################################################################
# View the pass dataframe.
df_pass.head()
##############################################################################
# Plotting
# Setup the pitch
pitch = Pitch(pitch_type='statsbomb',
pitch_color='#22312b',
line_color='#c7d5cc')
fig, ax = pitch.draw(figsize=(16, 11),
constrained_layout=True,
tight_layout=False)
fig.set_facecolor('#22312b')
# Plot the completed passes
pitch.arrows(df_pass[mask_complete].x,
df_pass[mask_complete].y,
df_pass[mask_complete].end_x,
df_pass[mask_complete].end_y,
width=2,
headwidth=10,
headlength=10,
color='#ad993c',
