This notebook is intended to document resources for obtaining particular types of NBA Statistics, provide examples on how to make requests, show what the response looks like and what kinds of operations can readily be performed on it.
In [1]:
import requests
from pandas import DataFrame
In [2]:
params = {
'PlayerID': 951, # Jesus Shuttlesworth
'Season': '2013-14',
'SeasonType': 'Regular Season'
}
r = requests.get('http://stats.nba.com/stats/playergamelog', params=params)
In [93]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(['Player_ID', 'SEASON_ID', 'Game_ID']); df
Out[93]:
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
FG3A
FG3_PCT
...
DREB
REB
AST
STL
BLK
TOV
PF
PTS
PLUS_MINUS
VIDEO_AVAILABLE
Player_ID
SEASON_ID
Game_ID
951
22013
0021301225
APR 16, 2014
MIA vs. PHI
L
24
4
9
0.444
1
5
0.200
...
3
3
3
0
0
1
0
11
-4
1
0021301206
APR 14, 2014
MIA @ WAS
L
19
4
7
0.571
3
5
0.600
...
2
2
1
1
0
0
1
13
-11
1
0021301192
APR 12, 2014
MIA @ ATL
L
24
1
2
0.500
1
2
0.500
...
0
0
1
0
0
0
1
3
-6
1
0021301180
APR 11, 2014
MIA vs. IND
W
29
3
7
0.429
1
3
0.333
...
4
4
2
2
0
2
2
10
2
1
0021301167
APR 09, 2014
MIA @ MEM
L
25
2
4
0.500
2
4
0.500
...
0
0
2
2
0
1
1
6
-5
1
0021301158
APR 08, 2014
MIA vs. BKN
L
23
2
5
0.400
2
4
0.500
...
3
4
0
0
1
1
4
6
2
1
0021301149
APR 06, 2014
MIA vs. NYK
W
29
4
10
0.400
4
9
0.444
...
1
1
1
1
0
1
1
12
15
1
0021301135
APR 04, 2014
MIA vs. MIN
L
28
4
10
0.400
2
6
0.333
...
2
3
1
2
0
1
4
10
1
1
0021301049
MAR 24, 2014
MIA vs. POR
W
32
3
10
0.300
2
4
0.500
...
3
3
3
0
0
0
2
8
-12
1
0021301036
MAR 22, 2014
MIA @ NOP
L
31
3
10
0.300
2
9
0.222
...
3
3
1
0
0
2
0
10
-10
1
0021301027
MAR 21, 2014
MIA vs. MEM
W
31
7
14
0.500
2
5
0.400
...
3
4
4
0
0
0
2
18
20
1
0021301009
MAR 19, 2014
MIA @ BOS
L
32
5
11
0.455
1
4
0.250
...
1
1
1
2
0
0
0
14
3
1
0021301003
MAR 18, 2014
MIA @ CLE
W
30
5
13
0.385
2
9
0.222
...
1
3
2
0
0
3
1
14
-5
1
0021300989
MAR 16, 2014
MIA vs. HOU
W
29
7
11
0.636
4
6
0.667
...
1
1
0
0
0
0
1
25
7
1
0021300976
MAR 14, 2014
MIA vs. DEN
L
29
7
9
0.778
5
7
0.714
...
3
4
2
1
0
1
3
22
14
1
0021300957
MAR 12, 2014
MIA vs. BKN
L
28
0
3
0.000
0
3
0.000
...
3
3
1
0
0
0
1
1
1
1
0021300945
MAR 10, 2014
MIA vs. WAS
W
29
5
9
0.556
3
6
0.500
...
2
3
2
0
0
0
1
13
3
1
0021300934
MAR 09, 2014
MIA @ CHI
L
21
2
5
0.400
2
3
0.667
...
1
1
0
0
0
1
0
7
-12
1
0021300914
MAR 06, 2014
MIA @ SAS
L
22
2
5
0.400
1
3
0.333
...
1
1
5
0
1
3
1
6
-11
1
0021300900
MAR 04, 2014
MIA @ HOU
L
26
5
8
0.625
2
5
0.400
...
3
4
1
1
0
0
1
13
11
1
0021300893
MAR 03, 2014
MIA vs. CHA
W
30
3
7
0.429
3
5
0.600
...
3
3
4
0
0
0
4
9
14
1
0021300874
MAR 01, 2014
MIA vs. ORL
W
24
3
7
0.429
1
4
0.250
...
1
1
1
0
0
1
0
7
11
2
0021300865
FEB 27, 2014
MIA vs. NYK
W
20
4
6
0.667
1
3
0.333
...
1
2
3
0
0
1
0
11
18
1
0021300834
FEB 23, 2014
MIA vs. CHI
W
31
4
8
0.500
2
3
0.667
...
5
5
0
0
0
0
1
10
18
1
0021300813
FEB 20, 2014
MIA @ OKC
W
25
2
6
0.333
1
3
0.333
...
3
3
1
1
0
1
0
6
3
1
0021300798
FEB 18, 2014
MIA @ DAL
W
22
0
5
0.000
0
3
0.000
...
1
1
1
0
0
0
1
0
4
1
0021300788
FEB 12, 2014
MIA @ GSW
W
31
3
7
0.429
3
5
0.600
...
3
3
3
2
0
0
2
9
-8
1
0021300774
FEB 11, 2014
MIA @ PHX
W
29
3
6
0.500
1
4
0.250
...
7
7
3
1
0
2
1
7
12
1
0021300755
FEB 08, 2014
MIA @ UTA
L
23
4
8
0.500
1
4
0.250
...
0
0
1
1
0
1
1
9
-5
1
0021300736
FEB 05, 2014
MIA @ LAC
W
27
4
8
0.500
2
5
0.400
...
0
0
2
1
0
2
3
15
-6
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
JAN 05, 2014
MIA vs. TOR
W
25
1
7
0.143
0
4
0.000
...
1
2
3
1
0
1
1
6
1
1
0021300500
JAN 04, 2014
MIA @ ORL
W
18
0
4
0.000
0
2
0.000
...
1
1
0
0
0
0
3
1
3
1
0021300490
JAN 02, 2014
MIA vs. GSW
L
20
1
4
0.250
0
1
0.000
...
0
0
2
0
0
0
3
5
7
1
0021300474
DEC 30, 2013
MIA @ DEN
W
31
4
7
0.571
2
4
0.500
...
6
7
2
0
0
1
1
13
-10
1
0021300457
DEC 28, 2013
MIA @ POR
W
26
4
11
0.364
1
6
0.167
...
2
2
2
3
0
1
1
9
-6
1
0021300446
DEC 25, 2013
MIA @ LAL
W
21
4
5
0.800
4
5
0.800
...
2
2
2
1
1
0
1
12
-5
1
0021300420
DEC 23, 2013
MIA vs. ATL
W
34
7
10
0.700
1
4
0.250
...
5
6
1
0
0
2
1
19
-3
1
0021300411
DEC 20, 2013
MIA vs. SAC
W
26
8
13
0.615
2
6
0.333
...
1
1
5
1
0
4
4
18
2
1
0021300387
DEC 18, 2013
MIA vs. IND
W
27
2
6
0.333
1
4
0.250
...
6
7
3
1
0
0
0
7
-1
1
0021300369
DEC 16, 2013
MIA vs. UTA
W
24
2
6
0.333
1
4
0.250
...
4
4
1
1
0
3
1
5
0
1
0021300360
DEC 14, 2013
MIA vs. CLE
W
25
3
5
0.600
2
4
0.500
...
2
2
2
1
0
3
4
12
13
1
0021300344
DEC 10, 2013
MIA @ IND
L
26
1
5
0.200
1
1
1.000
...
1
1
2
1
1
1
1
3
0
1
0021300311
DEC 08, 2013
MIA @ DET
W
25
6
10
0.600
2
5
0.400
...
1
1
3
1
0
0
1
18
15
1
0021300300
DEC 07, 2013
MIA @ MIN
W
21
4
8
0.500
0
3
0.000
...
3
3
2
2
0
2
2
11
13
1
0021300294
DEC 05, 2013
MIA @ CHI
L
27
2
4
0.500
1
3
0.333
...
1
1
1
1
0
0
2
9
-13
1
0021300279
DEC 03, 2013
MIA vs. DET
L
29
5
10
0.500
2
4
0.500
...
2
4
1
1
0
3
2
12
1
1
0021300264
DEC 01, 2013
MIA vs. CHA
W
16
1
4
0.250
0
1
0.000
...
0
0
2
2
0
2
4
2
-3
1
0021300252
NOV 29, 2013
MIA @ TOR
W
24
3
8
0.375
1
1
1.000
...
0
0
4
0
0
3
4
7
-8
1
0021300230
NOV 27, 2013
MIA @ CLE
W
20
3
9
0.333
1
4
0.250
...
3
3
2
1
1
0
3
7
5
1
0021300219
NOV 25, 2013
MIA vs. PHX
W
25
6
11
0.545
3
8
0.375
...
3
3
1
2
1
2
0
17
17
1
0021300205
NOV 23, 2013
MIA vs. ORL
W
26
1
5
0.200
1
3
0.333
...
2
3
2
0
0
1
1
5
0
1
0021300191
NOV 20, 2013
MIA @ ORL
W
20
2
8
0.250
2
3
0.667
...
1
2
0
1
0
3
0
6
3
1
0021300164
NOV 19, 2013
MIA vs. ATL
W
25
5
10
0.500
3
6
0.500
...
2
3
1
1
0
2
1
17
0
1
0021300158
NOV 09, 2013
MIA vs. BOS
L
24
2
4
0.500
0
1
0.000
...
4
4
3
0
0
1
1
5
-6
1
0021300089
NOV 07, 2013
MIA vs. LAC
W
26
5
8
0.625
0
3
0.000
...
1
1
2
1
0
0
0
12
14
1
0021300070
NOV 05, 2013
MIA @ TOR
W
30
5
11
0.455
3
7
0.429
...
4
4
2
1
0
2
3
14
21
1
0021300051
NOV 03, 2013
MIA vs. WAS
W
31
2
5
0.400
0
2
0.000
...
2
3
6
0
0
0
2
4
3
1
0021300042
NOV 01, 2013
MIA @ BKN
L
29
4
6
0.667
1
2
0.500
...
4
4
2
1
0
0
0
12
10
1
0021300028
OCT 30, 2013
MIA @ PHI
L
28
7
13
0.538
4
9
0.444
...
5
6
1
2
0
0
0
19
1
1
0021300005
OCT 29, 2013
MIA vs. CHI
W
26
3
7
0.429
3
6
0.500
...
1
1
7
0
1
3
4
11
9
1
73 rows × 24 columns
Summarize the data for us.
In [4]:
df.describe()
Out[4]:
MIN
FGM
FGA
FG_PCT
FG3M
FG3A
FG3_PCT
FTM
FTA
FT_PCT
...
DREB
REB
AST
STL
BLK
TOV
PF
PTS
PLUS_MINUS
VIDEO_AVAILABLE
count
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
...
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
73.000000
mean
26.534247
3.287671
7.438356
0.426932
1.589041
4.232877
0.363219
1.438356
1.589041
0.528767
...
2.493151
2.808219
1.958904
0.739726
0.109589
1.150685
1.575342
9.602740
2.219178
1.013699
std
4.885017
1.896484
2.793774
0.187429
1.211469
1.882083
0.253959
1.632877
1.801509
0.478835
...
1.803725
1.934034
1.447588
0.727007
0.314539
1.126342
1.322012
5.206139
10.030850
0.117041
min
15.000000
0.000000
2.000000
0.000000
0.000000
1.000000
0.000000
0.000000
0.000000
0.000000
...
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
0.000000
-24.000000
1.000000
25%
24.000000
2.000000
5.000000
0.333000
1.000000
3.000000
0.222000
0.000000
0.000000
0.000000
...
1.000000
1.000000
1.000000
0.000000
0.000000
0.000000
1.000000
6.000000
-5.000000
1.000000
50%
26.000000
3.000000
7.000000
0.429000
1.000000
4.000000
0.333000
1.000000
1.000000
0.750000
...
2.000000
3.000000
2.000000
1.000000
0.000000
1.000000
1.000000
9.000000
2.000000
1.000000
75%
29.000000
4.000000
10.000000
0.500000
2.000000
5.000000
0.500000
3.000000
3.000000
1.000000
...
3.000000
4.000000
3.000000
1.000000
0.000000
2.000000
2.000000
12.000000
10.000000
1.000000
max
43.000000
8.000000
14.000000
1.000000
5.000000
9.000000
1.000000
7.000000
7.000000
1.000000
...
9.000000
9.000000
7.000000
3.000000
1.000000
4.000000
5.000000
25.000000
22.000000
2.000000
8 rows × 21 columns
In [5]:
params = {
'LeagueID': '00',
'Season': '2013-14',
'IsOnlyCurrentSeason': 0
}
r = requests.get('http://stats.nba.com/stats/commonallplayers', params=params)
In [6]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('PERSON_ID'); df
Out[6]:
DISPLAY_LAST_COMMA_FIRST
ROSTERSTATUS
FROM_YEAR
TO_YEAR
PLAYERCODE
PERSON_ID
76001
Abdelnaby, Alaa
0
1990
1994
HISTADD_alaa_abdelnaby
76002
Abdul-Aziz, Zaid
0
1968
1977
HISTADD_zaid_abdul-aziz
76003
Abdul-Jabbar, Kareem
0
1969
1988
HISTADD_kareem_abdul-jabbar
51
Abdul-Rauf, Mahmoud
0
1990
2000
HISTADD_mahmoud_abdul-rauf
1505
Abdul-Wahad, Tariq
0
1997
2003
tariq_abdul-wahad
949
Abdur-Rahim, Shareef
0
1996
2007
shareef_abdur-rahim
76005
Abernethy, Tom
0
1976
1980
HISTADD_tom_abernethy
76006
Able, Forest
0
1956
1956
HISTADD_frosty_able
76007
Abramovic, John
0
1946
1947
HISTADD_brooms_abramovic
101165
Acker, Alex
0
2005
2008
alex_acker
76008
Ackerman, Donald
0
1953
1953
HISTADD_buddy_ackerman
76009
Acres, Mark
0
1987
1992
HISTADD_mark_acres
76010
Acton, Charles
0
1967
1967
HISTADD_bud_acton
203112
Acy, Quincy
1
2012
2014
quincy_acy
76011
Adams, Alvan
0
1975
1987
HISTADD_alvan_adams
76012
Adams, Don
0
1970
1976
HISTADD_don_adams
200801
Adams, Hassan
0
2006
2008
hassan_adams
203919
Adams, Jordan
0
2014
2014
jordan_adams
149
Adams, Michael
0
1985
1995
HISTADD_michael_adams
203500
Adams, Steven
1
2013
2014
steven_adams
912
Addison, Rafael
0
1986
1996
HISTADD_rafael_addison
76015
Adelman, Rick
0
1968
1974
HISTADD_rick_adelman
202399
Adrien, Jeff
1
2010
2014
jeff_adrien
201167
Afflalo, Arron
1
2007
2014
arron_afflalo
200772
Ager, Maurice
0
2006
2010
maurice_ager
76016
Aguirre, Mark
0
1981
1993
HISTADD_mark_aguirre
201336
Ahearn, Blake
0
2007
2011
blake_ahearn
76017
Ainge, Danny
0
1981
1994
HISTADD_danny_ainge
201582
Ajinca, Alexis
1
2008
2014
alexis_ajinca
76018
Akin, Henry
0
1966
1967
HISTADD_henry_akin
...
...
...
...
...
...
78631
Yelverton, Charlie
0
1971
1971
HISTADD_charlie_yelverton
201146
Yi Jianlian
0
2007
2011
yi_jianlian
78632
Yonakor, Rich
0
1981
1981
HISTADD_rich_yonakor
78633
Young, Danny
0
1984
1994
HISTADD_danny_young
203923
Young, James
0
2014
2014
james_young
1748
Young, Korleone
0
1998
1998
HISTADD_korleone_young
78634
Young, Michael
0
1984
1989
HISTADD_michael_young
201156
Young, Nick
1
2007
2014
nick_young
203942
Young, Patric
0
2014
2014
patric_young
78635
Young, Perry
0
1986
1986
HISTADD_perry_young
201970
Young, Sam
0
2009
2012
sam_young
201152
Young, Thaddeus
1
2007
2014
thaddeus_young
1937
Young, Tim
0
1999
1999
HISTADD_tim_young
201180
Yue, Sun
0
2008
2008
sun_yue
78638
Zaslofsky, Max
0
1946
1955
HISTADD_slats_zaslofsky
78639
Zawoluk, Robert
0
1952
1954
HISTADD_zeke_zawoluk
203469
Zeller, Cody
1
2013
2014
cody_zeller
78640
Zeller, Dave
0
1961
1961
HISTADD_dave_zeller
78641
Zeller, Gary
0
1970
1971
HISTADD_gary_zeller
78642
Zeller, Hank
0
1946
1946
HISTADD_hank_zeller
202545
Zeller, Luke
0
2012
2012
luke_zeller
203092
Zeller, Tyler
1
2012
2014
tyler_zeller
78643
Zeno, Tony
0
1979
1979
HISTADD_tony_zeno
78644
Zevenbergen, Phil
0
1987
1987
HISTADD_phil_zevenbergen
1917
Zhi-zhi, Wang
0
2000
2004
wang_zhizhi
678
Zidek, George
0
1995
1997
george_zidek
2583
Zimmerman, Derrick
0
2005
2005
derrick_zimmerman
78647
Zoet, Jim
0
1982
1982
HISTADD_jim_zoet
78648
Zopf, Bill
0
1970
1970
HISTADD_zip_zopf
78650
Zunic, Matt
0
1948
1948
HISTADD_matt_zunic
3990 rows × 5 columns
You'd be surprised how far a list like this can take us. By way of example, we can easily obtain an image of "Swaggy P" like so:
In [7]:
from IPython.display import Image
Image('http://i.cdn.turner.com/nba/nba/.element/img/2.0/sect/statscube/players/large/{player_code}.png' \
.format(player_code=df[df.DISPLAY_LAST_COMMA_FIRST=='Young, Nick'].iloc[0]['PLAYERCODE']))
Out[7]:
I'm sure you get the gist. In reality, this list will be much more useful as a starting point for a bot/spider to following each individual player's details.
In [84]:
params = {
'PlayerID' : 201167, # Arron Afflalo
'LeagueID' : '00',
'Season' : '2013-14',
'SeasonType' : 'Regular Season',
'GraphStartSeason' : '',
'GraphEndSeason' : '',
'GraphStat' : 'PTS',
}
r = requests.get('http://stats.nba.com/stats/playerprofile', params=params)
This one is interesting because it produces a bunch of different results.
In [85]:
result_dict = {data[u'name']: DataFrame(data[u'rowSet'], columns=data[u'headers']) for data in r.json()[u'resultSets']}; result_dict
Out[85]:
{u'GameLogs': Player_ID Game_ID GAME_DATE MATCHUP WL MIN FGM FGA FG_PCT \
0 201167 0021301227 APR 16, 2014 ORL vs. IND L 6 2 2 1.000
1 201167 0021301208 APR 14, 2014 ORL @ CHI L 22 5 7 0.714
2 201167 0021301199 APR 13, 2014 ORL @ BKN L 21 2 7 0.286
3 201167 0021301176 APR 11, 2014 ORL vs. WAS L 31 6 14 0.429
4 201167 0021301163 APR 09, 2014 ORL vs. BKN W 33 9 16 0.563
5 201167 0021301143 APR 05, 2014 ORL vs. MIN W 30 8 15 0.533
6 201167 0021301130 APR 04, 2014 ORL @ CHA L 23 2 10 0.200
7 201167 0021301116 APR 02, 2014 ORL vs. CLE L 29 3 10 0.300
8 201167 0021301096 MAR 30, 2014 ORL vs. TOR L 36 3 9 0.333
9 201167 0021301075 MAR 28, 2014 ORL vs. CHA W 39 3 9 0.333
FG3M ... OREB DREB REB AST STL BLK TOV PF PTS PLUS_MINUS
0 1 ... 0 1 1 1 1 0 1 0 7 4
1 1 ... 0 1 1 1 0 0 2 2 13 3
2 1 ... 0 0 0 5 0 0 0 1 5 2
3 1 ... 0 2 2 1 1 0 2 0 19 -1
4 2 ... 1 3 4 5 0 0 1 4 25 -14
5 1 ... 0 3 3 2 0 0 4 2 18 -5
6 0 ... 1 2 3 0 0 0 1 1 8 -21
7 1 ... 0 4 4 5 0 1 0 0 7 -18
8 1 ... 2 0 2 2 0 0 2 3 8 1
9 2 ... 0 2 2 1 0 0 3 1 17 -6
[10 rows x 25 columns],
u'GraphGameList': PLAYER_ID PLAYER_NAME GAME_ID GAME_DATE TEAM_ABBREVIATION \
0 201167 Arron Afflalo 0021300001 10/29/2013 ORL
1 201167 Arron Afflalo 0021300010 10/30/2013 ORL
2 201167 Arron Afflalo 0021300021 11/01/2013 ORL
3 201167 Arron Afflalo 0021300043 11/03/2013 ORL
4 201167 Arron Afflalo 0021300062 11/06/2013 ORL
5 201167 Arron Afflalo 0021300075 11/08/2013 ORL
6 201167 Arron Afflalo 0021300086 11/09/2013 ORL
7 201167 Arron Afflalo 0021300101 11/11/2013 ORL
8 201167 Arron Afflalo 0021300111 11/13/2013 ORL
9 201167 Arron Afflalo 0021300137 11/16/2013 ORL
10 201167 Arron Afflalo 0021300164 11/20/2013 ORL
11 201167 Arron Afflalo 0021300191 11/23/2013 ORL
12 201167 Arron Afflalo 0021300199 11/24/2013 ORL
13 201167 Arron Afflalo 0021300213 11/26/2013 ORL
14 201167 Arron Afflalo 0021300216 11/27/2013 ORL
15 201167 Arron Afflalo 0021300229 11/29/2013 ORL
16 201167 Arron Afflalo 0021300256 12/02/2013 ORL
17 201167 Arron Afflalo 0021300261 12/03/2013 ORL
18 201167 Arron Afflalo 0021300284 12/06/2013 ORL
19 201167 Arron Afflalo 0021300301 12/08/2013 ORL
20 201167 Arron Afflalo 0021300307 12/09/2013 ORL
21 201167 Arron Afflalo 0021300318 12/11/2013 ORL
22 201167 Arron Afflalo 0021300330 12/13/2013 ORL
23 201167 Arron Afflalo 0021300353 12/15/2013 ORL
24 201167 Arron Afflalo 0021300362 12/16/2013 ORL
25 201167 Arron Afflalo 0021300395 12/21/2013 ORL
26 201167 Arron Afflalo 0021300409 12/23/2013 ORL
27 201167 Arron Afflalo 0021300428 12/27/2013 ORL
28 201167 Arron Afflalo 0021300449 12/29/2013 ORL
29 201167 Arron Afflalo 0021300462 12/31/2013 ORL
.. ... ... ... ... ...
43 201167 Arron Afflalo 0021300689 01/31/2014 ORL
44 201167 Arron Afflalo 0021300709 02/02/2014 ORL
45 201167 Arron Afflalo 0021300710 02/03/2014 ORL
46 201167 Arron Afflalo 0021300733 02/05/2014 ORL
47 201167 Arron Afflalo 0021300740 02/07/2014 ORL
48 201167 Arron Afflalo 0021300761 02/09/2014 ORL
49 201167 Arron Afflalo 0021300778 02/12/2014 ORL
50 201167 Arron Afflalo 0021300797 02/18/2014 ORL
51 201167 Arron Afflalo 0021300802 02/19/2014 ORL
52 201167 Arron Afflalo 0021300816 02/21/2014 ORL
53 201167 Arron Afflalo 0021300905 03/05/2014 ORL
54 201167 Arron Afflalo 0021300932 03/08/2014 ORL
55 201167 Arron Afflalo 0021300947 03/10/2014 ORL
56 201167 Arron Afflalo 0021300958 03/12/2014 ORL
57 201167 Arron Afflalo 0021300972 03/14/2014 ORL
58 201167 Arron Afflalo 0021301007 03/18/2014 ORL
59 201167 Arron Afflalo 0021301016 03/19/2014 ORL
60 201167 Arron Afflalo 0021301037 03/22/2014 ORL
61 201167 Arron Afflalo 0021301046 03/23/2014 ORL
62 201167 Arron Afflalo 0021301058 03/25/2014 ORL
63 201167 Arron Afflalo 0021301075 03/28/2014 ORL
64 201167 Arron Afflalo 0021301096 03/30/2014 ORL
65 201167 Arron Afflalo 0021301116 04/02/2014 ORL
66 201167 Arron Afflalo 0021301130 04/04/2014 ORL
67 201167 Arron Afflalo 0021301143 04/05/2014 ORL
68 201167 Arron Afflalo 0021301163 04/09/2014 ORL
69 201167 Arron Afflalo 0021301176 04/11/2014 ORL
70 201167 Arron Afflalo 0021301199 04/13/2014 ORL
71 201167 Arron Afflalo 0021301208 04/14/2014 ORL
72 201167 Arron Afflalo 0021301227 04/16/2014 ORL
TEAM_NAME VS_TEAM_ID VS_TEAM_ABBREVIATION VS_TEAM_NAME PTS
0 Magic 1610612754 IND Pacers 9
1 Magic 1610612750 MIN Timberwolves 28
2 Magic 1610612740 NOP Pelicans 30
3 Magic 1610612751 BKN Nets 16
4 Magic 1610612746 LAC Clippers 11
5 Magic 1610612738 BOS Celtics 23
6 Magic 1610612737 ATL Hawks 21
7 Magic 1610612738 BOS Celtics 18
8 Magic 1610612749 MIL Bucks 36
9 Magic 1610612742 DAL Mavericks 25
10 Magic 1610612748 MIA Heat 30
11 Magic 1610612748 MIA Heat 18
12 Magic 1610612756 PHX Suns 16
13 Magic 1610612737 ATL Hawks 26
14 Magic 1610612755 PHI 76ers 18
15 Magic 1610612759 SAS Spurs 17
16 Magic 1610612764 WAS Wizards 21
17 Magic 1610612755 PHI 76ers 43
18 Magic 1610612752 NYK Knicks 20
19 Magic 1610612745 HOU Rockets 16
20 Magic 1610612763 MEM Grizzlies 17
21 Magic 1610612766 CHA Bobcats 16
22 Magic 1610612739 CLE Cavaliers 17
23 Magic 1610612760 OKC Thunder 25
24 Magic 1610612741 CHI Bulls 23
25 Magic 1610612758 SAC Kings 26
26 Magic 1610612752 NYK Knicks 26
27 Magic 1610612765 DET Pistons 23
28 Magic 1610612737 ATL Hawks 21
29 Magic 1610612744 GSW Warriors 15
.. ... ... ... ... ...
43 Magic 1610612749 MIL Bucks 21
44 Magic 1610612738 BOS Celtics 18
45 Magic 1610612754 IND Pacers 20
46 Magic 1610612765 DET Pistons 15
47 Magic 1610612760 OKC Thunder 16
48 Magic 1610612754 IND Pacers 7
49 Magic 1610612763 MEM Grizzlies 10
50 Magic 1610612749 MIL Bucks 21
51 Magic 1610612739 CLE Cavaliers 23
52 Magic 1610612752 NYK Knicks 32
53 Magic 1610612745 HOU Rockets 18
54 Magic 1610612759 SAS Spurs 17
55 Magic 1610612749 MIL Bucks 12
56 Magic 1610612743 DEN Nuggets 24
57 Magic 1610612764 WAS Wizards 18
58 Magic 1610612744 GSW Warriors 4
59 Magic 1610612756 PHX Suns 20
60 Magic 1610612762 UTA Jazz 18
61 Magic 1610612747 LAL Lakers 18
62 Magic 1610612757 POR Trail Blazers 10
63 Magic 1610612766 CHA Bobcats 17
64 Magic 1610612761 TOR Raptors 8
65 Magic 1610612739 CLE Cavaliers 7
66 Magic 1610612766 CHA Bobcats 8
67 Magic 1610612750 MIN Timberwolves 18
68 Magic 1610612751 BKN Nets 25
69 Magic 1610612764 WAS Wizards 19
70 Magic 1610612751 BKN Nets 5
71 Magic 1610612741 CHI Bulls 13
72 Magic 1610612754 IND Pacers 7
[73 rows x 10 columns],
u'GraphLeagueAvg': PTS
0 9.7,
u'GraphPlayerAvg': PLAYER_ID PLAYER_NAME PTS
0 201167 Arron Afflalo 18.2,
u'OverviewCareerAvg': PLAYER_ID PLAYER_NAME MIN FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM \
0 201167 Arron Afflalo 27.4 4 8.8 0.459 1.1 2.8 0.392 2
... FT_PCT OREB DREB REB AST TOV STL BLK PF PTS
0 ... 0.815 0.5 2.4 2.9 2 1.2 0.5 0.2 2 11.1
[1 rows x 21 columns],
u'OverviewCareerHigh': PLAYER_ID PLAYER_NAME MIN FGM FGA FG_PCT FG3M FG3A FG3_PCT FTM \
0 201167 Arron Afflalo 36 6.4 14.1 0.498 1.8 4.1 0.434 3.8
... FT_PCT OREB DREB REB AST TOV STL BLK PF PTS
0 ... 0.857 0.7 3.3 3.7 3.4 2.2 0.6 0.4 2.7 18.2
[1 rows x 21 columns],
u'OverviewCareerTotal': PLAYER_ID PLAYER_NAME MIN FGM FGA FG_PCT FG3M FG3A FG3_PCT \
0 201167 Arron Afflalo 13694 2004 4368 0.459 554 1413 0.392
FTM ... FT_PCT OREB DREB REB AST TOV STL BLK PF PTS
0 998 ... 0.815 271 1201 1472 1005 591 250 109 1012 5560
[1 rows x 21 columns],
u'OverviewSeasonAvg': PLAYER_ID PLAYER_NAME GP W L W_PCT MIN FGM FGA FG_PCT \
0 201167 Arron Afflalo 73 21 52 0.288 35 6.4 13.8 0.459
... TOV STL BLK BLKA PF PFD PTS PLUS_MINUS DD2 TD3
0 ... 2 0.5 0 0.8 1.9 3.3 18.2 -5 1 0
[1 rows x 30 columns],
u'OverviewSeasonHigh': PLAYER_ID PLAYER_NAME GP MIN FGM FGA FG_PCT FG3M FG3A \
0 201167 Arron Afflalo 73 52.416667 14 27 1 8 13
FG3_PCT ... REB AST TOV STL BLK BLKA PF PFD PTS PLUS_MINUS
0 1 ... 13 8 6 2 1 3 5 9 43 18
[1 rows x 25 columns]}
These are the names of the results returned.
In [86]:
result_dict.keys()
Out[86]:
[u'OverviewSeasonHigh',
u'GameLogs',
u'GraphGameList',
u'OverviewCareerAvg',
u'GraphLeagueAvg',
u'OverviewCareerTotal',
u'OverviewCareerHigh',
u'OverviewSeasonAvg',
u'GraphPlayerAvg']
Let's check one of them out.
In [87]:
result_dict['GameLogs'].set_index(['Player_ID', 'Game_ID'])
Out[87]:
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
FG3A
FG3_PCT
...
OREB
DREB
REB
AST
STL
BLK
TOV
PF
PTS
PLUS_MINUS
Player_ID
Game_ID
201167
0021301227
APR 16, 2014
ORL vs. IND
L
6
2
2
1.000
1
1
1.00
...
0
1
1
1
1
0
1
0
7
4
0021301208
APR 14, 2014
ORL @ CHI
L
22
5
7
0.714
1
1
1.00
...
0
1
1
1
0
0
2
2
13
3
0021301199
APR 13, 2014
ORL @ BKN
L
21
2
7
0.286
1
2
0.50
...
0
0
0
5
0
0
0
1
5
2
0021301176
APR 11, 2014
ORL vs. WAS
L
31
6
14
0.429
1
4
0.25
...
0
2
2
1
1
0
2
0
19
-1
0021301163
APR 09, 2014
ORL vs. BKN
W
33
9
16
0.563
2
5
0.40
...
1
3
4
5
0
0
1
4
25
-14
0021301143
APR 05, 2014
ORL vs. MIN
W
30
8
15
0.533
1
4
0.25
...
0
3
3
2
0
0
4
2
18
-5
0021301130
APR 04, 2014
ORL @ CHA
L
23
2
10
0.200
0
2
0.00
...
1
2
3
0
0
0
1
1
8
-21
0021301116
APR 02, 2014
ORL vs. CLE
L
29
3
10
0.300
1
5
0.20
...
0
4
4
5
0
1
0
0
7
-18
0021301096
MAR 30, 2014
ORL vs. TOR
L
36
3
9
0.333
1
4
0.25
...
2
0
2
2
0
0
2
3
8
1
0021301075
MAR 28, 2014
ORL vs. CHA
W
39
3
9
0.333
2
4
0.50
...
0
2
2
1
0
0
3
1
17
-6
10 rows × 23 columns
This is actually not unlike the playergamelog view we saw above.
In [89]:
result_dict[u'GraphGameList'].set_index(['PLAYER_ID', 'GAME_ID'])
Out[89]:
PLAYER_NAME
GAME_DATE
TEAM_ABBREVIATION
TEAM_NAME
VS_TEAM_ID
VS_TEAM_ABBREVIATION
VS_TEAM_NAME
PTS
PLAYER_ID
GAME_ID
201167
0021300001
Arron Afflalo
10/29/2013
ORL
Magic
1610612754
IND
Pacers
9
0021300010
Arron Afflalo
10/30/2013
ORL
Magic
1610612750
MIN
Timberwolves
28
0021300021
Arron Afflalo
11/01/2013
ORL
Magic
1610612740
NOP
Pelicans
30
0021300043
Arron Afflalo
11/03/2013
ORL
Magic
1610612751
BKN
Nets
16
0021300062
Arron Afflalo
11/06/2013
ORL
Magic
1610612746
LAC
Clippers
11
0021300075
Arron Afflalo
11/08/2013
ORL
Magic
1610612738
BOS
Celtics
23
0021300086
Arron Afflalo
11/09/2013
ORL
Magic
1610612737
ATL
Hawks
21
0021300101
Arron Afflalo
11/11/2013
ORL
Magic
1610612738
BOS
Celtics
18
0021300111
Arron Afflalo
11/13/2013
ORL
Magic
1610612749
MIL
Bucks
36
0021300137
Arron Afflalo
11/16/2013
ORL
Magic
1610612742
DAL
Mavericks
25
0021300164
Arron Afflalo
11/20/2013
ORL
Magic
1610612748
MIA
Heat
30
0021300191
Arron Afflalo
11/23/2013
ORL
Magic
1610612748
MIA
Heat
18
0021300199
Arron Afflalo
11/24/2013
ORL
Magic
1610612756
PHX
Suns
16
0021300213
Arron Afflalo
11/26/2013
ORL
Magic
1610612737
ATL
Hawks
26
0021300216
Arron Afflalo
11/27/2013
ORL
Magic
1610612755
PHI
76ers
18
0021300229
Arron Afflalo
11/29/2013
ORL
Magic
1610612759
SAS
Spurs
17
0021300256
Arron Afflalo
12/02/2013
ORL
Magic
1610612764
WAS
Wizards
21
0021300261
Arron Afflalo
12/03/2013
ORL
Magic
1610612755
PHI
76ers
43
0021300284
Arron Afflalo
12/06/2013
ORL
Magic
1610612752
NYK
Knicks
20
0021300301
Arron Afflalo
12/08/2013
ORL
Magic
1610612745
HOU
Rockets
16
0021300307
Arron Afflalo
12/09/2013
ORL
Magic
1610612763
MEM
Grizzlies
17
0021300318
Arron Afflalo
12/11/2013
ORL
Magic
1610612766
CHA
Bobcats
16
0021300330
Arron Afflalo
12/13/2013
ORL
Magic
1610612739
CLE
Cavaliers
17
0021300353
Arron Afflalo
12/15/2013
ORL
Magic
1610612760
OKC
Thunder
25
0021300362
Arron Afflalo
12/16/2013
ORL
Magic
1610612741
CHI
Bulls
23
0021300395
Arron Afflalo
12/21/2013
ORL
Magic
1610612758
SAC
Kings
26
0021300409
Arron Afflalo
12/23/2013
ORL
Magic
1610612752
NYK
Knicks
26
0021300428
Arron Afflalo
12/27/2013
ORL
Magic
1610612765
DET
Pistons
23
0021300449
Arron Afflalo
12/29/2013
ORL
Magic
1610612737
ATL
Hawks
21
0021300462
Arron Afflalo
12/31/2013
ORL
Magic
1610612744
GSW
Warriors
15
...
...
...
...
...
...
...
...
...
0021300689
Arron Afflalo
01/31/2014
ORL
Magic
1610612749
MIL
Bucks
21
0021300709
Arron Afflalo
02/02/2014
ORL
Magic
1610612738
BOS
Celtics
18
0021300710
Arron Afflalo
02/03/2014
ORL
Magic
1610612754
IND
Pacers
20
0021300733
Arron Afflalo
02/05/2014
ORL
Magic
1610612765
DET
Pistons
15
0021300740
Arron Afflalo
02/07/2014
ORL
Magic
1610612760
OKC
Thunder
16
0021300761
Arron Afflalo
02/09/2014
ORL
Magic
1610612754
IND
Pacers
7
0021300778
Arron Afflalo
02/12/2014
ORL
Magic
1610612763
MEM
Grizzlies
10
0021300797
Arron Afflalo
02/18/2014
ORL
Magic
1610612749
MIL
Bucks
21
0021300802
Arron Afflalo
02/19/2014
ORL
Magic
1610612739
CLE
Cavaliers
23
0021300816
Arron Afflalo
02/21/2014
ORL
Magic
1610612752
NYK
Knicks
32
0021300905
Arron Afflalo
03/05/2014
ORL
Magic
1610612745
HOU
Rockets
18
0021300932
Arron Afflalo
03/08/2014
ORL
Magic
1610612759
SAS
Spurs
17
0021300947
Arron Afflalo
03/10/2014
ORL
Magic
1610612749
MIL
Bucks
12
0021300958
Arron Afflalo
03/12/2014
ORL
Magic
1610612743
DEN
Nuggets
24
0021300972
Arron Afflalo
03/14/2014
ORL
Magic
1610612764
WAS
Wizards
18
0021301007
Arron Afflalo
03/18/2014
ORL
Magic
1610612744
GSW
Warriors
4
0021301016
Arron Afflalo
03/19/2014
ORL
Magic
1610612756
PHX
Suns
20
0021301037
Arron Afflalo
03/22/2014
ORL
Magic
1610612762
UTA
Jazz
18
0021301046
Arron Afflalo
03/23/2014
ORL
Magic
1610612747
LAL
Lakers
18
0021301058
Arron Afflalo
03/25/2014
ORL
Magic
1610612757
POR
Trail Blazers
10
0021301075
Arron Afflalo
03/28/2014
ORL
Magic
1610612766
CHA
Bobcats
17
0021301096
Arron Afflalo
03/30/2014
ORL
Magic
1610612761
TOR
Raptors
8
0021301116
Arron Afflalo
04/02/2014
ORL
Magic
1610612739
CLE
Cavaliers
7
0021301130
Arron Afflalo
04/04/2014
ORL
Magic
1610612766
CHA
Bobcats
8
0021301143
Arron Afflalo
04/05/2014
ORL
Magic
1610612750
MIN
Timberwolves
18
0021301163
Arron Afflalo
04/09/2014
ORL
Magic
1610612751
BKN
Nets
25
0021301176
Arron Afflalo
04/11/2014
ORL
Magic
1610612764
WAS
Wizards
19
0021301199
Arron Afflalo
04/13/2014
ORL
Magic
1610612751
BKN
Nets
5
0021301208
Arron Afflalo
04/14/2014
ORL
Magic
1610612741
CHI
Bulls
13
0021301227
Arron Afflalo
04/16/2014
ORL
Magic
1610612754
IND
Pacers
7
73 rows × 8 columns
Little summary... That's cool I guess. This view doesn't seem all that interesting to be honest, save maybe for the career summary which is of limited use to us. Let's move on.
In [16]:
params = {
'PlayerID': 76003 # Kareem Abdul-Jabbar
}
r = requests.get('http://stats.nba.com/stats/commonplayerinfo/', params=params)
In [20]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('PERSON_ID'); df
Out[20]:
FIRST_NAME
LAST_NAME
DISPLAY_FIRST_LAST
DISPLAY_LAST_COMMA_FIRST
DISPLAY_FI_LAST
BIRTHDATE
SCHOOL
COUNTRY
LAST_AFFILIATION
JERSEY
POSITION
ROSTERSTATUS
TEAM_ID
TEAM_NAME
TEAM_ABBREVIATION
TEAM_CODE
TEAM_CITY
PLAYERCODE
PERSON_ID
76003
Kareem
Abdul-Jabbar
Kareem Abdul-Jabbar
Abdul-Jabbar, Kareem
K. Abdul-Jabbar
1947-04-16T00:00:00
UCLA
US
UCLA/US
33
Center
Active
1610612747
Lakers
LAL
lakers
Los Angeles
HISTADD_kareem_abdul-jabbar
Not sure why ROSTERSTATUS is active here...
In [22]:
params = {
'PlayerID': 76003 # Kareem Abdul-Jabbar
}
r = requests.get('http://stats.nba.com/feeds/players/awards/{PlayerID}_Award.js'.format(**params))
In [23]:
data = r.json(); data
Out[23]:
{u'PlayerAwards': [{u'Award': [{u'AwardName': u'All-Defensive Team',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIADT',
u'Team': u'1'},
{u'YearsAwarded': [{u'SortOrder': u'/Date(315550800000-0500)/',
u'Year': u'1980-81'},
{u'SortOrder': u'/Date(284014800000-0500)/', u'Year': u'1979-80'},
{u'SortOrder': u'/Date(252478800000-0500)/', u'Year': u'1978-79'},
{u'SortOrder': u'/Date(126248400000-0500)/', u'Year': u'1974-75'},
{u'SortOrder': u'/Date(94712400000-0500)/', u'Year': u'1973-74'}]}]},
{u'Award': [{u'AwardName': u'All-Defensive Team',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIADT',
u'Team': u'2'},
{u'YearsAwarded': [{u'SortOrder': u'/Date(410245200000-0500)/',
u'Year': u'1983-84'},
{u'SortOrder': u'/Date(220942800000-0500)/', u'Year': u'1977-78'},
{u'SortOrder': u'/Date(189320400000-0500)/', u'Year': u'1976-77'},
{u'SortOrder': u'/Date(157784400000-0500)/', u'Year': u'1975-76'},
{u'SortOrder': u'/Date(18000000-0500)/', u'Year': u'1970-71'},
{u'SortOrder': u'/Date(-31518000000-0500)/', u'Year': u'1969-70'}]}]},
{u'Award': [{u'AwardName': u'All-NBA',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIANT',
u'Team': u'1'},
{u'YearsAwarded': [{u'SortOrder': u'/Date(473403600000-0500)/',
u'Year': u'1985-86'},
{u'SortOrder': u'/Date(410245200000-0500)/', u'Year': u'1983-84'},
{u'SortOrder': u'/Date(315550800000-0500)/', u'Year': u'1980-81'},
{u'SortOrder': u'/Date(284014800000-0500)/', u'Year': u'1979-80'},
{u'SortOrder': u'/Date(189320400000-0500)/', u'Year': u'1976-77'},
{u'SortOrder': u'/Date(157784400000-0500)/', u'Year': u'1975-76'},
{u'SortOrder': u'/Date(94712400000-0500)/', u'Year': u'1973-74'},
{u'SortOrder': u'/Date(63090000000-0500)/', u'Year': u'1972-73'},
{u'SortOrder': u'/Date(31554000000-0500)/', u'Year': u'1971-72'},
{u'SortOrder': u'/Date(18000000-0500)/', u'Year': u'1970-71'}]}]},
{u'Award': [{u'AwardName': u'All-NBA',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIANT',
u'Team': u'2'},
{u'YearsAwarded': [{u'SortOrder': u'/Date(441781200000-0500)/',
u'Year': u'1984-85'},
{u'SortOrder': u'/Date(378709200000-0500)/', u'Year': u'1982-83'},
{u'SortOrder': u'/Date(252478800000-0500)/', u'Year': u'1978-79'},
{u'SortOrder': u'/Date(220942800000-0500)/', u'Year': u'1977-78'},
{u'SortOrder': u'/Date(-31518000000-0500)/', u'Year': u'1969-70'}]}]},
{u'Award': [{u'AwardName': u'All-Rookie Team',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIART',
u'Team': u'1'},
{u'YearsAwarded': [{u'SortOrder': u'/Date(-31518000000-0500)/',
u'Year': u'1969-70'}]}]},
{u'Award': [{u'AwardName': u'Hall of Fame Inductee',
u'SubType1': u'Hall of Fame',
u'SubType2': u' ',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(788936400000-0500)/',
u'Year': u'1995'}]}]},
{u'Award': [{u'AwardName': u'NBA Finals Most Valuable Player',
u'SubType1': u'Kia Motors',
u'SubType2': u'KFMVP',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(441781200000-0500)/',
u'Year': u'1984-85'},
{u'SortOrder': u'/Date(18000000-0500)/', u'Year': u'1970-71'}]}]},
{u'Award': [{u'AwardName': u'NBA Most Valuable Player',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIMVP',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(284014800000-0500)/',
u'Year': u'1979-80'},
{u'SortOrder': u'/Date(189320400000-0500)/', u'Year': u'1976-77'},
{u'SortOrder': u'/Date(157784400000-0500)/', u'Year': u'1975-76'},
{u'SortOrder': u'/Date(94712400000-0500)/', u'Year': u'1973-74'},
{u'SortOrder': u'/Date(31554000000-0500)/', u'Year': u'1971-72'},
{u'SortOrder': u'/Date(18000000-0500)/', u'Year': u'1970-71'}]}]},
{u'Award': [{u'AwardName': u'NBA Player of the Month',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIPMO',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(446965200000-0500)/',
u'Year': u'Mar-1984'},
{u'SortOrder': u'/Date(312872400000-0500)/', u'Year': u'Dec-1979'}]}]},
{u'Award': [{u'AwardName': u'NBA Player of the Week',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIPWK',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(480484800000-0400)/',
u'Year': u'03/24/1985'},
{u'SortOrder': u'/Date(450244800000-0400)/', u'Year': u'04/08/1984'},
{u'SortOrder': u'/Date(445410000000-0500)/', u'Year': u'02/12/1984'},
{u'SortOrder': u'/Date(383720400000-0500)/', u'Year': u'02/28/1982'},
{u'SortOrder': u'/Date(346222800000-0500)/', u'Year': u'12/21/1980'},
{u'SortOrder': u'/Date(319611600000-0500)/', u'Year': u'02/17/1980'},
{u'SortOrder': u'/Date(313563600000-0500)/', u'Year': u'12/09/1979'}]}]},
{u'Award': [{u'AwardName': u'NBA Rookie of the Year',
u'SubType1': u'Kia Motors',
u'SubType2': u'KIROY',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(-31518000000-0500)/',
u'Year': u'1969-70'}]}]},
{u'Award': [{u'AwardName': u'NBA Sporting News Most Valuable Player of the Year',
u'SubType1': u'Sporting News',
u'SubType2': u'SNMVP',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(284014800000-0500)/',
u'Year': u'1979-80'},
{u'SortOrder': u'/Date(189320400000-0500)/', u'Year': u'1976-77'},
{u'SortOrder': u'/Date(157784400000-0500)/', u'Year': u'1975-76'},
{u'SortOrder': u'/Date(94712400000-0500)/', u'Year': u'1973-74'},
{u'SortOrder': u'/Date(31554000000-0500)/', u'Year': u'1971-72'},
{u'SortOrder': u'/Date(18000000-0500)/', u'Year': u'1970-71'}]}]},
{u'Award': [{u'AwardName': u'NBA Sporting News Rookie of the Year',
u'SubType1': u'Sporting News',
u'SubType2': u'SNROY',
u'Team': u' '},
{u'YearsAwarded': [{u'SortOrder': u'/Date(-31518000000-0500)/',
u'Year': u'1969-70'}]}]}]}
The JSON schema for this view is just messed up... Not even going to bother trying to get it into a DataFrame
In [25]:
params = {
'PlayerID': 2730 # Superman
}
r = requests.get('http://stats.nba.com/feeds/players/profile/{PlayerID}_Profile.js'.format(**params))
data = r.json(); data
Out[25]:
{u'PlayerProfile': [{u'PlayerBio': [{u'Age': 29,
u'Birthdate': u'December 8, 1985',
u'DraftPick': u'1',
u'DraftRound': u'1',
u'DraftTeam': u'ORL',
u'DraftYear': u'2004',
u'FirstSeason': u'2004',
u'Height': u'6-11',
u'LastSeason': u'2013',
u'Person_ID': 2730,
u'Rookie_Year': 2004,
u'School': u'SW Atlanta Christian Academy (GA)',
u'Season_Exp': 9,
u'Weight': u'265'}]},
{u'PlayerTeamSeasons': [{u'City': u'Orlando',
u'Nickname': u'Magic',
u'SeasonEnd': 2011,
u'SeasonStart': 2004,
u'TeamId': 1610612753},
{u'City': u'Los Angeles',
u'Nickname': u'Lakers',
u'SeasonEnd': 2012,
u'SeasonStart': 2012,
u'TeamId': 1610612747},
{u'City': u'Orlando',
u'Nickname': u'Magic',
u'SeasonEnd': 2012,
u'SeasonStart': 2012,
u'TeamId': 1610612753},
{u'City': u'Houston',
u'Nickname': u'Rockets',
u'SeasonEnd': 2013,
u'SeasonStart': 2013,
u'TeamId': 1610612745},
{u'City': u'Los Angeles',
u'Nickname': u'Lakers',
u'SeasonEnd': 2013,
u'SeasonStart': 2013,
u'TeamId': 1610612747}]}]}
This schema is still pretty terrible but let's get it into a DataFrame
In [36]:
DataFrame(data[u'PlayerProfile'][0][u'PlayerBio']).set_index('Person_ID')
Out[36]:
Age
Birthdate
DraftPick
DraftRound
DraftTeam
DraftYear
FirstSeason
Height
LastSeason
Rookie_Year
School
Season_Exp
Weight
Person_ID
2730
29
December 8, 1985
1
1
ORL
2004
2004
6-11
2013
2004
SW Atlanta Christian Academy (GA)
9
265
In [38]:
DataFrame(data[u'PlayerProfile'][1][u'PlayerTeamSeasons']).set_index('TeamId')
Out[38]:
City
Nickname
SeasonEnd
SeasonStart
TeamId
1610612753
Orlando
Magic
2011
2004
1610612747
Los Angeles
Lakers
2012
2012
1610612753
Orlando
Magic
2012
2012
1610612745
Houston
Rockets
2013
2013
1610612747
Los Angeles
Lakers
2013
2013
This gives more information such as height, draft pick number/round and the list of team affiliations, which could be easily used to build an affiliation network or something like that.
In [152]:
params = {
'GameID': '0021301223',
'StartPeriod': 0,
'EndPeriod': 0,
}
r = requests.get('http://stats.nba.com//stats/playbyplay', params=params)
In [153]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(['GAME_ID', 'EVENTNUM']); df
Out[153]:
EVENTMSGTYPE
EVENTMSGACTIONTYPE
PERIOD
WCTIMESTRING
PCTIMESTRING
HOMEDESCRIPTION
NEUTRALDESCRIPTION
VISITORDESCRIPTION
SCORE
SCOREMARGIN
GAME_ID
EVENTNUM
0021301223
0
12
0
1
7:10 PM
12:00
None
Start of 1st Period (7:10 PM EST)
None
None
None
1
10
0
1
7:11 PM
12:00
Jump Ball Jefferson vs. Noah: Tip to Boozer
None
None
None
None
2
1
5
1
7:11 PM
11:38
None
None
Boozer 1' Layup (2 PTS)
2 - 0
-2
3
2
1
1
7:11 PM
11:22
MISS Kidd-Gilchrist 3' Jump Shot
None
Dunleavy BLOCK (1 BLK)
None
None
5
4
0
1
7:11 PM
11:19
Kidd-Gilchrist REBOUND (Off:1 Def:0)
None
None
None
None
4
1
72
1
7:11 PM
11:19
Kidd-Gilchrist 2' Putback Layup (2 PTS)
None
None
2 - 2
TIE
6
1
5
1
7:12 PM
10:52
None
None
Noah 1' Layup (2 PTS) (Dunleavy 1 AST)
4 - 2
-2
7
6
2
1
7:12 PM
10:40
None
None
Noah S.FOUL (P1.T1)
None
None
8
3
11
1
7:12 PM
10:40
MISS Jefferson Free Throw 1 of 2
None
None
None
None
9
4
1
1
7:12 PM
10:40
BOBCATS Rebound
None
None
None
None
10
3
12
1
7:13 PM
10:40
Jefferson Free Throw 2 of 2 (1 PTS)
None
None
4 - 3
-1
12
6
1
1
7:13 PM
10:34
Kidd-Gilchrist P.FOUL (P1.T1)
None
None
None
None
13
2
1
1
7:13 PM
10:27
None
None
MISS Dunleavy 26' 3PT Jump Shot
None
None
14
4
0
1
7:13 PM
10:26
Walker REBOUND (Off:0 Def:1)
None
None
None
None
15
2
1
1
7:13 PM
10:17
MISS Walker 26' 3PT Jump Shot
None
None
None
None
16
4
0
1
7:13 PM
10:15
None
None
Noah REBOUND (Off:0 Def:1)
None
None
17
1
5
1
7:14 PM
10:02
None
None
Boozer 2' Layup (4 PTS) (Noah 1 AST)
6 - 3
-3
18
6
2
1
7:14 PM
9:41
None
None
Hinrich S.FOUL (P1.T2)
None
None
19
3
11
1
7:14 PM
9:41
Walker Free Throw 1 of 2 (1 PTS)
None
None
6 - 4
-2
21
3
12
1
7:15 PM
9:41
Walker Free Throw 2 of 2 (2 PTS)
None
None
6 - 5
-1
23
1
42
1
7:15 PM
9:28
None
None
Dunleavy 1' Driving Layup (2 PTS)
8 - 5
-3
24
1
5
1
7:15 PM
9:18
Henderson 3' Layup (2 PTS) (Walker 1 AST)
None
None
8 - 7
-1
25
6
2
1
7:15 PM
9:18
None
None
Boozer S.FOUL (P1.T3)
None
None
26
3
10
1
7:16 PM
9:18
Henderson Free Throw 1 of 1 (3 PTS)
None
None
8 - 8
TIE
28
2
46
1
7:16 PM
8:58
None
None
MISS Hinrich 12' Running Jump Shot
None
None
29
4
0
1
7:16 PM
8:57
Jefferson REBOUND (Off:0 Def:1)
None
None
None
None
30
1
1
1
7:16 PM
8:49
McRoberts 15' Jump Shot (2 PTS) (Walker 2 AST)
None
None
8 - 10
2
31
2
1
1
7:17 PM
8:32
None
None
MISS Dunleavy 20' Jump Shot
None
None
32
4
0
1
7:17 PM
8:31
McRoberts REBOUND (Off:0 Def:1)
None
None
None
None
33
2
1
1
7:17 PM
8:17
MISS McRoberts 27' 3PT Jump Shot
None
None
None
None
...
...
...
...
...
...
...
...
...
...
...
481
3
11
5
9:24 PM
1:30
None
None
MISS Dunleavy Free Throw 1 of 2
None
None
482
4
1
5
9:24 PM
1:30
None
None
Bulls Rebound
None
None
484
3
12
5
9:24 PM
1:30
None
None
Dunleavy Free Throw 2 of 2 (12 PTS)
81 - 84
3
487
1
5
5
9:25 PM
1:15
Walker 1' Layup (18 PTS)
None
None
81 - 86
5
489
6
2
5
9:25 PM
1:15
None
None
Gibson S.FOUL (P3.T2)
None
None
490
3
10
5
9:25 PM
1:15
MISS Walker Free Throw 1 of 1
None
None
None
None
492
4
0
5
9:25 PM
1:15
None
None
Noah REBOUND (Off:1 Def:12)
None
None
493
9
1
5
9:25 PM
1:15
None
None
Bulls Timeout: Regular (Reg.7 Short 1)
None
None
498
2
1
5
9:28 PM
1:05
McRoberts BLOCK (1 BLK)
None
MISS Augustin 26' 3PT Jump Shot
None
None
499
4
0
5
9:28 PM
1:03
Walker REBOUND (Off:0 Def:7)
None
None
None
None
500
1
42
5
9:28 PM
1:01
Walker 1' Driving Layup (20 PTS)
None
None
81 - 88
7
501
9
2
5
9:28 PM
1:01
None
None
Bulls Timeout: Short (Reg.7 Short 2)
None
None
503
5
1
5
9:29 PM
0:57
None
None
Gibson Bad Pass Turnover (P6.T11)
None
None
504
6
3
5
9:30 PM
0:57
None
None
Dunleavy L.B.FOUL (P3.PN)
None
None
505
18
1
5
9:30 PM
0:57
None
Overturn Ruling
None
None
None
506
3
11
5
9:32 PM
0:57
MISS Henderson Free Throw 1 of 2
None
None
None
None
507
4
1
5
9:32 PM
0:57
BOBCATS Rebound
None
None
None
None
508
3
12
5
9:32 PM
0:57
MISS Henderson Free Throw 2 of 2
None
None
None
None
510
4
0
5
9:32 PM
0:55
None
None
Dunleavy REBOUND (Off:1 Def:6)
None
None
511
5
1
5
9:32 PM
0:46
Jefferson STEAL (1 STL)
None
Noah Bad Pass Turnover (P2.T12)
None
None
512
6
28
5
9:32 PM
0:37
None
None
Augustin Personal Take Foul (P3.PN)
None
None
513
3
11
5
9:33 PM
0:37
Walker Free Throw 1 of 2 (21 PTS)
None
None
81 - 89
8
515
3
12
5
9:33 PM
0:37
Walker Free Throw 2 of 2 (22 PTS)
None
None
81 - 90
9
517
1
1
5
9:33 PM
0:31
None
None
Noah 17' Jump Shot (14 PTS) (Augustin 2 AST)
83 - 90
7
518
6
1
5
9:34 PM
0:20
None
None
Dunleavy P.FOUL (P4.PN)
None
None
519
3
11
5
9:34 PM
0:20
MISS Douglas-Roberts Free Throw 1 of 2
None
None
None
None
520
4
1
5
9:34 PM
0:20
BOBCATS Rebound
None
None
None
None
521
3
12
5
9:34 PM
0:20
Douglas-Roberts Free Throw 2 of 2 (13 PTS)
None
None
83 - 91
8
523
1
1
5
9:34 PM
0:13
None
None
Augustin 26' 3PT Jump Shot (17 PTS)
86 - 91
5
524
13
0
5
9:35 PM
0:00
None
End of 1st OT (9:35 PM EST)
None
86 - 91
5
448 rows × 10 columns
Get the list of active teams for a particular season
In [52]:
params = {
'DateFrom': '',
'DateTo': '',
'GameScope': '',
'GameSegment': '',
'LastNGames': 0,
'Location': '',
'MeasureType': 'Base',
'Month': 0,
'OpponentTeamID': 0,
'Outcome': '',
'PaceAdjust': 'N',
'PerMode': 'Totals',
'Period': 0,
'PlayerExperience': '',
'PlayerPosition': '',
'PlusMinus': 'N',
'Rank': 'N',
'Season': '2013-14',
'SeasonSegment': '',
'SeasonType': 'Regular Season',
'StarterBench': '',
'VsConference': '',
'VsDivision': ''
}
r = requests.get('http://stats.nba.com/stats/leaguedashteamstats', params=params)
In [54]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('TEAM_ID'); df
Out[54]:
TEAM_NAME
GP
W
L
W_PCT
MIN
FGM
FGA
FG_PCT
FG3M
...
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
CFID
CFPARAMS
TEAM_ID
1610612737
Atlanta Hawks
82
38
44
0.463
3966.000
3061
6688
0.458
768
...
1251
680
326
354
1577
1643
8282
-39
10
Atlanta Hawks
1610612738
Boston Celtics
82
25
57
0.305
3946.000
2996
6883
0.435
575
...
1261
584
343
373
1743
1564
7892
-366
10
Boston Celtics
1610612751
Brooklyn Nets
82
44
38
0.537
3976.000
2931
6391
0.459
709
...
1191
705
311
337
1777
1704
8079
-82
10
Brooklyn Nets
1610612766
Charlotte Bobcats
82
43
39
0.524
3981.000
2976
6730
0.442
516
...
1010
499
421
415
1493
1716
7942
-18
10
Charlotte Bobcats
1610612741
Chicago Bulls
82
48
34
0.585
3986.000
2843
6577
0.432
508
...
1223
594
424
490
1565
1713
7680
152
10
Chicago Bulls
1610612739
Cleveland Cavaliers
82
33
49
0.402
3986.000
3036
6955
0.437
584
...
1163
579
304
461
1640
1599
8054
-270
10
Cleveland Cavaliers
1610612742
Dallas Mavericks
82
49
33
0.598
3966.000
3249
6858
0.474
721
...
1110
704
356
288
1636
1633
8597
197
10
Dallas Mavericks
1610612743
Denver Nuggets
82
36
46
0.439
3950.985
3147
7042
0.447
702
...
1305
615
459
459
1890
1778
8559
-176
10
Denver Nuggets
1610612765
Detroit Pistons
82
29
53
0.354
3956.000
3182
7124
0.447
507
...
1193
687
395
403
1666
1699
8286
-300
10
Detroit Pistons
1610612744
Golden State Warriors
82
51
31
0.622
3966.000
3236
7005
0.462
774
...
1247
642
407
342
1784
1584
8549
394
10
Golden State Warriors
1610612745
Houston Rockets
82
54
28
0.659
3966.000
3118
6603
0.472
779
...
1323
621
461
460
1676
1993
8829
374
10
Houston Rockets
1610612754
Indiana Pacers
82
56
26
0.683
3956.000
2949
6573
0.449
550
...
1237
550
446
378
1675
1793
7933
361
10
Indiana Pacers
1610612746
Los Angeles Clippers
82
57
25
0.695
3951.000
3208
6761
0.474
693
...
1136
703
397
284
1767
1919
8850
572
10
Los Angeles Clippers
1610612747
Los Angeles Lakers
82
27
55
0.329
3941.000
3139
6980
0.450
774
...
1239
611
446
387
1627
1580
8442
-516
10
Los Angeles Lakers
1610612763
Memphis Grizzlies
82
50
32
0.610
3961.000
3122
6723
0.464
405
...
1124
631
375
410
1568
1553
7884
129
10
Memphis Grizzlies
1610612748
Miami Heat
82
54
28
0.659
3976.000
3142
6272
0.501
665
...
1212
732
367
256
1596
1675
8380
390
10
Miami Heat
1610612749
Milwaukee Bucks
82
15
67
0.183
3976.000
2952
6737
0.438
548
...
1238
541
403
413
1713
1690
7829
-671
10
Milwaukee Bucks
1610612750
Minnesota Timberwolves
82
40
42
0.488
3971.000
3189
7175
0.444
600
...
1142
718
297
457
1504
1936
8768
219
10
Minnesota Timberwolves
1610612740
New Orleans Pelicans
82
34
48
0.415
3971.000
3101
6761
0.459
486
...
1129
647
523
504
1857
1664
8177
-222
10
New Orleans Pelicans
1610612752
New York Knicks
82
37
45
0.451
3971.000
3027
6739
0.449
759
...
1063
631
367
277
1815
1611
8084
-65
10
New York Knicks
1610612760
Oklahoma City Thunder
82
59
23
0.720
3961.000
3194
6782
0.471
664
...
1256
678
501
296
1858
1672
8705
520
10
Oklahoma City Thunder
1610612753
Orlando Magic
82
23
59
0.280
3991.000
3022
6784
0.445
563
...
1222
630
350
445
1678
1500
7914
-446
10
Orlando Magic
1610612755
Philadelphia 76ers
82
19
63
0.232
3970.985
3108
7150
0.435
577
...
1384
765
330
558
1844
1686
8155
-857
10
Philadelphia 76ers
1610612756
Phoenix Suns
82
48
34
0.585
3951.000
3172
6845
0.463
765
...
1258
688
374
332
1798
1749
8629
216
10
Phoenix Suns
1610612757
Portland Trail Blazers
82
54
28
0.659
3971.000
3207
7134
0.450
770
...
1125
454
387
304
1576
1698
8753
327
10
Portland Trail Blazers
1610612758
Sacramento Kings
82
28
54
0.341
3966.000
3026
6766
0.447
491
...
1249
587
318
459
1849
1897
8241
-238
10
Sacramento Kings
1610612759
San Antonio Spurs
82
62
20
0.756
3951.000
3326
6844
0.486
698
...
1180
604
420
379
1495
1585
8639
633
10
San Antonio Spurs
1610612761
Toronto Raptors
82
48
34
0.585
3991.000
2992
6718
0.445
713
...
1159
577
343
352
1882
1815
8305
266
10
Toronto Raptors
1610612762
Utah Jazz
82
25
57
0.305
3956.000
2951
6652
0.444
543
...
1200
570
366
401
1699
1673
7791
-588
10
Utah Jazz
1610612764
Washington Wizards
82
44
38
0.537
4011.000
3177
6920
0.459
647
...
1204
668
377
320
1675
1600
8254
104
10
Washington Wizards
30 rows × 29 columns
Seems to only go as far back as 1995 though
In [70]:
params['Season'] = '1995-96'
r = requests.get('http://stats.nba.com/stats/leaguedashteamstats', params=params)
r.text
Out[70]:
u'{"resource":"leaguedashteamstats","parameters":{"MeasureType":"Base","PerMode":"Totals","PlusMinus":"N","PaceAdjust":"N","Rank":"N","LeagueID":null,"Season":"1995-96","SeasonType":"Regular Season","Outcome":null,"Location":null,"Month":0,"SeasonSegment":null,"DateFrom":null,"DateTo":null,"OpponentTeamID":0,"VsConference":null,"VsDivision":null,"GameSegment":null,"Period":0,"LastNGames":0,"GameScope":null,"PlayerExperience":null,"PlayerPosition":null,"StarterBench":null},"resultSets":[{"name":"LeagueDashTeamStats","headers":["TEAM_ID","TEAM_NAME","GP","W","L","W_PCT","MIN","FGM","FGA","FG_PCT","FG3M","FG3A","FG3_PCT","FTM","FTA","FT_PCT","OREB","DREB","REB","AST","TOV","STL","BLK","BLKA","PF","PFD","PTS","PLUS_MINUS","CFID","CFPARAMS"],"rowSet":[]}]}'
In [72]:
params['Season'] = '1996-97'
r = requests.get('http://stats.nba.com/stats/leaguedashteamstats', params=params)
In [74]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('TEAM_ID'); df
Out[74]:
TEAM_NAME
GP
W
L
W_PCT
MIN
FGM
FGA
FG_PCT
FG3M
...
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
CFID
CFPARAMS
TEAM_ID
1610612737
Atlanta Hawks
82
56
26
0.683
4076.438333
2812
6307
0.446
659
...
1228
701
427
332
1506
5
7774
446
10
Atlanta Hawks
1610612738
Boston Celtics
82
15
67
0.183
4110.655000
3066
6967
0.440
467
...
1342
811
315
557
1779
9
8248
-601
10
Boston Celtics
1610612740
Charlotte Hornets
82
54
28
0.659
4086.335000
2988
6341
0.471
591
...
1203
597
349
335
1628
15
8108
153
10
Charlotte Hornets
1610612741
Chicago Bulls
82
69
13
0.841
4068.781667
3277
6923
0.473
523
...
1109
717
332
286
1507
17
8458
886
10
Chicago Bulls
1610612739
Cleveland Cavaliers
82
42
40
0.512
4115.428333
2704
5972
0.453
483
...
1188
657
315
350
1728
20
7173
151
10
Cleveland Cavaliers
1610612742
Dallas Mavericks
82
24
58
0.293
4090.830000
2812
6451
0.436
429
...
1323
654
351
433
1678
15
7428
-522
10
Dallas Mavericks
1610612743
Denver Nuggets
82
21
61
0.256
4116.966667
2934
6687
0.439
636
...
1359
504
487
453
1669
19
8020
-515
10
Denver Nuggets
1610612765
Detroit Pistons
82
54
28
0.659
4090.888333
2827
6095
0.464
582
...
1042
633
283
281
1524
15
7723
430
10
Detroit Pistons
1610612744
Golden State Warriors
82
30
52
0.366
4108.676667
2996
6566
0.456
481
...
1410
612
359
395
1660
5
8169
-388
10
Golden State Warriors
1610612745
Houston Rockets
82
57
25
0.695
4089.153333
3037
6484
0.468
671
...
1365
688
347
365
1506
15
8248
367
10
Houston Rockets
1610612754
Indiana Pacers
82
39
43
0.476
4132.256667
2851
6254
0.456
430
...
1338
585
394
418
1813
28
7819
80
10
Indiana Pacers
1610612746
Los Angeles Clippers
82
36
46
0.439
4123.706667
2989
6696
0.446
474
...
1311
733
441
419
1864
10
7969
-193
10
Los Angeles Clippers
1610612747
Los Angeles Lakers
82
56
26
0.683
4121.368333
3018
6642
0.454
551
...
1222
740
575
388
1698
9
8200
350
10
Los Angeles Lakers
1610612748
Miami Heat
82
61
21
0.744
4100.851667
2822
6235
0.453
678
...
1306
651
439
392
1741
21
7776
450
10
Miami Heat
1610612749
Milwaukee Bucks
82
33
49
0.402
4078.358333
2967
6303
0.471
324
...
1285
634
348
392
1773
11
7818
-155
10
Milwaukee Bucks
1610612750
Minnesota Timberwolves
82
40
42
0.488
4087.591667
2937
6435
0.456
371
...
1243
618
557
444
1699
13
7882
-121
10
Minnesota Timberwolves
1610612751
New Jersey Nets
82
26
56
0.317
4078.453333
2994
7091
0.422
484
...
1290
678
481
516
1736
14
7974
-374
10
New Jersey Nets
1610612752
New York Knicks
82
57
25
0.695
4125.016667
2882
6227
0.463
470
...
1462
630
378
295
1862
14
7819
256
10
New York Knicks
1610612753
Orlando Magic
82
45
37
0.549
4087.943333
2839
6497
0.437
567
...
1250
697
362
417
1564
9
7719
-29
10
Orlando Magic
1610612755
Philadelphia 76ers
82
22
60
0.268
4071.365000
3003
6850
0.438
433
...
1437
683
394
494
1632
8
8215
-536
10
Philadelphia 76ers
1610612756
Phoenix Suns
82
40
42
0.488
4090.001667
3143
6705
0.469
527
...
1180
664
322
372
1635
9
8431
54
10
Phoenix Suns
1610612757
Portland Trail Blazers
82
49
33
0.598
4111.315000
3000
6465
0.464
501
...
1357
640
434
428
1767
15
8114
342
10
Portland Trail Blazers
1610612758
Sacramento Kings
82
34
48
0.415
4100.348333
2999
6610
0.454
414
...
1332
588
362
429
1872
9
7906
-277
10
Sacramento Kings
1610612759
San Antonio Spurs
82
20
62
0.244
4074.166667
2826
6390
0.442
378
...
1243
646
431
448
1628
13
7416
-646
10
San Antonio Spurs
1610612760
Seattle SuperSonics
82
57
25
0.695
4093.441667
2994
6413
0.467
559
...
1231
904
388
423
1699
14
8272
633
10
Seattle SuperSonics
1610612761
Toronto Raptors
82
30
52
0.366
4091.786667
2897
6632
0.437
589
...
1347
723
517
442
1801
7
7829
-256
10
Toronto Raptors
1610612762
Utah Jazz
82
64
18
0.780
4122.558333
3131
6216
0.504
334
...
1259
748
418
373
1857
18
8454
721
10
Utah Jazz
1610612763
Vancouver Grizzlies
82
14
68
0.171
4075.805000
2819
6453
0.437
445
...
1302
658
464
461
1641
5
7313
-839
10
Vancouver Grizzlies
1610612764
Washington Bullets
82
44
38
0.537
4100.316667
3208
6678
0.480
331
...
1290
715
402
334
1694
16
8147
133
10
Washington Bullets
29 rows × 29 columns
Worth noting are teams such as Washington Bullets, Vancouver Grizzlies, Seattle SuperSonics, etc.
In [94]:
params = {
'TeamID': 1610612752, # New York Knicks
'Season': '2013-14',
'SeasonType': 'Regular Season'
}
r = requests.get('http://stats.nba.com/stats/teamgamelog', params=params)
In [95]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(['Team_ID', 'Game_ID']); df
Out[95]:
GAME_DATE
MATCHUP
WL
MIN
FGM
FGA
FG_PCT
FG3M
FG3A
FG3_PCT
...
OREB
DREB
REB
AST
STL
BLK
TOV
PF
PTS
VIDEO_AVAILABLE
Team_ID
Game_ID
1610612752
0021301226
APR 16, 2014
NYK vs. TOR
W
240
37
81
0.457
8
17
0.471
...
11
25
36
13
12
6
16
23
95
1
0021301214
APR 15, 2014
NYK @ BKN
W
240
32
67
0.478
11
21
0.524
...
8
28
36
15
11
5
16
28
109
1
0021301197
APR 13, 2014
NYK vs. CHI
W
240
37
81
0.457
9
25
0.360
...
7
29
36
16
9
5
9
25
100
1
0021301177
APR 11, 2014
NYK @ TOR
W
240
35
73
0.479
6
24
0.250
...
7
32
39
16
6
3
8
24
108
1
0021301149
APR 06, 2014
NYK @ MIA
L
240
33
88
0.375
15
42
0.357
...
15
23
38
18
10
2
10
24
91
1
0021301136
APR 04, 2014
NYK vs. WAS
L
240
34
76
0.447
14
31
0.452
...
8
26
34
23
9
1
16
14
89
1
0021301115
APR 02, 2014
NYK vs. BKN
W
240
42
70
0.600
11
25
0.440
...
9
32
41
21
11
5
15
25
110
1
0021301110
MAR 31, 2014
NYK @ UTA
W
240
33
70
0.471
9
29
0.310
...
3
35
38
18
8
6
9
18
92
1
0021301098
MAR 30, 2014
NYK @ GSW
W
240
32
80
0.400
9
25
0.360
...
10
30
40
15
5
3
9
19
89
1
0021301085
MAR 28, 2014
NYK @ PHX
L
240
35
87
0.402
8
25
0.320
...
13
24
37
14
7
2
11
24
88
1
0021301069
MAR 26, 2014
NYK @ SAC
W
240
40
73
0.548
15
26
0.577
...
7
27
34
25
3
4
14
30
107
1
0021301060
MAR 25, 2014
NYK @ LAL
L
240
36
74
0.486
4
15
0.267
...
8
24
32
23
9
4
15
16
96
1
0021301045
MAR 23, 2014
NYK vs. CLE
L
240
35
77
0.455
12
27
0.444
...
9
25
34
21
7
2
12
13
100
1
0021301023
MAR 21, 2014
NYK @ PHI
W
240
34
82
0.415
7
29
0.241
...
9
38
47
29
6
2
11
17
93
1
0021301013
MAR 19, 2014
NYK vs. IND
W
240
32
82
0.390
4
17
0.235
...
10
35
45
15
6
2
10
26
92
1
0021300981
MAR 15, 2014
NYK vs. MIL
W
240
42
76
0.553
8
20
0.400
...
13
28
41
14
3
7
11
23
115
1
0021300962
MAR 12, 2014
NYK @ BOS
W
240
42
80
0.525
11
19
0.579
...
12
35
47
20
11
7
8
21
116
1
0021300946
MAR 10, 2014
NYK vs. PHI
W
240
44
81
0.543
13
32
0.406
...
10
25
35
20
10
6
13
24
123
1
0021300928
MAR 08, 2014
NYK @ CLE
W
240
38
88
0.432
14
31
0.452
...
13
36
49
26
7
9
13
16
107
1
0021300921
MAR 07, 2014
NYK vs. UTA
W
240
42
85
0.494
12
31
0.387
...
8
27
35
24
9
7
7
19
108
1
0021300911
MAR 05, 2014
NYK @ MIN
W
240
47
90
0.522
11
27
0.407
...
12
27
39
24
11
4
12
24
118
1
0021300892
MAR 03, 2014
NYK @ DET
L
240
32
83
0.386
10
28
0.357
...
12
28
40
16
8
6
16
27
85
1
0021300883
MAR 02, 2014
NYK @ CHI
L
240
35
79
0.443
5
18
0.278
...
12
32
44
17
0
5
12
23
90
1
0021300869
FEB 28, 2014
NYK vs. GSW
L
240
36
95
0.379
10
25
0.400
...
13
37
50
14
8
4
17
24
103
1
0021300865
FEB 27, 2014
NYK @ MIA
L
240
31
83
0.373
4
23
0.174
...
14
26
40
16
7
1
13
21
82
1
0021300843
FEB 24, 2014
NYK vs. DAL
L
240
40
76
0.526
14
33
0.424
...
11
30
41
23
3
3
19
24
108
1
0021300827
FEB 22, 2014
NYK @ ATL
L
240
39
89
0.438
10
28
0.357
...
8
33
41
22
6
4
10
24
98
1
0021300816
FEB 21, 2014
NYK @ ORL
L
290
45
95
0.474
15
34
0.441
...
7
37
44
24
6
4
13
21
121
1
0021300806
FEB 19, 2014
NYK @ NOP
W
240
37
83
0.446
10
21
0.476
...
13
25
38
20
8
6
9
17
98
1
0021300796
FEB 18, 2014
NYK @ MEM
L
240
35
85
0.412
6
27
0.222
...
14
25
39
15
8
5
12
23
93
1
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
0021300438
DEC 28, 2013
NYK @ TOR
L
240
39
80
0.488
7
21
0.333
...
7
30
37
23
11
4
14
26
100
1
0021300430
DEC 27, 2013
NYK vs. TOR
L
240
30
79
0.380
10
19
0.526
...
13
29
42
19
11
5
16
29
83
1
0021300419
DEC 25, 2013
NYK vs. OKC
L
240
38
90
0.422
6
23
0.261
...
12
30
42
23
5
2
12
22
94
1
0021300409
DEC 23, 2013
NYK @ ORL
W
240
39
86
0.453
10
21
0.476
...
9
38
47
19
3
4
9
21
103
1
0021300392
DEC 21, 2013
NYK vs. MEM
L
240
33
79
0.418
6
22
0.273
...
6
23
29
21
12
6
7
15
87
1
0021300375
DEC 18, 2013
NYK @ MIL
W
290
38
101
0.376
17
41
0.415
...
14
37
51
26
11
6
12
31
107
1
0021300361
DEC 16, 2013
NYK vs. WAS
L
240
33
79
0.418
11
30
0.367
...
9
26
35
21
9
6
9
20
101
1
0021300345
DEC 14, 2013
NYK vs. ATL
W
240
44
87
0.506
8
19
0.421
...
15
17
32
19
14
1
11
16
111
1
0021300333
DEC 13, 2013
NYK @ BOS
L
240
32
80
0.400
5
21
0.238
...
9
29
38
20
7
1
6
19
86
1
0021300324
DEC 11, 2013
NYK vs. CHI
W
240
31
76
0.408
5
19
0.263
...
10
29
39
17
16
7
13
21
83
1
0021300310
DEC 10, 2013
NYK @ CLE
L
240
40
87
0.460
10
31
0.323
...
9
22
31
27
4
5
13
19
94
1
0021300299
DEC 08, 2013
NYK vs. BOS
L
240
25
73
0.342
6
16
0.375
...
4
22
26
13
10
3
14
18
73
1
0021300284
DEC 06, 2013
NYK vs. ORL
W
240
44
83
0.530
17
34
0.500
...
10
35
45
25
8
5
10
20
121
1
0021300277
DEC 05, 2013
NYK @ BKN
W
240
44
77
0.571
16
27
0.593
...
7
23
30
24
11
7
12
29
113
2
0021300254
DEC 01, 2013
NYK vs. NOP
L
240
34
76
0.447
13
35
0.371
...
13
29
42
23
9
9
17
23
99
1
0021300238
NOV 29, 2013
NYK @ DEN
L
240
34
83
0.410
6
22
0.273
...
8
34
42
16
6
6
10
24
95
1
0021300227
NOV 27, 2013
NYK @ LAC
L
240
32
83
0.386
3
20
0.150
...
12
32
44
20
6
6
14
27
80
1
0021300210
NOV 25, 2013
NYK @ POR
L
240
38
85
0.447
6
15
0.400
...
10
34
44
24
5
3
9
17
91
1
0021300189
NOV 23, 2013
NYK @ WAS
L
240
36
82
0.439
11
29
0.379
...
11
29
40
24
7
3
9
24
89
1
0021300163
NOV 20, 2013
NYK vs. IND
L
265
37
100
0.370
8
30
0.267
...
16
36
52
15
9
5
15
32
96
1
0021300157
NOV 19, 2013
NYK @ DET
L
240
33
76
0.434
6
24
0.250
...
10
25
35
16
9
3
13
22
86
1
0021300139
NOV 16, 2013
NYK vs. ATL
L
240
34
90
0.378
10
30
0.333
...
19
29
48
19
6
3
14
20
90
1
0021300123
NOV 14, 2013
NYK vs. HOU
L
240
38
87
0.437
8
20
0.400
...
10
26
36
20
6
5
9
28
106
1
0021300116
NOV 13, 2013
NYK @ ATL
W
240
37
88
0.420
12
34
0.353
...
8
29
37
24
11
3
3
19
95
1
0021300094
NOV 10, 2013
NYK vs. SAS
L
240
31
82
0.378
6
22
0.273
...
9
24
33
16
6
1
10
22
89
1
0021300073
NOV 08, 2013
NYK @ CHA
W
240
42
90
0.467
12
26
0.462
...
12
25
37
25
5
9
12
22
101
1
0021300054
NOV 05, 2013
NYK vs. CHA
L
240
35
82
0.427
8
28
0.286
...
11
22
33
12
11
4
14
27
97
1
0021300045
NOV 03, 2013
NYK vs. MIN
L
240
40
89
0.449
9
32
0.281
...
14
35
49
21
7
3
16
26
100
1
0021300018
OCT 31, 2013
NYK @ CHI
L
240
31
86
0.360
9
24
0.375
...
13
29
42
20
12
8
15
24
81
1
0021300008
OCT 30, 2013
NYK vs. MIL
W
240
36
71
0.507
3
13
0.231
...
6
28
34
17
12
6
22
16
90
1
82 rows × 23 columns
In [96]:
df.describe()
Out[96]:
MIN
FGM
FGA
FG_PCT
FG3M
FG3A
FG3_PCT
FTM
FTA
FT_PCT
OREB
DREB
REB
AST
STL
BLK
TOV
PF
PTS
VIDEO_AVAILABLE
count
82.000000
82.000000
82.000000
82.00000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
82.000000
mean
242.134146
36.914634
82.182927
0.45061
9.256098
24.853659
0.368939
15.500000
20.365854
0.759927
10.609756
29.719512
40.329268
20.012195
7.695122
4.475610
12.402439
22.134146
98.585366
1.012195
std
8.959115
4.912069
6.778922
0.05996
3.438385
5.975510
0.096960
5.471024
6.553695
0.102890
3.180791
4.740877
5.864846
4.203155
3.117806
2.294522
3.637459
4.132862
11.167580
0.110432
min
240.000000
24.000000
67.000000
0.33800
3.000000
13.000000
0.150000
4.000000
5.000000
0.400000
3.000000
17.000000
26.000000
11.000000
0.000000
1.000000
3.000000
13.000000
73.000000
1.000000
25%
240.000000
33.250000
77.250000
0.40725
6.000000
20.250000
0.290500
12.250000
16.000000
0.700000
8.250000
26.000000
36.000000
16.000000
6.000000
3.000000
10.000000
19.000000
90.000000
1.000000
50%
240.000000
37.000000
82.000000
0.44650
9.000000
25.000000
0.375000
15.000000
20.000000
0.759500
10.000000
29.000000
40.000000
20.000000
7.500000
4.000000
12.000000
22.000000
98.000000
1.000000
75%
240.000000
40.000000
87.000000
0.48675
11.000000
28.750000
0.436000
18.000000
23.000000
0.833000
13.000000
33.750000
44.000000
23.750000
10.000000
6.000000
15.000000
24.000000
106.750000
1.000000
max
290.000000
50.000000
101.000000
0.60000
17.000000
42.000000
0.593000
34.000000
40.000000
1.000000
20.000000
41.000000
55.000000
29.000000
16.000000
12.000000
23.000000
32.000000
125.000000
2.000000
In [105]:
params = {
'TeamID': 1610612741, # Chicago Bulls
}
r = requests.get('http://stats.nba.com/feeds/teams/profile/{TeamID}_TeamProfile.js'.format(**params)); r.json()
Out[105]:
{u'TeamDetails': [{u'Details': [{u'Abbreviation': u'CHI',
u'Arena': u'United Center',
u'ArenaCapacity': u'21711',
u'City': u'Chicago',
u'DLeagueAffiliation': u'None',
u'GeneralManager': u'Gar Forman',
u'HeadCoach': u'Tom Thibodeau',
u'Nickname': u'Bulls',
u'Owner': u'Jerry Reinsdorf',
u'Team_Id': 1610612741,
u'YearActiveTill': u'present',
u'YearFounded': 1966}]},
{u'History': [{u'City': u'Chicago',
u'Nickname': u'Bulls',
u'Team_Id': 1610612741,
u'YearActiveTill': 2012,
u'YearFounded': 1966}]},
{u'SocialSites': [{u'AccountType': u'Facebook',
u'WebSite_Link': u'https://www.facebook.com/chicagobulls'},
{u'AccountType': u'Instagram',
u'WebSite_Link': u'http://instagram.com/chicagobulls'},
{u'AccountType': u'Twitter',
u'WebSite_Link': u'https://twitter.com/chicagobulls'}]},
{u'Awards': [{u'Championships': [{u'OppositeTeam': u'Los Angeles Lakers',
u'YearAwarded': 1991},
{u'OppositeTeam': u'Portland Trail Blazers', u'YearAwarded': 1992},
{u'OppositeTeam': u'Phoenix Suns', u'YearAwarded': 1993},
{u'OppositeTeam': u'Seattle SuperSonics', u'YearAwarded': 1996},
{u'OppositeTeam': u'Utah Jazz', u'YearAwarded': 1997},
{u'OppositeTeam': u'Utah Jazz', u'YearAwarded': 1998}]},
{u'ConferenceTitles': [{u'OppositeTeam': None, u'YearAwarded': 1991},
{u'OppositeTeam': None, u'YearAwarded': 1992},
{u'OppositeTeam': None, u'YearAwarded': 1993},
{u'OppositeTeam': None, u'YearAwarded': 1996},
{u'OppositeTeam': None, u'YearAwarded': 1997},
{u'OppositeTeam': None, u'YearAwarded': 1998}]},
{u'DivitionalTitles': [{u'OppositeTeam': None, u'YearAwarded': 1975},
{u'OppositeTeam': None, u'YearAwarded': 1991},
{u'OppositeTeam': None, u'YearAwarded': 1992},
{u'OppositeTeam': None, u'YearAwarded': 1993},
{u'OppositeTeam': None, u'YearAwarded': 1996},
{u'OppositeTeam': None, u'YearAwarded': 1997},
{u'OppositeTeam': None, u'YearAwarded': 1998},
{u'OppositeTeam': None, u'YearAwarded': 2011},
{u'OppositeTeam': None, u'YearAwarded': 2012}]}]},
{u'HallOfFameInductees': [{u'Jersey': None,
u'Player': u'Chet Walker',
u'PlayerID': 78435,
u'Position': u'F',
u'SeasonsWithTeam': u'1970-1975',
u'Year': 2012},
{u'Jersey': None,
u'Player': u'Artis Gilmore',
u'PlayerID': 600014,
u'Position': u'C',
u'SeasonsWithTeam': u'1977-1982, 1988',
u'Year': 2011},
{u'Jersey': None,
u'Player': u'Dennis Rodman',
u'PlayerID': 23,
u'Position': u'F',
u'SeasonsWithTeam': u'1996-1998',
u'Year': 2011},
{u'Jersey': None,
u'Player': u'Scottie Pippen',
u'PlayerID': 937,
u'Position': u'F',
u'SeasonsWithTeam': u'1988-1998, 2004',
u'Year': 2010},
{u'Jersey': None,
u'Player': u'Michael Jordan',
u'PlayerID': 893,
u'Position': u'G',
u'SeasonsWithTeam': u'1985-1993, 1995-1998',
u'Year': 2009},
{u'Jersey': None,
u'Player': u'George Gervin',
u'PlayerID': 76804,
u'Position': u'G/F',
u'SeasonsWithTeam': u'1986',
u'Year': 1996},
{u'Jersey': None,
u'Player': u'Nate Thurmond',
u'PlayerID': 600001,
u'Position': u'C',
u'SeasonsWithTeam': u'1975-1976',
u'Year': 1985}]},
{u'RetiredMembers': [{u'Jersey': u'33',
u'Player': u'Scottie Pippen',
u'PlayerID': 937,
u'Position': u'F',
u'SeasonsWithTeam': u'1988-1998, 2004',
u'Year': 2005},
{u'Jersey': u' ',
u'Player': u'Jerry Krause',
u'PlayerID': None,
u'Position': u'General Manager',
u'SeasonsWithTeam': u'1985-2003',
u'Year': 2003},
{u'Jersey': u' ',
u'Player': u'Phil Jackson',
u'PlayerID': 77106,
u'Position': u'Coach',
u'SeasonsWithTeam': u'1988-1998',
u'Year': 1999},
{u'Jersey': u'10',
u'Player': u'Bob Love',
u'PlayerID': 77412,
u'Position': u'F',
u'SeasonsWithTeam': u'1969-1977',
u'Year': 1994},
{u'Jersey': u'23',
u'Player': u'Michael Jordan',
u'PlayerID': 893,
u'Position': u'G',
u'SeasonsWithTeam': u'1985-1993, 1995-1998',
u'Year': 1994},
{u'Jersey': u'4',
u'Player': u'Jerry Sloan',
u'PlayerID': 78173,
u'Position': u'G',
u'SeasonsWithTeam': u'1967-1976',
u'Year': 1978},
{u'Jersey': u' ',
u'Player': u'Johny Kerr',
u'PlayerID': None,
u'Position': u'Coach, Business Manager, Broadcaster',
u'SeasonsWithTeam': u'1966-2009',
u'Year': None}]}]}
In [108]:
params = {
'TeamID': 1610612745, # Houston Rockets
'Season': '2013-14',
'SeasonType': 'Regular Season'
}
r = requests.get('http://stats.nba.com/stats/commonteamroster', params=params)
In [113]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(['TeamID', 'PLAYER_ID']); df
Out[113]:
SEASON
LeagueID
PLAYER
NUM
POSITION
HEIGHT
WEIGHT
BIRTH_DATE
AGE
EXP
SCHOOL
TeamID
PLAYER_ID
1610612745
203477
2013
00
Isaiah Canaan
1
G
6-0
188
MAY 02, 1991
22
R
Murray State
201976
2013
00
Patrick Beverley
2
G
6-1
185
JUL 12, 1988
24
1
Arkansas
201600
2013
00
Omer Asik
3
C
7-0
255
JUL 04, 1986
26
3
Turkey
202706
2013
00
Jordan Hamilton
5
F-G
6-7
220
OCT 06, 1990
22
2
Texas
203093
2013
00
Terrence Jones
6
F
6-9
252
JAN 09, 1992
21
1
Kentucky
202391
2013
00
Jeremy Lin
7
G
6-3
200
AUG 23, 1988
24
3
Harvard
2730
2013
00
Dwight Howard
12
C-F
6-11
265
DEC 08, 1985
27
9
SW Atlanta Christian Academy (GA)
201935
2013
00
James Harden
13
G
6-5
220
AUG 26, 1989
23
4
Arizona State
201956
2013
00
Omri Casspi
18
F
6-9
225
JUN 22, 1988
25
4
Yavne, Israel
202700
2013
00
Donatas Motiejunas
20
F-C
7-0
222
SEP 20, 1990
22
1
Kaunas, Lithuania
2694
2013
00
Josh Powell
21
F
6-9
240
JAN 25, 1983
30
6
North Carolina State
202718
2013
00
Chandler Parsons
25
F
6-9
227
OCT 25, 1988
24
2
Florida
203584
2013
00
Troy Daniels
30
G
6-4
204
JUL 15, 1991
21
R
Virginia Commonwealth
101128
2013
00
Francisco Garcia
32
G-F
6-7
195
DEC 31, 1980
32
8
Louisville
203496
2013
00
Robert Covington
33
F
6-9
215
DEC 14, 1990
22
R
Tennessee State
We can even get all the coaching staff right down to 'Strength and Conditioning Coach'
In [122]:
data = r.json()[u'resultSets'][1]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(['TEAM_ID', 'COACH_ID']); df
Out[122]:
SEASON
FIRST_NAME
LAST_NAME
COACH_NAME
COACH_CODE
IS_ASSISTANT
COACH_TYPE
SCHOOL
SORT_SEQUENCE
TEAM_ID
COACH_ID
1610612745
MCH414916
2013
Kevin
McHale
KevinMcHale
kevin_mchale
1
Head Coach
College - Minnesota
0
BIC363464
2013
J.B.
Bickerstaff
J.B.Bickerstaff
jb_bickerstaff
2
Assistant Coach
College - Minnesota
98
FIN079911
2013
Chris
Finch
ChrisFinch
chris_finch
2
Assistant Coach
College - Franklin & Marshall
99
BUC531121
2013
Greg
Buckner
GregBuckner
greg_buckner
2
Assistant Coach
College - Clemson
100
JON375806
2013
Keith
Jones
KeithJones
keith_jones
3
Trainer
College - Arkansas
102
BIL164480
2013
Jason
Biles
JasonBiles
jason_biles
4
Assistant Trainer
College - North Florida
103
ROG717320
2013
Joe
Rogowski
JoeRogowski
joe_rogowski
5
Strength and Conditioning Coach
College - DePauw
101
In [132]:
params = {
'TeamID': 1610612747 , # LA Lakers
}
r = requests.get('http://stats.nba.com/feeds/teams/stats/{TeamID}_HistoricalLeaders.js'.format(**params))
In [133]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']); df
Out[133]:
TEAM_ID
PTS
PTS_PERSON_ID
PTS_PLAYER
AST
AST_PERSON_ID
AST_PLAYER
REB
REB_PERSON_ID
REB_PLAYER
BLK
BLK_PERSON_ID
BLK_PLAYER
STL
STL_PERSON_ID
STL_PLAYER
SEASON_YEAR
0
1610612747
31700
977
Kobe Bryant
10141
77142
Magic Johnson
11463
76127
Elgin Baylor
2694
76003
Kareem Abdul-Jabbar
1835
977
Kobe Bryant
2013-14
This schema is turrible.
In [145]:
params = {
'Season': '2013-14',
'SeasonType': 'Regular Season',
'LeagueID': '00',
'TeamID': 1610612751, # Brooklyn Nets
'MeasureType':'Base',
'PerMode': 'Totals',
'PlusMinus': 'N',
'PaceAdjust': 'N',
'Rank': 'N',
'Outcome': '',
'Location': '',
'Month': '0',
'SeasonSegment': '',
'DateFrom': '',
'DateTo': '',
'OpponentTeamID': '0',
'VsConference': '',
'VsDivision': '',
'GameSegment': '',
'Period': '0',
'LastNGames': '0',
'GroupQuantity': 5,
'GameScope': '',
'GameID': ''
}
r = requests.get('http://stats.nba.com/stats/teamdashlineups', params=params)
In [146]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']); df
Out[146]:
GROUP_SET
GROUP_VALUE
TEAM_ID
TEAM_ABBREVIATION
TEAM_NAME
GP
W
L
W_PCT
MIN
...
REB
AST
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
0
Overall
2013-14
1610612751
BKN
Brooklyn Nets
82
44
38
0.537
3976
...
3128
1714
1191
705
311
337
1777
1704
8079
-82
1 rows × 31 columns
In [147]:
data = r.json()[u'resultSets'][1]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('GROUP_ID'); df
Out[147]:
GROUP_SET
GROUP_NAME
GP
W
L
W_PCT
MIN
FGM
FGA
FG_PCT
...
REB
AST
TOV
STL
BLK
BLKA
PF
PFD
PTS
PLUS_MINUS
GROUP_ID
2207 - 2733 - 1718 - 203486 - 101114
Lineups
Johnson,Joe - Livingston,Shaun - Pierce,Paul -...
26
18
8
0.692
284.376667
220
434
0.507
...
199
133
83
69
20
27
107
99
590
18
708 - 2207 - 2733 - 1718 - 101114
Lineups
Garnett,Kevin - Johnson,Joe - Livingston,Shaun...
13
9
4
0.692
129.441667
100
207
0.483
...
101
60
35
28
8
14
42
48
264
44
101187 - 708 - 2207 - 2733 - 1718
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
13
10
3
0.769
121.266667
86
190
0.453
...
97
47
35
21
13
8
51
43
218
13
101154 - 2207 - 2733 - 1718 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Livingston,Shau...
26
19
7
0.731
109.821667
84
174
0.483
...
79
43
30
27
4
8
41
47
242
25
708 - 2207 - 201572 - 1718 - 101114
Lineups
Garnett,Kevin - Johnson,Joe - Lopez,Brook - Pi...
10
4
6
0.400
89.685000
65
146
0.445
...
76
44
23
13
9
6
37
37
172
-14
101154 - 1905 - 203141 - 1891 - 101114
Lineups
Blatche,Andray - Kirilenko,Andrei - Teletovic,...
5
4
1
0.800
55.266667
52
94
0.553
...
41
37
10
12
4
3
22
20
144
28
101154 - 2207 - 201572 - 1718 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Lopez,Brook - P...
11
5
6
0.455
53.991667
46
95
0.484
...
62
28
22
14
5
5
26
22
128
26
101187 - 101154 - 2733 - 203141 - 201977
Lineups
Anderson,Alan - Blatche,Andray - Livingston,Sh...
8
6
2
0.750
51.766667
46
90
0.511
...
45
19
9
11
7
4
17
23
124
37
101187 - 101154 - 2207 - 2733 - 1718
Lineups
Anderson,Alan - Blatche,Andray - Johnson,Joe -...
16
11
5
0.688
49.705000
37
87
0.425
...
41
19
11
9
3
7
23
16
98
-1
101187 - 708 - 2207 - 203141 - 101114
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
5
1
4
0.200
45.100000
45
76
0.592
...
33
35
10
13
2
3
22
17
117
38
101187 - 708 - 2207 - 201572 - 101114
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
4
2
2
0.500
41.393333
34
59
0.576
...
32
19
12
3
2
1
21
17
92
-10
101154 - 2207 - 1718 - 201977 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Pierce,Paul - T...
10
6
4
0.600
40.750000
28
61
0.459
...
29
16
14
9
2
3
14
14
79
15
101154 - 708 - 2207 - 2733 - 1718
Lineups
Blatche,Andray - Garnett,Kevin - Johnson,Joe -...
4
1
3
0.250
40.016667
30
67
0.448
...
30
23
14
4
5
1
12
12
69
-25
101154 - 2207 - 1905 - 1718 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Kirilenko,Andre...
9
5
4
0.556
39.066667
30
66
0.455
...
39
15
11
6
2
5
12
19
83
23
708 - 2207 - 1905 - 1718 - 101114
Lineups
Garnett,Kevin - Johnson,Joe - Kirilenko,Andrei...
7
4
3
0.571
36.980000
27
60
0.450
...
28
14
12
5
1
5
13
21
75
-15
101187 - 101154 - 2733 - 1718 - 203486
Lineups
Anderson,Alan - Blatche,Andray - Livingston,Sh...
6
3
3
0.500
34.156667
22
42
0.524
...
22
14
15
5
1
2
20
20
71
-5
2207 - 1718 - 203486 - 201977 - 101114
Lineups
Johnson,Joe - Pierce,Paul - Plumlee,Mason - Th...
9
6
3
0.667
34.116667
30
57
0.526
...
28
14
10
8
5
2
10
14
72
4
101154 - 2207 - 1718 - 203141 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Pierce,Paul - T...
5
1
4
0.200
33.738333
23
61
0.377
...
32
12
10
2
3
0
14
16
69
2
101154 - 1905 - 203141 - 201977 - 101114
Lineups
Blatche,Andray - Kirilenko,Andrei - Teletovic,...
8
7
1
0.875
32.133333
22
54
0.407
...
26
15
8
11
1
5
16
24
62
-5
101187 - 2207 - 1718 - 203486 - 101114
Lineups
Anderson,Alan - Johnson,Joe - Pierce,Paul - Pl...
9
6
3
0.667
29.891667
25
49
0.510
...
24
12
8
8
3
1
12
12
70
16
101187 - 101154 - 2501 - 2207 - 2733
Lineups
Anderson,Alan - Blatche,Andray - Evans,Reggie ...
7
0
7
0.000
29.616667
19
51
0.373
...
28
8
7
2
1
2
12
11
56
1
101187 - 708 - 2207 - 2733 - 201572
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
4
2
2
0.500
28.983333
20
43
0.465
...
24
10
5
2
3
1
13
11
49
0
101154 - 2207 - 2733 - 201572 - 1718
Lineups
Blatche,Andray - Johnson,Joe - Livingston,Shau...
8
4
4
0.500
28.933333
18
50
0.360
...
29
7
7
3
5
4
8
14
51
0
101187 - 101154 - 2207 - 1718 - 101114
Lineups
Anderson,Alan - Blatche,Andray - Johnson,Joe -...
11
6
5
0.545
28.333333
24
47
0.511
...
17
13
6
4
2
2
16
9
64
-4
2207 - 1905 - 1718 - 203486 - 101114
Lineups
Johnson,Joe - Kirilenko,Andrei - Pierce,Paul -...
5
3
2
0.600
27.916667
27
42
0.643
...
20
20
11
8
2
2
12
13
68
-4
101187 - 708 - 2207 - 201572 - 203116
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
3
1
2
0.333
27.383333
18
47
0.383
...
23
11
8
5
2
3
7
7
44
-9
2501 - 2207 - 2733 - 1718 - 101114
Lineups
Evans,Reggie - Johnson,Joe - Livingston,Shaun ...
3
1
2
0.333
25.860000
18
38
0.474
...
13
12
8
3
3
3
9
10
50
3
101187 - 101154 - 2207 - 2733 - 201572
Lineups
Anderson,Alan - Blatche,Andray - Johnson,Joe -...
8
2
6
0.250
25.761667
20
41
0.488
...
24
9
10
4
3
2
9
10
55
10
708 - 2207 - 1718 - 203141 - 101114
Lineups
Garnett,Kevin - Johnson,Joe - Pierce,Paul - Te...
5
2
3
0.400
24.775000
19
37
0.514
...
11
10
9
3
1
0
15
6
48
-14
101187 - 203486 - 203129 - 203116 - 203141
Lineups
Anderson,Alan - Plumlee,Mason - Shengelia,Torn...
4
0
4
0.000
24.300000
18
32
0.563
...
26
3
11
3
1
3
10
13
54
4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
101187 - 708 - 1718 - 203486 - 203116
Lineups
Anderson,Alan - Garnett,Kevin - Pierce,Paul - ...
1
0
1
0.000
4.233333
2
6
0.333
...
5
2
3
1
2
1
2
0
6
2
101187 - 1905 - 2733 - 203486 - 101114
Lineups
Anderson,Alan - Kirilenko,Andrei - Livingston,...
2
2
0
1.000
4.216667
3
4
0.750
...
2
3
2
1
1
0
6
3
9
-1
101187 - 708 - 2207 - 203486 - 1891
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
2
1
1
0.500
4.183333
3
7
0.429
...
3
2
1
1
0
1
2
4
8
-2
101154 - 1905 - 203486 - 203104 - 201977
Lineups
Blatche,Andray - Kirilenko,Andrei - Plumlee,Ma...
1
0
1
0.000
4.183333
3
6
0.500
...
2
1
2
1
0
2
2
3
9
-1
101187 - 101154 - 1905 - 2733 - 1891
Lineups
Anderson,Alan - Blatche,Andray - Kirilenko,And...
2
1
1
0.500
4.166667
4
8
0.500
...
3
3
0
1
0
0
3
1
12
8
708 - 1905 - 2733 - 203486 - 1891
Lineups
Garnett,Kevin - Kirilenko,Andrei - Livingston,...
2
1
1
0.500
4.100000
1
4
0.250
...
3
1
3
0
0
0
3
3
2
-4
101187 - 2733 - 203129 - 203116 - 203141
Lineups
Anderson,Alan - Livingston,Shaun - Shengelia,T...
1
1
0
1.000
4.083333
1
7
0.143
...
2
0
2
0
0
0
4
0
2
-12
708 - 1905 - 2733 - 1718 - 203141
Lineups
Garnett,Kevin - Kirilenko,Andrei - Livingston,...
1
1
0
1.000
4.083333
4
7
0.571
...
5
1
2
0
1
0
2
1
9
1
101187 - 1905 - 1718 - 203486 - 1891
Lineups
Anderson,Alan - Kirilenko,Andrei - Pierce,Paul...
1
0
1
0.000
4.050000
2
6
0.333
...
3
1
1
1
0
1
1
4
7
2
101187 - 2215 - 2733 - 1718 - 201977
Lineups
Anderson,Alan - Collins,Jason - Livingston,Sha...
1
1
0
1.000
4.016667
6
6
1.000
...
1
1
1
0
0
0
3
0
13
-1
2501 - 708 - 2207 - 1718 - 101114
Lineups
Evans,Reggie - Garnett,Kevin - Johnson,Joe - P...
1
0
1
0.000
4.016667
3
4
0.750
...
1
3
2
0
0
0
1
1
10
5
101154 - 2207 - 1905 - 2733 - 201572
Lineups
Blatche,Andray - Johnson,Joe - Kirilenko,Andre...
2
1
1
0.500
4.000000
4
7
0.571
...
2
4
1
1
0
0
2
2
11
-3
101187 - 101154 - 1905 - 1718 - 203141
Lineups
Anderson,Alan - Blatche,Andray - Kirilenko,And...
1
0
1
0.000
3.983333
4
5
0.800
...
3
1
1
0
0
1
1
3
11
6
101154 - 2207 - 1905 - 201977 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Kirilenko,Andre...
2
1
1
0.500
3.971667
3
6
0.500
...
0
2
0
0
1
0
1
0
7
1
101154 - 2207 - 2733 - 203486 - 1891
Lineups
Blatche,Andray - Johnson,Joe - Livingston,Shau...
1
0
1
0.000
3.966667
3
7
0.429
...
3
2
2
0
0
0
1
2
8
-2
101187 - 2501 - 708 - 2733 - 1718
Lineups
Anderson,Alan - Evans,Reggie - Garnett,Kevin -...
2
0
2
0.000
3.950000
1
6
0.167
...
2
0
0
0
0
1
0
1
2
-10
101187 - 101154 - 201572 - 1891 - 101114
Lineups
Anderson,Alan - Blatche,Andray - Lopez,Brook -...
1
0
1
0.000
3.900000
2
6
0.333
...
4
2
2
0
0
1
3
3
6
-1
101154 - 2501 - 1905 - 2733 - 1891
Lineups
Blatche,Andray - Evans,Reggie - Kirilenko,Andr...
2
1
1
0.500
3.900000
6
8
0.750
...
3
1
1
0
0
0
4
1
12
-2
101154 - 2207 - 201572 - 1891 - 101114
Lineups
Blatche,Andray - Johnson,Joe - Lopez,Brook - T...
2
1
1
0.500
3.900000
3
8
0.375
...
6
2
2
0
1
1
1
1
6
-7
101154 - 1718 - 203486 - 1891 - 101114
Lineups
Blatche,Andray - Pierce,Paul - Plumlee,Mason -...
1
0
1
0.000
3.866667
3
8
0.375
...
4
1
2
2
0
0
1
0
6
-4
101187 - 708 - 2207 - 203486 - 101114
Lineups
Anderson,Alan - Garnett,Kevin - Johnson,Joe - ...
1
0
1
0.000
3.833333
1
5
0.200
...
4
1
1
0
1
0
0
3
6
0
2207 - 1905 - 203486 - 201977 - 101114
Lineups
Johnson,Joe - Kirilenko,Andrei - Plumlee,Mason...
2
1
1
0.500
3.816667
1
7
0.143
...
5
0
3
0
0
1
4
2
4
-4
101154 - 2207 - 201572 - 203116 - 203141
Lineups
Blatche,Andray - Johnson,Joe - Lopez,Brook - T...
2
1
1
0.500
3.778333
4
8
0.500
...
6
3
1
0
0
0
3
1
9
2
101187 - 2501 - 2207 - 1905 - 203141
Lineups
Anderson,Alan - Evans,Reggie - Johnson,Joe - K...
1
1
0
1.000
3.766667
3
7
0.429
...
3
3
0
1
0
0
0
3
10
4
101187 - 2501 - 2733 - 1718 - 203141
Lineups
Anderson,Alan - Evans,Reggie - Livingston,Shau...
2
2
0
1.000
3.766667
0
5
0.000
...
3
0
1
0
0
1
4
0
0
-9
101187 - 101154 - 2733 - 203486 - 101114
Lineups
Anderson,Alan - Blatche,Andray - Livingston,Sh...
1
0
1
0.000
3.733333
2
5
0.400
...
1
2
1
1
2
0
1
2
8
-3
2215 - 2207 - 1905 - 1718 - 101114
Lineups
Collins,Jason - Johnson,Joe - Kirilenko,Andrei...
2
2
0
1.000
3.706667
5
6
0.833
...
2
2
1
3
0
0
4
0
11
4
101187 - 2215 - 1905 - 203104 - 203141
Lineups
Anderson,Alan - Collins,Jason - Kirilenko,Andr...
1
1
0
1.000
3.683333
3
6
0.500
...
0
2
1
1
0
0
2
0
7
-6
101187 - 708 - 201572 - 1718 - 101114
Lineups
Anderson,Alan - Garnett,Kevin - Lopez,Brook - ...
1
1
0
1.000
3.683333
2
5
0.400
...
3
0
0
1
0
0
0
1
5
5
101187 - 2733 - 203486 - 203141 - 101114
Lineups
Anderson,Alan - Livingston,Shaun - Plumlee,Mas...
2
2
0
1.000
3.683333
3
7
0.429
...
1
2
0
0
0
0
2
1
8
3
250 rows × 28 columns
In [148]:
params = {
'GameID': '0021300962',
'RangeType': 0,
'StartPeriod': 0,
'EndPeriod': 0,
'StartRange': 0,
'EndRange': 0,
}
r = requests.get('http://stats.nba.com/stats/boxscoreadvanced', params=params)
In [149]:
len(r.json()[u'resultSets'])
Out[149]:
15
In [150]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']); df
Out[150]:
GAME_DATE_EST
GAME_SEQUENCE
GAME_ID
GAME_STATUS_ID
GAME_STATUS_TEXT
GAMECODE
HOME_TEAM_ID
VISITOR_TEAM_ID
SEASON
LIVE_PERIOD
LIVE_PC_TIME
NATL_TV_BROADCASTER_ABBREVIATION
LIVE_PERIOD_TIME_BCAST
WH_STATUS
0
2014-03-12T00:00:00
6
0021300962
3
Final
20140312/NYKBOS
1610612738
1610612752
2013
4
None
Q4 -
1
In [151]:
data = r.json()[u'resultSets'][1]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']); df
Out[151]:
GAME_DATE_EST
GAME_SEQUENCE
GAME_ID
TEAM_ID
TEAM_ABBREVIATION
TEAM_CITY_NAME
TEAM_WINS_LOSSES
PTS_QTR1
PTS_QTR2
PTS_QTR3
...
PTS_OT2
PTS_OT3
PTS_OT4
PTS_OT5
PTS_OT6
PTS_OT7
PTS_OT8
PTS_OT9
PTS_OT10
PTS
0
2014-03-12T00:00:00
6
0021300962
1610612738
BOS
Boston
22-43
24
24
27
...
0
0
0
0
0
0
0
0
0
92
1
2014-03-12T00:00:00
6
0021300962
1610612752
NYK
New York
26-40
36
32
17
...
0
0
0
0
0
0
0
0
0
116
2 rows × 22 columns
In [3]:
params = {
'LeagueID': '00',
'GameDate': '03/12/2014',
'DayOffset': 0
}
r = requests.get('http://stats.nba.com/stats/scoreboard', params=params)
In [5]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index('GAME_ID'); df
Out[5]:
GAME_DATE_EST
GAME_SEQUENCE
GAME_STATUS_ID
GAME_STATUS_TEXT
GAMECODE
HOME_TEAM_ID
VISITOR_TEAM_ID
SEASON
LIVE_PERIOD
LIVE_PC_TIME
NATL_TV_BROADCASTER_ABBREVIATION
LIVE_PERIOD_TIME_BCAST
WH_STATUS
GAME_ID
0021300957
2014-03-12T00:00:00
1
3
Final
20140312/BKNMIA
1610612748
1610612751
2013
4
ESPN
Q4 - ESPN
1
0021300958
2014-03-12T00:00:00
2
3
Final
20140312/DENORL
1610612753
1610612743
2013
4
None
Q4 -
1
0021300959
2014-03-12T00:00:00
3
3
Final
20140312/SACPHI
1610612755
1610612758
2013
4
None
Q4 -
1
0021300960
2014-03-12T00:00:00
4
3
Final
20140312/DETTOR
1610612761
1610612765
2013
4
None
Q4 -
1
0021300961
2014-03-12T00:00:00
5
3
Final
20140312/CHAWAS
1610612764
1610612766
2013
4
None
Q4 -
1
0021300962
2014-03-12T00:00:00
6
3
Final
20140312/NYKBOS
1610612738
1610612752
2013
4
None
Q4 -
1
0021300963
2014-03-12T00:00:00
7
3
Final
20140312/MEMNOP
1610612740
1610612763
2013
4
None
Q4 -
1
0021300964
2014-03-12T00:00:00
8
3
Final
20140312/DALUTA
1610612762
1610612742
2013
4
None
Q4 -
1
0021300965
2014-03-12T00:00:00
9
3
Final
20140312/PORSAS
1610612759
1610612757
2013
4
ESPN
Q4 - ESPN
1
0021300966
2014-03-12T00:00:00
10
3
Final
20140312/CLEPHX
1610612756
1610612739
2013
4
None
Q4 -
1
0021300967
2014-03-12T00:00:00
11
3
Final
20140312/GSWLAC
1610612746
1610612744
2013
4
None
Q4 -
1
In [9]:
params = {
# 'q': '03/12/2014'
}
r = requests.get('http://data.nba.com/data/5s/xml/nbacom/{season}/scores/{game_code}/boxscore.xml' \
.format(season='2013-14', game_code='0021300965'), params=params)
r.text
Out[9]:
u''
In [10]:
params = {
'GameID': '0021300962',
}
r = requests.get('http://stats.nba.com/stats/playertrackingboxscore', params=params)
r.text
Out[10]:
u'{"Message":"No HTTP resource was found that matches the request URI \'http://stats.nba.com/stats/playertrackingboxscore?GameID=0021300962\'."}'
/stats/playbyplay/stats/boxscoreadvanced/stats/boxscoremisc/stats/boxscorescoring/stats/boxscoreusage/stats/boxscorefourfactors
In [3]:
data = r.json()[u'resultSets'][0]
df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(None); df
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-3-e47a1d704438> in <module>()
1 data = r.json()[u'resultSets'][0]
----> 2 df = DataFrame(data[u'rowSet'], columns=data[u'headers']).set_index(None); df
/Users/tiao/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
2262 names.append(None)
2263 else:
-> 2264 level = frame[col].values
2265 names.append(col)
2266 if drop:
/Users/tiao/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
1682 return self._getitem_multilevel(key)
1683 else:
-> 1684 return self._getitem_column(key)
1685
1686 def _getitem_column(self, key):
/Users/tiao/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_column(self, key)
1689 # get column
1690 if self.columns.is_unique:
-> 1691 return self._get_item_cache(key)
1692
1693 # duplicate columns & possible reduce dimensionaility
/Users/tiao/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc in _get_item_cache(self, item)
1050 res = cache.get(item)
1051 if res is None:
-> 1052 values = self._data.get(item)
1053 res = self._box_item_values(item, values)
1054 cache[item] = res
/Users/tiao/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc in get(self, item)
2544 loc = indexer.item()
2545 else:
-> 2546 raise ValueError("cannot label index with a null key")
2547
2548 return self.iget(loc)
ValueError: cannot label index with a null key
In [ ]:
Content source: ltiao/notes
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