In [1]:
# Run the datacleaning notebook to get all the variables
%run 'Teknisk Tirsdag - Data Cleaning.ipynb'
/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py:2850: DtypeWarning: Columns (23,35) have mixed types. Specify dtype option on import or set low_memory=False.
if self.run_code(code, result):
Kolonnenavn: Name antal fyldte felter: 17981 datatype: object
Kolonnenavn: Age antal fyldte felter: 17981 datatype: int64
Kolonnenavn: Nationality antal fyldte felter: 17981 datatype: object
Kolonnenavn: Overall antal fyldte felter: 17981 datatype: int64
Kolonnenavn: Potential antal fyldte felter: 17981 datatype: int64
Kolonnenavn: Club antal fyldte felter: 17733 datatype: object
Kolonnenavn: Value antal fyldte felter: 17981 datatype: object
Kolonnenavn: Wage antal fyldte felter: 17981 datatype: object
Kolonnenavn: Special antal fyldte felter: 17981 datatype: int64
Kolonnenavn: Acceleration antal fyldte felter: 17981 datatype: object
Kolonnenavn: Aggression antal fyldte felter: 17981 datatype: object
Kolonnenavn: Agility antal fyldte felter: 17981 datatype: object
Kolonnenavn: Balance antal fyldte felter: 17981 datatype: object
Kolonnenavn: Ball control antal fyldte felter: 17981 datatype: object
Kolonnenavn: Composure antal fyldte felter: 17981 datatype: object
Kolonnenavn: Crossing antal fyldte felter: 17981 datatype: object
Kolonnenavn: Curve antal fyldte felter: 17981 datatype: object
Kolonnenavn: Dribbling antal fyldte felter: 17981 datatype: object
Kolonnenavn: Finishing antal fyldte felter: 17981 datatype: object
Kolonnenavn: Free kick accuracy antal fyldte felter: 17981 datatype: object
Kolonnenavn: GK diving antal fyldte felter: 17981 datatype: object
Kolonnenavn: GK handling antal fyldte felter: 17981 datatype: object
Kolonnenavn: GK kicking antal fyldte felter: 17981 datatype: object
Kolonnenavn: GK positioning antal fyldte felter: 17981 datatype: object
Kolonnenavn: GK reflexes antal fyldte felter: 17981 datatype: object
Kolonnenavn: Heading accuracy antal fyldte felter: 17981 datatype: object
Kolonnenavn: Interceptions antal fyldte felter: 17981 datatype: object
Kolonnenavn: Jumping antal fyldte felter: 17981 datatype: object
Kolonnenavn: Long passing antal fyldte felter: 17981 datatype: object
Kolonnenavn: Long shots antal fyldte felter: 17981 datatype: object
Kolonnenavn: Marking antal fyldte felter: 17981 datatype: object
Kolonnenavn: Penalties antal fyldte felter: 17981 datatype: object
Kolonnenavn: Positioning antal fyldte felter: 17981 datatype: object
Kolonnenavn: Reactions antal fyldte felter: 17981 datatype: object
Kolonnenavn: Short passing antal fyldte felter: 17981 datatype: object
Kolonnenavn: Shot power antal fyldte felter: 17981 datatype: object
Kolonnenavn: Sliding tackle antal fyldte felter: 17981 datatype: object
Kolonnenavn: Sprint speed antal fyldte felter: 17981 datatype: object
Kolonnenavn: Stamina antal fyldte felter: 17981 datatype: object
Kolonnenavn: Standing tackle antal fyldte felter: 17981 datatype: object
Kolonnenavn: Strength antal fyldte felter: 17981 datatype: object
Kolonnenavn: Vision antal fyldte felter: 17981 datatype: object
Kolonnenavn: Volleys antal fyldte felter: 17981 datatype: object
Kolonnenavn: CAM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: CB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: CDM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: CF antal fyldte felter: 15952 datatype: float64
Kolonnenavn: CM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: ID antal fyldte felter: 17981 datatype: int64
Kolonnenavn: LAM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LCB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LCM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LDM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LF antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LS antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LW antal fyldte felter: 15952 datatype: float64
Kolonnenavn: LWB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: Preferred Positions antal fyldte felter: 17981 datatype: object
Kolonnenavn: RAM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RCB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RCM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RDM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RF antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RM antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RS antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RW antal fyldte felter: 15952 datatype: float64
Kolonnenavn: RWB antal fyldte felter: 15952 datatype: float64
Kolonnenavn: ST antal fyldte felter: 15952 datatype: float64
('GK kicking', ['73-1', '68-2', '67+4', '65+1', '61-3', '63-7', '59-1', '60+2', '62+2', '60-1', '61+2', '60+1', '54-1', '65+4', '55+2', '68+8', '57+2', '55-1', '56+4'])
('GK diving', ['81-2', '76+1', '76-1', '75+1', '78+3', '72-1', '75+4', '70-2', '73+2', '71-2', '65+2', '68-1', '67+2', '63+2', '62-1', '66+1', '63+1', '64-3', '62+1', '64+5', '54-3', '56+2', '55+5', '55+4'])
('GK positioning', ['71-2', '69+1', '66-2', '69+2', '64+1', '65+4', '60+3', '66-1', '65+1', '63-1', '70+2', '62-1', '64+2', '62+4', '61-1', '60+1', '58+2', '58+4', '59+2', '51+3', '45-1'])
('GK handling', ['78-2', '72-1', '78-1', '67-1', '69+1', '75-1', '73+1', '66+3', '65+1', '63+2', '64+3', '66+2', '63-1', '65+3', '59+3', '58-1', '60+2', '60-1', '57+2', '52+2', '56+4', '53-1', '47-1', '52+3', '55+2'])
('GK reflexes', ['86-2', '83-1', '85-1', '79+1', '74+1', '81-1', '83+3', '78+2', '73-1', '75+2', '70+1', '70-1', '69+2', '67+2', '62+3', '68+2', '65+1', '65-3', '65-1', '61-3', '67+8', '60+1', '55+5', '56-4', '53+3', '55+3', '57+5'])
('Penalties', ['81+7', '65+2', '70-5', '64+6', '70+4', '67+11', '55+1', '70+2', '70+3', '36-10', '53+2', '67+15', '69+2', '61+5', '51+1', '58+13', '51+5', '56+7', '66+2', '58-4', '69-1', '40-1', '49-1', '61-6', '52-2', '60-7', '60-3', '58+5', '46-1', '61-1'])
('Volleys', ['70+1', '69+3', '72+1', '71+1', '69+4', '66+1', '68+2', '64+1', '69+1', '61+5', '62-4', '49+2', '39+4', '59+1', '60+2', '71+8', '51+1', '49+6', '63+5', '32-1', '52+8', '65-2', '53-1', '62+4', '59+2', '40-12', '57+1', '55-4', '57+2', '29-1', '54+10', '51-1', '56+6', '51+7', '33-1', '52-1', '15+8'])
('Free kick accuracy', ['81+1', '66-5', '73+1', '57+5', '60-4', '56+4', '60+8', '53+11', '63-7', '77-3', '58+2', '69+1', '52+10', '62-3', '69-2', '65+1', '70+5', '39+2', '58+6', '62-4', '65+5', '61+10', '71+4', '56+11', '64+1', '39+10', '55+21', '64+8', '70+3', '68+4', '70+17', '69+12', '65+24', '52+9', '70+21', '39-9', '66+30', '64-3', '70+4', '67-1', '39-6', '65+29', '56-2', '64-2', '36+4', '53+1', '32-1', '45-1'])
('Balance', ['90+2', '71+2', '85+2', '89+2', '65+7', '85+1', '47-3', '70+3', '72+6', '80+6', '64+1', '64+4', '52-2', '61-1', '73+4', '70+7', '66+6', '33+4', '53+1', '66+2', '63+2', '82+2', '66+4', '69+3', '80-6', '56-7', '67-2', '49+2', '64+2', '86-3', '62+1', '73-1', '72-2', '63+5', '81-1', '53-13', '50-5', '70+1', '55-3', '70+2', '73+5', '81+1', '65+1', '79-1', '75+1', '77+2', '63-2', '60+3', '68-4', '73-3', '58-1', '71+1'])
('Aggression', ['58-10', '65+10', '57+5', '66+7', '77+5', '68+1', '68+3', '87+1', '82+1', '76+3', '72+3', '80+3', '65-2', '72+5', '42+7', '78-3', '74+7', '82+2', '81+2', '70-1', '75+1', '76+7', '67+4', '66+3', '78+3', '60+12', '71+1', '65-1', '69+10', '74-6', '23+3', '57+1', '67+6', '67+39', '67-2', '53+14', '33+2', '51+2', '56+1', '65+2', '72-2', '66-2', '82+10', '78+17', '72+14', '59+1', '64+3', '58-1', '53+4', '49+4', '67-3', '65+4', '59-7', '61+1', '68-3', '85-2', '53+5', '51+1', '58+7', '48+2', '57+9', '40+2', '30-1', '61+2', '60+8', '46+1'])
('Jumping', ['74+1', '76-4', '67+2', '65+2', '73-8', '59+1', '77-4', '78+1', '86+1', '59+2', '80-2', '68+1', '76+4', '68-1', '81+2', '76-5', '72+4', '63+2', '67-2', '75-6', '73-3', '70+14', '70-3', '66-1', '64+4', '82+6', '75-4', '74+4', '73-4', '78-2', '72-1', '68+7', '60+1', '77-2', '79+2', '64+2', '67+8', '74+7', '65+1', '68+6', '64-4', '59-1', '76-3', '66-3', '69+7', '63+1', '77+2', '57+4', '62+1', '70-2', '71+2', '67+16', '71+9', '87+2', '66+2', '76-2', '46+2', '70+1', '68+2', '39-2', '60+3', '73+5', '66+5', '63+7', '52-2', '60-2'])
('Agility', ['60+6', '78+1', '58-2', '71+1', '70+1', '69+1', '68+2', '74+4', '64-1', '75-1', '64-2', '43-3', '84+1', '58+4', '68-2', '77-8', '76-3', '50-8', '58-4', '70+14', '70-2', '70+3', '81-2', '63-2', '65+4', '55-4', '65+1', '66+3', '48-3', '46+13', '67+8', '83+6', '68+1', '62-3', '83+3', '85+2', '82-8', '82+3', '61-3', '72-1', '83+1', '66-3', '62+1', '67+2', '79+2', '55+1', '71-4', '72+1', '59+4', '67-2', '60+7', '36+2', '62+3', '48+3', '35-2', '68+10', '53+2', '49+1', '63+3', '75+1', '50-1', '77+2', '46+1'])
('Curve', ['81+2', '77+2', '78+1', '60+7', '75-2', '74+2', '71+1', '76+1', '68+4', '39+7', '67+3', '79+1', '73-2', '75+10', '61+2', '67+4', '59+24', '70+1', '58+10', '72-3', '69+7', '65+3', '64+1', '61+5', '52-6', '66+3', '73+2', '73+1', '52+4', '39+8', '53+1', '64+2', '59+1', '68+2', '72+2', '58+3', '62+4', '66+5', '63+11', '71+3', '62+1', '63-2', '69+15', '55+22', '57+4', '58+21', '69+8', '68-3', '50-3', '63+19', '43+3', '45+5', '57-1', '64+9', '34+3', '37-1', '42-3', '28+5', '36+6', '56+2', '53+10', '46+12', '31-1', '56+1', '45-1', '41+5', '27+2'])
('Shot power', ['81+1', '73+3', '85+6', '83+3', '77+3', '78-1', '75+4', '74-1', '70+28', '73+2', '70+13', '75+3', '73+1', '68-1', '58+9', '66+1', '80+6', '63+2', '73-5', '56+2', '63-3', '64+29', '79-3', '67+2', '76+1', '74-2', '70+1', '54+1', '71-6', '71+4', '68+2', '62-1', '71+1', '64+10', '63+1', '67+1', '69+2', '58+5', '75+1', '60+5', '65+2', '69+4', '57+3', '81-4', '74+2', '65+3', '55+1', '64-1', '55-4', '43-4', '66-2', '56-1', '61+3', '57-1', '60-2', '58-3', '52+3', '67+10', '50+4', '54+2', '43+10', '45-1', '36+13', '56-2', '21+1'])
('Heading accuracy', ['85-1', '73+1', '76+2', '74+1', '72+3', '78+1', '78-1', '75+2', '75-1', '65+2', '68-7', '42-7', '69+2', '70+2', '56+2', '46+8', '74+2', '64+4', '72-2', '72+2', '60+2', '64-1', '55+7', '59+2', '68-4', '43+3', '69-1', '74-2', '61+1', '61+2', '63+4', '57+5', '71-1', '59-5', '65-1', '59-1', '62-1', '72+1', '62+3', '64-3', '63-3', '56-6', '53+6', '64+1', '60+4', '62+4', '65+1', '60-5', '50-10', '54+3', '48-2', '69-3', '63+1', '64+2', '52+5', '57+2', '37+2', '57-1', '60-1', '70+4', '48+1', '43-1', '60+6', '52-11', '47+7', '43+2', '41+2'])
('Long shots', ['80+3', '73+2', '72+2', '79+3', '62+2', '74+1', '73+1', '78+2', '64+2', '73-1', '66+2', '60+6', '70+5', '77-2', '68-2', '65+1', '69+1', '70-4', '67-1', '68-4', '55+5', '64+3', '55+25', '75+1', '52-3', '63+3', '68+1', '75+6', '66+4', '43+1', '53-14', '52+1', '72+5', '63+1', '59+2', '59-1', '69+4', '66+1', '53+9', '64-1', '59+1', '61+3', '54+8', '71+2', '62-1', '62+1', '60+7', '61+1', '41-4', '58+3', '66-2', '43-1', '60+4', '38+10', '59+20', '60-2', '63+10', '27+3', '42+20', '56+5', '57+1', '23-1', '50+1', '39-1', '35+5', '47+3', '17+10'])
('Acceleration', ['70+9', '80+1', '49-1', '67+2', '79-2', '65-2', '91-2', '74-3', '75+1', '41-6', '74+1', '70+3', '75+5', '74+2', '71+2', '68+1', '71+4', '89-2', '58-10', '78+1', '86+1', '66-1', '66+1', '74+4', '71-3', '80+2', '64-2', '57-4', '78+3', '73+9', '82-3', '68+3', '68+2', '55-8', '55-1', '43-2', '77+3', '82+10', '49-10', '72+1', '61+1', '79+8', '70-2', '60-2', '86+7', '81+4', '69+3', '65-10', '64-3', '73+4', '75-6', '64+5', '33+10', '92+2', '76-1', '62+2', '65+7', '58+8', '44-2', '77+1', '82+3', '68-1', '61+3', '73+3', '59+1', '64+12', '85-1', '78+14', '59-1', '75+4', '73+10', '71+6', '77+13', '62+1', '64-5'])
('Composure', ['79+1', '74+2', '70-1', '75+1', '75+2', '66+4', '74+1', '65-8', '68-3', '82+18', '72+6', '67-1', '70+3', '78+10', '72-2', '66+2', '64+4', '74-5', '65+2', '72-1', '68+2', '67+1', '68+3', '65+5', '64-3', '60-3', '69+2', '61+2', '69+1', '58-1', '61+3', '57-3', '59+2', '55-3', '61-1', '68-2', '68-1', '28+4', '63-1', '50+4', '60-1', '62+3', '59-2', '64+24', '62+1', '63+2', '63+3', '58+5', '62-2', '56+2', '52-3', '62-1', '70+5', '55+4', '52+1', '65-1', '64-1', '58-2', '54-4', '58+2', '55+3', '65-2', '56-1', '51+5', '51+3', '51-4', '56+7', '44+1', '56+1', '52-2', '43+1', '42+1', '40+1', '45+2', '44+5', '35+2'])
('Positioning', ['80+1', '80+3', '76-2', '76+1', '80+2', '72+2', '72+4', '73-2', '75+1', '68+2', '58-1', '74+1', '74+3', '56+4', '71-3', '57+2', '70+3', '68-1', '77+1', '72+8', '66+2', '66-2', '62-3', '69+2', '65+2', '63-3', '65+1', '74+2', '70+1', '58+10', '66+1', '68+6', '70-2', '63+4', '66-8', '61+2', '62-2', '64+1', '70+2', '70+9', '69-1', '68+4', '66+5', '67+4', '10-3', '62-1', '67+3', '67+1', '51+2', '55+3', '60+2', '67+2', '56+29', '69+3', '54+1', '51-4', '61+1', '46+4', '52-7', '40+14', '58+2', '56+3', '39+2', '60-2', '50+2', '30-1', '56-4', '53+1', '52+1', '50+7', '51-1', '47+6', '47+1', '56+7', '17+10', '27+3', '42+1', '52-1'])
('Sliding tackle', ['71+4', '82-1', '77-1', '31+8', '73+1', '79-2', '70+3', '78-1', '77+1', '58+37', '77+3', '78+1', '66+2', '72+1', '73+2', '70+1', '34+14', '65-1', '74+2', '73+3', '42+9', '32-4', '70+2', '80+5', '58+2', '66-3', '67-2', '65-2', '16+4', '68-1', '68+1', '69-1', '67-1', '68+3', '70-1', '69+2', '71-3', '18+3', '20+2', '29-9', '66-1', '52+12', '67-3', '48+6', '55+1', '65+1', '62+3', '44+26', '62-2', '63+5', '61+2', '64+1', '53-3', '59+2', '60+1', '61+5', '63+14', '62-4', '64+4', '60-2', '60-4', '65-3', '21+10', '67+2', '63+1', '31+10', '61-2', '23-2', '59+3', '51+3', '68-5', '34-1', '52+2', '66+1', '54+1', '52+1', '57+4', '56+2', '41+1', '54+3', '50-1'])
('Crossing', ['79+2', '85+1', '63+2', '79+4', '70+2', '74-5', '80+2', '68-8', '72+1', '74+2', '61+1', '71+2', '70-3', '65-3', '72+4', '66-3', '72-8', '79+3', '73+3', '56-3', '61-4', '67+1', '66-2', '60+2', '61-3', '67-2', '41+5', '62+1', '65-1', '66+2', '65+2', '64+2', '37-6', '66+1', '57-10', '66+7', '68+4', '36+10', '63+5', '67-1', '50+1', '62-3', '69-1', '60-1', '46+1', '71+1', '64-1', '58+2', '67+9', '53+1', '68+2', '59+2', '65+3', '54+10', '73-1', '64-5', '53+2', '54+2', '50+8', '58+1', '51+8', '60+8', '50+3', '65+6', '51-3', '54+7', '55+3', '61+29', '60+5', '48-7', '59+9', '55-2', '42+3', '58+11', '56-1', '38-9', '52-6', '56+3', '32-1', '45+9', '55+1', '65+1', '63-1', '36+6', '64+37', '48+7', '45+1', '33+3', '32+2'])
('Interceptions', ['37+1', '43+4', '81+2', '76+2', '64+13', '76+1', '69+1', '71+3', '73+1', '75-1', '74+1', '64+1', '72+1', '49+13', '49+17', '48-11', '36-10', '75-2', '57-2', '21-11', '66+1', '69-4', '67-2', '68-3', '67+1', '68+2', '66+2', '68+1', '68-1', '68-4', '67+3', '66-2', '61+1', '74+7', '17+1', '67-4', '62-1', '68+4', '64-1', '60+10', '67-1', '10-11', '45+6', '65+1', '64+12', '69+5', '59+4', '64+2', '36+24', '63+29', '39+10', '49+15', '59+2', '54+2', '64+4', '48+2', '56+4', '62+5', '23+10', '58+4', '58-6', '55+2', '63+2', '55+5', '57+3', '58-3', '18-6', '60+2', '58-4', '63-2', '48+1', '59+1', '57+1', '44+3', '56+1', '48+24', '24-1', '57-1', '50+5', '15-11', '34+5', '27+20', '49+2', '52+2', '31+1'])
('Strength', ['70+1', '79+1', '66+1', '34+3', '65-2', '76+2', '71+3', '68+1', '47+3', '52-5', '80+1', '68+6', '85-1', '75+5', '87-4', '75+2', '83+5', '77-1', '56-5', '85+1', '68+4', '76+1', '78+2', '65-1', '83-1', '54+5', '84+6', '60+2', '74+1', '81-3', '51-7', '68+2', '75+1', '57-3', '78-6', '69-3', '69+1', '65+3', '85-5', '78-2', '73-1', '70-1', '67-5', '74+2', '78+1', '72-2', '83+4', '74-1', '57-10', '84-4', '73+3', '48-2', '80-1', '66+5', '57+1', '40+10', '68-1', '55-6', '69+11', '79-1', '71-1', '54-7', '54+2', '82-5', '50+3', '73-2', '59-19', '57+4', '68+3', '74+4', '33+2', '87-3', '58+10', '70+3', '37+5', '71-4', '32+2', '55-1', '69+8', '74+8', '66+6', '65+2', '70+6', '45-10', '67+7', '47+9', '48+12', '72+3', '70+15'])
('Vision', ['72+2', '79+1', '77+1', '77-4', '73-2', '57-1', '73+1', '78+3', '78+1', '78+2', '66+6', '74+2', '67-3', '74+1', '71-2', '62+8', '67-1', '70+2', '66-2', '38-10', '70-4', '73-3', '68+3', '66+1', '68-3', '62+1', '63-4', '69+10', '36-10', '74-2', '69+1', '72+1', '52+5', '68+1', '61+2', '48+2', '58+5', '67+3', '57-4', '69+2', '54-4', '70-2', '66+10', '72+4', '60-5', '70-1', '52-1', '67+1', '65+2', '42-5', '65+1', '65+5', '63+2', '71+4', '53+1', '64+1', '29+15', '71+1', '71-1', '66+2', '57+8', '62+11', '60-3', '52+4', '60+8', '61+11', '55-2', '56+3', '54+1', '63+7', '66-5', '45+24', '52+1', '49+14', '60+4', '59+5', '52+3', '49+1', '61-1', '44+1', '31+3', '37+1', '58+3', '55+8', '51-2', '44+3', '30+1', '47+1', '50+1'])
('Stamina', ['68+2', '82+1', '85+2', '73+3', '75+1', '77+2', '84+7', '75+19', '58-2', '70+2', '74+2', '66+4', '79+2', '41+20', '72-3', '72+2', '72+1', '78-2', '84-3', '56+3', '86-3', '78-1', '80+2', '60+2', '69-1', '85+1', '62-4', '70-1', '63+2', '64+7', '76+3', '68-2', '66-8', '87+3', '85+3', '62-1', '60-4', '81-4', '78+3', '74+9', '74+1', '65-4', '72-1', '76+6', '67+2', '43-2', '73-2', '67-1', '65-1', '72-10', '65+4', '29-11', '59+3', '79-3', '72-2', '69+3', '65+1', '74+4', '64-3', '79+8', '68-1', '64-2', '64+2', '69+1', '73+10', '58-10', '67-2', '66+9', '80-2', '52+7', '61-22', '66+2', '75+20', '70-3', '51-8', '67+1', '74-1', '90+7', '61+30', '54+12', '60+4', '62+1', '73+22', '44-3', '48-1', '63+4', '40+1', '70+6', '69-7', '78+38', '29-9', '62+3', '58+2', '61+6', '53+2', '60-2'])
('Marking', ['84-1', '65+1', '77-2', '78-2', '73+2', '68+2', '78+1', '64+22', '63+1', '62+4', '25+4', '70-1', '74+1', '70+1', '42+21', '73-1', '72+1', '57+1', '79+4', '72+3', '41+7', '75-3', '70+2', '24-7', '67-4', '57+2', '68-1', '59+2', '71-2', '69-2', '74-4', '70-2', '65+2', '62-1', '69+1', '66+2', '59-3', '65+4', '66-2', '61+2', '68-3', '20+3', '70+4', '24+4', '64-3', '66+1', '34-3', '67-6', '55+16', '65-1', '72-1', '43+3', '60-2', '66+9', '60+1', '63+2', '62+6', '64+1', '68+4', '61+1', '56-5', '52+3', '63+4', '64+6', '31-1', '50-4', '62-3', '54+4', '60+2', '60-1', '57+5', '63-1', '64-4', '59+4', '58-2', '57-6', '61+3', '62-4', '22-2', '14-19', '55-1', '54+3', '59-2', '57+3', '40+6', '56-1', '23-1', '25-1', '56+1', '54+6', '15+10', '46+2', '50+1', '45+1', '47+3'])
('Finishing', ['52+2', '81+1', '79+1', '60+2', '79+3', '70+1', '69+4', '74+1', '72-1', '78+1', '69+1', '74+2', '64-3', '45+1', '73+1', '71+1', '80-3', '74+3', '75+1', '65-1', '71-1', '73-1', '77+1', '77-1', '67+3', '70-1', '73+2', '58-2', '58-1', '65+2', '57-3', '71-2', '47+10', '59+2', '65+3', '69+2', '68+2', '62+6', '52+1', '53+4', '69+5', '65-2', '70+2', '44+4', '66+3', '63-1', '58+3', '59+1', '66+1', '50+2', '36-12', '64-2', '68+4', '45+2', '52-3', '58+7', '53-2', '63-3', '33+3', '65+1', '58-4', '58+6', '55+3', '42+3', '64+2', '62-1', '61+1', '61+2', '40-1', '67+2', '38-1', '69-2', '61+3', '62+2', '57-5', '64+5', '51-1', '37+4', '68-3', '63-2', '32-8', '35+3', '49+2', '38+10', '46+1', '26-1', '53-4', '69+3', '54-1', '57-2', '49+1', '48+3', '13+7', '29+13', '56-1'])
('Sprint speed', ['73+7', '83+1', '53-1', '69+1', '84+1', '69+2', '89+1', '58+3', '80-3', '77-4', '73+1', '90+1', '67+3', '89+3', '70-9', '74+1', '38-3', '70+1', '49+8', '82-3', '71+1', '76+5', '78+2', '64+1', '69+3', '75-3', '85-4', '77+2', '63+4', '68+2', '88-2', '64+3', '55-9', '68-1', '77+4', '71-2', '85+1', '68+1', '72-2', '66+4', '80+2', '80-2', '65-3', '67-2', '80+3', '69+6', '79-2', '78+5', '47-2', '83-2', '57+1', '52-14', '73+6', '37-10', '86-1', '58+4', '54+11', '84+5', '77+3', '66+5', '76+1', '55-10', '69-1', '83+11', '68-4', '77+7', '68+6', '71+5', '70-2', '80-5', '68+7', '85+5', '74+4', '73+2', '71-5', '66-10', '90-2', '60-2', '70-10', '67+12', '55+17', '32-1', '87-2', '72-1', '66+6', '64+2', '81+2', '70+5', '66+8', '46-3', '75+3', '70+15', '71+3', '65+3', '71+39', '70+3', '74+2', '61+1', '68+4', '95+2', '65+2', '75+8', '58-2', '73-3', '72+12', '79+17', '60+1', '63-11'])
('Reactions', ['79+2', '78+2', '73+1', '74-2', '76-1', '74+6', '74+4', '77+2', '76+3', '71-2', '72+1', '79+4', '77+1', '75-2', '71+1', '73+3', '71-1', '69-1', '70-1', '72+4', '73+7', '63-2', '66-1', '76+1', '68+1', '66-3', '67+1', '59+4', '61+2', '68+3', '62-3', '75+1', '66+2', '64+3', '76+2', '56+4', '62-2', '69-2', '61-2', '60-2', '65+1', '69+6', '62+1', '55-1', '66+13', '62+2', '58-2', '61+1', '63+4', '53-1', '65-1', '59-3', '64+5', '59+2', '65-2', '67+2', '58-1', '60-5', '62+6', '68+2', '57-2', '63-9', '62+9', '63-1', '59+1', '52-1', '57-1', '59-1', '59+10', '55+2', '56+1', '61-1', '56-1', '54+6', '56-2', '56+3', '57+3', '51+1', '52+7', '49+1', '51+2', '57+5', '55-3', '43-7', '54-2', '48+1', '45+1', '49+5', '52+1', '39-6', '54+3'])
('Long passing', ['80+1', '70+3', '72+2', '81+1', '70+1', '76+1', '82-1', '65+10', '55+24', '77+1', '73-1', '74+2', '61+1', '75-2', '76-1', '74+6', '62+13', '62+4', '72+4', '55-2', '66+6', '71+3', '52+10', '60-2', '62+2', '56-10', '70-2', '53-3', '65+6', '66+2', '70-1', '66+1', '53+3', '61+2', '59-1', '56-2', '74+4', '65+4', '67+5', '71+1', '74-5', '65-2', '61-1', '67+1', '65+1', '44+5', '54+5', '65+3', '59+8', '47+3', '67-1', '53-2', '57+6', '64-3', '75+1', '57+1', '52+3', '60-1', '69+2', '47+4', '43+3', '67+4', '62+1', '65+2', '55-3', '58+4', '57+2', '65+8', '57+21', '54+10', '44+4', '47-4', '48+8', '64+2', '57-1', '56+2', '46-6', '63-3', '45-10', '55+11', '51-3', '51+2', '38+3', '60-3', '40+12', '48+2', '61+4', '50+6', '38+1', '26+16', '26+4', '58+2', '34-1', '62+20', '49-5', '49-1', '38+6', '36+1', '48+10', '59+6', '34+10', '30+6', '25+4'])
('Standing tackle', ['82-1', '72-5', '83-1', '34+8', '76-2', '80-2', '69+3', '80+2', '64+35', '76+2', '46+10', '72+1', '71+2', '39+5', '77+3', '75-2', '75+1', '73+2', '35+6', '71-1', '74+1', '66-3', '72-1', '76+3', '41+10', '70+3', '73+1', '34-5', '68-1', '67-1', '59+2', '69-2', '40+10', '70-1', '65+3', '71-2', '72-4', '67+2', '66-2', '71+1', '60-3', '66+3', '44+10', '64-1', '72-2', '63+4', '73-2', '60+5', '17+2', '27+4', '65-2', '37-3', '67+1', '67-2', '58+10', '69-3', '70+1', '49+4', '69-1', '64+1', '69+4', '67+4', '66+4', '63+1', '51-9', '65+1', '68+1', '68+2', '62+1', '60+2', '62+2', '48+4', '45+2', '68+15', '65-3', '63-2', '63+2', '57-4', '63-1', '13-2', '61-1', '60-2', '37+10', '20-1', '30-2', '64+2', '59+7', '60+1', '55+2', '33+2', '73+5', '26-1', '52+4', '57+1', '54+7', '60+4', '54+4', '58+2', '47+1'])
('Dribbling', ['78+3', '87+1', '84+1', '68+3', '77+1', '80+1', '76+1', '76-2', '74+2', '71-3', '73+2', '71+2', '77+3', '74+4', '76-1', '75-2', '75+1', '72+4', '73-2', '69-5', '72-1', '72+3', '72+2', '74+1', '70+1', '74-1', '78-1', '67-1', '72+1', '38+2', '66-2', '59-2', '64-1', '71+4', '67+2', '56+1', '73+1', '60-1', '66-1', '68+1', '79+1', '66+2', '67+1', '48+15', '69-1', '71+1', '58+4', '42+2', '72-2', '64+3', '63-2', '65+1', '68+2', '36+2', '66+1', '64+1', '66+4', '65-2', '59+10', '63-1', '60-2', '54+8', '62-2', '66+3', '65-1', '57-1', '54-4', '54+10', '62-1', '65+2', '55-2', '59+5', '60+5', '34+3', '58-10', '64+4', '54+1', '49+1', '64-2', '47-2', '65+3', '63+2', '35+7', '49+3', '52+4', '37-1', '58-1', '59-1', '37+7', '31+2', '47+4', '62+5', '50+6', '53+2', '11+6', '36+4', '55-1', '59+1', '41+1', '53+1', '43-1'])
('Ball control', ['83+2', '77+1', '79+1', '85+1', '80+1', '70+1', '76+1', '82+6', '68+6', '74+1', '66+1', '74+5', '63-3', '75-2', '71-2', '74+3', '72-2', '74+2', '59-3', '70-1', '71-1', '68+1', '75+1', '38+3', '67+2', '48+3', '56-2', '70-3', '72+3', '62+2', '61-2', '67+1', '64+1', '72-1', '72-3', '56+2', '55-2', '72+2', '66-1', '63-1', '71+2', '63+1', '58-2', '65-1', '65+5', '59-2', '62-1', '66+3', '67-1', '68+4', '55-4', '71+1', '63+3', '61-3', '63-2', '65+2', '60-1', '61+2', '66+2', '68-1', '67+3', '72+1', '60+2', '69+1', '65+1', '67+6', '61-1', '56+14', '48+5', '50+4', '60-4', '61+4', '60-2', '58+1', '61+1', '57+1', '43+6', '51+2', '64+3', '55+2', '50+2', '58+4', '39-1', '60+5', '64+4', '64-1', '43+7', '48+1', '56+5', '47+6', '30+19', '41+11', '40+1', '48+4', '14-2', '38+1'])
('Short passing', ['79+2', '73+3', '84+1', '82+1', '78+2', '80+1', '81+3', '67-5', '76+3', '67+1', '78+1', '75+1', '72-2', '77-2', '77-1', '68-2', '65+2', '59-6', '74-2', '71+1', '66+1', '69-3', '79-1', '68+1', '67+6', '58+1', '64-2', '70+1', '74+3', '74+2', '55-10', '70-2', '67+7', '67-1', '72+2', '56-1', '64+4', '73+1', '72+3', '69+4', '65+5', '64-1', '73+2', '63-1', '69+1', '66+4', '67+2', '61+4', '62+1', '70-3', '73+4', '67-2', '66+2', '68-1', '64+3', '65+6', '57+1', '60-3', '67+3', '63+2', '54+5', '75+4', '66-1', '65-1', '59-2', '54-1', '65+1', '59+4', '69-1', '61+6', '62-2', '70+3', '62-5', '64+1', '63+4', '74+5', '67+13', '69+3', '62+4', '62+2', '63-2', '63+3', '60+13', '68+15', '60+6', '66-3', '65+9', '60+4', '68+2', '54+1', '61+3', '45+4', '64+2', '58-2', '54+6', '65-3', '50-10', '59+8', '62+7', '54-2', '60+1', '49+1', '46+10', '54+2', '53+6', '49+4', '18+11', '42+1', '37-1', '58+12', '61+2', '52-6', '62-1', '45+5', '60+3', '58+8', '58+4', '40+10', '44+3', '38+8', '46+1', '33+3', '38+1', '56+1', '31+3', '42-2'])
('Club', ['Real Madrid CF', 'FC Barcelona', 'Paris Saint-Germain', 'FC Bayern Munich', 'Manchester United', 'Chelsea', 'Juventus', 'Manchester City', 'Arsenal', 'Atlético Madrid', 'Borussia Dortmund', 'Milan', 'Tottenham Hotspur', 'Napoli', 'Inter', 'Liverpool', 'Roma', 'Beşiktaş JK', 'AS Monaco', 'Bayer 04 Leverkusen', 'AS Saint-Étienne', 'Athletic Club de Bilbao', '1. FC Köln', 'Villarreal CF', 'FC Schalke 04', 'Olympique de Marseille', 'Atalanta', 'RB Leipzig', 'Real Sociedad', 'Torino', 'Sporting CP', 'Leicester City', 'Southampton', 'FC Porto', 'UD Las Palmas', 'Olympique Lyonnais', 'Lazio', 'Genoa', 'Everton', 'RC Celta de Vigo', 'Valencia CF', nan, 'Sevilla FC', 'Toronto FC', 'Borussia Mönchengladbach', 'SL Benfica', 'RCD Espanyol', 'OGC Nice', 'Spartak Moscow', 'Swansea City', 'Sassuolo', 'TSG 1899 Hoffenheim', 'Stoke City', 'Shakhtar Donetsk', 'West Ham United', 'SV Werder Bremen', 'Watford', 'Galatasaray SK', 'Lokomotiv Moscow', 'Zenit St. Petersburg', 'Bournemouth', 'Sampdoria', 'Antalyaspor', 'Girondins de Bordeaux', 'VfL Wolfsburg', 'New York City Football Club', 'Hertha BSC Berlin', 'SD Eibar', 'Ajax', 'RC Deportivo de La Coruña', 'Crystal Palace', 'West Bromwich Albion', 'CSKA Moscow', 'Eintracht Frankfurt', 'Real Betis Balompié', 'Fenerbahçe SK', 'Fiorentina', 'Burnley', 'Tigres U.A.N.L.', 'San Lorenzo de Almagro', 'Chicago Fire Soccer Club', 'Feyenoord', 'FC Krasnodar', 'Angers SCO', 'U.N.A.M.', 'Montreal Impact', 'Chievo Verona', 'LA Galaxy', 'Vissel Kobe', 'Bologna', 'LOSC Lille', 'Orlando City Soccer Club', 'Atlanta United FC', 'Independiente', 'Club Atlético Lanús', 'RSC Anderlecht', 'İstanbul Başakşehir FK', 'Hannover 96', 'Newcastle United', 'Málaga CF', 'Trabzonspor', 'PSV', 'FC Augsburg', 'Club Tijuana', 'VfB Stuttgart', 'Hamburger SV', 'CD Leganés', 'Getafe CF', 'Deportivo Alavés', 'Portland Timbers', 'Kayserispor', 'Udinese', 'Standard de Liège', 'Alanyaspor', '1. FSV Mainz 05', 'Sparta Praha', 'FC Nantes', 'Al Ahli', 'SC Braga', 'Brighton & Hove Albion', 'Levante UD', 'Boca Juniors', 'Columbus Crew SC', 'Querétaro', 'Dijon FCO', 'Olympiakos CFP', 'KRC Genk', 'FC Basel', 'Club América', 'Montpellier Hérault SC', 'Monterrey', 'Seattle Sounders FC', 'River Plate', 'CPD Junior Barranquilla', 'Racing Club de Avellaneda', 'En Avant de Guingamp', 'Rangers', 'Colorado Rapids', 'Necaxa', 'Aston Villa', 'KV Oostende', 'Akhisar Belediyespor', 'Rubin Kazan', 'Rosario Central', 'Ferrara (SPAL)', 'Wolverhampton Wanderers', 'Stade Rennais FC', 'Pachuca', 'Granada CF', 'Colo-Colo', 'KAA Gent', 'Middlesbrough', 'Toulouse FC', 'BSC Young Boys', 'FC St. Gallen', 'Santos Futebol Clube', 'Cagliari', 'Rio Ave FC', 'Norwich City', 'Grêmio Foot-Ball Porto Alegrense', 'Estudiantes de La Plata', 'Cruzeiro', 'Universidad de Chile', 'Atletico Nacional Medellin', 'FC Ufa', 'Girona CF', 'SM Caen', 'Independiente Medellín', 'Djurgårdens IF', 'Vitória Guimarães', 'São Paulo Futebol Clube', 'Royal Antwerp FC', 'Kaizer Chiefs', 'Fulham', 'Sunderland', 'Huddersfield Town', 'PAOK Thessaloniki', 'Fluminense Football Club', 'Leeds United', 'Derby County', 'AC Ajaccio', 'Club León', 'Sociedade Esportiva Palmeiras', 'Hellas Verona', 'Rayo Vallecano', 'Club Brugge KV', 'Real Sporting de Gijón', 'Celtic', 'Hull City', 'Universidad Católica', "CD O'Higgins", 'Club Atlas', 'SC Freiburg', 'Melbourne City', 'Grupo Desportivo de Chaves', 'Al Hilal', 'CA Osasuna', 'Perth Glory', 'Panathinaikos FC', 'FC Utrecht', 'Club Atlético Huracán', 'Birmingham City', 'Botafogo de Futebol e Regatas', 'Deportivo Toluca', 'Amiens SC Football', 'Clube Atlético Paranaense', 'Göztepe', 'FC Groningen', 'FC Ingolstadt 04', 'New England Revolution', 'FC Red Bull Salzburg', 'Clube Atlético Mineiro', 'Crotone', 'Banfield', 'Grasshopper Club Zürich', 'Vitesse', 'AZ Alkmaar', 'SV Darmstadt 98', 'Avaí Futebol Clube', 'Coritiba Foot Ball Club', 'Legia Warszawa', 'RC Strasbourg', 'Brescia', 'Vancouver Whitecaps FC', 'Western Sydney Wanderers', 'Guadalajara', 'Cerezo Osaka', 'CS Marítimo', 'Osmanlıspor', 'Santos Laguna', 'Dinamo Moscow', 'Monarcas Morelia', 'Frosinone', 'Cruz Azul', 'Real Zaragoza', 'Deportes Iquique', 'Sydney FC', 'Brisbane Roar', 'AEK Athens', 'Colon de Santa Fe', 'Atiker Konyaspor', 'Unión Española', 'Reading', 'Asociacion Deportivo Cali', 'Palermo', 'FC Metz', 'Sporting Kansas City', 'FC Seoul', 'Bursaspor', 'Benevento Calcio', 'FC Rostov', 'Associação Atlética Ponte Preta', 'Al Nassr', 'San Jose Earthquakes', 'Nîmes Olympique', 'Al Ittihad', 'Godoy Cruz', 'Gimnàstic de Tarragona', 'Philadelphia Union', 'Sport Club do Recife', 'CD Universidad de Concepción', 'FC Paços de Ferreira', 'New York Red Bulls', 'Club Atlético Tigre', 'FC Lorient', 'Belgrano de Córdoba', 'Sheffield Wednesday', 'CD Tenerife', 'SC Heerenveen', 'Independiente Santa Fe', 'Puebla', 'Vitória Setúbal', 'FC København', 'Kalmar FF', 'CD Aves', "Newell's Old Boys", 'Terek Grozny', '1. FC Union Berlin', 'Real Valladolid', 'ADO Den Haag', 'Santiago Wanderers', 'Bari', 'Ulsan Hyundai Horang-i', '1. FC Heidenheim', 'Argentinos Juniors', 'Brentford', 'Gençlerbirliği SK', 'FC St. Pauli', 'Melbourne Victory', 'VfL Bochum', 'Córdoba CF', 'Atlético Clube Goianiense', 'Kardemir Karabükspor', 'AJ Auxerre', 'Real Oviedo', 'Vitória ', 'Arsenal Tula', 'RC Lens', 'CF Os Belenenses', 'SK Rapid Wien', 'Everton de Viña del Mar', 'CD Los Millionarios Bogota', 'GwangJu FC', 'Sangju Sangmu FC', 'Jeonbuk Hyundai Motors', 'FC Dallas', 'ES Troyes AC', 'F.B.C. Unione Venezia', 'Al Qadisiyah', 'Fortuna Düsseldorf', 'Boavista FC', 'D.C. United', 'Medicana Sivasspor', 'CD Once Caldas Manizales', 'FC Ural', 'Kashiwa Reysol', 'Malmö FF', 'Cádiz C.F.', 'Portimonense SC', 'Urawa Red Diamonds', 'UD Almería', 'AIK Solna', 'CD Huachipato', 'Estoril Praia', '1. FC Nürnberg', 'Pescara', 'Stade de Reims', 'Moreirense FC', 'Empoli', 'Orlando Pirates', 'FC Anzhi Makhachkala', 'Heracles Almelo', 'Eintracht Braunschweig', 'Cremonese', 'CD Antofagasta', 'AS Nancy Lorraine', 'Lobos de la BUAP', 'Suwon Samsung Bluewings', 'Defensa y Justicia', 'Ipswich Town', 'Rosenborg BK', 'KAS Eupen', 'FC Sion', 'Adelaide United', 'Real Salt Lake', 'FC Midtjylland', 'Minnesota Thunder', 'AD Alcorcón', 'Al Faisaly', 'FC Tosno', 'FC Twente', 'F.C. Tokyo', 'IF Elfsborg', 'Carpi', 'Parma', 'IFK Norrköping', 'Unión de Santa Fe', 'Corporación Club Deportivo Tuluá', 'Kawasaki Frontale', 'Associação Chapecoense de Futebol', '1. FC Kaiserslautern', 'Preston North End', 'Molde FK', 'Bristol City', 'Brøndby IF', 'Albacete Balompié', 'GFC Ajaccio', 'KV Kortrijk', 'Kashima Antlers', 'CD Feirense', 'Oxford United', 'Talleres de Cordoba', 'FC Lugano', 'SV Zulte-Waregem', 'Deportes Tolima', 'Amkar Perm', 'Kasimpaşa SK', 'Gimnasia y Esgrima La Plata', 'Queens Park Rangers', 'Shimizu S-Pulse', 'Aalborg BK', 'Águilas Doradas', 'FC Lausanne-Sports', 'Tiburones Rojos de Veracruz', 'Perugia', 'La Spezia', 'Pohang Steelers', 'FC Barcelona B', 'Evkur Yeni Malatyaspor', 'Willem II', 'Novara', 'Sporting Charleroi', 'FK Austria Wien', 'Gamba Osaka', 'DSC Arminia Bielefeld', 'Nottingham Forest', 'Salernitana', 'Vercelli', 'Śląsk Wrocław', 'Sagan Tosu', 'CD Numancia', 'Strømsgodset IF', 'Yokohama F. Marinos', 'Cardiff City', 'FC Sochaux-Montbéliard', 'Jeju United FC', 'Lechia Gdańsk', 'Piast Gliwice', 'SD Huesca', 'FC Zürich', 'Heart of Midlothian', 'Atlético Tucumán', 'BK Häcken', 'Audax Italiano', 'Club de Deportes Temuco', 'San Luis de Quillota', 'Al Taawoun', 'Lech Poznań', 'FC Luzern', 'Cesena', 'Chacarita Juniors', 'Aberdeen', 'Excelsior', 'SpVgg Greuther Fürth', 'San Martín de San Juan', 'Wisła Kraków', 'Hammarby IF', 'Virtus Entella', 'Vegalta Sendai', 'Alianza Petrolera', 'CD Palestino', 'KV Mechelen', 'Central Coast Mariners', 'La Equidad', 'Avellino', 'Omiya Ardija', 'Cracovia', 'Curicó Unido', 'CF Reus Deportiu', 'SK Brann', 'C.D. Leonesa S.A.D.', 'US Quevilly-Rouen', 'Sporting Lokeren', 'Houston Dynamo', 'Termalica Bruk-Bet Nieciecza', 'Tondela', 'SV Sandhausen', 'Al Fayha', 'Jeonnam Dragons', 'Sandefjord Fotball', 'Pogoń Szczecin', 'Östersunds FK', 'Burton Albion', 'Al Raed', 'Jagiellonia Białystok', 'Bahía Blanca', 'Ventforet Kofu', 'Stade Brestois 29', 'CD Lugo', 'Korona Kielce', 'PEC Zwolle', 'MSV Duisburg', 'AFC Eskilstuna', 'Waasland-Beveren', 'Club Atlético Patronato', 'La Berrichonne de Châteauroux', 'SK Sturm Graz', 'Royal Excel Mouscron', 'Júbilo Iwata', 'SønderjyskE', 'Al Shabab', 'US Orléans Loiret Football', 'Bourg en Bresse Péronnas 01', 'FC Nordsjælland', 'FC Thun', 'CD America de Cali', 'VVV-Venlo', 'Charlton Athletic', 'Sevilla Atlético', 'Ternana', 'Gangwon FC', 'IFK Göteborg', 'Odense Boldklub', 'Arsenal de Sarandí', 'SG Dynamo Dresden', 'Bolton Wanderers', 'Plymouth Argyle', 'Southend United', 'Vélez Sarsfield', 'Daegu FC', 'Sanfrecce Hiroshima', 'Odds BK', 'Aarhus GF', 'Blackburn Rovers', 'Lyngby BK', 'FC Erzgebirge Aue', 'Sint-Truidense VV', 'Karlsruher SC', 'Jaguares Fútbol Club', 'Sarpsborg 08 FF', 'Temperley', ' SSV Jahn Regensburg', 'Patriotas Boyacá FC', 'Barnsley', 'Sandecja Nowy Sącz', 'Górnik Zabrze', 'VfL Osnabrück', 'Valenciennes FC', 'Tours FC', 'Lorca Deportiva CF', 'Albirex Niigata', 'Cittadella', 'Aalesunds FK', 'Deportivo Pasto', 'Roda JC Kerkrade', 'Motherwell', 'Holstein Kiel', 'Scunthorpe United', 'Atlético Huila', 'Wisła Płock', 'Atlético Bucaramanga', 'Sparta Rotterdam', 'Stabæk Fotball', 'Würzburger FV', 'VfR Aalen', 'Clermont Foot 63', 'Randers FC', 'Vålerenga Fotball', 'Incheon United FC', 'Hibernian', 'SCR Altach', 'Sheffield United', 'Le Havre AC', 'Walsall', 'Chamois Niortais FC', 'Sportfreunde Lotte', 'Millwall', 'SpVgg Unterhaching', 'FC SKA-Energiya Khabarovsk', 'Hansa Rostock', 'Wellington Phoenix', 'Hobro IK', 'Wigan Athletic', 'Peterborough United', 'Chemnitzer FC', 'Grimsby Town', 'Ascoli', 'Al Fateh', 'Foggia', 'Bristol Rovers', '1. FC Magdeburg', 'SG Sonnenhof Großaspach', 'Bury', 'Lillestrøm SK', 'Arka Gdynia', 'Ross County FC', 'Paris FC', 'Bradford City', 'Hamilton Academical FC', 'Luton Town', 'NAC Breda', 'Portsmouth', 'Zagłębie Lubin', 'Kilmarnock', 'Hokkaido Consadole Sapporo', 'Örebro SK', 'SV Wehen Wiesbaden', 'Coventry City', 'Milton Keynes Dons', 'Rochdale', 'FK Haugesund', 'Dundee FC', 'Oldham Athletic', 'Ohod Club', 'Rotherham United', 'HJK Helsinki', 'LASK Linz', 'AC Horsens', 'SC Fortuna Köln', 'St. Johnstone FC', 'Sogndal', 'Envigado FC', 'Blackpool', 'SC Preußen Münster', 'Hallescher FC', 'Ettifaq FC', 'Crawley Town', 'Exeter City', 'AFC Wimbledon', 'Doncaster Rovers', 'IK Sirius', 'Jönköpings Södra IF', 'Rot-Weiß Erfurt', 'Notts County', 'SV Mattersburg', 'Viking FK', 'Cambridge United', 'Tigres FC', 'Mansfield Town', 'SV Meppen', 'Tromsø IL', 'Swindon Town', 'Werder Bremen II', 'FC Admira Wacker Mödling', 'SC Paderborn 07', 'Partick Thistle F.C.', 'Silkeborg IF', 'Al Batin', 'Colchester United', 'FSV Zwickau', 'Crewe Alexandra', 'Northampton Town', 'Kristiansund BK', 'Lincoln City', 'Dundalk', 'Wycombe Wanderers', 'Yeovil Town', 'GIF Sundsvall', 'FC Carl Zeiss Jena', "St. Patrick's Athletic", 'Fleetwood Town', 'Wolfsberger AC', 'Cork City', 'Gillingham', 'SKN St. Pölten', 'Carlisle United', 'Chesterfield', 'Newcastle Jets', 'Morecambe', 'Port Vale', 'Newport County', 'Shrewsbury', 'Accrington Stanley', 'Forest Green', 'Bohemian FC', 'Cheltenham Town', 'Barnet', 'Halmstads BK', 'Shamrock Rovers', 'Derry City', 'Stevenage', 'Finn Harps', 'FC Helsingør', 'Sligo Rovers', 'Bray Wanderers', 'Limerick FC', 'Galway United', 'Drogheda United'])
Teknisk Tirsdag - Data Cleaning.ipynb:4: 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
"cell_type": "markdown",
Teknisk Tirsdag - Data Cleaning.ipynb: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
{
Teknisk Tirsdag - Data Cleaning.ipynb:2: 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
"cells": [
Variablenavn: Name Variabletype: object
Variablenavn: Age Variabletype: int64
Variablenavn: Nationality Variabletype: object
Variablenavn: Overall Variabletype: int64
Variablenavn: Potential Variabletype: int64
Variablenavn: Club Variabletype: object
Variablenavn: Value Variabletype: float64
Variablenavn: Wage Variabletype: float64
Variablenavn: Special Variabletype: int64
Variablenavn: Acceleration Variabletype: float64
Variablenavn: Aggression Variabletype: float64
Variablenavn: Agility Variabletype: float64
Variablenavn: Balance Variabletype: float64
Variablenavn: Ball control Variabletype: float64
Variablenavn: Composure Variabletype: float64
Variablenavn: Crossing Variabletype: float64
Variablenavn: Curve Variabletype: float64
Variablenavn: Dribbling Variabletype: float64
Variablenavn: Finishing Variabletype: float64
Variablenavn: Free kick accuracy Variabletype: float64
Variablenavn: GK diving Variabletype: float64
Variablenavn: GK handling Variabletype: float64
Variablenavn: GK kicking Variabletype: float64
Variablenavn: GK positioning Variabletype: float64
Variablenavn: GK reflexes Variabletype: float64
Variablenavn: Heading accuracy Variabletype: float64
Variablenavn: Interceptions Variabletype: float64
Variablenavn: Jumping Variabletype: float64
Variablenavn: Long passing Variabletype: float64
Variablenavn: Long shots Variabletype: float64
Variablenavn: Marking Variabletype: float64
Variablenavn: Penalties Variabletype: float64
Variablenavn: Positioning Variabletype: float64
Variablenavn: Reactions Variabletype: float64
Variablenavn: Short passing Variabletype: float64
Variablenavn: Shot power Variabletype: float64
Variablenavn: Sliding tackle Variabletype: float64
Variablenavn: Sprint speed Variabletype: float64
Variablenavn: Stamina Variabletype: float64
Variablenavn: Standing tackle Variabletype: float64
Variablenavn: Strength Variabletype: float64
Variablenavn: Vision Variabletype: float64
Variablenavn: Volleys Variabletype: float64
Variablenavn: CAM Variabletype: float64
Variablenavn: CB Variabletype: float64
Variablenavn: CDM Variabletype: float64
Variablenavn: CF Variabletype: float64
Variablenavn: CM Variabletype: float64
Variablenavn: ID Variabletype: int64
Variablenavn: LAM Variabletype: float64
Variablenavn: LB Variabletype: float64
Variablenavn: LCB Variabletype: float64
Variablenavn: LCM Variabletype: float64
Variablenavn: LDM Variabletype: float64
Variablenavn: LF Variabletype: float64
Variablenavn: LM Variabletype: float64
Variablenavn: LS Variabletype: float64
Variablenavn: LW Variabletype: float64
Variablenavn: LWB Variabletype: float64
Variablenavn: Preferred Positions Variabletype: object
Variablenavn: RAM Variabletype: float64
Variablenavn: RB Variabletype: float64
Variablenavn: RCB Variabletype: float64
Variablenavn: RCM Variabletype: float64
Variablenavn: RDM Variabletype: float64
Variablenavn: RF Variabletype: float64
Variablenavn: RM Variabletype: float64
Variablenavn: RS Variabletype: float64
Variablenavn: RW Variabletype: float64
Variablenavn: RWB Variabletype: float64
Variablenavn: ST Variabletype: float64
Træningsæt størrelse: 1272
Efter at have hentet vores rensede data, hvor vi minder os selv om at vi har:
Det første, vi gerne vil kigge lidt på, er, om vi var grundige nok i vores foranalyse. Derfor laver vi et heatmap, der skal fortælle os hvor stor sammenhængen er (korrelation) mellem kolonnerne i forhold til hinanden.
In [2]:
corr = overall_set.corr()
fig = plt.figure(figsize=(20, 16))
ax = sb.heatmap(corr, xticklabels=corr.columns.values,
yticklabels=corr.columns.values,
linewidths=0.25, vmax=1.0, square=True,
linecolor='black', annot=False
)
plt.show()
Hvad vi ser her, er en korrelationsmatrix. Jo mørkere farver, des højere korrelation, rød for positiv- og blå for negativ-korrelation.
Vi ser altså at der er høj korrelation, i vores nedre højre hjørne; Dette er spilpositionerne. Vi ser også et stort blåt kryds, som er målmandsdata. Disse har meget negativ korrelation med resten af vores datasæt. (Dobbeltklik evt. på plottet, hvis det er meget svært at læse teksten)
Derudover kan vi se, at ID kolonnen slet ikke korrelere. Man kan derfor vælge at tage den ud.
Vi tilføjer nu vores "kendte" labels til vores data. (Hvis man spiller for en af vores topklubber, får man et 1-tal, og ellers får man et 0)
Vi deler også vores træningssæt op i en X matrix med alle vores numeriske features, og en y vektor med alle vores labels.
In [3]:
overall_set['label'] = overall_set['Club'].isin(topklub_set.Club).astype(int)
y = overall_set['label']
X = overall_set.iloc[:,0:-1].select_dtypes(include=['float64', 'int64'])
Vi kan kigge lidt overordnet på tallene mellem de 2 klasser.
In [4]:
overall_set.groupby('label').mean()
Out[4]:
Age
Overall
Potential
Value
Wage
Special
Acceleration
Aggression
Agility
Balance
...
RB
RCB
RCM
RDM
RF
RM
RS
RW
RWB
ST
label
0
25.026730
65.619497
70.600629
1.655747e+06
9361.635220
1571.663522
63.682390
54.246855
62.617925
62.869497
...
49.207547
47.996855
50.830189
49.125786
51.566038
52.295597
51.045597
51.738994
49.856918
51.045597
1
25.166667
77.099057
81.856918
1.630118e+07
71496.855346
1817.268868
69.382075
62.413522
68.784591
66.122642
...
58.400943
56.378931
61.515723
58.900943
61.589623
62.281447
60.044025
61.707547
59.371069
60.044025
2 rows × 67 columns
Vi er nu klar til at gå i gang med vores første Machine Learning algoritme.
På forhånd ved vi, at der i vores træningssæt er {{y.where(y==1).count()}} som spiller i topklubber, og {{y.where(y==0).count()}} der ikke gør.
Der er en 50/50 chance for at ramme rigtigt, hvis man bare gætte tilfældigt. Vi håber derfor, at algoritmen kan slå den 50% svarrate.
In [5]:
# hent nødvendige pakker fra Scikit Learn biblioteket (generelt super hvis man vil lave data science)
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
Vi fitter nu en logistic regression classifier til vores data, og fitter en model, så den kan genkende om man spiller for en topklub eller ej, og evaluere resultatet:
In [6]:
model = LogisticRegression()
model = model.fit(X,y)
model.score(X,y)
Out[6]:
0.83411949685534592
Altså har vores model ret i
{{'{:.0f}'.format(100*model.score(X, y))}}% af tiden i træningssættet.
Pretty good!! Den har altså fundet nogle mønstre der kan mappe data til labels, og gætter ikke bare.
Men vi kan ikke vide, om den har overfittet, og derved har tilpasset sig for godt til sit kendte data, så nyt data vil blive fejlmappet.
Hvad vi kan prøve, er at splitte vores træningssæt op i et trænings- og testsæt. På den måde kan vi først fitte og derefter evaluere på "nyt" kendt data, om den stadig performer som forventet.
In [7]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
print('Træningsæt størrelse: {} - Testsæt størrelse: {}'.format(len(X_train), len(X_test)))
Træningsæt størrelse: 1017 - Testsæt størrelse: 255
Og vi er nu klar til at prøve igen!
In [8]:
model2 = LogisticRegression()
model2 = model2.fit(X_train, y_train)
model2.score(X_train, y_train)
Out[8]:
0.82595870206489674
Modellen matcher nu
{{'{:.0f}'.format(100*model2.score(X, y))}}% af tiden i træningssættet.
Men har den overfittet?
In [9]:
y_pred = model2.predict(X_test)
y_probs = model2.predict_proba(X_test)
In [10]:
# Evalueringsmålinger
from sklearn import metrics
print('Nøjagtigheden af vores logistiske regressions models prediction på testsættet er {:.0f}'.format(100*metrics.accuracy_score(y_test, y_pred))+'%', '\n')
print('Arealet under vores ROC AUC kurve er {:.0f}'.format(100*metrics.roc_auc_score(y_test, y_probs[:, 1]))+'%')
Nøjagtigheden af vores logistiske regressions models prediction på testsættet er 84%
Arealet under vores ROC AUC kurve er 90%
Det ser jo ret fornuftigt ud.
For at sige noget om vores nye model, kan vi også lave en "confusion_matrix"
<img src='http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb08c97955970d-pi', align="center" width="40%" alt="confusion matrix">
T og F står for henholdsvist True og False
P og N står for henholdsvist Positive og Negative
In [11]:
confusion_matrix = metrics.confusion_matrix(y_test, y_pred)
print(confusion_matrix)
[[112 9]
[ 32 102]]
Resultatet fortæller os, at vi har {{confusion_matrix[0,0]}}+{{confusion_matrix[1,1]}} = {{confusion_matrix[0,0]+confusion_matrix[1,1]}} korrekte forudsigelser og {{confusion_matrix[0,1]}}+{{confusion_matrix[1,0]}} = {{confusion_matrix[0,1]+confusion_matrix[1,0]}} ukorrekte
Man kan også bede classifieren om en rapport:
In [12]:
print(metrics.classification_report(y_test, y_pred))
precision recall f1-score support
0 0.78 0.93 0.85 121
1 0.92 0.76 0.83 134
avg / total 0.85 0.84 0.84 255
In [13]:
# 10-folds cross-validation
from sklearn.model_selection import cross_val_score
scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)
print(scores,'\n')
print(scores.mean())
[ 0.9296875 0.9453125 0.9140625 0.921875 0.9765625 0.9375
0.94444444 0.74603175 0.46825397 0.44444444]
0.822817460317
Her preformer modellen altså i gennemsnit {{'{:.0f}'.format(100*scores.mean())}}%.
Det lyder meget lovende, men vi holder os til vores model2 og kan nu prøve modellen af på det rigtige datasæt
Vi skal nu prøve vores model på vores danske spillere
Vi skal lave prediction og probability på vores danske spillere, ligesom vi gjorde tidligere for testsættet. (Lige under Evaluering af modellen)
Husk din dataframe kun må indeholder numeriske værdier, når vi bruger modellen.
Fx. "df.select_dtypes(include=['float64', 'int64'])"
In [14]:
dansker_pred = None ### Fjern NONE og UDFYLD MIG ###
dansker_probs = None ### Fjern NONE og UDFYLD MIG ###
Modellen har fundet {{np.bincount(dansker_pred)[0]}} nuller og {{np.bincount(dansker_pred)[1]}} ét-taller
Hvis du satte top_klub_ratio til 75 i Opgave 1 i Data Cleaning, skulle der være omkring 27-28 ét-taller.
top_klub_ratio blev sat til: {{top_klub_ratio}}
Vi tilføjer disse kolonner til vores dataframe.
In [18]:
dansker_set_df = dansker_set.copy()
dansker_set_df[['prob1','prob2']] = pd.DataFrame(dansker_probs, index=dansker_set.index)
dansker_set_df['Probabilities [0,1]'] = dansker_set_df[['prob1','prob2']].values.tolist()
dansker_set_df['Prediction'] = pd.Series(dansker_pred, index=dansker_set.index)
del dansker_set_df['prob1'], dansker_set_df['prob2']
# dansker_set_df.head()
Og sortere listen, så de bedste danske spillere står øvers, og tilføjer et index, så vi kan få et bedre overblik
In [16]:
dansker_set_df.loc[:,'pred=1'] = dansker_set_df['Probabilities [0,1]'].map(lambda x: x[1]).sort_values(ascending=False)
dansker_sorted = dansker_set_df.sort_values('pred=1', ascending=False)
dansker_sorted = dansker_sorted[['Name', 'Club', 'Overall', 'Potential', 'Probabilities [0,1]', 'Prediction']]
dansker_sorted.loc[:,'in'] = np.arange(1, len(dansker_set_df)+1)
dansker_sorted.set_index('in')
Out[16]:
Name
Club
Overall
Potential
Probabilities [0,1]
Prediction
in
1
C. Eriksen
Tottenham Hotspur
87
91
[1.2361997292487104e-07, 0.9999998763800271]
1
2
A. Christensen
Chelsea
81
89
[0.00595764605638005, 0.99404235394362]
1
3
K. Schmeichel
Leicester City
83
84
[0.014948898649614573, 0.9850511013503854]
1
4
S. Kjær
Sevilla FC
81
82
[0.0792499037714689, 0.9207500962285311]
1
5
D. Wass
RC Celta de Vigo
80
80
[0.08093367436833954, 0.9190663256316605]
1
6
J. Vestergaard
Borussia Mönchengladbach
79
84
[0.09443758718579498, 0.905562412814205]
1
7
N. Jørgensen
Feyenoord
79
81
[0.09662360026251093, 0.9033763997374891]
1
8
Y. Poulsen
RB Leipzig
76
83
[0.11856360569546154, 0.8814363943045385]
1
9
K. Dolberg
Ajax
78
88
[0.1340973698375778, 0.8659026301624222]
1
10
P. Højbjerg
Southampton
75
81
[0.19744945518739987, 0.8025505448126001]
1
11
M. Braithwaite
Middlesbrough
77
80
[0.20992890568457212, 0.7900710943154279]
1
12
L. Andersen
Grasshopper Club Zürich
76
83
[0.23658486733875572, 0.7634151326612443]
1
13
F. Sørensen
1. FC Köln
77
81
[0.2387127042826872, 0.7612872957173128]
1
14
L. Lerager
Girondins de Bordeaux
75
80
[0.28360075282343045, 0.7163992471765696]
1
15
V. Fischer
1. FSV Mainz 05
75
82
[0.28803632021595915, 0.7119636797840408]
1
16
M. Jørgensen
Huddersfield Town
75
78
[0.30175956933231884, 0.6982404306676812]
1
17
M. Krohn-Dehli
Sevilla FC
79
79
[0.34163848506912053, 0.6583615149308795]
1
18
P. Sisto
RC Celta de Vigo
74
83
[0.35254475144372266, 0.6474552485562773]
1
19
L. Schøne
Ajax
77
77
[0.3653813781431249, 0.6346186218568751]
1
20
J. Poulsen
FC Midtjylland
71
71
[0.41554897703083404, 0.584451022969166]
1
21
R. Falk Jensen
FC København
74
77
[0.41712565782872424, 0.5828743421712758]
1
22
J. Lössl
Huddersfield Town
75
76
[0.4321175862344081, 0.5678824137655919]
1
23
E. Sviatchenko
Celtic
73
75
[0.4361724537479308, 0.5638275462520692]
1
24
A. Bjelland
Brentford
73
73
[0.4629540156936526, 0.5370459843063474]
1
25
A. Cornelius
Atalanta
73
78
[0.4666099871886722, 0.5333900128113278]
1
26
P. Mtiliga
FC Nordsjælland
69
69
[0.4794912216897943, 0.5205087783102057]
1
27
M. Rasmussen
Aarhus GF
69
69
[0.48675749765285403, 0.513242502347146]
1
28
M. Jensen
Rosenborg BK
74
74
[0.5020672719966166, 0.49793272800338345]
0
29
A. Scholz
Standard de Liège
74
79
[0.5049879774285326, 0.4950120225714674]
0
30
A. Christiansen
Malmö FF
74
75
[0.5096762833242563, 0.4903237166757437]
0
...
...
...
...
...
...
...
317
M. Roerslev
FC København
52
68
[0.8631321739682192, 0.1368678260317808]
0
318
K. Tshiembe
Lyngby BK
52
68
[0.8634039506795222, 0.13659604932047775]
0
319
O. Albayrak
Hobro IK
56
64
[0.8636862223098634, 0.13631377769013664]
0
320
O. Snorre
Lyngby BK
52
73
[0.8637484895953144, 0.13625151040468567]
0
321
T. Damsgaard
Randers FC
51
66
[0.8637660405380061, 0.13623395946199385]
0
322
J. Eskesen
Odense Boldklub
52
68
[0.8638436078001344, 0.1361563921998657]
0
323
N. Lyngø
Aalborg BK
54
65
[0.8638480408791316, 0.13615195912086842]
0
324
O. Ottesen
FC Midtjylland
57
67
[0.863986086521014, 0.1360139134789859]
0
325
D. Thøgersen
Aarhus GF
52
70
[0.863995687351485, 0.13600431264851498]
0
326
M. Warming
Brøndby IF
51
66
[0.864179921766968, 0.135820078233032]
0
327
M. Mattsson
Silkeborg IF
54
76
[0.8642832619995633, 0.13571673800043674]
0
328
E. Simonsen
Lyngby BK
53
77
[0.8643983530602066, 0.13560164693979332]
0
329
G. Marcussen
Lyngby BK
50
69
[0.8644066756136866, 0.13559332438631339]
0
330
M. Andersen
Aalborg BK
52
66
[0.8644086683355966, 0.13559133166440343]
0
331
F. Roepstorff
FC Helsingør
51
70
[0.8644360366151669, 0.1355639633848331]
0
332
M. Haarup
Hobro IK
54
66
[0.8644842744974983, 0.13551572550250168]
0
333
A. Vaporakis
FC Helsingør
53
63
[0.8644874549734057, 0.13551254502659427]
0
334
M. Hegaard
FC Helsingør
51
69
[0.864535621341468, 0.13546437865853198]
0
335
A. Kappenberger
FC Helsingør
52
66
[0.8645552105643415, 0.13544478943565855]
0
336
M. Johannsen
FC Helsingør
54
58
[0.8645567173499312, 0.13544328265006875]
0
337
G. Arndal-Lauritzen
Brøndby IF
50
67
[0.864584838948699, 0.135415161051301]
0
338
A. Skov Olsen
FC Nordsjælland
53
66
[0.8646176815512268, 0.13538231844877324]
0
339
T. Arndal
AC Horsens
52
65
[0.864647326784709, 0.135352673215291]
0
340
C. Enemark
Brøndby IF
50
69
[0.8646551987933163, 0.13534480120668368]
0
341
A. Ammitzbøl
Aarhus GF
55
64
[0.8649192100937313, 0.13508078990626865]
0
342
I. Esen
FC Helsingør
50
69
[0.8649298106428374, 0.13507018935716258]
0
343
A. Junge
FC Helsingør
50
56
[0.865287155002264, 0.13471284499773598]
0
344
M. Iversen
Aalborg BK
50
62
[0.8666456075747598, 0.13335439242524016]
0
345
O. Drost
AC Horsens
51
58
[0.8668261929640377, 0.13317380703596227]
0
346
F. Nørgaard
AC Horsens
50
62
[0.8669208380435555, 0.13307916195644454]
0
346 rows × 6 columns
Efter flot hattrick mod Irland, kan man vidst ikke være i tvivl om Kong Christian tager pladsen på tronen <img src='kongen.png', align="center" width="40%" alt="kongen">
Men hvilke danske spillere spiller egentlig for topklubber, og hvordan er de rangeret i forhold til vores model?
In [39]:
dansker_sorted[dansker_sorted['Club'].isin(top_clubs)].set_index('in')
Out[39]:
Name
Club
Overall
Potential
Probabilities [0,1]
Prediction
in
2
A. Christensen
Chelsea
81
89
[0.00469680685795848, 0.9953031931420415]
1
7
S. Kjær
Sevilla FC
81
82
[0.10268761388443448, 0.8973123861155655]
1
17
M. Krohn-Dehli
Sevilla FC
79
79
[0.32668814694969617, 0.6733118530503038]
1
198
J. Larsen
Borussia Dortmund
62
79
[0.8181799678362287, 0.18182003216377135]
0
224
R. Corlu
Roma
58
69
[0.8314270782764579, 0.16857292172354213]
0
Man kan undre sig over hvad Jacob Larsen laver hos stopklubben Borussia Dortmund, men en hurtig googling viser, at han simpelthen blev headhuntet til klubben som 16-årig.
Og så er der jo nok nogen, der vil spørger - Hvad med Bendtner? Så han skal da også lige have en plads i vores analyse:
In [40]:
dansker_sorted.loc[dansker_sorted.Name == 'N. Bendtner'].set_index('in')
Out[40]:
Name
Club
Overall
Potential
Probabilities [0,1]
Prediction
in
42
N. Bendtner
Rosenborg BK
73
73
[0.568000951323095, 0.43199904867690503]
0
In [41]:
df.loc[df.Name == 'N. Bendtner']
Out[41]:
Name
Age
Nationality
Overall
Potential
Club
Value
Wage
Special
Acceleration
...
RB
RCB
RCM
RDM
RF
RM
RS
RW
RWB
ST
2697
N. Bendtner
29
Denmark
73
73
Rosenborg BK
4000000.0
10000.0
1790
70.0
...
51.0
51.0
65.0
54.0
70.0
69.0
72.0
69.0
53.0
72.0
1 rows × 71 columns
Danske Rezan Corlu som ellers ligger ret lavt selv på potentiale har alligevel sikret sig en plads hos A.S. Roma i en alder af 20 år. Men hvordan var det egentlig med de topklub spillere? Hvor langt ned kan man gå i potentiale, og stadig spille for en topklub?
In [42]:
top_df = df[df.Club.isin(top_clubs)]
top_df[top_df.Overall < 70].sort_values('Overall', ascending=True)
Out[42]:
Name
Age
Nationality
Overall
Potential
Club
Value
Wage
Special
Acceleration
...
RB
RCB
RCM
RDM
RF
RM
RS
RW
RWB
ST
17331
A. Zerbin
18
Italy
53
73
Napoli
120000.0
4000.0
1324
42.0
...
39.0
37.0
49.0
41.0
52.0
51.0
51.0
53.0
41.0
51.0
16714
P. Fritsch
18
Germany
55
71
Borussia Dortmund
160000.0
3000.0
1349
52.0
...
49.0
54.0
44.0
50.0
41.0
42.0
42.0
42.0
48.0
42.0
16406
D. Sauerland
20
Germany
56
68
Borussia Dortmund
160000.0
5000.0
1526
55.0
...
46.0
41.0
55.0
47.0
57.0
56.0
53.0
58.0
49.0
53.0
15718
J. Maddox
18
England
58
74
Chelsea
260000.0
7000.0
1402
71.0
...
42.0
34.0
51.0
41.0
56.0
57.0
52.0
57.0
45.0
52.0
15549
R. Corlu
19
Denmark
58
69
Roma
210000.0
4000.0
1473
73.0
...
43.0
34.0
51.0
40.0
56.0
57.0
53.0
58.0
45.0
53.0
15479
T. Bola
18
England
59
72
Arsenal
250000.0
6000.0
1344
59.0
...
54.0
58.0
41.0
50.0
40.0
40.0
42.0
40.0
51.0
42.0
15409
E. Nketiah
18
England
59
80
Arsenal
350000.0
8000.0
1404
77.0
...
37.0
33.0
50.0
37.0
59.0
56.0
58.0
58.0
39.0
58.0
15389
Obama
17
Spain
59
76
Atlético Madrid
300000.0
4000.0
1408
66.0
...
37.0
29.0
48.0
34.0
58.0
56.0
58.0
58.0
40.0
58.0
15341
A. Guarnone
18
Italy
59
74
Milan
260000.0
3000.0
997
54.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15180
J. Beste
18
Germany
59
78
Borussia Dortmund
290000.0
3000.0
1510
69.0
...
58.0
53.0
52.0
54.0
50.0
55.0
47.0
54.0
59.0
47.0
15103
R. Nelson
17
England
59
83
Arsenal
350000.0
7000.0
1471
84.0
...
42.0
36.0
48.0
39.0
59.0
58.0
56.0
61.0
44.0
56.0
15063
G. McEachran
16
England
59
79
Chelsea
300000.0
4000.0
1521
64.0
...
52.0
47.0
57.0
52.0
55.0
58.0
49.0
56.0
53.0
49.0
14374
T. Chalobah
17
England
60
77
Chelsea
375000.0
6000.0
1491
71.0
...
58.0
59.0
48.0
55.0
47.0
50.0
49.0
49.0
56.0
49.0
14511
K. Scott
19
United States
60
75
Chelsea
450000.0
7000.0
1553
61.0
...
53.0
46.0
59.0
53.0
57.0
59.0
52.0
59.0
55.0
52.0
15044
D. Reimann
20
Germany
60
74
Borussia Dortmund
375000.0
4000.0
989
44.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
15034
Javi Díaz
20
Spain
60
72
Sevilla FC
290000.0
1000.0
1125
58.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
14655
Tachi
19
Spain
60
77
Atlético Madrid
400000.0
3000.0
1369
63.0
...
57.0
59.0
46.0
54.0
42.0
45.0
44.0
43.0
54.0
44.0
13783
A. Bernede
18
France
61
73
Paris Saint-Germain
425000.0
7000.0
1565
65.0
...
50.0
46.0
57.0
51.0
58.0
59.0
55.0
59.0
51.0
55.0
13738
S. Schreck
18
Germany
61
78
Bayer 04 Leverkusen
525000.0
5000.0
1457
67.0
...
46.0
40.0
56.0
47.0
57.0
59.0
51.0
58.0
48.0
51.0
13771
H. Wilson
20
Wales
61
79
Liverpool
550000.0
13000.0
1556
77.0
...
47.0
40.0
53.0
44.0
60.0
60.0
58.0
61.0
48.0
58.0
13831
N. Dorsch
19
Germany
61
78
FC Bayern Munich
500000.0
7000.0
1607
68.0
...
59.0
59.0
58.0
60.0
56.0
58.0
53.0
57.0
59.0
53.0
14172
M. Pantović
20
Serbia
61
69
FC Bayern Munich
375000.0
12000.0
1497
67.0
...
45.0
39.0
54.0
43.0
60.0
60.0
59.0
61.0
47.0
59.0
13928
Juan Moreno
20
Spain
61
70
Atlético Madrid
400000.0
7000.0
1543
62.0
...
50.0
45.0
57.0
49.0
59.0
60.0
54.0
61.0
52.0
54.0
14061
Olabe
21
Spain
61
70
Atlético Madrid
400000.0
7000.0
1500
62.0
...
50.0
46.0
58.0
51.0
60.0
60.0
57.0
60.0
52.0
57.0
14073
M. Gabbia
17
Italy
61
76
Milan
450000.0
7000.0
1386
62.0
...
55.0
60.0
41.0
51.0
41.0
42.0
43.0
42.0
52.0
43.0
13834
F. Benko
19
Germany
61
78
FC Bayern Munich
550000.0
8000.0
1643
65.0
...
54.0
53.0
60.0
56.0
59.0
59.0
56.0
59.0
55.0
56.0
12876
L. Jones
21
England
62
70
Liverpool
400000.0
11000.0
1328
46.0
...
52.0
61.0
43.0
54.0
38.0
39.0
41.0
38.0
50.0
41.0
12985
N. Schiappacasse
18
Uruguay
62
78
Atlético Madrid
625000.0
5000.0
1453
64.0
...
42.0
35.0
52.0
39.0
62.0
59.0
61.0
62.0
44.0
61.0
13274
K. Bare
19
Albania
62
75
Atlético Madrid
575000.0
5000.0
1629
69.0
...
56.0
55.0
62.0
59.0
60.0
60.0
58.0
58.0
57.0
58.0
13074
A. Pinamonti
18
Italy
62
80
Inter
625000.0
6000.0
1446
55.0
...
37.0
36.0
50.0
37.0
60.0
55.0
61.0
58.0
39.0
61.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
8967
João Costa
21
Portugal
66
76
FC Porto
800000.0
2000.0
935
18.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9512
Moreto Cassamá
19
Portugal
66
81
FC Porto
1400000.0
2000.0
1803
74.0
...
58.0
53.0
65.0
58.0
67.0
67.0
64.0
67.0
59.0
64.0
9418
J. Riley
20
England
66
78
Manchester United
1000000.0
18000.0
1719
78.0
...
65.0
62.0
59.0
62.0
58.0
63.0
56.0
60.0
65.0
56.0
9580
N. Lomb
23
Germany
66
71
Bayer 04 Leverkusen
625000.0
8000.0
1073
38.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9690
O. Ejaria
19
England
66
81
Liverpool
1400000.0
13000.0
1703
71.0
...
56.0
53.0
63.0
57.0
64.0
65.0
63.0
64.0
58.0
63.0
9711
A. Donnarumma
26
Italy
66
70
Milan
575000.0
16000.0
930
13.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
9514
Álvaro Tejero
20
Spain
66
78
Real Madrid CF
1000000.0
22000.0
1758
76.0
...
65.0
62.0
56.0
60.0
58.0
59.0
56.0
59.0
65.0
56.0
8741
C. Willock
19
England
67
82
SL Benfica
1600000.0
4000.0
1502
83.0
...
43.0
33.0
56.0
41.0
65.0
66.0
60.0
66.0
48.0
60.0
8737
Caio Henrique
19
Brazil
67
81
Atlético Madrid
1600000.0
15000.0
1603
67.0
...
49.0
42.0
64.0
52.0
63.0
63.0
57.0
63.0
51.0
57.0
8581
Unai Simón
20
Spain
67
80
Athletic Club de Bilbao
1200000.0
7000.0
1095
46.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7995
R. Habran
23
France
67
72
Paris Saint-Germain
1000000.0
32000.0
1779
87.0
...
55.0
51.0
59.0
53.0
65.0
66.0
64.0
67.0
56.0
64.0
7871
Gerson
20
Brazil
67
80
Roma
1500000.0
24000.0
1701
71.0
...
52.0
45.0
66.0
55.0
66.0
68.0
62.0
67.0
56.0
62.0
7789
Paulo Lopes
39
Portugal
67
67
SL Benfica
50000.0
3000.0
1120
38.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7759
V. Yurchenko
23
Ukraine
67
73
Bayer 04 Leverkusen
1000000.0
23000.0
1769
67.0
...
59.0
55.0
66.0
61.0
66.0
67.0
63.0
67.0
61.0
63.0
6678
A. Isak
17
Sweden
68
84
Borussia Dortmund
1800000.0
18000.0
1574
80.0
...
44.0
38.0
56.0
42.0
66.0
64.0
67.0
66.0
47.0
67.0
6987
Diogo Dalot
18
Portugal
68
83
FC Porto
1600000.0
4000.0
1686
71.0
...
67.0
67.0
58.0
63.0
61.0
63.0
61.0
62.0
67.0
61.0
7093
M. Chrien
21
Slovakia
68
77
SL Benfica
1400000.0
6000.0
1744
67.0
...
61.0
59.0
67.0
62.0
66.0
68.0
64.0
66.0
62.0
64.0
7164
Galeno
19
Brazil
68
79
FC Porto
1500000.0
5000.0
1695
79.0
...
52.0
45.0
60.0
51.0
68.0
67.0
66.0
68.0
55.0
66.0
7506
Rúben Dias
20
Portugal
68
78
SL Benfica
1300000.0
5000.0
1458
62.0
...
62.0
67.0
48.0
60.0
47.0
48.0
48.0
46.0
59.0
48.0
7604
Óscar
19
Spain
68
81
Real Madrid CF
1700000.0
37000.0
1667
64.0
...
51.0
48.0
63.0
55.0
65.0
63.0
61.0
65.0
53.0
61.0
7500
Wallyson Mallmann
23
Brazil
68
75
Sporting CP
1300000.0
8000.0
1890
70.0
...
61.0
60.0
66.0
62.0
67.0
66.0
67.0
67.0
62.0
67.0
6545
Wallace Oliveira
23
Brazil
69
78
Chelsea
1400000.0
40000.0
1888
76.0
...
68.0
66.0
67.0
68.0
67.0
69.0
64.0
68.0
70.0
64.0
6486
R. Bentancur
20
Uruguay
69
83
Juventus
2300000.0
37000.0
1760
62.0
...
62.0
59.0
68.0
64.0
65.0
66.0
61.0
65.0
63.0
61.0
6369
A. Werner
21
Argentina
69
81
Atlético Madrid
1500000.0
14000.0
1135
41.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6358
Pedro Pereira
19
Portugal
69
82
SL Benfica
1700000.0
4000.0
1723
75.0
...
68.0
66.0
62.0
65.0
60.0
64.0
56.0
62.0
68.0
56.0
6322
C. Pinsoglio
27
Italy
69
69
Juventus
700000.0
34000.0
1062
43.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
6253
J. Wilson
21
England
69
76
Manchester United
1500000.0
48000.0
1632
74.0
...
45.0
40.0
59.0
45.0
67.0
65.0
68.0
66.0
48.0
68.0
6160
T. Alexander-Arnold
18
England
69
85
Liverpool
1900000.0
23000.0
1904
81.0
...
68.0
64.0
66.0
66.0
65.0
68.0
62.0
67.0
69.0
62.0
5876
M. Delač
24
Croatia
69
72
Chelsea
950000.0
31000.0
1008
31.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5808
C. Brannagan
21
England
69
79
Liverpool
1700000.0
38000.0
1881
64.0
...
65.0
64.0
68.0
67.0
65.0
66.0
63.0
65.0
66.0
63.0
106 rows × 71 columns
Vi kan altså se, at der bliver satset på ungdommen, hvor deres kommende potentiale nok taler for deres plads i en storklub.
Men hvad så med ikke-topklubsspillere og deres performance?
In [43]:
bund_df = df[~df.Club.isin(top_clubs)]
bund_df[bund_df.Overall > 70]
Out[43]:
Name
Age
Nationality
Overall
Potential
Club
Value
Wage
Special
Acceleration
...
RB
RCB
RCM
RDM
RF
RM
RS
RW
RWB
ST
11
K. De Bruyne
26
Belgium
89
92
Manchester City
83000000.0
285000.0
2162
76.0
...
66.0
57.0
84.0
70.0
85.0
85.0
81.0
85.0
71.0
81.0
16
S. Agüero
29
Argentina
89
89
Manchester City
66500000.0
325000.0
2074
90.0
...
52.0
44.0
75.0
54.0
87.0
84.0
86.0
86.0
57.0
86.0
29
H. Lloris
30
France
88
88
Tottenham Hotspur
38000000.0
165000.0
1318
65.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
36
C. Eriksen
25
Denmark
87
91
Tottenham Hotspur
65000000.0
165000.0
2064
77.0
...
64.0
53.0
83.0
68.0
82.0
84.0
77.0
83.0
69.0
77.0
42
David Silva
31
Spain
87
87
Manchester City
44000000.0
220000.0
1977
72.0
...
59.0
50.0
81.0
64.0
81.0
82.0
75.0
82.0
65.0
75.0
46
H. Kane
23
England
86
90
Tottenham Hotspur
59000000.0
165000.0
2057
68.0
...
60.0
57.0
74.0
62.0
82.0
78.0
84.0
79.0
62.0
84.0
56
T. Alderweireld
28
Belgium
86
87
Tottenham Hotspur
40500000.0
165000.0
2047
62.0
...
79.0
84.0
74.0
81.0
67.0
67.0
67.0
65.0
77.0
67.0
76
D. Subašić
32
Croatia
85
85
AS Monaco
22000000.0
46000.0
1305
51.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
79
K. Glik
29
Poland
85
85
AS Monaco
30000000.0
60000.0
1612
53.0
...
72.0
83.0
55.0
74.0
48.0
50.0
52.0
45.0
68.0
52.0
81
I. Gündoğan
26
Germany
85
87
Manchester City
46000000.0
190000.0
2148
73.0
...
73.0
69.0
83.0
77.0
81.0
81.0
76.0
81.0
75.0
76.0
86
J. Vertonghen
30
Belgium
85
85
Tottenham Hotspur
28500000.0
130000.0
2079
68.0
...
80.0
83.0
74.0
81.0
70.0
70.0
70.0
69.0
78.0
70.0
88
S. Ruffier
30
France
85
85
AS Saint-Étienne
24500000.0
49000.0
1257
45.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
90
V. Kompany
31
Belgium
85
85
Manchester City
26000000.0
170000.0
1913
61.0
...
76.0
82.0
67.0
77.0
62.0
63.0
63.0
61.0
74.0
63.0
94
Bernardo Silva
22
Portugal
84
91
Manchester City
43500000.0
165000.0
2012
85.0
...
63.0
53.0
79.0
66.0
82.0
83.0
75.0
83.0
68.0
75.0
96
D. Alli
21
England
84
90
Tottenham Hotspur
43000000.0
115000.0
2122
77.0
...
72.0
69.0
81.0
75.0
83.0
81.0
81.0
81.0
73.0
81.0
103
T. Horn
24
Germany
84
90
1. FC Köln
31000000.0
39000.0
1260
45.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
111
Bruno
33
Spain
84
84
Villarreal CF
18500000.0
59000.0
2059
42.0
...
74.0
78.0
82.0
82.0
74.0
72.0
71.0
71.0
75.0
71.0
116
R. Fährmann
28
Germany
84
85
FC Schalke 04
25000000.0
54000.0
1190
38.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
117
Sergio Asenjo
28
Spain
84
85
Villarreal CF
25000000.0
46000.0
1341
59.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
120
D. Payet
30
France
84
84
Olympique de Marseille
29500000.0
60000.0
2076
79.0
...
60.0
51.0
79.0
63.0
82.0
83.0
78.0
83.0
64.0
78.0
121
Falcao
31
Colombia
84
84
AS Monaco
28000000.0
68000.0
2010
73.0
...
54.0
54.0
71.0
57.0
79.0
74.0
82.0
76.0
56.0
82.0
125
A. Gómez
29
Argentina
84
84
Atalanta
31000000.0
60000.0
2013
94.0
...
59.0
45.0
77.0
59.0
83.0
84.0
76.0
85.0
65.0
76.0
129
N. Keïta
22
Guinea
83
88
RB Leipzig
34000000.0
68000.0
2055
82.0
...
71.0
65.0
82.0
76.0
80.0
81.0
74.0
80.0
75.0
74.0
131
G. Rulli
25
Argentina
83
89
Real Sociedad
25500000.0
28000.0
1284
54.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
132
T. Lemar
21
France
83
91
AS Monaco
38500000.0
37000.0
2171
86.0
...
72.0
66.0
78.0
72.0
81.0
82.0
77.0
82.0
75.0
77.0
135
Ederson
23
Brazil
83
89
Manchester City
26000000.0
87000.0
1320
64.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
136
Fabinho
23
Brazil
83
88
AS Monaco
29500000.0
37000.0
2114
73.0
...
82.0
82.0
77.0
82.0
74.0
76.0
72.0
74.0
82.0
72.0
138
A. Belotti
23
Italy
83
90
Torino
37000000.0
58000.0
1935
80.0
...
52.0
51.0
65.0
52.0
78.0
72.0
82.0
75.0
54.0
82.0
139
E. Forsberg
25
Sweden
83
85
RB Leipzig
31500000.0
75000.0
1949
78.0
...
55.0
45.0
76.0
59.0
79.0
81.0
74.0
81.0
61.0
74.0
143
R. Mahrez
26
Algeria
83
84
Leicester City
30500000.0
88000.0
1935
85.0
...
54.0
41.0
74.0
55.0
80.0
81.0
74.0
82.0
60.0
74.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
4822
Jair
28
Brazil
71
71
Jeonnam Dragons
2400000.0
7000.0
1828
76.0
...
55.0
49.0
66.0
56.0
70.0
70.0
70.0
71.0
58.0
70.0
4823
K. Vandendriessche
27
France
71
71
KV Oostende
2400000.0
12000.0
1924
71.0
...
69.0
70.0
70.0
70.0
68.0
68.0
70.0
66.0
69.0
70.0
4824
C. Fai
24
Cameroon
71
74
Standard de Liège
2500000.0
9000.0
1740
88.0
...
70.0
67.0
62.0
67.0
59.0
65.0
53.0
62.0
70.0
53.0
4825
S. Hagen
31
Norway
71
71
Odds BK
1600000.0
4000.0
1530
56.0
...
66.0
70.0
58.0
68.0
48.0
51.0
49.0
47.0
64.0
49.0
4826
M. Rygaard
26
Denmark
71
73
Lyngby BK
2900000.0
9000.0
1818
78.0
...
54.0
50.0
66.0
57.0
68.0
69.0
64.0
69.0
57.0
64.0
4827
M. Männel
29
Germany
71
71
FC Erzgebirge Aue
1600000.0
5000.0
1184
52.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4828
Cristian López
28
Spain
71
71
RC Lens
2400000.0
8000.0
1705
80.0
...
49.0
48.0
58.0
49.0
69.0
64.0
70.0
66.0
51.0
70.0
4829
Eduardo Fonseira
29
Brazil
71
71
Clube Atlético Mineiro
1600000.0
18000.0
963
32.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4830
L. Rosić
24
Serbia
71
77
SC Braga
2800000.0
7000.0
1442
58.0
...
60.0
70.0
47.0
61.0
43.0
44.0
46.0
42.0
57.0
46.0
4831
R. Beerens
29
Netherlands
71
71
Reading
2300000.0
27000.0
1760
80.0
...
49.0
39.0
64.0
49.0
69.0
71.0
64.0
71.0
54.0
64.0
4832
B. Douglas
27
Scotland
71
71
Wolverhampton Wanderers
2000000.0
31000.0
1956
76.0
...
70.0
67.0
66.0
67.0
64.0
68.0
62.0
66.0
71.0
62.0
4833
S. Johnstone
24
England
71
79
Aston Villa
2600000.0
40000.0
1162
45.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4834
R. Elliot
31
Republic of Ireland
71
71
Newcastle United
1400000.0
30000.0
1128
47.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4835
D. Capelli
31
Italy
71
71
La Spezia
1600000.0
4000.0
1457
34.0
...
60.0
70.0
49.0
63.0
41.0
43.0
45.0
40.0
58.0
45.0
4836
K. Ziani
34
France
71
71
US Orléans Loiret Football
1200000.0
5000.0
1836
75.0
...
50.0
42.0
65.0
51.0
68.0
69.0
64.0
69.0
53.0
64.0
4837
A. Kozlov
30
Russia
71
71
Dinamo Moscow
1700000.0
21000.0
1801
71.0
...
70.0
70.0
63.0
69.0
58.0
62.0
59.0
60.0
69.0
59.0
4838
S. Berge
19
Norway
71
82
KRC Genk
3500000.0
6000.0
1851
75.0
...
68.0
69.0
69.0
70.0
66.0
67.0
64.0
65.0
68.0
64.0
4839
O. Pérez
44
Mexico
71
71
Pachuca
160000.0
9000.0
1244
60.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4840
C. Pasquato
27
Italy
71
71
Legia Warszawa
2500000.0
8000.0
1707
90.0
...
44.0
33.0
62.0
44.0
68.0
70.0
64.0
71.0
49.0
64.0
4841
A. Kolomeytsev
28
Russia
71
72
Lokomotiv Moscow
2200000.0
26000.0
1904
58.0
...
68.0
68.0
72.0
70.0
69.0
69.0
69.0
68.0
69.0
69.0
4842
S. Kitsiou
23
Greece
71
75
Sint-Truidense VV
2600000.0
8000.0
1878
78.0
...
70.0
67.0
68.0
69.0
67.0
68.0
65.0
68.0
70.0
65.0
4843
Y. Yotún
27
Peru
71
72
Orlando City Soccer Club
2700000.0
7000.0
1937
71.0
...
67.0
63.0
70.0
68.0
66.0
69.0
61.0
67.0
69.0
61.0
4844
G. Zardes
25
United States
71
72
LA Galaxy
2900000.0
7000.0
1812
85.0
...
56.0
54.0
62.0
54.0
70.0
69.0
70.0
70.0
58.0
70.0
4845
C. Menéndez
29
Argentina
71
71
Tiburones Rojos de Veracruz
2400000.0
8000.0
1670
66.0
...
45.0
45.0
61.0
47.0
69.0
64.0
70.0
65.0
47.0
70.0
4846
T. Zentena
25
Chile
71
71
Everton de Viña del Mar
2600000.0
4000.0
1744
81.0
...
48.0
42.0
60.0
47.0
71.0
70.0
71.0
72.0
52.0
71.0
4847
Vágner Corraldo
29
Brazil
71
71
Santos Futebol Clube
1800000.0
13000.0
1915
78.0
...
70.0
67.0
65.0
67.0
65.0
67.0
63.0
67.0
71.0
63.0
4848
D. Sjölund
34
Finland
71
71
IFK Norrköping
1200000.0
5000.0
1839
46.0
...
60.0
61.0
70.0
68.0
63.0
64.0
60.0
63.0
63.0
60.0
4849
C. Mavinga
26
DR Congo
71
72
Toronto FC
2300000.0
6000.0
1736
77.0
...
70.0
70.0
64.0
69.0
60.0
66.0
58.0
63.0
70.0
58.0
4850
E. Conferio
33
Chile
71
71
CD Antofagasta
1500000.0
4000.0
1763
86.0
...
50.0
40.0
62.0
47.0
72.0
69.0
70.0
71.0
52.0
70.0
4851
F. Ben Khalfallah
34
Tunisia
71
71
Brisbane Roar
1200000.0
6000.0
1808
75.0
...
53.0
48.0
67.0
56.0
70.0
71.0
67.0
71.0
57.0
67.0
4336 rows × 71 columns
Måske er de 22 klubber, vi har udvalgt ikke helt nok til at beskrive topklubber
In [44]:
top_clubs
Out[44]:
220 FC Barcelona
331 Juventus
467 Real Madrid CF
223 FC Bayern Munich
433 Paris Saint-Germain
378 Manchester United
398 Napoli
478 Roma
530 Sevilla FC
324 Inter
143 Chelsea
551 Sporting CP
241 FC Porto
369 Liverpool
68 Atlético Madrid
52 Arsenal
92 Borussia Dortmund
61 Athletic Club de Bilbao
83 Beşiktaş JK
502 SL Benfica
384 Milan
80 Bayer 04 Leverkusen
Name: Club, dtype: object
Du kan evt. gå tilbage til Data Cleaning notebooken, og prøve at ændre tallet for top_klub_ratio
In [ ]:
Content source: mssalvador/Fifa2018
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