In [192]:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns

import highlight_text

In [209]:

# the famous import font code to use Andale Mono
import matplotlib.font_manager
from IPython.core.display import HTML

def make_html(fontname):
    return "<p>{font}: <span style='font-family:{font}; font-size: 24px;'>{font}</p>".format(font=fontname)

code = "\n".join([make_html(font) for font in sorted(set([f.name for f in matplotlib.font_manager.fontManager.ttflist]))])

In [51]:

#import data
df = pd.read_csv('beeswarmTutorial.csv')

In [202]:

#set default colors
text_color = 'white'
background = '#313332'

In [53]:

#look at top of dataframe
df.head()

Out[53]:

	Player	Pos	Squad	90s	Prog
0	Patrick van Aanholt\Patrick-van-Aanholt	DF	Crystal Palace	11.7	76
1	Tammy Abraham\Tammy-Abraham	FW	Chelsea	10.6	11
2	Che Adams\Che-Adams	FW	Southampton	19.0	38
3	Tosin Adarabioyo\Tosin-Adarabioyo	DF	Fulham	18.0	36
4	Adrián\Adrian	GK	Liverpool	2.0	0

In [54]:

#create a new column for progressive passes per 90
df['per90'] = df['Prog']/df['90s']
df

Out[54]:

	Player	Pos	Squad	90s	Prog	per90
0	Patrick van Aanholt\Patrick-van-Aanholt	DF	Crystal Palace	11.7	76	6.495726
1	Tammy Abraham\Tammy-Abraham	FW	Chelsea	10.6	11	1.037736
2	Che Adams\Che-Adams	FW	Southampton	19.0	38	2.000000
3	Tosin Adarabioyo\Tosin-Adarabioyo	DF	Fulham	18.0	36	2.000000
4	Adrián\Adrian	GK	Liverpool	2.0	0	0.000000
...	...	...	...	...	...	...
492	Andre-Frank Zambo Anguissa\Andre-Frank-Zambo-A...	MF	Fulham	18.7	74	3.957219
493	Andi Zeqiri\Andi-Zeqiri	FWDF	Brighton	0.8	1	1.250000
494	Oleksandr Zinchenko\Oleksandr-Zinchenko	DF	Manchester City	7.1	39	5.492958
495	Hakim Ziyech\Hakim-Ziyech	FWMF	Chelsea	7.0	42	6.000000
496	Kurt Zouma\Kurt-Zouma	DF	Chelsea	15.0	46	3.066667

497 rows × 6 columns

In [55]:

#filter the dataframe so it is only players who have played more than 6.5 90's which is about 585 minutes
df = df[df['90s']>=6.5].reset_index()
df

Out[55]:

	index	Player	Pos	Squad	90s	Prog	per90
0	0	Patrick van Aanholt\Patrick-van-Aanholt	DF	Crystal Palace	11.7	76	6.495726
1	1	Tammy Abraham\Tammy-Abraham	FW	Chelsea	10.6	11	1.037736
2	2	Che Adams\Che-Adams	FW	Southampton	19.0	38	2.000000
3	3	Tosin Adarabioyo\Tosin-Adarabioyo	DF	Fulham	18.0	36	2.000000
4	6	Ola Aina\Ola-Aina	DF	Fulham	17.7	69	3.898305
...	...	...	...	...	...	...	...
300	491	Wilfried Zaha\Wilfried-Zaha	FW	Crystal Palace	18.5	39	2.108108
301	492	Andre-Frank Zambo Anguissa\Andre-Frank-Zambo-A...	MF	Fulham	18.7	74	3.957219
302	494	Oleksandr Zinchenko\Oleksandr-Zinchenko	DF	Manchester City	7.1	39	5.492958
303	495	Hakim Ziyech\Hakim-Ziyech	FWMF	Chelsea	7.0	42	6.000000
304	496	Kurt Zouma\Kurt-Zouma	DF	Chelsea	15.0	46	3.066667

305 rows × 7 columns

In [56]:

df.describe()

Out[56]:

	index	90s	Prog	per90
count	305.000000	305.000000	305.000000	305.000000
mean	241.468852	14.688525	43.655738	3.019502
std	146.384632	4.758721	31.651934	1.798697
min	0.000000	6.600000	0.000000	0.000000
25%	110.000000	10.900000	21.000000	1.932773
50%	243.000000	14.700000	37.000000	2.857143
75%	371.000000	18.800000	60.000000	4.000000
max	496.000000	23.000000	171.000000	9.879518

In [60]:

df = df.sort_values(by='per90',ascending=False)

In [62]:

df = df[df['Pos'] != 'GK']

In [80]:

df.head(10)

Out[80]:

	index	Player	Pos	Squad	90s	Prog	per90
8	11	Thiago Alcántara\Thiago-Alcantara	MF	Liverpool	8.3	82	9.879518
10	14	Trent Alexander-Arnold\Trent-Alexander-Arnold	DF	Liverpool	19.5	166	8.512821
148	234	Mateo Kovačić\Mateo-Kovacic	MF	Chelsea	13.1	105	8.015267
299	487	Granit Xhaka\Granit-Xhaka	MF	Arsenal	17.1	136	7.953216
122	187	Jordan Henderson\Jordan-Henderson	MFDF	Liverpool	17.6	134	7.613636
77	111	Kevin De Bruyne\Kevin-De-Bruyne	MF	Manchester City	15.6	118	7.564103
190	301	James Milner\James-Milner	MFDF	Liverpool	6.8	51	7.500000
230	375	Andrew Robertson\Andrew-Robertson	DF	Liverpool	22.9	171	7.467249
236	384	James Rodríguez\James-Rodriguez	FWMF	Everton	13.3	99	7.443609
96	140	Bruno Fernandes\Bruno-Fernandes	MF	Manchester Utd	21.4	158	7.383178

In [85]:

fig, ax = plt.subplots(figsize=(10,5))
fig.set_facecolor(background)
ax.patch.set_facecolor(background)

#set up our base layer
mpl.rcParams['xtick.color'] = text_color
mpl.rcParams['ytick.color'] = text_color

ax.grid(ls='dotted',lw=.5,color='lightgrey',axis='y',zorder=1)
spines = ['top','bottom','left','right']
for x in spines:
    if x in spines:
        ax.spines[x].set_visible(False)

sns.swarmplot(x='per90',data=df,color='white',zorder=1)

#plot thiago
plt.scatter(x=9.87,y=0,c='red',edgecolor='white',s=200,zorder=2)
plt.text(s='Thiago',x=9.87,y=-.04,c=text_color)

#plot de bruyne
plt.scatter(x=7.564,y=0,c='blue',edgecolor='white',s=200,zorder=2)

plt.title('Progressive Passes in the Premier League 2020/21',c=text_color,fontsize=14)

plt.xlabel('Progressive Passes per 90',c=text_color)



#plt.savefig('swarm.png',dpi=500,bbox_inches = 'tight',facecolor=background)

Out[85]:

Text(0.5, 0, 'Progressive Passes per 90')

In [184]:

#import our next dataframe
df2 = pd.read_csv('beeswarm2.csv')

In [185]:

df2.head(10)

Out[185]:

	Player	Nation	Pos	Squad	90s	Cmp	Att	Cmp%	TotDist	PrgDist	Ast	xA	A-xA	KP	1/3	PPA	CrsPA	Prog
0	Patrick van Aanholt\Patrick-van-Aanholt	nl NED	DF	Crystal Palace	11.7	573	705	81.3	9485	4365	1	0.8	0.2	11	43	21	4	76
1	Tammy Abraham\Tammy-Abraham	eng ENG	FW	Chelsea	10.6	138	201	68.7	1925	327	1	0.9	0.1	8	12	2	0	11
2	Che Adams\Che-Adams	eng ENG	FW	Southampton	19.0	288	413	69.7	4739	1079	4	4.3	-0.3	22	20	12	0	38
3	Tosin Adarabioyo\Tosin-Adarabioyo	eng ENG	DF	Fulham	18.0	779	905	86.1	18458	6406	0	0.1	-0.1	3	39	2	0	36
4	Adrián\Adrian	es ESP	GK	Liverpool	2.0	43	62	69.4	1147	723	0	0.0	0.0	0	0	0	0	0
5	Sergio Agüero\Sergio-Aguero	ar ARG	FW	Manchester City	1.6	33	42	78.6	386	58	0	0.6	-0.6	3	2	1	0	1
6	Ola Aina\Ola-Aina	ng NGA	DF	Fulham	17.7	782	1013	77.2	15293	6132	0	0.3	-0.3	8	66	11	6	69
7	Rayan Aït Nouri\Rayan-Ait-Nouri	fr FRA	DF	Wolves	7.7	299	357	83.8	4712	1364	1	0.7	0.3	6	13	6	3	18
8	Semi Ajayi\Semi-Ajayi	ng NGA	DF	West Brom	22.0	553	763	72.5	12087	5164	0	0.1	-0.1	2	50	3	0	46
9	Nathan Aké\Nathan-Ake	nl NED	DF	Manchester City	5.2	429	461	93.1	7845	2158	0	0.0	0.0	1	36	0	0	23

In [186]:

#do some data preprocessing and cleaning

#split the player names
df2['Player'] = df2['Player'].str.split('\\',expand=True)[0]

df2 = df2[df2['Pos'] != 'GK']

df2 = df2[df2['90s'] > 6.5].reset_index()

In [187]:

#make the per 90 stats
#metrics = ['Prog90','1/390','xA90','Cmp%','KP90','PPA90']
df2['Prog'] = df2['Prog'] / df2['90s']
df2['1/3'] = df2['1/3'] / df2['90s']
df2['xA'] = df2['xA'] / df2['90s']
df2['KP'] = df2['KP'] / df2['90s']
df2['PPA'] = df2['PPA'] / df2['90s']

In [188]:

print(df2.head())

df2.Player.unique()

   index               Player   Nation Pos           Squad   90s  Cmp   Att  \
0      0  Patrick van Aanholt   nl NED  DF  Crystal Palace  11.7  573   705   
1      1        Tammy Abraham  eng ENG  FW         Chelsea  10.6  138   201   
2      2            Che Adams  eng ENG  FW     Southampton  19.0  288   413   
3      3     Tosin Adarabioyo  eng ENG  DF          Fulham  18.0  779   905   
4      6             Ola Aina   ng NGA  DF          Fulham  17.7  782  1013   

   Cmp%  TotDist  PrgDist  Ast        xA  A-xA        KP       1/3       PPA  \
0  81.3     9485     4365    1  0.068376   0.2  0.940171  3.675214  1.794872   
1  68.7     1925      327    1  0.084906   0.1  0.754717  1.132075  0.188679   
2  69.7     4739     1079    4  0.226316  -0.3  1.157895  1.052632  0.631579   
3  86.1    18458     6406    0  0.005556  -0.1  0.166667  2.166667  0.111111   
4  77.2    15293     6132    0  0.016949  -0.3  0.451977  3.728814  0.621469   

   CrsPA      Prog  
0      4  6.495726  
1      0  1.037736  
2      0  2.000000  
3      0  2.000000  
4      6  3.898305

Out[188]:

array(['Patrick van Aanholt', 'Tammy Abraham', 'Che Adams',
       'Tosin Adarabioyo', 'Ola Aina', 'Rayan Aït Nouri', 'Semi Ajayi',
       'Marc Albrighton', 'Thiago Alcántara', 'Toby Alderweireld',
       'Trent Alexander-Arnold', 'Ezgjan Alioski', 'Allan',
       'Miguel Almirón', 'Steven Alzate', 'Ethan Ampadu',
       'Joachim Andersen', 'Michail Antonio', 'Stuart Armstrong',
       'Pierre-Emerick Aubameyang', 'Serge Aurier', 'Jordan Ayew',
       'Luke Ayling', 'César Azpilicueta', 'Fabián Balbuena',
       'George Baldock', 'Patrick Bamford', 'Ross Barkley',
       'Ashley Barnes', 'Harvey Barnes', 'Kyle Bartley', 'Chris Basham',
       'Michy Batshuayi', 'Jan Bednarek', 'Héctor Bellerín',
       'Saïd Benrahma', 'Christian Benteke', 'Jóhann Berg Guðmundsson',
       'Sander Berge', 'Steven Bergwijn', 'Ryan Bertrand',
       'Yves Bissouma', 'Jayden Bogle', 'Willy Boly', 'Jarrod Bowen',
       'Robbie Brady', 'Rhian Brewster', 'Josh Brownhill', 'Oliver Burke',
       'Dan Burn', 'Gary Cahill', 'Tom Cairney', 'Dominic Calvert-Lewin',
       'João Cancelo', 'Matty Cash', 'Timothy Castagne', 'Ivan Cavaleiro',
       'Edinson Cavani', 'Dani Ceballos', 'Ben Chilwell', 'Ciaran Clark',
       'Nathaniel Clyne', 'Conor Coady', 'Séamus Coleman',
       'Aaron Connolly', 'Liam Cooper', 'Hélder Costa', 'Vladimír Coufal',
       'Aaron Cresswell', 'Stuart Dallas', 'Scott Dann', 'Ben Davies',
       'Tom Davies', 'Craig Dawson', 'Kevin De Bruyne',
       'Leander Dendoncker', 'Grady Diangana', 'Raphael Dias Belloli',
       'Rúben Dias', 'Eric Dier', 'Lucas Digne', 'Moussa Djenepo',
       'Matt Doherty', 'Gabriel Dos Santos', 'Abdoulaye Doucouré',
       'Lewis Dunk', 'John Egan', 'Mohamed Elneny', 'Jonny Evans',
       'Eberechi Eze', 'Fabinho', 'Bruno Fernandes', 'Federico Fernández',
       'Roberto Firmino', 'John Fleck', 'Phil Foden', 'Wesley Fofana',
       'Pablo Fornals', 'Fred', 'Christian Fuchs', 'Darnell Furlong',
       'Conor Gallagher', 'Kieran Gibbs', 'Ben Godfrey', 'André Gomes',
       'Joe Gomez', 'Karlan Grant', 'Jack Grealish', 'Mason Greenwood',
       'Pascal Groß', 'İlkay Gündoğan', 'Sébastien Haller',
       'Jack Harrison', 'Kai Havertz', 'Isaac Hayden', 'Jordan Henderson',
       'Jeff Hendrick', 'Son Heung-min', 'Rob Holding', 'Mason Holgate',
       'Callum Hudson-Odoi', 'Pierre Højbjerg', 'Danny Ings',
       'Branislav Ivanović', 'Alex Iwobi', 'Reece James', 'Gabriel Jesus',
       'Raúl Jiménez', 'Joelinton', 'Curtis Jones', 'Jorginho',
       'James Justin', 'Harry Kane', "N'Golo Kanté", 'Michael Keane',
       'Max Kilman', 'Mateusz Klich', 'Robin Koch', 'Ezri Konsa',
       'Cheikhou Kouyaté', 'Mateo Kovačić', 'Alexandre Lacazette',
       'Adam Lallana', 'Tariq Lamptey', 'Jamaal Lascelles',
       'Mario Lemina', 'Jamal Lewis', 'Victor Lindelöf', 'Jake Livermore',
       'Ruben Loftus-Cheek', 'Sean Longstaff', 'Ademola Lookman',
       'Matthew Lowton', 'David Luiz', 'Douglas Luiz', 'John Lundstram',
       'James Maddison', 'Harry Maguire', 'Riyad Mahrez', 'Sadio Mané',
       'Javier Manquillo', 'Solly March', 'Anthony Martial',
       'Arthur Masuaku', 'Nemanja Matić', 'Joël Matip', 'Neal Maupay',
       'James McArthur', 'Oliver McBurnie', 'John McGinn',
       'David McGoldrick', 'Dwight McNeil', 'Scott McTominay', 'Ben Mee',
       'Nampalys Mendy', 'Luka Milivojević', 'James Milner', 'Yerry Mina',
       'Tyrone Mings', 'Tyrick Mitchell', 'Aleksandar Mitrović',
       'Mason Mount', 'Lucas Moura', 'João Moutinho', 'Jacob Murphy',
       'Wilfred Ndidi', 'Tanguy Ndombele', 'Pedro Neto', 'Rúben Neves',
       'Oliver Norwood', "Dara O'Shea", 'Angelo Ogbonna', 'Ben Osborn',
       'Thomas Partey', 'Nicolas Pépé', 'Matheus Pereira', 'Ayoze Pérez',
       'Kalvin Phillips', 'Matt Phillips', 'Erik Pieters',
       'Daniel Podence', 'Paul Pogba', 'Dennis Praet',
       'Christian Pulisic', 'Marcus Rashford', 'Nathan Redmond',
       'Harrison Reed', 'Sergio Reguilón', 'Bobby Reid', 'Declan Rice',
       'Richarlison', 'Jaïro Riedewald', 'Andrew Robertson',
       'Antonee Robinson', 'Callum Robinson', 'Jack Robinson', 'Rodri',
       'Rodrigo', 'James Rodríguez', 'Jay Rodriguez', 'Oriol Romeu',
       'Antonio Rüdiger', 'Allan Saint-Maximin', 'Romain Saïss',
       'Bukayo Saka', 'Mohamed Salah', 'Davinson Sánchez',
       'Romaine Sawyers', 'Fabian Schär', 'Jeffrey Schlupp',
       'Nélson Semedo', 'Luke Shaw', 'Jonjo Shelvey', 'Gylfi Sigurðsson',
       'Bernardo Silva', 'Fábio Silva', 'Thiago Silva', 'Moussa Sissoko',
       'Emile Smith-Rowe', 'Tomáš Souček', 'Çağlar Söyüncü',
       'Jack Stephens', 'Raheem Sterling', 'Enda Stevens', 'John Stones',
       'Pascal Struijk', 'Matt Targett', 'James Tarkowski',
       'Charlie Taylor', 'Kenny Tete', 'Youri Tielemans',
       'Kieran Tierney', 'Ferrán Torres', 'Andros Townsend',
       'Conor Townsend', 'Adama Traoré', 'Bertrand Traoré', 'Trézéguet',
       'Leandro Trossard', 'Jamie Vardy', 'Joël Veltman',
       'Jannik Vestergaard', 'Theo Walcott', 'Kyle Walker',
       'Kyle Walker-Peters', 'Aaron Wan-Bissaka', 'Joel Ward',
       'James Ward-Prowse', 'Ollie Watkins', 'Adam Webster',
       'Danny Welbeck', 'Timo Werner', 'Ashley Westwood', 'Ben White',
       'Georginio Wijnaldum', 'Willian', 'Callum Wilson', 'Chris Wood',
       'Granit Xhaka', 'Wilfried Zaha', 'Andre-Frank Zambo Anguissa',
       'Oleksandr Zinchenko', 'Hakim Ziyech', 'Kurt Zouma'], dtype=object)

In [189]:

#create a list of 6 metrics to compare
metrics = ['Prog','1/3','xA','Cmp%','KP','PPA']

In [232]:

fig,axes = plt.subplots(3,2,figsize=(14,10))
fig.set_facecolor(background)
ax.patch.set_facecolor(background)

#set up our base layer
mpl.rcParams['xtick.color'] = text_color
mpl.rcParams['ytick.color'] = text_color

#create a list of comparisons
counter=0
counter2=0
met_counter = 0

for i,ax in zip(df2['Player'],axes.flatten()):
    ax.set_facecolor(background)
    ax.grid(ls='dotted',lw=.5,color='lightgrey',axis='y',zorder=1)
    
    spines = ['top','bottom','left','right']
    for x in spines:
        if x in spines:
            ax.spines[x].set_visible(False)
            
    sns.swarmplot(x=metrics[met_counter],data=df2,ax=axes[counter,counter2],zorder=1,color='#64645e')
    ax.set_xlabel(f'{metrics[met_counter]}',c='white')
    
    for x in range(len(df2['Player'])):
        #if df2['Player'][x] == 'Thiago Alcántara':
            #ax.scatter(x=df2[metrics[met_counter]][x],y=0,s=200,c='red',zorder=2)
        if df2['Player'][x] == 'İlkay Gündoğan':
            ax.scatter(x=df2[metrics[met_counter]][x],y=0,s=200,c='#6CABDD',zorder=2)
                        
    met_counter+=1
    if counter2 == 0:
        counter2 = 1
        continue
    if counter2 == 1:
        counter2 = 0
        counter+=1
        

        
s='<Ilkay Gündogan> Premier League Passing Stats'
highlight_text.fig_text(s=s,
                x=.25, y=.88,
                #highlight_weights = ['bold'],
                fontsize=22,
                fontfamily = 'Andale Mono',
                color = text_color,
                highlight_colors = ['#6CABDD'],
                va='center'
               )

fig.text(.12,.05,"all stats per 90 / Goal Keepers and those with less than 6.5 90's played excluded",fontsize=11, fontfamily='Andale Mono',color=text_color)
fig.text(.12,.03,"@mckayjohns / data via fbref.com + Statsbomb", fontstyle='italic',fontsize=11, fontfamily='Andale Mono',color=text_color)

plt.savefig('test.png',dpi=500,bbox_inches = 'tight',facecolor=background)

In [ ]: