Introduction

This report uses data downloaded from ???.

Read the data

Per game

Read the file:

df_per_game <- read_csv(
  './data/2022-12-27-per-player-per-game.csv'
) %>% 
  clean_names() %>% 
  remove_empty(quiet = FALSE) %>% 
  remove_constant(quiet = FALSE)
## value for "which" not specified, defaulting to c("rows", "cols")
## Rows: 812 Columns: 31
## ── Column specification ─────────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (4): Player, Pos, Tm, Player-additional
## dbl (27): Rk, Age, G, GS, MP, FG, FGA, FG%, 3P, 3PA, 3P%, 2P, 2PA, 2P%, eFG%, FT,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## No empty rows to remove.
## 
## No empty columns to remove.
## 
## No constant columns to remove.

First look at the data:

df_per_game %>% glimpse()
## Rows: 812
## Columns: 31
## $ rk                <dbl> 1, 2, 3, 4, 5, 6, 6, 6, 7, 8, 9, 10, 10, 10, 11, 12, 13, …
## $ player            <chr> "Precious Achiuwa", "Steven Adams", "Bam Adebayo", "Santi…
## $ pos               <chr> "C", "C", "C", "PF", "C", "SG", "SG", "SG", "SG", "C", "P…
## $ age               <dbl> 22, 28, 24, 21, 36, 23, 23, 23, 26, 23, 23, 28, 28, 28, 2…
## $ tm                <chr> "TOR", "MEM", "MIA", "MEM", "BRK", "TOT", "NOP", "UTA", "…
## $ g                 <dbl> 73, 76, 56, 32, 47, 65, 50, 15, 66, 56, 54, 16, 3, 13, 69…
## $ gs                <dbl> 28, 75, 56, 0, 12, 21, 19, 2, 61, 56, 1, 6, 0, 6, 11, 67,…
## $ mp                <dbl> 23,6, 26,3, 32,6, 11,3, 22,3, 22,6, 26,3, 9,9, 27,3, 32,3…
## $ fg                <dbl> 3,6, 2,8, 7,3, 1,7, 5,4, 3,9, 4,7, 1,1, 3,9, 6,6, 2,4, 2,…
## $ fga               <dbl> 8,3, 5,1, 13,0, 4,1, 9,7, 10,5, 12,6, 3,2, 8,6, 9,7, 5,4,…
## $ fg_percent        <dbl> 0,439, 0,547, 0,557, 0,402, 0,550, 0,372, 0,375, 0,333, 0…
## $ x3p               <dbl> 0,8, 0,0, 0,0, 0,2, 0,3, 1,6, 1,9, 0,7, 2,4, 0,0, 0,6, 0,…
## $ x3pa              <dbl> 2,1, 0,0, 0,1, 1,5, 1,0, 5,2, 6,1, 2,2, 5,9, 0,2, 2,0, 3,…
## $ x3p_percent       <dbl> 0,359, 0,000, 0,000, 0,125, 0,304, 0,311, 0,311, 0,303, 0…
## $ x2p               <dbl> 2,9, 2,8, 7,3, 1,5, 5,1, 2,3, 2,8, 0,4, 1,5, 6,6, 1,8, 1,…
## $ x2pa              <dbl> 6,1, 5,0, 12,9, 2,6, 8,8, 5,3, 6,5, 1,0, 2,7, 9,6, 3,4, 2…
## $ x2p_percent       <dbl> 0,468, 0,548, 0,562, 0,560, 0,578, 0,433, 0,434, 0,400, 0…
## $ e_fg_percent      <dbl> 0,486, 0,547, 0,557, 0,424, 0,566, 0,449, 0,450, 0,438, 0…
## $ ft                <dbl> 1,1, 1,4, 4,6, 0,6, 1,9, 1,2, 1,4, 0,7, 1,0, 2,9, 0,7, 0,…
## $ fta               <dbl> 1,8, 2,6, 6,1, 1,0, 2,2, 1,7, 1,9, 0,8, 1,1, 4,2, 1,0, 1,…
## $ ft_percent        <dbl> 0,595, 0,543, 0,753, 0,625, 0,873, 0,743, 0,722, 0,917, 0…
## $ orb               <dbl> 2,0, 4,6, 2,4, 1,0, 1,6, 0,6, 0,7, 0,1, 0,5, 3,4, 0,5, 0,…
## $ drb               <dbl> 4,5, 5,4, 7,6, 1,7, 3,9, 2,3, 2,6, 1,5, 2,9, 7,3, 1,4, 2,…
## $ trb               <dbl> 6,5, 10,0, 10,1, 2,7, 5,5, 2,9, 3,3, 1,5, 3,4, 10,8, 1,9,…
## $ ast               <dbl> 1,1, 3,4, 3,4, 0,7, 0,9, 2,4, 2,8, 1,1, 1,5, 1,6, 2,8, 2,…
## $ stl               <dbl> 0,5, 0,9, 1,4, 0,2, 0,3, 0,7, 0,8, 0,3, 0,7, 0,8, 1,3, 0,…
## $ blk               <dbl> 0,6, 0,8, 0,8, 0,3, 1,0, 0,4, 0,4, 0,3, 0,3, 1,3, 0,1, 0,…
## $ tov               <dbl> 1,2, 1,5, 2,6, 0,5, 0,9, 1,4, 1,7, 0,5, 0,7, 1,7, 0,7, 0,…
## $ pf                <dbl> 2,1, 2,0, 3,1, 1,1, 1,7, 1,6, 1,8, 1,0, 1,5, 1,7, 1,4, 1,…
## $ pts               <dbl> 9,1, 6,9, 19,1, 4,1, 12,9, 10,6, 12,8, 3,5, 11,1, 16,1, 6…
## $ player_additional <chr> "achiupr01", "adamsst01", "adebaba01", "aldamsa01", "aldr…

Advanced

Read the file:

df_advanced <- read_csv(
  './data/2022-12-27-per-player-advanced.csv'
) %>% 
  clean_names() %>% 
  remove_empty(quiet = FALSE) %>% 
  remove_constant(quiet = FALSE)
## value for "which" not specified, defaulting to c("rows", "cols")
## New names:
## Rows: 812 Columns: 30
## ── Column specification
## ───────────────────────────────────────────────────────────── Delimiter: "," chr
## (4): Player, Pos, Tm, Player-additional dbl (24): Rk, Age, G, MP, PER, TS%, 3PAr,
## FTr, ORB%, DRB%, TRB%, AST%, STL%, BLK%... lgl (2): ...20, ...25
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ Specify
## the column types or set `show_col_types = FALSE` to quiet this message.
## No empty rows to remove.
## Removing 2 empty columns of 30 columns total (Removed: x20, x25).
## No constant columns to remove.
## • `` -> `...20`
## • `` -> `...25`

First look at the data:

df_advanced %>% glimpse()
## Rows: 812
## Columns: 28
## $ rk                <dbl> 1, 2, 3, 4, 5, 6, 6, 6, 7, 8, 9, 10, 10, 10, 11, 12, 13, …
## $ player            <chr> "Precious Achiuwa", "Steven Adams", "Bam Adebayo", "Santi…
## $ pos               <chr> "C", "C", "C", "PF", "C", "SG", "SG", "SG", "SG", "C", "P…
## $ age               <dbl> 22, 28, 24, 21, 36, 23, 23, 23, 26, 23, 23, 28, 28, 28, 2…
## $ tm                <chr> "TOR", "MEM", "MIA", "MEM", "BRK", "TOT", "NOP", "UTA", "…
## $ g                 <dbl> 73, 76, 56, 32, 47, 65, 50, 15, 66, 56, 54, 16, 3, 13, 69…
## $ mp                <dbl> 1725, 1999, 1825, 360, 1050, 1466, 1317, 149, 1805, 1809,…
## $ per               <dbl> 12,7, 17,6, 21,8, 10,2, 19,6, 10,5, 10,5, 10,2, 12,7, 23,…
## $ ts_percent        <dbl> 0,503, 0,560, 0,608, 0,452, 0,604, 0,475, 0,474, 0,497, 0…
## $ x3p_ar            <dbl> 0,259, 0,003, 0,008, 0,364, 0,100, 0,497, 0,483, 0,688, 0…
## $ f_tr              <dbl> 0,217, 0,518, 0,466, 0,242, 0,223, 0,160, 0,153, 0,250, 0…
## $ orb_percent       <dbl> 8,7, 17,9, 8,7, 9,4, 7,8, 2,7, 3,0, 0,8, 1,9, 12,0, 3,2, …
## $ drb_percent       <dbl> 21,7, 22,0, 26,1, 16,1, 18,7, 11,5, 11,0, 15,6, 10,9, 24,…
## $ trb_percent       <dbl> 14,9, 19,9, 17,5, 12,6, 13,4, 7,1, 6,9, 8,5, 6,5, 18,4, 6…
## $ ast_percent       <dbl> 6,9, 16,1, 17,5, 7,7, 6,3, 16,1, 16,1, 15,5, 7,6, 8,2, 26…
## $ stl_percent       <dbl> 1,1, 1,6, 2,2, 0,8, 0,6, 1,5, 1,5, 1,7, 1,2, 1,2, 4,2, 1,…
## $ blk_percent       <dbl> 2,3, 2,7, 2,6, 2,5, 4,0, 1,5, 1,4, 2,4, 1,0, 3,7, 0,8, 1,…
## $ tov_percent       <dbl> 11,3, 19,6, 14,4, 9,9, 8,0, 11,3, 11,2, 13,1, 6,7, 12,7, …
## $ usg_percent       <dbl> 18,5, 12,0, 25,0, 18,4, 22,4, 24,1, 24,8, 17,9, 15,2, 18,…
## $ ows               <dbl> 0,4, 3,8, 3,6, -0,1, 2,1, -1,1, -1,1, 0,0, 2,8, 5,4, 1,0,…
## $ dws               <dbl> 2,1, 3,0, 3,5, 0,4, 1,0, 1,1, 0,9, 0,2, 1,4, 3,0, 1,1, 0,…
## $ ws                <dbl> 2,5, 6,8, 7,2, 0,3, 3,1, 0,1, -0,1, 0,2, 4,2, 8,5, 2,1, 0…
## $ ws_48             <dbl> 0,070, 0,163, 0,188, 0,044, 0,141, 0,003, -0,005, 0,070, …
## $ obpm              <dbl> -2,0, 1,0, 1,7, -4,2, 1,3, -1,8, -1,7, -2,9, 0,6, 2,7, -0…
## $ dbpm              <dbl> -0,6, 1,0, 2,1, -1,5, -0,6, -1,1, -1,3, 1,2, -0,2, 1,2, 2…
## $ bpm               <dbl> -2,6, 2,0, 3,8, -5,7, 0,7, -2,9, -3,0, -1,7, 0,4, 3,9, 1,…
## $ vorp              <dbl> -0,2, 2,0, 2,7, -0,3, 0,7, -0,3, -0,3, 0,0, 1,1, 2,7, 0,8…
## $ player_additional <chr> "achiupr01", "adamsst01", "adebaba01", "aldamsa01", "aldr…

Data dictionary

Per game

  • Rk: Rank.

  • Player: Player name.

  • Pos: Position.

  • Age: Player’s age on February 1 of the season.

  • Tm: Team.

  • G: Games.

  • GS: Games Started.

  • MP: Minutes Played Per Game.

  • FG: Field Goals Per Game.

  • FGA: Field Goal Attempts Per Game.

  • FG%: Field Goal Percentage.

  • 3P: 3-Point Field Goals Per Game.

  • 3PA: 3-Point Field Goal Attempts Per Game.

  • 3P%: 3-Point Field Goal Percentage.

  • 2P: 2-Point Field Goals Per Game.

  • 2PA: 2-Point Field Goal Attempts Per Game.

  • 2P%: 2-Point Field Goal Percentage.

  • eFG%: Effective Field Goal Percentage. Adjusts for a 3-point field goal being worth one more point than a 2-point field goal.

  • FT: Free Throws Per Game.

  • FTA: Free Throw Attempts Per Game.

  • FT%: Free Throw Percentage.

  • ORB: Offensive Rebounds Per Game.

  • DRB: Defensive Rebounds Per Game.

  • TRB: Total Rebounds Per Game.

  • AST: Assists Per Game.

  • STL: Steals Per Game.

  • BLK: Blocks Per Game.

  • TOV: Turnovers Per Game.

  • PF: Personal Fouls Per Game.

  • PTS: Points Per Game.

  • Player-additional: Unique identifier.

Advanced

  • Rk: Rank.

  • Player: Player name.

  • Pos: Position.

  • Age: Player’s age on February 1 of the season.

  • Tm: Team.

  • G: Games.

  • MP: Minutes Played.

  • PER: Player Efficiency Rating. A measure of per-minute production standardized such that the league average is 15.

  • TS%: True Shooting Percentage. A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws.

  • 3PAr: 3-Point Attempt Rate. Percentage of FG Attempts from 3-Point Range.

  • FTr: Free Throw Attempt Rate. Number of FT Attempts Per FG Attempt.

  • ORB%: Offensive Rebound Percentage. An estimate of the percentage of available offensive rebounds a player grabbed while they were on the floor.

  • DRB%: Defensive Rebound Percentage. An estimate of the percentage of available defensive rebounds a player grabbed while they were on the floor.

  • TRB%: Total Rebound Percentage. An estimate of the percentage of available rebounds a player grabbed while they were on the floor.

  • AST%: Assist Percentage. An estimate of the percentage of teammate field goals a player assisted while they were on the floor.

  • STL%: Steal Percentage. An estimate of the percentage of opponent possessions that end with a steal by the player while they were on the floor.

  • BLK%: Block Percentage. An estimate of the percentage of opponent two-point field goal attempts blocked by the player while they were on the floor.

  • TOV%: Turnover Percentage. An estimate of turnovers committed per 100 plays.

  • USG%: Usage Percentage. An estimate of the percentage of team plays used by a player while they were on the floor.

  • OWS: Offensive Win Shares. An estimate of the number of wins contributed by a player due to offense.

  • DWS: Defensive Win Shares. An estimate of the number of wins contributed by a player due to defense.

  • WS: Win Shares. An estimate of the number of wins contributed by a player.

  • WS/48: Win Shares Per 48 Minutes. An estimate of the number of wins contributed by a player per 48 minutes (league average is approximately .100).

  • OBPM: Offensive Box Plus/Minus. A box score estimate of the offensive points per 100 possessions a player contributed above a league-average player, translated to an average team.

  • DBPM: Defensive Box Plus/Minus. A box score estimate of the defensive points per 100 possessions a player contributed above a league-average player, translated to an average team.

  • BPM: Box Plus/Minus. A box score estimate of the points per 100 possessions a player contributed above a league-average player, translated to an average team.

  • VORP: Value over Replacement Player. A box score estimate of the points per 100 TEAM possessions that a player contributed above a replacement-level (-2.0) player, translated to an average team and prorated to an 82-game season. Multiply by 2.70 to convert to wins over replacement.

  • Player-additional: Unique identifier.

Cleaning the data

Per game

  • Delete rk column:

    df_per_game <- 
  df_per_game %>% 
    select(-rk)

  • Rename all columns:

    original_names <- names(df_per_game)
new_names <- c(
  'player',
  'position',
  'age',
  'team',
  'games',
  'games_started',
  'minutes_played_average',
  'goals_scored',
  'goal_attempts',
  'goal_pct',
  'goals_scored_3p',
  'goal_attempts_3p',
  'goal_pct_3p',
  'goals_scored_2p',
  'goal_attempts_2p',
  'goal_pct_2p',
  'goals_effective_pct',
  'free_throws_scored',
  'free_throw_attempts',
  'free_throw_pct',
  'rebounds_offense',
  'rebounds_defense',
  'rebounds_total',
  'assists',
  'steals',
  'blocks',
  'turnovers',
  'fouls',
  'points_scored',
  'player_id'
)

names(new_names) <- original_names

paste(
  names(new_names), 
  new_names, 
  sep = ' -> ', 
  collapse = '\n'
) %>% 
  cat()
    ## player -> player
## pos -> position
## age -> age
## tm -> team
## g -> games
## gs -> games_started
## mp -> minutes_played_average
## fg -> goals_scored
## fga -> goal_attempts
## fg_percent -> goal_pct
## x3p -> goals_scored_3p
## x3pa -> goal_attempts_3p
## x3p_percent -> goal_pct_3p
## x2p -> goals_scored_2p
## x2pa -> goal_attempts_2p
## x2p_percent -> goal_pct_2p
## e_fg_percent -> goals_effective_pct
## ft -> free_throws_scored
## fta -> free_throw_attempts
## ft_percent -> free_throw_pct
## orb -> rebounds_offense
## drb -> rebounds_defense
## trb -> rebounds_total
## ast -> assists
## stl -> steals
## blk -> blocks
## tov -> turnovers
## pf -> fouls
## pts -> points_scored
## player_additional -> player_id
    df_per_game <- df_per_game %>% 
  rename_with(
    function(x) { new_names[x] }
  )

  • Find players that appear more than once and keep only the row that has the totals:

    dupes <- df_per_game %>% 
  get_dupes(player_id)
    dupes %>% 
  select(player, team, dupe_count) %>% 
  arrange(desc(dupe_count))

    For these players, we keep only the row for the totals (TOT):

    df_per_game <- df_per_game %>% 
  keep_only_totals(dupes)

Per game: summary 

df_per_game %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
Variable Stats / Values Freqs (% of Valid) Graph Missing
player [character]
1. Aaron Gordon
2. Aaron Henry
3. Aaron Holiday
4. Aaron Nesmith
5. Aaron Wiggins
6. Abdel Nader
7. Ade Murkey
8. Admiral Schofield
9. Ahmad Caver
10. Al Horford
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)
position [character]
1. SG
2. SF
3. PG
4. C
5. PF
6. SF-SG
7. SG-PG
8. SG-SF
9. PG-SG
10. C-PF
[ 3 others ]
153 ( 25,3% )
116 ( 19,2% )
107 ( 17,7% )
106 ( 17,5% )
106 ( 17,5% )
4 ( 0,7% )
4 ( 0,7% )
3 ( 0,5% )
2 ( 0,3% )
1 ( 0,2% )
3 ( 0,5% )
0 (0,0%)
age [numeric]
Mean (sd) : 25,7 (4,1)
min ≤ med ≤ max:
19 ≤ 25 ≤ 41
IQR (CV) : 5 (0,2)
 22 distinct values 0 (0,0%)
team [character]
1. TOT
2. OKC
3. DET
4. MEM
5. NYK
6. ATL
7. CLE
8. ORL
9. PHO
10. CHI
[ 21 others ]
97 ( 16,0% )
25 ( 4,1% )
22 ( 3,6% )
22 ( 3,6% )
20 ( 3,3% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
18 ( 3,0% )
325 ( 53,7% )
0 (0,0%)
games [numeric]
Mean (sd) : 43 (25,8)
min ≤ med ≤ max:
1 ≤ 48 ≤ 82
IQR (CV) : 49 (0,6)
 82 distinct values 0 (0,0%)
games_started [numeric]
Mean (sd) : 20,3 (25,8)
min ≤ med ≤ max:
0 ≤ 7 ≤ 82
IQR (CV) : 35 (1,3)
 78 distinct values 0 (0,0%)
minutes_played_average [numeric]
Mean (sd) : 18,9 (9,7)
min ≤ med ≤ max:
1 ≤ 18,1 ≤ 43,5
IQR (CV) : 16,2 (0,5)
 280 distinct values 0 (0,0%)
goals_scored [numeric]
Mean (sd) : 3 (2,3)
min ≤ med ≤ max:
0 ≤ 2,6 ≤ 11,4
IQR (CV) : 2,8 (0,8)
 95 distinct values 0 (0,0%)
goal_attempts [numeric]
Mean (sd) : 6,7 (4,8)
min ≤ med ≤ max:
0 ≤ 5,5 ≤ 21,8
IQR (CV) : 5,9 (0,7)
 170 distinct values 0 (0,0%)
goal_pct [numeric]
Mean (sd) : 0,4 (0,1)
min ≤ med ≤ max:
0 ≤ 0,4 ≤ 1
IQR (CV) : 0,1 (0,3)
 259 distinct values 9 (1,5%)
goals_scored_3p [numeric]
Mean (sd) : 0,9 (0,9)
min ≤ med ≤ max:
0 ≤ 0,7 ≤ 4,5
IQR (CV) : 1,2 (0,9)
 38 distinct values 0 (0,0%)
goal_attempts_3p [numeric]
Mean (sd) : 2,7 (2,3)
min ≤ med ≤ max:
0 ≤ 2,1 ≤ 11,7
IQR (CV) : 3,2 (0,8)
 91 distinct values 0 (0,0%)
goal_pct_3p [numeric]
Mean (sd) : 0,3 (0,1)
min ≤ med ≤ max:
0 ≤ 0,3 ≤ 1
IQR (CV) : 0,1 (0,4)
 192 distinct values 44 (7,3%)
goals_scored_2p [numeric]
Mean (sd) : 2,1 (1,8)
min ≤ med ≤ max:
0 ≤ 1,7 ≤ 9,5
IQR (CV) : 2,1 (0,9)
 74 distinct values 0 (0,0%)
goal_attempts_2p [numeric]
Mean (sd) : 4 (3,3)
min ≤ med ≤ max:
0 ≤ 3,2 ≤ 18,3
IQR (CV) : 3,9 (0,8)
 132 distinct values 0 (0,0%)
goal_pct_2p [numeric]
Mean (sd) : 0,5 (0,2)
min ≤ med ≤ max:
0 ≤ 0,5 ≤ 1
IQR (CV) : 0,1 (0,3)
 260 distinct values 16 (2,6%)
goals_effective_pct [numeric]
Mean (sd) : 0,5 (0,1)
min ≤ med ≤ max:
0 ≤ 0,5 ≤ 1
IQR (CV) : 0,1 (0,3)
 248 distinct values 9 (1,5%)
free_throws_scored [numeric]
Mean (sd) : 1,3 (1,3)
min ≤ med ≤ max:
0 ≤ 0,9 ≤ 9,6
IQR (CV) : 1,2 (1)
 59 distinct values 0 (0,0%)
free_throw_attempts [numeric]
Mean (sd) : 1,6 (1,6)
min ≤ med ≤ max:
0 ≤ 1,2 ≤ 11,8
IQR (CV) : 1,6 (1)
 69 distinct values 0 (0,0%)
free_throw_pct [numeric]
Mean (sd) : 0,7 (0,1)
min ≤ med ≤ max:
0 ≤ 0,8 ≤ 1
IQR (CV) : 0,2 (0,2)
 247 distinct values 59 (9,8%)
rebounds_offense [numeric]
Mean (sd) : 0,8 (0,7)
min ≤ med ≤ max:
0 ≤ 0,6 ≤ 4,6
IQR (CV) : 0,8 (0,9)
 39 distinct values 0 (0,0%)
rebounds_defense [numeric]
Mean (sd) : 2,6 (1,8)
min ≤ med ≤ max:
0 ≤ 2,4 ≤ 11
IQR (CV) : 2,2 (0,7)
 80 distinct values 0 (0,0%)
rebounds_total [numeric]
Mean (sd) : 3,4 (2,4)
min ≤ med ≤ max:
0 ≤ 3 ≤ 14,7
IQR (CV) : 2,7 (0,7)
 106 distinct values 0 (0,0%)
assists [numeric]
Mean (sd) : 1,9 (1,8)
min ≤ med ≤ max:
0 ≤ 1,2 ≤ 10,8
IQR (CV) : 1,9 (1)
 77 distinct values 0 (0,0%)
steals [numeric]
Mean (sd) : 0,6 (0,4)
min ≤ med ≤ max:
0 ≤ 0,5 ≤ 2,3
IQR (CV) : 0,6 (0,7)
 23 distinct values 0 (0,0%)
blocks [numeric]
Mean (sd) : 0,4 (0,4)
min ≤ med ≤ max:
0 ≤ 0,3 ≤ 2,8
IQR (CV) : 0,4 (1)
 23 distinct values 0 (0,0%)
turnovers [numeric]
Mean (sd) : 1 (0,8)
min ≤ med ≤ max:
0 ≤ 0,8 ≤ 4,5
IQR (CV) : 0,8 (0,8)
 43 distinct values 0 (0,0%)
fouls [numeric]
Mean (sd) : 1,6 (0,8)
min ≤ med ≤ max:
0 ≤ 1,6 ≤ 3,8
IQR (CV) : 1,2 (0,5)
 39 distinct values 0 (0,0%)
points_scored [numeric]
Mean (sd) : 8,2 (6,3)
min ≤ med ≤ max:
0 ≤ 6,9 ≤ 30,6
IQR (CV) : 7,6 (0,8)
 202 distinct values 0 (0,0%)
player_id [character]
1. achiupr01
2. adamsst01
3. adebaba01
4. aldamsa01
5. aldrila01
6. alexani01
7. allengr01
8. allenja01
9. alvarjo01
10. anderju01
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)

Generated by summarytools 1.0.1 (R version 4.2.2)
2022-12-30

Per game: notes

  • Approximately \(16\%\) of players played for two or more teams during the season.

  • On average, a player plays only \(19\) minutes per game.

  • Columns that should contain percentages actually contain proportions.

  • All columns that contain proportions have some missing values. Fortunately, all of them will be discarded before we build the model, as they are derived — therefore, perfectly correlated with other columns.

  • What are the “other” positions?

    df_per_game %>% 
  count(position, sort = TRUE) %>% 
  slice_tail(n = 3)

Per game: more cleaning

  • Turn proportions into percentages:

    df_per_game <- df_per_game %>% 
  mutate(
    across(contains('_pct'), ~ .x * 100)
  )

  • Why are some percentages NA?

    df_per_game %>% 
  filter(is.na(goal_pct)) %>% 
  select(starts_with('goal'))

    Because they are \(0\%\) of \(0\).

    I will replace the NAs with zeroes:

    df_per_game <- df_per_game %>% 
  mutate(
    across(
      contains('_pct'),
      ~ if_else(
        is.na(.x), 0, .x
      )
    )
  )

Per game: another summary 

df_per_game %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
Variable Stats / Values Freqs (% of Valid) Graph Missing
player [character]
1. Aaron Gordon
2. Aaron Henry
3. Aaron Holiday
4. Aaron Nesmith
5. Aaron Wiggins
6. Abdel Nader
7. Ade Murkey
8. Admiral Schofield
9. Ahmad Caver
10. Al Horford
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)
position [character]
1. SG
2. SF
3. PG
4. C
5. PF
6. SF-SG
7. SG-PG
8. SG-SF
9. PG-SG
10. C-PF
[ 3 others ]
153 ( 25,3% )
116 ( 19,2% )
107 ( 17,7% )
106 ( 17,5% )
106 ( 17,5% )
4 ( 0,7% )
4 ( 0,7% )
3 ( 0,5% )
2 ( 0,3% )
1 ( 0,2% )
3 ( 0,5% )
0 (0,0%)
age [numeric]
Mean (sd) : 25,7 (4,1)
min ≤ med ≤ max:
19 ≤ 25 ≤ 41
IQR (CV) : 5 (0,2)
 22 distinct values 0 (0,0%)
team [character]
1. TOT
2. OKC
3. DET
4. MEM
5. NYK
6. ATL
7. CLE
8. ORL
9. PHO
10. CHI
[ 21 others ]
97 ( 16,0% )
25 ( 4,1% )
22 ( 3,6% )
22 ( 3,6% )
20 ( 3,3% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
18 ( 3,0% )
325 ( 53,7% )
0 (0,0%)
games [numeric]
Mean (sd) : 43 (25,8)
min ≤ med ≤ max:
1 ≤ 48 ≤ 82
IQR (CV) : 49 (0,6)
 82 distinct values 0 (0,0%)
games_started [numeric]
Mean (sd) : 20,3 (25,8)
min ≤ med ≤ max:
0 ≤ 7 ≤ 82
IQR (CV) : 35 (1,3)
 78 distinct values 0 (0,0%)
minutes_played_average [numeric]
Mean (sd) : 18,9 (9,7)
min ≤ med ≤ max:
1 ≤ 18,1 ≤ 43,5
IQR (CV) : 16,2 (0,5)
 280 distinct values 0 (0,0%)
goals_scored [numeric]
Mean (sd) : 3 (2,3)
min ≤ med ≤ max:
0 ≤ 2,6 ≤ 11,4
IQR (CV) : 2,8 (0,8)
 95 distinct values 0 (0,0%)
goal_attempts [numeric]
Mean (sd) : 6,7 (4,8)
min ≤ med ≤ max:
0 ≤ 5,5 ≤ 21,8
IQR (CV) : 5,9 (0,7)
 170 distinct values 0 (0,0%)
goal_pct [numeric]
Mean (sd) : 43,2 (14,2)
min ≤ med ≤ max:
0 ≤ 44,1 ≤ 100
IQR (CV) : 10,3 (0,3)
 259 distinct values 0 (0,0%)
goals_scored_3p [numeric]
Mean (sd) : 0,9 (0,9)
min ≤ med ≤ max:
0 ≤ 0,7 ≤ 4,5
IQR (CV) : 1,2 (0,9)
 38 distinct values 0 (0,0%)
goal_attempts_3p [numeric]
Mean (sd) : 2,7 (2,3)
min ≤ med ≤ max:
0 ≤ 2,1 ≤ 11,7
IQR (CV) : 3,2 (0,8)
 91 distinct values 0 (0,0%)
goal_pct_3p [numeric]
Mean (sd) : 28,3 (14,3)
min ≤ med ≤ max:
0 ≤ 32,7 ≤ 100
IQR (CV) : 13,2 (0,5)
 192 distinct values 0 (0,0%)
goals_scored_2p [numeric]
Mean (sd) : 2,1 (1,8)
min ≤ med ≤ max:
0 ≤ 1,7 ≤ 9,5
IQR (CV) : 2,1 (0,9)
 74 distinct values 0 (0,0%)
goal_attempts_2p [numeric]
Mean (sd) : 4 (3,3)
min ≤ med ≤ max:
0 ≤ 3,2 ≤ 18,3
IQR (CV) : 3,9 (0,8)
 132 distinct values 0 (0,0%)
goal_pct_2p [numeric]
Mean (sd) : 49,7 (17,1)
min ≤ med ≤ max:
0 ≤ 51,9 ≤ 100
IQR (CV) : 12,4 (0,3)
 260 distinct values 0 (0,0%)
goals_effective_pct [numeric]
Mean (sd) : 49,4 (14,7)
min ≤ med ≤ max:
0 ≤ 52 ≤ 100
IQR (CV) : 9,5 (0,3)
 248 distinct values 0 (0,0%)
free_throws_scored [numeric]
Mean (sd) : 1,3 (1,3)
min ≤ med ≤ max:
0 ≤ 0,9 ≤ 9,6
IQR (CV) : 1,2 (1)
 59 distinct values 0 (0,0%)
free_throw_attempts [numeric]
Mean (sd) : 1,6 (1,6)
min ≤ med ≤ max:
0 ≤ 1,2 ≤ 11,8
IQR (CV) : 1,6 (1)
 69 distinct values 0 (0,0%)
free_throw_pct [numeric]
Mean (sd) : 67,5 (26,3)
min ≤ med ≤ max:
0 ≤ 75,3 ≤ 100
IQR (CV) : 21,1 (0,4)
 247 distinct values 0 (0,0%)
rebounds_offense [numeric]
Mean (sd) : 0,8 (0,7)
min ≤ med ≤ max:
0 ≤ 0,6 ≤ 4,6
IQR (CV) : 0,8 (0,9)
 39 distinct values 0 (0,0%)
rebounds_defense [numeric]
Mean (sd) : 2,6 (1,8)
min ≤ med ≤ max:
0 ≤ 2,4 ≤ 11
IQR (CV) : 2,2 (0,7)
 80 distinct values 0 (0,0%)
rebounds_total [numeric]
Mean (sd) : 3,4 (2,4)
min ≤ med ≤ max:
0 ≤ 3 ≤ 14,7
IQR (CV) : 2,7 (0,7)
 106 distinct values 0 (0,0%)
assists [numeric]
Mean (sd) : 1,9 (1,8)
min ≤ med ≤ max:
0 ≤ 1,2 ≤ 10,8
IQR (CV) : 1,9 (1)
 77 distinct values 0 (0,0%)
steals [numeric]
Mean (sd) : 0,6 (0,4)
min ≤ med ≤ max:
0 ≤ 0,5 ≤ 2,3
IQR (CV) : 0,6 (0,7)
 23 distinct values 0 (0,0%)
blocks [numeric]
Mean (sd) : 0,4 (0,4)
min ≤ med ≤ max:
0 ≤ 0,3 ≤ 2,8
IQR (CV) : 0,4 (1)
 23 distinct values 0 (0,0%)
turnovers [numeric]
Mean (sd) : 1 (0,8)
min ≤ med ≤ max:
0 ≤ 0,8 ≤ 4,5
IQR (CV) : 0,8 (0,8)
 43 distinct values 0 (0,0%)
fouls [numeric]
Mean (sd) : 1,6 (0,8)
min ≤ med ≤ max:
0 ≤ 1,6 ≤ 3,8
IQR (CV) : 1,2 (0,5)
 39 distinct values 0 (0,0%)
points_scored [numeric]
Mean (sd) : 8,2 (6,3)
min ≤ med ≤ max:
0 ≤ 6,9 ≤ 30,6
IQR (CV) : 7,6 (0,8)
 202 distinct values 0 (0,0%)
player_id [character]
1. achiupr01
2. adamsst01
3. adebaba01
4. aldamsa01
5. aldrila01
6. alexani01
7. allengr01
8. allenja01
9. alvarjo01
10. anderju01
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)

Generated by summarytools 1.0.1 (R version 4.2.2)
2022-12-30

Advanced

  • Delete rk column:

    df_advanced <- 
  df_advanced %>% 
    select(-rk)

  • Rename all columns:

    original_names <- names(df_advanced)
new_names <- c(
  'player',
  'position',
  'age',
  'team',
  'games',
  'minutes_played_total',
  'efficiency',
  'true_shooting_pct',
  'attempt_rate_3p',
  'attempt_rate_free_throw',
  'rebound_offense_pct',
  'rebound_defense_pct',
  'rebound_total_pct',
  'assist_pct',
  'steal_pct',
  'block_pct',
  'turnover_pct',
  'usage_pct',
  'win_shares_offense',
  'win_shares_defense',
  'win_shares',
  'win_shares_48',
  'plus_minus_offense',
  'plus_minus_defense',
  'plus_minus',
  'value_over_replacement',
  'player_id'
)

names(new_names) <- original_names

paste(
  names(new_names), 
  new_names, 
  sep = ' -> ', 
  collapse = '\n'
) %>% 
  cat()
    ## player -> player
## pos -> position
## age -> age
## tm -> team
## g -> games
## mp -> minutes_played_total
## per -> efficiency
## ts_percent -> true_shooting_pct
## x3p_ar -> attempt_rate_3p
## f_tr -> attempt_rate_free_throw
## orb_percent -> rebound_offense_pct
## drb_percent -> rebound_defense_pct
## trb_percent -> rebound_total_pct
## ast_percent -> assist_pct
## stl_percent -> steal_pct
## blk_percent -> block_pct
## tov_percent -> turnover_pct
## usg_percent -> usage_pct
## ows -> win_shares_offense
## dws -> win_shares_defense
## ws -> win_shares
## ws_48 -> win_shares_48
## obpm -> plus_minus_offense
## dbpm -> plus_minus_defense
## bpm -> plus_minus
## vorp -> value_over_replacement
## player_additional -> player_id
    df_advanced <- df_advanced %>% 
  rename_with(
    function(x) { new_names[x] }
  )

  • Find players that appear more than once and keep only the row that has the totals:

    dupes <- df_advanced %>% 
  get_dupes(player_id)
    dupes %>% 
  select(player, team, dupe_count) %>% 
  arrange(desc(dupe_count))

    For these players, we keep only the row for the totals (TOT):

    df_advanced <- df_advanced %>% 
  keep_only_totals(dupes)

Advanced: summary 

df_advanced %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
Variable Stats / Values Freqs (% of Valid) Graph Missing
player [character]
1. Aaron Gordon
2. Aaron Henry
3. Aaron Holiday
4. Aaron Nesmith
5. Aaron Wiggins
6. Abdel Nader
7. Ade Murkey
8. Admiral Schofield
9. Ahmad Caver
10. Al Horford
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)
position [character]
1. SG
2. SF
3. PG
4. C
5. PF
6. SF-SG
7. SG-PG
8. SG-SF
9. PG-SG
10. C-PF
[ 3 others ]
153 ( 25,3% )
116 ( 19,2% )
107 ( 17,7% )
106 ( 17,5% )
106 ( 17,5% )
4 ( 0,7% )
4 ( 0,7% )
3 ( 0,5% )
2 ( 0,3% )
1 ( 0,2% )
3 ( 0,5% )
0 (0,0%)
age [numeric]
Mean (sd) : 25,7 (4,1)
min ≤ med ≤ max:
19 ≤ 25 ≤ 41
IQR (CV) : 5 (0,2)
 22 distinct values 0 (0,0%)
team [character]
1. TOT
2. OKC
3. DET
4. MEM
5. NYK
6. ATL
7. CLE
8. ORL
9. PHO
10. CHI
[ 21 others ]
97 ( 16,0% )
25 ( 4,1% )
22 ( 3,6% )
22 ( 3,6% )
20 ( 3,3% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
18 ( 3,0% )
325 ( 53,7% )
0 (0,0%)
games [numeric]
Mean (sd) : 43 (25,8)
min ≤ med ≤ max:
1 ≤ 48 ≤ 82
IQR (CV) : 49 (0,6)
 82 distinct values 0 (0,0%)
minutes_played_total [numeric]
Mean (sd) : 981,4 (809,5)
min ≤ med ≤ max:
1 ≤ 883 ≤ 2854
IQR (CV) : 1474 (0,8)
 501 distinct values 0 (0,0%)
efficiency [numeric]
Mean (sd) : 12,5 (7,8)
min ≤ med ≤ max:
-45,2 ≤ 12,7 ≤ 76,2
IQR (CV) : 6,4 (0,6)
 228 distinct values 0 (0,0%)
true_shooting_pct [numeric]
Mean (sd) : 0,5 (0,1)
min ≤ med ≤ max:
0 ≤ 0,6 ≤ 1
IQR (CV) : 0,1 (0,3)
 261 distinct values 8 (1,3%)
attempt_rate_3p [numeric]
Mean (sd) : 0,4 (0,2)
min ≤ med ≤ max:
0 ≤ 0,4 ≤ 1
IQR (CV) : 0,3 (0,6)
 366 distinct values 9 (1,5%)
attempt_rate_free_throw [numeric]
Mean (sd) : 0,2 (0,2)
min ≤ med ≤ max:
0 ≤ 0,2 ≤ 2
IQR (CV) : 0,2 (0,8)
 312 distinct values 9 (1,5%)
rebound_offense_pct [numeric]
Mean (sd) : 5,2 (5,2)
min ≤ med ≤ max:
0 ≤ 3,5 ≤ 56,6
IQR (CV) : 5,1 (1)
 152 distinct values 0 (0,0%)
rebound_defense_pct [numeric]
Mean (sd) : 14,7 (6,8)
min ≤ med ≤ max:
0 ≤ 13,4 ≤ 40,6
IQR (CV) : 8 (0,5)
 213 distinct values 0 (0,0%)
rebound_total_pct [numeric]
Mean (sd) : 9,9 (5,2)
min ≤ med ≤ max:
0 ≤ 8,7 ≤ 44,7
IQR (CV) : 6,3 (0,5)
 177 distinct values 0 (0,0%)
assist_pct [numeric]
Mean (sd) : 13 (9,2)
min ≤ med ≤ max:
0 ≤ 10,5 ≤ 64,1
IQR (CV) : 10,1 (0,7)
 239 distinct values 0 (0,0%)
steal_pct [numeric]
Mean (sd) : 1,7 (1,9)
min ≤ med ≤ max:
0 ≤ 1,4 ≤ 25
IQR (CV) : 0,9 (1,1)
 55 distinct values 0 (0,0%)
block_pct [numeric]
Mean (sd) : 1,8 (2)
min ≤ med ≤ max:
0 ≤ 1,3 ≤ 30
IQR (CV) : 1,8 (1,1)
 72 distinct values 0 (0,0%)
turnover_pct [numeric]
Mean (sd) : 12,2 (6,7)
min ≤ med ≤ max:
0 ≤ 11,6 ≤ 58,1
IQR (CV) : 5,6 (0,6)
 176 distinct values 8 (1,3%)
usage_pct [numeric]
Mean (sd) : 18,2 (6,4)
min ≤ med ≤ max:
0 ≤ 17,5 ≤ 54,6
IQR (CV) : 7 (0,3)
 213 distinct values 0 (0,0%)
win_shares_offense [numeric]
Mean (sd) : 1,1 (1,7)
min ≤ med ≤ max:
-3 ≤ 0,5 ≤ 10,8
IQR (CV) : 1,8 (1,6)
 78 distinct values 0 (0,0%)
win_shares_defense [numeric]
Mean (sd) : 1 (1)
min ≤ med ≤ max:
-0,1 ≤ 0,7 ≤ 4,6
IQR (CV) : 1,3 (1)
 45 distinct values 0 (0,0%)
win_shares [numeric]
Mean (sd) : 2,1 (2,5)
min ≤ med ≤ max:
-1,6 ≤ 1,2 ≤ 15,2
IQR (CV) : 3,1 (1,2)
 98 distinct values 0 (0,0%)
win_shares_48 [numeric]
Mean (sd) : 0,1 (0,1)
min ≤ med ≤ max:
-1,2 ≤ 0,1 ≤ 1,2
IQR (CV) : 0,1 (1,9)
 263 distinct values 0 (0,0%)
plus_minus_offense [numeric]
Mean (sd) : -1,8 (4,5)
min ≤ med ≤ max:
-33,9 ≤ -1,4 ≤ 31
IQR (CV) : 3,6 (-2,5)
 160 distinct values 0 (0,0%)
plus_minus_defense [numeric]
Mean (sd) : -0,2 (2,2)
min ≤ med ≤ max:
-14,5 ≤ -0,2 ≤ 11,8
IQR (CV) : 1,9 (-8,9)
 97 distinct values 0 (0,0%)
plus_minus [numeric]
Mean (sd) : -2,1 (5,7)
min ≤ med ≤ max:
-42,6 ≤ -1,5 ≤ 36,9
IQR (CV) : 4,4 (-2,7)
 180 distinct values 0 (0,0%)
value_over_replacement [numeric]
Mean (sd) : 0,5 (1,1)
min ≤ med ≤ max:
-1,2 ≤ 0 ≤ 9,8
IQR (CV) : 0,9 (2,3)
 57 distinct values 0 (0,0%)
player_id [character]
1. achiupr01
2. adamsst01
3. adebaba01
4. aldamsa01
5. aldrila01
6. alexani01
7. allengr01
8. allenja01
9. alvarjo01
10. anderju01
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)

Generated by summarytools 1.0.1 (R version 4.2.2)
2022-12-30

Advanced: notes

  • Here, percentages are really percentages (between \(0\) and \(100\)), except for true_shooting_pct.

  • Offensive rebounds are harder than defensive rebounds.

  • Most statistics here have right-skewed distributions.

  • What are the “other” positions?

    df_advanced %>% 
  count(position, sort = TRUE) %>% 
  slice_tail(n = 3)

  • Are the players in the advanced data frame the same as in the per game data frame?

    identical(
  sort(df_per_game$player_id),
  sort(df_advanced$player_id)
)
    ## [1] TRUE

Advanced: more cleaning

  • Fix true_shooting_pct:

    df_advanced <- df_advanced %>% 
  mutate(true_shooting_pct = 100 * true_shooting_pct)

  • Replace the NAs with zeroes:

    df_advanced <- df_advanced %>% 
  mutate(
    across(
      .fns = 
        ~ if (is.numeric(.x)) {
            if_else(is.na(.x), 0, .x)
        } else { .x }
    )
  )

Advanced: another summary 

df_advanced %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
Variable Stats / Values Freqs (% of Valid) Graph Missing
player [character]
1. Aaron Gordon
2. Aaron Henry
3. Aaron Holiday
4. Aaron Nesmith
5. Aaron Wiggins
6. Abdel Nader
7. Ade Murkey
8. Admiral Schofield
9. Ahmad Caver
10. Al Horford
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)
position [character]
1. SG
2. SF
3. PG
4. C
5. PF
6. SF-SG
7. SG-PG
8. SG-SF
9. PG-SG
10. C-PF
[ 3 others ]
153 ( 25,3% )
116 ( 19,2% )
107 ( 17,7% )
106 ( 17,5% )
106 ( 17,5% )
4 ( 0,7% )
4 ( 0,7% )
3 ( 0,5% )
2 ( 0,3% )
1 ( 0,2% )
3 ( 0,5% )
0 (0,0%)
age [numeric]
Mean (sd) : 25,7 (4,1)
min ≤ med ≤ max:
19 ≤ 25 ≤ 41
IQR (CV) : 5 (0,2)
 22 distinct values 0 (0,0%)
team [character]
1. TOT
2. OKC
3. DET
4. MEM
5. NYK
6. ATL
7. CLE
8. ORL
9. PHO
10. CHI
[ 21 others ]
97 ( 16,0% )
25 ( 4,1% )
22 ( 3,6% )
22 ( 3,6% )
20 ( 3,3% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
19 ( 3,1% )
18 ( 3,0% )
325 ( 53,7% )
0 (0,0%)
games [numeric]
Mean (sd) : 43 (25,8)
min ≤ med ≤ max:
1 ≤ 48 ≤ 82
IQR (CV) : 49 (0,6)
 82 distinct values 0 (0,0%)
minutes_played_total [numeric]
Mean (sd) : 981,4 (809,5)
min ≤ med ≤ max:
1 ≤ 883 ≤ 2854
IQR (CV) : 1474 (0,8)
 501 distinct values 0 (0,0%)
efficiency [numeric]
Mean (sd) : 12,5 (7,8)
min ≤ med ≤ max:
-45,2 ≤ 12,7 ≤ 76,2
IQR (CV) : 6,4 (0,6)
 228 distinct values 0 (0,0%)
true_shooting_pct [numeric]
Mean (sd) : 52,4 (14,5)
min ≤ med ≤ max:
0 ≤ 54,9 ≤ 100
IQR (CV) : 9,3 (0,3)
 261 distinct values 0 (0,0%)
attempt_rate_3p [numeric]
Mean (sd) : 0,4 (0,2)
min ≤ med ≤ max:
0 ≤ 0,4 ≤ 1
IQR (CV) : 0,3 (0,6)
 366 distinct values 0 (0,0%)
attempt_rate_free_throw [numeric]
Mean (sd) : 0,2 (0,2)
min ≤ med ≤ max:
0 ≤ 0,2 ≤ 2
IQR (CV) : 0,2 (0,8)
 312 distinct values 0 (0,0%)
rebound_offense_pct [numeric]
Mean (sd) : 5,2 (5,2)
min ≤ med ≤ max:
0 ≤ 3,5 ≤ 56,6
IQR (CV) : 5,1 (1)
 152 distinct values 0 (0,0%)
rebound_defense_pct [numeric]
Mean (sd) : 14,7 (6,8)
min ≤ med ≤ max:
0 ≤ 13,4 ≤ 40,6
IQR (CV) : 8 (0,5)
 213 distinct values 0 (0,0%)
rebound_total_pct [numeric]
Mean (sd) : 9,9 (5,2)
min ≤ med ≤ max:
0 ≤ 8,7 ≤ 44,7
IQR (CV) : 6,3 (0,5)
 177 distinct values 0 (0,0%)
assist_pct [numeric]
Mean (sd) : 13 (9,2)
min ≤ med ≤ max:
0 ≤ 10,5 ≤ 64,1
IQR (CV) : 10,1 (0,7)
 239 distinct values 0 (0,0%)
steal_pct [numeric]
Mean (sd) : 1,7 (1,9)
min ≤ med ≤ max:
0 ≤ 1,4 ≤ 25
IQR (CV) : 0,9 (1,1)
 55 distinct values 0 (0,0%)
block_pct [numeric]
Mean (sd) : 1,8 (2)
min ≤ med ≤ max:
0 ≤ 1,3 ≤ 30
IQR (CV) : 1,8 (1,1)
 72 distinct values 0 (0,0%)
turnover_pct [numeric]
Mean (sd) : 12 (6,8)
min ≤ med ≤ max:
0 ≤ 11,5 ≤ 58,1
IQR (CV) : 5,8 (0,6)
 176 distinct values 0 (0,0%)
usage_pct [numeric]
Mean (sd) : 18,2 (6,4)
min ≤ med ≤ max:
0 ≤ 17,5 ≤ 54,6
IQR (CV) : 7 (0,3)
 213 distinct values 0 (0,0%)
win_shares_offense [numeric]
Mean (sd) : 1,1 (1,7)
min ≤ med ≤ max:
-3 ≤ 0,5 ≤ 10,8
IQR (CV) : 1,8 (1,6)
 78 distinct values 0 (0,0%)
win_shares_defense [numeric]
Mean (sd) : 1 (1)
min ≤ med ≤ max:
-0,1 ≤ 0,7 ≤ 4,6
IQR (CV) : 1,3 (1)
 45 distinct values 0 (0,0%)
win_shares [numeric]
Mean (sd) : 2,1 (2,5)
min ≤ med ≤ max:
-1,6 ≤ 1,2 ≤ 15,2
IQR (CV) : 3,1 (1,2)
 98 distinct values 0 (0,0%)
win_shares_48 [numeric]
Mean (sd) : 0,1 (0,1)
min ≤ med ≤ max:
-1,2 ≤ 0,1 ≤ 1,2
IQR (CV) : 0,1 (1,9)
 263 distinct values 0 (0,0%)
plus_minus_offense [numeric]
Mean (sd) : -1,8 (4,5)
min ≤ med ≤ max:
-33,9 ≤ -1,4 ≤ 31
IQR (CV) : 3,6 (-2,5)
 160 distinct values 0 (0,0%)
plus_minus_defense [numeric]
Mean (sd) : -0,2 (2,2)
min ≤ med ≤ max:
-14,5 ≤ -0,2 ≤ 11,8
IQR (CV) : 1,9 (-8,9)
 97 distinct values 0 (0,0%)
plus_minus [numeric]
Mean (sd) : -2,1 (5,7)
min ≤ med ≤ max:
-42,6 ≤ -1,5 ≤ 36,9
IQR (CV) : 4,4 (-2,7)
 180 distinct values 0 (0,0%)
value_over_replacement [numeric]
Mean (sd) : 0,5 (1,1)
min ≤ med ≤ max:
-1,2 ≤ 0 ≤ 9,8
IQR (CV) : 0,9 (2,3)
 57 distinct values 0 (0,0%)
player_id [character]
1. achiupr01
2. adamsst01
3. adebaba01
4. aldamsa01
5. aldrila01
6. alexani01
7. allengr01
8. allenja01
9. alvarjo01
10. anderju01
[ 595 others ]
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
1 ( 0,2% )
595 ( 98,3% )
0 (0,0%)

Generated by summarytools 1.0.1 (R version 4.2.2)
2022-12-30

---
title: 'Linear regression: defense ~ .'
subtitle: '2021--2022'
author: 'fnaufel, romulor3'
email: 'https://fnaufel.github.io/'
date: '   (v. `r format(Sys.Date(), "%d/%m/%Y")`)'
lang: 'en'

output: 
  # To install these output formats, run
  #   install.packages("devtools")
  #   devtools::install_github("fnaufel/fnaufelRmd")
  fnaufelRmd::html_report:
    number_sections: no
    code_folding: 'hide'
  fnaufelRmd::pdf_report:
    number_sections: no

# LaTeX / pdf options
#
# For more options, see
# https://pandoc.org/MANUAL.html#variables-for-latex
documentclass: article
classoption: '11pt'
geometry: 'margin=1in'
bibliography: []
biblio-style: apalike
link-citations: yes
---

```{r setup, include=FALSE}
# The next command configures MANY things and loads quite a few packages.
# 
# If you want to see what's being done, execute 
# 
#   cat(
#     system.file(
#       "rmarkdown/resources/R/_common_report.R", 
#       package = "fnaufelRmd"
#     )
#   )
# 
# to find out the location of the file. Then open the file.
# 
# If you want to change the configuration, copy the file, edit it, and
# source it instead of the package file. 
# 
# Or simply write your commands here in this code chunk.

source(
  system.file(
    "rmarkdown/resources/R/_common_report.R",
    package = "fnaufelRmd"
  )
)

# Summarytools options
st_options(
  lang = 'en',
)

library(janitor)

source('R/utils.R')
```


# Introduction

This report uses data downloaded from ???.


# Read the data

## Per game

Read the file:

```{r cache=TRUE}
df_per_game <- read_csv(
  './data/2022-12-27-per-player-per-game.csv'
) %>% 
  clean_names() %>% 
  remove_empty(quiet = FALSE) %>% 
  remove_constant(quiet = FALSE)
```

First look at the data:

```{r}
df_per_game %>% glimpse()
```


## Advanced

Read the file:

```{r cache=TRUE}
df_advanced <- read_csv(
  './data/2022-12-27-per-player-advanced.csv'
) %>% 
  clean_names() %>% 
  remove_empty(quiet = FALSE) %>% 
  remove_constant(quiet = FALSE)
```

First look at the data:

```{r}
df_advanced %>% glimpse()
```


# Data dictionary

## Per game

- **Rk:** Rank.

- **Player:** Player name.

- **Pos:** Position.

- **Age:** Player's age on February 1 of the season.

- **Tm:** Team.

- **G:** Games.

- **GS:** Games Started.

- **MP:** Minutes Played Per Game.

- **FG:** Field Goals Per Game.

- **FGA:** Field Goal Attempts Per Game.

- **FG%:** Field Goal Percentage.

- **3P:** 3-Point Field Goals Per Game.

- **3PA:** 3-Point Field Goal Attempts Per Game.

- **3P%:** 3-Point Field Goal Percentage.

- **2P:** 2-Point Field Goals Per Game.

- **2PA:** 2-Point Field Goal Attempts Per Game.

- **2P%:** 2-Point Field Goal Percentage.

- **eFG%:** Effective Field Goal Percentage. Adjusts for a 3-point field goal being worth one more point than a 2-point field goal.

- **FT:** Free Throws Per Game.

- **FTA:** Free Throw Attempts Per Game.

- **FT%:** Free Throw Percentage.

- **ORB:** Offensive Rebounds Per Game.

- **DRB:** Defensive Rebounds Per Game.

- **TRB:** Total Rebounds Per Game.

- **AST:** Assists Per Game.

- **STL:** Steals Per Game.

- **BLK:** Blocks Per Game.

- **TOV:** Turnovers Per Game.

- **PF:** Personal Fouls Per Game.

- **PTS:** Points Per Game.

- **Player-additional:** Unique identifier.


## Advanced

- **Rk:** Rank.

- **Player:** Player name.

- **Pos:** Position.

- **Age:** Player's age on February 1 of the season.

- **Tm:** Team.

- **G:** Games.

- **MP:** Minutes Played.

- **PER:** Player Efficiency Rating. A measure of per-minute production [standardized such that the league average is 15]{.hl}.

- **TS%:** True Shooting Percentage. A measure of shooting efficiency that takes into account 2-point field goals, 3-point field goals, and free throws.

- **3PAr:** 3-Point Attempt Rate. Percentage of FG Attempts from 3-Point Range.

- **FTr:** Free Throw Attempt Rate. Number of FT Attempts [Per FG Attempt]{.hl}.

- **ORB%:** Offensive Rebound Percentage. An estimate of the percentage of available offensive rebounds a player grabbed while they were on the floor.

- **DRB%:** Defensive Rebound Percentage. An estimate of the percentage of available defensive rebounds a player grabbed while they were on the floor.

- **TRB%:** Total Rebound Percentage. An estimate of the percentage of available rebounds a player grabbed while they were on the floor.

- **AST%:** Assist Percentage. An estimate of the percentage of teammate field goals a player assisted while they were on the floor.

- **STL%:** Steal Percentage. An estimate of the percentage of opponent possessions that end with a steal by the player while they were on the floor.

- **BLK%:** Block Percentage. An estimate of the percentage of opponent [two-point]{.hl} field goal attempts blocked by the player while they were on the floor.

- **TOV%:** Turnover Percentage. An estimate of turnovers committed per 100 plays.

- **USG%:** Usage Percentage. An estimate of the percentage of team plays used by a player while they were on the floor.

- **OWS:** Offensive Win Shares. An estimate of the number of wins contributed by a player due to offense.

- **DWS:** Defensive Win Shares. An estimate of the number of wins contributed by a player due to defense.

- **WS:** Win Shares. An estimate of the number of wins contributed by a player.

- **WS/48:** Win Shares Per 48 Minutes. An estimate of the number of wins contributed by a player per 48 minutes (league average is approximately .100).

- **OBPM:** Offensive Box Plus/Minus. A box score estimate of the offensive points per 100 possessions a player contributed above a league-average player, translated to an average team.

- **DBPM:** Defensive Box Plus/Minus. A box score estimate of the defensive points per 100 possessions a player contributed above a league-average player, translated to an average team.

- **BPM:** Box Plus/Minus. A box score estimate of the points per 100 possessions a player contributed above a league-average player, translated to an average team.

- **VORP:** Value over Replacement Player. A box score estimate of the points per 100 TEAM possessions that a player contributed above a replacement-level (-2.0) player, translated to an average team and prorated to an 82-game season. Multiply by 2.70 to convert to wins over replacement.

- **Player-additional:** Unique identifier.


# Cleaning the data

## Per game

* Delete `rk` column:

    ```{r}
    df_per_game <- 
      df_per_game %>% 
        select(-rk)
    ```

* Rename all columns:

    ```{r}
    original_names <- names(df_per_game)
    new_names <- c(
      'player',
      'position',
      'age',
      'team',
      'games',
      'games_started',
      'minutes_played_average',
      'goals_scored',
      'goal_attempts',
      'goal_pct',
      'goals_scored_3p',
      'goal_attempts_3p',
      'goal_pct_3p',
      'goals_scored_2p',
      'goal_attempts_2p',
      'goal_pct_2p',
      'goals_effective_pct',
      'free_throws_scored',
      'free_throw_attempts',
      'free_throw_pct',
      'rebounds_offense',
      'rebounds_defense',
      'rebounds_total',
      'assists',
      'steals',
      'blocks',
      'turnovers',
      'fouls',
      'points_scored',
      'player_id'
    )
    
    names(new_names) <- original_names
    
    paste(
      names(new_names), 
      new_names, 
      sep = ' -> ', 
      collapse = '\n'
    ) %>% 
      cat()
    ```

    ```{r}
    df_per_game <- df_per_game %>% 
      rename_with(
        function(x) { new_names[x] }
      )
    ```

* Find players that appear more than once and keep only the row that has the totals:

    ```{r}
    dupes <- df_per_game %>% 
      get_dupes(player_id)
    ```

  ```{r}
  dupes %>% 
    select(player, team, dupe_count) %>% 
    arrange(desc(dupe_count))
  ```

  For these players, we keep only the row for the totals (`TOT`):

    ```{r}
    df_per_game <- df_per_game %>% 
      keep_only_totals(dupes)
    ```


### Per game: summary

```{r}
df_per_game %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
```


### Per game: notes

* Approximately $16\%$ of players played for two or more teams during the season.

* On average, a player plays only $19$ minutes per game.

* Columns that should contain percentages actually contain proportions.

* All columns that contain proportions have some missing values. Fortunately, all of them will be discarded before we build the model, as they are derived --- therefore, perfectly correlated with other columns.

* What are the "other" positions?

  ```{r}
  df_per_game %>% 
    count(position, sort = TRUE) %>% 
    slice_tail(n = 3)
  ```


### Per game: more cleaning

* Turn proportions into percentages:

    ```{r}
    df_per_game <- df_per_game %>% 
      mutate(
        across(contains('_pct'), ~ .x * 100)
      )
    ```

* Why are some percentages `NA`?

  ```{r}
  df_per_game %>% 
    filter(is.na(goal_pct)) %>% 
    select(starts_with('goal'))
  ```
  
  Because they are $0\%$ of $0$.
  
  I will replace the `NA`s with zeroes:
  
    ```{r}
    df_per_game <- df_per_game %>% 
      mutate(
        across(
          contains('_pct'),
          ~ if_else(
            is.na(.x), 0, .x
          )
        )
      )
    ```
  

### Per game: another summary

```{r}
df_per_game %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
```


## Advanced

* Delete `rk` column:

    ```{r}
    df_advanced <- 
      df_advanced %>% 
        select(-rk)
    ```

* Rename all columns:

    ```{r}
    original_names <- names(df_advanced)
    new_names <- c(
      'player',
      'position',
      'age',
      'team',
      'games',
      'minutes_played_total',
      'efficiency',
      'true_shooting_pct',
      'attempt_rate_3p',
      'attempt_rate_free_throw',
      'rebound_offense_pct',
      'rebound_defense_pct',
      'rebound_total_pct',
      'assist_pct',
      'steal_pct',
      'block_pct',
      'turnover_pct',
      'usage_pct',
      'win_shares_offense',
      'win_shares_defense',
      'win_shares',
      'win_shares_48',
      'plus_minus_offense',
      'plus_minus_defense',
      'plus_minus',
      'value_over_replacement',
      'player_id'
    )
    
    names(new_names) <- original_names
    
    paste(
      names(new_names), 
      new_names, 
      sep = ' -> ', 
      collapse = '\n'
    ) %>% 
      cat()
    ```

    ```{r}
    df_advanced <- df_advanced %>% 
      rename_with(
        function(x) { new_names[x] }
      )
    ```

* Find players that appear more than once and keep only the row that has the totals:

    ```{r}
    dupes <- df_advanced %>% 
      get_dupes(player_id)
    ```

  ```{r}
  dupes %>% 
    select(player, team, dupe_count) %>% 
    arrange(desc(dupe_count))
  ```

  For these players, we keep only the row for the totals (`TOT`):

    ```{r}
    df_advanced <- df_advanced %>% 
      keep_only_totals(dupes)
    ```


### Advanced: summary

```{r}
df_advanced %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
```


### Advanced: notes

* Here, percentages are really percentages (between $0$ and $100$), [except for `true_shooting_pct`]{.hl}. 

* Offensive rebounds are harder than defensive rebounds.

* Most statistics here have right-skewed distributions.

* What are the "other" positions?

  ```{r}
  df_advanced %>% 
    count(position, sort = TRUE) %>% 
    slice_tail(n = 3)
  ```

* Are the players in the `advanced` data frame the same as in the `per game` data frame?

  ```{r}
  identical(
    sort(df_per_game$player_id),
    sort(df_advanced$player_id)
  )
  ```

### Advanced: more cleaning

* Fix `true_shooting_pct`:

    ```{r}
    df_advanced <- df_advanced %>% 
      mutate(true_shooting_pct = 100 * true_shooting_pct)
    ```

* Replace the `NA`s with zeroes:
  
    ```{r}
    df_advanced <- df_advanced %>% 
      mutate(
        across(
          .fns = 
            ~ if (is.numeric(.x)) {
                if_else(is.na(.x), 0, .x)
            } else { .x }
        )
      )
    ```


### Advanced: another summary

```{r}
df_advanced %>% 
  dfSummary(silent = TRUE) %>% 
  print(method = 'render')
```


