# plotting to show that events can move from their pitch markings
# without relative scaling to the pitch markings
# first setup the figure
FIGWIDTH = 16
FIGHEIGHT = 6
fig = plt.figure(figsize=(FIGWIDTH, FIGHEIGHT))
opta_pitch = VerticalPitch(pitch_type='opta',
line_color='black',
half=True,
line_zorder=3)
ax_opta = fig.add_axes(
(0.05, 0.05, 0.4, 0.4 * FIGWIDTH / FIGHEIGHT / opta_pitch.ax_aspect))
opta_pitch.draw(ax=ax_opta)
custom_pitch = VerticalPitch(pitch_type='custom',
line_color='black',
half=True,
pitch_length=105,
pitch_width=68,
line_zorder=3)
ax_custom = fig.add_axes(
(0.55, 0.05, 0.4, 0.4 * FIGWIDTH / FIGHEIGHT / custom_pitch.ax_aspect))
custom_pitch.draw(ax=ax_custom)
# draw the original event points
fm = FontManager() # a mplsoccer fontmanager with the default Robotto font
opta_pitch.scatter(x,
y,
SHOT_ID = '8bb8bbc2-68a6-4c01-93de-53a194e7a1cf'
df_freeze_frame = df_freeze[df_freeze.id == SHOT_ID].copy()
df_shot_event = df_event[df_event.id == SHOT_ID].dropna(axis=1,
how='all').copy()
##############################################################################
# Location dataset
df = pd.concat([df_shot_event[['x', 'y']], df_freeze_frame[['x', 'y']]])
x = df.x.values
y = df.y.values
teams = np.concatenate([[True], df_freeze_frame.player_teammate.values])
##############################################################################
# Plotting
# draw plot
pitch = VerticalPitch(half=True)
fig, ax = pitch.draw(figsize=(8, 6.2))
# Plot Voronoi
team1, team2 = pitch.voronoi(x, y, teams)
t1 = pitch.polygon(team1, ax=ax, fc='#c34c45', ec='white', lw=3, alpha=0.4)
t2 = pitch.polygon(team2, ax=ax, fc='#6f63c5', ec='white', lw=3, alpha=0.4)
# Plot players
sc1 = pitch.scatter(x[teams], y[teams], ax=ax, c='#c34c45', s=150)
sc2 = pitch.scatter(x[~teams], y[~teams], ax=ax, c='#6f63c5', s=150)
# ---------------
# I like the look of the voltage colormap so let's plot in against a light and
# dark background
#
# You can reverse any of the colormaps, by putting
# _r at the end, for example cmr.arctic_r (this also applies to matplotlib cmaps).
#
# Reversing the colormaps is sometimes helpful so the high value colors do not bleed into the
# background. I prefer dark to light colormaps on dark background, and light to dark
# colormaps on light backgrounds. I have shown this below using the same colormap in reverse.
# dark
pitch_dark = VerticalPitch(line_color='#cfcfcf',
line_zorder=2,
pitch_color='#122c3d')
fig, ax = pitch_dark.draw()
kdeplot_dark = pitch_dark.kdeplot(df.x,
df.y,
ax=ax,
cmap=cmr.voltage,
shade=True,
levels=100)
# light
pitch_light = VerticalPitch(line_zorder=2)
fig, ax = pitch_light.draw()
kdeplot_light = pitch_light.kdeplot(df.x,
df.y,
ax=ax,
cmap=cmr.voltage_r,
shade=True,
# Shot map Barcelona
# ------------------
# First let's plot Barcelona's shots with the scatter marker size varying
# by the expected goals amount. The maximum of 1 (100% expected chance of scoring)
# has been given size 1000 (points**2).
# By multiplying the expected goals amount by 900 and adding 100
# we essentially get a size that varies between 100 and 1000.
# For choosing color schemes, I really like this website
# `iWantHue <https://medialab.github.io/iwanthue/>`_.
pitch = VerticalPitch(
pad_bottom=0.5, # pitch extends slightly below halfway line
half=True, # half of a pitch
goal_type='box',
goal_alpha=0.8) # control the goal transparency
fig, ax = pitch.draw(figsize=(12, 10))
sc = pitch.scatter(
df_shots_barca.x,
df_shots_barca.y,
# size varies between 100 and 1000 (points squared)
s=(df_shots_barca.shot_statsbomb_xg * 900) + 100,
c='#b94b75', # color for scatter in hex format
edgecolors='#383838', # give the markers a charcoal border
# for other markers types see: https://matplotlib.org/api/markers_api.html
marker='h',
ax=ax)
txt = ax.text(
x=40,
y=80,
s='Barcelona shots\nversus Sevilla',
size=30,
# fontmanager for google font (robotto)
robotto_regular = FontManager()
path_eff = [path_effects.Stroke(linewidth=3, foreground='black'),
path_effects.Normal()]
##############################################################################
# Plot positional heatmap
# -----------------------
# setup pitch
pitch = VerticalPitch(pitch_type='statsbomb', line_zorder=2,
pitch_color='#22312b', line_color='white')
# 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, title_space=0,
df_before_false9 = df_before_false9.loc[df_before_false9.player_id == 5503,
['x', 'y']]
##############################################################################
# Create a custom colormap.
# Note see the `custom colormaps
# <https://mplsoccer.readthedocs.io/en/latest/gallery/pitch_plots/plot_cmap.html>`_
# example for more ideas.
flamingo_cmap = LinearSegmentedColormap.from_list("Flamingo - 10 colors",
['#e3aca7', '#c03a1d'],
N=10)
##############################################################################
# Plot Messi's first game as a false-9.
pitch = VerticalPitch(line_color='#000009', line_zorder=2, pitch_color='white')
fig, ax = pitch.draw(figsize=(4.4, 6.4))
hexmap = pitch.hexbin(df_false9.x,
df_false9.y,
ax=ax,
edgecolors='#f4f4f4',
gridsize=(8, 8),
cmap=flamingo_cmap)
##############################################################################
# Load a custom font.
URL = 'https://github.com/googlefonts/roboto/blob/main/src/hinted/Roboto-Regular.ttf?raw=true'
URL2 = 'https://github.com/google/fonts/blob/main/apache/roboto/static/Roboto-Bold.ttf?raw=true'
robotto_regular = FontManager(URL)
robboto_bold = FontManager(URL2)
##############################################################################
##############################################################################
# Add the last name to the dataframes
df_statsbomb['last_name'] = df_statsbomb.player_name.str.split(' ').str[-1]
df_wyscout['last_name'] = df_wyscout.player_name.str.split(' ').str[-1]
##############################################################################
# Plot the standardized data
# --------------------------
pitch = VerticalPitch(pitch_type='statsbomb',
half=True,
pad_left=-10,
pad_right=-10,
pad_bottom=-20)
fig, ax = pitch.draw(figsize=(16, 9))
fm = FontManager() # a mplsoccer fontmanager with the default Robotto font
# subset portugals shots for both data providers
mask_portugal_sb = df_statsbomb.team_name == 'Portugal'
mask_shot_sb = df_statsbomb.type_name == 'Shot'
mask_portugal_wyscout = df_wyscout.team_name == 'Portugal'
mask_shot_wyscout = df_wyscout.event_type == 'SHOT'
df_wyscout_portugal = df_wyscout[mask_shot_wyscout
& mask_portugal_wyscout].copy()
df_statsbomb_portugal = df_statsbomb[mask_shot_sb & mask_portugal_sb].copy()
# plotting the shots as a scatter plot with a legend
pitch.scatter(df_wyscout_portugal.coordinates_x,
df_wyscout_portugal.coordinates_y,
ax=ax,
##############################################################################
# View the pass dataframe.
df_pass
##############################################################################
# Plotting
# Setup the pitch
pitch = VerticalPitch(pitch_type='statsbomb',
pitch_color='#22312b',
line_color='#c7d5cc',
half=True,
pad_top=2)
fig, ax = pitch.draw(figsize=(16, 11), tight_layout=True)
# Plot the completed passes
pitch.lines(df_pass.x,
df_pass.y,
df_pass.end_x,
df_pass.end_y,
lw=10,
transparent=True,
comet=True,
cmap='jet',
label='pass leading to shot',
ax=ax)
# Plot the goals
pitch.scatter(df_pass[mask_goal].end_x,
match_files = ['19789.json', '19794.json', '19805.json']
kwargs = {'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 = VerticalPitch(pitch_type='statsbomb', line_zorder=2,
pitch_color='#22312b', line_color='white')
# draw
fig, ax = pitch.draw(figsize=(16, 9), ncols=3, nrows=1)
positions = ['full', 'horizontal', 'vertical']
for i, pos in enumerate(positions):
bin_statistic = pitch.bin_statistic_positional(df.x, df.y, statistic='count', positional=pos)
pitch.heatmap_positional(bin_statistic, ax=ax[i], cmap='coolwarm', edgecolors='#22312b')
pitch.scatter(df.x, df.y, c='white', s=2, ax=ax[i])
total = np.array([bs['statistic'].sum() for bs in bin_statistic]).sum()
# replace raw counts with percentages and add percentage
# sign (note immutable named tuple so used _replace)
for bs in bin_statistic:
bs['statistic'] = (pd.DataFrame(bs['statistic'] / total)
.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'
left2 = (TOP_SPACE * 2) + TOP_WIDTH bottom2 = (1 - height2) / 2 - top_offset ax2 = fig.add_axes((left2, bottom2, TOP_WIDTH, height2)) pitch2.draw(ax=ax2) # top right left3 = (TOP_SPACE * 3) + (TOP_WIDTH * 2) bottom3 = (1 - height3) / 2 - top_offset ax3 = fig.add_axes((left3, bottom3, TOP_WIDTH, height3)) pitch3.draw(ax=ax3) # bottom left LEFT4 = BOTTOM_SPACE bottom4 = (1 - height4) / 2 - bottom_offset ax4 = fig.add_axes((LEFT4, bottom4, BOTTOM_WIDTH, height4)) pitch4.draw(ax=ax4) # bottom middle left5 = (BOTTOM_SPACE * 2) + BOTTOM_WIDTH bottom5 = (1 - height5) / 2 - bottom_offset ax5 = fig.add_axes((left5, bottom5, BOTTOM_WIDTH, height5)) pitch5.draw(ax=ax5) # bottom right left6 = (BOTTOM_SPACE * 3) + (BOTTOM_WIDTH * 2) bottom6 = (1 - height6) / 2 - bottom_offset ax6 = fig.add_axes((left6, bottom6, BOTTOM_WIDTH, height6)) pitch6.draw(ax=ax6) # draw titles
