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 Toronto Raptors* 59 23 0.720 — 60 22 111.7 103.9
## 2 Boston Celtics* 55 27 0.671 4 51 31 104.0 100.4
## 3 Philadelphia 76ers* 52 30 0.634 7 53 29 109.8 105.3
## 4 Cleveland Cavaliers* 50 32 0.610 9 43 39 110.9 109.9
## 5 Indiana Pacers* 48 34 0.585 11 45 37 105.6 104.2
## 6 Miami Heat* 44 38 0.537 15 42 40 103.4 102.9
## 7 Milwaukee Bucks* 44 38 0.537 15 40 42 106.5 106.8
## 8 Washington Wizards* 43 39 0.524 16 43 39 106.6 106.0
## 9 Detroit Pistons 39 43 0.476 20 41 41 103.8 103.9
## 10 Charlotte Hornets 36 46 0.439 23 42 40 108.2 108.0
## 11 New York Knicks 29 53 0.354 30 32 50 104.5 108.0
## 12 Brooklyn Nets 28 54 0.341 31 31 51 106.6 110.3
## 13 Chicago Bulls 27 55 0.329 32 23 59 102.9 110.0
## 14 Orlando Magic 25 57 0.305 34 28 54 103.4 108.2
## 15 Atlanta Hawks 24 58 0.293 35 27 55 103.4 108.8
##
## $West
## western_conference w l w_lpercent gb pw pl ps_g pa_g
## 1 Houston Rockets* 65 17 0.793 — 61 21 112.4 103.9
## 2 Golden State Warriors* 58 24 0.707 7 56 26 113.5 107.5
## 3 Portland Trail Blazers* 49 33 0.598 16 48 34 105.6 103.0
## 4 New Orleans Pelicans* 48 34 0.585 17 44 38 111.7 110.4
## 5 Oklahoma City Thunder* 48 34 0.585 17 50 32 107.9 104.4
## 6 Utah Jazz* 48 34 0.585 17 53 29 104.1 99.8
## 7 San Antonio Spurs* 47 35 0.573 18 49 33 102.7 99.8
## 8 Minnesota Timberwolves* 47 35 0.573 18 47 35 109.5 107.3
## 9 Denver Nuggets 46 36 0.561 19 45 37 110.0 108.5
## 10 Los Angeles Clippers 42 40 0.512 23 41 41 109.0 109.0
## 11 Los Angeles Lakers 35 47 0.427 30 37 45 108.1 109.6
## 12 Sacramento Kings 27 55 0.329 38 23 59 98.8 105.8
## 13 Dallas Mavericks 24 58 0.293 41 33 49 102.3 105.4
## 14 Memphis Grizzlies 22 60 0.268 43 25 57 99.3 105.5
## 15 Phoenix Suns 21 61 0.256 44 19 63 103.9 113.3Standings 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: 664 x 31
## rk player pos age tm g gs mp fg fga fgpercent
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Alex … SG 24 OKC 75 8 15.1 1.5 3.9 0.395
## 2 2 Quinc… PF 27 BRK 70 8 19.4 1.9 5.2 0.356
## 3 3 Steve… C 24 OKC 76 76 32.7 5.9 9.4 0.629
## 4 4 Bam A… C 20 MIA 69 19 19.8 2.5 4.9 0.512
## 5 5 Arron… SG 32 ORL 53 3 12.9 1.2 3.1 0.401
## 6 6 Cole … C 29 MIN 21 0 2.3 0.2 0.7 0.333
## 7 7 LaMar… C 32 SAS 75 75 33.5 9.2 18 0.51
## 8 8 Jarre… C 19 BRK 72 31 20 3.3 5.5 0.589
## 9 9 Kadee… PG 25 BOS 18 1 5.9 0.3 1.2 0.273
## 10 10 Tony … SF 36 NOP 22 0 12.4 2 4.1 0.484
## # ... with 654 more rows, and 20 more variables: 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: 51 x 2
## player link
## <chr> <chr>
## 1 Justin Anderson /players/a/anderju01.html
## 2 Giannis Antetokounmpo /players/a/antetgi01.html
## 3 Carmelo Anthony /players/a/anthoca01.html
## 4 Trevor Ariza /players/a/arizatr01.html
## 5 Matt Barnes /players/b/barnema02.html
## 6 Kent Bazemore /players/b/bazemke01.html
## 7 Bojan Bogdanovic /players/b/bogdabo02.html
## 8 Jimmy Butler /players/b/butleji01.html
## 9 DeMarre Carroll /players/c/carrode01.html
## 10 Vince Carter /players/c/cartevi01.html
## # ... with 41 more rowsExample 4
players <- NBAPerGameStatisticsPer36Min(season = 2017)players## # A tibble: 595 x 30
## rk player pos age tm g gs mp fg fga fgpercent
## <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Alex … SG 23 OKC 68 6 1055 4.6 11.6 0.393
## 2 2 Quinc… PF 26 TOT 38 1 558 4.5 11 0.412
## 3 2 Quinc… PF 26 DAL 6 0 48 3.7 12.7 0.294
## 4 2 Quinc… PF 26 BRK 32 1 510 4.6 10.8 0.425
## 5 3 Steve… C 23 OKC 80 80 2389 5.6 9.9 0.571
## 6 4 Arron… SG 31 SAC 61 45 1580 4.2 9.6 0.44
## 7 5 Alexi… C 28 NOP 39 15 584 5.5 11 0.5
## 8 6 Cole … C 28 MIN 62 0 531 3.1 5.8 0.523
## 9 7 LaMar… PF 31 SAS 72 72 2335 7.7 16.2 0.477
## 10 8 Lavoy… PF 27 IND 61 5 871 3.2 6.9 0.458
## # ... with 585 more rows, and 19 more variables: 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 Kevin Durant /players/d/duranke01.html
## 4 Joel Embiid /players/e/embiijo01.html
## 5 Enes Kanter /players/k/kanteen01.html
## 6 Brook Lopez /players/l/lopezbr01.html
## 7 Boban Marjanovic /players/m/marjabo01.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
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 75 8 1134 5.0 12.7 0.395 3.7 9.7
## 2 2 Quincy Acy PF 27 BRK 70 8 1359 4.6 13.0 0.356 3.6 10.4
## 3 3 Steven Adams C 24 OKC 76 76 2487 8.9 14.2 0.629 0.0 0.0
## 4 4 Bam Adebayo C 20 MIA 69 19 1368 6.4 12.5 0.512 0.0 0.3
## 5 5 Arron Afflalo SG 32 ORL 53 3 682 4.7 11.6 0.401 1.9 5.0
## 6 6 Cole Aldrich C 29 MIN 21 0 49 5.1 15.3 0.333 0.0 0.0
## x3ppercent x2p x2pa x2ppercent ft fta ftpercent orb drb trb ast stl
## 1 0.380 1.4 3.1 0.443 1.7 2.0 0.848 1.1 3.9 5.0 1.2 1.7
## 2 0.349 1.0 2.6 0.384 1.8 2.1 0.817 1.4 7.8 9.2 2.0 1.2
## 3 0.000 8.9 14.2 0.631 3.2 5.7 0.559 7.7 6.0 13.7 1.8 1.8
## 4 0.000 6.4 12.2 0.523 4.7 6.6 0.721 4.3 9.7 14.0 3.7 1.2
## 5 0.386 2.7 6.6 0.413 1.6 1.9 0.846 0.3 4.5 4.7 2.2 0.3
## 6 NA 5.1 15.3 0.333 2.0 6.1 0.333 3.1 12.2 15.3 3.1 2.0
## blk tov pf pts x ortg drtg link
## 1 0.4 1.1 5.4 15.4 NA 116 110 /players/a/abrinal01.html
## 2 1.0 2.1 5.3 14.7 NA 99 110 /players/a/acyqu01.html
## 3 1.6 2.6 4.3 21.1 NA 125 107 /players/a/adamsst01.html
## 4 1.5 2.4 5.1 17.5 NA 116 105 /players/a/adebaba01.html
## 5 0.6 1.5 4.0 12.8 NA 98 115 /players/a/afflaar01.html
## 6 1.0 1.0 11.2 12.2 NA 85 107 /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 75 1134 9.0 0.567 0.759 0.158
## 2 2 Quincy Acy PF 27 BRK 70 1359 8.2 0.525 0.800 0.164
## 3 3 Steven Adams C 24 OKC 76 2487 20.6 0.630 0.003 0.402
## 4 4 Bam Adebayo C 20 MIA 69 1368 15.7 0.570 0.021 0.526
## 5 5 Arron Afflalo SG 32 ORL 53 682 5.8 0.516 0.432 0.160
## 6 6 Cole Aldrich C 29 MIN 21 49 6.0 0.340 0.000 0.400
## orbpercent drbpercent trbpercent astpercent stlpercent blkpercent
## 1 2.5 8.9 5.6 3.4 1.7 0.6
## 2 3.1 17.0 10.0 6.0 1.2 1.6
## 3 16.6 13.9 15.3 5.5 1.8 2.8
## 4 9.7 21.6 15.6 11.0 1.2 2.5
## 5 0.6 10.1 5.3 6.2 0.3 1.1
## 6 7.0 28.6 17.7 8.2 2.0 1.8
## tovpercent usgpercent x ows dws ws ws_48 x_2 obpm dbpm bpm vorp
## 1 7.4 12.7 NA 1.3 1.0 2.2 0.094 NA -0.5 -1.7 -2.2 -0.1
## 2 13.3 14.4 NA -0.1 1.1 1.0 0.036 NA -2.0 -0.2 -2.2 -0.1
## 3 13.2 16.7 NA 6.7 3.0 9.7 0.187 NA 2.2 1.1 3.3 3.3
## 4 13.6 15.9 NA 2.3 1.9 4.2 0.148 NA -1.6 1.8 0.2 0.8
## 5 10.8 12.5 NA -0.1 0.2 0.1 0.009 NA -4.1 -1.8 -5.8 -0.7
## 6 5.4 16.8 NA -0.1 0.1 0.0 -0.013 NA -7.0 0.1 -6.9 -0.1
## 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"