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In [ ]:



    


# Analyzes relationships between points and potential features for model



    











In [1]:



    


#Import Packages
import pandas as pd
import numpy as np
from Player.Players import Players
from Regression.Reg_Model import Reg_Model



    


















    






//anaconda/lib/python2.7/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used

  UserWarning)

















In [2]:



    


# Analyze Player's past points, assists, and minutes
Player_List = Players('2015-16').players



    











In [3]:



    


all_stats=pd.DataFrame()
all_game_logs=pd.DataFrame()

for key in Player_List:
    p=Player_List[key]
    game_logs = p.game_logs
    game_logs['PTS_MIN'] = [x*1.0/y for x,y in zip(game_logs['PTS'], game_logs['MIN'])]
    
    all_game_logs = all_game_logs.append(game_logs)
    
    stats = Player_List[key].get_stats(game_logs = game_logs, date_col='GAME_DATE', 
                                       id_col='Game_ID', target_col='PTS', 
                                       col_list=['PTS', 'MIN', 'PTS_MIN', 'AST', 'FGM', 'FGA', 'FG_PCT'], n=10)
    if len(stats>0):
        stats=stats[stats['n_games']>=5]
    
    all_stats = all_stats.append(stats)



    











In [4]:



    


cor = pd.DataFrame()
feature_list = ['PTS_avg_10', 'AST_avg_10', 'PTS_MIN_avg_10']
for f in feature_list:
    cor.set_value('PTS', f, np.corrcoef(x=stats[f], y=stats['PTS'])[0,1])
cor



    


















    
Out[4]:










  
    

      
      PTS_avg_10
      AST_avg_10
      PTS_MIN_avg_10
    

  
  
    

      PTS
      0.116314
      -0.042724
      0.135762
    

  




















In [5]:



    


%matplotlib inline
x=stats['PTS_avg_10']
y=stats['PTS']
import matplotlib.pyplot as plt
plt.scatter(x, y, alpha=0.5)
plt.show



    


















    
Out[5]:








<function matplotlib.pyplot.show>










    






//anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == str('face'):










    
























In [6]:



    


%matplotlib inline
x=stats['PTS_MIN_avg_10']
y=stats['PTS']
import matplotlib.pyplot as plt
plt.scatter(x, y, alpha=0.5)
plt.show



    


















    
Out[6]:








<function matplotlib.pyplot.show>










    
























In [8]:



    


# Looks at team's past performance using ELO
from Regression.ELO import ELO
from Schedule.Schedule import Schedule
sched_2014 = Schedule(b_dt = '10/1/2014')
sched_2015 = Schedule(b_dt = '10/1/2015')
games = sched_2014.get_games().append(sched_2015.get_games()).reset_index(drop=True)
elo = ELO(games, 'GAME_DATE', 'Home Team', 'Away Team', 'H_PTS', 'A_PTS')
elo_data = elo.create_elo(1500, 20, 100, '538')



    











In [9]:



    


# Joins ELO data back to games
for index, game in games.iterrows():
    game_dt = game['GAME_DATE']
    poss_elo = elo_data[elo_data.index < game_dt]
    
    h_team = game['Home Team']
    if len(poss_elo) == 0:
        h_elo_score = 1500
    else:
        h_elo_score = poss_elo.tail(1)[h_team][0]
    games.set_value(index, 'H_ELO', h_elo_score)
    
    a_team = game['Away Team']
    if len(poss_elo) == 0:
        a_elo_score = 1500
    else:
        a_elo_score = poss_elo.tail(1)[a_team][0]
    games.set_value(index, 'A_ELO', a_elo_score)



    











In [ ]:



    


elo_data



    











In [ ]:



    


# Joins Player data and ELO data
game_logs



    











In [ ]:



    


for index, g in game_logs.iterrows():
    elo=elo_data.ix[str(g['GAME_DATE'])[0:10], g['MATCHUP'][0:3]]
    game_logs.set_value(index, 'ELO', elo)
    opp_team = g['MATCHUP'].split(' ')[2]
    opp_elo = elo_data.ix[str(g['GAME_DATE'])[0:10], opp_team]
    game_logs.set_value(index, 'Opp_ELO', opp_elo)



    











In [ ]:



    


game_logs



    











In [ ]:



    


%matplotlib inline
x=[a-b for a,b in zip(game_logs['ELO'],game_logs['Opp_ELO'])]
y=game_logs['PTS']
import matplotlib.pyplot as plt
plt.scatter(x, y, alpha=0.5)
plt.show



    











In [ ]:



    


np.corrcoef(x, y)[0,1]



    











In [10]:



    


#Trying to get points per team
home_games = games[['GAME_DATE', 'Home Team', 'H_PTS']].rename({'Home Team': 'Team', 'H_PTS': 'PTS'})
away_games = games[['GAME_DATE', 'Away Team', 'A_PTS']].rename({'Away Team': 'Team', 'A_PTS': 'PTS'})
game_stats = home_games.append(away_games).reset_index(drop=True)
print len(games), len(home_games), len(away_games), len(game_stats)



    


















    






2460 2460 2460 4920

















In [17]:



    


from Schedule.Stats import Stats
my_team_stats = Stats(games, 'avg', 'GAME_DATE', 'Home Team', 'Away Team', 'Pts_diff', ['Game_ID', 'GAME_DATE', 'Home Team', 'Away Team'])
last_10_team_stats = my_team_stats.get_lastn_stats(10)



    











In [18]:



    


last_all_team_stats = my_team_stats.get_lastn_stats(100)



    











In [ ]:



    


last_10_team_stats.to_csv('tstats.csv', index=False)
game_logs.to_csv('gl.csv', index=False)
                    
                    # I need to plan this merge out--probably will need join home and away separately



    











In [ ]:



    


last_10_team_stats = pd.read_csv('tstats.csv')
game_logs = pd.read_csv('gl.csv')



    











In [28]:



    


team_stats_10 = last_10_team_stats[['Game_ID', 'GAME_DATE', 'Home Team', 'Away Team', 'H_PTS_10', 'A_PTS_10', 'H_BTB', 'A_BTB']]
team_stats_100 = last_all_team_stats[['Game_ID', 'H_PTS_100', 'A_PTS_100']]
team_stats = pd.merge(team_stats_10, team_stats_100, on='Game_ID')
team_stats['H_PTS_Trend'] = [(a-b)*1.0/b for a,b in zip(team_stats['H_PTS_10'], team_stats['H_PTS_100'])]
team_stats['A_PTS_Trend'] = [(a-b)*1.0/b for a,b in zip(team_stats['A_PTS_10'], team_stats['A_PTS_100'])]
team_stats



    


















    
Out[28]:










  
    

      
      Game_ID
      GAME_DATE
      Home Team
      Away Team
      H_PTS_10
      A_PTS_10
      H_BTB
      A_BTB
      H_PTS_100
      A_PTS_100
      H_PTS_Trend
      A_PTS_Trend
    

  
  
    

      0
      21401205.0
      2015-04-13
      ATL
      NYK
      103.400000
      87.700000
      1.0
      0.0
      102.666667
      89.125000
      0.007143
      -0.015989
    

    

      1
      21401176.0
      2015-04-10
      ATL
      CHA
      104.100000
      94.200000
      0.0
      0.0
      102.333333
      94.187500
      0.017264
      0.000133
    

    

      2
      21401154.0
      2015-04-07
      ATL
      PHX
      103.600000
      94.700000
      0.0
      0.0
      103.928571
      93.846154
      -0.003162
      0.009098
    

    

      3
      21401138.0
      2015-04-04
      ATL
      BKN
      99.600000
      106.400000
      0.0
      1.0
      100.933333
      103.562500
      -0.013210
      0.027399
    

    

      4
      21401103.0
      2015-03-30
      ATL
      MIL
      99.800000
      98.200000
      0.0
      0.0
      101.000000
      95.933333
      -0.011881
      0.023628
    

    

      5
      21401077.0
      2015-03-27
      ATL
      MIA
      101.300000
      97.800000
      0.0
      0.0
      101.000000
      98.500000
      0.002970
      -0.007107
    

    

      6
      21401039.0
      2015-03-22
      ATL
      SAS
      103.300000
      112.900000
      0.0
      0.0
      100.133333
      107.928571
      0.031625
      0.046062
    

    

      7
      21400939.0
      2015-03-09
      ATL
      SAC
      96.800000
      104.000000
      0.0
      0.0
      96.000000
      100.076923
      0.008333
      0.039201
    

    

      8
      21400915.0
      2015-03-06
      ATL
      CLE
      99.000000
      107.000000
      0.0
      0.0
      99.545455
      107.230769
      -0.005479
      -0.002152
    

    

      9
      21400892.0
      2015-03-03
      ATL
      HOU
      99.100000
      106.500000
      0.0
      0.0
      99.181818
      106.000000
      -0.000825
      0.004717
    

    

      10
      21400858.0
      2015-02-27
      ATL
      ORL
      99.400000
      95.000000
      0.0
      0.0
      101.000000
      95.454545
      -0.015842
      -0.004762
    

    

      11
      21400843.0
      2015-02-25
      ATL
      DAL
      99.500000
      100.200000
      0.0
      1.0
      100.727273
      99.071429
      -0.012184
      0.011391
    

    

      12
      21400805.0
      2015-02-20
      ATL
      TOR
      104.300000
      105.700000
      0.0
      0.0
      104.666667
      102.833333
      -0.003503
      0.027877
    

    

      13
      21400749.0
      2015-02-06
      ATL
      GSW
      103.900000
      118.500000
      0.0
      0.0
      105.062500
      116.600000
      -0.011065
      0.016295
    

    

      14
      21400732.0
      2015-02-04
      ATL
      WAS
      104.400000
      100.600000
      0.0
      0.0
      105.187500
      99.750000
      -0.007487
      0.008521
    

    

      15
      21400708.0
      2015-01-31
      ATL
      PHI
      106.300000
      87.100000
      1.0
      1.0
      106.562500
      88.235294
      -0.002463
      -0.012867
    

    

      16
      21400697.0
      2015-01-30
      ATL
      POR
      107.800000
      99.400000
      0.0
      0.0
      106.666667
      99.846154
      0.010625
      -0.004468
    

    

      17
      21400683.0
      2015-01-28
      ATL
      BKN
      107.100000
      90.500000
      0.0
      0.0
      106.400000
      92.000000
      0.006579
      -0.016304
    

    

      18
      21400661.0
      2015-01-25
      ATL
      MIN
      105.500000
      95.000000
      0.0
      0.0
      103.187500
      95.933333
      0.022411
      -0.009729
    

    

      19
      21400642.0
      2015-01-23
      ATL
      OKC
      105.900000
      107.300000
      0.0
      0.0
      103.200000
      107.076923
      0.026163
      0.002083
    

    

      20
      21400628.0
      2015-01-21
      ATL
      IND
      106.400000
      95.300000
      0.0
      0.0
      103.125000
      97.600000
      0.031758
      -0.023566
    

    

      21
      21400613.0
      2015-01-19
      ATL
      DET
      106.900000
      103.800000
      0.0
      0.0
      103.812500
      105.285714
      0.029741
      -0.014111
    

    

      22
      21400558.0
      2015-01-11
      ATL
      WAS
      101.000000
      97.400000
      0.0
      0.0
      101.333333
      98.466667
      -0.003289
      -0.010833
    

    

      23
      21400526.0
      2015-01-07
      ATL
      MEM
      103.900000
      100.400000
      0.0
      0.0
      101.400000
      104.000000
      0.024655
      -0.034615
    

    

      24
      21400466.0
      2014-12-30
      ATL
      CLE
      98.400000
      99.700000
      0.0
      0.0
      100.466667
      101.200000
      -0.020571
      -0.014822
    

    

      25
      21400434.0
      2014-12-26
      ATL
      MIL
      102.100000
      103.200000
      0.0
      0.0
      103.750000
      103.571429
      -0.015904
      -0.003586
    

    

      26
      21400420.0
      2014-12-23
      ATL
      LAC
      101.200000
      109.100000
      1.0
      1.0
      103.687500
      109.058824
      -0.023990
      0.000378
    

    

      27
      21400358.0
      2014-12-15
      ATL
      CHI
      100.900000
      106.300000
      0.0
      1.0
      102.133333
      102.357143
      -0.012076
      0.038521
    

    

      28
      21400330.0
      2014-12-12
      ATL
      ORL
      104.400000
      91.300000
      0.0
      0.0
      104.000000
      94.125000
      0.003846
      -0.030013
    

    

      29
      21400319.0
      2014-12-10
      ATL
      PHI
      104.800000
      95.400000
      0.0
      0.0
      103.733333
      91.230769
      0.010283
      0.045700
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      2430
      21500860.0
      2016-02-25
      UTA
      SAS
      101.700000
      108.700000
      0.0
      1.0
      101.833333
      110.000000
      -0.001309
      -0.011818
    

    

      2431
      21500843.0
      2016-02-23
      UTA
      HOU
      100.300000
      107.500000
      0.0
      0.0
      99.750000
      108.181818
      0.005514
      -0.006303
    

    

      2432
      21500817.0
      2016-02-19
      UTA
      BOS
      97.500000
      113.500000
      1.0
      0.0
      99.500000
      112.769231
      -0.020101
      0.006480
    

    

      2433
      21500758.0
      2016-02-05
      UTA
      MIL
      103.500000
      99.300000
      0.0
      0.0
      99.733333
      98.857143
      0.037767
      0.004480
    

    

      2434
      21500743.0
      2016-02-03
      UTA
      DEN
      103.500000
      107.500000
      0.0
      0.0
      100.133333
      103.142857
      0.033622
      0.042244
    

    

      2435
      21500728.0
      2016-02-01
      UTA
      CHI
      101.600000
      98.600000
      0.0
      1.0
      99.266667
      101.600000
      0.023506
      -0.029528
    

    

      2436
      21500704.0
      2016-01-29
      UTA
      MIN
      101.100000
      102.600000
      0.0
      0.0
      98.437500
      97.937500
      0.027048
      0.047607
    

    

      2437
      21500690.0
      2016-01-27
      UTA
      CHA
      100.300000
      104.600000
      0.0
      0.0
      98.000000
      103.625000
      0.023469
      0.009409
    

    

      2438
      21500673.0
      2016-01-25
      UTA
      DET
      100.900000
      105.400000
      0.0
      0.0
      98.750000
      102.857143
      0.021772
      0.024722
    

    

      2439
      21500608.0
      2016-01-16
      UTA
      LAL
      93.400000
      98.300000
      0.0
      0.0
      95.000000
      95.250000
      -0.016842
      0.032021
    

    

      2440
      21500591.0
      2016-01-14
      UTA
      SAC
      92.800000
      111.500000
      1.0
      1.0
      94.533333
      108.571429
      -0.018336
      0.026974
    

    

      2441
      21500555.0
      2016-01-09
      UTA
      MIA
      95.800000
      98.800000
      0.0
      1.0
      94.533333
      99.062500
      0.013399
      -0.002650
    

    

      2442
      21500518.0
      2016-01-04
      UTA
      HOU
      94.700000
      103.800000
      0.0
      0.0
      97.933333
      107.066667
      -0.033016
      -0.030511
    

    

      2443
      21500503.0
      2016-01-02
      UTA
      MEM
      95.300000
      97.600000
      0.0
      0.0
      98.066667
      95.600000
      -0.028212
      0.020921
    

    

      2444
      21500489.0
      2015-12-31
      UTA
      POR
      93.400000
      100.700000
      1.0
      1.0
      97.285714
      102.812500
      -0.039941
      -0.020547
    

    

      2445
      21500467.0
      2015-12-28
      UTA
      PHI
      97.100000
      94.800000
      0.0
      0.0
      99.357143
      93.000000
      -0.022717
      0.019355
    

    

      2446
      21500452.0
      2015-12-26
      UTA
      LAC
      98.900000
      100.900000
      0.0
      1.0
      99.000000
      101.533333
      -0.001010
      -0.006238
    

    

      2447
      21500417.0
      2015-12-21
      UTA
      PHX
      99.100000
      100.900000
      0.0
      1.0
      98.692308
      103.470588
      0.004131
      -0.024844
    

    

      2448
      21500395.0
      2015-12-18
      UTA
      DEN
      99.500000
      95.000000
      0.0
      0.0
      98.000000
      96.714286
      0.015306
      -0.017725
    

    

      2449
      21500380.0
      2015-12-16
      UTA
      NOP
      100.300000
      101.100000
      0.0
      0.0
      98.307692
      102.714286
      0.020266
      -0.015716
    

    

      2450
      21500341.0
      2015-12-11
      UTA
      OKC
      100.900000
      105.600000
      0.0
      1.0
      99.230769
      104.785714
      0.016822
      0.007771
    

    

      2451
      21500327.0
      2015-12-09
      UTA
      NYK
      100.000000
      95.300000
      1.0
      0.0
      99.846154
      95.866667
      0.001541
      -0.005911
    

    

      2452
      21500297.0
      2015-12-05
      UTA
      IND
      95.600000
      108.700000
      0.0
      0.0
      96.538462
      105.461538
      -0.009721
      0.030708
    

    

      2453
      21500279.0
      2015-12-03
      UTA
      ORL
      97.600000
      102.000000
      0.0
      0.0
      96.384615
      101.200000
      0.012610
      0.007905
    

    

      2454
      21500259.0
      2015-11-30
      UTA
      GSW
      96.200000
      116.500000
      0.0
      0.0
      95.923077
      116.312500
      0.002887
      0.001612
    

    

      2455
      21500243.0
      2015-11-28
      UTA
      NOP
      95.700000
      102.300000
      0.0
      1.0
      95.769231
      103.571429
      -0.000723
      -0.012276
    

    

      2456
      21500208.0
      2015-11-23
      UTA
      OKC
      95.500000
      106.300000
      0.0
      1.0
      95.083333
      109.714286
      0.004382
      -0.031120
    

    

      2457
      21500173.0
      2015-11-18
      UTA
      TOR
      95.500000
      101.300000
      0.0
      1.0
      95.500000
      102.666667
      0.000000
      -0.013312
    

    

      2458
      21500091.0
      2015-11-07
      UTA
      MEM
      94.200000
      92.833333
      0.0
      0.0
      94.200000
      92.833333
      0.000000
      0.000000
    

    

      2459
      21500068.0
      2015-11-04
      UTA
      POR
      94.333333
      100.000000
      0.0
      0.0
      94.333333
      100.000000
      0.000000
      0.000000
    

  



2460 rows × 12 columns



















In [46]:



    


print len(team_stats)
h_stats = team_stats[['Game_ID', 'GAME_DATE', 'Home Team', 'H_PTS_Trend', 'H_BTB']].rename(index = str, columns={'Home Team':'Team', 'H_PTS_Trend':'PTS_Trend', 'H_BTB':'BTB'})
print len(h_stats)
a_stats = team_stats[['Game_ID', 'GAME_DATE', 'Away Team', 'A_PTS_Trend', 'A_BTB']].rename(index = str, columns={'Away Team':'Team', 'A_PTS_Trend':'PTS_Trend', 'A_BTB':'BTB'})
print len(a_stats)
team_stats_sep = h_stats.append(a_stats).reset_index(drop=True)
print len(team_stats_sep)



    


















    






2460
2460
2460
4920

















In [59]:



    


game_logs



    


















    
Out[59]:










  
    

      
      SEASON_ID
      Player_ID
      Game_ID
      GAME_DATE
      MATCHUP
      WL
      MIN
      FGM
      FGA
      FG_PCT
      ...
      AST
      STL
      BLK
      TOV
      PF
      PTS
      PLUS_MINUS
      VIDEO_AVAILABLE
      PTS_MIN
      Team
    

  
  
    

      5047
      22015.0
      2037.0
      21501196.0
      2016-04-10
      LAC vs. DAL
      W
      26.0
      7.0
      13.0
      0.538
      ...
      2.0
      1.0
      0.0
      2.0
      1.0
      22.0
      6.0
      1.0
      0.846154
      LAC
    

    

      5048
      22015.0
      2037.0
      21501187.0
      2016-04-08
      LAC @ UTA
      W
      49.0
      9.0
      20.0
      0.450
      ...
      4.0
      1.0
      1.0
      1.0
      3.0
      30.0
      6.0
      1.0
      0.612245
      LAC
    

    

      5049
      22015.0
      2037.0
      21501164.0
      2016-04-05
      LAC vs. LAL
      W
      18.0
      2.0
      6.0
      0.333
      ...
      1.0
      1.0
      0.0
      3.0
      2.0
      4.0
      -5.0
      1.0
      0.222222
      LAC
    

    

      5050
      22015.0
      2037.0
      21501147.0
      2016-04-03
      LAC vs. WAS
      W
      25.0
      6.0
      12.0
      0.500
      ...
      6.0
      0.0
      0.0
      2.0
      2.0
      19.0
      1.0
      1.0
      0.760000
      LAC
    

    

      5051
      22015.0
      2037.0
      21501126.0
      2016-03-31
      LAC @ OKC
      L
      40.0
      10.0
      20.0
      0.500
      ...
      7.0
      2.0
      1.0
      3.0
      0.0
      32.0
      -9.0
      1.0
      0.800000
      LAC
    

    

      5052
      22015.0
      2037.0
      21501116.0
      2016-03-30
      LAC @ MIN
      W
      18.0
      3.0
      6.0
      0.500
      ...
      1.0
      2.0
      0.0
      0.0
      0.0
      10.0
      15.0
      1.0
      0.555556
      LAC
    

    

      5053
      22015.0
      2037.0
      21501106.0
      2016-03-28
      LAC vs. BOS
      W
      29.0
      5.0
      12.0
      0.417
      ...
      2.0
      1.0
      0.0
      3.0
      2.0
      15.0
      19.0
      1.0
      0.517241
      LAC
    

    

      5054
      22015.0
      2037.0
      21501092.0
      2016-03-27
      LAC vs. DEN
      W
      21.0
      3.0
      8.0
      0.375
      ...
      2.0
      0.0
      0.0
      1.0
      0.0
      14.0
      1.0
      1.0
      0.666667
      LAC
    

    

      5055
      22015.0
      2037.0
      21501072.0
      2016-03-24
      LAC vs. POR
      W
      33.0
      11.0
      21.0
      0.524
      ...
      2.0
      1.0
      0.0
      1.0
      2.0
      25.0
      11.0
      1.0
      0.757576
      LAC
    

    

      5056
      22015.0
      2037.0
      21501066.0
      2016-03-23
      LAC @ GSW
      L
      32.0
      4.0
      11.0
      0.364
      ...
      6.0
      2.0
      1.0
      2.0
      1.0
      12.0
      -4.0
      1.0
      0.375000
      LAC
    

    

      5057
      22015.0
      2037.0
      21501040.0
      2016-03-20
      LAC @ NOP
      L
      33.0
      5.0
      12.0
      0.417
      ...
      3.0
      1.0
      0.0
      3.0
      4.0
      14.0
      6.0
      1.0
      0.424242
      LAC
    

    

      5058
      22015.0
      2037.0
      21501035.0
      2016-03-19
      LAC @ MEM
      L
      28.0
      2.0
      9.0
      0.222
      ...
      1.0
      1.0
      0.0
      0.0
      2.0
      7.0
      1.0
      1.0
      0.250000
      LAC
    

    

      5059
      22015.0
      2037.0
      21501009.0
      2016-03-16
      LAC @ HOU
      W
      27.0
      4.0
      9.0
      0.444
      ...
      7.0
      1.0
      1.0
      0.0
      1.0
      14.0
      9.0
      1.0
      0.518519
      LAC
    

    

      5060
      22015.0
      2037.0
      21501001.0
      2016-03-15
      LAC @ SAS
      L
      22.0
      1.0
      9.0
      0.111
      ...
      3.0
      0.0
      0.0
      0.0
      2.0
      6.0
      -20.0
      1.0
      0.272727
      LAC
    

    

      5061
      22015.0
      2037.0
      21500983.0
      2016-03-13
      LAC vs. CLE
      L
      25.0
      3.0
      11.0
      0.273
      ...
      1.0
      0.0
      0.0
      2.0
      2.0
      8.0
      -27.0
      1.0
      0.320000
      LAC
    

    

      5062
      22015.0
      2037.0
      21500972.0
      2016-03-11
      LAC vs. NYK
      W
      19.0
      4.0
      13.0
      0.308
      ...
      1.0
      0.0
      0.0
      1.0
      2.0
      10.0
      -9.0
      1.0
      0.526316
      LAC
    

    

      5063
      22015.0
      2037.0
      21500955.0
      2016-03-09
      LAC @ OKC
      L
      27.0
      6.0
      12.0
      0.500
      ...
      2.0
      1.0
      0.0
      1.0
      0.0
      16.0
      -8.0
      1.0
      0.592593
      LAC
    

    

      5064
      22015.0
      2037.0
      21500942.0
      2016-03-07
      LAC @ DAL
      W
      29.0
      5.0
      15.0
      0.333
      ...
      3.0
      1.0
      0.0
      0.0
      2.0
      15.0
      24.0
      1.0
      0.517241
      LAC
    

    

      5065
      22015.0
      2037.0
      21500929.0
      2016-03-05
      LAC vs. ATL
      L
      29.0
      2.0
      7.0
      0.286
      ...
      1.0
      1.0
      0.0
      2.0
      1.0
      5.0
      -5.0
      1.0
      0.172414
      LAC
    

    

      5066
      22015.0
      2037.0
      21500907.0
      2016-03-02
      LAC vs. OKC
      W
      32.0
      5.0
      14.0
      0.357
      ...
      2.0
      1.0
      0.0
      0.0
      0.0
      12.0
      7.0
      1.0
      0.375000
      LAC
    

    

      5067
      22015.0
      2037.0
      21500890.0
      2016-02-29
      LAC vs. BKN
      W
      25.0
      10.0
      17.0
      0.588
      ...
      1.0
      2.0
      0.0
      3.0
      4.0
      26.0
      11.0
      1.0
      1.040000
      LAC
    

    

      5068
      22015.0
      2037.0
      21500867.0
      2016-02-26
      LAC @ SAC
      W
      26.0
      7.0
      19.0
      0.368
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      21.0
      -3.0
      1.0
      0.807692
      LAC
    

    

      5069
      22015.0
      2037.0
      21500853.0
      2016-02-24
      LAC vs. DEN
      L
      28.0
      8.0
      18.0
      0.444
      ...
      1.0
      0.0
      0.0
      1.0
      3.0
      20.0
      -1.0
      1.0
      0.714286
      LAC
    

    

      5070
      22015.0
      2037.0
      21500839.0
      2016-02-22
      LAC vs. PHX
      W
      17.0
      0.0
      4.0
      0.000
      ...
      0.0
      0.0
      0.0
      1.0
      1.0
      2.0
      -2.0
      1.0
      0.117647
      LAC
    

    

      5071
      22015.0
      2037.0
      21500822.0
      2016-02-20
      LAC vs. GSW
      L
      38.0
      7.0
      18.0
      0.389
      ...
      3.0
      3.0
      0.0
      2.0
      1.0
      25.0
      -11.0
      1.0
      0.657895
      LAC
    

    

      5072
      22015.0
      2037.0
      21500804.0
      2016-02-18
      LAC vs. SAS
      W
      30.0
      7.0
      15.0
      0.467
      ...
      6.0
      0.0
      0.0
      7.0
      1.0
      19.0
      6.0
      1.0
      0.633333
      LAC
    

    

      5073
      22015.0
      2037.0
      21500792.0
      2016-02-10
      LAC @ BOS
      L
      38.0
      4.0
      17.0
      0.235
      ...
      4.0
      0.0
      0.0
      2.0
      3.0
      18.0
      4.0
      1.0
      0.473684
      LAC
    

    

      5074
      22015.0
      2037.0
      21500777.0
      2016-02-08
      LAC @ PHI
      W
      39.0
      9.0
      21.0
      0.429
      ...
      3.0
      2.0
      0.0
      1.0
      1.0
      23.0
      11.0
      1.0
      0.589744
      LAC
    

    

      5075
      22015.0
      2037.0
      21500772.0
      2016-02-07
      LAC @ MIA
      W
      34.0
      7.0
      19.0
      0.368
      ...
      2.0
      0.0
      0.0
      1.0
      1.0
      20.0
      8.0
      1.0
      0.588235
      LAC
    

    

      5076
      22015.0
      2037.0
      21500752.0
      2016-02-05
      LAC @ ORL
      W
      35.0
      8.0
      14.0
      0.571
      ...
      2.0
      3.0
      1.0
      4.0
      4.0
      20.0
      9.0
      1.0
      0.571429
      LAC
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      5096
      22015.0
      2037.0
      21500440.0
      2015-12-25
      LAC @ LAL
      W
      20.0
      4.0
      10.0
      0.400
      ...
      0.0
      1.0
      0.0
      1.0
      1.0
      13.0
      -5.0
      1.0
      0.650000
      LAC
    

    

      5097
      22015.0
      2037.0
      21500418.0
      2015-12-21
      LAC vs. OKC
      L
      24.0
      4.0
      10.0
      0.400
      ...
      0.0
      0.0
      0.0
      0.0
      3.0
      8.0
      0.0
      1.0
      0.333333
      LAC
    

    

      5098
      22015.0
      2037.0
      21500401.0
      2015-12-19
      LAC @ HOU
      L
      31.0
      1.0
      8.0
      0.125
      ...
      1.0
      0.0
      1.0
      1.0
      4.0
      4.0
      7.0
      1.0
      0.129032
      LAC
    

    

      5099
      22015.0
      2037.0
      21500393.0
      2015-12-18
      LAC @ SAS
      L
      17.0
      0.0
      3.0
      0.000
      ...
      3.0
      0.0
      1.0
      0.0
      2.0
      0.0
      -10.0
      1.0
      0.000000
      LAC
    

    

      5100
      22015.0
      2037.0
      21500382.0
      2015-12-16
      LAC vs. MIL
      W
      21.0
      0.0
      3.0
      0.000
      ...
      1.0
      1.0
      0.0
      1.0
      1.0
      2.0
      3.0
      1.0
      0.095238
      LAC
    

    

      5101
      22015.0
      2037.0
      21500359.0
      2015-12-14
      LAC @ DET
      W
      26.0
      5.0
      8.0
      0.625
      ...
      4.0
      1.0
      0.0
      1.0
      0.0
      14.0
      -3.0
      1.0
      0.538462
      LAC
    

    

      5102
      22015.0
      2037.0
      21500344.0
      2015-12-12
      LAC @ BKN
      W
      25.0
      4.0
      8.0
      0.500
      ...
      3.0
      2.0
      0.0
      2.0
      3.0
      9.0
      -3.0
      1.0
      0.360000
      LAC
    

    

      5103
      22015.0
      2037.0
      21500330.0
      2015-12-10
      LAC @ CHI
      L
      22.0
      1.0
      5.0
      0.200
      ...
      2.0
      0.0
      0.0
      1.0
      0.0
      2.0
      -11.0
      1.0
      0.090909
      LAC
    

    

      5104
      22015.0
      2037.0
      21500324.0
      2015-12-09
      LAC @ MIL
      W
      22.0
      3.0
      11.0
      0.273
      ...
      0.0
      0.0
      0.0
      1.0
      0.0
      8.0
      -1.0
      1.0
      0.363636
      LAC
    

    

      5105
      22015.0
      2037.0
      21500311.0
      2015-12-07
      LAC @ MIN
      W
      27.0
      5.0
      11.0
      0.455
      ...
      7.0
      1.0
      0.0
      2.0
      2.0
      18.0
      13.0
      1.0
      0.666667
      LAC
    

    

      5106
      22015.0
      2037.0
      21500298.0
      2015-12-05
      LAC vs. ORL
      W
      31.0
      10.0
      19.0
      0.526
      ...
      5.0
      0.0
      0.0
      1.0
      3.0
      32.0
      7.0
      1.0
      1.032258
      LAC
    

    

      5107
      22015.0
      2037.0
      21500276.0
      2015-12-02
      LAC vs. IND
      L
      37.0
      5.0
      16.0
      0.313
      ...
      3.0
      0.0
      0.0
      4.0
      5.0
      16.0
      -10.0
      0.0
      0.432432
      LAC
    

    

      5108
      22015.0
      2037.0
      21500261.0
      2015-11-30
      LAC vs. POR
      W
      23.0
      4.0
      10.0
      0.400
      ...
      4.0
      1.0
      0.0
      0.0
      2.0
      11.0
      0.0
      1.0
      0.478261
      LAC
    

    

      5109
      22015.0
      2037.0
      21500247.0
      2015-11-29
      LAC vs. MIN
      W
      15.0
      0.0
      4.0
      0.000
      ...
      2.0
      0.0
      0.0
      3.0
      1.0
      0.0
      -1.0
      1.0
      0.000000
      LAC
    

    

      5110
      22015.0
      2037.0
      21500238.0
      2015-11-27
      LAC vs. NOP
      W
      27.0
      2.0
      8.0
      0.250
      ...
      0.0
      1.0
      0.0
      3.0
      0.0
      9.0
      7.0
      1.0
      0.333333
      LAC
    

    

      5111
      22015.0
      2037.0
      21500226.0
      2015-11-25
      LAC vs. UTA
      L
      30.0
      1.0
      9.0
      0.111
      ...
      2.0
      0.0
      0.0
      2.0
      1.0
      3.0
      -2.0
      1.0
      0.100000
      LAC
    

    

      5112
      22015.0
      2037.0
      21500212.0
      2015-11-24
      LAC @ DEN
      W
      24.0
      4.0
      9.0
      0.444
      ...
      2.0
      1.0
      0.0
      0.0
      1.0
      15.0
      7.0
      1.0
      0.625000
      LAC
    

    

      5113
      22015.0
      2037.0
      21500196.0
      2015-11-22
      LAC vs. TOR
      L
      29.0
      1.0
      9.0
      0.111
      ...
      2.0
      1.0
      0.0
      1.0
      1.0
      8.0
      1.0
      1.0
      0.275862
      LAC
    

    

      5114
      22015.0
      2037.0
      21500186.0
      2015-11-20
      LAC @ POR
      L
      27.0
      6.0
      15.0
      0.400
      ...
      1.0
      0.0
      0.0
      1.0
      2.0
      20.0
      -5.0
      1.0
      0.740741
      LAC
    

    

      5115
      22015.0
      2037.0
      21500177.0
      2015-11-19
      LAC vs. GSW
      L
      40.0
      6.0
      16.0
      0.375
      ...
      2.0
      0.0
      0.0
      2.0
      5.0
      15.0
      -9.0
      1.0
      0.375000
      LAC
    

    

      5116
      22015.0
      2037.0
      21500138.0
      2015-11-14
      LAC vs. DET
      W
      41.0
      12.0
      27.0
      0.444
      ...
      8.0
      0.0
      1.0
      3.0
      4.0
      37.0
      21.0
      1.0
      0.902439
      LAC
    

    

      5117
      22015.0
      2037.0
      21500126.0
      2015-11-12
      LAC @ PHX
      L
      25.0
      7.0
      14.0
      0.500
      ...
      2.0
      3.0
      0.0
      5.0
      2.0
      18.0
      -8.0
      1.0
      0.720000
      LAC
    

    

      5118
      22015.0
      2037.0
      21500118.0
      2015-11-11
      LAC @ DAL
      L
      29.0
      3.0
      8.0
      0.375
      ...
      1.0
      0.0
      0.0
      1.0
      4.0
      11.0
      -9.0
      1.0
      0.379310
      LAC
    

    

      5119
      22015.0
      2037.0
      21500105.0
      2015-11-09
      LAC vs. MEM
      W
      20.0
      2.0
      7.0
      0.286
      ...
      1.0
      0.0
      0.0
      2.0
      0.0
      13.0
      19.0
      1.0
      0.650000
      LAC
    

    

      5120
      22015.0
      2037.0
      21500093.0
      2015-11-07
      LAC vs. HOU
      L
      19.0
      1.0
      3.0
      0.333
      ...
      4.0
      1.0
      0.0
      3.0
      0.0
      2.0
      -1.0
      1.0
      0.105263
      LAC
    

    

      5121
      22015.0
      2037.0
      21500069.0
      2015-11-04
      LAC @ GSW
      L
      20.0
      4.0
      15.0
      0.267
      ...
      1.0
      1.0
      0.0
      0.0
      0.0
      8.0
      -10.0
      1.0
      0.400000
      LAC
    

    

      5122
      22015.0
      2037.0
      21500052.0
      2015-11-02
      LAC vs. PHX
      W
      26.0
      2.0
      8.0
      0.250
      ...
      3.0
      1.0
      0.0
      0.0
      2.0
      11.0
      8.0
      1.0
      0.423077
      LAC
    

    

      5123
      22015.0
      2037.0
      21500038.0
      2015-10-31
      LAC vs. SAC
      W
      23.0
      3.0
      10.0
      0.300
      ...
      0.0
      0.0
      0.0
      0.0
      1.0
      10.0
      -11.0
      1.0
      0.434783
      LAC
    

    

      5124
      22015.0
      2037.0
      21500020.0
      2015-10-29
      LAC vs. DAL
      W
      17.0
      6.0
      11.0
      0.545
      ...
      2.0
      0.0
      0.0
      0.0
      0.0
      15.0
      12.0
      1.0
      0.882353
      LAC
    

    

      5125
      22015.0
      2037.0
      21500016.0
      2015-10-28
      LAC @ SAC
      W
      21.0
      5.0
      9.0
      0.556
      ...
      1.0
      1.0
      0.0
      0.0
      2.0
      11.0
      -5.0
      1.0
      0.523810
      LAC
    

  



79 rows × 29 columns



















In [60]:



    


all_game_logs['Team'] = [a[0:3] for a in all_game_logs['MATCHUP']]
all_game_logs['GAME_DATE'] = pd.to_datetime(all_game_logs['GAME_DATE'])
team_stats_sep['GAME_DATE'] = pd.to_datetime(team_stats_sep['GAME_DATE'])
team_data = pd.merge(all_game_logs, team_stats_sep, on=['Team', 'GAME_DATE'])
print len(all_game_logs), len(team_stats_sep), len(team_data)



    


















    






25417 4920 25417

















In [55]:



    


team_data.columns



    


















    
Out[55]:








Index([u'SEASON_ID', u'Player_ID', u'Game_ID_x', u'GAME_DATE', u'MATCHUP',
       u'WL', u'MIN', u'FGM', u'FGA', u'FG_PCT', u'FG3M', u'FG3A', u'FG3_PCT',
       u'FTM', u'FTA', u'FT_PCT', u'OREB', u'DREB', u'REB', u'AST', u'STL',
       u'BLK', u'TOV', u'PF', u'PTS', u'PLUS_MINUS', u'VIDEO_AVAILABLE',
       u'PTS_MIN', u'Team', u'Game_ID_y', u'PTS_Trend', u'BTB'],
      dtype='object')

















In [61]:



    


cor = pd.DataFrame()
feature_list = ['PTS_Trend', 'BTB']
for f in feature_list:
    cor.set_value('PTS', f, np.corrcoef(x=team_data[f], y=team_data['PTS'])[0,1])
cor



    


















    
Out[61]:










  
    

      
      PTS_Trend
      BTB
    

  
  
    

      PTS
      -0.000703
      -0.001779
    

  




















In [62]:



    


%matplotlib inline
x=team_data['PTS_Trend']
y=team_data['PTS']
import matplotlib.pyplot as plt
plt.scatter(x, y, alpha=0.5)
plt.show



    


















    
Out[62]:








<function matplotlib.pyplot.show>










    
























In [63]:



    


%matplotlib inline
x=team_data['BTB']
y=team_data['PTS']
import matplotlib.pyplot as plt
plt.scatter(x, y, alpha=0.5)
plt.show



    


















    
Out[63]:








<function matplotlib.pyplot.show>










    
























In [79]:



    


print len(Player_List)
min_list = []
for p in Player_List:
    minutes = np.mean(Player_List[p].game_logs['MIN'])
    if not np.isnan(minutes):
        min_list.append(minutes)
min_list



    


















    






450










    
Out[79]:








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 11.709090909090909,
 20.551724137931036,
 9.692307692307692,
 4.571428571428571,
 8.518518518518519,
 30.527027027027028,
 2.0,
 28.2125,
 31.951219512195124,
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 34.02857142857143,
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 23.986301369863014,
 32.888888888888886,
 30.954545454545453,
 21.865671641791046,
 21.81967213114754,
 18.575757575757574,
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 16.82456140350877,
 18.641025641025642,
 11.9,
 20.94,
 14.660714285714286,
 32.50632911392405,
 23.666666666666668,
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 14.245901639344263,
 22.64516129032258,
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 37.05194805194805,
 28.01298701298701,
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 15.833333333333334,
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 35.63157894736842,
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 33.54716981132076,
 30.513513513513512,
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 13.695652173913043,
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 42.0,
 18.19736842105263,
 14.64864864864865,
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 18.583333333333332,
 17.017241379310345,
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 32.12676056338028,
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 19.076923076923077,
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 17.36764705882353,
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 28.196969696969695,
 19.893939393939394,
 5.333333333333333,
 14.745454545454546,
 10.872727272727273,
 8.863636363636363,
 27.98780487804878,
 14.282608695652174,
 15.919354838709678,
 28.733333333333334,
 32.35443037974684,
 8.892857142857142,
 6.324324324324325,
 7.260869565217392,
 32.10526315789474,
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 32.21518987341772,
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 6.862068965517241,
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 6.375,
 20.265822784810126,
 21.440677966101696,
 7.914893617021277,
 26.911392405063292]

















In [80]:



    


#plt.hist(min_list, bins=np.arange(np.min(min_list), np.max(min_list)+1))
plt.hist(min_list)



    


















    
Out[80]:








(array([ 19.,  39.,  45.,  71.,  75.,  49.,  54.,  59.,  31.,   2.]),
 array([  2.,   6.,  10.,  14.,  18.,  22.,  26.,  30.,  34.,  38.,  42.]),
 <a list of 10 Patch objects>)










    
























In [ ]:

Content source: mprego/NBA

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