Introduction
Welcome to the ballr [baw-ler], as in baller1. This is the R resource for your basketball-reference.com needs.
library(ballr)library(magrittr)
library(ggplot2)
library(janitor)
library(scales)Example 1
Current standings
standings <- NBAStandingsByDate() # "YEAR-MO-DY"
standings## $East
## eastern_conference w l w_lpercent gb pw pl ps_g pa_g
## 1 Boston Celtics 18 3 0.857 — 16 5 103.6 95.8
## 2 Detroit Pistons 12 6 0.667 4.5 10 8 103.7 101.8
## 3 Cleveland Cavaliers 12 7 0.632 5 10 9 110.8 110.1
## 4 Toronto Raptors 12 7 0.632 5 14 5 109.6 102.6
## 5 Philadelphia 76ers 11 7 0.611 5.5 10 8 109.6 107.4
## 6 Indiana Pacers 11 9 0.550 6.5 11 9 108.3 107.2
## 7 New York Knicks 10 9 0.526 7 9 10 104.7 104.7
## 8 Washington Wizards 10 9 0.526 7 12 7 108.3 104.5
## 9 Milwaukee Bucks 9 9 0.500 7.5 7 11 102.4 105.6
## 10 Miami Heat 9 9 0.500 7.5 8 10 100.9 102.5
## 11 Charlotte Hornets 8 11 0.421 9 9 10 105.4 106.1
## 12 Orlando Magic 8 12 0.400 9.5 8 12 107.3 110.5
## 13 Brooklyn Nets 6 12 0.333 10.5 7 11 111.3 114.9
## 14 Atlanta Hawks 4 16 0.200 13.5 6 14 102.2 108.2
## 15 Chicago Bulls 3 14 0.176 13 2 15 94.4 107.3
##
## $West
## western_conference w l w_lpercent gb pw pl ps_g pa_g
## 1 Houston Rockets 15 4 0.789 — 15 4 113.5 103.4
## 2 Golden State Warriors 15 5 0.750 0.5 16 4 117.4 106.2
## 3 San Antonio Spurs 12 7 0.632 3 11 8 100.9 98.1
## 4 Portland Trail Blazers 12 8 0.600 3.5 13 7 103.2 99.2
## 5 Denver Nuggets 11 8 0.579 4 10 9 107.8 106.5
## 6 Minnesota Timberwolves 11 8 0.579 4 9 10 107.5 108.3
## 7 New Orleans Pelicans 11 9 0.550 4.5 10 10 108.3 108.4
## 8 Utah Jazz 9 11 0.450 6.5 11 9 101.3 100.4
## 9 Oklahoma City Thunder 8 11 0.421 7 12 7 102.0 98.1
## 10 Los Angeles Lakers 8 11 0.421 7 8 11 105.3 107.1
## 11 Los Angeles Clippers 7 11 0.389 7.5 9 9 105.1 105.7
## 12 Memphis Grizzlies 7 11 0.389 7.5 8 10 99.4 101.1
## 13 Phoenix Suns 7 13 0.350 8.5 5 15 107.0 115.8
## 14 Sacramento Kings 5 14 0.263 10 3 16 94.3 105.1
## 15 Dallas Mavericks 5 15 0.250 10.5 7 13 98.8 104.0Standings on an arbitrary date
standings <- NBAStandingsByDate("2015-12-31")
standings## $East
## eastern_conference w l w_lpercent gb pw pl ps_g pa_g
## 1 Cleveland Cavaliers* 21 9 0.700 — 20 10 99.7 95.1
## 2 Atlanta Hawks* 21 13 0.618 2 19 15 102.0 100.1
## 3 Toronto Raptors* 20 13 0.606 2.5 20 13 99.8 96.4
## 4 Chicago Bulls 18 12 0.600 3 16 14 101.1 100.0
## 5 Orlando Magic 19 13 0.594 3 19 13 101.0 98.4
## 6 Miami Heat* 18 13 0.581 3.5 17 14 97.0 95.5
## 7 Indiana Pacers* 18 13 0.581 3.5 20 11 102.3 98.5
## 8 Boston Celtics* 18 14 0.563 4 20 12 103.1 99.1
## 9 Charlotte Hornets* 17 14 0.548 4.5 18 13 102.5 99.7
## 10 Detroit Pistons* 17 15 0.531 5 17 15 101.0 100.2
## 11 New York Knicks 15 18 0.455 7.5 15 18 98.0 99.5
## 12 Washington Wizards 14 16 0.467 7 12 18 101.5 104.4
## 13 Milwaukee Bucks 12 21 0.364 10.5 10 23 97.1 103.2
## 14 Brooklyn Nets 9 23 0.281 13 9 23 97.1 103.4
## 15 Philadelphia 76ers 3 31 0.088 20 5 29 92.5 104.4
##
## $West
## western_conference w l w_lpercent gb pw pl ps_g pa_g
## 1 Golden State Warriors* 29 2 0.935 — 26 5 114.1 101.8
## 2 San Antonio Spurs* 28 6 0.824 2.5 30 4 102.0 88.6
## 3 Oklahoma City Thunder* 22 10 0.688 7.5 24 8 108.6 100.4
## 4 Los Angeles Clippers* 20 13 0.606 10 19 14 103.1 100.9
## 5 Dallas Mavericks* 19 13 0.594 10.5 18 14 102.3 100.8
## 6 Memphis Grizzlies* 18 16 0.529 12.5 13 21 96.4 99.4
## 7 Houston Rockets* 16 17 0.485 14 15 18 104.1 105.5
## 8 Portland Trail Blazers* 14 20 0.412 16.5 16 18 101.3 102.0
## 9 Utah Jazz 13 17 0.433 15.5 14 16 96.6 97.3
## 10 Minnesota Timberwolves 12 20 0.375 17.5 14 18 100.4 102.6
## 11 Sacramento Kings 12 20 0.375 17.5 13 19 104.2 107.3
## 12 Denver Nuggets 12 21 0.364 18 11 22 98.9 103.8
## 13 Phoenix Suns 12 22 0.353 18.5 14 20 102.7 105.4
## 14 New Orleans Pelicans 10 21 0.323 19 11 20 102.1 107.0
## 15 Los Angeles Lakers 6 27 0.182 24 6 27 96.8 107.2Example 2
players <- NBAPerGameStatistics()
players## # A tibble: 456 x 31
## rk player pos age tm g gs mp fg fga
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Alex Abrines SG 24 OKC 19 0 15.2 1.4 3.7
## 2 2 Quincy Acy PF 27 BRK 14 0 18.1 1.6 4.8
## 3 3 Steven Adams C 24 OKC 16 16 31.3 5.3 8.3
## 4 4 Bam Adebayo C 20 MIA 10 3 12.3 1.3 2.6
## 5 5 Arron Afflalo SG 32 ORL 15 0 11.0 0.7 2.1
## 6 6 Cole Aldrich C 29 MIN 5 0 2.0 0.0 0.4
## 7 7 LaMarcus Aldridge PF 32 SAS 19 19 32.9 8.4 16.8
## 8 8 Jarrett Allen C 19 BRK 10 0 15.4 1.9 4.1
## 9 9 Tony Allen SG 36 NOP 16 0 13.4 2.3 4.5
## 10 10 Al-Farouq Aminu PF 27 POR 8 8 30.1 3.3 7.5
## # ... with 446 more rows, and 21 more variables: fgpercent <dbl>,
## # x3p <dbl>, x3pa <dbl>, x3ppercent <dbl>, x2p <dbl>, x2pa <dbl>,
## # x2ppercent <dbl>, efgpercent <dbl>, ft <dbl>, fta <dbl>,
## # ftpercent <dbl>, orb <dbl>, drb <dbl>, trb <dbl>, ast <dbl>,
## # stl <dbl>, blk <dbl>, tov <dbl>, pf <dbl>, ps_g <dbl>, link <chr>Example 3
players <- NBAPerGameStatistics(season = 2017)players %>%
dplyr::filter(mp > 20, pos %in% c("SF")) %>%
dplyr::select(player, link) %>%
dplyr::distinct()## # A tibble: 55 x 2
## player link
## <chr> <chr>
## 1 Al-Farouq Aminu /players/a/aminual01.html
## 2 Justin Anderson /players/a/anderju01.html
## 3 Giannis Antetokounmpo /players/a/antetgi01.html
## 4 Carmelo Anthony /players/a/anthoca01.html
## 5 Trevor Ariza /players/a/arizatr01.html
## 6 Matt Barnes /players/b/barnema02.html
## 7 Kent Bazemore /players/b/bazemke01.html
## 8 Bojan Bogdanovic /players/b/bogdabo02.html
## 9 Jimmy Butler /players/b/butleji01.html
## 10 DeMarre Carroll /players/c/carrode01.html
## # ... with 45 more rowsExample 4
players <- NBAPerGameStatisticsPer36Min(season = 2017)players## # A tibble: 595 x 30
## rk player pos age tm g gs mp fg fga
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Alex Abrines SG 23 OKC 68 6 1055 4.6 11.6
## 2 2 Quincy Acy PF 26 TOT 38 1 558 4.5 11.0
## 3 2 Quincy Acy PF 26 DAL 6 0 48 3.7 12.7
## 4 2 Quincy Acy PF 26 BRK 32 1 510 4.6 10.8
## 5 3 Steven Adams C 23 OKC 80 80 2389 5.6 9.9
## 6 4 Arron Afflalo SG 31 SAC 61 45 1580 4.2 9.6
## 7 5 Alexis Ajinca C 28 NOP 39 15 584 5.5 11.0
## 8 6 Cole Aldrich C 28 MIN 62 0 531 3.1 5.8
## 9 7 LaMarcus Aldridge PF 31 SAS 72 72 2335 7.7 16.2
## 10 8 Lavoy Allen PF 27 IND 61 5 871 3.2 6.9
## # ... with 585 more rows, and 20 more variables: fgpercent <dbl>,
## # x3p <dbl>, x3pa <dbl>, x3ppercent <dbl>, x2p <dbl>, x2pa <dbl>,
## # x2ppercent <dbl>, ft <dbl>, fta <dbl>, ftpercent <dbl>, orb <dbl>,
## # drb <dbl>, trb <dbl>, ast <dbl>, stl <dbl>, blk <dbl>, tov <dbl>,
## # pf <dbl>, pts <dbl>, link <chr>Example - Look at Centers and Power Forwards averaging more than 10 MPG
players <- NBAPerGameStatisticsPer36Min(season = 2017) %>%
dplyr::filter(pos %in% c("C", "PF")) %>%
dplyr::top_n(n = 10, pts) %>%
dplyr::select(player, link) %>%
dplyr::distinct()players## # A tibble: 8 x 2
## player link
## <chr> <chr>
## 1 DeMarcus Cousins /players/c/couside01.html
## 2 Anthony Davis /players/d/davisan02.html
## 3 Joel Embiid /players/e/embiijo01.html
## 4 Enes Kanter /players/k/kanteen01.html
## 5 Brook Lopez /players/l/lopezbr01.html
## 6 Boban Marjanovic /players/m/marjabo01.html
## 7 JaVale McGee /players/m/mcgeeja01.html
## 8 Karl-Anthony Towns /players/t/townska01.htmlQuery each player in the list
player_stats <- NBAPlayerPerGameStats(players[1, 2]) %>%
dplyr::filter(!is.na(age)) %>%
dplyr::mutate(player = as.character(players[1, 1]))Append the stats from each player into a df
for(i in 2:dim(players)[1]){
tmp <- NBAPlayerPerGameStats(players[i, 2]) %>%
dplyr::filter(!is.na(age)) %>%
dplyr::mutate(player = as.character(players[i, 1]))
player_stats <- dplyr::bind_rows(player_stats, tmp)
}Plot everything
#player_stats <- clean_names(player_stats)
p <- ggplot2::ggplot(data = player_stats,
aes(x = age, y = efgpercent, group = player))
p + ggplot2::geom_line(alpha = .25) +
ggplot2::geom_point(alpha = .25) +
ggplot2::scale_y_continuous("effective field goal %age", limit = c(0, 1),
labels = percent) +
ggplot2::geom_line(data = dplyr::filter(player_stats, player == "Anthony Davis"),
aes(x = age, y = efgpercent), size = 1, col = "#1f78b4") +
ggplot2::geom_point(data = dplyr::filter(player_stats, player == "Anthony Davis"),
aes(x = age, y = efgpercent), size = 1, col = "#1f78b4") +
ggplot2::geom_line(data = dplyr::filter(player_stats, player == "DeMarcus Cousins"),
aes(x = age, y = efgpercent), size = 1, col = "#33a02c") +
ggplot2::geom_point(data = dplyr::filter(player_stats, player == "DeMarcus Cousins"),
aes(x = age, y = efgpercent), size = 1, col = "#33a02c") +
ggplot2::theme_bw()
Advanced Statistics
per_100 <- NBAPerGameStatisticsPer100Poss(season = 2018)
utils::head(per_100)## rk player pos age tm g gs mp fg fga fgpercent x3p x3pa
## 1 1 Alex Abrines SG 24 OKC 19 0 289 4.5 12.0 0.371 2.6 8.1
## 2 2 Quincy Acy PF 27 BRK 14 0 254 4.0 12.2 0.328 3.4 9.8
## 3 3 Steven Adams C 24 OKC 16 16 500 8.3 13.1 0.636 0.0 0.0
## 4 4 Bam Adebayo C 20 MIA 10 3 123 5.3 10.5 0.500 0.0 0.0
## 5 5 Arron Afflalo SG 32 ORL 15 0 165 2.9 9.0 0.323 1.4 4.6
## 6 6 Cole Aldrich C 29 MIN 5 0 10 0.0 9.8 0.000 0.0 0.0
## x3ppercent x2p x2pa x2ppercent ft fta ftpercent orb drb trb ast stl
## 1 0.319 1.9 3.9 0.478 1.5 1.9 0.818 1.2 3.4 4.6 1.2 1.7
## 2 0.352 0.5 2.4 0.231 2.5 3.1 0.824 1.5 8.9 10.3 2.0 0.9
## 3 NA 8.3 13.1 0.636 3.0 4.2 0.714 6.6 6.3 12.9 1.4 2.2
## 4 NA 5.3 10.5 0.500 3.6 5.3 0.692 5.7 8.9 14.6 0.0 1.6
## 5 0.313 1.4 4.3 0.333 1.4 2.3 0.625 0.3 6.6 6.9 2.6 0.3
## 6 NA 0.0 9.8 0.000 4.9 9.8 0.500 0.0 4.9 4.9 4.9 4.9
## blk tov pf pts x ortg drtg link
## 1 0.2 1.2 5.7 13.0 NA 103 106 /players/a/abrinal01.html
## 2 0.5 2.9 6.2 14.0 NA 94 111 /players/a/acyqu01.html
## 3 1.9 3.0 4.1 19.6 NA 122 101 /players/a/adamsst01.html
## 4 1.6 2.0 5.3 14.1 NA 112 104 /players/a/adebaba01.html
## 5 0.6 1.4 4.0 8.7 NA 88 113 /players/a/afflaar01.html
## 6 0.0 0.0 9.8 4.9 NA 59 105 /players/a/aldrico01.htmlAdvanced Statistics
adv_stats <- NBAPerGameAdvStatistics(season = 2018)
utils::head(adv_stats)## rk player pos age tm g mp per tspercent x3par ftr
## 1 1 Alex Abrines SG 24 OKC 19 289 6.8 0.508 0.671 0.157
## 2 2 Quincy Acy PF 27 BRK 14 254 6.7 0.517 0.806 0.254
## 3 3 Steven Adams C 24 OKC 16 500 20.1 0.658 0.000 0.318
## 4 4 Bam Adebayo C 20 MIA 10 123 13.5 0.552 0.000 0.500
## 5 5 Arron Afflalo SG 32 ORL 15 165 3.8 0.435 0.516 0.258
## 6 6 Cole Aldrich C 29 MIN 5 10 0.8 0.174 0.000 1.000
## orbpercent drbpercent trbpercent astpercent stlpercent blkpercent
## 1 2.7 8.0 5.2 3.5 1.7 0.3
## 2 3.1 20.7 11.6 5.8 0.9 0.9
## 3 14.7 14.5 14.6 4.5 2.2 3.6
## 4 12.8 19.3 16.1 0.0 1.6 2.6
## 5 0.7 14.7 7.8 7.1 0.3 0.9
## 6 0.0 11.7 5.7 12.3 4.9 0.0
## tovpercent usgpercent x ows dws ws ws_48 x_2 obpm dbpm bpm vorp
## 1 8.6 12.3 NA 0.1 0.3 0.4 0.071 NA -2.4 -1.1 -3.5 -0.1
## 2 17.7 14.6 NA -0.1 0.1 0.0 0.009 NA -2.7 -1.0 -3.6 -0.1
## 3 16.6 15.7 NA 1.2 0.9 2.1 0.200 NA 0.9 2.2 3.1 0.6
## 4 13.6 13.3 NA 0.1 0.2 0.3 0.123 NA -4.4 -1.6 -6.0 -0.1
## 5 12.7 10.4 NA -0.1 0.0 -0.1 -0.019 NA -4.5 -1.1 -5.6 -0.2
## 6 0.0 12.5 NA 0.0 0.0 0.0 -0.078 NA -7.4 1.3 -6.1 0.0
## link
## 1 /players/a/abrinal01.html
## 2 /players/a/acyqu01.html
## 3 /players/a/adamsst01.html
## 4 /players/a/adebaba01.html
## 5 /players/a/afflaar01.html
## 6 /players/a/aldrico01.htmlExample
Look at selector gadget for a team’s website, e.g. Denver Nuggets. Suppose you want to find everybody who played for the Nuggets last year, and then their stats. Remember to use Chrome (ugh).
library(rvest)## Loading required package: xml2url <- "http://www.basketball-reference.com/teams/DEN/2017.html"
links <- xml2::read_html(url) %>%
rvest::html_nodes(".center+ .left a") %>%
rvest::html_attr('href')links ## [1] "/players/a/arthuda01.html" "/players/b/bartowi01.html"
## [3] "/players/b/beaslma01.html" "/players/c/chandwi01.html"
## [5] "/players/f/farieke01.html" "/players/g/gallida01.html"
## [7] "/players/g/geeal01.html" "/players/h/harriga01.html"
## [9] "/players/h/hernaju01.html" "/players/h/hibbero01.html"
## [11] "/players/j/jokicni01.html" "/players/m/millemi01.html"
## [13] "/players/m/mudiaem01.html" "/players/m/murraja01.html"
## [15] "/players/n/nelsoja01.html" "/players/n/nurkiju01.html"
## [17] "/players/o/obryajo01.html" "/players/p/plumlma01.html"
## [19] "/players/s/stokeja01.html"