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)
fig.set_facecolor('#22312b')
# 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)
##############################################################################
# Plotting using a cmap and scaled arrows
nrows=4,
tight_layout=False,
constrained_layout=True)
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[:len(teams)]):
ax.set_title(teams[i], fontsize=20)
# fill in the bin statistics from df and plot the heatmap
bin_statistic['statistic'] = df.loc[df.Squad == teams[i],
pressure_cols].values
heatmap = pitch.heatmap(bin_statistic,
ax=ax,
cmap='coolwarm',
vmin=vmin,
vmax=vmax)
# format and plot labels
bin_statistic['statistic'] = (pd.DataFrame(
bin_statistic['statistic']).round(0).astype(
np.int32).applymap(lambda x: '{:d}%'.format(x)).values)
annotate = pitch.label_heatmap(bin_statistic,
color='white',
fontsize=20,
ax=ax,
ha='center',
va='center')
# set a black path effect around the labels
for label in annotate:
label.set_path_effects([
# plot without endnote/ title axes
title_height=0,
endnote_height=0)
bs1 = pitch.bin_statistic(df_team1.x, df_team1.y, bins=(18, 12))
bs2 = pitch.bin_statistic(df_team2.x, df_team2.y, bins=(18, 12))
# get the min/ max values for normalizing across both teams
vmax = max(bs2['statistic'].max(), bs1['statistic'].max())
vmin = max(bs2['statistic'].min(), bs1['statistic'].min())
# set values where zero shots to nan values so it does not show up in the heatmap
# i.e. zero values take the background color
bs1['statistic'][bs1['statistic'] == 0] = np.nan
bs2['statistic'][bs2['statistic'] == 0] = np.nan
# set the vmin/ vmax so the colors depend on the minimum/maximum value for both teams
hm1 = pitch.heatmap(bs1,
ax=axes[0],
cmap='Reds',
vmin=vmin,
vmax=vmax,
edgecolor='#f9f9f9')
hm2 = pitch.heatmap(bs2,
ax=axes[0],
cmap='Blues',
vmin=vmin,
vmax=vmax,
edgecolor='#f9f9f9')
# histograms with kdeplot
team1_hist_y = sns.histplot(y=df_team1.y,
ax=axes[1],
color=red,
linewidth=1,
kde=True)
team1_hist_x = sns.histplot(x=df_team1.x,
'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)
