探索 NBA 数据

我们首先安装 Goldsberry 包，项目源地址：

https://github.com/bradleyfay/py-Goldsberry

使用 pip 安装：

pip install py-goldsberry

该包的接口与 pandas 兼容，可以与 pandas 的 DataFrame 一起使用。

In [1]:

import goldsberry as gb
import pandas as pd

当前使用的版本号为：

In [2]:

gb.__version__

Out[2]:

'0.8.0.1'

球员信息

获得 2015-2016 赛季运动员的名单：

In [3]:

players = gb.PlayerList().players()
players = pd.DataFrame(players)

players.head()

Out[3]:

	DISPLAY_LAST_COMMA_FIRST	FROM_YEAR	GAMES_PLAYED_FLAG	PERSON_ID	PLAYERCODE	ROSTERSTATUS	TEAM_ABBREVIATION	TEAM_CITY	TEAM_CODE	TEAM_ID	TEAM_NAME	TO_YEAR
0	Acy, Quincy	2012	Y	203112	quincy_acy	1	SAC	Sacramento	kings	1610612758	Kings	2015
1	Adams, Jordan	2014	Y	203919	jordan_adams	1	MEM	Memphis	grizzlies	1610612763	Grizzlies	2015
2	Adams, Steven	2013	Y	203500	steven_adams	1	OKC	Oklahoma City	thunder	1610612760	Thunder	2015
3	Afflalo, Arron	2007	Y	201167	arron_afflalo	1	NYK	New York	knicks	1610612752	Knicks	2015
4	Ajinca, Alexis	2008	Y	201582	alexis_ajinca	1	NOP	New Orleans	pelicans	1610612740	Pelicans	2015

球员总数为：

In [4]:

print len(players)

通过查询特定的 TEAM_ABBREVIATION，我们可以查看某个球队本赛季的球员，比如 2014-2015 赛季的总冠军金州勇士 GSW：

In [5]:

gsw_players = players.ix[players["TEAM_ABBREVIATION"] == "GSW"]

gsw_players[["DISPLAY_LAST_COMMA_FIRST", "FROM_YEAR", "TEAM_ABBREVIATION", "TEAM_CITY", "TEAM_NAME", "PERSON_ID"]]

Out[5]:

	DISPLAY_LAST_COMMA_FIRST	FROM_YEAR	TEAM_ABBREVIATION	TEAM_CITY	TEAM_NAME	PERSON_ID
30	Barbosa, Leandro	2003	GSW	Golden State	Warriors	2571
33	Barnes, Harrison	2012	GSW	Golden State	Warriors	203084
52	Bogut, Andrew	2005	GSW	Golden State	Warriors	101106
86	Clark, Ian	2013	GSW	Golden State	Warriors	203546
103	Curry, Stephen	2009	GSW	Golden State	Warriors	201939
135	Ezeli, Festus	2012	GSW	Golden State	Warriors	203105
164	Green, Draymond	2012	GSW	Golden State	Warriors	203110
209	Iguodala, Andre	2004	GSW	Golden State	Warriors	2738
262	Livingston, Shaun	2004	GSW	Golden State	Warriors	2733
263	Looney, Kevon	2015	GSW	Golden State	Warriors	1626172
279	McAdoo, James Michael	2014	GSW	Golden State	Warriors	203949
377	Rush, Brandon	2008	GSW	Golden State	Warriors	201575
398	Speights, Marreese	2008	GSW	Golden State	Warriors	201578
414	Thompson, Jason	2008	GSW	Golden State	Warriors	201574
415	Thompson, Klay	2011	GSW	Golden State	Warriors	202691

球员比赛数据

通过 DISPLAY_LAST_COMMA_FIRST，我们来查询宣布本赛季之后退役的科比布莱恩特（Kobe, Bryant）的信息：

In [6]:

kobe = players.ix[players["DISPLAY_LAST_COMMA_FIRST"].str.contains("Kobe")]

kobe

Out[6]:

	DISPLAY_LAST_COMMA_FIRST	FROM_YEAR	GAMES_PLAYED_FLAG	PERSON_ID	PLAYERCODE	ROSTERSTATUS	TEAM_ABBREVIATION	TEAM_CITY	TEAM_CODE	TEAM_ID	TEAM_NAME	TO_YEAR
64	Bryant, Kobe	1996	Y	977	kobe_bryant	1	LAL	Los Angeles	lakers	1610612747	Lakers	2015

为了方便，我们将 Kobe 的 ID 放到变量中去：

In [7]:

kobe_id = 977

我们来看本赛季 Kobe 的比赛记录：

In [8]:

kobe_logs = gb.player.game_logs(kobe_id)

kobe_logs = pd.DataFrame(kobe_logs.logs())

# 最近五场比赛
kobe_logs.head()

Out[8]:

	AST	BLK	DREB	FG3A	FG3M	FG3_PCT	FGA	FGM	FG_PCT	FTA	...	PF	PLUS_MINUS	PTS	Player_ID	REB	SEASON_ID	STL	TOV	VIDEO_AVAILABLE	WL
0	3	0	6	7	3	0.429	16	5	0.313	4	...	2	-19	17	977	6	22015	1	3	1	L
1	0	0	4	14	4	0.286	25	6	0.240	4	...	0	-6	19	977	5	22015	0	0	1	L
2	4	1	1	14	4	0.286	28	9	0.321	3	...	4	-2	25	977	2	22015	0	2	1	L
3	2	0	9	11	4	0.364	24	10	0.417	4	...	0	16	27	977	12	22015	2	1	1	W
4	5	0	3	11	7	0.636	21	10	0.476	12	...	3	6	38	977	5	22015	2	2	1	W

5 rows × 27 columns

截至到全明星赛前，本赛季 Kobe 一共参加了 44 场比赛，其场均数据为：

In [9]:

kobe_logs.Game_ID

Out[9]:

0     0021500795
1     0021500776
2     0021500767
3     0021500747
4     0021500734
5     0021500720
6     0021500697
7     0021500662
8     0021500653
9     0021500638
10    0021500614
11    0021500608
12    0021500592
13    0021500576
14    0021500549
15    0021500539
16    0021500476
17    0021500458
18    0021500455
19    0021500440
20    0021500435
21    0021500422
22    0021500385
23    0021500370
24    0021500349
25    0021500342
26    0021500325
27    0021500308
28    0021500301
29    0021500286
30    0021500269
31    0021500263
32    0021500253
33    0021500244
34    0021500214
35    0021500201
36    0021500188
37    0021500151
38    0021500135
39    0021500095
40    0021500077
41    0021500059
42    0021500045
43    0021500031
44    0021500017
Name: Game_ID, dtype: object

In [10]:

def show_avg_info(avg):
    print "得分：{:.1f}".format(avg.ix["PTS"])
    print "篮板：{:.1f}".format(avg.ix["REB"])
    print "助攻：{:.1f}".format(avg.ix["AST"])
    print "盖帽：{:.1f}".format(avg.ix["BLK"])
    print "时间：{:.1f}".format(avg.ix["MIN"])
    print "抢断：{:.1f}".format(avg.ix["STL"])
    print "失误：{:.1f}".format(avg.ix["TOV"])
    print "犯规：{:.1f}".format(avg.ix["PF"])
    print "投篮：{:.1f}%".format(avg.ix["FGM"] * 100 / avg.ix["FGA"])
    print "三分：{:.1f}%".format(avg.ix["FG3M"] * 100 / avg.ix["FG3A"])
    print "罚篮：{:.1f}%".format(avg.ix["FTM"] * 100 / avg.ix["FTA"])
    print "后篮板：{:.1f}".format(avg.ix["DREB"])
    print "前篮板：{:.1f}".format(avg.ix["OREB"])
    print "正负值：{:.1f}".format(avg.ix["PLUS_MINUS"])

show_avg_info(kobe_logs.mean())

得分：16.9
篮板：4.2
助攻：3.4
盖帽：0.2
时间：29.3
抢断：1.0
失误：2.2
犯规：1.9
投篮：34.9%
三分：28.0%
罚篮：80.3%
后篮板：3.5
前篮板：0.7
正负值：-7.9

再看一下史提芬库里的场均数据（不要问我为什么跪着看球）：

In [11]:

curry_id = 201939
curry_logs = gb.player.game_logs(curry_id)
curry_logs = pd.DataFrame(curry_logs.logs())

show_avg_info(curry_logs.mean())

得分：29.8
篮板：5.3
助攻：6.6
盖帽：0.2
时间：33.9
抢断：2.1
失误：3.3
犯规：2.0
投篮：50.8%
三分：45.4%
罚篮：91.2%
后篮板：4.5
前篮板：0.9
正负值：15.5

当然我们也可以对比一下职业生涯的数据：

In [12]:

kobe_career = gb.player.career_stats(kobe_id)
curry_career = gb.player.career_stats(curry_id)

职业生涯最高：

In [13]:

def show_career_high(career):
    career_high = pd.DataFrame(career.career_high()).ix[[0,1,5]]
    print career_high[["GAME_DATE", "STAT", "STAT_VALUE", "VS_TEAM_CITY", "VS_TEAM_NAME"]]

print "Kobe"
show_career_high(kobe_career)

print "Curry"
show_career_high(curry_career)

Kobe
     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME
0  JAN 22 2006  PTS          81      Toronto      Raptors
1  JAN 24 2010  REB          16      Toronto      Raptors
5  JAN 15 2015  AST          17    Cleveland    Cavaliers
Curry
     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME
0  FEB 27 2013  PTS          54     New York       Knicks
1  DEC 28 2015  REB          14   Sacramento        Kings
5  DEC 27 2013  AST          16      Phoenix         Suns

本赛季最高：

In [14]:

def show_season_high(career):
    career_high = pd.DataFrame(career.season_high()).ix[[0,1,5]]
    print career_high[["GAME_DATE", "STAT", "STAT_VALUE", "VS_TEAM_CITY", "VS_TEAM_NAME"]]

print "Kobe"
show_season_high(kobe_career)

print "Curry"
show_season_high(curry_career)

Kobe
     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY  VS_TEAM_NAME
0  FEB 02 2016  PTS          38    Minnesota  Timberwolves
1  FEB 04 2016  REB          12  New Orleans      Pelicans
5  NOV 15 2015  AST           9      Detroit       Pistons
Curry
     GAME_DATE STAT  STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME
0  OCT 31 2015  PTS          53  New Orleans     Pelicans
1  DEC 28 2015  REB          14   Sacramento        Kings
5  JAN 25 2016  STL           5  San Antonio        Spurs

比赛信息

In [15]:

game_ids = gb.GameIDs()
game_ids = pd.DataFrame(game_ids.game_list())

game_ids.head()

Out[15]:

	AST	BLK	DREB	FG3A	FG3M	FG3_PCT	FGA	FGM	FG_PCT	FTA	...	PTS	REB	SEASON_ID	STL	TEAM_ABBREVIATION	TEAM_ID	TEAM_NAME	TOV	VIDEO_AVAILABLE	WL
0	28	4	45	29	8	0.276	124	56	0.452	46	...	147	64	22015	7	DET	1610612765	Detroit Pistons	11	1	W
1	30	2	36	23	9	0.391	87	53	0.609	34	...	142	46	22015	9	SAC	1610612758	Sacramento Kings	15	1	W
2	34	2	30	21	9	0.429	86	52	0.605	13	...	123	38	22015	10	SAS	1610612759	San Antonio Spurs	13	1	W
3	29	6	36	35	16	0.457	95	52	0.547	15	...	131	46	22015	10	GSW	1610612744	Golden State Warriors	15	1	W
4	34	8	38	31	8	0.258	104	52	0.500	16	...	122	46	22015	10	SAC	1610612758	Sacramento Kings	20	1	L

5 rows × 29 columns

获得运动员的头像

In [16]:

from IPython.display import Image

Image("http://stats.nba.com/media/players/230x185/"+str(kobe_id)+".png")

Out[16]:

In [17]:

Image("http://stats.nba.com/media/players/230x185/"+str(curry_id)+".png")

Out[17]:

More

修改了 goldsberry\player\_Player.py 代码中的错误，使之能够查询退役球员的信息，修改后的代码在本文件夹下，放到安装目录之后下面的代码均可以运行：

In [18]:

from goldsberry.player import _Player as pl_old

1997 年的球员列表：

In [19]:

players_1997 = pl_old.PlayerList(1997)

players_1997 = pd.DataFrame(players_1997)

乔丹的球员 ID：

In [20]:

jordan_id = players_1997["PERSON_ID"].ix[players_1997["DISPLAY_LAST_COMMA_FIRST"].str.contains("Jordan, Michael")]
jordan_id = jordan_id[jordan_id.index[0]]
jordan_id

Out[20]:

乔丹在 1997-1998 赛季常规赛表现：

In [21]:

jordan_logs_1997 = pl_old.game_logs(jordan_id, season="1997")
jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())

show_avg_info(jordan_logs_1997.mean())

得分：28.7
篮板：5.8
助攻：3.5
盖帽：0.5
时间：38.9
抢断：1.7
失误：2.3
犯规：1.8
投篮：46.5%
三分：23.8%
罚篮：78.4%
后篮板：4.2
前篮板：1.6
正负值：7.3

乔丹在 1997-1998 赛季季后赛表现：

In [22]:

jordan_logs_1997 = pl_old.game_logs(jordan_id, season="1997", seasontype=2)
jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())

show_avg_info(jordan_logs_1997.mean())

得分：32.4
篮板：5.1
助攻：3.5
盖帽：0.6
时间：41.0
抢断：1.5
失误：2.1
犯规：2.2
投篮：46.2%
三分：30.2%
罚篮：81.2%
后篮板：3.5
前篮板：1.6
正负值：7.5

头像：

In [23]:

Image("http://stats.nba.com/media/players/230x185/"+str(jordan_id)+".png")

Out[23]:

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探索 NBA 数据

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探索 NBA 数据

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球员比赛数据

比赛信息

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