figsize=(16, 9),
ncols=3,
nrows=1,
)
# heatmap specified by (nx, ny) for horizontal pitch
bins = [(6, 5), (1, 5), (6, 1)]
for i, bin_dimension in enumerate(bins):
bin_statistic = pitch.bin_statistic(df.x,
df.y,
statistic='count',
bins=bin_dimension)
# 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],
ha='center',
va='bottom')
TITLE_STR = 'Location of pressure events - 3 home games for Chelsea FC Women'
title = fig.suptitle(TITLE_STR, x=0.5, y=0.98, fontsize=30)
# draw
fig, ax = pitch.draw(figsize=(4.125, 6))
bin_statistic = pitch.bin_statistic_positional(df.x,
df.y,
statistic='count',
positional='full',
normalize=True)
pitch.heatmap_positional(bin_statistic,
ax=ax,
cmap='coolwarm',
edgecolors='#22312b')
pitch.scatter(df.x, df.y, c='white', s=2, ax=ax)
labels = pitch.label_heatmap(bin_statistic,
color='#f4edf0',
fontsize=18,
ax=ax,
ha='center',
va='center',
str_format='{:.0%}',
path_effects=path_eff)
##############################################################################
# Plot the chart again with a title
# ---------------------------------
# We will use mplsoccer's grid function to plot a pitch with a title and endnote axes.
pitch = VerticalPitch(pitch_type='statsbomb',
line_zorder=2,
pitch_color='#1e4259')
fig, axs = pitch.grid(
endnote_height=0.03,
endnote_space=0,
title_height=0.08,