def defensesmap(Df,team,teamid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,6), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
defensedf = df.query("(type_displayName in ['Aerial','Clearance','Interception','Tackle','BlockedPass','Challenge'])\
&(teamId==@teamid)").reset_index()
defensedf['defensive'] = [1 for i in range(len(defensedf))]
for i in range(len(defensedf)):
if(defensedf.loc[i,['type_displayName']][0]=='Aerial'):
quals = defensedf.quals[i]
if(286 in quals):
defensedf.loc[i,['defensive']] = 0
if(defensedf.loc[i,['type_displayName']][0]=='Challenge'):
quals = defensedf.quals[i]
if(286 in quals):
defensedf.loc[i,['defensive']] = 0
defensedf = defensedf[defensedf.defensive==1]
defensedf['y'] = 80 - defensedf['y']
ppda = defensedf[defensedf.x>=48]
ppda = ppda.query("type_displayName in ['Interception','Tackle','Challenge']")
deepdf = defensedf[defensedf.x<48]
bin_statistic = pitch.bin_statistic(ppda.x, ppda.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[0], cmap='Reds', edgecolors=None)
ax[0].set_title(team+'\n'+' Pressurizing'+'\n'+ 'Defensive activities',fontsize=30)
bin_statistic = pitch.bin_statistic(deepdf.x, deepdf.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[1], cmap='Reds', edgecolors=None)
# cbar = fig.colorbar(pcm, ax=ax[1])
ax[1].set_title(team+'\n'+' Goal Protecting'+'\n'+'Defensive activities',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.15, "Pressurizing defensive activities include tackles, interceptions and challenges"+'\n'+
"made high up in opposition territory",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.05, "Goal protecting defensive activities include tackles, interceptions, defensive aerials, challenges,"
+'\n'+"clearances and blocked passes deep in own territory",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def touchmap(Df,team,teamid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(12,7), line_zorder=2, layout=(4,4),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
touchdf = df[(df.isTouch==True)&(df.teamId==teamid)&(df.name!='')].reset_index()
touchdf['y'] = 80 - touchdf['y']
players = touchdf.name.unique().tolist()
Nplayers = len(players)
for i in range(Nplayers):
pdf = touchdf[touchdf.name==players[i]]
bin_statistic = pitch.bin_statistic(pdf.x, pdf.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[i//4,i%4], cmap='Reds', edgecolors=None)
ax[i//4,i%4].set_title(players[i],fontsize=15)
for i in range(Nplayers,16):
ax[i//4,i%4].remove()
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.3, 1.01, team+" Touch-based heatmaps",fontsize=25,color='#edece9')
# add_image(bu_alt, fig, left=0.95, bottom=0.97, width=0.07)
return fig
##############################################################################
# Plot the percentages
# setup a mplsoccer pitch
pitch = Pitch(line_zorder=2,
line_color='black',
figsize=(16, 9),
layout=(4, 5),
tight_layout=False,
constrained_layout=True)
# 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))
# load the StatsBomb logo
sb_logo = Image.open(
urlopen((
'https://github.com/statsbomb/open-data/blob/fb54bd7fe20dbd5299fafc64f1f6f0d919a5e40d/'
'stats-bomb-logo.png?raw=true')))
# Plot
fig, axes = pitch.draw()
axes = axes.ravel()
teams = df['Squad'].values
vmin = df[pressure_cols].min().min(
) # we normalise the heatmaps with the min / max values
vmax = df[pressure_cols].max().max()
for i, ax in enumerate(axes):
# setup pitch
pitch = Pitch(pitch_type='statsbomb',
figsize=(16, 9),
layout=(1, 3),
line_zorder=2,
pitch_color='#22312b',
line_color='white',
orientation='vertical')
# draw
fig, ax = pitch.draw()
# heatmap specified by (nx, ny) for horizontal pitch
bins = [(6, 5), (1, 5), (6, 1)]
for i, bin in enumerate(bins):
bin_statistic = pitch.bin_statistic(df.x,
df.y,
statistic='count',
bins=bin)
# draw
pitch.heatmap(bin_statistic,
ax=ax[i],
cmap='coolwarm',
edgecolors='#22312b')
pitch.scatter(df.x, df.y, c='white', s=2, ax=ax[i])
# replace raw counts with percentages and add percentage sign (note immutable named tuple so used _replace)
bin_statistic['statistic'] = (pd.DataFrame(
(bin_statistic['statistic'] / bin_statistic['statistic'].sum()
)).applymap(lambda x: '{:.0%}'.format(x)).values)
pitch.label_heatmap(bin_statistic,
color='white',
fontsize=18,
ax=ax[i],
pitch = Pitch(pitch_type='statsbomb',
orientation='horizontal',
figsize=(16, 11),
line_zorder=2,
line_color='#c7d5cc',
constrained_layout=True,
tight_layout=False,
pitch_color='#22312b')
bins = (6, 4)
##############################################################################
# Plotting using a single color and length
fig, ax = pitch.draw()
# 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)
fm = pitch.flow(df_pass.x,
df_pass.y,
df_pass.end_x,
df_pass.end_y,
color='black',
arrow_type='same',
arrow_length=5,
bins=bins,
ax=ax)
ax.set_title(f'{team1} pass flow map vs {team2}', fontsize=30, pad=-20)
fig.set_facecolor('#22312b')
xT_Players = barcamoves.groupby('name')[[
'xT_value', 'end_zone_value', 'start_zone_value'
]].sum().sort_values('xT_value', ascending=False).round(4)
pitch = Pitch(pitch_type='uefa',
figsize=(6.8, 10.5),
line_zorder=2,
line_color='white',
orientation='vertical')
# draw
fig, ax = pitch.draw()
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
cmaplist = ['#082630', '#0682fe', "#eff3ff"]
cmap = LinearSegmentedColormap.from_list("", cmaplist)
bin_statistic = pitch.bin_statistic(barcamoves.x,
barcamoves.y,
values=barcamoves.xT_value,
statistic='sum',
bins=(38, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
vm = bin_statistic['statistic'].min()
vma = bin_statistic['statistic'].max()
pitch.heatmap(bin_statistic,
ax=ax,
cmap='inferno',
edgecolors=None,
vmin=bin_statistic['statistic'].min(),
vmax=bin_statistic['statistic'].max())
ax.set_title('Barcelona' + '\n' + 'Open-play Threat-generation hotspots',
fontsize=25)
fig.set_facecolor('white')
plt.savefig('xt.png', dpi=600)