New stats format in acb.com

Playing with the new stats format in acb.com (launched in October 2019)



In [1]:

    
import pandas as pd



In [2]:

    
season = 2019
urls = [
    'http://www.acb.com/estadisticas-individuales/{}/temporada_id/{}/tipo_id/0'.format(x, season)
    for x in 
    [
        'valoracion', 
        'puntos', 
        'rebotes', 
        'asistencias', 
        'robos', 'tapones', 
        'mas-menos', 
        'minutos', 
        'tiros3', 
        'tiros3-porciento',
        'tiros2', 
        'tiros2-porciento',
        'tiros1', 
        'tiros1-porciento',
        'rebotes-defensivos',
        'rebotes-ofensivos',
        'faltas-recibidas',
        'faltas-cometidas',
        'mates'
    ]
]



In [3]:

    
data = pd.concat([pd.read_html(url)[0].iloc[:, 1:] for url in urls], axis=0).drop_duplicates()



In [4]:

    
data.columns = [
    'name', 'games', 'minutes', 'points',
    '3p_converted', '3p_attempted', '3p_percentage',
    '2p_converted', '2p_attempted', '2p_percentage',
    '1p_converted', '1p_attempted', '1p_percentage',
    'offensive_rebounds', 'deffensive_rebounds', 'rebounds',
    'assists', 'steals', 'turnovers',
    'blocks', 'received_blocks',
    'dunks', 'faults', 'received_faults',
    'plus_minus', 'pir'
]
data = data.set_index('name')



In [5]:

    
data.describe()









    Out[5]:







  
    
      
      games
      minutes
      points
      3p_converted
      3p_attempted
      2p_converted
      2p_attempted
      1p_converted
      1p_attempted
      offensive_rebounds
      ...
      assists
      steals
      turnovers
      blocks
      received_blocks
      dunks
      faults
      received_faults
      plus_minus
      pir
    
  
  
    
      count
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      ...
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
      196.000000
    
    
      mean
      3.571429
      66.923469
      27.591837
      3.188776
      8.765306
      6.724490
      12.897959
      4.576531
      6.020408
      3.153061
      ...
      5.122449
      2.117347
      4.275510
      0.943878
      0.882653
      0.974490
      6.867347
      6.913265
      0.535714
      28.382653
    
    
      std
      0.835126
      27.259047
      16.912345
      3.386258
      7.851573
      5.165528
      8.793925
      4.595344
      5.644111
      3.260807
      ...
      5.133630
      1.919269
      2.994972
      1.529844
      1.141975
      1.500209
      3.576670
      5.205286
      21.015104
      19.204206
    
    
      min
      1.000000
      1.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      ...
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      -69.000000
      -14.000000
    
    
      25%
      3.000000
      50.000000
      16.000000
      1.000000
      2.000000
      3.000000
      6.000000
      1.000000
      2.000000
      1.000000
      ...
      1.000000
      1.000000
      2.000000
      0.000000
      0.000000
      0.000000
      4.000000
      3.000000
      -14.000000
      15.000000
    
    
      50%
      4.000000
      67.500000
      24.000000
      2.000000
      7.000000
      6.000000
      11.000000
      4.000000
      5.000000
      2.000000
      ...
      3.000000
      2.000000
      4.000000
      0.000000
      1.000000
      0.000000
      7.000000
      6.000000
      1.000000
      25.000000
    
    
      75%
      4.000000
      87.000000
      36.250000
      5.000000
      13.000000
      10.000000
      18.000000
      7.000000
      8.000000
      4.000000
      ...
      7.000000
      3.000000
      6.000000
      1.000000
      1.000000
      1.000000
      9.000000
      9.250000
      14.250000
      37.250000
    
    
      max
      4.000000
      124.000000
      95.000000
      17.000000
      39.000000
      31.000000
      44.000000
      33.000000
      38.000000
      19.000000
      ...
      27.000000
      11.000000
      14.000000
      7.000000
      6.000000
      8.000000
      17.000000
      30.000000
      56.000000
      98.000000
    
  

8 rows × 22 columns

PIR and plus-minus



In [6]:

    
data[['pir', 'plus_minus']].sum(axis=1).sort_values(ascending=False).head(18)









    Out[6]:





name
Nikola Mirotic Stajovic           130
Ferran Bassas Navarra             109
Walter Samuel Tavares Da Veiga    101
Giorgi Shermadini                 100
Thaddus Dewayn Mcfadden           100
Clark, Earl                        98
Augusto Cesar Lima Brito           94
Marcelo Tieppo Huertas             89
Cheikh Moussa Diagné               88
Brandon Davies                     87
Costello, Matthew                  87
Adams, Josh                        86
Facundo Campazzo                   84
Daniel Díez De La Faya             83
Harper, Demonte                    80
Gabriel Alejandro Deck             77
Ondrej Balvin                      76
Nicolás Laprovittola               73
dtype: int64

Offensive players



In [7]:

    
(
    data[
        ['points', 
         'offensive_rebounds', 
         'assists', 
         'received_faults', 
         '3p_converted', 
         '2p_converted', 
         '1p_converted',
         'plus_minus']
    ].sum(axis=1) - 
    data[
        ['3p_attempted', 
         '2p_attempted', 
         '1p_attempted',
         'turnovers',
         'received_blocks'
        ]
    ].sum(axis=1)
).sort_values(ascending=False).head(18)









    Out[7]:





name
Nikola Mirotic Stajovic           114
Ferran Bassas Navarra             102
Thaddus Dewayn Mcfadden           101
Adams, Josh                        92
Giorgi Shermadini                  90
Augusto Cesar Lima Brito           84
Harper, Demonte                    83
Clark, Earl                        81
Marcelo Tieppo Huertas             81
Facundo Campazzo                   77
Walter Samuel Tavares Da Veiga     77
Cheikh Moussa Diagné               75
Brandon Davies                     72
Sergio Llull Meliá                 70
Nicolás Laprovittola               70
Jaycee Don Carroll                 69
Vitor Alves Benite                 69
Gabriel Alejandro Deck             69
dtype: int64

Deffensive players



In [8]:

    
(
    data[
        ['deffensive_rebounds', 
         'steals', 
         'blocks', 
         'plus_minus']
    ].sum(axis=1) - data['faults']
).sort_values(ascending=False).head(18)









    Out[8]:





name
Walter Samuel Tavares Da Veiga    78
Clark, Earl                       73
Ferran Bassas Navarra             56
Nikola Mirotic Stajovic           53
Augusto Cesar Lima Brito          49
Facundo Campazzo                  49
Jonathan Barreiro Rodríguez       48
Fabien Causeur                    47
Usman Garuba Alari                43
Jean-Pierre Tokoto II             43
Costello, Matthew                 42
Daniel Díez De La Faya            42
Mickey, Jordan                    40
Louis Labeyrie                    39
Gabriel Alejandro Deck            38
Nicolás Laprovittola              37
Marcelo Tieppo Huertas            37
Bojan Dubljevic                   37
dtype: int64

Team players



In [9]:

    
(data['plus_minus'] + data['minutes'] / 2 - data['pir']).sort_values(ascending=False).head(18)









    Out[9]:





name
Clark, Earl                         65.5
Fabien Causeur                      63.5
Thomas Schreiner                    47.5
Walter Samuel Tavares Da Veiga      45.5
Jean-Pierre Tokoto II               45.5
Vitor Alves Benite                  44.0
Anthony Erwin Randolph Jr           43.0
Adam Hanga                          42.0
Rousselle, Jonathan                 40.5
Omar Sharif Cook                    39.5
Ferran Bassas Navarra               39.5
Sergio Llull Meliá                  39.0
Massenat, Frantz                    38.0
Sakota, Dusan                       37.5
Harper, Demonte                     36.5
Higgins, Courdon                    36.0
Rodrigo San Miguel de la Iglesia    36.0
Delaney, Malcolm                    35.0
dtype: float64

Assists by turnover



In [10]:

    
((data['assists'] + 1) / (data['turnovers'] + 1)).sort_values(ascending=False).head(18)









    Out[10]:





name
David Navarro Brugal                6.000000
Omar Sharif Cook                    4.666667
Rodrigo San Miguel de la Iglesia    4.200000
Kelvin Creswell Rivers              4.000000
Albert Oliver Campos                3.250000
Sam Van Rossom                      3.166667
John William Shurna                 3.000000
Polonara, Achille                   3.000000
Izundu, Ebuka                       3.000000
Nicholas James Spires               3.000000
Rinalds Malmanis                    3.000000
Dovydas Giedraitis                  3.000000
Luca Vildoza                        2.600000
Daniel Pérez Otero                  2.600000
Scrubb, Philip                      2.600000
Earl Genard Calloway                2.500000
Atkins, Darion                      2.500000
Alberto Díaz Ortiz                  2.500000
dtype: float64

Up in the air



In [11]:

    
(
    data['dunks'] + data['blocks'] - data['received_blocks'] + data['2p_converted'] - data['2p_attempted']
).sort_values(ascending=False).head(18)









    Out[11]:





name
Lammers, Benjamin                 7
Fall, Youssoupha                  6
Mickey, Jordan                    4
Ilimane Diop Gaye                 4
Tryggvi Hlinason                  4
Louis Labeyrie                    2
Sy, Bandja                        2
Walter Samuel Tavares Da Veiga    2
Rolands Smits                     2
Víctor Claver Arocas              1
Pere Tomàs Noguera                1
Javier Vega Merayo                1
Vit Krejci                        1
Joel Parra López                  1
Alejandro Abrines Redondo         1
Edgar Benjamin Vicedo Ayala       1
Oriol Paulí Fornells              1
Vyacheslav Bobrov                 1
dtype: int64

Greedy



In [12]:

    
(
    data[['3p_attempted', '2p_attempted', 'turnovers', 'received_blocks']].sum(axis=1) - 
    data[['assists','plus_minus']].sum(axis=1)
).sort_values(ascending=False).head(18)









    Out[12]:





name
Luke Cameron Harangody            121
Klemen Prepelic                   104
Alen Omic                          92
Kravic, Dejan                      92
Christian Eyenga Moenge            90
Eddie, Jarell                      71
Anderson, Karvel                   70
EJ Rowland                         69
Shayne Mitchell Whittington        69
Konstantinos Vasileiadis           68
Askia Toussaint Booker             67
Magee, Fletcher                    64
Davis, Jordan                      63
Demitrius Rayelle Conger           61
Dino Radoncic                      60
Christopher Daniel Czerapowicz     58
Nikos Zisis                        57
Vladimir Brodziansky               56
dtype: int64



In [ ]:



In [ ]:

	games	minutes	points	3p_converted	3p_attempted	2p_converted	2p_attempted	1p_converted	1p_attempted	offensive_rebounds	...	assists	steals	turnovers	blocks	received_blocks	dunks	faults	received_faults	plus_minus	pir
count	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	...	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000	196.000000
mean	3.571429	66.923469	27.591837	3.188776	8.765306	6.724490	12.897959	4.576531	6.020408	3.153061	...	5.122449	2.117347	4.275510	0.943878	0.882653	0.974490	6.867347	6.913265	0.535714	28.382653
std	0.835126	27.259047	16.912345	3.386258	7.851573	5.165528	8.793925	4.595344	5.644111	3.260807	...	5.133630	1.919269	2.994972	1.529844	1.141975	1.500209	3.576670	5.205286	21.015104	19.204206
min	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-69.000000	-14.000000
25%	3.000000	50.000000	16.000000	1.000000	2.000000	3.000000	6.000000	1.000000	2.000000	1.000000	...	1.000000	1.000000	2.000000	0.000000	0.000000	0.000000	4.000000	3.000000	-14.000000	15.000000
50%	4.000000	67.500000	24.000000	2.000000	7.000000	6.000000	11.000000	4.000000	5.000000	2.000000	...	3.000000	2.000000	4.000000	0.000000	1.000000	0.000000	7.000000	6.000000	1.000000	25.000000
75%	4.000000	87.000000	36.250000	5.000000	13.000000	10.000000	18.000000	7.000000	8.000000	4.000000	...	7.000000	3.000000	6.000000	1.000000	1.000000	1.000000	9.000000	9.250000	14.250000	37.250000
max	4.000000	124.000000	95.000000	17.000000	39.000000	31.000000	44.000000	33.000000	38.000000	19.000000	...	27.000000	11.000000	14.000000	7.000000	6.000000	8.000000	17.000000	30.000000	56.000000	98.000000