##############################################################################
# Set color to make the lines more transparent when fewer passes are made
MIN_TRANSPARENCY = 0.3
color = np.array(to_rgba('white'))
color = np.tile(color, (len(passes_between), 1))
c_transparency = passes_between.pass_count / passes_between.pass_count.max()
c_transparency = (c_transparency * (1 - MIN_TRANSPARENCY)) + MIN_TRANSPARENCY
color[:, 3] = c_transparency
##############################################################################
# Plotting
pitch = Pitch(
pitch_type='statsbomb',
pitch_color='#22312b',
line_color='#c7d5cc',
)
fig, ax = pitch.draw(figsize=(16, 11),
constrained_layout=True,
tight_layout=False)
pass_lines = pitch.lines(passes_between.x,
passes_between.y,
passes_between.x_end,
passes_between.y_end,
lw=passes_between.width,
color=color,
zorder=1,
ax=ax)
pass_nodes = pitch.scatter(average_locs_and_count.x,
average_locs_and_count.y,
import cmasher as cmr import matplotlib.pyplot as plt import numpy as np from PIL import Image from highlight_text import ax_text from mplsoccer import Pitch, VerticalPitch, add_image, FontManager 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
============ Pitch Basics ============ First we import the Pitch classes and matplotlib """ import matplotlib.pyplot as plt from mplsoccer import Pitch, VerticalPitch ############################################################################## # Draw a pitch on a new axis # -------------------------- # Let's plot on a new axis first. pitch = Pitch() # specifying figure size (width, height) fig, ax = pitch.draw(figsize=(8, 4)) ############################################################################## # Draw on an existing axis # ------------------------ # mplsoccer also plays nicely with other matplotlib figures. To draw a pitch on an # existing matplotlib axis specify an ``ax`` in the ``draw`` method. fig, axs = plt.subplots(nrows=1, ncols=2) pitch = Pitch() pie = axs[0].pie(x=[5, 15]) pitch.draw(ax=axs[1]) ##############################################################################
# Show the away data
df_away.head()
##############################################################################
# Show the home data
df_home.head()
##############################################################################
# Show the ball data
df_ball.head()
##############################################################################
# Plot the animation
# First set up the figure, the axis
pitch = Pitch(pitch_type='metricasports', goal_type='line', pitch_width=68, pitch_length=105)
fig, ax = pitch.draw(figsize=(16, 10.4))
# then setup the pitch plot markers we want to animate
marker_kwargs = {'marker': 'o', 'markeredgecolor': 'black', 'linestyle': 'None'}
ball, = ax.plot([], [], ms=6, markerfacecolor='w', zorder=3, **marker_kwargs)
away, = ax.plot([], [], ms=10, markerfacecolor='#b94b75', **marker_kwargs) # red/maroon
home, = ax.plot([], [], ms=10, markerfacecolor='#7f63b8', **marker_kwargs) # purple
# animation function
def animate(i):
""" Function to animate the data. Each frame it sets the data for the players and the ball."""
# set the ball data with the x and y positions for the ith frame
ball.set_data(df_ball.iloc[i, 3], df_ball.iloc[i, 4])
# get the frame id for the ith frame
'connectionstyle': 'angle3,angleA=0,angleB=-90',
'color': FONTCOLOR
}
font_kwargs = {
'fontsize': 14,
'ha': 'center',
'va': 'bottom',
'fontweight': 'bold',
'fontstyle': 'italic',
'c': FONTCOLOR
}
for idx, pt in enumerate(pitch_types):
if pt in ['tracab', 'metricasports', 'custom', 'skillcorner']:
pitch = Pitch(pitch_type=pt,
pitch_length=105,
pitch_width=68,
**pitch_kwargs)
else:
pitch = Pitch(pitch_type=pt, **pitch_kwargs)
pitch.draw(axes[idx])
xmin, xmax, ymin, ymax = pitch.extent
if pitch.dim.aspect != 1:
TEXT = 'data coordinates \n are square (1:1) \n scale up to a real-pitch size'
axes[idx].annotate(TEXT,
xy=(xmin, ymin),
xytext=(0 + (xmax - xmin) / 2, ymin),
**font_kwargs)
axes[idx].xaxis.set_ticks([xmin, xmax])
axes[idx].yaxis.set_ticks([ymin, ymax])
axes[idx].tick_params(labelsize=15)
if pt == 'skillcorner':
coord['team'], player_size,
player_opacity)
st.image(draw.compose_image(sensitivity))
with o_col3:
draw = PitchDraw(image, original=False)
for pid, coord in dfCoords.iterrows():
draw.draw_circle(coord[['x', 'y']].values,
coord['team'], 2, player_opacity)
draw.draw_text(coord[['x', 'y']] + 0.5, f"{pid}",
coord['team'])
st.image(draw.compose_image(sensitivity))
st.markdown(get_table_download_link(
dfCoords[['team', 'x', 'y']]),
unsafe_allow_html=True)
#mplsoccer based output viz
_, mpl_output, _ = st.beta_columns(
(1, 2,
1)) #temporary, as fig size doesn't seem to be working
from mplsoccer import Pitch
pitch = Pitch(pitch_type='statsbomb', pitch_color='#22312b')
fig, ax = pitch.draw()
pitch.scatter(dfCoords.x,
dfCoords.y,
c=dfCoords.team,
ax=ax,
s=120,
edgecolors='white')
with mpl_output:
st.pyplot(fig)
##############################################################################
# Rotated markers
# ---------------
# I also included a method for rotating markers in mplsoccer.
#
# Warning: The rotation angle is in degrees and assumes the original marker is pointing upwards ↑.
# If it's not you will have to modify the rotation degrees.
# Rotates the marker in degrees, clockwise. 0 degrees is facing the
# direction of play (left to right).
# In a horizontal pitch, 0 degrees is this way →, in a vertical pitch, 0 degrees is this way ↑
#
# We are going to plot pass data as an arrowhead marker with the
# arrow facing in the direction of the pass
# The marker size is going to relate to the pass distance,
# so larger markers mean the pass was longer.
pitch = Pitch()
fig, ax = pitch.draw(figsize=(14, 12))
angle, distance = pitch.calculate_angle_and_distance(df_pass_barca.x,
df_pass_barca.y,
df_pass_barca.end_x,
df_pass_barca.end_y,
standardized=False,
degrees=True)
sc = pitch.scatter(
df_pass_barca.x,
df_pass_barca.y,
rotation_degrees=angle,
c='#b94b75', # color for scatter in hex format
edgecolors='#383838',
alpha=0.9,
s=(distance / distance.max()) * 900,
##############################################################################
# Boolean mask for filtering the dataset by team
team1, team2 = df.team_name.unique()
mask_team1 = (df.type_name == 'Pass') & (df.team_name == team1)
##############################################################################
# Filter dataset to only include one teams passes and get boolean mask for the completed passes
df_pass = df.loc[mask_team1, ['x', 'y', 'end_x', 'end_y', 'outcome_name']]
mask_complete = df_pass.outcome_name.isnull()
##############################################################################
# Setup the pitch and number of bins
pitch = Pitch(pitch_type='statsbomb',
line_zorder=2,
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')
# Get the StatsBomb logo and Fonts
LOGO_URL = 'https://raw.githubusercontent.com/statsbomb/open-data/master/img/statsbomb-logo.jpg'
sb_logo = Image.open(urlopen(LOGO_URL))
# a FontManager object for using a google font (default Robotto)
fm = FontManager()
# path effects
path_eff = [path_effects.Stroke(linewidth=3, foreground='black'),
path_effects.Normal()]
##############################################################################
# Plot the percentages
# setup a mplsoccer pitch
pitch = Pitch(line_zorder=2, line_color='black', pad_top=20)
# 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))
GRID_HEIGHT = 0.8
CBAR_WIDTH = 0.03
fig, axs = pitch.grid(nrows=4, ncols=5, figheight=20,
# leaves some space on the right hand side for the colorbar
grid_width=0.88, left=0.025,
endnote_height=0.06, endnote_space=0,
# Turn off the endnote/title axis. I usually do this after
# I am happy with the chart layout and text placement
df_total = pd.DataFrame(df[pressure_cols].sum())
df_total.columns = ['total']
df_total = df_total.T
df_total = df_total.divide(df_total.sum(axis=1), axis=0) * 100
##############################################################################
# Calculate the percentages for each team and sort so that the teams which press higher are last
df[pressure_cols] = df[pressure_cols].divide(df[pressure_cols].sum(axis=1),
axis=0) * 100.
df.sort_values(['Att 3rd', 'Def 3rd'], ascending=[True, False], inplace=True)
##############################################################################
# Plot the percentages
# setup a mplsoccer pitch
pitch = Pitch(line_zorder=2, line_color='black')
# 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))
# Plot
fig, axes = pitch.draw(figsize=(16, 9),
ncols=5,
nrows=4,
tight_layout=False,
constrained_layout=True)
axes = axes.ravel()
teams = df['Squad'].values
kwargs = {
'related_event_df': False,
'shot_freeze_frame_df': False,
'tactics_lineup_df': False,
'warn': False
}
df_false9 = read_event(f'{EVENT_SLUG}/69249.json', **kwargs)['event']
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']]
##############################################################################
# plotting
pitch = Pitch(pitch_type='statsbomb',
pitch_color='#22312b',
stripe=False,
line_zorder=2)
fig, ax = pitch.draw(
figsize=(16, 9),
nrows=1,
ncols=2,
)
pitch.hexbin(df_before_false9.x, df_before_false9.y, ax=ax[0], cmap='Blues')
pitch.hexbin(df_false9.x, df_false9.y, ax=ax[1], cmap='Blues')
TITLE_STR1 = 'Messi in the game directly before \n playing in the false 9 role'
TITLE_STR2 = 'The first Game Messi \nplayed in the false 9 role'
title1 = ax[0].set_title(TITLE_STR1, fontsize=25, pad=20)
title2 = ax[1].set_title(TITLE_STR2, fontsize=25, pad=20)
'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)
fig = plt.figure(figsize=FIGSIZE)
fm_rubik = FontManager(('https://github.com/google/fonts/blob/main/ofl/rubikmonoone/'
'RubikMonoOne-Regular.ttf?raw=true'))
# 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
# Load an image of Messi
# ######################
# load the image
IMAGE_URL = 'https://upload.wikimedia.org/wikipedia/commons/b/b8/Messi_vs_Nigeria_2018.jpg'
image = Image.open(urlopen(IMAGE_URL))
##############################################################################
# Plotting an image over a pitch
# ##############################
#
# To plot images you use ``Axes.imshow()`` in matplotlib.
# We are going to draw a pitch and then overlay ontop an image of Messi on a new axis.
# draw the pitch
pitch = Pitch(line_zorder=2)
fig, ax = pitch.draw(figsize=(16, 9), tight_layout=False)
# add an image
ax_image = add_image(image, fig, left=0.55, bottom=0.2, width=0.2,
alpha=0.9, interpolation='hanning')
##############################################################################
# Photo from: https://en.wikipedia.org/wiki/Lionel_Messi#/media/File:Messi_vs_Nigeria_2018.jpg;
# License: https://creativecommons.org/licenses/by-sa/3.0/;
# Creator: Кирилл Венедиктов
##############################################################################
# More control over the images and axis
# #####################################
#
""" =========== Quick start =========== """ from mplsoccer import Pitch pitch = Pitch(pitch_color='grass', line_color='white', stripe=True) fig, ax = pitch.draw()
# Now creating a DataFrame and Storing the DataFrame in a dictionary with match id as Key
df = json_normalize(data, sep='_').assign(match_id=filename[-9:-5])
# In[13]:
# A dataframe of only Shots
shots = df[df['type_name'] == 'Shot'].set_index('id')
shots.head()
# In[15]:
# fig,ax = createPitch(pitchLengthX,pitchWidthY,'yards','gray')
pitch = Pitch(
pitch_type='custom', # example plotting a tracab pitch
pitch_length=pitchLengthX,
pitch_width=pitchWidthY,
axis=True,
label=True) # showing axis labels is optional
fig, ax = pitch.draw(figsize=(16, 11),
constrained_layout=False,
tight_layout=True)
# Now lets plot the shots
for i, shot in shots.iterrows():
x = shot['location'][0]
y = shot['location'][1]
goal = shot['shot_outcome_name'] == 'Goal'
team_name = shot['team_name']
circleSize = 2
#circleSize=np.sqrt(shot['shot_statsbomb_xg'])*12
# the pass comet lines when using capstyle='round'
ALPHA_PITCH_LINE = 0.3
ALPHA_PASS_LINE = 0.15
# The colors are borrowed from mplcyberpunk. Try some of the following alternatives
# '#08F7FE' (teal/cyan), '#FE53BB' (pink), '#F5D300' (yellow),
# '#00ff41' (matrix green), 'r' (red), '#9467bd' (viloet)
BACKGROUND_COLOR = '#212946'
PASS_COLOR = '#FE53BB'
LINE_COLOR = '#08F7FE'
# plot as initial pitch and the lines with alpha=1
# I have used grid to get a title and endnote axis automatically, but you could you pitch.draw()
pitch = Pitch(line_color=LINE_COLOR,
pitch_color=BACKGROUND_COLOR,
linewidth=LINEWIDTH,
line_alpha=1,
goal_alpha=1,
goal_type='box')
fig, ax = pitch.grid(grid_height=0.9,
title_height=0.06,
axis=False,
endnote_height=0.04,
title_space=0,
endnote_space=0)
fig.set_facecolor(BACKGROUND_COLOR)
pitch.lines(
df_pass.x,
df_pass.y,
df_pass.end_x,
df_pass.end_y,
capstyle='butt', # cut-off the line at the end-location.
##############################################################################
# Filter the dataframes to only include Messi's events and the 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']]
##############################################################################
# View a dataframe
df_false9.head()
##############################################################################
# Plotting Messi's first game as a False-9
pitch = Pitch(pitch_type='statsbomb', pitch_color='grass', stripe=True)
fig, ax = pitch.draw(figsize=(16, 11))
# plotting
ax.set_title('The first Game Messi played in the false 9 role',
fontsize=30,
pad=20)
# plot the kernel density estimation
pitch.kdeplot(df_false9.x, df_false9.y, ax=ax, cmap='plasma', linewidths=3)
# annotate
pitch.annotate('6-2 thrashing \nof Real Madrid', (25, 10),
color='white',
fontsize=25,
ha='center',
# I created a function to calculate the maximum dimensions you can get away with while
# having a set figure size. Let's use this to create the largest pitch possible
# with a 16:9 figure aspect ratio.
FIGWIDTH = 16
FIGHEIGHT = 9
NROWS = 1
NCOLS = 1
# here we want the maximum side in proportion to the figure dimensions
# (height in this case) to take up all of the image
MAX_GRID = 1
# pitch with minimal padding (2 each side)
pitch = Pitch(pad_top=2,
pad_bottom=2,
pad_left=2,
pad_right=2,
pitch_color='#22312b')
# calculate the maximum grid_height/ width
GRID_WIDTH, GRID_HEIGHT = pitch.calculate_grid_dimensions(figwidth=FIGWIDTH,
figheight=FIGHEIGHT,
nrows=NROWS,
ncols=NCOLS,
max_grid=MAX_GRID,
space=0)
# plot using the mplsoccer grid function
fig, ax = pitch.grid(figheight=FIGHEIGHT,
grid_width=GRID_WIDTH,
grid_height=GRID_HEIGHT,