In [15]:
from nba_py import team
import pandas as pd
In [3]:
#get list of teams
team_list = team.TeamList()
team_list.info()
Out[3]:
LEAGUE_ID
TEAM_ID
MIN_YEAR
MAX_YEAR
ABBREVIATION
0
00
1610612737
1949
2015
ATL
1
00
1610612738
1946
2015
BOS
2
00
1610612739
1970
2015
CLE
3
00
1610612740
2002
2015
NOP
4
00
1610612741
1966
2015
CHI
5
00
1610612742
1980
2015
DAL
6
00
1610612743
1976
2015
DEN
7
00
1610612744
1946
2015
GSW
8
00
1610612745
1967
2015
HOU
9
00
1610612746
1970
2015
LAC
10
00
1610612747
1948
2015
LAL
11
00
1610612748
1988
2015
MIA
12
00
1610612749
1968
2015
MIL
13
00
1610612750
1989
2015
MIN
14
00
1610612751
1976
2015
BKN
15
00
1610612752
1946
2015
NYK
16
00
1610612753
1989
2015
ORL
17
00
1610612754
1976
2015
IND
18
00
1610612755
1949
2015
PHI
19
00
1610612756
1968
2015
PHX
20
00
1610612757
1970
2015
POR
21
00
1610612758
1948
2015
SAC
22
00
1610612759
1976
2015
SAS
23
00
1610612760
1967
2015
OKC
24
00
1610612761
1995
2015
TOR
25
00
1610612763
1995
2015
MEM
26
00
1610612764
1961
2015
WAS
27
00
1610612765
1948
2015
DET
28
00
1610612766
1988
2015
CHA
29
00
1610612762
1974
2015
UTA
30
00
1610610029
1948
1948
None
31
00
1610610025
1946
1949
None
32
00
1610610034
1946
1949
None
33
00
1610610036
1946
1950
None
34
00
1610610024
1947
1954
None
35
00
1610610027
1949
1949
None
36
00
1610610030
1949
1952
None
37
00
1610610033
1949
1949
None
38
00
1610610037
1949
1949
None
39
00
1610610031
1946
1946
None
40
00
1610610023
1949
1949
None
41
00
1610610028
1946
1946
None
42
00
1610610026
1946
1946
None
43
00
1610610032
1946
1948
None
44
00
1610610035
1946
1946
None
In [7]:
cle_id = '1610612739'
cle_summary = team.TeamSummary(team_id=cle_id, season = '2015-16')
#cle_summary.info()
cle_summary.season_ranks()
Out[7]:
LEAGUE_ID
SEASON_ID
TEAM_ID
PTS_RANK
PTS_PG
REB_RANK
REB_PG
AST_RANK
AST_PG
OPP_PTS_RANK
OPP_PTS_PG
0
00
22015
1610612739
8
104.3
9
44.5
13
22.7
4
98.3
In [8]:
#General dashboard
cle_db = team._TeamDashboard(team_id = cle_id, )
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
<ipython-input-8-c2cc7cbcf5bd> in <module>()
1 #General dashboard
----> 2 cle_db = team._TeamDashboard(team_id = cle_id)
//anaconda/lib/python2.7/site-packages/nba_py/team.pyc in __init__(self, team_id, measure_type, per_mode, plus_minus, pace_adjust, rank, league_id, season, season_type, po_round, outcome, location, month, season_segment, date_from, date_to, opponent_team_id, vs_conference, vs_division, game_segment, period, shot_clock_range, last_n_games)
103 'Period': period,
104 'ShotClockRange': shot_clock_range,
--> 105 'LastNGames': last_n_games})
106
107 def overall(self):
//anaconda/lib/python2.7/site-packages/nba_py/__init__.pyc in _get_json(endpoint, params)
72 headers=HEADERS)
73 # print _get.url
---> 74 _get.raise_for_status()
75 return _get.json()
76
//anaconda/lib/python2.7/site-packages/requests/models.pyc in raise_for_status(self)
838
839 if http_error_msg:
--> 840 raise HTTPError(http_error_msg, response=self)
841
842 def close(self):
HTTPError: 404 Client Error: Not Found for url: http://stats.nba.com/stats/?PlusMinus=N&TeamID=1610612739&Location=&Month=0&SeasonType=Regular+Season&Season=2015-16&PaceAdjust=N&DateFrom=&VsConference=&OpponentTeamID=0&DateTo=&GameSegment=&LastNGames=0&VsDivision=&LeagueID=00&Outcome=&MeasureType=Base&PORound=0&PerMode=PerGame&SeasonSegment=&Period=0&Rank=N&ShotClockRange=
In [12]:
#Splits
cle_splits = team.TeamGeneralSplits(team_id=cle_id)
cle_splits.days_rest()
Out[12]:
GROUP_SET
GROUP_VALUE
TEAM_DAYS_REST_RANGE
GP
W
L
W_PCT
MIN
FGM
FGA
...
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
CFID
CFPARAMS
0
Days Rest
0 Days Rest
0 Days Rest
19
11
8
0.579
48.8
37.6
84.5
...
14.3
6.6
4.0
4.7
21.1
19.5
100.2
1.1
152
0 Days Rest
1
Days Rest
1 Days Rest
1 Days Rest
44
34
10
0.773
48.3
39.7
84.3
...
13.6
6.8
3.7
4.3
20.0
20.6
107.2
7.7
152
1 Days Rest
2
Days Rest
2 Days Rest
2 Days Rest
14
8
6
0.571
48.4
37.1
81.4
...
13.6
6.7
3.5
3.9
20.9
22.3
102.4
4.5
152
2 Days Rest
3
Days Rest
3 Days Rest
3 Days Rest
3
3
0
1.000
48.0
37.3
83.3
...
10.7
6.7
4.3
4.3
17.7
19.7
100.3
20.7
152
3 Days Rest
4
Days Rest
6+ Days Rest
6+ Days Rest
2
1
1
0.500
48.0
40.0
92.5
...
11.0
6.5
7.5
7.0
21.5
20.5
100.5
4.5
152
6+ Days Rest
5 rows × 31 columns
In [14]:
#last game splits
cle_g_splits = team.TeamLastNGamesSplits(team_id = cle_id)
cle_g_splits.last5()
Out[14]:
GROUP_SET
GROUP_VALUE
GP
W
L
W_PCT
MIN
FGM
FGA
FG_PCT
...
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
CFID
CFPARAMS
0
Last 5 Games
Last 5 Games
5
2
3
0.4
49.0
39.6
82.4
0.481
...
13.4
5.8
5.4
5.0
17.6
18.6
107.8
5.0
162
Last 5 Games
1 rows × 30 columns
In [17]:
#game logs
cle_logs = team.TeamGameLogs(team_id = cle_id)
cle_logs.info()
Out[17]:
Team_ID
Game_ID
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
...
FT_PCT
OREB
DREB
REB
AST
STL
BLK
TOV
PF
PTS
0
1610612739
0021501220
APR 13, 2016
CLE vs. DET
L
265
46
97
0.474
7
...
0.733
8
35
43
21
4
7
10
23
110
1
1610612739
0021501203
APR 11, 2016
CLE vs. ATL
W
240
40
83
0.482
11
...
0.900
9
38
47
17
9
4
15
14
109
2
1610612739
0021501191
APR 09, 2016
CLE @ CHI
L
240
36
83
0.434
19
...
0.611
12
30
42
24
5
5
15
18
102
3
1610612739
0021501165
APR 06, 2016
CLE @ IND
L
240
35
74
0.473
8
...
0.912
7
26
33
15
7
3
10
19
109
4
1610612739
0021501159
APR 05, 2016
CLE @ MIL
W
240
41
75
0.547
18
...
0.900
2
39
41
30
4
8
16
14
109
5
1610612739
0021501144
APR 03, 2016
CLE vs. CHA
W
240
45
83
0.542
16
...
0.462
15
31
46
34
7
1
17
19
112
6
1610612739
0021501131
APR 01, 2016
CLE @ ATL
W
265
39
98
0.398
12
...
0.645
11
46
57
27
9
6
12
23
110
7
1610612739
0021501122
MAR 31, 2016
CLE vs. BKN
W
240
38
87
0.437
12
...
0.792
9
41
50
29
10
7
17
17
107
8
1610612739
0021501111
MAR 29, 2016
CLE vs. HOU
L
240
31
86
0.360
14
...
0.857
10
28
38
21
5
5
11
31
100
9
1610612739
0021501086
MAR 26, 2016
CLE @ NYK
W
240
37
83
0.446
14
...
0.760
15
38
53
21
6
5
9
23
107
10
1610612739
0021501069
MAR 24, 2016
CLE @ BKN
L
240
39
89
0.438
10
...
0.778
11
34
45
22
3
2
14
20
95
11
1610612739
0021501059
MAR 23, 2016
CLE vs. MIL
W
240
40
83
0.482
10
...
0.852
17
25
42
29
6
8
12
17
113
12
1610612739
0021501044
MAR 21, 2016
CLE vs. DEN
W
240
48
86
0.558
15
...
0.765
8
35
43
38
10
4
12
19
124
13
1610612739
0021501033
MAR 19, 2016
CLE @ MIA
L
240
40
76
0.526
10
...
0.846
3
23
26
19
6
3
14
22
101
14
1610612739
0021501020
MAR 18, 2016
CLE @ ORL
W
240
38
76
0.500
13
...
0.741
9
32
41
23
7
4
19
21
109
15
1610612739
0021501005
MAR 16, 2016
CLE vs. DAL
W
240
39
88
0.443
10
...
0.846
13
35
48
21
9
4
16
19
99
16
1610612739
0021500994
MAR 14, 2016
CLE @ UTA
L
240
35
88
0.398
10
...
0.625
13
26
39
18
6
3
8
16
85
17
1610612739
0021500983
MAR 13, 2016
CLE @ LAC
W
240
41
84
0.488
18
...
0.700
9
40
49
23
3
4
8
19
114
18
1610612739
0021500962
MAR 10, 2016
CLE @ LAL
W
240
45
85
0.529
16
...
0.700
9
29
38
22
4
6
8
20
120
19
1610612739
0021500957
MAR 09, 2016
CLE @ SAC
W
240
39
90
0.433
13
...
0.829
15
36
51
17
5
1
13
21
120
20
1610612739
0021500938
MAR 07, 2016
CLE vs. MEM
L
240
36
80
0.450
7
...
0.828
13
36
49
23
9
7
25
22
103
21
1610612739
0021500922
MAR 05, 2016
CLE vs. BOS
W
240
42
82
0.512
10
...
0.765
15
32
47
27
8
4
15
21
120
22
1610612739
0021500917
MAR 04, 2016
CLE vs. WAS
W
240
42
90
0.467
12
...
0.750
8
39
47
24
9
5
9
19
108
23
1610612739
0021500884
FEB 29, 2016
CLE vs. IND
W
240
35
83
0.422
9
...
0.840
9
31
40
22
4
2
11
15
100
24
1610612739
0021500877
FEB 28, 2016
CLE @ WAS
L
240
32
80
0.400
9
...
0.867
6
33
39
20
6
2
14
26
99
25
1610612739
0021500864
FEB 26, 2016
CLE @ TOR
L
240
35
74
0.473
12
...
0.714
10
29
39
21
4
0
14
26
97
26
1610612739
0021500845
FEB 24, 2016
CLE vs. CHA
W
240
45
91
0.495
13
...
0.647
12
35
47
26
11
1
9
20
114
27
1610612739
0021500833
FEB 22, 2016
CLE vs. DET
L
240
34
79
0.430
8
...
0.923
5
35
40
19
8
2
17
21
88
28
1610612739
0021500824
FEB 21, 2016
CLE @ OKC
W
240
41
80
0.513
10
...
0.852
13
36
49
25
4
1
12
21
115
29
1610612739
0021500803
FEB 18, 2016
CLE vs. CHI
W
240
42
91
0.462
7
...
0.882
11
41
52
21
8
8
10
22
106
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
52
1610612739
0021500473
DEC 29, 2015
CLE @ DEN
W
240
38
86
0.442
4
...
0.591
9
35
44
17
12
3
12
16
93
53
1610612739
0021500466
DEC 28, 2015
CLE @ PHX
W
240
33
71
0.465
17
...
0.720
10
27
37
21
8
8
17
18
101
54
1610612739
0021500453
DEC 26, 2015
CLE @ POR
L
240
28
77
0.364
11
...
0.600
9
33
42
21
4
2
17
20
76
55
1610612739
0021500438
DEC 25, 2015
CLE @ GSW
L
240
30
95
0.316
5
...
0.720
17
38
55
12
6
4
11
22
83
56
1610612739
0021500424
DEC 23, 2015
CLE vs. NYK
W
240
32
83
0.386
5
...
0.880
14
34
48
23
6
4
7
15
91
57
1610612739
0021500405
DEC 20, 2015
CLE vs. PHI
W
240
40
85
0.471
11
...
0.810
6
34
40
25
11
6
10
19
108
58
1610612739
0021500384
DEC 17, 2015
CLE vs. OKC
W
240
41
88
0.466
12
...
0.769
16
26
42
29
6
1
16
22
104
59
1610612739
0021500367
DEC 15, 2015
CLE @ BOS
W
240
36
78
0.462
8
...
0.900
6
40
46
17
5
7
14
17
89
60
1610612739
0021500334
DEC 11, 2015
CLE @ ORL
W
240
41
72
0.569
11
...
0.667
9
36
45
28
12
0
18
19
111
61
1610612739
0021500313
DEC 08, 2015
CLE vs. POR
W
240
40
79
0.506
9
...
0.800
6
24
30
18
10
4
8
18
105
62
1610612739
0021500291
DEC 05, 2015
CLE @ MIA
L
240
31
85
0.365
6
...
0.667
11
25
36
14
8
1
14
26
84
63
1610612739
0021500288
DEC 04, 2015
CLE @ NOP
L
265
39
89
0.438
10
...
0.833
12
31
43
18
8
1
14
26
108
64
1610612739
0021500262
DEC 01, 2015
CLE vs. WAS
L
240
28
83
0.337
9
...
0.870
10
37
47
15
8
3
18
18
85
65
1610612739
0021500240
NOV 28, 2015
CLE vs. BKN
W
240
35
87
0.402
9
...
0.647
12
37
49
20
9
11
19
19
90
66
1610612739
0021500227
NOV 27, 2015
CLE @ CHA
W
240
34
81
0.420
8
...
0.760
12
38
50
17
3
2
16
22
95
67
1610612739
0021500219
NOV 25, 2015
CLE @ TOR
L
240
36
82
0.439
14
...
0.813
9
33
42
22
4
2
10
20
99
68
1610612739
0021500203
NOV 23, 2015
CLE vs. ORL
W
240
43
81
0.531
18
...
0.591
8
33
41
34
5
3
10
15
117
69
1610612739
0021500191
NOV 21, 2015
CLE vs. ATL
W
240
41
85
0.482
11
...
0.842
11
40
51
27
4
8
14
22
109
70
1610612739
0021500176
NOV 19, 2015
CLE vs. MIL
W
240
40
71
0.563
11
...
0.800
13
30
43
29
3
2
17
24
115
71
1610612739
0021500160
NOV 17, 2015
CLE @ DET
L
240
38
80
0.475
11
...
0.600
3
37
40
21
8
2
14
25
99
72
1610612739
0021500141
NOV 14, 2015
CLE @ MIL
L
290
37
91
0.407
14
...
0.630
14
40
54
21
6
8
20
24
105
73
1610612739
0021500130
NOV 13, 2015
CLE @ NYK
W
240
33
78
0.423
6
...
0.581
12
37
49
12
6
1
12
19
90
74
1610612739
0021500106
NOV 10, 2015
CLE vs. UTA
W
240
37
74
0.500
11
...
0.767
11
24
35
24
9
6
16
21
118
75
1610612739
0021500094
NOV 08, 2015
CLE vs. IND
W
240
38
83
0.458
8
...
0.680
11
37
48
25
5
4
10
20
101
76
1610612739
0021500078
NOV 06, 2015
CLE vs. PHI
W
240
45
88
0.511
10
...
0.667
11
34
45
29
9
0
17
20
108
77
1610612739
0021500063
NOV 04, 2015
CLE vs. NYK
W
240
33
83
0.398
7
...
0.719
9
39
48
21
5
2
11
12
96
78
1610612739
0021500046
NOV 02, 2015
CLE @ PHI
W
240
42
81
0.519
8
...
0.750
7
36
43
28
5
9
16
19
107
79
1610612739
0021500021
OCT 30, 2015
CLE vs. MIA
W
240
39
86
0.453
6
...
0.857
14
35
49
25
4
4
11
19
102
80
1610612739
0021500011
OCT 28, 2015
CLE @ MEM
W
240
41
84
0.488
13
...
0.647
12
42
54
29
7
2
19
25
106
81
1610612739
0021500002
OCT 27, 2015
CLE @ CHI
L
240
38
94
0.404
9
...
0.588
11
39
50
26
5
7
10
21
95
82 rows × 24 columns
In [2]:
## Start of code that gets all all gamelogs for all teams within a season
team_list = team.TeamList().info()
team_list = team_list[team_list['MAX_YEAR']=='2015'].reset_index()
team_list
Out[2]:
index
LEAGUE_ID
TEAM_ID
MIN_YEAR
MAX_YEAR
ABBREVIATION
0
0
00
1610612737
1949
2015
ATL
1
1
00
1610612738
1946
2015
BOS
2
2
00
1610612739
1970
2015
CLE
3
3
00
1610612740
2002
2015
NOP
4
4
00
1610612741
1966
2015
CHI
5
5
00
1610612742
1980
2015
DAL
6
6
00
1610612743
1976
2015
DEN
7
7
00
1610612744
1946
2015
GSW
8
8
00
1610612745
1967
2015
HOU
9
9
00
1610612746
1970
2015
LAC
10
10
00
1610612747
1948
2015
LAL
11
11
00
1610612748
1988
2015
MIA
12
12
00
1610612749
1968
2015
MIL
13
13
00
1610612750
1989
2015
MIN
14
14
00
1610612751
1976
2015
BKN
15
15
00
1610612752
1946
2015
NYK
16
16
00
1610612753
1989
2015
ORL
17
17
00
1610612754
1976
2015
IND
18
18
00
1610612755
1949
2015
PHI
19
19
00
1610612756
1968
2015
PHX
20
20
00
1610612757
1970
2015
POR
21
21
00
1610612758
1948
2015
SAC
22
22
00
1610612759
1976
2015
SAS
23
23
00
1610612760
1967
2015
OKC
24
24
00
1610612761
1995
2015
TOR
25
25
00
1610612763
1995
2015
MEM
26
26
00
1610612764
1961
2015
WAS
27
27
00
1610612765
1948
2015
DET
28
28
00
1610612766
1988
2015
CHA
29
29
00
1610612762
1974
2015
UTA
In [8]:
df = pd.DataFrame()
for index, tm in team_list.iterrows():
log = team.TeamGameLogs(team_id=tm['TEAM_ID'])
log_df = log.info().set_index('MATCHUP')
df=df.append(log.info())
df = df.reset_index(drop=True)
df
Out[8]:
Team_ID
Game_ID
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
...
FT_PCT
OREB
DREB
REB
AST
STL
BLK
TOV
PF
PTS
0
1610612737
0021501221
APR 13, 2016
ATL @ WAS
L
240
32
81
0.395
11
...
0.742
9
38
47
22
13
5
22
21
98
1
1610612737
0021501203
APR 11, 2016
ATL @ CLE
L
240
39
87
0.448
8
...
0.533
10
32
42
23
8
6
15
18
94
2
1610612737
0021501188
APR 09, 2016
ATL vs. BOS
W
240
46
88
0.523
17
...
0.818
5
39
44
31
10
10
17
22
118
3
1610612737
0021501173
APR 07, 2016
ATL vs. TOR
W
240
33
76
0.434
12
...
0.810
5
36
41
23
4
12
13
19
95
4
1610612737
0021501157
APR 05, 2016
ATL vs. PHX
W
240
39
95
0.411
11
...
0.737
13
37
50
26
16
3
16
21
103
5
1610612737
0021501131
APR 01, 2016
ATL vs. CLE
L
265
38
95
0.400
9
...
0.885
5
43
48
25
5
8
15
27
108
6
1610612737
0021501113
MAR 30, 2016
ATL @ TOR
L
240
37
83
0.446
14
...
0.692
11
33
44
24
3
7
18
19
97
7
1610612737
0021501099
MAR 28, 2016
ATL @ CHI
W
240
36
85
0.424
5
...
0.893
7
41
48
22
4
13
8
13
102
8
1610612737
0021501085
MAR 26, 2016
ATL @ DET
W
240
43
95
0.453
13
...
0.929
5
34
39
34
6
7
4
21
112
9
1610612737
0021501076
MAR 25, 2016
ATL vs. MIL
W
240
41
97
0.423
5
...
0.824
17
28
45
26
10
6
11
21
101
10
1610612737
0021501056
MAR 23, 2016
ATL @ WAS
W
240
45
84
0.536
17
...
0.682
7
34
41
32
10
5
16
17
122
11
1610612737
0021501048
MAR 21, 2016
ATL vs. WAS
L
240
38
78
0.487
13
...
0.765
2
31
33
23
5
6
14
15
102
12
1610612737
0021501029
MAR 19, 2016
ATL vs. HOU
W
240
44
88
0.500
14
...
0.583
9
31
40
32
12
6
16
20
109
13
1610612737
0021501015
MAR 17, 2016
ATL vs. DEN
W
240
40
80
0.500
12
...
0.857
7
33
40
32
5
8
12
16
116
14
1610612737
0021501007
MAR 16, 2016
ATL @ DET
W
240
39
89
0.438
12
...
0.824
8
38
46
25
10
3
12
26
118
15
1610612737
0021500984
MAR 13, 2016
ATL vs. IND
W
240
40
85
0.471
15
...
0.900
9
41
50
27
11
6
15
13
104
16
1610612737
0021500974
MAR 12, 2016
ATL vs. MEM
W
240
36
84
0.429
11
...
0.706
8
39
47
27
9
12
11
14
95
17
1610612737
0021500959
MAR 10, 2016
ATL @ TOR
L
240
35
82
0.427
7
...
0.704
6
27
33
17
8
5
11
24
96
18
1610612737
0021500947
MAR 08, 2016
ATL @ UTA
W
240
38
80
0.475
8
...
0.500
4
37
41
15
9
4
16
19
91
19
1610612737
0021500929
MAR 05, 2016
ATL @ LAC
W
240
40
89
0.449
10
...
0.850
10
43
53
26
9
3
18
26
107
20
1610612737
0021500921
MAR 04, 2016
ATL @ LAL
W
240
37
68
0.544
13
...
0.731
3
36
39
31
6
8
10
18
106
21
1610612737
0021500895
MAR 01, 2016
ATL @ GSW
L
265
37
80
0.463
12
...
0.792
7
35
42
25
9
5
17
17
105
22
1610612737
0021500878
FEB 28, 2016
ATL vs. CHA
W
240
38
77
0.494
8
...
0.600
6
42
48
29
4
5
15
19
87
23
1610612737
0021500865
FEB 26, 2016
ATL vs. CHI
W
240
37
89
0.416
7
...
0.917
13
35
48
28
14
11
11
21
103
24
1610612737
0021500836
FEB 22, 2016
ATL vs. GSW
L
240
36
86
0.419
10
...
0.625
8
39
47
23
7
7
17
17
92
25
1610612737
0021500819
FEB 20, 2016
ATL vs. MIL
L
290
44
106
0.415
9
...
0.667
10
39
49
31
11
8
16
27
109
26
1610612737
0021500808
FEB 19, 2016
ATL vs. MIA
L
240
41
87
0.471
16
...
1.000
8
34
42
27
7
4
21
23
111
27
1610612737
0021500796
FEB 10, 2016
ATL @ CHI
W
240
43
90
0.478
13
...
0.933
11
35
46
25
16
7
12
14
113
28
1610612737
0021500780
FEB 08, 2016
ATL vs. ORL
L
265
43
92
0.467
12
...
0.600
8
39
47
31
9
4
14
20
110
29
1610612737
0021500771
FEB 07, 2016
ATL @ ORL
L
240
35
91
0.385
13
...
0.733
18
32
50
22
7
7
16
13
94
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2430
1610612762
0021500479
DEC 30, 2015
UTA @ MIN
L
240
28
80
0.350
10
...
0.700
15
29
44
15
8
8
19
24
80
2431
1610612762
0021500467
DEC 28, 2015
UTA vs. PHI
W
240
28
84
0.333
7
...
0.889
15
38
53
13
11
9
15
18
95
2432
1610612762
0021500452
DEC 26, 2015
UTA vs. LAC
L
240
36
74
0.486
10
...
0.815
8
28
36
18
7
4
16
22
104
2433
1610612762
0021500434
DEC 23, 2015
UTA @ GSW
L
240
33
80
0.413
7
...
0.857
12
28
40
14
9
3
19
18
85
2434
1610612762
0021500417
DEC 21, 2015
UTA vs. PHX
W
240
36
79
0.456
9
...
0.784
11
36
47
16
8
6
10
16
110
2435
1610612762
0021500395
DEC 18, 2015
UTA vs. DEN
W
240
34
73
0.466
10
...
0.760
5
34
39
15
8
1
14
19
97
2436
1610612762
0021500380
DEC 16, 2015
UTA vs. NOP
L
240
32
67
0.478
7
...
0.793
5
29
34
17
4
4
12
20
94
2437
1610612762
0021500364
DEC 14, 2015
UTA @ SAS
L
240
32
79
0.405
3
...
0.824
7
25
32
18
8
3
11
23
81
2438
1610612762
0021500356
DEC 13, 2015
UTA @ OKC
L
265
39
94
0.415
8
...
0.600
16
28
44
14
6
2
11
24
98
2439
1610612762
0021500341
DEC 11, 2015
UTA vs. OKC
L
240
33
78
0.423
8
...
0.667
11
28
39
17
7
4
11
16
90
2440
1610612762
0021500327
DEC 09, 2015
UTA vs. NYK
W
240
39
80
0.488
9
...
0.826
9
42
51
26
6
2
14
26
106
2441
1610612762
0021500318
DEC 08, 2015
UTA @ SAC
L
240
38
92
0.413
15
...
0.682
19
25
44
23
7
0
12
22
106
2442
1610612762
0021500297
DEC 05, 2015
UTA vs. IND
W
265
43
92
0.467
8
...
0.757
19
35
54
21
7
3
15
30
122
2443
1610612762
0021500279
DEC 03, 2015
UTA vs. ORL
L
240
31
72
0.431
14
...
0.750
5
34
39
18
4
8
19
16
94
2444
1610612762
0021500259
NOV 30, 2015
UTA vs. GSW
L
240
40
89
0.449
6
...
0.773
10
25
35
18
9
2
8
16
103
2445
1610612762
0021500243
NOV 28, 2015
UTA vs. NOP
W
240
38
82
0.463
9
...
0.762
12
37
49
18
11
9
15
25
101
2446
1610612762
0021500226
NOV 25, 2015
UTA @ LAC
W
240
39
77
0.506
6
...
0.720
11
28
39
19
12
4
16
20
102
2447
1610612762
0021500208
NOV 23, 2015
UTA vs. OKC
L
240
28
73
0.384
5
...
0.700
13
28
41
15
11
3
21
18
89
2448
1610612762
0021500184
NOV 20, 2015
UTA @ DAL
L
240
35
80
0.438
6
...
0.810
18
30
48
14
4
2
17
24
93
2449
1610612762
0021500173
NOV 18, 2015
UTA vs. TOR
W
240
35
72
0.486
7
...
0.727
7
34
41
15
9
5
17
20
93
2450
1610612762
0021500148
NOV 15, 2015
UTA @ ATL
W
240
39
76
0.513
7
...
0.706
10
30
40
21
7
7
17
15
97
2451
1610612762
0021500128
NOV 13, 2015
UTA @ ORL
L
240
34
87
0.391
8
...
0.739
12
29
41
24
14
6
17
24
93
2452
1610612762
0021500124
NOV 12, 2015
UTA @ MIA
L
240
34
89
0.382
4
...
0.760
11
31
42
13
10
9
10
23
91
2453
1610612762
0021500106
NOV 10, 2015
UTA @ CLE
L
240
40
82
0.488
10
...
0.857
13
24
37
30
11
0
16
35
114
2454
1610612762
0021500091
NOV 07, 2015
UTA vs. MEM
W
240
31
74
0.419
12
...
0.833
9
40
49
18
7
10
21
20
89
2455
1610612762
0021500073
NOV 05, 2015
UTA @ DEN
W
240
37
85
0.435
11
...
0.647
12
31
43
23
6
6
10
26
96
2456
1610612762
0021500068
NOV 04, 2015
UTA vs. POR
L
240
33
88
0.375
5
...
0.636
16
24
40
9
12
8
9
22
92
2457
1610612762
0021500033
OCT 31, 2015
UTA @ IND
W
240
39
88
0.443
6
...
0.722
15
32
47
15
13
4
17
24
97
2458
1610612762
0021500023
OCT 30, 2015
UTA @ PHI
W
240
36
88
0.409
7
...
0.800
13
38
51
19
10
9
7
22
99
2459
1610612762
0021500007
OCT 28, 2015
UTA @ DET
L
240
35
75
0.467
2
...
0.714
4
34
38
15
4
5
12
25
87
2460 rows × 24 columns
In [9]:
for index, game in df.iterrows():
splits = game['MATCHUP'].split(' ')
if splits[1] == '@':
df.set_value(index, 'Away Team', splits[0])
df.set_value(index, 'Home Team', splits[2])
else:
df.set_value(index, 'Home Team', splits[0])
df.set_value(index, 'Away Team', splits[2])
if splits[0] == df.ix[index, 'Home Team']:
df.set_value(index, 'Home Stats', 1)
else:
df.set_value(index, 'Home Stats', 0)
df
Out[9]:
Team_ID
Game_ID
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
...
REB
AST
STL
BLK
TOV
PF
PTS
Away Team
Home Team
Home Stats
0
1610612737
0021501221
APR 13, 2016
ATL @ WAS
L
240
32
81
0.395
11
...
47
22
13
5
22
21
98
ATL
WAS
0.0
1
1610612737
0021501203
APR 11, 2016
ATL @ CLE
L
240
39
87
0.448
8
...
42
23
8
6
15
18
94
ATL
CLE
0.0
2
1610612737
0021501188
APR 09, 2016
ATL vs. BOS
W
240
46
88
0.523
17
...
44
31
10
10
17
22
118
BOS
ATL
1.0
3
1610612737
0021501173
APR 07, 2016
ATL vs. TOR
W
240
33
76
0.434
12
...
41
23
4
12
13
19
95
TOR
ATL
1.0
4
1610612737
0021501157
APR 05, 2016
ATL vs. PHX
W
240
39
95
0.411
11
...
50
26
16
3
16
21
103
PHX
ATL
1.0
5
1610612737
0021501131
APR 01, 2016
ATL vs. CLE
L
265
38
95
0.400
9
...
48
25
5
8
15
27
108
CLE
ATL
1.0
6
1610612737
0021501113
MAR 30, 2016
ATL @ TOR
L
240
37
83
0.446
14
...
44
24
3
7
18
19
97
ATL
TOR
0.0
7
1610612737
0021501099
MAR 28, 2016
ATL @ CHI
W
240
36
85
0.424
5
...
48
22
4
13
8
13
102
ATL
CHI
0.0
8
1610612737
0021501085
MAR 26, 2016
ATL @ DET
W
240
43
95
0.453
13
...
39
34
6
7
4
21
112
ATL
DET
0.0
9
1610612737
0021501076
MAR 25, 2016
ATL vs. MIL
W
240
41
97
0.423
5
...
45
26
10
6
11
21
101
MIL
ATL
1.0
10
1610612737
0021501056
MAR 23, 2016
ATL @ WAS
W
240
45
84
0.536
17
...
41
32
10
5
16
17
122
ATL
WAS
0.0
11
1610612737
0021501048
MAR 21, 2016
ATL vs. WAS
L
240
38
78
0.487
13
...
33
23
5
6
14
15
102
WAS
ATL
1.0
12
1610612737
0021501029
MAR 19, 2016
ATL vs. HOU
W
240
44
88
0.500
14
...
40
32
12
6
16
20
109
HOU
ATL
1.0
13
1610612737
0021501015
MAR 17, 2016
ATL vs. DEN
W
240
40
80
0.500
12
...
40
32
5
8
12
16
116
DEN
ATL
1.0
14
1610612737
0021501007
MAR 16, 2016
ATL @ DET
W
240
39
89
0.438
12
...
46
25
10
3
12
26
118
ATL
DET
0.0
15
1610612737
0021500984
MAR 13, 2016
ATL vs. IND
W
240
40
85
0.471
15
...
50
27
11
6
15
13
104
IND
ATL
1.0
16
1610612737
0021500974
MAR 12, 2016
ATL vs. MEM
W
240
36
84
0.429
11
...
47
27
9
12
11
14
95
MEM
ATL
1.0
17
1610612737
0021500959
MAR 10, 2016
ATL @ TOR
L
240
35
82
0.427
7
...
33
17
8
5
11
24
96
ATL
TOR
0.0
18
1610612737
0021500947
MAR 08, 2016
ATL @ UTA
W
240
38
80
0.475
8
...
41
15
9
4
16
19
91
ATL
UTA
0.0
19
1610612737
0021500929
MAR 05, 2016
ATL @ LAC
W
240
40
89
0.449
10
...
53
26
9
3
18
26
107
ATL
LAC
0.0
20
1610612737
0021500921
MAR 04, 2016
ATL @ LAL
W
240
37
68
0.544
13
...
39
31
6
8
10
18
106
ATL
LAL
0.0
21
1610612737
0021500895
MAR 01, 2016
ATL @ GSW
L
265
37
80
0.463
12
...
42
25
9
5
17
17
105
ATL
GSW
0.0
22
1610612737
0021500878
FEB 28, 2016
ATL vs. CHA
W
240
38
77
0.494
8
...
48
29
4
5
15
19
87
CHA
ATL
1.0
23
1610612737
0021500865
FEB 26, 2016
ATL vs. CHI
W
240
37
89
0.416
7
...
48
28
14
11
11
21
103
CHI
ATL
1.0
24
1610612737
0021500836
FEB 22, 2016
ATL vs. GSW
L
240
36
86
0.419
10
...
47
23
7
7
17
17
92
GSW
ATL
1.0
25
1610612737
0021500819
FEB 20, 2016
ATL vs. MIL
L
290
44
106
0.415
9
...
49
31
11
8
16
27
109
MIL
ATL
1.0
26
1610612737
0021500808
FEB 19, 2016
ATL vs. MIA
L
240
41
87
0.471
16
...
42
27
7
4
21
23
111
MIA
ATL
1.0
27
1610612737
0021500796
FEB 10, 2016
ATL @ CHI
W
240
43
90
0.478
13
...
46
25
16
7
12
14
113
ATL
CHI
0.0
28
1610612737
0021500780
FEB 08, 2016
ATL vs. ORL
L
265
43
92
0.467
12
...
47
31
9
4
14
20
110
ORL
ATL
1.0
29
1610612737
0021500771
FEB 07, 2016
ATL @ ORL
L
240
35
91
0.385
13
...
50
22
7
7
16
13
94
ATL
ORL
0.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
2430
1610612762
0021500479
DEC 30, 2015
UTA @ MIN
L
240
28
80
0.350
10
...
44
15
8
8
19
24
80
UTA
MIN
0.0
2431
1610612762
0021500467
DEC 28, 2015
UTA vs. PHI
W
240
28
84
0.333
7
...
53
13
11
9
15
18
95
PHI
UTA
1.0
2432
1610612762
0021500452
DEC 26, 2015
UTA vs. LAC
L
240
36
74
0.486
10
...
36
18
7
4
16
22
104
LAC
UTA
1.0
2433
1610612762
0021500434
DEC 23, 2015
UTA @ GSW
L
240
33
80
0.413
7
...
40
14
9
3
19
18
85
UTA
GSW
0.0
2434
1610612762
0021500417
DEC 21, 2015
UTA vs. PHX
W
240
36
79
0.456
9
...
47
16
8
6
10
16
110
PHX
UTA
1.0
2435
1610612762
0021500395
DEC 18, 2015
UTA vs. DEN
W
240
34
73
0.466
10
...
39
15
8
1
14
19
97
DEN
UTA
1.0
2436
1610612762
0021500380
DEC 16, 2015
UTA vs. NOP
L
240
32
67
0.478
7
...
34
17
4
4
12
20
94
NOP
UTA
1.0
2437
1610612762
0021500364
DEC 14, 2015
UTA @ SAS
L
240
32
79
0.405
3
...
32
18
8
3
11
23
81
UTA
SAS
0.0
2438
1610612762
0021500356
DEC 13, 2015
UTA @ OKC
L
265
39
94
0.415
8
...
44
14
6
2
11
24
98
UTA
OKC
0.0
2439
1610612762
0021500341
DEC 11, 2015
UTA vs. OKC
L
240
33
78
0.423
8
...
39
17
7
4
11
16
90
OKC
UTA
1.0
2440
1610612762
0021500327
DEC 09, 2015
UTA vs. NYK
W
240
39
80
0.488
9
...
51
26
6
2
14
26
106
NYK
UTA
1.0
2441
1610612762
0021500318
DEC 08, 2015
UTA @ SAC
L
240
38
92
0.413
15
...
44
23
7
0
12
22
106
UTA
SAC
0.0
2442
1610612762
0021500297
DEC 05, 2015
UTA vs. IND
W
265
43
92
0.467
8
...
54
21
7
3
15
30
122
IND
UTA
1.0
2443
1610612762
0021500279
DEC 03, 2015
UTA vs. ORL
L
240
31
72
0.431
14
...
39
18
4
8
19
16
94
ORL
UTA
1.0
2444
1610612762
0021500259
NOV 30, 2015
UTA vs. GSW
L
240
40
89
0.449
6
...
35
18
9
2
8
16
103
GSW
UTA
1.0
2445
1610612762
0021500243
NOV 28, 2015
UTA vs. NOP
W
240
38
82
0.463
9
...
49
18
11
9
15
25
101
NOP
UTA
1.0
2446
1610612762
0021500226
NOV 25, 2015
UTA @ LAC
W
240
39
77
0.506
6
...
39
19
12
4
16
20
102
UTA
LAC
0.0
2447
1610612762
0021500208
NOV 23, 2015
UTA vs. OKC
L
240
28
73
0.384
5
...
41
15
11
3
21
18
89
OKC
UTA
1.0
2448
1610612762
0021500184
NOV 20, 2015
UTA @ DAL
L
240
35
80
0.438
6
...
48
14
4
2
17
24
93
UTA
DAL
0.0
2449
1610612762
0021500173
NOV 18, 2015
UTA vs. TOR
W
240
35
72
0.486
7
...
41
15
9
5
17
20
93
TOR
UTA
1.0
2450
1610612762
0021500148
NOV 15, 2015
UTA @ ATL
W
240
39
76
0.513
7
...
40
21
7
7
17
15
97
UTA
ATL
0.0
2451
1610612762
0021500128
NOV 13, 2015
UTA @ ORL
L
240
34
87
0.391
8
...
41
24
14
6
17
24
93
UTA
ORL
0.0
2452
1610612762
0021500124
NOV 12, 2015
UTA @ MIA
L
240
34
89
0.382
4
...
42
13
10
9
10
23
91
UTA
MIA
0.0
2453
1610612762
0021500106
NOV 10, 2015
UTA @ CLE
L
240
40
82
0.488
10
...
37
30
11
0
16
35
114
UTA
CLE
0.0
2454
1610612762
0021500091
NOV 07, 2015
UTA vs. MEM
W
240
31
74
0.419
12
...
49
18
7
10
21
20
89
MEM
UTA
1.0
2455
1610612762
0021500073
NOV 05, 2015
UTA @ DEN
W
240
37
85
0.435
11
...
43
23
6
6
10
26
96
UTA
DEN
0.0
2456
1610612762
0021500068
NOV 04, 2015
UTA vs. POR
L
240
33
88
0.375
5
...
40
9
12
8
9
22
92
POR
UTA
1.0
2457
1610612762
0021500033
OCT 31, 2015
UTA @ IND
W
240
39
88
0.443
6
...
47
15
13
4
17
24
97
UTA
IND
0.0
2458
1610612762
0021500023
OCT 30, 2015
UTA @ PHI
W
240
36
88
0.409
7
...
51
19
10
9
7
22
99
UTA
PHI
0.0
2459
1610612762
0021500007
OCT 28, 2015
UTA @ DET
L
240
35
75
0.467
2
...
38
15
4
5
12
25
87
UTA
DET
0.0
2460 rows × 27 columns
In [10]:
h_df = df[df['Home Stats'] == 1]
len(h_df)
Out[10]:
1230
In [11]:
a_df = df[df['Home Stats'] == 0]
len(a_df)
Out[11]:
1230
In [12]:
full_sched = pd.merge(h_df, a_df, on = 'Game_ID', suffixes = ['_home', '_away']).reset_index(drop=True)
full_sched
Out[12]:
Team_ID_home
Game_ID
GAME_DATE_home
MATCHUP_home
WL_home
MIN_home
FGM_home
FGA_home
FG_PCT_home
FG3M_home
...
REB_away
AST_away
STL_away
BLK_away
TOV_away
PF_away
PTS_away
Away Team_away
Home Team_away
Home Stats_away
0
1610612737
0021501188
APR 09, 2016
ATL vs. BOS
W
240
46
88
0.523
17
...
40
26
10
6
15
21
107
BOS
ATL
0.0
1
1610612737
0021501173
APR 07, 2016
ATL vs. TOR
W
240
33
76
0.434
12
...
46
16
10
4
11
16
87
TOR
ATL
0.0
2
1610612737
0021501157
APR 05, 2016
ATL vs. PHX
W
240
39
95
0.411
11
...
51
19
13
4
24
19
90
PHX
ATL
0.0
3
1610612737
0021501131
APR 01, 2016
ATL vs. CLE
L
265
38
95
0.400
9
...
57
27
9
6
12
23
110
CLE
ATL
0.0
4
1610612737
0021501076
MAR 25, 2016
ATL vs. MIL
W
240
41
97
0.423
5
...
49
16
4
9
15
16
90
MIL
ATL
0.0
5
1610612737
0021501048
MAR 21, 2016
ATL vs. WAS
L
240
38
78
0.487
13
...
44
27
10
3
9
17
117
WAS
ATL
0.0
6
1610612737
0021501029
MAR 19, 2016
ATL vs. HOU
W
240
44
88
0.500
14
...
51
15
11
3
17
17
97
HOU
ATL
0.0
7
1610612737
0021501015
MAR 17, 2016
ATL vs. DEN
W
240
40
80
0.500
12
...
39
22
6
4
14
22
98
DEN
ATL
0.0
8
1610612737
0021500984
MAR 13, 2016
ATL vs. IND
W
240
40
85
0.471
15
...
38
24
9
5
16
14
75
IND
ATL
0.0
9
1610612737
0021500974
MAR 12, 2016
ATL vs. MEM
W
240
36
84
0.429
11
...
58
15
10
5
16
14
83
MEM
ATL
0.0
10
1610612737
0021500878
FEB 28, 2016
ATL vs. CHA
W
240
38
77
0.494
8
...
41
16
7
4
6
12
76
CHA
ATL
0.0
11
1610612737
0021500865
FEB 26, 2016
ATL vs. CHI
W
240
37
89
0.416
7
...
49
19
3
2
20
20
88
CHI
ATL
0.0
12
1610612737
0021500836
FEB 22, 2016
ATL vs. GSW
L
240
36
86
0.419
10
...
43
30
14
8
13
17
102
GSW
ATL
0.0
13
1610612737
0021500819
FEB 20, 2016
ATL vs. MIL
L
290
44
106
0.415
9
...
65
23
12
10
17
21
117
MIL
ATL
0.0
14
1610612737
0021500808
FEB 19, 2016
ATL vs. MIA
L
240
41
87
0.471
16
...
46
27
13
1
14
12
115
MIA
ATL
0.0
15
1610612737
0021500780
FEB 08, 2016
ATL vs. ORL
L
265
43
92
0.467
12
...
47
37
11
8
13
22
117
ORL
ATL
0.0
16
1610612737
0021500750
FEB 05, 2016
ATL vs. IND
W
240
39
76
0.513
10
...
53
23
6
4
19
18
96
IND
ATL
0.0
17
1610612737
0021500723
FEB 01, 2016
ATL vs. DAL
W
240
42
80
0.525
14
...
37
14
5
2
6
18
97
DAL
ATL
0.0
18
1610612737
0021500687
JAN 27, 2016
ATL vs. LAC
L
240
33
79
0.418
10
...
46
18
16
5
11
14
85
LAC
ATL
0.0
19
1610612737
0021500620
JAN 18, 2016
ATL vs. ORL
W
240
41
80
0.513
9
...
42
21
11
5
13
13
81
ORL
ATL
0.0
20
1610612737
0021500602
JAN 16, 2016
ATL vs. BKN
W
240
44
79
0.557
8
...
36
22
5
3
17
17
86
BKN
ATL
0.0
21
1610612737
0021500551
JAN 09, 2016
ATL vs. CHI
W
240
49
94
0.521
10
...
46
16
7
6
21
13
105
CHI
ATL
0.0
22
1610612737
0021500521
JAN 05, 2016
ATL vs. NYK
L
240
37
87
0.425
15
...
46
20
7
6
9
20
107
NYK
ATL
0.0
23
1610612737
0021500442
DEC 26, 2015
ATL vs. NYK
W
240
47
88
0.534
8
...
42
25
6
8
22
15
98
NYK
ATL
0.0
24
1610612737
0021500429
DEC 23, 2015
ATL vs. DET
W
240
43
89
0.483
6
...
46
18
7
9
14
20
100
DET
ATL
0.0
25
1610612737
0021500413
DEC 21, 2015
ATL vs. POR
W
240
37
77
0.481
9
...
50
24
5
4
18
20
97
POR
ATL
0.0
26
1610612737
0021500376
DEC 16, 2015
ATL vs. PHI
W
240
48
78
0.615
10
...
32
20
5
2
22
21
106
PHI
ATL
0.0
27
1610612737
0021500360
DEC 14, 2015
ATL vs. MIA
L
240
33
84
0.393
8
...
54
26
6
5
15
16
100
MIA
ATL
0.0
28
1610612737
0021500347
DEC 12, 2015
ATL vs. SAS
L
240
30
80
0.375
5
...
49
26
11
2
23
16
103
SAS
ATL
0.0
29
1610612737
0021500286
DEC 04, 2015
ATL vs. LAL
W
240
37
74
0.500
10
...
42
19
10
7
16
14
87
LAL
ATL
0.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
1200
1610612762
0021500860
FEB 25, 2016
UTA vs. SAS
L
240
33
77
0.429
3
...
43
17
5
5
11
16
96
SAS
UTA
0.0
1201
1610612762
0021500843
FEB 23, 2016
UTA vs. HOU
W
265
38
74
0.514
10
...
35
17
15
4
14
28
114
HOU
UTA
0.0
1202
1610612762
0021500817
FEB 19, 2016
UTA vs. BOS
W
240
37
68
0.544
10
...
34
18
8
3
7
31
93
BOS
UTA
0.0
1203
1610612762
0021500758
FEB 05, 2016
UTA vs. MIL
W
240
30
77
0.390
8
...
41
9
12
7
20
17
81
MIL
UTA
0.0
1204
1610612762
0021500743
FEB 03, 2016
UTA vs. DEN
W
240
33
74
0.446
5
...
41
11
12
2
14
19
81
DEN
UTA
0.0
1205
1610612762
0021500728
FEB 01, 2016
UTA vs. CHI
W
265
38
85
0.447
7
...
42
19
4
7
14
25
96
CHI
UTA
0.0
1206
1610612762
0021500704
JAN 29, 2016
UTA vs. MIN
W
240
40
76
0.526
10
...
38
17
9
2
14
13
90
MIN
UTA
0.0
1207
1610612762
0021500690
JAN 27, 2016
UTA vs. CHA
W
240
40
81
0.494
12
...
35
10
6
0
18
15
73
CHA
UTA
0.0
1208
1610612762
0021500673
JAN 25, 2016
UTA vs. DET
L
240
34
85
0.400
9
...
42
19
7
1
10
18
95
DET
UTA
0.0
1209
1610612762
0021500608
JAN 16, 2016
UTA vs. LAL
W
240
41
83
0.494
12
...
44
16
4
4
14
14
82
LAL
UTA
0.0
1210
1610612762
0021500591
JAN 14, 2016
UTA vs. SAC
L
240
35
83
0.422
6
...
54
19
12
2
15
29
103
SAC
UTA
0.0
1211
1610612762
0021500555
JAN 09, 2016
UTA vs. MIA
W
240
39
71
0.549
9
...
39
14
7
8
17
16
83
MIA
UTA
0.0
1212
1610612762
0021500518
JAN 04, 2016
UTA vs. HOU
L
240
30
75
0.400
12
...
34
17
8
2
16
16
93
HOU
UTA
0.0
1213
1610612762
0021500503
JAN 02, 2016
UTA vs. MEM
W
265
31
75
0.413
9
...
40
17
8
4
11
24
87
MEM
UTA
0.0
1214
1610612762
0021500489
DEC 31, 2015
UTA vs. POR
W
240
43
86
0.500
15
...
39
19
1
0
10
16
96
POR
UTA
0.0
1215
1610612762
0021500467
DEC 28, 2015
UTA vs. PHI
W
240
28
84
0.333
7
...
37
20
8
9
18
27
91
PHI
UTA
0.0
1216
1610612762
0021500452
DEC 26, 2015
UTA vs. LAC
L
240
36
74
0.486
10
...
34
24
9
6
12
23
109
LAC
UTA
0.0
1217
1610612762
0021500417
DEC 21, 2015
UTA vs. PHX
W
240
36
79
0.456
9
...
40
12
3
0
13
26
89
PHX
UTA
0.0
1218
1610612762
0021500395
DEC 18, 2015
UTA vs. DEN
W
240
34
73
0.466
10
...
44
15
10
4
14
20
88
DEN
UTA
0.0
1219
1610612762
0021500380
DEC 16, 2015
UTA vs. NOP
L
240
32
67
0.478
7
...
36
14
9
2
10
24
104
NOP
UTA
0.0
1220
1610612762
0021500341
DEC 11, 2015
UTA vs. OKC
L
240
33
78
0.423
8
...
45
14
4
2
13
19
94
OKC
UTA
0.0
1221
1610612762
0021500327
DEC 09, 2015
UTA vs. NYK
W
240
39
80
0.488
9
...
36
20
10
3
10
24
85
NYK
UTA
0.0
1222
1610612762
0021500297
DEC 05, 2015
UTA vs. IND
W
265
43
92
0.467
8
...
44
17
10
2
15
34
119
IND
UTA
0.0
1223
1610612762
0021500279
DEC 03, 2015
UTA vs. ORL
L
240
31
72
0.431
14
...
40
19
13
4
10
20
103
ORL
UTA
0.0
1224
1610612762
0021500259
NOV 30, 2015
UTA vs. GSW
L
240
40
89
0.449
6
...
45
21
2
5
15
19
106
GSW
UTA
0.0
1225
1610612762
0021500243
NOV 28, 2015
UTA vs. NOP
W
240
38
82
0.463
9
...
35
19
8
4
16
19
87
NOP
UTA
0.0
1226
1610612762
0021500208
NOV 23, 2015
UTA vs. OKC
L
240
28
73
0.384
5
...
36
22
12
10
16
31
111
OKC
UTA
0.0
1227
1610612762
0021500173
NOV 18, 2015
UTA vs. TOR
W
240
35
72
0.486
7
...
37
14
10
4
14
17
89
TOR
UTA
0.0
1228
1610612762
0021500091
NOV 07, 2015
UTA vs. MEM
W
240
31
74
0.419
12
...
44
16
8
1
14
21
79
MEM
UTA
0.0
1229
1610612762
0021500068
NOV 04, 2015
UTA vs. POR
L
240
33
88
0.375
5
...
48
11
5
7
19
25
108
POR
UTA
0.0
1230 rows × 53 columns
In [14]:
full_sched.columns
Out[14]:
Index([ u'Team_ID_home', u'Game_ID', u'GAME_DATE_home',
u'MATCHUP_home', u'WL_home', u'MIN_home',
u'FGM_home', u'FGA_home', u'FG_PCT_home',
u'FG3M_home', u'FG3A_home', u'FG3_PCT_home',
u'FTM_home', u'FTA_home', u'FT_PCT_home',
u'OREB_home', u'DREB_home', u'REB_home',
u'AST_home', u'STL_home', u'BLK_home',
u'TOV_home', u'PF_home', u'PTS_home',
u'Away Team_home', u'Home Team_home', u'Home Stats_home',
u'Team_ID_away', u'GAME_DATE_away', u'MATCHUP_away',
u'WL_away', u'MIN_away', u'FGM_away',
u'FGA_away', u'FG_PCT_away', u'FG3M_away',
u'FG3A_away', u'FG3_PCT_away', u'FTM_away',
u'FTA_away', u'FT_PCT_away', u'OREB_away',
u'DREB_away', u'REB_away', u'AST_away',
u'STL_away', u'BLK_away', u'TOV_away',
u'PF_away', u'PTS_away', u'Away Team_away',
u'Home Team_away', u'Home Stats_away'],
dtype='object')
In [ ]:
cle_id = '1610612739'
cle_dunks = team.TeamShootingSplits(team_id=cle_id, season = '2015-16')
#cle_summary.info()
cle_summary.season_ranks()
Content source: mprego/NBA
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