Пример #1
0
                     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
    if (team_name == team1):
        if goal:
            # shotCircle=plt.Circle((x,pitchWidthY-y),circleSize,color="red")
            shotCircle = pitch.scatter(x,
                                       pitchWidthY - y,
                                       marker='football',
                                       ax=ax)
            # plt.text((x+1),pitchWidthY-y+1,shot['player_name'])
        else:
            shotCircle = pitch.scatter(x, pitchWidthY - y, ax=ax)
            # shotCircle=plt.Circle((x,pitchWidthY-y),circleSize,color="red")
    elif (team_name == team2):
        if goal:
            shotCircle = pitch.scatter(pitchLengthX - x,
                                       y,
                                       marker='football',
                                       ax=ax)
            # shotCircle=plt.Circle((pitchLengthX-x,y),circleSize,color="blue")
            # plt.text((pitchLengthX-x+1),y+1,shot['player_name'])
        else:
            shotCircle = pitch.scatter(pitchLengthX - x, y, ax=ax)
Пример #2
0
)
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,
                           s=average_locs_and_count.marker_size,
                           color='red',
                           edgecolors='black',
                           linewidth=1,
                           alpha=1,
                           ax=ax)
for index, row in average_locs_and_count.iterrows():
    pitch.annotate(row.name,
                   xy=(row.x, row.y),
                   c='white',
                   va='center',
                   ha='center',
                   size=16,
                   weight='bold',
                   ax=ax)
title = ax.set_title("{} {} Formation vs {}".format(TEAM, FORMATION, OPPONENT),
                     size=28,
                     y=0.97,
Пример #3
0
fig, axes, _, _ = pitch.jointgrid(
    figheight=10,  # the figure is 10 inches high
    left=None,  # joint grid center-aligned
    bottom=0.075,  # grid starts 7.5% in from the bottom of the figure
    marginal=0.1,  # marginal axes heights are 10% of grid height
    space=0,  # 0% of the grid height reserved for space between axes
    grid_width=0.9,  # the grid width takes up 90% of the figure width
    title_height=0,  # plot without a title axes
    endnote_height=0,  # plot without an endnote axes
    grid_height=0.8)  # grid takes up 80% of the figure height
# we plot a usual scatter plot but the scatter size is based on expected goals
# note that the size is the expected goals * 700
# so any shots with an expected goals = 1 would take a size of 700 (points**2)
sc_team1 = pitch.scatter(df_team1.x,
                         df_team1.y,
                         s=df_team1.shot_statsbomb_xg * 700,
                         ec='black',
                         color='#ba495c',
                         ax=axes[0])
sc_team2 = pitch.scatter(df_team2.x,
                         df_team2.y,
                         s=df_team1.shot_statsbomb_xg * 700,
                         ec='black',
                         color='#697cd4',
                         ax=axes[0])
# (step) histograms on each of the left, top, and right marginal axes
team1_hist_y = sns.histplot(y=df_team1.y,
                            ax=axes[1],
                            element='step',
                            color='#ba495c')
team1_hist_x = sns.histplot(x=df_team1.x,
                            ax=axes[2],
Пример #4
0
            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=axs['pitch'])

# Plot the goals
pitch.scatter(df_pass[mask_goal].end_x,
              df_pass[mask_goal].end_y,
              s=700,
              marker='football',
              edgecolors='black',
              c='white',
              zorder=2,
              label='goal',
              ax=axs['pitch'])
pitch.scatter(df_pass[~mask_goal].end_x,
              df_pass[~mask_goal].end_y,
              edgecolors='white',
              c='#22312b',
              s=700,
              zorder=2,
              label='shot',
              ax=axs['pitch'])

# endnote and title
axs['endnote'].text(1,
Пример #5
0
import matplotlib.pyplot as plt

# read data
df = read_event(f'{EVENT_SLUG}/7478.json',
                related_event_df=False,
                shot_freeze_frame_df=False,
                tactics_lineup_df=False)['event']

##############################################################################
# Filter passes by Jodie Taylor
df = df[(df.player_name == 'Jodie Taylor') & (df.type_name == 'Pass')].copy()

##############################################################################
# Plotting

pitch = Pitch()
fig, ax = pitch.draw(figsize=(8, 6))
hull = pitch.convexhull(df.x, df.y)
poly = pitch.polygon(hull,
                     ax=ax,
                     edgecolor='cornflowerblue',
                     facecolor='cornflowerblue',
                     alpha=0.3)
scatter = pitch.scatter(df.x,
                        df.y,
                        ax=ax,
                        edgecolor='black',
                        facecolor='cornflowerblue')
plt.show(
)  # if you are not using a Jupyter notebook this is necessary to show the plot