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NBA Leaderboard

Saiem Gilani
@saiemgilani @saiemgilani

Source: vignettes/leaderboard-nba.Rmd
leaderboard-nba.Rmd
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Updated 2026-10-12: the offseason, so this is the final 2025-26 regular season; the 2026-27 season has no games yet.

This page is rebuilt every week with the site. It builds the standings with offensive, defensive and net ratings, charts net rating with logos and ranks the scoring leaders with their headshots, for the latest NBA season with games: the season to date from October to April, the final regular season once it is over. Data: hoopR’s ESPN box scores, read from release files (no stats.nba.com calls).

Picking the season

NBA seasons are named by the year they end (2025-26 is 2026) and tip off in October, so until then the calendar points at the season that ended in June. For a season with no file yet hoopR::load_nba_team_box() warns and returns no rows, and a new season’s file holds only preseason games at first, so the helper treats “no regular-season games” as “not published” and the page steps back one season. The status line says what the numbers cover.

library(sdvplotR)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
library(gt)

today <- Sys.Date()
day_label <- function(day, month = "%b") sub(" 0", " ", format(as.Date(day), paste(month, "%d"))) # "Oct 5"
# hoopR names a season by the year it ends (2025-26 is 2026); it tips off in
# October, so until then the calendar points at the season that ended in June
current <- as.integer(format(today, "%Y")) + (format(today, "%m") >= "10")
label <- function(season) sprintf("%d-%02d", season - 1, season %% 100)

# A season's team box scores, or NULL when it has no regular-season games yet:
# load_nba_team_box() warns and returns no rows for a season it has no file for,
# and a new season's file holds only preseason games at first
team_games <- function(season) {
  box <- tryCatch(suppressWarnings(hoopR::load_nba_team_box(seasons = season)), error = function(e) NULL)
  if (NROW(box) == 0 || !any(box$season_type == 2)) NULL else box
}

season <- current
box <- team_games(season)
if (is.null(box)) {
  season <- current - 1
  box <- team_games(season)
}
stopifnot(!is.null(box))

last_game <- max(box$game_date)
playoffs <- filter(box, season_type == 3)
if (season < current) {
  status <- sprintf(
    "the offseason, so this is the final %s regular season; the %s season has no games yet.",
    label(season), label(current)
  )
  through <- "final regular season"
} else if (nrow(playoffs) == 0) {
  status <- sprintf("the %s season through %s.", label(season), day_label(last_game, "%B"))
  through <- paste("through", day_label(last_game))
} else if (as.numeric(today - max(playoffs$game_date)) <= 10) {
  status <- sprintf("the final %s regular season; the playoffs are under way.", label(season))
  through <- "final regular season"
} else {
  status <- sprintf("the offseason, so this is the final %s regular season.", label(season))
  through <- "final regular season"
}

ESPN files the All-Star Game and the in-season cup final as regular-season games, though neither counts in the standings. The schedule marks them in type_abbreviation (ALLSTAR, CC), so keep the standard games (STD) only. Possessions are the usual box-score estimate, averaged with the opponent’s.

standard <- hoopR::load_nba_schedule(seasons = season) |>
  filter(season_type == 2, type_abbreviation == "STD")
stopifnot(identical(class(box$game_id), class(standard$game_id)))

games <- box |>
  filter(season_type == 2, game_id %in% standard$game_id) |>
  mutate(poss = field_goals_attempted - offensive_rebounds + total_turnovers +
    0.44 * free_throws_attempted) |>
  group_by(game_id) |>
  mutate(poss = mean(poss, na.rm = TRUE)) |> # one side's box can miss a count; the other covers it
  ungroup()

teams <- sdv_teams("nba") |>
  transmute(team = abbr, name = short_name, conference = sub(" Conference$", "", conference))
ratings <- games |>
  arrange(game_date, game_id) |>
  group_by(team = team_abbreviation) |>
  summarise(
    w = sum(team_winner), l = sum(!team_winner),
    l10 = sum(tail(team_winner, 10)),
    ortg = 100 * sum(team_score) / sum(poss),
    drtg = 100 * sum(opponent_team_score) / sum(poss),
    .groups = "drop"
  ) |>
  mutate(pct = w / (w + l), net = ortg - drtg) |>
  inner_join(teams, by = "team") |>
  arrange(desc(pct), desc(net), team) # the team breaks a tie, so an unchanged week renders the same
head(ratings)
#> # A tibble: 6 × 10
#>   team      w     l   l10  ortg  drtg   pct   net name    conference
#>   <chr> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>   <chr>     
#> 1 OKC      64    18     7  116.  105. 0.780 10.9  Thunder Western   
#> 2 SA       62    20     8  117.  109. 0.756  8.08 Spurs   Western   
#> 3 DET      60    22     8  114.  106. 0.732  7.93 Pistons Eastern   
#> 4 BOS      56    26     8  117.  109. 0.683  7.85 Celtics Eastern   
#> 5 DEN      54    28    10  120.  115. 0.659  5.04 Nuggets Western   
#> 6 NY       53    29     6  117.  110. 0.646  6.34 Knicks  Eastern

1. Standings with net rating

Each conference ordered by winning percentage, with every team’s points scored and allowed per 100 possessions. gt_sdv_logos() draws the logos from ESPN’s abbreviations, which are sdvplotR’s keys for the NBA.

standings <- ratings |>
  group_by(conference) |>
  mutate(seed = row_number()) |>
  ungroup() |>
  mutate(
    record = paste(w, l, sep = "-"),
    last10 = paste(l10, pmin(10, w + l) - l10, sep = "-"),
    logo = team
  ) |>
  arrange(conference, seed) |>
  select(conference, seed, logo, name, record, pct, last10, ortg, drtg, net)

standings |>
  gt(groupname_col = "conference", id = "nba-standings") |>
  tab_header(
    title = paste("NBA standings and ratings,", label(season)),
    subtitle = paste0(toupper(substr(through, 1, 1)), substring(through, 2))
  ) |>
  fmt_number(pct, decimals = 3) |>
  fmt_number(c(ortg, drtg), decimals = 1) |>
  fmt_number(net, decimals = 1, force_sign = TRUE) |>
  data_color(
    net,
    palette = c("#c84630", "#f7f7f7", "#2e8b57"), domain = c(-1, 1) * max(abs(standings$net))
  ) |>
  tab_spanner("Per 100 possessions", c(ortg, drtg, net)) |>
  cols_label(
    seed = "", logo = "", name = "Team", record = "W-L", pct = "Pct",
    last10 = "Last 10", ortg = "Off", drtg = "Def", net = "Net"
  ) |>
  tab_source_note(paste(
    "Data: hoopR (ESPN box scores). Possessions estimated from the box score;",
    "ties ordered by net rating. Viz: sdvplotR"
  )) |>
  gt_sdv_logos(columns = logo, league = "nba", height = 24) |>
  gt_theme_sofa()
NBA standings and ratings, 2025-26
Final regular season
Team W-L Pct Last 10
Per 100 possessions
Off Def Net
Eastern
1 Detroit Pistons Pistons 60-22 0.732 8-2 114.4 106.5 +7.9
2 Boston Celtics Celtics 56-26 0.683 8-2 117.2 109.4 +7.9
3 New York Knicks Knicks 53-29 0.646 6-4 116.7 110.4 +6.3
4 Cleveland Cavaliers Cavaliers 52-30 0.634 7-3 116.1 112.1 +4.0
5 Toronto Raptors Raptors 46-36 0.561 6-4 112.9 110.1 +2.8
6 Atlanta Hawks Hawks 46-36 0.561 6-4 113.3 110.9 +2.3
7 Orlando Magic Magic 45-37 0.549 7-3 112.0 111.3 +0.6
8 Philadelphia 76ers 76ers 45-37 0.549 6-4 112.2 112.4 −0.2
9 Charlotte Hornets Hornets 44-38 0.537 6-4 115.7 110.9 +4.8
10 Miami Heat Heat 43-39 0.524 5-5 113.8 111.6 +2.2
11 Milwaukee Bucks Bucks 32-50 0.390 3-7 110.3 116.5 −6.2
12 Chicago Bulls Bulls 31-51 0.378 2-8 110.6 115.5 −4.9
13 Brooklyn Nets Nets 20-62 0.244 3-7 105.9 115.9 −10.0
14 Indiana Pacers Pacers 19-63 0.232 3-7 108.3 116.0 −7.7
15 Washington Wizards Wizards 17-65 0.207 0-10 108.1 119.6 −11.5
Western
1 Oklahoma City Thunder Thunder 64-18 0.780 7-3 116.0 105.1 +10.9
2 San Antonio Spurs Spurs 62-20 0.756 8-2 116.6 108.5 +8.1
3 Denver Nuggets Nuggets 54-28 0.659 10-0 119.6 114.5 +5.0
4 Los Angeles Lakers Lakers 53-29 0.646 7-3 115.4 113.7 +1.7
5 Houston Rockets Rockets 52-30 0.634 9-1 115.1 109.9 +5.2
6 Minnesota Timberwolves Timberwolves 49-33 0.598 5-5 113.8 110.5 +3.2
7 Phoenix Suns Suns 45-37 0.549 5-5 112.1 110.6 +1.5
8 LA Clippers Clippers 42-40 0.512 6-4 114.5 113.3 +1.1
9 Portland Trail Blazers Trail Blazers 42-40 0.512 7-3 111.0 111.3 −0.3
10 Golden State Warriors Warriors 37-45 0.451 3-7 112.0 112.6 −0.5
11 New Orleans Pelicans Pelicans 26-56 0.317 1-9 111.3 115.6 −4.3
12 Dallas Mavericks Mavericks 26-56 0.317 3-7 108.6 113.8 −5.2
13 Memphis Grizzlies Grizzlies 25-57 0.305 1-9 109.9 115.6 −5.8
14 Utah Jazz Jazz 22-60 0.268 1-9 111.3 119.2 −8.0
15 Sacramento Kings Kings 22-60 0.268 3-7 108.6 118.4 −9.8
Data: hoopR (ESPN box scores). Possessions estimated from the box score; ties ordered by net rating. Viz: sdvplotR

2. Net rating, best to worst

The same net ratings as bars in team colors. scale_fill_sdv() maps the abbreviations to each team’s primary color, and scale_x_sdv() with theme_x_sdv() swaps the abbreviations on the x axis for logos.

ggplot(ratings, aes(x = reorder(team, -net), y = net, fill = team)) +
  geom_col(width = 0.75) +
  geom_hline(yintercept = 0, colour = "grey20", linewidth = 0.4) +
  scale_fill_sdv(league = "nba", guide = "none") +
  scale_x_sdv(league = "nba", size = 20) +
  labs(
    title = paste("NBA net rating,", label(season), through),
    subtitle = "Points scored minus points allowed per 100 possessions",
    x = NULL, y = "Net rating",
    caption = "Data: hoopR (ESPN box scores) | Viz: sdvplotR"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold"),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  theme_x_sdv()

Bar chart of every NBA team's net rating in points per 100 possessions, best to worst, each bar in the team's primary color with the team's logo under it on the horizontal axis.

3. Scoring leaders with headshots

Points per game from the player box scores (standard games only, as above), for players who appeared in at least half of the most games any team has played. ESPN athlete ids are sdvplotR’s default id for NBA headshots.

players <- hoopR::load_nba_player_box(seasons = season) |>
  filter(season_type == 2, !did_not_play, !is.na(minutes), game_id %in% standard$game_id)
most_games <- max(count(distinct(players, team_abbreviation, game_id), team_abbreviation)$n)

leaders <- players |>
  arrange(game_date, game_id) |>
  group_by(athlete_id) |>
  summarise(
    name = last(athlete_display_name), team = last(team_abbreviation),
    gp = n(), ppg = mean(points), .groups = "drop"
  ) |>
  filter(gp >= most_games / 2) |>
  arrange(desc(ppg), athlete_id) |>
  slice_head(n = 12) |>
  mutate(name = factor(name, rev(name)))
top <- max(leaders$ppg)

ggplot(leaders, aes(y = name)) +
  geom_col(aes(x = ppg, fill = team), width = 0.7) +
  geom_sdv_headshots(aes(x = -0.06 * top, player_id = athlete_id), league = "nba", height = 0.075) +
  geom_sdv_logos(aes(x = ppg + 0.05 * top, team = team), league = "nba", height = 0.055) +
  geom_text(aes(x = ppg + 0.1 * top, label = sprintf("%.1f  (%d games)", ppg, gp)), hjust = 0, size = 3.4) +
  scale_fill_sdv(league = "nba", guide = "none") +
  scale_x_continuous(limits = c(-0.12 * top, 1.4 * top), expand = c(0, 0)) +
  labs(
    title = paste("NBA scoring leaders,", label(season), through),
    subtitle = paste("Points per game, minimum", ceiling(most_games / 2), "games"),
    x = "Points per game", y = NULL,
    caption = "Data: hoopR (ESPN box scores) | Viz: sdvplotR"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold"),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )

Horizontal bar chart of the twelve NBA scoring leaders in points per game, each bar in the player's team color, with the player's headshot at the left of the bar and the team logo at its end.

4. Interactive version

The net rating bars from section 2 with ggiraph: hover a bar for the team’s logo, record and offensive and defensive ratings, and the other bars fade. The chart is the same ggplot with geom_col_interactive() in place of geom_col(). Here the axis keeps the abbreviations and the logo goes in the tooltip as a link (sdv_logo_url()), which the browser loads on hover; for logos on the axis too, add scale_x_sdv() and theme_x_sdv() as in section 2 (ggiraph embeds them, about 100 KB for 30).

p_net <- ggplot(ratings, aes(x = reorder(team, -net), y = net, fill = team)) +
  ggiraph::geom_col_interactive(
    aes(data_id = team, tooltip = sprintf(
      "<img src='%s' height='48'><br><b>%s</b>, %d-%d<br>Offense %.1f, defense %.1f<br>Net %+.1f per 100 possessions",
      sdv_logo_url(team, league = "nba"), name, w, l, ortg, drtg, net
    )),
    width = 0.75
  ) +
  geom_hline(yintercept = 0, colour = "grey20", linewidth = 0.4) +
  scale_fill_sdv(league = "nba", guide = "none") +
  labs(title = paste("NBA net rating,", label(season), through), x = NULL, y = "Net rating") +
  theme_minimal(base_size = 13) +
  theme(
    panel.grid.major.x = element_blank(), panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 90, vjust = 0.5, size = 8)
  )

ggiraph::girafe(
  ggobj = p_net, width_svg = 8, height_svg = 4.5,
  options = list(
    ggiraph::opts_hover_inv(css = "opacity:0.3;"),
    ggiraph::opts_tooltip(css = "background:white;color:black;padding:6px;border-radius:6px;")
  )
)

More recipes like this one, in plotly, ggiraph, leaflet and reactable, are in Interactive charts.

  • NBA walks through hoopR and sdvplotR more broadly.
  • WNBA builds the same standings from wehoop.
  • Social graphics, automated posts leaderboards like these on a schedule.

Our Authors

Citation

To cite the sdvplotR R package in publications, use:

BibTeX Citation

@misc{gilani_carl_lee_kay_weatherman_sdvplotR,
  author = {Gilani, Saiem and Carl, Sebastian and Lee, Jared and Kay, Camden and Weatherman, Andrew},
  title = {sdvplotR: The SportsDataverse's R Package for Multi-League Sports Plotting.},
  url = {https://sdvplotR.sportsdataverse.org},
  year = {2026}
}