def turnovermap(Df,hometeam,homeid,awayteam,awayid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,6), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
homedef = df.query("(teamId==@homeid)&\
(type_displayName in ['Clearance','Tackle','Interception','Aerial','BlockedPass'])&\
(outcomeType_displayName=='Successful')")
awaydef = df.query("(teamId==@awayid)&\
(type_displayName in ['Clearance','Tackle','Interception','Aerial','BlockedPass'])&\
(outcomeType_displayName=='Successful')")
for i in homedef.index.tolist():
if(i<len(df)-1):
if((df.teamId[i+1]==homeid)&(df.type_displayName[i+1] in ['BallRecovery','Pass','TakeOn'])&
(df.outcomeType_displayName[i+1]=='Successful')):
pitch.scatter(homedef.x[i],80-homedef.y[i],marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='#fee090',linewidth=3,ax=ax[0])
for i in awaydef.index.tolist():
if(i<len(df)-1):
if((df.teamId[i+1]==homeid)&(df.type_displayName[i+1] in ['BallRecovery','Pass','TakeOn'])&
(df.outcomeType_displayName[i+1]=='Successful')):
pitch.scatter(awaydef.x[i],80-awaydef.y[i],marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='#fee090',linewidth=3,ax=ax[1])
ax[0].set_title(hometeam+'\n'+'Turnover creating'+'\n'+'Defensive activities',fontsize=30)
ax[1].set_title(awayteam+'\n'+'Turnover creating'+'\n'+'Defensive activities',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.05, "Turnover creating defensive activities include tackles, interceptions, aerials, clearances,"
+'\n'+" and blocked passes leading to winning possession",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def badpasses(Df,hometeam,homeid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,4.5), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
def3rd = df.query("(teamId==@homeid)&(type_displayName == 'Pass')&(x<40)")
mid3rd = df.query("(teamId==@homeid)&(type_displayName == 'Pass')&(x>=40)&(x<80)")
defmask = def3rd.outcomeType_displayName == 'Successful'
midmask = mid3rd.outcomeType_displayName == 'Successful'
# awaymask = awaydef.outcomeType_displayName == 'Successful'
pitch.arrows(def3rd[~defmask].x,80-def3rd[~defmask].y,def3rd[~defmask].endX,80-def3rd[~defmask].endY,
headwidth=5, headlength=5,zorder=5,color='tab:red',width=1,ax=ax[0])
# pitch.lines(homedef[~homemask].x,80-homedef[~homemask].y,homedef[~homemask].endX,80-homedef[~homemask].endY,
# zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[0])
pitch.arrows(mid3rd[~midmask].x,80-mid3rd[~midmask].y,mid3rd[~midmask].endX,80-mid3rd[~midmask].endY,
headwidth=5, headlength=5,zorder=5,color='tab:red',width=1,ax=ax[1])
# pitch.lines(awaydef[~awaymask].x,80-awaydef[~awaymask].y,awaydef[~awaymask].endX,80-awaydef[~awaymask].endY,
# zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[1])
ax[0].set_title(hometeam+' Unsuccessful Passes'+'\n'+'from defensive 3rd',fontsize=20)
ax[1].set_title(hometeam+' Unsuccessful Passes'+'\n'+'from middle 3rd',fontsize=20)
# fig.text(0.0, 0.05, "All unsuccessful passes originating from defensive third of the pitch",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.0, width=0.07)
return fig
def switches(Df,hometeam,homeid,awayteam,awayid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,5), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
homedef = df.query("(teamId==@homeid)&(type_displayName == 'Pass')&((endY-y)**2>=1600.0)")
awaydef = df.query("(teamId==@awayid)&(type_displayName == 'Pass')&((endY-y)**2>=1600.0)")
homemask = homedef.outcomeType_displayName == 'Successful'
awaymask = awaydef.outcomeType_displayName == 'Successful'
pitch.lines(homedef[homemask].x,80-homedef[homemask].y,homedef[homemask].endX,80-homedef[homemask].endY,
zorder=5,color='#fee090',linewidth=2,comet=True,ax=ax[0])
pitch.lines(homedef[~homemask].x,80-homedef[~homemask].y,homedef[~homemask].endX,80-homedef[~homemask].endY,
zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[0])
pitch.lines(awaydef[awaymask].x,80-awaydef[awaymask].y,awaydef[awaymask].endX,80-awaydef[awaymask].endY,
zorder=5,color='#fee090',linewidth=2,comet=True,ax=ax[1])
pitch.lines(awaydef[~awaymask].x,80-awaydef[~awaymask].y,awaydef[~awaymask].endX,80-awaydef[~awaymask].endY,
zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[1])
ax[0].set_title(hometeam+'\n'+'Switches',fontsize=30)
ax[1].set_title(awayteam+'\n'+'Switches',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.05, "Red lines for unsuccessful switches, yellow for successful ones",
fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def keypasses(Df,hometeam,homeid,awayteam,awayid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,5), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
homedef = df.query("(teamId==@homeid)&(KP == 1)&(endX!=0)&(endY!=0)")
awaydef = df.query("(teamId==@awayid)&(KP == 1)&(endX!=0)&(endY!=0)")
# homemask = homedef.outcomeType_displayName == 'Successful'
# awaymask = awaydef.outcomeType_displayName == 'Successful'
pitch.arrows(homedef.x,80-homedef.y,homedef.endX,80-homedef.endY,headwidth=5, headlength=5,
zorder=5,color='#fee090',width=1,ax=ax[0])
# pitch.lines(homedef[~homemask].x,80-homedef[~homemask].y,homedef[~homemask].endX,80-homedef[~homemask].endY,
# zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[0])
pitch.arrows(awaydef.x,80-awaydef.y,awaydef.endX,80-awaydef.endY,headwidth=5, headlength=5,
zorder=5,color='#fee090',width=1,ax=ax[1])
# pitch.lines(awaydef[~awaymask].x,80-awaydef[~awaymask].y,awaydef[~awaymask].endX,80-awaydef[~awaymask].endY,
# zorder=5,color='tab:red',linewidth=2,comet=True,ax=ax[1])
ax[0].set_title(hometeam+'\n'+'Key Passes',fontsize=30)
ax[1].set_title(awayteam+'\n'+'Key Passes',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def takeonmap(Df,hometeam,homeid,awayteam,awayid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,5), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
homedef = df.query("(teamId==@homeid)&(type_displayName == 'TakeOn')")
awaydef = df.query("(teamId==@awayid)&(type_displayName == 'TakeOn')")
homemask = homedef.outcomeType_displayName == 'Successful'
awaymask = awaydef.outcomeType_displayName == 'Successful'
pitch.scatter(homedef[homemask].x,80-homedef[homemask].y,marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='#fee090',linewidth=3,ax=ax[0])
pitch.scatter(homedef[~homemask].x,80-homedef[~homemask].y,marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='tab:red',linewidth=3,ax=ax[0])
pitch.scatter(awaydef[awaymask].x,80-awaydef[awaymask].y,marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='#fee090',linewidth=3,ax=ax[1])
pitch.scatter(awaydef[~awaymask].x,80-awaydef[~awaymask].y,marker='o',s=50,zorder=5,facecolors='#082630',
edgecolors='tab:red',linewidth=3,ax=ax[1])
ax[0].set_title(hometeam+'\n'+'TakeOns',fontsize=30)
ax[1].set_title(awayteam+'\n'+'TakeOns',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.05, "Red markers for unsuccessful takeons, yellow for successful ones",
fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def ind_heatmap(Df, player):
from matplotlib.colors import LinearSegmentedColormap
import cmasher as cmr
#from mplsoccer import VerticalPitch
df = Df.copy()
pitch = Pitch(pitch_type='uefa',
figsize=(10.5, 6.8),
line_zorder=2,
line_color='#636363',
orientation='horizontal',
constrained_layout=True,
tight_layout=False,
pitch_color='black')
fig, ax = pitch.draw()
df = df[df.name == player]
touchdf = df[(df.isTouch == True) & (df.name == player)].reset_index()
#pitch.kdeplot(touchdf.x, touchdf.y, ax=ax, cmap=cmap,
#linewidths=0.3,fill=True,levels=1000)
pitch.kdeplot(touchdf.x,
touchdf.y,
ax=ax,
cmap=cmr.voltage,
shade=True,
levels=1000)
ax.set_title(player + ' Touch-based heatmap', fontsize=25, color='white')
fig.text(0.2,
0.0,
"Created by Nikhil Rajesh / @nikhilrajesh231",
fontstyle="italic",
fontsize=15,
color='black')
fig.set_facecolor('black')
ax.set_facecolor('black')
return fig
def init_pitch():
pitch = Pitch(pitch_color=None, line_color='whitesmoke', stripe=False)
fig, ax = pitch.draw()
# SB coordinates start on the left side, so we should invert the axes
ax.invert_yaxis()
return fig, ax
def alineacion_442(valor_total, edad):
try:
valor_jugador = valor_total / 11
porteros = goalkeeper(1, valor_jugador, edad).reset_index()
defensas = defenders(4, valor_jugador, edad).reset_index()
medios = midfielders(4, valor_jugador, edad).reset_index()
delanteros = forwards(2, valor_jugador, edad).reset_index()
pitch = Pitch(pitch_color='grass', line_color='white', stripe=True)
fig, ax = pitch.draw(figsize=(20, 20))
annotation = ax.annotate(porteros['short_name'][0], (5, 40),
fontsize=40,
ha='center')
annotation = ax.annotate(defensas['short_name'][0], (24, 30),
fontsize=40,
ha='center')
annotation = ax.annotate(defensas['short_name'][1], (24, 52),
fontsize=40,
ha='center')
annotation = ax.annotate(defensas['short_name'][2], (32, 12),
fontsize=40,
ha='center')
annotation = ax.annotate(defensas['short_name'][3], (32, 68),
fontsize=40,
ha='center')
annotation = ax.annotate(medios['short_name'][0], (60, 30),
fontsize=40,
ha='center')
annotation = ax.annotate(medios['short_name'][1], (60, 52),
fontsize=40,
ha='center')
annotation = ax.annotate(medios['short_name'][2], (60, 12),
fontsize=40,
ha='center')
annotation = ax.annotate(medios['short_name'][3], (60, 68),
fontsize=40,
ha='center')
annotation = ax.annotate(delanteros['short_name'][0], (89, 24),
fontsize=40,
ha='center')
annotation = ax.annotate(delanteros['short_name'][1], (89, 60),
fontsize=40,
ha='center')
plt.savefig('output/images/442.jpg')
result = pd.concat([porteros, defensas, medios,
delanteros]).reset_index()
result = result[[
'short_name', 'nationality', 'position', 'age', 'wage_eur',
'value_eur', 'value_pred'
]]
return result
except:
plt.savefig('output/images/442.jpg')
return "No se han encontrado jugadores con esos parametros"
def show_buts(club_name):
identifiant_club = df_2.loc[df_2['name'] == club_name, ['wyId']]
identifiant_club = identifiant_club.iat[0,0]
df_goals = df_4.loc[(df_4['teamId'] == identifiant_club) & (df_4['subEventName'] == 'Goal kick')]
x, y = list(), list()
for i in df_goals['positions']:
x.append(i[1]['x'])
y.append(i[1]['y'])
pitch = Pitch(pitch_color='grass', line_color='white', stripe=True)
fig, ax = pitch.draw()
sc = pitch.scatter(y, x, s=100, ax=ax)
def createShotmap(match_data, events_df, team, pitchcolor, shotcolor, goalcolor, titlecolor, legendcolor, marker_size):
# getting team id and venue
if match_data['home']['name'] == team:
teamId = match_data['home']['teamId']
venue = 'home'
else:
teamId = match_data['away']['teamId']
venue = 'away'
# getting opponent
if venue == 'home':
opponent = match_data['away']['name']
else:
opponent = match_data['home']['name']
total_shots = events_df.loc[[9 in row for row in list(events_df['satisfiedEventsTypes'])]]
team_shots = total_shots.loc[total_shots['teamId'] == teamId].reset_index(drop=True)
mask_goal = team_shots.isGoal == True
# Setup the pitch
pitch = Pitch(pitch_type='statsbomb', orientation='vertical', pitch_color=pitchcolor, line_color='#c7d5cc',
figsize=(16, 11), view='half', pad_top=2, tight_layout=True)
fig, ax = pitch.draw()
# Plot the goals
pitch.scatter(team_shots[mask_goal].x/100*120, 80-team_shots[mask_goal].y/100*80, s=marker_size,
edgecolors='black', c=goalcolor, zorder=2,
label='goal', ax=ax)
pitch.scatter(team_shots[~mask_goal].x/100*120, 80-team_shots[~mask_goal].y/100*80,
edgecolors='white', c=shotcolor, s=marker_size, zorder=2,
label='shot', ax=ax)
# Set the title
ax.set_title(f'{team} shotmap \n vs {opponent}', fontsize=30, color=titlecolor)
# set legend
leg = ax.legend(facecolor=pitchcolor, edgecolor='None', fontsize=20, loc='lower center', handlelength=4)
leg_texts = leg.get_texts() # list of matplotlib Text instances.
leg_texts[0].set_color(legendcolor)
leg_texts[1].set_color(legendcolor)
# Set the figure facecolor
fig.set_facecolor(pitchcolor)
def passtypes(Df,team,teamid):
df = Df.copy()
# matchdict = Dict
pitch = Pitch(pitch_type='statsbomb', figsize=(11,6), line_zorder=2, layout=(2,3), view='half',
line_color='#c7d5cc', orientation='vertical',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
passdf = df.query("(type_displayName == 'Pass')&(outcomeType_displayName=='Successful')\
&(teamId==@teamid)").reset_index()
passdf['y'] = 80 - passdf['y']
passdf['endY'] = 80 - passdf['endY']
progtocentre = passdf.query("(x<80)&(endX>80)&(endY>18)&(endY<62)")[['x','y','endX','endY']]
wingplay = passdf.query("(x<80)&(endX>80)&((endY<18)or(endY>62))")[['x','y','endX','endY']]
allbox = passdf.query("(endX>102)&(endY>18)&(endY<62)")[['x','y','endX','endY']]
wingtobox = passdf.query("(x>80)&((y<18)or(y>62))&(endX>102)&(endY>18)&(endY<62)")[['x','y','endX','endY']]
halfspaceplay = passdf.query("(x>80)&(((y>18)&(y<30))or((y>50)&(y<62)))&(endX>x)")[['x','y','endX','endY']]
zone14play = passdf.query("(x>80)&(x<102)&(y>30)&(y<50)&(endX>x)")[['x','y','endX','endY']]
pitch.lines(progtocentre.x, progtocentre.y, progtocentre.endX, progtocentre.endY, transparent=True, lw=2,
comet=True, color='#a1d76a',ax=ax[0,0])
ax[0,0].set_title('Middle 3rd'+'\n'+'to Centre of final 3rd',fontsize=15)
pitch.lines(wingplay.x, wingplay.y, wingplay.endX, wingplay.endY, transparent=True, lw=2,
comet=True, color='#a1d76a',ax=ax[0,1])
ax[0,1].set_title('Middle 3rd'+'\n'+'to wing of final 3rd',fontsize=15)
pitch.lines(allbox.x, allbox.y, allbox.endX, allbox.endY, transparent=True, lw=2, comet=True,
color='#a1d76a',ax=ax[0,2])
ax[0,2].set_title('All passes to box',fontsize=15)
pitch.lines(wingtobox.x, wingtobox.y, wingtobox.endX, wingtobox.endY, transparent=True, lw=2, comet=True,
color='#a1d76a',ax=ax[1,0])
ax[1,0].set_title('Wing to box',fontsize=15)
pitch.lines(halfspaceplay.x, halfspaceplay.y, halfspaceplay.endX, halfspaceplay.endY, transparent=True,
lw=2, comet=True, color='#a1d76a',ax=ax[1,1])
ax[1,1].set_title('Attacking Passes'+'\n'+'From halfspace',fontsize=15)
pitch.lines(zone14play.x, zone14play.y, zone14play.endX, zone14play.endY, transparent=True, lw=2, comet=True,
color='#a1d76a',ax=ax[1,2])
ax[1,2].set_title('Attacking Passes'+'\n'+'From Zone 14',fontsize=15)
fig.suptitle(team+' Progressive/Attacking Passes',fontsize=20)
fig.text(0.05, -0.05, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.9, width=0.07)
return fig
def defensesmap(Df,team,teamid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(10,6), line_zorder=2, layout=(1,2),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
defensedf = df.query("(type_displayName in ['Aerial','Clearance','Interception','Tackle','BlockedPass','Challenge'])\
&(teamId==@teamid)").reset_index()
defensedf['defensive'] = [1 for i in range(len(defensedf))]
for i in range(len(defensedf)):
if(defensedf.loc[i,['type_displayName']][0]=='Aerial'):
quals = defensedf.quals[i]
if(286 in quals):
defensedf.loc[i,['defensive']] = 0
if(defensedf.loc[i,['type_displayName']][0]=='Challenge'):
quals = defensedf.quals[i]
if(286 in quals):
defensedf.loc[i,['defensive']] = 0
defensedf = defensedf[defensedf.defensive==1]
defensedf['y'] = 80 - defensedf['y']
ppda = defensedf[defensedf.x>=48]
ppda = ppda.query("type_displayName in ['Interception','Tackle','Challenge']")
deepdf = defensedf[defensedf.x<48]
bin_statistic = pitch.bin_statistic(ppda.x, ppda.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[0], cmap='Reds', edgecolors=None)
ax[0].set_title(team+'\n'+' Pressurizing'+'\n'+ 'Defensive activities',fontsize=30)
bin_statistic = pitch.bin_statistic(deepdf.x, deepdf.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[1], cmap='Reds', edgecolors=None)
# cbar = fig.colorbar(pcm, ax=ax[1])
ax[1].set_title(team+'\n'+' Goal Protecting'+'\n'+'Defensive activities',fontsize=30)
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.15, "Pressurizing defensive activities include tackles, interceptions and challenges"+'\n'+
"made high up in opposition territory",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.0, 0.05, "Goal protecting defensive activities include tackles, interceptions, defensive aerials, challenges,"
+'\n'+"clearances and blocked passes deep in own territory",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=0.01, width=0.07)
return fig
def touchmap(Df,team,teamid):
df = Df.copy()
pitch = Pitch(pitch_type='statsbomb', figsize=(12,7), line_zorder=2, layout=(4,4),
line_color='k', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
touchdf = df[(df.isTouch==True)&(df.teamId==teamid)&(df.name!='')].reset_index()
touchdf['y'] = 80 - touchdf['y']
players = touchdf.name.unique().tolist()
Nplayers = len(players)
for i in range(Nplayers):
pdf = touchdf[touchdf.name==players[i]]
bin_statistic = pitch.bin_statistic(pdf.x, pdf.y, statistic='count', bins=(25, 25))
bin_statistic['statistic'] = gaussian_filter(bin_statistic['statistic'], 1)
pcm = pitch.heatmap(bin_statistic, ax=ax[i//4,i%4], cmap='Reds', edgecolors=None)
ax[i//4,i%4].set_title(players[i],fontsize=15)
for i in range(Nplayers,16):
ax[i//4,i%4].remove()
fig.text(0.0, 0.0, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
fig.text(0.3, 1.01, team+" Touch-based heatmaps",fontsize=25,color='#edece9')
# add_image(bu_alt, fig, left=0.95, bottom=0.97, width=0.07)
return fig
def progpassplotter(Df,team,teamid):
pitch = Pitch(pitch_type='statsbomb', figsize=(12,10), line_zorder=2, layout=(4,4), view='full',
line_color='#c7d5cc', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
df = Df[(Df.teamId==teamid)&(Df.name!='')].reset_index()
players = df.name.unique().tolist()
Nplayers = len(players)
for i in range(Nplayers):
p = players[i]
Passp = playerpasses(df,p)
pitch.lines(Passp.x, Passp.y, Passp.endX, Passp.endY, transparent=True, lw=2,
comet=True, color='#a1d76a',ax=ax[i//4,i%4])
ax[i//4,i%4].set_title(p,fontsize=15)
for i in range(Nplayers,16):
ax[i//4,i%4].remove()
fig.text(0.05, -0.04, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=15,color='#edece9')
# add_image(bu_alt, fig, left=0.85, bottom=-0.07, width=0.05)
fig_htext(s = "<Completed Progressive Passes> by all players of "+team,
x = 0.1, y = 1, highlight_colors = ['#a1d76a'],
highlight_weights=['bold'],
string_weight='bold',fontsize=20,color='#edece9')
return fig
Basics
======
First we import the Pitch class and set the matplotlib style sheet.
"""
import matplotlib.pyplot as plt
from mplsoccer.pitch import Pitch
plt.style.use('ggplot')
##############################################################################
# Draw a pitch on a new axis
# --------------------------
# Let's plot on a new axis first.
pitch = Pitch(figsize=(8, 4)) # specifying figure size is optional (width, height)
fig, ax = pitch.draw()
##############################################################################
# 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, ax = plt.subplots(nrows=1, ncols=2)
pitch = Pitch()
pitch.draw(ax=ax[1])
##############################################################################
# Supported data providers
# ------------------------
# mplsoccer supports 7 pitch types by specifying the ``pitch_type`` argument:
def shotMapMatch(shots_home, shots_away, mask_goal_h, mask_goal_a, home_name, away_name, home_score, away_score):
"""
Creates a combined shot map (similar to caley style) for a both teams on a full, horizontal pitch
Parameters
----------
shots_XXXX : A pandas dataframe
- Subset of team dataframe for home and away
- contains ['id', 'minute', 'X', 'Y', 'player', 'shotType', 'situation', 'result', 'xG', 'pos_x', 'pos_y']
- pos_x and pos_y are scaled to the 120x80 statsbomb sized pitch
mask_goal_X : Pandas index mask
- Mask for all goals separated home and away
- Lets us separate points where the shot is scored
team_name : string
- Name of home/away team
score : int
- Actual team score
- used for display options
Returns
-------
shotmap : figure
- Pitch shot map for a both teams
"""
max_marker_size = 500
home_xg = round(shots_home['xG'].sum(),1)
away_xg = round(shots_away['xG'].sum(),1)
fig, ax = plt.subplots(figsize=(10,6))
fig.set_facecolor('#3d4849')
ax.patch.set_facecolor('#3d4849')
pitch = Pitch(pitch_type='statsbomb', orientation='horizontal',
pitch_color='#22312b', line_color='#c7d5cc', figsize=(10,6),
constrained_layout=False, tight_layout=True, view='full',
pad_top=2)
# Home
pitch.scatter(120-shots_home[~mask_goal_h].pos_x, 80-shots_home[~mask_goal_h].pos_y,
s=shots_home[~mask_goal_h].xG*max_marker_size, edgecolors='white', c='#1f77b4',
marker='s', zorder=1, label=home_name, ax=ax, alpha=0.8)
pitch.scatter(120-shots_home[mask_goal_h].pos_x, 80-shots_home[mask_goal_h].pos_y,
edgecolors='black', c='white', s=shots_home[mask_goal_h].xG*max_marker_size,
zorder=2, marker='s', alpha=0.95, ax=ax)
# Away
pitch.scatter(shots_away[~mask_goal_a].pos_x, shots_away[~mask_goal_a].pos_y,
s=shots_away[~mask_goal_a].xG*max_marker_size, edgecolors='white', c='#ff7f0e',
marker='s', zorder=1, label=away_name, ax=ax, alpha=0.8)
pitch.scatter(shots_away[mask_goal_a].pos_x, shots_away[mask_goal_a].pos_y,
edgecolors='black', c='white', s=shots_away[mask_goal_a].xG*max_marker_size,
zorder=2, marker='s', alpha=0.95, label='Goal', ax=ax)
pitch.draw(ax=ax)
# Flip so goal is on bottom
plt.gca().invert_yaxis()
# Set the title
ax.set_title(f'{home_name} (H) vs {away_name} (A) Shot Map', fontsize=20, color='w')
legend = plt.legend(loc='center', markerscale=1, framealpha=1.0, bbox_to_anchor=[0.5, 0.88])
# make the markers the same size in the legend
for handle in legend.legendHandles:
handle.set_sizes([100])
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='white', alpha=1.0)
textstr = f'Score: (H) {home_score} - {away_score} (A)\nxG: (H) {home_xg} - {away_xg} (A)'
ax.text(0.5, 0.125, textstr, transform=ax.transAxes, fontsize=14,
verticalalignment='top', bbox=props, horizontalalignment='center')
plt.show()
def shotMapTeam(shots, mask_goal, home_name, away_name, home=True):
"""
Creates a team shot map (similar to caley style) for a single team on half pitch
Parameters
----------
shots : A pandas dataframe
- Subset of team dataframe
- contains ['id', 'minute', 'X', 'Y', 'player', 'shotType', 'situation', 'result', 'xG', 'pos_x', 'pos_y']
- pos_x and pos_y are scaled to the 120x80 statsbomb sized pitch
mask_goal : Pandas index mask
- Mask for all goals
- Lets us separate points where the shot is scored
team_name : string
- Name of home/away team
home : boolean
- Defaults to true
- True if looking at home team's shots.
- Responsible for changing draw color options and title text
Returns
-------
shotmap : figure
- Pitch shot map for a single team
"""
max_marker_size = 500
## Plotting options for single team or both teams
orientation = 'vertical'
view = 'half'
if home:
color = '#1f77b4'
team_name = home_name
opp_name = away_name
else:
color = '#ff7f0e'
team_name = away_name
opp_name = home_name
fig, ax = plt.subplots(figsize=(10,6))
fig.set_facecolor('#3d4849')
ax.patch.set_facecolor('#3d4849')
pitch = Pitch(pitch_type='statsbomb', orientation=orientation,
pitch_color='#22312b', line_color='#c7d5cc', figsize=(8,5.5),
constrained_layout=False, tight_layout=True, view=view,
pad_top=2)
pitch.scatter(shots[~mask_goal].pos_x, shots[~mask_goal].pos_y,
s=shots[~mask_goal].xG*max_marker_size, edgecolors='white', c=color,
marker='s', zorder=1, label='Shot', ax=ax, alpha=0.8)
pitch.scatter(shots[mask_goal].pos_x, shots[mask_goal].pos_y,
edgecolors='black', c='white', s=shots[mask_goal].xG*max_marker_size,
zorder=2, marker='s', alpha=0.95, label='Goal', ax=ax)
pitch.draw(ax=ax)
# Flip so goal is on bottom
plt.gca().invert_yaxis()
# Set the title
ax.set_title(f'{team_name} Shots \n vs {opp_name}', fontsize=20, color='w')
def getTeamSuccessfulBoxPasses(events_df, teamId, team, pitch_color, cmap):
"""
Parameters
----------
events_df : DataFrame of all events.
teamId : ID of the team, the passes of which are required.
team : Name of the team, the passes of which are required.
pitch_color : color of the pitch.
cmap : color design of the pass lines.
You can select more cmaps here:
https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html
Returns
-------
Pitch Plot.
"""
# Get Total Passes
passes_df = events_df.loc[[row['displayName'] == 'Pass' for row in list(events_df['type'])]].reset_index(drop=True)
# Get Team Passes
team_passes = passes_df.loc[passes_df['teamId'] == teamId]
# Extracting Box Passes from Total Passes
box_passes = team_passes.copy()
for i,pas in box_passes.iterrows():
X = pas["x"]/100*120
Xend = pas["endX"]/100*120
Y = pas["y"]/100*80
Yend = pas["endY"]/100*80
if Xend >= 102 and Yend >= 18 and Yend <= 62:
if X >=102 and Y >= 18 and Y <= 62:
box_passes = box_passes.drop([i])
else:
pass
else:
box_passes = box_passes.drop([i])
successful_box_passes = box_passes.copy()
unsuccessful_box_passes = box_passes.copy()
for i,pas in box_passes.iterrows():
if pas['outcomeType']['value'] == 0:
successful_box_passes = successful_box_passes.drop([i])
else:
unsuccessful_box_passes = unsuccessful_box_passes.drop([i])
pitch = Pitch(pitch_type='statsbomb', orientation='vertical', pitch_color=pitch_color, line_color='#c7d5cc',
figsize=(16, 11), view='half', pad_top=2, tight_layout=True)
fig, ax = pitch.draw()
# Plot the completed passes
pitch.lines(successful_box_passes.x/100*120, 80-successful_box_passes.y/100*80,
successful_box_passes.endX/100*120, 80-successful_box_passes.endY/100*80,
lw=5, opp_transparent=True, opp_comet=True, cmap=cmap,
label='Successful Passes', ax=ax)
pitch.scatter(successful_box_passes.x/100*120, 80-successful_box_passes.y/100*80,
edgecolors='white', c='white', s=50, zorder=2,
ax=ax)
# Set the title
fig.suptitle(f'Completed Box Passes - {team}', fontsize=15)
# Set the subtitle
ax.set_title('Data : Whoscored', fontsize=8, loc='right', fontstyle='italic', fontweight='bold')
# set legend
#ax.legend(facecolor='#22312b', edgecolor='None', fontsize=8, loc='lower center', handlelength=4)
# Set the figure facecolor
fig.set_facecolor(pitch_color)
def createAttPassNetworks(match_data, matches_df, events_df, team, pitch_color, max_lw,
marker_size, marker_color, marker_label='kit_no', marker_label_size=20):
"""
Parameters
----------
match_data : Data containing everything about the match
matches_df : DataFrame containing match data.
events_df : DataFrame containing event data for that match.
team : Name of the required team.
pitch_color : color of the pitch and figure.
max_lw : maximum line width of network lines.
marker_size : size of the circle markers.
marker_color : color of the circle markers.
Returns
-------
Pitch Plot.
"""
# getting match_id
matchId = match_data['matchId']
# getting team id and venue
if match_data['home']['name'] == team:
teamId = match_data['home']['teamId']
venue = 'home'
else:
teamId = match_data['away']['teamId']
venue = 'away'
# getting opponent
if venue == 'home':
opponent = match_data['away']['name']
else:
opponent = match_data['home']['name']
# getting player dictionary
team_players_dict = {}
for player in match_data[venue]['players']:
team_players_dict[player['playerId']] = player['name']
# getting player dictionary with kit no
team_playerskitno_dict = {}
for player in matches_df[venue][matchId]['players']:
team_playerskitno_dict[player['shirtNo']] = player['name']
# getting minute of first substitution
for i,row in events_df.iterrows():
if row['type']['displayName'] == 'SubstitutionOn' and row['teamId'] == teamId:
sub_minute = str(row['minute'])
break
# getting players dataframe
match_players_df = pd.DataFrame()
player_names = []
player_ids = []
player_pos = []
player_kit_number = []
for player in match_data[venue]['players']:
player_names.append(player['name'])
player_ids.append(player['playerId'])
player_pos.append(player['position'])
player_kit_number.append(player['shirtNo'])
match_players_df['playerId'] = player_ids
match_players_df['playerName'] = player_names
match_players_df['playerPos'] = player_pos
match_players_df['playerKitNumber'] = player_kit_number
# extracting passes
passes_df = events_df.loc[events_df['teamId'] == teamId].reset_index().drop('index', axis=1)
passes_df.dropna(subset=["playerId"], inplace=True)
passes_df.insert(27, column='playerName', value=[team_players_dict[i] for i in list(passes_df['playerId'])])
passes_df.insert(28, column='passRecipientId', value=passes_df['playerId'].shift(-1))
passes_df.insert(29, column='passRecipientName', value=passes_df['playerName'].shift(-1))
passes_df.dropna(subset=["passRecipientName"], inplace=True)
passes_df = passes_df.loc[[row['displayName'] == 'Pass' for row in list(passes_df['type'])]].reset_index(drop=True)
passes_df = passes_df.loc[[row['displayName'] == 'Successful' for row in list(passes_df['outcomeType'])]].reset_index(drop=True)
index_names = passes_df.loc[passes_df['playerName']==passes_df['passRecipientName']].index
passes_df.drop(index_names, inplace=True)
passes_df = passes_df.merge(match_players_df, on=['playerId', 'playerName'], how='left', validate='m:1')
passes_df = passes_df.merge(match_players_df.rename({'playerId': 'passRecipientId', 'playerName':'passRecipientName'},
axis='columns'), on=['passRecipientId', 'passRecipientName'],
how='left', validate='m:1', suffixes=['', 'Receipt'])
passes_df = passes_df[passes_df['playerPos'] != 'Sub']
# getting team formation
formation = match_data[venue]['formations'][0]['formationName']
formation = '-'.join(formation)
# getting player average locations
location_formation = passes_df[['playerKitNumber', 'x', 'y']]
average_locs_and_count = location_formation.groupby('playerKitNumber').agg({'x': ['mean'], 'y': ['mean', 'count']})
average_locs_and_count.columns = ['x', 'y', 'count']
# filtering progressive passes
passes_df = passes_df.loc[passes_df['EPV_difference'] > 0]
# getting separate dataframe for selected columns
passes_formation = passes_df[['id', 'playerKitNumber', 'playerKitNumberReceipt']].copy()
passes_formation['EPV'] = passes_df['EPV_difference']
# getting dataframe for passes between players
passes_between = passes_formation.groupby(['playerKitNumber', 'playerKitNumberReceipt']).agg({ 'id' : 'count', 'EPV' : 'sum'}).reset_index()
passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True)
passes_between = passes_between.merge(average_locs_and_count, left_on='playerKitNumberReceipt', right_index=True)
passes_between = passes_between.merge(average_locs_and_count, left_on='playerKitNumber', right_index=True,
suffixes=['', '_end'])
# filtering passes
pass_filter = int(passes_between['pass_count'].mean())
passes_between = passes_between.loc[passes_between['pass_count'] > pass_filter*2]
# calculating the line width
max_line_width = max_lw
passes_between['width'] = passes_between.pass_count / passes_between.pass_count.max() * max_line_width
passes_between = passes_between.reset_index(drop=True)
# setting 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.EPV / passes_between.EPV.max()
c_transparency = (c_transparency * (1 - min_transparency)) + min_transparency
color[:, 3] = c_transparency
passes_between['alpha'] = color.tolist()
# Plotting name on markers
if marker_label == 'name':
average_locs_and_count.index = average_locs_and_count.index.map(team_playerskitno_dict)
# plotting
pitch = Pitch(pitch_type='statsbomb', orientation='horizontal',
pitch_color=pitch_color, line_color='#c7d5cc', figsize=(16, 11),
constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
average_locs_and_count['zorder'] = list(np.linspace(2,6,11, dtype='int'))
pitch.lines(passes_between.x/100*120, 80-passes_between.y/100*80,
passes_between.x_end/100*120, 80-passes_between.y_end/100*80, lw=passes_between.width,
color=color, zorder=1, ax=ax)
for index, row in average_locs_and_count.iterrows():
pitch.scatter(row.x/100*120, 80-row.y/100*80, s=marker_size,
color=marker_color, edgecolors='black', linewidth=1,
alpha=1, zorder=row.zorder, ax=ax)
for index, row in average_locs_and_count.iterrows():
pitch.annotate(row.name, xy=(row.x/100*120, 80-row.y/100*80), family='DejaVu Sans', c='white',
va='center', ha='center', zorder=row.zorder, size=marker_label_size, weight='bold', ax=ax)
ax.set_title("{} Progressive Pass Network vs {}".format(team, opponent), size=15, y=0.97, color='#c7d5cc')
fig.set_facecolor(pitch_color)
def getTeamTotalPasses(events_df, teamId, team, opponent, pitch_color):
"""
Parameters
----------
events_df : DataFrame of all events.
teamId : ID of the team, the passes of which are required.
team : Name of the team, the passes of which are required.
opponent : Name of opponent team.
pitch_color : color of the pitch.
Returns
-------
Pitch Plot.
"""
# Get Total Passes
passes_df = events_df.loc[events_df['type'] == 'Pass'].reset_index(
drop=True)
# Get Team Passes
team_passes = passes_df.loc[passes_df['teamId'] == teamId]
successful_passes = team_passes.loc[team_passes['outcomeType'] ==
'Successful'].reset_index(drop=True)
unsuccessful_passes = team_passes.loc[
team_passes['outcomeType'] == 'Unsuccessful'].reset_index(drop=True)
# Setup the pitch
pitch = Pitch(pitch_type='statsbomb',
pitch_color=pitch_color,
line_color='#c7d5cc',
figsize=(16, 11))
fig, ax = pitch.draw(constrained_layout=True, tight_layout=False)
# Plot the completed passes
pitch.arrows(successful_passes.x / 100 * 120,
80 - successful_passes.y / 100 * 80,
successful_passes.endX / 100 * 120,
80 - successful_passes.endY / 100 * 80,
width=1,
headwidth=10,
headlength=10,
color='#ad993c',
ax=ax,
label='Completed')
# Plot the other passes
pitch.arrows(unsuccessful_passes.x / 100 * 120,
80 - unsuccessful_passes.y / 100 * 80,
unsuccessful_passes.endX / 100 * 120,
80 - unsuccessful_passes.endY / 100 * 80,
width=1,
headwidth=6,
headlength=5,
headaxislength=12,
color='#ba4f45',
ax=ax,
label='Blocked')
# setup the legend
ax.legend(facecolor=pitch_color,
handlelength=5,
edgecolor='None',
fontsize=8,
loc='upper left',
shadow=True)
# Set the title
fig.suptitle(f'{team} Passes vs {opponent}', y=1, fontsize=15)
# Set the subtitle
ax.set_title('Data : Whoscored/Opta',
fontsize=8,
loc='right',
fontstyle='italic',
fontweight='bold')
# Set the figure facecolor
fig.set_facecolor(pitch_color)
def createPassNetworks(match_data, matches_df, events_df, team,
pitch_color, max_lw, marker_size, marker_color, marker_label='kit_no', marker_label_size=20):
"""
Parameters
----------
match_data : Data containing everything about the match
matches_df : DataFrame containing match data.
events_df : DataFrame containing event data for that match.
team : Name of the required team.
pitch_color : color of the pitch and figure.
max_lw : maximum line width of network lines.
marker_size : size of the circle markers.
marker_color : color of the circle markers.
Returns
-------
Pitch Plot.
"""
matchId = match_data['matchId']
# getting team id and venue
if match_data['home']['name'] == team:
teamId = match_data['home']['teamId']
venue = 'home'
else:
teamId = match_data['away']['teamId']
venue = 'away'
# getting opponent
if venue == 'home':
opponent = match_data['away']['name']
else:
opponent = match_data['home']['name']
team_players_dict = {}
for player in matches_df[venue][matchId]['players']:
team_players_dict[player['playerId']] = player['name']
team_playerskitno_dict = {}
for player in matches_df[venue][matchId]['players']:
team_playerskitno_dict[player['shirtNo']] = player['name']
passes_df = events_df.loc[[row['displayName'] == 'Pass' for row in list(events_df['type'])]].reset_index(drop=True)
passes_df = passes_df.loc[passes_df['teamId'] == teamId].reset_index().drop('index', axis=1)
passes_df = passes_df.loc[[row['displayName'] == 'Successful' for row in list(passes_df['outcomeType'])]].reset_index(drop=True)
passes_df.insert(27, column='playerName', value=[team_players_dict[i] for i in list(passes_df['playerId'])])
passes_df.insert(28, column='passRecipientId', value=passes_df['playerId'].shift(-1))
passes_df.insert(29, column='passRecipientName', value=passes_df['playerName'].shift(-1))
passes_df.dropna(subset=["passRecipientName"], inplace=True)
match_players_df = pd.DataFrame()
player_names = []
player_ids = []
player_pos = []
player_kit_number = []
for player in matches_df[venue][matchId]['players']:
player_names.append(player['name'])
player_ids.append(player['playerId'])
player_pos.append(player['position'])
player_kit_number.append(player['shirtNo'])
match_players_df['playerId'] = player_ids
match_players_df['playerName'] = player_names
match_players_df['playerPos'] = player_pos
match_players_df['playerKitNumber'] = player_kit_number
passes_df = passes_df.merge(match_players_df, on=['playerId', 'playerName'], how='left', validate='m:1')
passes_df = passes_df.merge(match_players_df.rename({'playerId': 'passRecipientId', 'playerName':'passRecipientName'},
axis='columns'), on=['passRecipientId', 'passRecipientName'],
how='left', validate='m:1', suffixes=['', 'Receipt'])
passes_df = passes_df[passes_df['playerPos'] != 'Sub']
passes_formation = passes_df[['id', 'playerKitNumber', 'playerKitNumberReceipt']].copy()
location_formation = passes_df[['playerKitNumber', 'x', 'y']]
average_locs_and_count = location_formation.groupby('playerKitNumber').agg({'x': ['mean'], 'y': ['mean', 'count']})
average_locs_and_count.columns = ['x', 'y', 'count']
passes_formation['kitNo_max'] = passes_formation[['playerKitNumber',
'playerKitNumberReceipt']].max(axis='columns')
passes_formation['kitNo_min'] = passes_formation[['playerKitNumber',
'playerKitNumberReceipt']].min(axis='columns')
passes_between = passes_formation.groupby(['kitNo_max', 'kitNo_min']).id.count().reset_index()
passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True)
# add on the location of each player so we have the start and end positions of the lines
passes_between = passes_between.merge(average_locs_and_count, left_on='kitNo_min', right_index=True)
passes_between = passes_between.merge(average_locs_and_count, left_on='kitNo_max', right_index=True,
suffixes=['', '_end'])
##############################################################################
# Calculate the line width and marker sizes relative to the largest counts
max_line_width = max_lw
passes_between['width'] = passes_between.pass_count / passes_between.pass_count.max() * max_line_width
#average_locs_and_count['marker_size'] = (average_locs_and_count['count']
# / average_locs_and_count['count'].max() * max_marker_size)
##############################################################################
# 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 name on markers
if marker_label == 'name':
average_locs_and_count.index = average_locs_and_count.index.map(team_playerskitno_dict)
##############################################################################
# Plotting
pitch = Pitch(pitch_type='statsbomb', orientation='horizontal',
pitch_color=pitch_color, line_color='#c7d5cc', figsize=(16, 11),
constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
pitch.lines(passes_between.x/100*120, 80-passes_between.y/100*80,
passes_between.x_end/100*120, 80-passes_between.y_end/100*80, lw=passes_between.width,
color=color, zorder=1, ax=ax)
pitch.scatter(average_locs_and_count.x/100*120, 80-average_locs_and_count.y/100*80, s=marker_size,
color=marker_color, edgecolors='black', linewidth=1, alpha=1, ax=ax)
for index, row in average_locs_and_count.iterrows():
pitch.annotate(row.name, xy=(row.x/100*120, 80-row.y/100*80), c='white', va='center', ha='center', size=marker_label_size, weight='bold', ax=ax)
ax.set_title("{} Pass Network vs {}".format(team, opponent), size=15, y=0.97, color='#c7d5cc')
fig.set_facecolor(pitch_color)
def createPVFormationMap(match_data, events_df, team, color_palette,
markerstyle, markersize, markeredgewidth, labelsize,
labelcolor, ax):
# getting team id and venue
if match_data['home']['name'] == team:
teamId = match_data['home']['teamId']
venue = 'home'
else:
teamId = match_data['away']['teamId']
venue = 'away'
# getting opponent
if venue == 'home':
opponent = match_data['away']['name']
else:
opponent = match_data['home']['name']
# getting player dictionary
team_players_dict = {}
for player in match_data[venue]['players']:
team_players_dict[player['playerId']] = player['name']
# getting minute of first substitution
for i, row in events_df.iterrows():
if row['type'] == 'SubstitutionOn' and row['teamId'] == teamId:
sub_minute = str(row['minute'])
break
# getting players dataframe
match_players_df = pd.DataFrame()
player_names = []
player_ids = []
player_pos = []
player_kit_number = []
for player in match_data[venue]['players']:
player_names.append(player['name'])
player_ids.append(player['playerId'])
player_pos.append(player['position'])
player_kit_number.append(player['shirtNo'])
match_players_df['playerId'] = player_ids
match_players_df['playerName'] = player_names
match_players_df['playerPos'] = player_pos
match_players_df['playerKitNumber'] = player_kit_number
# extracting passes
passes_df = events_df.loc[events_df['teamId'] ==
teamId].reset_index().drop('index', axis=1)
passes_df['playerId'] = passes_df['playerId'].astype('float').astype(
'Int64')
if 'playerName' in passes_df.columns:
passes_df = passes_df.drop(columns='playerName')
passes_df.dropna(subset=["playerId"], inplace=True)
passes_df.insert(
27,
column='playerName',
value=[team_players_dict[i] for i in list(passes_df['playerId'])])
if 'passRecipientId' in passes_df.columns:
passes_df = passes_df.drop(columns='passRecipientId')
passes_df = passes_df.drop(columns='passRecipientName')
passes_df.insert(28,
column='passRecipientId',
value=passes_df['playerId'].shift(-1))
passes_df.insert(29,
column='passRecipientName',
value=passes_df['playerName'].shift(-1))
passes_df.dropna(subset=["passRecipientName"], inplace=True)
passes_df = passes_df.loc[events_df['type'] == 'Pass', :].reset_index(
drop=True)
passes_df = passes_df.loc[events_df['outcomeType'] ==
'Successful', :].reset_index(drop=True)
index_names = passes_df.loc[passes_df['playerName'] ==
passes_df['passRecipientName']].index
passes_df.drop(index_names, inplace=True)
passes_df = passes_df.merge(match_players_df,
on=['playerId', 'playerName'],
how='left',
validate='m:1')
passes_df = passes_df.merge(match_players_df.rename(
{
'playerId': 'passRecipientId',
'playerName': 'passRecipientName'
},
axis='columns'),
on=['passRecipientId', 'passRecipientName'],
how='left',
validate='m:1',
suffixes=['', 'Receipt'])
# passes_df = passes_df[passes_df['playerPos'] != 'Sub']
# Getting net possesion value for passes
netPVPassed = passes_df.groupby(['playerId',
'playerName'])['EPV'].sum().reset_index()
netPVReceived = passes_df.groupby(['passRecipientId', 'passRecipientName'
])['EPV'].sum().reset_index()
# Getting formation and player ids for first 11
formation = match_data[venue]['formations'][0]['formationName']
formation_positions = match_data[venue]['formations'][0][
'formationPositions']
playerIds = match_data[venue]['formations'][0]['playerIds'][:11]
# Getting all data in a dataframe
formation_data = []
for playerId, pos in zip(playerIds, formation_positions):
pl_dict = {'playerId': playerId}
pl_dict.update(pos)
formation_data.append(pl_dict)
formation_data = pd.DataFrame(formation_data)
formation_data['vertical'] = normalize(
formation_data['vertical'], {
'actual': {
'lower': 0,
'upper': 10
},
'desired': {
'lower': 10,
'upper': 110
}
})
formation_data['horizontal'] = normalize(formation_data['horizontal'], {
'actual': {
'lower': 0,
'upper': 10
},
'desired': {
'lower': 80,
'upper': 0
}
})
formation_data = netPVPassed.join(formation_data.set_index('playerId'),
on='playerId',
how='inner').reset_index(drop=True)
formation_data = formation_data.rename(columns={"EPV": "PV"})
# Plotting
pitch = Pitch(pitch_type='statsbomb',
pitch_color='#171717',
line_color='#5c5c5c',
goal_type='box')
pitch.draw(ax=ax, constrained_layout=True, tight_layout=True)
sns.scatterplot(x='vertical',
y='horizontal',
data=formation_data,
hue='PV',
s=markersize,
marker=markerstyle,
legend=False,
palette=color_palette,
linewidth=markeredgewidth,
ax=ax)
ax.text(2, 78, '{}'.format('-'.join(formation)), size=20, c='grey')
for index, row in formation_data.iterrows():
pitch.annotate(str(round(row.PV * 100, 2)) + '%',
xy=(row.vertical, row.horizontal),
c=labelcolor,
va='center',
ha='center',
size=labelsize,
zorder=2,
weight='bold',
ax=ax)
pitch.annotate(row.playerName,
xy=(row.vertical, row.horizontal + 5),
c=labelcolor,
va='center',
ha='center',
size=labelsize,
zorder=2,
weight='bold',
ax=ax)
axes = axes.ravel()
pitch_kwargs = {'line_color': '#94A7AE', 'axis': True, 'label': True, 'pad_left': 0,
'pad_right': 0, 'pad_top': 0, 'pad_bottom': 0, 'linewidth': 1}
pitch_types = ['statsbomb', 'statsperform', 'tracab', 'wyscout', 'metricasports', 'opta', 'stats']
fontcolor = '#b6b9ea'
arrowprops = {'arrowstyle': '->', 'lw': 4,
'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']:
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.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)
axes[idx].set_title(pt, fontsize=30, c='#9749b9', pad=15)
if pitch.invert_y:
text = 'inverted y axis'
if pt == 'stats':
xytext = (0 + (xmax - xmin)/2, 50)
else:
xytext = (0 + (xmax - xmin)/2, ymin + (ymax - ymin)/2)
def createPassNetworks(match_data,
events_df,
matchId,
team,
max_line_width,
marker_size,
edgewidth,
dh_arrow_width,
marker_color,
marker_edge_color,
shrink,
ax,
kit_no_size=20):
# getting team id and venue
if match_data['home']['name'] == team:
teamId = match_data['home']['teamId']
venue = 'home'
else:
teamId = match_data['away']['teamId']
venue = 'away'
# getting opponent
if venue == 'home':
opponent = match_data['away']['name']
else:
opponent = match_data['home']['name']
# getting player dictionary
team_players_dict = {}
for player in match_data[venue]['players']:
team_players_dict[player['playerId']] = player['name']
# getting minute of first substitution
for i in events_df.index:
if events_df.loc[i, 'type'] == 'SubstitutionOn' and events_df.loc[
i, 'teamId'] == teamId:
sub_minute = str(events_df.loc[i, 'minute'])
break
# getting players dataframe
match_players_df = pd.DataFrame()
player_names = []
player_ids = []
player_pos = []
player_kit_number = []
for player in match_data[venue]['players']:
player_names.append(player['name'])
player_ids.append(player['playerId'])
player_pos.append(player['position'])
player_kit_number.append(player['shirtNo'])
match_players_df['playerId'] = player_ids
match_players_df['playerName'] = player_names
match_players_df['playerPos'] = player_pos
match_players_df['playerKitNumber'] = player_kit_number
# extracting passes
passes_df = events_df.loc[events_df['teamId'] ==
teamId].reset_index().drop('index', axis=1)
passes_df['playerId'] = passes_df['playerId'].astype('float').astype(
'Int64')
if 'playerName' in passes_df.columns:
passes_df = passes_df.drop(columns='playerName')
passes_df.dropna(subset=["playerId"], inplace=True)
passes_df.insert(
27,
column='playerName',
value=[team_players_dict[i] for i in list(passes_df['playerId'])])
if 'passRecipientId' in passes_df.columns:
passes_df = passes_df.drop(columns='passRecipientId')
passes_df = passes_df.drop(columns='passRecipientName')
passes_df.insert(28,
column='passRecipientId',
value=passes_df['playerId'].shift(-1))
passes_df.insert(29,
column='passRecipientName',
value=passes_df['playerName'].shift(-1))
passes_df.dropna(subset=["passRecipientName"], inplace=True)
passes_df = passes_df.loc[events_df['type'] == 'Pass', :].reset_index(
drop=True)
passes_df = passes_df.loc[events_df['outcomeType'] ==
'Successful', :].reset_index(drop=True)
index_names = passes_df.loc[passes_df['playerName'] ==
passes_df['passRecipientName']].index
passes_df.drop(index_names, inplace=True)
passes_df = passes_df.merge(match_players_df,
on=['playerId', 'playerName'],
how='left',
validate='m:1')
passes_df = passes_df.merge(match_players_df.rename(
{
'playerId': 'passRecipientId',
'playerName': 'passRecipientName'
},
axis='columns'),
on=['passRecipientId', 'passRecipientName'],
how='left',
validate='m:1',
suffixes=['', 'Receipt'])
passes_df = passes_df[passes_df['playerPos'] != 'Sub']
# getting team formation
formation = match_data[venue]['formations'][0]['formationName']
formation = '-'.join(formation)
# getting player average locations
location_formation = passes_df[['playerKitNumber', 'x', 'y']]
average_locs_and_count = location_formation.groupby('playerKitNumber').agg(
{
'x': ['mean'],
'y': ['mean', 'count']
})
average_locs_and_count.columns = ['x', 'y', 'count']
# getting separate dataframe for selected columns
passes_formation = passes_df[[
'id', 'playerKitNumber', 'playerKitNumberReceipt'
]].copy()
passes_formation['EPV'] = passes_df['EPV']
# getting dataframe for passes between players
passes_between = passes_formation.groupby(
['playerKitNumber', 'playerKitNumberReceipt']).agg({
'id': 'count',
'EPV': 'sum'
}).reset_index()
passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True)
passes_between = passes_between.merge(average_locs_and_count,
left_on='playerKitNumberReceipt',
right_index=True)
passes_between = passes_between.merge(average_locs_and_count,
left_on='playerKitNumber',
right_index=True,
suffixes=['', '_end'])
# filtering passes
pass_filter = int(passes_between['pass_count'].mean())
passes_between = passes_between.loc[
passes_between['pass_count'] > pass_filter]
# calculating the line width
passes_between[
'width'] = passes_between.pass_count / passes_between.pass_count.max(
) * max_line_width
passes_between = passes_between.reset_index(drop=True)
# setting 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
passes_between['alpha'] = color.tolist()
# separating paired passes from normal passes
passes_between_threshold = 15
filtered_pair_df = []
pair_list = [
comb
for comb in combinations(passes_between['playerKitNumber'].unique(), 2)
]
for pair in pair_list:
df = passes_between[(
(passes_between['playerKitNumber'] == pair[0]) &
(passes_between['playerKitNumberReceipt'] == pair[1])) | (
(passes_between['playerKitNumber'] == pair[1]) &
(passes_between['playerKitNumberReceipt'] == pair[0]))]
if df.shape[0] == 2:
if (np.array(df.pass_count)[0] >= passes_between_threshold) and (
np.array(df.pass_count)[1] >= passes_between_threshold):
filtered_pair_df.append(df)
passes_between.drop(df.index, inplace=True)
if len(filtered_pair_df) > 0:
filtered_pair_df = pd.concat(filtered_pair_df).reset_index(drop=True)
passes_between = passes_between.reset_index(drop=True)
# plotting
pitch = Pitch(pitch_type='opta',
pitch_color='#171717',
line_color='#5c5c5c',
goal_type='box')
pitch.draw(ax=ax, constrained_layout=True, tight_layout=True)
average_locs_and_count['zorder'] = list(np.linspace(1, 5, 11))
for i in average_locs_and_count.index:
pitch.scatter(average_locs_and_count.loc[i, 'x'],
average_locs_and_count.loc[i, 'y'],
s=marker_size,
color=marker_color,
edgecolors=marker_edge_color,
linewidth=edgewidth,
alpha=1,
zorder=average_locs_and_count.loc[i, 'zorder'],
ax=ax)
for i in passes_between.index:
x = passes_between.loc[i, 'x']
y = passes_between.loc[i, 'y']
endX = passes_between.loc[i, 'x_end']
endY = passes_between.loc[i, 'y_end']
coordsA = "data"
coordsB = "data"
con = ConnectionPatch([endX, endY], [x, y],
coordsA,
coordsB,
arrowstyle="simple",
shrinkA=shrink,
shrinkB=shrink,
mutation_scale=passes_between.loc[i, 'width'] *
max_line_width,
color=passes_between.loc[i, 'alpha'])
ax.add_artist(con)
if len(filtered_pair_df) > 0:
for i in filtered_pair_df.index:
x = filtered_pair_df.loc[i, 'x']
y = filtered_pair_df.loc[i, 'y']
endX = filtered_pair_df.loc[i, 'x_end']
endY = filtered_pair_df.loc[i, 'y_end']
coordsA = "data"
coordsB = "data"
con = ConnectionPatch([endX, endY], [x, y],
coordsA,
coordsB,
arrowstyle="<|-|>",
shrinkA=shrink,
shrinkB=shrink,
mutation_scale=dh_arrow_width,
lw=filtered_pair_df.loc[i, 'width'] *
max_line_width / 5,
color=filtered_pair_df.loc[i, 'alpha'])
ax.add_artist(con)
for i in average_locs_and_count.index:
pitch.annotate(i,
xy=(average_locs_and_count.loc[i, 'x'],
average_locs_and_count.loc[i, 'y']),
family='DejaVu Sans',
c='white',
va='center',
ha='center',
zorder=average_locs_and_count.loc[i, 'zorder'],
size=kit_no_size,
weight='bold',
ax=ax)
ax.text(50,
104,
"{} (Mins 1-{})".format(team, sub_minute).upper(),
size=10,
fontweight='bold',
ha='center',
va='center')
ax.text(2, 3, '{}'.format(formation), size=9, c='grey')
passes_between['width'] = passes_between.pass_count / passes_between.pass_count.max() * max_line_width
average_locs_and_count['marker_size'] = (average_locs_and_count['count']
/ average_locs_and_count['count'].max() * max_marker_size)
##############################################################################
# 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', orientation='horizontal',
pitch_color='#22312b', line_color='#c7d5cc', figsize=(16, 11),
constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
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, color='#c7d5cc')
fig.set_facecolor("#22312b")
def passmap(Df,teamname,teamid,min1,max1):
pitch = Pitch(pitch_type='statsbomb', figsize=(20,13), line_zorder=2,
line_color='#c7d5cc', orientation='horizontal',constrained_layout=True, tight_layout=False)
fig, ax = pitch.draw()
df = Df.copy()
df = df[(df.expandedMinute>=min1)&(df.expandedMinute<=max1)]
allplayers = df[(df.teamId==teamid)&(df.name!='')].name.tolist()
playersubbedoff = df[(df.type_displayName == 'SubstitutionOff')&(df.teamId==teamid)]['name'].tolist()
timeoff = df[(df.type_displayName == 'SubstitutionOff')&(df.teamId==teamid)]['expandedMinute'].tolist()
playersubbedon = df[(df.type_displayName == 'SubstitutionOn')&(df.teamId==teamid)]['name'].tolist()
timeon = df[(df.type_displayName == 'SubstitutionOn')&(df.teamId==teamid)]['expandedMinute'].tolist()
majoritylist = []
minoritylist = []
for i in range(len(timeon)):
if((timeon[i]>=min1)&(timeon[i]<=max1)):
player1min = timeon[i] - min1
player2min = max1 - timeon[i]
if(player1min >= player2min):
majoritylist.append(playersubbedoff[i])
minoritylist.append(playersubbedon[i])
else:
majoritylist.append(playersubbedon[i])
minoritylist.append(playersubbedoff[i])
players = list(set(allplayers) - set(minoritylist))
# return players
shirtNo = []
for p in players:
shirtNo.append(int(df[df.name==p]['shirtNo'].values[0]))
passdf = df.query("(type_displayName=='Pass')&(name in @players)&(receiver in @players)&\
(outcomeType_displayName == 'Successful')&(teamId==@teamid)")
passdf['x'] = passdf['x']
passdf['y'] = 80 - passdf['y']
passdf['endX'] = passdf['endX']
passdf['endY'] = 80 - passdf['endY']
cols = ['name','receiver']
gamedf = passdf[cols]
totalpassdf = gamedf.groupby(cols).size().reset_index(name="count")
totalpassdf[cols] = np.sort(totalpassdf[cols],axis=1)
totalpassdf = totalpassdf.groupby(cols)['count'].agg(['sum']).reset_index()
avg_x = []
avg_y = []
blank=np.zeros(len(totalpassdf))
totalpassdf['passer_x'] = blank
totalpassdf['passer_y'] = blank
totalpassdf['receiver_x'] = blank
totalpassdf['receiver_y'] = blank
uniquenames = np.array(players)
uniquejerseys = np.array(shirtNo)
totalpasses = []
for name in uniquenames:
player_pass_df = passdf.query("(name == @name)")
x = np.mean(player_pass_df['x'])
y = np.mean(player_pass_df['y'])
totalpasses.append(len(player_pass_df))
avg_x.append(x)
avg_y.append(y)
for i in range(len(totalpassdf)):
passername = totalpassdf.iloc[i,0]
receivername = totalpassdf.iloc[i,1]
totalpassdf.iloc[i,3] = avg_x[np.where(uniquenames==passername)[0][0]]
totalpassdf.iloc[i,4] = avg_y[np.where(uniquenames==passername)[0][0]]
totalpassdf.iloc[i,5] = avg_x[np.where(uniquenames==receivername)[0][0]]
totalpassdf.iloc[i,6] = avg_y[np.where(uniquenames==receivername)[0][0]]
link = totalpassdf.iloc[i,2]
passerx = totalpassdf.iloc[i,3]
receiverx = totalpassdf.iloc[i,5]
passery = totalpassdf.iloc[i,4]
receivery = totalpassdf.iloc[i,6]
if(link>=1):
ax.plot([passerx, receiverx],[passery, receivery],linewidth=link/2.5, color ='lightgrey')
for indx in range(len(uniquenames)):
name = uniquenames[indx]
size = 50*totalpasses[np.where(uniquenames==name)[0][0]]
avgx = avg_x[np.where(uniquenames==name)[0][0]]
avgy = avg_y[np.where(uniquenames==name)[0][0]]
jersey = shirtNo[np.where(uniquenames==name)[0][0]]
ax.scatter(avgx,avgy,color='#d7191c',s=1000,zorder=3)
ax.annotate(jersey, (avgx, avgy),alpha=1,fontsize=25,color='w',
horizontalalignment='center',
verticalalignment='center').set_path_effects([path_effects.Stroke(linewidth=2,
foreground='black'), path_effects.Normal()])
ax.text(125,3,'# Player (Acc passes)',fontsize=25)
for i in range(12):
ax.text(127,3+4*i,'|',fontsize=50)
for i in range(30):
ax.text(125+i,3.5,'_',fontsize=50)
# ax.plot([126,3],[126,50])
for indx in range(len(players)):
ax.text(125,7+4*indx,str(shirtNo[indx]),fontsize=25)
for indx in range(len(players)):
ax.text(130,7+4*indx,players[indx]+'('+str(totalpasses[indx])+')',fontsize=25)
ax.set_title(teamname+' Passmap, Exp. Minutes '+str(min1)+ " to "+str(max1),fontsize=50)
fig.text(0.02, 0.05, "Created by Soumyajit Bose / @Soumyaj15209314",fontstyle="italic",fontsize=20,color='#edece9')
fig.text(0.02, -0.05, "Acc passes mentioned in brackets next to player names "+"\n"
"Width of line is proportional to number of passes exchanged between two players"+"\n"+
"A minimum of 1 pass need to be exchanged to show up as a line",
fontstyle="italic",fontsize=25,color='#edece9')
# add_image(bu_alt, fig, left=0.9, bottom=-0.05, width=0.07)
return fig
df = pd.read_csv('C:/Users/dev/Desktop/messibetis.csv')
df['x'] = df['x'] * 1.2
df['y'] = df['y'] * 0.8
df['endX'] = df['endX'] * 1.2
df['endY'] = df['endY'] * 0.8
fig, ax = plt.subplots(figsize=(13, 8.5))
fig.set_facecolor('#22312b')
ax.patch.set_facecolor('#22312b')
pitch = Pitch(pitch_type='statsbomb',
pitch_color='#22312b',
line_color='#c7d5cc',
figsize=(16, 11),
constrained_layout=True,
tight_layout=False)
pitch.draw(ax=ax)
plt.gca().invert_yaxis()
kde = sns.kdeplot(df['x'],
df['y'],
shade=True,
shade_lowest=False,
alpha=0.5,
n_levels=11,
cmap='magma')
for x in range(len(df['x'])):
if df['outcome'][x] == 'Successful':
plt.plot((df['x'][x], df['endX'][x]), (df['y'][x], df['endY'][x]),
color='green')
plt.scatter(df['x'][x], df['y'][x], color='Black')
plt.xlim(0, 120)
plt.ylim(0, 80)
"""
from urllib.request import urlopen
from PIL import Image
import matplotlib.pyplot as plt
from mplsoccer.pitch import Pitch
from mplsoccer.utils import add_image
# opening the background image
# pic by webtreats: https://www.flickr.com/photos/webtreatsetc/
# available at: https://www.flickr.com/photos/webtreatsetc/5756834840
IMAGE_URL = 'https://live.staticflickr.com/5065/5756834840_e31c559b26_c_d.jpg'
image = Image.open(urlopen(IMAGE_URL))
pitch = Pitch(pitch_color='None')
fig, ax = pitch.draw(tight_layout=False)
# adding the image and the image credits
ax_image = add_image(image, fig, left=0, bottom=0, width=1, height=1)
ax.text(70, 75, 'cloud pic by\nwebtreats (flickr)', color='white')
# set the pitch to be plotted after the image
# note these numbers can be anything like 0.5, 0.2 as long
# as the image zorder is behind the pitch zorder
ax.set_zorder(1)
ax_image.set_zorder(0)
# save with 'tight' and no padding to avoid borders
# fig.savefig('cloud.png', bbox_inches='tight', pad_inches=0)
plt.show() # If you are using a Jupyter notebook you do not need this line
tight_layout=False,
constrained_layout=True)
# 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))
# load the StatsBomb logo
sb_logo = Image.open(
urlopen((
'https://github.com/statsbomb/open-data/blob/fb54bd7fe20dbd5299fafc64f1f6f0d919a5e40d/'
'stats-bomb-logo.png?raw=true')))
# Plot
fig, axes = pitch.draw()
axes = axes.ravel()
teams = df['Squad'].values
vmin = df[pressure_cols].min().min(
) # we normalise the heatmaps with the min / max values
vmax = df[pressure_cols].max().max()
for i, ax in enumerate(axes):
ax.set_title(teams[i], fontsize=20)
# fill in the bin statistics from df
bin_statistic['statistic'] = df.loc[df.Squad == teams[i],
pressure_cols].values
heatmap = pitch.heatmap(bin_statistic,
ax=ax,
cmap='coolwarm',
vmin=vmin,
vmax=vmax) # plot the heatmap
def passmap(Df, teamid, teamname, min1, max1):
pitch = Pitch(pitch_type='statsbomb',
orientation='vertical',
pitch_color='#000000',
line_color='#a9a9a9',
constrained_layout=True,
tight_layout=False,
linewidth=0.5)
fig, ax = pitch.draw()
df = Df.copy()
df = df[(df.expandedMinute >= min1) & (df.expandedMinute <= max1)]
allplayers = df[(df.teamId == teamid) & (df.name.notna())].name.tolist()
playersubbedoff = df[(df.type_displayName == 'SubstitutionOff')
& (df.teamId == teamid)]['name'].tolist()
timeoff = df[(df.type_displayName == 'SubstitutionOff')
& (df.teamId == teamid)]['expandedMinute'].tolist()
playersubbedon = df[(df.type_displayName == 'SubstitutionOn')
& (df.teamId == teamid)]['name'].tolist()
timeon = df[(df.type_displayName == 'SubstitutionOn')
& (df.teamId == teamid)]['expandedMinute'].tolist()
majoritylist = []
minoritylist = []
for i in range(len(timeon)):
if ((timeon[i] >= min1) & (timeon[i] <= max1)):
player1min = timeon[i] - min1
player2min = max1 - timeon[i]
if (player1min >= player2min):
majoritylist.append(playersubbedoff[i])
minoritylist.append(playersubbedon[i])
else:
majoritylist.append(playersubbedon[i])
minoritylist.append(playersubbedoff[i])
players = list(set(allplayers) - set(minoritylist))
#return players
shirtNo = []
for p in players:
shirtNo.append(int(df[df.name == p]['shirtNo'].values[0]))
passes_df = df.query(
"(type_displayName=='Pass')&(name in @players)&(receiver in @players)&\
(outcomeType_displayName == 'Successful')&(teamId==@teamid)")
#passes_df.insert(29, column='passRecipientName', value=passes_df['name'].shift(-1))
passes_df.dropna(subset=["receiver"], inplace=True)
#passes_df['passer'] = passes_df['playerId']
#passes_df['recipient'] = passes_df['passer'].shift(-1)
passes_df = passes_df[passes_df.columns[~passes_df.isnull().all()]]
passes_df['playerKitNumber'] = passes_df['shirtNo'].fillna(0).astype(
np.int)
passes_df['playerKitNumberReceipt'] = passes_df['shirtNo'].shift(
-1).fillna(0).astype(np.int)
passer_avg = passes_df.groupby('playerKitNumber').agg({
'x': ['median'],
'y': ['median', 'count'],
'epv_value': ['sum']
})
passer_avg.columns = ['x', 'y', 'count', 'epv']
passer_avg.index = passer_avg.index.astype(int)
passes_formation = passes_df[[
'id', 'playerKitNumber', 'playerKitNumberReceipt'
]].copy()
passes_formation['kitNo_max'] = passes_formation[[
'playerKitNumber', 'playerKitNumberReceipt'
]].max(axis='columns')
passes_formation['kitNo_min'] = passes_formation[[
'playerKitNumber', 'playerKitNumberReceipt'
]].min(axis='columns')
passes_between = passes_formation.groupby(['kitNo_max', 'kitNo_min'
]).id.count().reset_index()
passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True)
# add on the location of each player so we have the start and end positions of the lines
passes_between = passes_between.merge(passer_avg,
left_on='kitNo_min',
right_index=True)
passes_between = passes_between.merge(passer_avg,
left_on='kitNo_max',
right_index=True,
suffixes=['', '_end'])
'''
#Between Passer and Recipient
passes_between = passes_df.groupby(['passer', 'recipient']).id.count().reset_index()
passes_between.rename({'id': 'pass_count'}, axis='columns', inplace=True)
passes_between = passes_between.merge(passer_avg, left_on='passer', right_index=True)
passes_between = passes_between.merge(passer_avg, left_on='recipient', right_index=True,
suffixes=['', '_end'])
'''
#Minimum No. of Passes
passes_between = passes_between.loc[(passes_between['pass_count'] >= 3)]
#Scaling for StatsBomb
passes_between['x'] = passes_between['x'] * 1.2
passes_between['y'] = passes_between['y'] * 0.8
passer_avg['x'] = passer_avg['x'] * 1.2
passer_avg['y'] = passer_avg['y'] * 0.8
passes_between['x_end'] = passes_between['x_end'] * 1.2
passes_between['y_end'] = passes_between['y_end'] * 0.8
#Width Variable
yo = passes_between.pass_count / passes_between.pass_count.max()
b = 1
min_transparency = 0.3
color = np.array(to_rgba('#00bfff'))
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
a = plt.scatter(passer_avg.y,
passer_avg.x,
s=100,
c="#111111",
facecolor='none',
lw=1,
cmap="winter",
alpha=1,
zorder=2,
vmin=0,
vmax=0.6,
marker='h')
c = plt.scatter(passer_avg.y,
passer_avg.x,
s=60,
c='#FF0000',
alpha=1,
zorder=3,
marker='h')
pitch.arrows(passes_between.x,
passes_between.y,
passes_between.x_end,
passes_between.y_end,
color=color,
ax=ax,
zorder=1,
width=1.5)
cbar = plt.colorbar(a,
orientation="horizontal",
shrink=0.3,
pad=0,
ticks=[0, 0.2, 0.4, 0.6])
cbar.set_label('Expected Possession Value (EPV)', color='#a9a9a9', size=6)
cbar.outline.set_edgecolor('#a9a9a9')
cbar.ax.xaxis.set_tick_params(color='#a9a9a9')
cbar.ax.xaxis.set_tick_params(labelcolor='#a9a9a9')
cbar.ax.tick_params(labelsize=5)
plt.gca().invert_xaxis()
for index, row in passer_avg.iterrows():
pitch.annotate(row.name,
xy=(row.x, row.y),
c='#a9a9a9',
va='center',
ha='center',
size=5,
ax=ax)
plt.text(
79,
2,
"Positions = Median Location of Successful Passes\nArrows = Pass Direction\nTransparency = Frequency of Combination\nMinimum of 3 Passes ",
color='#a9a9a9',
fontsize=5,
alpha=0.5,
zorder=1)
plt.text(80, 122, "Minutes 45-90", color='#a9a9a9', fontsize=5)
plt.text(18, 122, "@nikhilrajesh231", color='#a9a9a9', fontsize=5)
ax.set_title("Barcelona PV Pass Network\n5-2 vs Getafe (H)",
fontsize=8,
color="#a9a9a9",
fontweight='bold',
y=1.01)
fig = plt.savefig('pn2.png',
bbox_inches="tight",
facecolor="#000000",
dpi=600)
return fig