from mplsoccer.statsbomb import read_event, read_lineup, EVENT_SLUG, LINEUP_SLUG
##############################################################################
# Single Pitch
# ------------
# You can also use grid to plot a single pitch with an endnote and title axis.
# The defaults setup a pitch with a 2.5%
# (of the figure height/ width) border around the sides.
pitch = Pitch()
fig, axs = pitch.grid()
##############################################################################
# Multiple Pitches side by side
# -----------------------------
# Next up let's plot 3 pitches side-by-side.
pitch = VerticalPitch()
fig, axs = pitch.grid(figheight=15, ncols=3)
##############################################################################
# Grid of Pitches
# ---------------
# Here's how to plot a grid of pitches
pitch = Pitch(linewidth=4)
fig, axs = pitch.grid(nrows=3, ncols=4, # number of rows/ columns
figheight=25, # the figure height in inches
bottom=0.025, # starts 2.5% in from the figure bottom
# grid takes up 83% of the figure height
# I calculated this so most of the figure is pitches
# 1 - (bottom + endnote_height + endnote_space +
# title_height + title_space) - 0.025 [space at top]
grid_height=0.83,
pitch_to='custom',
length_to=105,
width_to=68)
x_relative, y_relative = opta_to_custom.transform(x, y)
# 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)
#
# See the docs for more info: https://cmasher.readthedoavorite cs.io/.
cmap_dict = cmr.cm.cmap_cd
all_cmap_dict = {}
for cmap_type_key in cmap_dict:
for key, cmap in cmap_dict[cmap_type_key].items():
if key[-2:] != '_r':
all_cmap_dict[key] = cmap
##############################################################################
# Cmasher kdeplot
# ---------------
# Here's all the cmasher colormaps plotted as a grid so you can find your
# favorite.
pitch = VerticalPitch(line_color='#cfcfcf',
line_zorder=2,
pitch_color='#122c3d')
fig, axs = pitch.grid(
nrows=11,
ncols=4,
space=0.1,
figheight=40,
title_height=0,
endnote_height=0, # no title/ endnote
grid_width=0.9,
grid_height=0.98,
bottom=0.01,
left=0.05)
cmap_names = list(all_cmap_dict.keys())
for idx, ax in enumerate(axs['pitch'].flat):
cmap_name = f'cmr.{cmap_names[idx]}'
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)
############################################################################## # Pitch orientation # ----------------- # There are four basic pitch orientations. # To get vertical pitches use the VerticalPitch class. # To get half pitches use the half=True argument. # # Horizontal full pitch = Pitch(half=False) fig, ax = pitch.draw() ############################################################################## # Vertical full pitch = VerticalPitch(half=False) fig, ax = pitch.draw() ############################################################################## # Horizontal half pitch = Pitch(half=True) fig, ax = pitch.draw() ############################################################################## # Vertical half pitch = VerticalPitch(half=True) fig, ax = pitch.draw() ############################################################################## # You can also adjust the pitch orientations with the ``pad_left``, ``pad_right``, # ``pad_bottom`` and ``pad_top`` arguments to make arbitrary pitch shapes.
import matplotlib.pyplot as plt
from mplsoccer import Pitch, VerticalPitch
from mplsoccer.statsbomb import read_event, EVENT_SLUG
from mplsoccer.utils import FontManager
# 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'))
##############################################################################
# Subset the shots for each team and move Barcelona's shots to the other side of the pitch.
# subset the shots
df_shots = df[df.type_name == 'Shot'].copy()
# subset the shots for each team
team1, team2 = df_shots.team_name.unique()
df_team1 = df_shots[df_shots.team_name == team1].copy()
df_shot = (df.loc[(df.type_name == 'Shot') & (df.team_name == TEAM1),
['id', 'outcome_name', 'shot_statsbomb_xg']].rename(
{'id': 'pass_assisted_shot_id'}, axis=1))
df_pass = df_pass.merge(df_shot, how='left').drop('pass_assisted_shot_id',
axis=1)
mask_goal = df_pass.outcome_name == 'Goal'
##############################################################################
# This example shows how to plot all passes leading to shots from a team using a colormap (cmap).
# Setup the pitch
pitch = VerticalPitch(pitch_type='statsbomb',
pitch_color='#22312b',
line_color='#c7d5cc',
half=True,
pad_top=2)
fig, axs = pitch.grid(endnote_height=0.03,
endnote_space=0,
figheight=12,
title_height=0.08,
title_space=0,
axis=False,
grid_height=0.82)
fig.set_facecolor('#22312b')
# Plot the completed passes
pitch.lines(df_pass.x,
df_pass.y,
df_pass.end_x,
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']]
##############################################################################
# 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)
'RubikMonoOne-Regular.ttf?raw=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,
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']]
##############################################################################
# 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 - 100 colors",
['#e3aca7', '#c03a1d'], N=100)
##############################################################################
# Plot Messi's first game as a false-9.
pitch = VerticalPitch(line_color='#000009', line_zorder=2)
fig, ax = pitch.draw(figsize=(4.4, 6.4))
kde = pitch.kdeplot(df_false9.x, df_false9.y, ax=ax,
# shade using 100 levels so it looks smooth
shade=True, levels=100,
# shade the lowest area so it looks smooth
# so even if there are no events it gets some color
shade_lowest=True,
cut=4, # extended the cut so it reaches the bottom edge
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)
df_wyscout['coordinates_x'] = x_std
df_wyscout['coordinates_y'] = y_std
##############################################################################
# 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
axis=1)
mask_goal = df_pass.outcome_name == 'Goal'
##############################################################################
# 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)
# 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
# calculate the horizontal space between axes (and figure sides) in percent of the figure width
TOP_SPACE = (1 - (TOP_WIDTH * 3)) / 4
BOTTOM_SPACE = (1 - (BOTTOM_WIDTH * 3)) / 4
# calculate the height of the pitch axes in percent of the figure height
# strings for team names
team1 = df_shot_event.team_name.iloc[0]
team2 = list(set(df_event.team_name.unique()) - {team1})[0]
# subset the team shooting, and the opposition (goalkeeper/ other)
df_team1 = df_freeze_frame[df_freeze_frame.team_name == team1]
df_team2_goal = df_freeze_frame[(df_freeze_frame.team_name == team2) & (
df_freeze_frame.player_position_name == 'Goalkeeper')]
df_team2_other = df_freeze_frame[(df_freeze_frame.team_name == team2) & (
df_freeze_frame.player_position_name != 'Goalkeeper')]
##############################################################################
# Plotting
# Setup the pitch
pitch = VerticalPitch(half=True, goal_type='box', pad_bottom=-20)
# We will use mplsoccer's grid function to plot a pitch with a title axis.
fig, axs = pitch.grid(
figheight=8,
endnote_height=0, # no endnote
title_height=0.1,
title_space=0.02,
# Turn off the endnote/title axis. I usually do this after
# I am happy with the chart layout and text placement
axis=False,
grid_height=0.83)
# Plot the players
sc1 = pitch.scatter(df_team1.x,
df_team1.y,
# 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',
'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'))
##############################################################################
# Subset the shots for each team and move Barcelona's shots to the other side of the pitch.
# subset the shots
df_shots = df[df.type_name == 'Shot'].copy()
}
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,
)
# 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
# 16 by 9: three vertical pitches
# -------------------------------
# Three vertical pitches fits nicely in the 16:9 aspect ratio.
# Here we plot with a title and endnote axis too.
FIGWIDTH = 16
FIGHEIGHT = 9
NROWS = 1
NCOLS = 3
SPACE = 0.09
MAX_GRID = 0.95
pitch = VerticalPitch(pad_top=1,
pad_bottom=1,
pad_left=1,
pad_right=1,
pitch_color='grass',
stripe=True,
line_color='white')
GRID_WIDTH, GRID_HEIGHT = pitch.calculate_grid_dimensions(figwidth=FIGWIDTH,
figheight=FIGHEIGHT,
nrows=NROWS,
ncols=NCOLS,
max_grid=MAX_GRID,
space=SPACE)
TITLE_HEIGHT = 0.1
ENDNOTE_HEIGHT = MAX_GRID - (GRID_HEIGHT + TITLE_HEIGHT)
fig, ax = pitch.grid(figheight=FIGHEIGHT,
