前言
1. 这篇数据分析案例,我做了个 视频版本 ,对代码做了解读,讲了运行的注意事项和一些有的没的,供需。对于视频制作还不是很有经验,多包涵。(视频里有彩蛋)
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NBA球星出手点分布 https://www.zhihu.com/video/1129401682580348928 2. 本文在案例的代码写完后,我把它发在了 虎扑 上。结果,被推荐上了首页, 25万多次浏览,近800回复 ……感谢各位JRs赏脸!
Python学习交流群:1004391443
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3. 获取原图、更多球员生成的结果及完整代码,请在公众号“ Crossin的编程教室 ”内回复关键字: nba
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NBA 2018-19 赛季已经落下帷幕,猛龙击败勇士,成为新科冠军球队。
我想,不如来做个 NBA 相关的数据分析 案例好了。实际上,数据分析团队对于现在的 NBA 球队已经算是标配了,无论是在球员选择还是战术制定上都起着重要的作用。当下整个联盟越来越盛行的“ 魔球化 ”打法,其实也正是基于数据分析的产物。
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于是,我们就借助 官方数据 和 Python 的绘图函数 ,来看一看 不同的球员都是在什么位置出手投篮的 。
0. 效果展示
照例先看结果,每个点是一次投篮, 蓝色点是投中,红色点是未中 。挑几个有特点的:
哈登 ,魔球得很明显了,长两分这种低效率的事绝对不干
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西蒙斯 ,古典中锋
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德拉赞 ,中投小王子
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字母哥 ,篮下都给你扣糊了!
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库里 ,这个得放全场……
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科比 ,生涯累计图,全方位无死角,包括篮板后负角度
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下面来看具体步骤,我们以刚刚获得本赛季冠军戒指的林书豪作为例子。
1. 获取数据
NBA 有一个官方统计网站: http:// stats.nba.com ,上面有各种数据,可以说是一个宝藏了。
从网页上,我没有找到这次所需的数据。但通过一番搜索,我找到了网站的开放接口 API。相关的接口和文档你可以从这个项目里查看:
nba_py - http:// stats.nba.com API for python
https:// github.com/seemethere/n ba_py/
通过以下接口,我们可以获取某个球员在指定赛季的投篮详细数据:
https:// stats.nba.com/stats/sho tchartdetail?CFID=33&CFPARAMS=2018-19&ContextFilter=&ContextMeasure=FGA&DateFrom=&DateTo=&GameID=&GameSegment=&LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PaceAdjust=N&PerMode=PerGame&Period=0&PlayerID=201935&PlusMinus=N&Position=&Rank=N&RookieYear=&Season=2018-19&SeasonSegment=&SeasonType=Regular+Season&TeamID=0&VsConference=&VsDivision=&mode=Advanced&showDetails=0&showShots=1&showZones=0&PlayerPosition=
其中参数 PlayerID 是查询球员的 ID,这个 ID 可以通过在网页上搜索球员姓名,从对应的 URL 中获取。比如林书豪就是 202391
https:// stats.nba.com/player/20 2391/
链接中的两处 2018-19 是赛季参数,你可以改成其他赛季,不过我试过不能太早,大约 96 年往前就没数据了,而且早期数据精度要差些。
其他参数可以不用管。
用 requests 库可轻松获取结果(需加上 headers):
<pre spellcheck="false" style="box-sizing: border-box; margin: 5px 0px; padding: 5px 10px; border: 0px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-weight: 400; font-stretch: inherit; font-size: 16px; line-height: inherit; font-family: inherit; vertical-align: baseline; cursor: text; counter-reset: list-1 0 list-2 0 list-3 0 list-4 0 list-5 0 list-6 0 list-7 0 list-8 0 list-9 0; background-color: rgb(240, 240, 240); border-radius: 3px; white-space: pre-wrap; color: rgb(34, 34, 34); letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">response = requests.get(url, headers=headers, timeout=5)
</pre>
2. 解析数据
返回的数据是 JSON 格式,用 pandas 转成 DataFrame 格式,方便后续处理:
<pre spellcheck="false" style="box-sizing: border-box; margin: 5px 0px; padding: 5px 10px; border: 0px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-weight: 400; font-stretch: inherit; font-size: 16px; line-height: inherit; font-family: inherit; vertical-align: baseline; cursor: text; counter-reset: list-1 0 list-2 0 list-3 0 list-4 0 list-5 0 list-6 0 list-7 0 list-8 0 list-9 0; background-color: rgb(240, 240, 240); border-radius: 3px; white-space: pre-wrap; color: rgb(34, 34, 34); letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">data = response.json()
获取列名即每项投球数据的意思
headers = data['resultSets'][0]['headers']
获取投球的相关数据
shots = data['resultSets'][0]['rowSet']
转 DataFrame
shot_df = pd.DataFrame(shots, columns=headers)
</pre>
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我们关心的数据就是 LOC_X 、 LOC_Y (出手位置)和 SHOT_MADE_FLAG (是否投中)这三列。
3. 绘制投篮点
使用 matplotlib 库的散点图绘制 scatter 方法就可以把投篮点画出来,这个我们之前的各种案例里也用过不少次了:
<pre spellcheck="false" style="box-sizing: border-box; margin: 5px 0px; padding: 5px 10px; border: 0px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-weight: 400; font-stretch: inherit; font-size: 16px; line-height: inherit; font-family: inherit; vertical-align: baseline; cursor: text; counter-reset: list-1 0 list-2 0 list-3 0 list-4 0 list-5 0 list-6 0 list-7 0 list-8 0 list-9 0; background-color: rgb(240, 240, 240); border-radius: 3px; white-space: pre-wrap; color: rgb(34, 34, 34); letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">made = shot_df[shot_df['SHOT_MADE_FLAG']==1]
miss = shot_df[shot_df['SHOT_MADE_FLAG']==0]
plt.scatter(miss.LOC_X, miss.LOC_Y, color='r', marker='.', alpha=0.3)
plt.scatter(made.LOC_X, made.LOC_Y, color='b', marker='.', alpha=0.3)
</pre>
我们通过参数设定点的大小和透明度,让显示更清楚。
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4. 绘制球场
投篮点画出来了,不过只是这样不够直观。我们再来加个球场的图。
<tt-image data-tteditor-tag="tteditorTag" contenteditable="false" class="syl1562138615553" data-render-status="finished" data-syl-blot="image" style="box-sizing: border-box; cursor: text; color: rgb(34, 34, 34); font-family: "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei", "Helvetica Neue", Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: pre-wrap; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial; display: block;"> image<input class="pgc-img-caption-ipt" placeholder="图片描述(最多50字)" value="" style="box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) 0s;"></tt-image>
这个是通过 matplotlib 里的 Circle 、 Rectangle 、 Arc 等方法拼出来的,没啥特别的技术,但代码比较繁琐,这边就不贴了。(只要你愿意,甚至可以用它画出小猪佩奇)
5. 添加头像
最后,再给数据图加上一个头像,让它看起来更完整。头像地址:
https:// stats.nba.com/media/pla yers/230x185/202391.png
或者
https:// ak-static.cms.nba.com/w p-content/uploads/headshots/nba/latest/260x190/202391.png
文件名依然是球员的 ID,但这个地址并不是对所有球员有效,可做下异常处理。
<pre spellcheck="false" style="box-sizing: border-box; margin: 5px 0px; padding: 5px 10px; border: 0px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-variant-numeric: inherit; font-variant-east-asian: inherit; font-weight: 400; font-stretch: inherit; font-size: 16px; line-height: inherit; font-family: inherit; vertical-align: baseline; cursor: text; counter-reset: list-1 0 list-2 0 list-3 0 list-4 0 list-5 0 list-6 0 list-7 0 list-8 0 list-9 0; background-color: rgb(240, 240, 240); border-radius: 3px; white-space: pre-wrap; color: rgb(34, 34, 34); letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial;">pic = urllib.request.urlretrieve("http://stats.nba.com/media/players/230x185/202391.png")
imread 读取的图像可以被 matplotlib 绘制
head_pic = plt.imread(pic[0])
将球员图片放置于右上角,并设置缩小等级以配合整个图片
img = OffsetImage(head_pic, zoom=0.6)
(x, y) 控制将球员放在你想要放的位置
img.set_offset((540,640))
添加球员图片
fig.gca().add_artist(img)
</pre>
如果在你的电脑上位置不对,记得修改下 offset 的值(不同系统,甚至不同编译器都可能存在差异)。
最终效果:
<tt-image data-tteditor-tag="tteditorTag" contenteditable="false" class="syl1562138615561" data-render-status="finished" data-syl-blot="image" style="box-sizing: border-box; cursor: text; color: rgb(34, 34, 34); font-family: "PingFang SC", "Hiragino Sans GB", "Microsoft YaHei", "WenQuanYi Micro Hei", "Helvetica Neue", Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: pre-wrap; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration-style: initial; text-decoration-color: initial; display: block;"> image<input class="pgc-img-caption-ipt" placeholder="图片描述(最多50字)" value="" style="box-sizing: border-box; outline: 0px; color: rgb(102, 102, 102); position: absolute; left: 187.5px; transform: translateX(-50%); padding: 6px 7px; max-width: 100%; width: 375px; text-align: center; cursor: text; font-size: 12px; line-height: 1.5; background-color: rgb(255, 255, 255); background-image: none; border: 0px solid rgb(217, 217, 217); border-radius: 4px; transition: all 0.2s cubic-bezier(0.645, 0.045, 0.355, 1) 0s;"></tt-image>
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