NBA25岁以下得分后卫排名

用户7010445

发布于 2020-03-03 14:48:26

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发布于 2020-03-03 14:48:26

文章被收录于专栏：小明的数据分析笔记本

今天在知乎看到一个问题：25岁以下前五得分后卫你觉得是谁，怎么排？

意识到样本排序问题

主成分分析可以为每个样本打分，根据分值大小排序

首先第一个问题：25岁以下（包括25岁）得分后卫都有谁？

R语言里 ballr包里有一个函数 NBAPerGameAdvStatistics()可以抓取NBA指定赛季所有球员的统计数据；数据来源是https://www.basketball-reference.com。

抓取2019赛季所有球员的统计数据

> library(ballr)
> players<-NBAPerGameAdvStatistics("2019")
> dim(players)

[1] 708  30

2018-2019赛季NBA总共有708位球员，统计指标中包括30各变量。可以通过 colnames(players)命令查看所有的变量名称。通过 head(players)命令查看前六行数据。

> colnames(players)
[1] "rk"         "player"     "pos"        "age"        "tm"         "g"         
 [7] "mp"         "per"        "tspercent"  "x3par"      "ftr"        "orbpercent"
[13] "drbpercent" "trbpercent" "astpercent" "stlpercent" "blkpercent" "tovpercent"
[19] "usgpercent" "x"          "ows"        "dws"        "ws"         "ws_48"     
[25] "x_2"        "obpm"       "dbpm"       "bpm"        "vorp"       "link"     

> head(players)

  rk       player pos age  tm  g   mp  per tspercent x3par   ftr orbpercent drbpercent trbpercent
1  1 Alex Abrines  SG  25 OKC 31  588  6.3     0.507 0.809 0.083        0.9        7.8        4.2
2  2   Quincy Acy  PF  28 PHO 10  123  2.9     0.379 0.833 0.556        2.7       20.1       11.3
3  3 Jaylen Adams  PG  22 ATL 34  428  7.6     0.474 0.673 0.082        2.6       12.3        7.4
4  4 Steven Adams   C  25 OKC 80 2669 18.5     0.591 0.002 0.361       14.7       14.8       14.7
5  5  Bam Adebayo   C  21 MIA 82 1913 17.9     0.623 0.031 0.465        9.2       24.0       16.6
6  6    Deng Adel  SF  21 CLE 19  194  2.7     0.424 0.639 0.111        1.6        9.6        5.4
  astpercent stlpercent blkpercent tovpercent usgpercent  x  ows dws   ws  ws_48 x_2 obpm dbpm  bpm
1        4.3        1.3        0.9        7.9       12.2 NA  0.1 0.6  0.6  0.053  NA -2.4 -0.9 -3.4
2        8.2        0.4        2.7       15.2        9.2 NA -0.1 0.0 -0.1 -0.022  NA -5.7 -0.3 -5.9
3       19.8        1.5        1.0       19.7       13.5 NA -0.1 0.2  0.1  0.011  NA -3.1 -1.3 -4.4
4        6.6        2.0        2.4       12.6       16.4 NA  5.1 4.0  9.1  0.163  NA  0.6  2.1  2.7
5       14.2        1.8        3.0       17.1       15.8 NA  3.4 3.4  6.8  0.171  NA -0.6  3.6  3.0
6        3.4        0.3        1.8       13.7        9.9 NA -0.2 0.0 -0.2 -0.054  NA -5.3 -2.0 -7.3
  vorp                      link
1 -0.2 /players/a/abrinal01.html
2 -0.1   /players/a/acyqu01.html
3 -0.3 /players/a/adamsja01.html
4  3.2 /players/a/adamsst01.html
5  2.4 /players/a/adebaba01.html
6 -0.3  /players/a/adelde01.html

rank和link变量没有用，在后续分析中可以去掉；还有两列x和x_2都是缺失值也可以在后续的分析中删除。

接下来筛选25岁以下（包括25岁）的得分后卫

> df<-players%>%
  filter(pos == "PG")%>%
  filter(age<=25)%>%
  select(-rk,-link,-x,-x_2)
> dim(df)
[1] 62 26

可以看到25岁以下的得分后卫总共有62位。

接下来通过加标签的方式看下这些人的名字

#构造数据集
df1<-data.frame(A=sort(c(rep(1:8,7),rep(9,6))),
                B=c(rep(1:7,8),1:6),
                Name=gsub(" ","\n",df$player))
head(df1)
#绘图
library(ggplot2)
ggplot(df1,aes(x=A,y=B))+
  geom_text(aes(label=Name))+
  theme_bw()+xlim(0.5,9.5)+ylim(0,8)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank())

放眼望去好像没有太熟悉的名字。

接下来展示比赛场次、上场时间、PER前十位的球员

（数据中竟然没有得分篮板助攻等数据，回头检查才发现 NBAPerGameAdvStatistics()拿到的是高阶统计数据，还有一个函数是 NBAPerGameStatisticsPer100Poss(season=2018)可以拿到常规数据。数据还得整合。）

场次前十

df2<-df%>%
  filter(g >= sort(df$g,decreasing = T)[10])
df2$rank<-1:dim(df2)[1]
df2$Name<-gsub(" ","\n",df2$player)
ggplot(df2,aes(x=reorder(rank,g),y=g,fill=Name))+
  geom_bar(stat="identity")+ylim(0,100)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=g),hjust=2)+
  theme(axis.title = element_blank(),
                   axis.text = element_blank(),
                   axis.ticks = element_blank(),
                   panel.border = element_blank(),
        legend.position = "none")+
    coord_flip()+scale_fill_brewer(palette = "Spectral")+
  labs(title="比赛场次",caption="Author: MingYan")

自己能认出来的吹杨和西蒙斯

出场时间前十

df3<-df%>%
  filter(mp >= sort(df$mp,decreasing = T)[10])
df3$rank<-1:dim(df3)[1]
df3$Name<-gsub(" ","\n",df3$player)
df3
ggplot(df3,aes(x=reorder(rank,mp),y=mp,fill=Name))+
  geom_bar(stat="identity")+ylim(0,2900)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=mp),hjust=2)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank(),
        legend.position = "none")+
  coord_flip()+scale_fill_brewer(palette = "Spectral")+
  labs(title="出场时间（分钟）",caption="Author: MingYan")

PER

df4<-df%>%
  filter(per >= sort(df$per,decreasing = T)[10])
df4$rank<-1:dim(df4)[1]
df4$Name<-gsub(" ","\n",df4$player)
df3
ggplot(df4,aes(x=reorder(rank,per),y=per,fill=Name))+
  geom_bar(stat="identity")+ylim(0,40)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=per),hjust=2)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank(),
        legend.position = "none")+
  coord_flip()+scale_fill_brewer(palette = "Spectral")+
  labs(title="PER",caption="Author: MingYan")

更新20190601

重新构造数据集，选择的变量包括：

Player 球员姓名
Pos 位置
Tm 球队
G 比赛场次
GS 首发场次
MP 场均上场时间
FGA 出手次数
FG% 命中率
3PA 三分出手次数
3PA% 三分命中率
FTA 场均罚球
FT% 罚球命中率
TRB 篮板
AST 助攻
STL 抢断
BLK 盖帽
TOV 失误
PF 犯规
PTS 得分
PER (暂时不知道是什么意思。英文解释：Palyer Efficiency Rating: A measure of per-minute production standardized such that the league average is 15.)
TS% 真实命中率 True Shooting Percentage
WS 赢球贡献值

library(ballr)
help(package="ballr")
players_a<-NBAPerGameStatistics("2019")
players_b<-NBAPerGameAdvStatistics("2019")
dim(players_a)
dim(players_b)
df<-data.frame(player=players_a$player,
               pos=players_a$pos,
               age=players_a$age,
               tm=players_a$tm,
               g=players_a$g,
               gs=players_a$gs,
               mp=players_a$mp,
               fga=players_a$fga,
               fgpercent=players_a$fgpercent,
               three_papercent=players_a$x3ppercent,
               three_pa=players_a$x3pa,
               fta=players_a$fta,
               ftpercent=players_a$ftpercent,
               trb=players_a$trb,
               ast=players_a$ast,
               stl=players_a$stl,
               blk=players_a$blk,
               tov=players_a$tov,
               pts=players_a$pts,
               per=players_b$per,
               ts=players_b$tspercent,
               ws=players_b$ws)
dim(df)

#选择25岁以下的得分后卫
library(dplyr)
df1<-df%>%
  filter(pos == "PG")%>%
  filter(age <= 25)
dim(df1)
head(df)


[1] 62 22

        player pos age  tm  g gs   mp  fga fgpercent three_papercent three_pa fta ftpercent trb
1 Alex Abrines  SG  25 OKC 31  2 19.0  5.1     0.357           0.323      4.1 0.4     0.923 1.5
2   Quincy Acy  PF  28 PHO 10  0 12.3  1.8     0.222           0.133      1.5 1.0     0.700 2.5
3 Jaylen Adams  PG  22 ATL 34  1 12.6  3.2     0.345           0.338      2.2 0.3     0.778 1.8
4 Steven Adams   C  25 OKC 80 80 33.4 10.1     0.595           0.000      0.0 3.7     0.500 9.5
5  Bam Adebayo   C  21 MIA 82 28 23.3  5.9     0.576           0.200      0.2 2.8     0.735 7.3
6    Deng Adel  SF  21 CLE 19  3 10.2  1.9     0.306           0.261      1.2 0.2     1.000 1.0
  ast stl blk tov  pts  per    ts   ws
1 0.6 0.5 0.2 0.5  5.3  6.3 0.507  0.6
2 0.8 0.1 0.4 0.4  1.7  2.9 0.379 -0.1
3 1.9 0.4 0.1 0.8  3.2  7.6 0.474  0.1
4 1.6 1.5 1.0 1.7 13.9 18.5 0.591  9.1
5 2.2 0.9 0.8 1.5  8.9 17.9 0.623  6.8
6 0.3 0.1 0.2 0.3  1.7  2.7 0.424 -0.2

场均得分前十名

df_points<-df1%>%
  filter(pts >= sort(df1$pts,decreasing = T)[10])
df_points
df_points$rank<-1:dim(df_points)[1]
df_points$Name<-gsub(" ","\n",df_points$player)

library(ggplot2)

ggplot(df_points,aes(x=reorder(rank,pts),y=pts,fill=Name))+
  geom_bar(stat="identity")+ylim(0,23)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=pts),hjust=2)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank(),
        legend.position = "none")+
  coord_flip()+scale_fill_manual(values=rainbow(10))+
  labs(title="Points Per Game",caption="Author: MingYan")

25岁以下得分后卫场均得分前十名：1、篮网队的拉塞尔；2、老鹰队的特雷杨；3、掘金队的贾马尔穆雷；4、国王队的福克斯（不确定）；5、76人的西蒙斯；6-9光看英文名还真想不起来是谁；10、是原来小牛队的丹尼尔史密斯吗？

场均篮板前十

df_rebound<-df1%>%
  filter(trb >= sort(df1$trb,decreasing = T)[10])
df_rebound
df_rebound$rank<-1:dim(df_rebound)[1]
df_rebound$Name<-gsub(" ","\n",df_rebound$player)
ggplot(df_rebound,aes(x=reorder(rank,trb),y=trb,fill=Name))+
  geom_bar(stat="identity")+ylim(0,10)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=trb),hjust=2)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank(),
        legend.position = "none")+
  coord_flip()+scale_fill_manual(values=rainbow(10))+
  labs(title="Total Rebounds Per Game",
       caption="Source: https://www.basketball-reference.com\nAuthor: MingYan")

场均篮板前两名：1、76人的西蒙斯；2、湖人队的朗佐鲍尔；

场均助攻前十

library(ggsci)
df_assists<-df1%>%
  filter(ast >= sort(df1$ast,decreasing = T)[10])
df_assists
df_assists$rank<-1:dim(df_assists)[1]
df_assists$Name<-gsub(" ","\n",df_assists$player)
df_assists<-df_assists[-4,]
ggplot(df_assists,aes(x=reorder(rank,ast),y=ast,fill=Name))+
  geom_bar(stat="identity")+ylim(0,10)+
  theme_bw()+geom_text(aes(label=Name),hjust=0)+
  geom_text(aes(label=ast),hjust=2)+
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.border = element_blank(),
        legend.position = "none")+
  coord_flip()+
  labs(title="",
       caption="Source: https://www.basketball-reference.com\nAuthor: MingYan")+
  scale_fill_ucscgb()