3D Pass Networks wit Python & Cinema4d¶

make sure to download Sergio's repository at https://github.com/Friends-of-Tracking-Data-FoTD/passing-networks-in-python

replace 'Demo Eventing.ipynb' with this notebook

In [1]:

# Make sure that the path is the root of the project (it can be checked with '%pwd')
%cd ..

/Users/DML/OneDrive/Soccer Python/passing-networks-in-python-master

In [2]:

import pandas as pd
from pandas.io.json import json_normalize
from utils import read_json

In [3]:

lineups_path = "open-data-master/data/lineups/{0}.json"
events_path = "open-data-master/data/events/{0}.json"

team_name = "Spain"
match_id = 7576

Step 1: Read data¶

In [5]:

lineups = read_json(lineups_path.format(match_id))
names_dict = {player["player_name"]: player["player_nickname"]
              for team in lineups for player in team["lineup"]}

names_dict

Out[5]:

{'André Miguel Valente Silva': 'André Silva',
 'Andrés Iniesta Luján': 'Andrés Iniesta',
 'Bernardo Mota Veiga de Carvalho e Silva': 'Bernardo Silva',
 'Bruno Miguel Borges Fernandes': 'Bruno Fernandes',
 'Cristiano Ronaldo dos Santos Aveiro': 'Cristiano Ronaldo',
 'Cédric Ricardo Alves Soares': 'Cédric Soares',
 'David Josué Jiménez Silva': 'David Silva',
 'David de Gea Quintana': 'David de Gea',
 'Diego da Silva Costa': 'Diego Costa',
 'Francisco Román Alarcón Suárez': 'Isco',
 'Gerard Piqué Bernabéu': 'Gerard Piqué',
 'Gonçalo Manuel Ganchinho Guedes': 'Gonçalo Guedes',
 'Iago Aspas Juncal': 'Iago Aspas',
 'Jordi Alba Ramos': 'Jordi Alba',
 'Jorge Resurrección Merodio': 'Koke',
 'José Ignacio Fernández Iglesias': 'Nacho',
 'José Miguel da Rocha Fonte': 'José Fonte',
 'João Filipe Iria Santos Moutinho': 'João Moutinho',
 'João Mário Naval da Costa Eduardo': 'João Mário',
 'Kléper Laveran Lima Ferreira': 'Pepe',
 'Lucas Vázquez Iglesias': 'Lucas Vázquez',
 'Raphaël Adelino José Guerreiro': 'Raphaël Guerreiro',
 'Ricardo Andrade Quaresma Bernardo': 'Ricardo Quaresma',
 'Rui Pedro dos Santos Patrício': 'Rui Patrício',
 'Sergio Busquets i Burgos': 'Sergio Busquets',
 'Sergio Ramos García': 'Sergio Ramos',
 'Thiago Alcântara do Nascimento': 'Thiago Alcântara',
 'William Silva de Carvalho': 'William Carvalho'}

In [6]:

events = read_json(events_path.format(match_id))
df_events = json_normalize(events, sep="_").assign(match_id=match_id)

Step 2: Compute max. minutes¶

In [7]:

first_red_card_minute = df_events[df_events.foul_committed_card_name.isin(["Second Yellow", "Red Card"])].minute.min()
first_substitution_minute = df_events[df_events.type_name == "Substitution"].minute.min()
max_minute = df_events.minute.max()

num_minutes = min(first_substitution_minute, first_red_card_minute, max_minute)
num_minutes

Out[7]:

Step 3: Set text information¶

In [8]:

plot_name = "statsbomb_match{0}_{1}".format(match_id, team_name)

opponent_team = [x for x in df_events.team_name.unique() if x != team_name][0]
plot_title ="{0}'s passing network against {1} (StatsBomb eventing data)".format(team_name, opponent_team)

plot_legend = "Location: pass origin\nSize: number of passes\nColor: number of passes"

Step 4: Prepare data¶

In [9]:

def _statsbomb_to_point(location, max_width=120, max_height=80):
    '''
    Convert a point's coordinates from a StatsBomb's range to 0-1 range.
    '''
    return location[0] / max_width, 1-(location[1] / max_height)

In [10]:

df_passes = df_events[(df_events.type_name == "Pass") &
                      (df_events.pass_outcome_name.isna()) &
                      (df_events.team_name == team_name) &
                      (df_events.minute < num_minutes)].copy()

# If available, use player's nickname instead of full name to optimize space in plot
df_passes["pass_recipient_name"] = df_passes.pass_recipient_name.apply(lambda x: names_dict[x] if names_dict[x] else x)
df_passes["player_name"] = df_passes.player_name.apply(lambda x: names_dict[x] if names_dict[x] else x)

df_passes.head()
# df_passes.to_csv('passes.csv')

Out[10]:

	ball_receipt_outcome_id	ball_receipt_outcome_name	ball_recovery_recovery_failure	carry_end_location	clearance_aerial_won	counterpress	dribble_outcome_id	dribble_outcome_name	dribble_overrun	duel_outcome_id	...	substitution_replacement_name	tactics_formation	tactics_lineup	team_id	team_name	timestamp	type_id	type_name	under_pressure	match_id
5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	772	Spain	00:00:00.480	30	Pass	True	7576
7	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	772	Spain	00:00:01.173	30	Pass	True	7576
10	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	772	Spain	00:00:03.600	30	Pass	NaN	7576
14	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	772	Spain	00:00:04.413	30	Pass	True	7576
17	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	772	Spain	00:00:07.253	30	Pass	NaN	7576

5 rows × 108 columns

In [11]:

df_passes["origin_pos_x"] = df_passes.location.apply(lambda x: _statsbomb_to_point(x)[0])
df_passes["origin_pos_y"] = df_passes.location.apply(lambda x: _statsbomb_to_point(x)[1])
player_position = df_passes.groupby("player_name").agg({"origin_pos_x": "median", "origin_pos_y": "median"})

player_position

Out[11]:

	origin_pos_x	origin_pos_y
player_name
Andrés Iniesta	0.566667	0.78750
David Silva	0.716667	0.26250
David de Gea	0.079167	0.51875
Diego Costa	0.716667	0.72500
Gerard Piqué	0.366667	0.27500
Isco	0.591667	0.83750
Jordi Alba	0.541667	0.95000
Koke	0.562500	0.22500
Nacho	0.579167	0.02500
Sergio Busquets	0.466667	0.48750
Sergio Ramos	0.425000	0.70000

In [12]:

player_pass_count = df_passes.groupby("player_name").size().to_frame("num_passes")
player_pass_value = df_passes.groupby("player_name").size().to_frame("pass_value")

player_pass_count

Out[12]:

	num_passes
player_name
Andrés Iniesta	61
David Silva	33
David de Gea	10
Diego Costa	15
Gerard Piqué	51
Isco	71
Jordi Alba	65
Koke	50
Nacho	42
Sergio Busquets	49
Sergio Ramos	95

In [13]:

player_info = pd.concat([player_position, player_pass_count], axis=1, join_axes=[player_position.index])
player_info.to_csv('player_info.csv')
player_info

Out[13]:

	origin_pos_x	origin_pos_y	num_passes
player_name
Andrés Iniesta	0.566667	0.78750	61
David Silva	0.716667	0.26250	33
David de Gea	0.079167	0.51875	10
Diego Costa	0.716667	0.72500	15
Gerard Piqué	0.366667	0.27500	51
Isco	0.591667	0.83750	71
Jordi Alba	0.541667	0.95000	65
Koke	0.562500	0.22500	50
Nacho	0.579167	0.02500	42
Sergio Busquets	0.466667	0.48750	49
Sergio Ramos	0.425000	0.70000	95

In [14]:

df_passes_low = df_passes[(df_passes.pass_height_name == "Ground Pass") | (df_passes.pass_height_name == "Low Pass")]
df_passes_high = df_passes[(df_passes.pass_height_name == "High Pass")]

In [15]:

df_passes["pair_key"] = df_passes.apply(lambda x: "_".join(sorted([x["player_name"], x["pass_recipient_name"]])), axis=1)

pair_pass_count = df_passes.groupby("pair_key").size().to_frame("num_passes")
pair_pass_value = df_passes.groupby("pair_key").size().to_frame("pass_value")

pair_pass_value['origin_pos_player_x'] = 0
pair_pass_value['origin_pos_player_y'] = 0
pair_pass_value['origin_pos_recipe_x'] = 0
pair_pass_value['origin_pos_recipe_y'] = 0

for index, row in pair_pass_value.iterrows():
    pair_pass_value.loc[index,'origin_pos_player_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[0]] 
    pair_pass_value.loc[index,'origin_pos_player_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[0]] 
    pair_pass_value.loc[index,'origin_pos_recipe_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[1]] 
    pair_pass_value.loc[index,'origin_pos_recipe_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[1]]
    #print(index.rsplit('_',1)[0])
    #print(index.rsplit('_',1)[1])
pair_pass_value.to_csv('pair_pass_value.csv')    
pair_pass_value

Out[15]:

	pass_value	origin_pos_player_x	origin_pos_player_y	origin_pos_recipe_x	origin_pos_recipe_y
pair_key
Andrés Iniesta_David Silva	5	0.566667	0.78750	0.716667	0.2625
Andrés Iniesta_Diego Costa	7	0.566667	0.78750	0.716667	0.7250
Andrés Iniesta_Gerard Piqué	6	0.566667	0.78750	0.366667	0.2750
Andrés Iniesta_Isco	28	0.566667	0.78750	0.591667	0.8375
Andrés Iniesta_Jordi Alba	27	0.566667	0.78750	0.541667	0.9500
Andrés Iniesta_Koke	7	0.566667	0.78750	0.562500	0.2250
Andrés Iniesta_Nacho	1	0.566667	0.78750	0.579167	0.0250
Andrés Iniesta_Sergio Busquets	9	0.566667	0.78750	0.466667	0.4875
Andrés Iniesta_Sergio Ramos	38	0.566667	0.78750	0.425000	0.7000
David Silva_Diego Costa	3	0.716667	0.26250	0.716667	0.7250
David Silva_Gerard Piqué	5	0.716667	0.26250	0.366667	0.2750
David Silva_Isco	9	0.716667	0.26250	0.591667	0.8375
David Silva_Jordi Alba	4	0.716667	0.26250	0.541667	0.9500
David Silva_Koke	17	0.716667	0.26250	0.562500	0.2250
David Silva_Nacho	11	0.716667	0.26250	0.579167	0.0250
David Silva_Sergio Busquets	6	0.716667	0.26250	0.466667	0.4875
David Silva_Sergio Ramos	7	0.716667	0.26250	0.425000	0.7000
David de Gea_Gerard Piqué	5	0.079167	0.51875	0.366667	0.2750
David de Gea_Isco	1	0.079167	0.51875	0.591667	0.8375
David de Gea_Jordi Alba	4	0.079167	0.51875	0.541667	0.9500
David de Gea_Nacho	3	0.079167	0.51875	0.579167	0.0250
David de Gea_Sergio Busquets	1	0.079167	0.51875	0.466667	0.4875
David de Gea_Sergio Ramos	5	0.079167	0.51875	0.425000	0.7000
Diego Costa_Gerard Piqué	1	0.716667	0.72500	0.366667	0.2750
Diego Costa_Isco	6	0.716667	0.72500	0.591667	0.8375
Diego Costa_Jordi Alba	7	0.716667	0.72500	0.541667	0.9500
Diego Costa_Nacho	1	0.716667	0.72500	0.579167	0.0250
Diego Costa_Sergio Busquets	4	0.716667	0.72500	0.466667	0.4875
Diego Costa_Sergio Ramos	2	0.716667	0.72500	0.425000	0.7000
Gerard Piqué_Isco	3	0.366667	0.27500	0.591667	0.8375
Gerard Piqué_Jordi Alba	2	0.366667	0.27500	0.541667	0.9500
Gerard Piqué_Koke	21	0.366667	0.27500	0.562500	0.2250
Gerard Piqué_Nacho	12	0.366667	0.27500	0.579167	0.0250
Gerard Piqué_Sergio Busquets	14	0.366667	0.27500	0.466667	0.4875
Gerard Piqué_Sergio Ramos	26	0.366667	0.27500	0.425000	0.7000
Isco_Jordi Alba	44	0.591667	0.83750	0.541667	0.9500
Isco_Koke	11	0.591667	0.83750	0.562500	0.2250
Isco_Nacho	13	0.591667	0.83750	0.579167	0.0250
Isco_Sergio Busquets	10	0.591667	0.83750	0.466667	0.4875
Isco_Sergio Ramos	26	0.591667	0.83750	0.425000	0.7000
Jordi Alba_Koke	1	0.541667	0.95000	0.562500	0.2250
Jordi Alba_Nacho	1	0.541667	0.95000	0.579167	0.0250
Jordi Alba_Sergio Ramos	44	0.541667	0.95000	0.425000	0.7000
Koke_Nacho	21	0.562500	0.22500	0.579167	0.0250
Koke_Sergio Busquets	20	0.562500	0.22500	0.466667	0.4875
Koke_Sergio Ramos	7	0.562500	0.22500	0.425000	0.7000
Nacho_Sergio Busquets	11	0.579167	0.02500	0.466667	0.4875
Nacho_Sergio Ramos	2	0.579167	0.02500	0.425000	0.7000
Sergio Busquets_Sergio Ramos	23	0.466667	0.48750	0.425000	0.7000

In [16]:

df_passes_low["pair_key"] = df_passes_low.apply(lambda x: "_".join(sorted([x["player_name"], x["pass_recipient_name"]])), axis=1)

pair_pass_count_low = df_passes_low.groupby("pair_key").size().to_frame("num_passes")
pair_pass_value_low = df_passes_low.groupby("pair_key").size().to_frame("pass_value")

pair_pass_value_low['origin_pos_player_x'] = 0
pair_pass_value_low['origin_pos_player_y'] = 0
pair_pass_value_low['origin_pos_recipe_x'] = 0
pair_pass_value_low['origin_pos_recipe_y'] = 0

for index, row in pair_pass_value_low.iterrows():
    pair_pass_value_low.loc[index,'origin_pos_player_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[0]] 
    pair_pass_value_low.loc[index,'origin_pos_player_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[0]] 
    pair_pass_value_low.loc[index,'origin_pos_recipe_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[1]] 
    pair_pass_value_low.loc[index,'origin_pos_recipe_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[1]]
    #print(index.rsplit('_',1)[0])
    #print(index.rsplit('_',1)[1])
pair_pass_value_low = pair_pass_value_low.add_suffix('_low')
  
pair_pass_value_low

/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

Out[16]:

	pass_value_low	origin_pos_player_x_low	origin_pos_player_y_low	origin_pos_recipe_x_low	origin_pos_recipe_y_low
pair_key
Andrés Iniesta_David Silva	5	0.566667	0.78750	0.716667	0.2625
Andrés Iniesta_Diego Costa	7	0.566667	0.78750	0.716667	0.7250
Andrés Iniesta_Gerard Piqué	6	0.566667	0.78750	0.366667	0.2750
Andrés Iniesta_Isco	28	0.566667	0.78750	0.591667	0.8375
Andrés Iniesta_Jordi Alba	27	0.566667	0.78750	0.541667	0.9500
Andrés Iniesta_Koke	7	0.566667	0.78750	0.562500	0.2250
Andrés Iniesta_Sergio Busquets	9	0.566667	0.78750	0.466667	0.4875
Andrés Iniesta_Sergio Ramos	38	0.566667	0.78750	0.425000	0.7000
David Silva_Diego Costa	3	0.716667	0.26250	0.716667	0.7250
David Silva_Gerard Piqué	5	0.716667	0.26250	0.366667	0.2750
David Silva_Isco	9	0.716667	0.26250	0.591667	0.8375
David Silva_Jordi Alba	4	0.716667	0.26250	0.541667	0.9500
David Silva_Koke	17	0.716667	0.26250	0.562500	0.2250
David Silva_Nacho	11	0.716667	0.26250	0.579167	0.0250
David Silva_Sergio Busquets	4	0.716667	0.26250	0.466667	0.4875
David Silva_Sergio Ramos	7	0.716667	0.26250	0.425000	0.7000
David de Gea_Gerard Piqué	5	0.079167	0.51875	0.366667	0.2750
David de Gea_Jordi Alba	4	0.079167	0.51875	0.541667	0.9500
David de Gea_Nacho	1	0.079167	0.51875	0.579167	0.0250
David de Gea_Sergio Busquets	1	0.079167	0.51875	0.466667	0.4875
David de Gea_Sergio Ramos	5	0.079167	0.51875	0.425000	0.7000
Diego Costa_Gerard Piqué	1	0.716667	0.72500	0.366667	0.2750
Diego Costa_Isco	4	0.716667	0.72500	0.591667	0.8375
Diego Costa_Jordi Alba	6	0.716667	0.72500	0.541667	0.9500
Diego Costa_Nacho	1	0.716667	0.72500	0.579167	0.0250
Diego Costa_Sergio Busquets	4	0.716667	0.72500	0.466667	0.4875
Diego Costa_Sergio Ramos	2	0.716667	0.72500	0.425000	0.7000
Gerard Piqué_Isco	3	0.366667	0.27500	0.591667	0.8375
Gerard Piqué_Jordi Alba	1	0.366667	0.27500	0.541667	0.9500
Gerard Piqué_Koke	21	0.366667	0.27500	0.562500	0.2250
Gerard Piqué_Nacho	12	0.366667	0.27500	0.579167	0.0250
Gerard Piqué_Sergio Busquets	13	0.366667	0.27500	0.466667	0.4875
Gerard Piqué_Sergio Ramos	26	0.366667	0.27500	0.425000	0.7000
Isco_Jordi Alba	44	0.591667	0.83750	0.541667	0.9500
Isco_Koke	11	0.591667	0.83750	0.562500	0.2250
Isco_Nacho	11	0.591667	0.83750	0.579167	0.0250
Isco_Sergio Busquets	10	0.591667	0.83750	0.466667	0.4875
Isco_Sergio Ramos	26	0.591667	0.83750	0.425000	0.7000
Jordi Alba_Koke	1	0.541667	0.95000	0.562500	0.2250
Jordi Alba_Nacho	1	0.541667	0.95000	0.579167	0.0250
Jordi Alba_Sergio Ramos	43	0.541667	0.95000	0.425000	0.7000
Koke_Nacho	21	0.562500	0.22500	0.579167	0.0250
Koke_Sergio Busquets	20	0.562500	0.22500	0.466667	0.4875
Koke_Sergio Ramos	6	0.562500	0.22500	0.425000	0.7000
Nacho_Sergio Busquets	11	0.579167	0.02500	0.466667	0.4875
Sergio Busquets_Sergio Ramos	23	0.466667	0.48750	0.425000	0.7000

In [17]:

df_passes_high["pair_key"] = df_passes_high.apply(lambda x: "_".join(sorted([x["player_name"], x["pass_recipient_name"]])), axis=1)

pair_pass_count_high = df_passes_high.groupby("pair_key").size().to_frame("num_passes")
pair_pass_value_high = df_passes_high.groupby("pair_key").size().to_frame("pass_value")

pair_pass_value_high['origin_pos_player_x'] = 0
pair_pass_value_high['origin_pos_player_y'] = 0
pair_pass_value_high['origin_pos_recipe_x'] = 0
pair_pass_value_high['origin_pos_recipe_y'] = 0

for index, row in pair_pass_value_high.iterrows():
    pair_pass_value_high.loc[index,'origin_pos_player_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[0]] 
    pair_pass_value_high.loc[index,'origin_pos_player_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[0]] 
    pair_pass_value_high.loc[index,'origin_pos_recipe_x'] = player_info['origin_pos_x'][index.rsplit('_',1)[1]] 
    pair_pass_value_high.loc[index,'origin_pos_recipe_y'] = player_info['origin_pos_y'][index.rsplit('_',1)[1]]
    #print(index.rsplit('_',1)[0])
    #print(index.rsplit('_',1)[1])
    
pair_pass_value_high = pair_pass_value_high.add_suffix('_high')
# pair_pass_value_high = pair_pass_value_high.to_csv('pair_pass_value_high.csv')    
pair_pass_value_high

/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

Out[17]:

	pass_value_high	origin_pos_player_x_high	origin_pos_player_y_high	origin_pos_recipe_x_high	origin_pos_recipe_y_high
pair_key
Andrés Iniesta_Nacho	1	0.566667	0.78750	0.579167	0.0250
David Silva_Sergio Busquets	2	0.716667	0.26250	0.466667	0.4875
David de Gea_Isco	1	0.079167	0.51875	0.591667	0.8375
David de Gea_Nacho	2	0.079167	0.51875	0.579167	0.0250
Diego Costa_Isco	2	0.716667	0.72500	0.591667	0.8375
Diego Costa_Jordi Alba	1	0.716667	0.72500	0.541667	0.9500
Gerard Piqué_Jordi Alba	1	0.366667	0.27500	0.541667	0.9500
Gerard Piqué_Sergio Busquets	1	0.366667	0.27500	0.466667	0.4875
Isco_Nacho	2	0.591667	0.83750	0.579167	0.0250
Jordi Alba_Sergio Ramos	1	0.541667	0.95000	0.425000	0.7000
Koke_Sergio Ramos	1	0.562500	0.22500	0.425000	0.7000
Nacho_Sergio Ramos	2	0.579167	0.02500	0.425000	0.7000

In [18]:

joined = pd.concat([pair_pass_value, pair_pass_value_low['pass_value_low'].reindex(pair_pass_value.index)], axis=1)
joined = pd.concat([joined, pair_pass_value_high['pass_value_high'].reindex(joined.index)], axis=1)
joined = joined.fillna(0)

joined = joined[['pass_value', 'pass_value_low','pass_value_high', 'origin_pos_player_x' , 'origin_pos_player_y', 'origin_pos_recipe_x' , 'origin_pos_recipe_y' ]]
joined.to_csv('pair_pass_values.csv')  
joined

Out[18]:

	pass_value	pass_value_low	pass_value_high	origin_pos_player_x	origin_pos_player_y	origin_pos_recipe_x	origin_pos_recipe_y
pair_key
Andrés Iniesta_David Silva	5	5.0	0.0	0.566667	0.78750	0.716667	0.2625
Andrés Iniesta_Diego Costa	7	7.0	0.0	0.566667	0.78750	0.716667	0.7250
Andrés Iniesta_Gerard Piqué	6	6.0	0.0	0.566667	0.78750	0.366667	0.2750
Andrés Iniesta_Isco	28	28.0	0.0	0.566667	0.78750	0.591667	0.8375
Andrés Iniesta_Jordi Alba	27	27.0	0.0	0.566667	0.78750	0.541667	0.9500
Andrés Iniesta_Koke	7	7.0	0.0	0.566667	0.78750	0.562500	0.2250
Andrés Iniesta_Nacho	1	0.0	1.0	0.566667	0.78750	0.579167	0.0250
Andrés Iniesta_Sergio Busquets	9	9.0	0.0	0.566667	0.78750	0.466667	0.4875
Andrés Iniesta_Sergio Ramos	38	38.0	0.0	0.566667	0.78750	0.425000	0.7000
David Silva_Diego Costa	3	3.0	0.0	0.716667	0.26250	0.716667	0.7250
David Silva_Gerard Piqué	5	5.0	0.0	0.716667	0.26250	0.366667	0.2750
David Silva_Isco	9	9.0	0.0	0.716667	0.26250	0.591667	0.8375
David Silva_Jordi Alba	4	4.0	0.0	0.716667	0.26250	0.541667	0.9500
David Silva_Koke	17	17.0	0.0	0.716667	0.26250	0.562500	0.2250
David Silva_Nacho	11	11.0	0.0	0.716667	0.26250	0.579167	0.0250
David Silva_Sergio Busquets	6	4.0	2.0	0.716667	0.26250	0.466667	0.4875
David Silva_Sergio Ramos	7	7.0	0.0	0.716667	0.26250	0.425000	0.7000
David de Gea_Gerard Piqué	5	5.0	0.0	0.079167	0.51875	0.366667	0.2750
David de Gea_Isco	1	0.0	1.0	0.079167	0.51875	0.591667	0.8375
David de Gea_Jordi Alba	4	4.0	0.0	0.079167	0.51875	0.541667	0.9500
David de Gea_Nacho	3	1.0	2.0	0.079167	0.51875	0.579167	0.0250
David de Gea_Sergio Busquets	1	1.0	0.0	0.079167	0.51875	0.466667	0.4875
David de Gea_Sergio Ramos	5	5.0	0.0	0.079167	0.51875	0.425000	0.7000
Diego Costa_Gerard Piqué	1	1.0	0.0	0.716667	0.72500	0.366667	0.2750
Diego Costa_Isco	6	4.0	2.0	0.716667	0.72500	0.591667	0.8375
Diego Costa_Jordi Alba	7	6.0	1.0	0.716667	0.72500	0.541667	0.9500
Diego Costa_Nacho	1	1.0	0.0	0.716667	0.72500	0.579167	0.0250
Diego Costa_Sergio Busquets	4	4.0	0.0	0.716667	0.72500	0.466667	0.4875
Diego Costa_Sergio Ramos	2	2.0	0.0	0.716667	0.72500	0.425000	0.7000
Gerard Piqué_Isco	3	3.0	0.0	0.366667	0.27500	0.591667	0.8375
Gerard Piqué_Jordi Alba	2	1.0	1.0	0.366667	0.27500	0.541667	0.9500
Gerard Piqué_Koke	21	21.0	0.0	0.366667	0.27500	0.562500	0.2250
Gerard Piqué_Nacho	12	12.0	0.0	0.366667	0.27500	0.579167	0.0250
Gerard Piqué_Sergio Busquets	14	13.0	1.0	0.366667	0.27500	0.466667	0.4875
Gerard Piqué_Sergio Ramos	26	26.0	0.0	0.366667	0.27500	0.425000	0.7000
Isco_Jordi Alba	44	44.0	0.0	0.591667	0.83750	0.541667	0.9500
Isco_Koke	11	11.0	0.0	0.591667	0.83750	0.562500	0.2250
Isco_Nacho	13	11.0	2.0	0.591667	0.83750	0.579167	0.0250
Isco_Sergio Busquets	10	10.0	0.0	0.591667	0.83750	0.466667	0.4875
Isco_Sergio Ramos	26	26.0	0.0	0.591667	0.83750	0.425000	0.7000
Jordi Alba_Koke	1	1.0	0.0	0.541667	0.95000	0.562500	0.2250
Jordi Alba_Nacho	1	1.0	0.0	0.541667	0.95000	0.579167	0.0250
Jordi Alba_Sergio Ramos	44	43.0	1.0	0.541667	0.95000	0.425000	0.7000
Koke_Nacho	21	21.0	0.0	0.562500	0.22500	0.579167	0.0250
Koke_Sergio Busquets	20	20.0	0.0	0.562500	0.22500	0.466667	0.4875
Koke_Sergio Ramos	7	6.0	1.0	0.562500	0.22500	0.425000	0.7000
Nacho_Sergio Busquets	11	11.0	0.0	0.579167	0.02500	0.466667	0.4875
Nacho_Sergio Ramos	2	0.0	2.0	0.579167	0.02500	0.425000	0.7000
Sergio Busquets_Sergio Ramos	23	23.0	0.0	0.466667	0.48750	0.425000	0.7000