In [20]:
#Import Packages
import pandas as pd
import numpy as np
from scipy.stats import norm
from Schedule.Schedule import Schedule
from Schedule.Stats import Stats
from Regression.ELO import ELO
from Regression.Game_Scores_v2 import Game_Scores
%matplotlib inline
In [2]:
#Get schedule of games
sched_2014 = Schedule(b_dt = '10/1/2014')
sched_2015 = Schedule(b_dt = '10/1/2015')
In [3]:
#Adds four factors for each game
sched_2014.add_four_factors()
sched_2015.add_four_factors()
Out[3]:
Team_ID_home
Game_ID
FGM_home
FGA_home
FG_PCT_home
FG3M_home
FG3A_home
FG3_PCT_home
FTM_home
FTA_home
...
Home Team
Away Team
H_FF_EFG
H_FF_ORB
H_FF_FTFGA
H_FF_TOV
A_FF_EFG
A_FF_ORB
A_FF_FTFGA
A_FF_TOV
0
1610612737
21501188
46
88
0.523
17
33
0.515
9
11
...
ATL
BOS
0.619318
0.142857
0.102273
0.162152
0.505556
0.204082
0.177778
0.142099
1
1610612737
21501173
33
76
0.434
12
32
0.375
17
21
...
ATL
TOR
0.513158
0.116279
0.223684
0.139425
0.428571
0.181818
0.178571
0.114823
2
1610612737
21501157
39
95
0.411
11
34
0.324
14
19
...
ATL
PHX
0.468421
0.245283
0.147368
0.150432
0.444444
0.229167
0.222222
0.223881
3
1610612737
21501131
38
95
0.400
9
30
0.300
23
26
...
ATL
CLE
0.447368
0.098039
0.242105
0.128822
0.459184
0.203704
0.204082
0.106534
4
1610612737
21501076
41
97
0.423
5
32
0.156
14
17
...
ATL
MIL
0.448454
0.340000
0.144330
0.111698
0.434524
0.363636
0.202381
0.158831
5
1610612737
21501048
38
78
0.487
13
33
0.394
13
17
...
ATL
WAS
0.570513
0.054054
0.166667
0.143619
0.576923
0.225000
0.131868
0.090180
6
1610612737
21501029
44
88
0.500
14
38
0.368
7
12
...
ATL
HOU
0.579545
0.200000
0.079545
0.159553
0.422619
0.326087
0.309524
0.170614
7
1610612737
21501015
40
80
0.500
12
26
0.462
24
28
...
ATL
DEN
0.575000
0.189189
0.300000
0.123305
0.482759
0.214286
0.160920
0.140732
8
1610612737
21500984
40
85
0.471
15
30
0.500
9
10
...
ATL
IND
0.558824
0.225000
0.105882
0.157233
0.408046
0.145833
0.045977
0.161486
9
1610612737
21500974
36
84
0.429
11
34
0.324
12
17
...
ATL
MEM
0.494048
0.173913
0.142857
0.116427
0.378947
0.338983
0.115789
0.164677
10
1610612737
21500878
38
77
0.494
8
17
0.471
3
5
...
ATL
CHA
0.545455
0.162162
0.038961
0.170068
0.376543
0.192308
0.185185
0.068871
11
1610612737
21500865
37
89
0.416
7
34
0.206
22
24
...
ATL
CHI
0.455056
0.288889
0.247191
0.112751
0.392045
0.326923
0.215909
0.198649
12
1610612737
21500836
36
86
0.419
10
34
0.294
10
16
...
ATL
GSW
0.476744
0.177778
0.116279
0.166601
0.516854
0.133333
0.112360
0.128357
13
1610612737
21500819
44
106
0.415
9
41
0.220
12
18
...
ATL
MIL
0.457547
0.172414
0.113208
0.133422
0.456731
0.303571
0.211538
0.143970
14
1610612737
21500808
41
87
0.471
16
36
0.444
13
13
...
ATL
MIA
0.563218
0.181818
0.149425
0.198638
0.545455
0.227273
0.215909
0.134202
15
1610612737
21500780
43
92
0.467
12
29
0.414
12
20
...
ATL
ORL
0.532609
0.163265
0.130435
0.131086
0.543011
0.133333
0.172043
0.118527
16
1610612737
21500750
39
76
0.513
10
27
0.370
14
20
...
ATL
IND
0.578947
0.081081
0.184211
0.154959
0.489011
0.395833
0.076923
0.200084
17
1610612737
21500723
42
80
0.525
14
35
0.400
14
19
...
ATL
DAL
0.612500
0.156250
0.175000
0.152501
0.405882
0.181818
0.329412
0.061400
18
1610612737
21500687
33
79
0.418
10
23
0.435
7
14
...
ATL
LAC
0.481013
0.177778
0.088608
0.221864
0.417647
0.180000
0.164706
0.114298
19
1610612737
21500620
41
80
0.513
9
19
0.474
7
10
...
ATL
ORL
0.568750
0.114286
0.087500
0.174538
0.379310
0.203704
0.172414
0.132924
20
1610612737
21500602
44
79
0.557
8
29
0.276
18
24
...
ATL
BKN
0.607595
0.147059
0.227848
0.105753
0.474359
0.166667
0.153846
0.176422
21
1610612737
21500551
49
94
0.521
10
24
0.417
12
15
...
ATL
CHI
0.574468
0.190476
0.127660
0.139405
0.475904
0.272727
0.313253
0.198788
22
1610612737
21500521
37
87
0.425
15
39
0.385
12
19
...
ATL
NYK
0.511494
0.285714
0.137931
0.137770
0.505952
0.289474
0.261905
0.097234
23
1610612737
21500442
47
88
0.534
8
24
0.333
15
18
...
ATL
NYK
0.579545
0.250000
0.170455
0.094817
0.524691
0.307692
0.160494
0.223396
24
1610612737
21500429
43
89
0.483
6
20
0.300
15
20
...
ATL
DET
0.516854
0.232558
0.168539
0.128968
0.466292
0.288889
0.191011
0.133946
25
1610612737
21500413
37
77
0.481
9
29
0.310
23
28
...
ATL
POR
0.538961
0.052632
0.298701
0.102754
0.500000
0.325581
0.197531
0.190114
26
1610612737
21500376
48
78
0.615
10
21
0.476
21
24
...
ATL
PHI
0.679487
0.074074
0.269231
0.121753
0.600000
0.233333
0.213333
0.218775
27
1610612737
21500360
33
84
0.393
8
33
0.242
14
15
...
ATL
MIA
0.440476
0.152174
0.166667
0.143443
0.488764
0.333333
0.146067
0.156904
28
1610612737
21500347
30
80
0.375
5
24
0.208
13
16
...
ATL
SAS
0.406250
0.140000
0.162500
0.175185
0.554054
0.187500
0.283784
0.228447
29
1610612737
21500286
37
74
0.500
10
26
0.385
16
20
...
ATL
LAL
0.567568
0.250000
0.216216
0.187225
0.447674
0.306122
0.116279
0.172563
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1200
1610612762
21500860
33
77
0.429
3
17
0.176
9
12
...
UTA
SAS
0.448052
0.105263
0.116883
0.123208
0.537975
0.250000
0.139241
0.127492
1201
1610612762
21500843
38
74
0.514
10
24
0.417
31
38
...
UTA
HOU
0.581081
0.250000
0.418919
0.202468
0.477011
0.250000
0.356322
0.129582
1202
1610612762
21500817
37
68
0.544
10
23
0.435
27
44
...
UTA
BOS
0.617647
0.235294
0.397059
0.158966
0.413580
0.156863
0.320988
0.073038
1203
1610612762
21500758
30
77
0.390
8
27
0.296
16
23
...
UTA
MIL
0.441558
0.260870
0.207792
0.166445
0.471831
0.175000
0.197183
0.214500
1204
1610612762
21500743
33
74
0.446
5
22
0.227
14
20
...
UTA
DEN
0.479730
0.200000
0.189189
0.157658
0.400000
0.230769
0.357143
0.156390
1205
1610612762
21500728
38
85
0.447
7
18
0.389
22
33
...
UTA
CHI
0.488235
0.227273
0.258824
0.135240
0.461111
0.166667
0.144444
0.133588
1206
1610612762
21500704
40
76
0.526
10
25
0.400
13
16
...
UTA
MIN
0.592105
0.285714
0.171053
0.151093
0.480000
0.302326
0.240000
0.160110
1207
1610612762
21500690
40
81
0.494
12
31
0.387
10
15
...
UTA
CHA
0.567901
0.189189
0.123457
0.110375
0.411765
0.116279
0.250000
0.199468
1208
1610612762
21500673
34
85
0.400
9
29
0.310
15
18
...
UTA
DET
0.452941
0.255319
0.176471
0.138416
0.526667
0.159091
0.213333
0.108085
1209
1610612762
21500608
41
83
0.494
12
24
0.500
15
18
...
UTA
LAL
0.566265
0.282051
0.180723
0.111210
0.394444
0.285714
0.122222
0.145955
1210
1610612762
21500591
35
83
0.422
6
34
0.176
25
34
...
UTA
SAC
0.457831
0.200000
0.301205
0.128764
0.493243
0.333333
0.405405
0.160462
1211
1610612762
21500555
39
71
0.549
9
20
0.450
11
18
...
UTA
MIA
0.612676
0.172414
0.154930
0.177936
0.451220
0.319149
0.109756
0.185833
1212
1610612762
21500518
30
75
0.400
12
29
0.414
19
25
...
UTA
HOU
0.480000
0.348837
0.253333
0.174419
0.566176
0.171429
0.235294
0.183402
1213
1610612762
21500503
31
75
0.413
9
27
0.333
21
28
...
UTA
MEM
0.473333
0.219512
0.280000
0.169635
0.450617
0.170213
0.172840
0.115256
1214
1610612762
21500489
43
86
0.500
15
33
0.455
8
11
...
UTA
POR
0.587209
0.279070
0.093023
0.059637
0.551282
0.210526
0.128205
0.116063
1215
1610612762
21500467
28
84
0.333
7
26
0.269
32
36
...
UTA
PHI
0.375000
0.340909
0.380952
0.150240
0.447674
0.173913
0.162791
0.172480
1216
1610612762
21500452
36
74
0.486
10
27
0.370
22
27
...
UTA
LAC
0.554054
0.228571
0.297297
0.170430
0.563291
0.200000
0.253165
0.126904
1217
1610612762
21500417
36
79
0.456
9
25
0.360
29
37
...
UTA
PHX
0.512658
0.261905
0.367089
0.106067
0.436709
0.200000
0.253165
0.140268
1218
1610612762
21500395
34
73
0.466
10
25
0.400
19
25
...
UTA
DEN
0.534247
0.147059
0.260274
0.150538
0.430380
0.306122
0.253165
0.157303
1219
1610612762
21500380
32
67
0.478
7
20
0.350
23
29
...
UTA
NOP
0.529851
0.151515
0.343284
0.138313
0.534247
0.216216
0.356164
0.114521
1220
1610612762
21500341
33
78
0.423
8
28
0.286
16
24
...
UTA
OKC
0.474359
0.244444
0.205128
0.124210
0.506329
0.282051
0.177215
0.147660
1221
1610612762
21500327
39
80
0.488
9
21
0.429
19
23
...
UTA
NYK
0.543750
0.236842
0.237500
0.147183
0.448718
0.142857
0.192308
0.107666
1222
1610612762
21500297
43
92
0.467
8
23
0.348
28
37
...
UTA
IND
0.510870
0.372549
0.304348
0.143843
0.494624
0.255319
0.290323
0.135184
1223
1610612762
21500279
31
72
0.431
14
33
0.424
18
24
...
UTA
ORL
0.527778
0.138889
0.250000
0.196769
0.494382
0.209302
0.168539
0.103520
1224
1610612762
21500259
40
89
0.449
6
19
0.316
17
22
...
UTA
GSW
0.483146
0.243902
0.191011
0.082747
0.573171
0.358974
0.146341
0.167411
1225
1610612762
21500243
38
82
0.463
9
18
0.500
16
21
...
UTA
NOP
0.518293
0.279070
0.195122
0.159168
0.456522
0.097561
0.347826
0.169851
1226
1610612762
21500208
28
73
0.384
5
19
0.263
28
40
...
UTA
OKC
0.417808
0.302326
0.383562
0.212982
0.616883
0.176471
0.207792
0.168563
1227
1610612762
21500173
35
72
0.486
7
22
0.318
16
22
...
UTA
TOR
0.534722
0.200000
0.222222
0.185428
0.456250
0.209302
0.200000
0.150667
1228
1610612762
21500091
31
74
0.419
12
27
0.444
15
18
...
UTA
MEM
0.500000
0.225000
0.202703
0.223595
0.362637
0.245283
0.142857
0.142624
1229
1610612762
21500068
33
88
0.375
5
24
0.208
21
33
...
UTA
POR
0.403409
0.313725
0.238636
0.094221
0.603896
0.351351
0.194805
0.195715
1230 rows × 52 columns
In [4]:
#Gets and filters games
games_2014 = sched_2014.get_games()
games_2015 = sched_2015.get_games()
#games_2014 = games_2014.sort_values(by='GAME_DATE').reset_index(drop=True).ix[100:, :]
#games_2015 = games_2015.sort_values(by='GAME_DATE').reset_index(drop=True).ix[100:, :]
In [ ]:
games_2014.columns
In [16]:
#Creates ELO scores
games = games_2014.append(games_2015).reset_index(drop=True)
elo = ELO(games, 'GAME_DATE', 'Home Team', 'Away Team', 'PTS_home', 'PTS_away')
elo_data = elo.create_elo(1500, 20, 100, '538')
elo_list = []
for tm in elo_data.columns.values:
elo_list.append(elo_data[tm])
elo_list
Out[16]:
[2014-10-28 1500
2014-10-29 1499.47
2014-10-30 1499.47
2014-10-31 1500.74
2014-11-01 1499.54
2014-11-02 1499.54
2014-11-03 1499.54
2014-11-04 1498.82
2014-11-05 1499.72
2014-11-06 1499.72
2014-11-07 1499.12
2014-11-08 1499.55
2014-11-09 1499.55
2014-11-10 1499.55
2014-11-11 1500.47
2014-11-12 1500.47
2014-11-13 1500.47
2014-11-14 1498.94
2014-11-15 1498.94
2014-11-16 1498.16
2014-11-17 1498.16
2014-11-18 1498.85
2014-11-19 1498.26
2014-11-20 1498.26
2014-11-21 1495.24
2014-11-22 1496.25
2014-11-23 1496.25
2014-11-24 1496.25
2014-11-25 1497.52
2014-11-26 1496.16
...
2016-03-14 1487.75
2016-03-15 1489.14
2016-03-16 1489.14
2016-03-17 1490.29
2016-03-18 1490.29
2016-03-19 1490.29
2016-03-20 1491.19
2016-03-21 1490.75
2016-03-22 1490.75
2016-03-23 1489.71
2016-03-24 1489.71
2016-03-25 1488.54
2016-03-26 1490.25
2016-03-27 1490.25
2016-03-28 1490.25
2016-03-29 1490.25
2016-03-30 1491.44
2016-03-31 1491.44
2016-04-01 1492.05
2016-04-02 1492.05
2016-04-03 1492.63
2016-04-05 1494.57
2016-04-06 1494.57
2016-04-07 1494.57
2016-04-08 1493.12
2016-04-09 1493.12
2016-04-10 1492.75
2016-04-11 1491.69
2016-04-12 1491.69
2016-04-13 1492.33
Name: MIL, dtype: object, 2014-10-28 1500
2014-10-29 1498.59
2014-10-30 1498.59
2014-10-31 1498.59
2014-11-01 1500.56
2014-11-02 1499.91
2014-11-03 1499.91
2014-11-04 1499.91
2014-11-05 1501.5
2014-11-06 1501.5
2014-11-07 1501.5
2014-11-08 1500.48
2014-11-09 1499.22
2014-11-10 1499.22
2014-11-11 1500.35
2014-11-12 1500.35
2014-11-13 1501.34
2014-11-14 1501.34
2014-11-15 1503.42
2014-11-16 1501.85
2014-11-17 1501.85
2014-11-18 1501.85
2014-11-19 1501.85
2014-11-20 1501.85
2014-11-21 1503.18
2014-11-22 1503.18
2014-11-23 1502.54
2014-11-24 1502.54
2014-11-25 1501.18
2014-11-26 1499.94
...
2016-03-14 1517.26
2016-03-15 1517.26
2016-03-16 1519.93
2016-03-17 1519.93
2016-03-18 1518.54
2016-03-19 1517.55
2016-03-20 1517.55
2016-03-21 1516.91
2016-03-22 1516.91
2016-03-23 1518.42
2016-03-24 1518.42
2016-03-25 1519.4
2016-03-26 1519.4
2016-03-27 1520.62
2016-03-28 1520.62
2016-03-29 1521.6
2016-03-30 1520.83
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2014-11-22 1499.24
2014-11-23 1499.24
2014-11-24 1496.76
2014-11-25 1496.76
2014-11-26 1498
...
2016-03-14 1490.91
2016-03-15 1491.75
2016-03-16 1490.77
2016-03-17 1490.77
2016-03-18 1491.48
2016-03-19 1491.48
2016-03-20 1490.72
2016-03-21 1489.54
2016-03-22 1489.54
2016-03-23 1488.02
2016-03-24 1488.02
2016-03-25 1486.83
2016-03-26 1488.75
2016-03-27 1488.75
2016-03-28 1488.75
2016-03-29 1491.37
2016-03-30 1491.37
2016-03-31 1489.83
2016-04-01 1489.23
2016-04-02 1489.23
2016-04-03 1490.51
2016-04-05 1490.51
2016-04-06 1491.1
2016-04-07 1491.1
2016-04-08 1491.71
2016-04-09 1491.71
2016-04-10 1489.81
2016-04-11 1490.87
2016-04-12 1490.87
2016-04-13 1489.47
Name: ORL, dtype: object, 2014-10-28 1500
2014-10-29 1501.78
2014-10-30 1501.78
2014-10-31 1502.55
2014-11-01 1500.34
2014-11-02 1500.34
2014-11-03 1500.34
2014-11-04 1499.62
2014-11-05 1500.64
2014-11-06 1500.64
2014-11-07 1501.09
2014-11-08 1501.09
2014-11-09 1502.35
2014-11-10 1502.35
2014-11-11 1502.35
2014-11-12 1503.34
2014-11-13 1503.34
2014-11-14 1504.18
2014-11-15 1502.85
2014-11-16 1502.85
2014-11-17 1502.26
2014-11-18 1502.26
2014-11-19 1501.82
2014-11-20 1501.82
2014-11-21 1500
2014-11-22 1498.33
2014-11-23 1498.33
2014-11-24 1497.65
2014-11-25 1497.65
2014-11-26 1498.63
...
2016-03-14 1485.6
2016-03-15 1485.6
2016-03-16 1485.6
2016-03-17 1483.03
2016-03-18 1482.38
2016-03-19 1482.38
2016-03-20 1482.38
2016-03-21 1483.08
2016-03-22 1483.08
2016-03-23 1484.35
2016-03-24 1484.35
2016-03-25 1482.46
2016-03-26 1482.96
2016-03-27 1482.96
2016-03-28 1482.2
2016-03-29 1482.2
2016-03-30 1481.01
2016-03-31 1481.01
2016-04-01 1481.78
2016-04-02 1481.78
2016-04-03 1483.01
2016-04-05 1481.71
2016-04-06 1481.71
2016-04-07 1480.8
2016-04-08 1480.8
2016-04-09 1479.21
2016-04-10 1479.21
2016-04-11 1479.79
2016-04-12 1479.79
2016-04-13 1480.88
Name: PHX, dtype: object, 2014-10-28 1500
2014-10-29 1501.26
2014-10-30 1501.26
2014-10-31 1502.11
2014-11-01 1500.97
2014-11-02 1500.97
2014-11-03 1500.97
2014-11-04 1501.69
2014-11-05 1501.16
2014-11-06 1501.16
2014-11-07 1500.56
2014-11-08 1501.34
2014-11-09 1501.34
2014-11-10 1502.54
2014-11-11 1502.54
2014-11-12 1501.82
2014-11-13 1501.82
2014-11-14 1503.39
2014-11-15 1502.49
2014-11-16 1502.49
2014-11-17 1502.49
2014-11-18 1502.49
2014-11-19 1503.02
2014-11-20 1503.02
2014-11-21 1503.02
2014-11-22 1504.69
2014-11-23 1504.69
2014-11-24 1503.67
2014-11-25 1503.67
2014-11-26 1502.83
...
2016-03-14 1505.64
2016-03-15 1506.4
2016-03-16 1506.4
2016-03-17 1507.18
2016-03-18 1507.18
2016-03-19 1507.77
2016-03-20 1507.77
2016-03-21 1509.27
2016-03-22 1509.27
2016-03-23 1509.27
2016-03-24 1510.24
2016-03-25 1510.24
2016-03-26 1509.1
2016-03-27 1509.7
2016-03-28 1509.7
2016-03-29 1510.16
2016-03-30 1510.16
2016-03-31 1511.69
2016-04-01 1511.69
2016-04-02 1510.58
2016-04-03 1509.94
2016-04-05 1509.94
2016-04-06 1511.34
2016-04-07 1511.34
2016-04-08 1510
2016-04-09 1510
2016-04-10 1511.99
2016-04-11 1511.99
2016-04-12 1513.23
2016-04-13 1512.59
Name: IND, dtype: object, 2014-10-28 1501.59
2014-10-29 1501.59
2014-10-30 1501.59
2014-10-31 1501.59
2014-11-01 1502.24
2014-11-02 1502.24
2014-11-03 1500.98
2014-11-04 1502.03
2014-11-05 1502.03
2014-11-06 1502.03
2014-11-07 1502.03
2014-11-08 1501.66
2014-11-09 1501.66
2014-11-10 1500.74
2014-11-11 1500.74
2014-11-12 1501.66
2014-11-13 1501.66
2014-11-14 1505.01
2014-11-15 1505.01
2014-11-16 1505.01
2014-11-17 1503.96
2014-11-18 1503.24
2014-11-19 1503.24
2014-11-20 1503.24
2014-11-21 1501.45
2014-11-22 1500.38
2014-11-23 1500.38
2014-11-24 1500.38
2014-11-25 1501.34
2014-11-26 1501.34
...
2016-03-14 1493.81
2016-03-15 1493.81
2016-03-16 1492.57
2016-03-17 1492.57
2016-03-18 1493.21
2016-03-19 1493.21
2016-03-20 1493.91
2016-03-21 1493.91
2016-03-22 1495.1
2016-03-23 1495.1
2016-03-24 1494.12
2016-03-25 1494.12
2016-03-26 1495.84
2016-03-27 1495.84
2016-03-28 1496.82
2016-03-29 1496.82
2016-03-30 1495.85
2016-03-31 1496.68
2016-04-01 1496.68
2016-04-02 1496.68
2016-04-03 1495.23
2016-04-05 1493.81
2016-04-06 1492.91
2016-04-07 1492.91
2016-04-08 1493.89
2016-04-09 1495.48
2016-04-10 1495.48
2016-04-11 1496.13
2016-04-12 1496.13
2016-04-13 1493.45
Name: NOP, dtype: object]
In [22]:
#Adds ELO to schedule
for index, game in games_2014.iterrows():
h_team = game['Home Team']
game_dt = game['GAME_DATE']
poss_elo = elo_data[elo_data.index < game_dt]
if len(poss_elo) == 0:
h_elo_score = 1500
else:
h_elo_score = poss_elo.tail(1)[h_team][0]
games_2014.set_value(index, 'H_ELO', h_elo_score)
games_2014.set_value(index, 'H_ELO_std', norm.cdf((h_elo_score - elo_mean)*1.0/elo_std))
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_2014.set_value(index, 'A_ELO', a_elo_score)
games_2014.set_value(index, 'A_ELO_std', norm.cdf((a_elo_score - elo_mean)*1.0/elo_std))
In [24]:
#Normalizes by ELO
hff_cols = [x for x in games_2014.columns.values if 'H_FF' in x]
for col in hff_cols:
norm_ff = [x*y for x,y in zip(games_2014[col], games_2014['H_ELO_std'])]
games_2014[col] = norm_ff
aff_cols = [x for x in games_2014.columns.values if 'A_FF' in x]
for col in aff_cols:
norm_ff = [x*y for x,y in zip(games_2014[col], games_2014['A_ELO_std'])]
games_2014[col] = norm_ff
In [26]:
#Create last n statistics
games_2014['Pts_diff'] = [x-y for x,y in zip(games_2014['PTS_home'], games_2014['PTS_away'])]
games_2014 = games_2014[['Pts_diff', 'PTS_home', 'PTS_away', 'GAME_DATE', 'WL', 'Home Team', 'Away Team', 'H_FF_EFG', 'H_FF_ORB', 'H_FF_FTFGA', 'H_FF_TOV', 'A_FF_EFG', 'A_FF_ORB', 'A_FF_FTFGA', 'A_FF_TOV']]
games_2014 = games_2014.rename(index = str, columns = {'PTS_away':'A_PTS', 'PTS_home':'H_PTS', 'WL':'H_WL'})
games_2014['H_WL'] = [1 if x=='W' else 0 for x in games_2014['H_WL']]
games_2014['A_WL'] = [1-x for x in games_2014['H_WL']]
stats_2014 = Stats(games_2014, 'avg', 'GAME_DATE', 'Home Team', 'Away Team', 'Pts_diff')
stats_5 = stats_2014.get_lastn_stats(5)
games_2015['Pts_diff'] = [x-y for x,y in zip(games_2015['PTS_home'], games_2015['PTS_away'])]
games_2015 = games_2015[['Pts_diff', 'PTS_home', 'PTS_away', 'GAME_DATE', 'WL', 'Home Team', 'Away Team', 'H_FF_EFG', 'H_FF_ORB', 'H_FF_FTFGA', 'H_FF_TOV', 'A_FF_EFG', 'A_FF_ORB', 'A_FF_FTFGA', 'A_FF_TOV']]
games_2015 = games_2015.rename(index = str, columns = {'PTS_away':'A_PTS', 'PTS_home':'H_PTS', 'WL':'H_WL'})
games_2015['H_WL'] = [1 if x=='W' else 0 for x in games_2015['H_WL']]
games_2015['A_WL'] = [1-x for x in games_2015['H_WL']]
stats_2015 = Stats(games_2015, 'avg', 'GAME_DATE', 'Home Team', 'Away Team', 'Pts_diff')
stats_5 = stats_5.append(stats_2015.get_lastn_stats(5))
stats_5.to_csv('stats_5.csv', index = False)
In [27]:
#filters out games with insufficient data
print len(stats_5)
stats_5 = stats_5[stats_5['H_PTS_5']!=-1]
print len(stats_5)
stats_5 = stats_5[stats_5['A_PTS_5']!=-1]
print len(stats_5)
2460
2427
2426
In [28]:
# Gets correlation of variables to target variable
stats_2015.get_correl(stats_5)
Out[28]:
Correlation
H_PTS_5
0.181956
H_O_PTS_5
-0.102344
A_PTS_5
-0.129266
A_O_PTS_5
0.128906
H_WL_5
0.218901
H_O_WL_5
-0.218901
A_WL_5
-0.186963
A_O_WL_5
0.186963
H_FF_EFG_5
0.081710
H_O_FF_EFG_5
0.005367
A_FF_EFG_5
-0.064832
A_O_FF_EFG_5
0.035255
H_FF_ORB_5
0.069607
H_O_FF_ORB_5
0.006655
A_FF_ORB_5
-0.057203
A_O_FF_ORB_5
0.024671
H_FF_FTFGA_5
0.049785
H_O_FF_FTFGA_5
0.013526
A_FF_FTFGA_5
-0.050100
A_O_FF_FTFGA_5
0.034866
H_FF_TOV_5
0.052600
H_O_FF_TOV_5
0.017478
A_FF_TOV_5
-0.050366
A_O_FF_TOV_5
0.008414
In [29]:
x = stats_5.drop('Pts_diff', axis=1)
gs = Game_Scores(stats_5, x, 'Pts_diff')
In [30]:
gs.create_rank_order_graph()
//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 [31]:
gs.get_model().get_mse()
Out[31]:
144.2439012108438
In [ ]:
Content source: mprego/NBA
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