ESPN basketball endpoints -- NBA & MBB
Saiem Gilani
Source: vignettes/espn-endpoints.Rmd
espn-endpoints.RmdWhat this vignette covers
If you’ve used hoopR before, you’ve probably reached for
espn_mbb_pbp(), espn_nba_pbp(), and the box
score wrappers. Those get you the bulk of any analysis built around
individual games. The package now also wraps a much wider slice of
ESPN’s basketball API – team rosters, schedules, league news, athlete
biographies and gamelogs, in-game win probabilities, officials,
broadcast info, the NBA draft, free agency, transactions, and
league-wide catalogs of venues, coaches, and statistical leaders. Nearly
two hundred ESPN endpoint wrappers in total, split between men’s college
basketball (espn_mbb_*) and the NBA
(espn_nba_*).
This vignette is a tour of what’s available and how to compose the pieces. Most of the chunks run live when the package website is built, so the tables below are real ESPN responses – and every one of them works the same way when you copy it into an interactive session.
A note on the API surface
ESPN exposes basketball data through three public, unauthenticated
API hosts, and hoopR reaches into each:
-
site.api.espn.com/apis/site/v2/sports/basketball/{league}is the most stable. It serves scoreboards, teams, rosters, schedules, news, injuries, standings, and player and team season stats. -
sports.core.api.espn.com/v2/sports/basketball/{league}carries the deeper resources: athlete indexes, per-event detail (odds, win probabilities, officials, broadcasts), seasons, venues, coaches, the NBA draft, and transactions. Big collections paginate via$reflinks. -
site.web.api.espn.com/apis/common/v3/sports/basketball/{league}carries per-athlete deep detail – season overview, stats by category, game-by-game logs, situational splits. It’s the least stable of the three; older seasons sometimes return 404, and not every athlete is represented.
There’s a fourth host (cdn.espn.com/core) that surfaces
the same information as the site-v2 /summary endpoint we
already parse, so we don’t wrap it.
The league slugs in URL paths are
mens-college-basketball and nba. Those only
matter if you’re building requests by hand.
Rate limits. ESPN doesn’t publish them. In practice,
requests arriving faster than about one per second occasionally come
back as HTTP 429 or as silently empty payloads. If you’re looping over
many games or athletes, drop a Sys.sleep(1) between
calls.
Proxies. ESPN wrappers don’t accept a per-call
proxy = argument; they call into the package’s HTTP layer
directly. Set the proxy once at the top of your session with
options(hoopR.proxy = "http://host:port") (or a list, for
authenticated proxies) and every ESPN call will pick it up
automatically.
What’s available, by use case
The tables below are grouped by what you’re likely trying to do. Function names are clickable on the pkgdown reference.
Game data
These all key off game_id (also called
event_id in some endpoints – they’re the same thing).
| Function | Returns |
|---|---|
espn_mbb_game_all() /
espn_nba_game_all()
|
Full game summary as a named list |
espn_mbb_pbp() / espn_nba_pbp()
|
Play-by-play |
espn_mbb_team_box() /
espn_nba_team_box()
|
Team box score |
espn_mbb_player_box() /
espn_nba_player_box()
|
Player box score |
espn_mbb_game_rosters() /
espn_nba_game_rosters()
|
Game-day rosters |
Scoreboard, conferences, and league reference
| Function | Returns |
|---|---|
espn_mbb_scoreboard() /
espn_nba_scoreboard()
|
Daily scoreboard |
espn_mbb_teams() / espn_nba_teams()
|
All teams |
espn_mbb_conferences() /
espn_nba_conferences()
|
Conferences |
espn_mbb_rankings() |
AP / coaches poll (MBB only – NBA has no poll) |
espn_mbb_standings() /
espn_nba_standings()
|
Conference / league standings |
espn_mbb_news() / espn_nba_news()
|
League-wide news feed |
espn_mbb_calendar() /
espn_nba_calendar()
|
Season calendar weeks |
Team detail
| Function | Returns |
|---|---|
espn_mbb_team() / espn_nba_team()
|
Single-team info, record, coaches |
espn_mbb_team_roster() /
espn_nba_team_roster()
|
Roster |
espn_mbb_team_schedule() /
espn_nba_team_schedule()
|
Schedule |
espn_mbb_team_leaders() /
espn_nba_team_leaders()
|
Statistical leaders |
espn_mbb_team_news() /
espn_nba_team_news()
|
Team news feed |
espn_mbb_team_stats() /
espn_nba_team_stats()
|
Team-level season stats |
espn_mbb_team_injuries() /
espn_nba_team_injuries()
|
Team injury report |
espn_mbb_injuries() /
espn_nba_injuries()
|
League-wide injury report |
Athlete detail
| Function | Returns |
|---|---|
espn_mbb_athlete_info() /
espn_nba_athlete_info()
|
Bio and profile |
espn_mbb_athlete_overview() /
espn_nba_athlete_overview()
|
Season overview (web-common-v3) |
espn_mbb_athlete_stats() /
espn_nba_athlete_stats()
|
Season stats by category |
espn_mbb_athlete_gamelog() /
espn_nba_athlete_gamelog()
|
Game-by-game log |
espn_mbb_athlete_splits() /
espn_nba_athlete_splits()
|
Situational splits |
espn_mbb_athlete_eventlog() /
espn_nba_athlete_eventlog()
|
Event log ($ref links) |
espn_mbb_athlete_awards() /
espn_nba_athlete_awards()
|
Awards history |
espn_mbb_athlete_statisticslog() /
espn_nba_athlete_statisticslog()
|
Stats log (core-v2) |
espn_mbb_player_stats() /
espn_nba_player_stats()
|
Cross-athlete season stats |
Per-event enrichment
These take an event_id and complement the
play-by-play.
| Function | Returns |
|---|---|
espn_mbb_event_odds() /
espn_nba_event_odds()
|
Opening / closing lines (NBA only – empty for MBB) |
espn_mbb_event_probabilities() /
espn_nba_event_probabilities()
|
Win probability per play |
espn_mbb_event_officials() /
espn_nba_event_officials()
|
Officials |
espn_mbb_event_broadcasts() /
espn_nba_event_broadcasts()
|
Broadcast outlets |
League-wide catalogs
| Function | Returns |
|---|---|
espn_mbb_leaders() /
espn_nba_leaders()
|
League statistical leaders |
espn_mbb_venues() / espn_nba_venues()
|
Arenas |
espn_mbb_coaches() /
espn_nba_coaches()
|
Coaches |
espn_mbb_athletes_index() /
espn_nba_athletes_index()
|
Full athlete index |
espn_mbb_seasons() /
espn_nba_seasons()
|
Seasons on record |
espn_mbb_season_info() /
espn_nba_season_info()
|
Single-season metadata |
NBA-only (draft, free agency, transactions)
These three endpoints exist only on the NBA side – the NCAA does not run an in-league draft and ESPN tracks free agency / transactions only at the pro level.
| Function | Returns |
|---|---|
espn_nba_draft() |
Draft picks by season |
espn_nba_freeagents() |
Free agents (during the FA window) |
espn_nba_transactions() |
Transactions log |
Worked examples
The examples below use Duke (team_id = 150) for MBB and
the Los Angeles Lakers (team_id = 13) for the NBA. Most
ESPN team IDs and athlete IDs are easy to discover with
espn_mbb_teams() / espn_nba_teams() and the
various roster endpoints.
Browsing news and the season calendar
When you’re starting a new analysis, the easiest way to confirm the season is active and you’re hitting current data is to pull the news feed and the calendar.
library(hoopR)
# Latest 10 MBB news items
mbb_news <- espn_mbb_news(limit = 10)
head(mbb_news[, c("headline", "published")])
#> # A tibble: 6 × 2
#> headline published
#> <chr> <chr>
#> 1 Big 12 first power conference to have full CSC participation 2026-05-…
#> 2 2026 NBA draft big board rankings: Top 100 prospects 2026-05-…
#> 3 College teams that won -- or lost -- at the NBA draft withdrawal de… 2026-05-…
#> 4 Arizona star Koa Peat to remain in NBA draft 2026-05-…
#> 5 Vanderbilt PG Tyler Tanner withdraws from NBA draft 2026-05-…
#> 6 Milan Momcilovic withdraws from NBA draft, set for college return 2026-05-…
# 2025 MBB season calendar
mbb_cal <- espn_mbb_calendar(season = 2025)
head(mbb_cal)
#> # A tibble: 6 × 12
#> season season_type season_type_label season_start_date season_end_date
#> <chr> <chr> <chr> <chr> <chr>
#> 1 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> 2 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> 3 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> 4 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> 5 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> 6 2025 NA NA 2024-07-13T07:00Z 2025-04-09T06:59Z
#> # ℹ 7 more variables: calendar_type <chr>, label <chr>, alternate_label <chr>,
#> # detail <chr>, value <chr>, start_date <chr>, end_date <chr>
# Same on the NBA side
nba_news <- espn_nba_news(limit = 10)
nba_cal <- espn_nba_calendar(season = 2025)The calendar tibble carries one row per scheduling block (preseason, regular season, postseason, championship weeks, etc.), with start and end dates. It’s useful for filtering schedules and scoreboards down to a specific portion of the year.
Looking at a team
espn_mbb_team() and espn_nba_team() return
a small named list with high-level identity, record, next event, and
coaching info. Pair it with the roster, schedule, and team-leader
wrappers when you want a fuller picture.
# Duke at a glance
duke <- espn_mbb_team(team_id = 150, season = 2025)
names(uconn) # Info, Record, NextEvent, StandingSummary, Coaches
#> Error:
#> ! object 'uconn' not found
# Current roster
duke_roster <- espn_mbb_team_roster(team_id = 150, season = 2025)
head(uconn_roster[, c("athlete_display_name", "position_name", "jersey")])
#> Error:
#> ! object 'uconn_roster' not found
# Regular-season schedule
duke_sched <- espn_mbb_team_schedule(
team_id = 150, season = 2025, season_type = 2
)
head(uconn_sched[, c("name", "date", "home_away", "score")])
#> Error:
#> ! object 'uconn_sched' not found
# Statistical leaders for the team
duke_ldrs <- espn_mbb_team_leaders(team_id = 150, season = 2025)
head(uconn_ldrs)
#> Error:
#> ! object 'uconn_ldrs' not foundThe same shape applies to NBA teams –
espn_nba_team(17, 2025) for the Aces, plus
_team_roster(), _team_schedule(), and so
on.
A small caveat on the roster and leaders endpoints: ESPN serves only
the current roster and leaders for each team, regardless of any
season you pass. The argument is preserved in the function
signature for API symmetry, but it doesn’t change the request URL.
Tracking injuries
Injury data is a soft spot in ESPN’s MBB coverage – most college games don’t carry an active injury report. NBA injuries are more reliably populated when the league is in season.
# Whole league
nba_inj <- espn_nba_injuries(season = 2025)
# Single team
lakers_inj <- espn_nba_team_injuries(team_id = 13)
# MBB injuries are sparse on ESPN -- an empty tibble is the normal case
mbb_inj <- espn_mbb_injuries(season = 2025)If you’re building a workflow that depends on injury data, gate
downstream code on nrow(...) > 0.
Following an athlete
The athlete endpoints are the deepest part of the surface. Pull a
roster, pick a player, and you have biographical data, season-level
totals, a per-game log, situational splits, and an awards history all
keyed off the same athlete_id.
library(dplyr)
duke_roster <- espn_mbb_team_roster(team_id = 150, season = 2025)
athlete_id <- uconn_roster$athlete_id[1]
#> Error:
#> ! object 'uconn_roster' not found
athlete_name <- uconn_roster$athlete_display_name[1]
#> Error:
#> ! object 'uconn_roster' not found
message("Selected: ", athlete_name, " (", athlete_id, ")")
#> Error:
#> ! object 'athlete_name' not found
# Bio and profile
bio <- espn_mbb_athlete_info(athlete_id = athlete_id)
#> Error:
#> ! object 'athlete_id' not found
glimpse(bio)
#> Error:
#> ! object 'bio' not found
# Season overview (web-common-v3)
overview <- espn_mbb_athlete_overview(
athlete_id = athlete_id, season = 2025
)
#> Error:
#> ! object 'athlete_id' not found
names(overview)
#> Error:
#> ! object 'overview' not found
# Game-by-game log
gamelog <- espn_mbb_athlete_gamelog(
athlete_id = athlete_id, season = 2025
)
#> Error:
#> ! object 'athlete_id' not found
head(gamelog[, c("game_date", "opponent", "points", "rebounds", "assists")])
#> Error:
#> ! object 'gamelog' not found
# Situational splits (home/away, by month, vs ranked, ...)
splits <- espn_mbb_athlete_splits(
athlete_id = athlete_id, season = 2025
)
#> Error:
#> ! object 'athlete_id' not found
head(splits)
#> Error:
#> ! object 'splits' not foundEverything above works the same on the NBA side – swap
espn_mbb_* for espn_nba_* and use a NBA
athlete_id (A’ja Wilson is "1966").
A few things to know about the athlete endpoints:
-
espn_*_athlete_eventlog()returns$refURL columns rather than parsed game stats. Reach forespn_*_athlete_gamelog()if you want per-game numbers in tibble form. -
espn_*_athlete_awards()is sparse. Many athletes have no ESPN-recorded awards, so an empty tibble is normal. - The web-common-v3 endpoints (
_athlete_overview,_athlete_stats,_athlete_gamelog,_athlete_splits) are less stable than the rest of the surface. Some seasons before roughly 2018 return HTTP 404, and not every athlete is in the index.
Charting win probability
The combination of _pbp() and
_event_probabilities() is the quickest way to chart a
game’s momentum. Event '401283399' below is a 2024 NBA
regular-season game.
library(hoopR)
library(dplyr)
library(ggplot2)
game_id <- "401283399"
pbp <- espn_nba_pbp(game_id = game_id)
probs <- espn_nba_event_probabilities(event_id = game_id, limit = 200)
plot_data <- probs %>%
mutate(seq = as.integer(sequence_number)) %>%
arrange(seq)
ggplot(plot_data, aes(x = seq, y = as.numeric(home_win_percentage))) +
geom_line(color = "#221A4D", linewidth = 0.8) +
geom_hline(yintercept = 0.5, linetype = "dashed", color = "grey50") +
labs(
title = paste("Win probability --", game_id),
x = "Play sequence",
y = "Home team win probability"
) +
theme_minimal()
You can layer in scoring plays from pbp to label
momentum swings, or join _event_officials() and
_event_broadcasts() if you want contextual annotations.
# Pre-game odds (populated when ESPN carries lines)
odds <- espn_nba_event_odds(event_id = game_id)
# Officials
officials <- espn_nba_event_officials(event_id = game_id)
officials[, c("full_name", "position")]
#> Error in `officials[, c("full_name", "position")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `position` doesn't exist.
# Broadcast outlets
broadcasts <- espn_nba_event_broadcasts(event_id = game_id)
broadcasts[, c("market", "names")]
#> Error in `broadcasts[, c("market", "names")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `market` doesn't exist.ESPN doesn’t carry betting lines for NCAA games, so
espn_mbb_event_odds() always returns an empty tibble. The
function exists for API symmetry; it’s not a bug. Win probabilities,
officials, and broadcasts are all populated for both leagues.
Working with the NBA draft and transactions
The draft, free agency, and transaction logs only exist on the NBA side – the NCAA doesn’t have a pro-style draft on ESPN, and the transfer portal isn’t part of ESPN’s basketball API.
# 2025 NBA draft picks
draft_2025 <- espn_nba_draft(season = 2025)
head(draft_2025[, c("pick", "round", "team_name", "athlete_display_name")])
#> Error in `draft_2025[, c("pick", "round", "team_name", "athlete_display_name")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Columns `team_name` and `athlete_display_name` don't exist.
# Recent transactions (waivers, trades, signings)
txn <- espn_nba_transactions(season = 2025, limit = 100)
head(txn[, c("date", "type", "athlete_display_name", "team_name")])
#> Error in `txn[, c("date", "type", "athlete_display_name", "team_name")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Columns `athlete_display_name` and `team_name` don't exist.
# Free agents -- empty outside the FA window, which is roughly
# January through April each year
fa <- espn_nba_freeagents(season = 2025)
nrow(fa)
#> [1] 0If you’re stitching together a roster history, the natural sequence
is draft -> free agents -> transactions, joined on
athlete_id.
Browsing league-wide catalogs
Sometimes you don’t have a specific team or athlete in mind – you want the league-wide leaderboard, every venue, every coach, or the full athlete index. The catalog endpoints cover that.
# Statistical leaders (points, rebounds, assists, ...)
nba_ldrs <- espn_nba_leaders(season = 2024, season_type = 2)
head(nba_ldrs)
#> # A tibble: 6 × 11
#> season season_type category abbreviation athlete_id athlete_name team_id
#> <int> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 2024 2 pointsPerGame PTS 3059318 NA 20
#> 2 2024 2 pointsPerGame PTS 3945274 NA 6
#> 3 2024 2 pointsPerGame PTS 3032977 NA 15
#> 4 2024 2 pointsPerGame PTS 4278073 NA 25
#> 5 2024 2 pointsPerGame PTS 3934672 NA 18
#> 6 2024 2 pointsPerGame PTS 3202 NA 21
#> # ℹ 4 more variables: team_abbrev <chr>, value <dbl>, rank <int>,
#> # display_value <chr>
# Every NBA arena ESPN tracks
nba_venues <- espn_nba_venues()
head(nba_venues[, c("full_name", "city", "state", "capacity")])
#> Error in `nba_venues[, c("full_name", "city", "state", "capacity")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Columns `city` and `state` don't exist.
# Coaches (current season)
mbb_coaches <- espn_mbb_coaches(season = 2025)
head(mbb_coaches[, c("full_name", "team_name", "experience")])
#> Error in `mbb_coaches[, c("full_name", "team_name", "experience")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `team_name` doesn't exist.The athlete index is the largest of the catalog endpoints – expect
6,000+ rows for a recent MBB season. Pagination is handled internally,
but for exploratory pulls you’ll usually want a small
limit.
# NBA -- a couple hundred active players
nba_athletes <- espn_nba_athletes_index(
season = 2025, active = TRUE, limit = 5000
)
nrow(nba_athletes)
#> [1] 845
head(nba_athletes[, c("display_name", "position_name", "team_name")])
#> Error in `nba_athletes[, c("display_name", "position_name", "team_name")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Columns `display_name`, `position_name`, and `team_name` don't exist.
# MBB -- cap at 50 rows just to peek; the default limit is 25,000
mbb_athletes <- espn_mbb_athletes_index(season = 2025, limit = 50)
head(mbb_athletes[, c("display_name", "position_name", "team_name")])
#> Error in `mbb_athletes[, c("display_name", "position_name", "team_name")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Columns `display_name`, `position_name`, and `team_name` don't exist.The seasons catalog is mostly handy when you want to know which years ESPN has on record before you start a multi-season pull.
nba_seasons <- espn_nba_seasons()
head(nba_seasons[, c("year", "start_date", "end_date")])
#> Error in `nba_seasons[, c("year", "start_date", "end_date")]`:
#> ! Can't subset columns that don't exist.
#> ✖ Column `year` doesn't exist.
# Single-season metadata is mostly $ref URLs to sub-resources, so
# espn_*_seasons() is usually the more useful starting point
nba_s2025 <- espn_nba_season_info(season = 2025)Core-v2 deep expansion
The post-3.0.0 release adds another ~73 ESPN core-v2 wrappers to
espn_nba_* + espn_mbb_*, bringing the total
ESPN basketball surface in hoopR to 199
functions. Everything here is shimmed over an internal
.espn_basketball_* helper so NBA and MBB share parsing
logic and bug fixes propagate to both leagues at once.
Function overview
| Tier | Family | NBA | MBB | What it gives you |
|---|---|---|---|---|
| 1 / 2A | athlete_career_stats |
✓ | ✓ | Long-format career stats (1 row per stat). Default fetches RS + post and binds. |
| 1 / 2A | athlete_seasons |
✓ | ✓ | Index of seasons an athlete played, with $refs to
season-scoped detail |
| 1 / 2A |
franchises / franchise
|
✓ | ✓ | League franchise index + per-franchise detail (stable across relocations) |
| 1 / 2A | futures |
✓ | ✓ | Per-season betting futures markets in long format |
| 1 / 2A |
tournaments / tournament /
tournament_seasons
|
✓ | ✓ | NBA Cup (1) / NCAA tournament (3) index + per-tournament + per-season list |
| 1 / 2A |
team_record / team_odds_records
|
✓ | ✓ | W-L / record types and against-the-spread records by season |
| 1 / 2A | team_depthchart |
✓ | — | NBA-only depth chart |
| 1 / 2A |
team_season_profile /
team_season_roster
|
✓ | ✓ | Per-(team × season) profile and roster |
| 1 / 2A |
coach / coach_season
|
✓ | ✓ | Coach detail; coach-in-a-season |
| 1 / 2A | powerindex |
✓ | ✓ | Per-season power-index entries per team |
| 1 / 2A |
season_info / season_type /
season_types
|
✓ | ✓ | Season metadata (top-level + per-season-type) |
| 1 / 2A | season_leaders |
✓ | ✓ | Per-(season × season-type) leader board, long format. Defaults fetch RS + post. |
| 1 / 2A |
season_rankings / season_ranking
|
✓ | ✓ | Per-season poll/ranking indexes |
| 1 / 2A |
season_weeks / season_week
|
✓ | ✓ | Weeks within a season (week-based leagues) |
| 1 / 2A |
week_rankings / week_ranking
|
✓ | ✓ | Per-week ranking lookups |
| 1 / 2A |
season_groups / season_group /
season_group_children /
season_group_teams
|
✓ | ✓ | Conferences / divisions / D-I groups within a season |
| 1 / 2A |
season_awards / award
|
✓ | ✓ | Per-season awards index + per-award detail |
| 1 / 2A | draft_pick |
✓ | — | Single pick lookup |
| 2B.1 | athlete_eventlog_v2 |
✓ | ✓ | Per-(athlete × season) event log (core-v2; richer than the web-common-v3 variant) |
| 2B.1 |
draft_rounds / draft_athletes /
draft_status
|
✓ | — | Draft year completion: rounds, draftees, in-progress status |
| 2B.2 | event_situation |
✓ | ✓ | Live game situation: timeouts, fouls, bonus state, last play
$ref
|
| 2B.2 | event_predictor |
✓ | ✓ | Pre-game predictor stats (one row per team × stat) |
| 2B.2 | event_powerindex |
✓ | ✓ | Per-event power-index index. Sparse coverage. |
| 2B.2 | event_propbets |
✓ | ✓ | Per-(event × provider) prop-bet markets in long format |
| 2B.3 | event_competitor_linescores |
✓ | ✓ | Per-quarter scoring for one team in one event |
| 2B.3 | event_competitor_leaders |
✓ | ✓ | Top performers per team in long format |
| 2B.3 | event_competitor_roster |
✓ | ✓ | Game-day roster index — athlete ids + $refs |
| 2B.3 | event_competitor_statistics |
✓ | ✓ | Full team-game statistics in long format |
| 2B.3 | event_competitor_records |
✓ | ✓ | Team records as of the event (overall / home / away / conf / div) |
| 2D |
positions / position
|
✓ | ✓ | Position dictionary index + detail (id-by-league, not shared) |
| 2E.1 | event_player_box |
✓ | ✓ | Per-game player box score, long format |
| 2E.1 | event_competitor_roster_entry |
✓ | ✓ | Single roster row: starter / DNP / ejection / sub slot |
| 2E.1 | event_play |
✓ | ✓ | Single-play detail (sequence, period, clock, scoring flags, coords) |
| 2E.1 | event_play_personnel |
✓ | ✓ | Players on court at a specific play. Sparse coverage. |
| 2E.2 | event_competitor_score |
✓ | ✓ | Single-row final score (value, displayValue, winner, source) |
| 2E.2 | team_season_statistics |
✓ | ✓ | Full team-season stat sheet in long format, with league rank per stat |
| 2E.2 | season_draft |
✓ | — | Draft-year top-level metadata |
| 2F | event_official_detail |
✓ | ✓ | Per-official detail. URL uses crew order (1-indexed),
not the ESPN stable id. |
| 2F | team_record_detail |
✓ | ✓ | Per-record stat array in long format |
| 2F | coach_record |
✓ | ✓ | Coach career record by type (Total / Pre Season / Regular Season / Post Season) |
| 2F | tournament_season |
✓ | ✓ | Single tournament-year detail |
| 2F | draft_athlete_detail |
✓ | — | Rich single-row drafted-player record |
Dashes mark families that don’t apply at ESPN for that league (no MBB in-league draft, no MBB depth chart endpoint).
Athlete career, contracts, splits, and event log
Beyond the gamelog and per-season stats wrappers we shipped in the initial ESPN batch, the core-v2 surface adds career-level rollups, season-by-season indexes, and a richer event log.
library(hoopR)
# Career stats — long format, one row per (stat_type x category x stat).
# Default `stat_type = c(0L, 1L)` fetches regular season + postseason and
# binds them so a single call gives you the player's full career sheet.
flagg_career <- espn_mbb_athlete_career_stats(athlete_id = 4432809)
lebron_career <- espn_nba_athlete_career_stats(athlete_id = 1966)
# Per-(athlete x season) event log -- core-v2 variant. Returns
# event_id + result + per-event stats in long format.
espn_nba_athlete_eventlog_v2(athlete_id = 1966, season = 2024)
#> # A tibble: 25 × 8
#> league athlete_id season event_id team_id played event_ref competition_ref
#> <chr> <chr> <int> <chr> <chr> <lgl> <chr> <chr>
#> 1 nba 1966 2024 401591880 13 TRUE http://spo… http://sports.…
#> 2 nba 1966 2024 401591892 13 FALSE http://spo… http://sports.…
#> 3 nba 1966 2024 401591902 13 TRUE http://spo… http://sports.…
#> 4 nba 1966 2024 401591909 13 FALSE http://spo… http://sports.…
#> 5 nba 1966 2024 401591931 13 TRUE http://spo… http://sports.…
#> 6 nba 1966 2024 401584689 13 TRUE http://spo… http://sports.…
#> 7 nba 1966 2024 401584704 13 TRUE http://spo… http://sports.…
#> 8 nba 1966 2024 401584728 13 TRUE http://spo… http://sports.…
#> 9 nba 1966 2024 401584739 13 TRUE http://spo… http://sports.…
#> 10 nba 1966 2024 401584755 13 TRUE http://spo… http://sports.…
#> # ℹ 15 more rows
# Index of seasons an athlete has on file (good for iterating).
espn_nba_athlete_seasons(athlete_id = 1966)
#> # A tibble: 23 × 4
#> league athlete_id season ref
#> <chr> <chr> <int> <chr>
#> 1 nba 1966 2026 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 2 nba 1966 2025 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 3 nba 1966 2024 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 4 nba 1966 2023 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 5 nba 1966 2022 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 6 nba 1966 2021 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 7 nba 1966 2020 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 8 nba 1966 2019 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 9 nba 1966 2018 http://sports.core.api.espn.com/v2/sports/basketbal…
#> 10 nba 1966 2017 http://sports.core.api.espn.com/v2/sports/basketbal…
#> # ℹ 13 more rowsFranchises, tournaments, and league dictionaries
# Franchise records are stable across relocations and rebrands.
espn_nba_franchises()
#> # A tibble: 30 × 3
#> franchise_id ref league
#> <chr> <chr> <chr>
#> 1 1 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 2 2 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 3 3 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 4 4 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 5 5 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 6 6 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 7 7 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 8 8 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 9 9 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> 10 10 http://sports.core.api.espn.com/v2/sports/basketball/lea… nba
#> # ℹ 20 more rows
espn_nba_franchise(franchise_id = 13) # one franchise
#> # A tibble: 1 × 16
#> id uid slug location name nickname abbreviation display_name
#> <chr> <chr> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 13 s:40~l:46~f:13 los-an… Los Ang… Lake… NA LAL Los Angeles…
#> # ℹ 8 more variables: short_display_name <chr>, color <chr>, is_active <lgl>,
#> # league <chr>, logo <chr>, logo_dark <chr>, venue_ref <chr>, team_ref <chr>
# MBB-only: NCAA tournament index + detail + season list.
espn_mbb_tournaments()
#> # A tibble: 38 × 3
#> tournament_id ref league
#> <chr> <chr> <chr>
#> 1 3 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 2 1 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 3 39 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 4 2 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 5 4 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 6 5 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 7 6 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 8 7 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 9 8 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> 10 9 http://sports.core.api.espn.com/v2/sports/basketball/le… mens-…
#> # ℹ 28 more rows
espn_mbb_tournament(tournament_id = 3)
#> # A tibble: 1 × 4
#> tournament_id display_name seasons_ref league
#> <chr> <chr> <chr> <chr>
#> 1 3 Atlantic Coast Conf Tournament http://sports.core.api.es… mens-…
espn_mbb_tournament_seasons(tournament_id = 3)
#> # A tibble: 14 × 4
#> league tournament_id season ref
#> <chr> <chr> <int> <chr>
#> 1 mens-college-basketball 3 2009 http://sports.core.api.espn.com…
#> 2 mens-college-basketball 3 2010 http://sports.core.api.espn.com…
#> 3 mens-college-basketball 3 2011 http://sports.core.api.espn.com…
#> 4 mens-college-basketball 3 2012 http://sports.core.api.espn.com…
#> 5 mens-college-basketball 3 2013 http://sports.core.api.espn.com…
#> 6 mens-college-basketball 3 2014 http://sports.core.api.espn.com…
#> 7 mens-college-basketball 3 2015 http://sports.core.api.espn.com…
#> 8 mens-college-basketball 3 2016 http://sports.core.api.espn.com…
#> 9 mens-college-basketball 3 2017 http://sports.core.api.espn.com…
#> 10 mens-college-basketball 3 2018 http://sports.core.api.espn.com…
#> 11 mens-college-basketball 3 2019 http://sports.core.api.espn.com…
#> 12 mens-college-basketball 3 2020 http://sports.core.api.espn.com…
#> 13 mens-college-basketball 3 2022 http://sports.core.api.espn.com…
#> 14 mens-college-basketball 3 2023 http://sports.core.api.espn.com…
# Position dictionary. Position ids are NOT shared across leagues --
# id 1 is "Point Guard" in NBA but "Center" in MBB.
espn_nba_positions()
#> # A tibble: 11 × 3
#> position_id ref league
#> <chr> <chr> <chr>
#> 1 0 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 2 1 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 3 2 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 4 3 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 5 4 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 6 5 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 7 6 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 8 7 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 9 8 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 10 9 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
#> 11 30 http://sports.core.api.espn.com/v2/sports/basketball/leag… nba
espn_nba_position(position_id = 1)
#> # A tibble: 1 × 7
#> position_id name display_name abbreviation leaf parent_ref league
#> <chr> <chr> <chr> <chr> <lgl> <chr> <chr>
#> 1 1 Point Guard Point Guard PG TRUE http://sports.… nbaSeason metadata, rankings, awards
# Per-season-type leaders, long format. Default fetches RS + post.
espn_nba_season_leaders(season = 2024)
#> # A tibble: 800 × 15
#> league season season_type category_name category_display category_short
#> <chr> <int> <int> <chr> <chr> <chr>
#> 1 nba 2024 2 pointsPerGame Points Per Game PPG
#> 2 nba 2024 2 pointsPerGame Points Per Game PPG
#> 3 nba 2024 2 pointsPerGame Points Per Game PPG
#> 4 nba 2024 2 pointsPerGame Points Per Game PPG
#> 5 nba 2024 2 pointsPerGame Points Per Game PPG
#> 6 nba 2024 2 pointsPerGame Points Per Game PPG
#> 7 nba 2024 2 pointsPerGame Points Per Game PPG
#> 8 nba 2024 2 pointsPerGame Points Per Game PPG
#> 9 nba 2024 2 pointsPerGame Points Per Game PPG
#> 10 nba 2024 2 pointsPerGame Points Per Game PPG
#> # ℹ 790 more rows
#> # ℹ 9 more variables: category_abbrev <chr>, rank <int>, athlete_id <chr>,
#> # team_id <chr>, display_value <chr>, value <dbl>, rel <chr>,
#> # athlete_ref <chr>, team_ref <chr>
# Award index for a season + per-award detail.
aw <- espn_nba_season_awards(season = 2024)
espn_nba_award(award_id = aw$award_id[1])
#> # A tibble: 1 × 9
#> league season award_id name description athlete_id team_id athlete_ref
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 2026 33 MVP NBA Most Valuable… 4278073 25 http://spo…
#> # ℹ 1 more variable: team_ref <chr>
# Season group structure (conferences / D-I groups) -- mostly relevant
# for MBB, which has a deep conference hierarchy.
espn_mbb_season_groups(season = 2024, season_type = 2)
#> # A tibble: 2 × 5
#> league season season_type group_id ref
#> <chr> <int> <int> <chr> <chr>
#> 1 mens-college-basketball 2024 2 50 http://sports.core.api.es…
#> 2 mens-college-basketball 2024 2 51 http://sports.core.api.es…
espn_mbb_season_group(group_id = 50, season = 2024, season_type = 2)
#> # A tibble: 1 × 15
#> league season season_type group_id uid name abbreviation short_name
#> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 mens-college-… 2024 2 50 s:40… NCAA… NCAA Division I
#> # ℹ 7 more variables: midsize_name <chr>, is_conference <lgl>, slug <chr>,
#> # parent_ref <chr>, children_ref <chr>, teams_ref <chr>, standings_ref <chr>
espn_mbb_season_group_teams(group_id = 50, season = 2024, season_type = 2)
#> # A tibble: 200 × 6
#> league season season_type group_id team_id ref
#> <chr> <int> <int> <chr> <chr> <chr>
#> 1 mens-college-basketball 2024 2 50 2 http://sports.co…
#> 2 mens-college-basketball 2024 2 50 5 http://sports.co…
#> 3 mens-college-basketball 2024 2 50 6 http://sports.co…
#> 4 mens-college-basketball 2024 2 50 8 http://sports.co…
#> 5 mens-college-basketball 2024 2 50 9 http://sports.co…
#> 6 mens-college-basketball 2024 2 50 12 http://sports.co…
#> 7 mens-college-basketball 2024 2 50 13 http://sports.co…
#> 8 mens-college-basketball 2024 2 50 16 http://sports.co…
#> 9 mens-college-basketball 2024 2 50 21 http://sports.co…
#> 10 mens-college-basketball 2024 2 50 23 http://sports.co…
#> # ℹ 190 more rowsTeam season profile, roster, and stats
# Per-(team x season) profile — a $ref index for that team-season's
# deeper resources (record, statistics, leaders, roster, ...).
espn_mbb_team_season_profile(team_id = 150, season = 2024)
#> # A tibble: 1 × 35
#> id guid uid slug location name nickname abbreviation display_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 150 c4430c6c-… s:40… duke… Duke Blue… Duke DUKE Duke Blue D…
#> # ℹ 26 more variables: short_display_name <chr>, color <chr>,
#> # alternate_color <chr>, is_active <lgl>, is_all_star <lgl>, season <int>,
#> # logo <chr>, logo_dark <chr>, record_ref <chr>, venue_ref <chr>,
#> # groups_ref <chr>, ranks_ref <chr>, statistics_ref <chr>, leaders_ref <chr>,
#> # injuries_ref <chr>, notes_ref <chr>, against_the_spread_records_ref <chr>,
#> # awards_ref <chr>, franchise_ref <chr>, depth_charts_ref <chr>,
#> # events_ref <chr>, transactions_ref <chr>, coaches_ref <chr>, …
# Roster the same team carried for that season.
espn_mbb_team_season_roster(team_id = 150, season = 2024)
#> # A tibble: 18 × 5
#> league team_id season athlete_id ref
#> <chr> <chr> <int> <chr> <chr>
#> 1 mens-college-basketball 150 2024 4431890 http://sports.core.api.esp…
#> 2 mens-college-basketball 150 2024 4683735 http://sports.core.api.esp…
#> 3 mens-college-basketball 150 2024 4897107 http://sports.core.api.esp…
#> 4 mens-college-basketball 150 2024 4397971 http://sports.core.api.esp…
#> 5 mens-college-basketball 150 2024 4684793 http://sports.core.api.esp…
#> 6 mens-college-basketball 150 2024 4711256 http://sports.core.api.esp…
#> 7 mens-college-basketball 150 2024 4280073 http://sports.core.api.esp…
#> 8 mens-college-basketball 150 2024 4897108 http://sports.core.api.esp…
#> 9 mens-college-basketball 150 2024 4398003 http://sports.core.api.esp…
#> 10 mens-college-basketball 150 2024 4683778 http://sports.core.api.esp…
#> 11 mens-college-basketball 150 2024 4433285 http://sports.core.api.esp…
#> 12 mens-college-basketball 150 2024 4684843 http://sports.core.api.esp…
#> 13 mens-college-basketball 150 2024 5023693 http://sports.core.api.esp…
#> 14 mens-college-basketball 150 2024 5105444 http://sports.core.api.esp…
#> 15 mens-college-basketball 150 2024 4433135 http://sports.core.api.esp…
#> 16 mens-college-basketball 150 2024 4712846 http://sports.core.api.esp…
#> 17 mens-college-basketball 150 2024 4869739 http://sports.core.api.esp…
#> 18 mens-college-basketball 150 2024 4397232 http://sports.core.api.esp…
# Full team-season stat sheet in long format, with league rank embedded
# per stat. Smoke-tested at 109 rows x 13 cols for a single NBA team --
# MBB shapes are comparable.
espn_mbb_team_season_statistics(team_id = 150, season = 2024,
season_type = 2)
#> # A tibble: 77 × 13
#> league season season_type team_id category_name category_display stat_name
#> <chr> <int> <int> <chr> <chr> <chr> <chr>
#> 1 mens-col… 2024 2 150 defensive Defensive blocks
#> 2 mens-col… 2024 2 150 defensive Defensive defensiv…
#> 3 mens-col… 2024 2 150 defensive Defensive steals
#> 4 mens-col… 2024 2 150 defensive Defensive turnover…
#> 5 mens-col… 2024 2 150 defensive Defensive avgDefen…
#> 6 mens-col… 2024 2 150 defensive Defensive avgBlocks
#> 7 mens-col… 2024 2 150 defensive Defensive avgSteals
#> 8 mens-col… 2024 2 150 general General disquali…
#> 9 mens-col… 2024 2 150 general General flagrant…
#> 10 mens-col… 2024 2 150 general General fouls
#> # ℹ 67 more rows
#> # ℹ 6 more variables: stat_abbrev <chr>, stat_display <chr>, value <dbl>,
#> # display_value <chr>, rank <int>, rank_display_value <chr>Coach detail, coach-in-season, and coach career record
# Coach detail (single-row).
espn_mbb_coach(coach_id = 32116)
#> # A tibble: 1 × 12
#> coach_id uid first_name last_name date_of_birth birth_city birth_state
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 32116 s:40~l:41~… Steven Pearl 1987-09-14T0… Knoxville TN
#> # ℹ 5 more variables: n_career_records <int>, n_coach_seasons <int>,
#> # college_ref <chr>, team_ref <chr>, league <chr>
# Coach-in-a-season: per-season stats and record.
espn_mbb_coach_season(coach_id = 32116, season = 2024)
#> # A tibble: 1 × 13
#> league season coach_id uid first_name last_name date_of_birth birth_city
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 mens-coll… 2024 32116 s:40… Steven Pearl 1987-09-14T0… Knoxville
#> # ℹ 5 more variables: birth_state <chr>, n_records <int>, person_ref <chr>,
#> # college_ref <chr>, team_ref <chr>
# Career record by type. Type codes: 0 = Total, 1 = Pre Season,
# 2 = Regular Season, 3 = Post Season.
espn_mbb_coach_record(coach_id = 32116, record_type = 2)
#> # A tibble: 5 × 12
#> league coach_id record_type_id record_name record_type record_summary
#> <chr> <chr> <int> <chr> <chr> <chr>
#> 1 mens-college-b… 32116 2 Regular Se… Regular Se… 16-15-0
#> 2 mens-college-b… 32116 2 Regular Se… Regular Se… 16-15-0
#> 3 mens-college-b… 32116 2 Regular Se… Regular Se… 16-15-0
#> 4 mens-college-b… 32116 2 Regular Se… Regular Se… 16-15-0
#> 5 mens-college-b… 32116 2 Regular Se… Regular Se… 16-15-0
#> # ℹ 6 more variables: record_display <chr>, stat_name <chr>, stat_abbrev <chr>,
#> # stat_display <chr>, value <dbl>, stat_display_value <chr>Event meta: live situation, predictor, power index, prop bets
# All four take an event_id from espn_*_schedule() or any boxscore.
EID <- "401283399"
# Live game situation -- timeouts, fouls, bonus state, last play $ref.
espn_nba_event_situation(event_id = EID)
#> # A tibble: 1 × 15
#> league event_id home_timeouts_current home_timeouts_remaining
#> <chr> <chr> <int> <int>
#> 1 nba 401283399 3 0
#> # ℹ 11 more variables: away_timeouts_current <int>,
#> # away_timeouts_remaining <int>, home_team_fouls <int>,
#> # home_team_fouls_current <int>, home_fouls_to_give <int>,
#> # home_bonus_state <chr>, away_team_fouls <int>,
#> # away_team_fouls_current <int>, away_fouls_to_give <int>,
#> # away_bonus_state <chr>, last_play_ref <chr>
# Pre-game predictor -- one row per (team x stat). Empty for past games.
espn_nba_event_predictor(event_id = EID)
#> # A tibble: 6 × 13
#> league event_id name short_name last_modified side team_id stat_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 gameProj…
#> 2 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 matchupQ…
#> 3 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 teamChan…
#> 4 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 teamPred…
#> 5 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 teamExpe…
#> 6 nba 401283399 Oklahoma Ci… OKC @ MEM 2021-07-21T1… away 25 oppExpec…
#> # ℹ 5 more variables: stat_display <chr>, description <chr>, value <dbl>,
#> # display_value <chr>, team_ref <chr>
# Per-event power index. Coverage is sparse -- many events return zero.
espn_nba_event_powerindex(event_id = EID)
#> # A tibble: 2 × 4
#> league event_id team_id ref
#> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 25 http://sports.core.api.espn.com/v2/sports/basketball…
#> 2 nba 401283399 29 http://sports.core.api.espn.com/v2/sports/basketball…
# Prop bet markets per (event x provider). 58 = ESPN BET.
espn_nba_event_propbets(event_id = EID, provider_id = 58)
#> # A tibble: 0 × 13
#> # ℹ 13 variables: league <chr>, event_id <chr>, provider_id <chr>,
#> # athlete_id <chr>, prop_type_id <chr>, prop_type_name <chr>, american <chr>,
#> # decimal <dbl>, fraction <chr>, total <dbl>, current_target <dbl>,
#> # last_updated <chr>, athlete_ref <chr>Event competitor sub-resources
Five resource families under
events/{eid}/competitions/{cid}/competitors/{team_id}/,
each shimmed for NBA + MBB. Pair team_id with
event_id from espn_nba_schedule()$id (or the
team_id from any boxscore-style call).
EID <- "401283399"; TID <- 29 # team_id of one competitor
# Per-quarter scoring for one team in one event.
espn_nba_event_competitor_linescores(event_id = EID, team_id = TID)
#> # A tibble: 8 × 7
#> league event_id team_id period value display_value source
#> <chr> <chr> <chr> <int> <dbl> <chr> <chr>
#> 1 nba 401283399 29 1 30 30 1
#> 2 nba 401283399 29 1 30 30 Basic/Manual
#> 3 nba 401283399 29 2 27 27 1
#> 4 nba 401283399 29 2 27 27 Basic/Manual
#> 5 nba 401283399 29 3 29 29 1
#> 6 nba 401283399 29 3 29 29 Basic/Manual
#> 7 nba 401283399 29 4 36 36 1
#> 8 nba 401283399 29 4 36 36 Basic/Manual
# Top performers per team in long format (category x rank).
espn_nba_event_competitor_leaders(event_id = EID, team_id = TID)
#> # A tibble: 48 × 11
#> league event_id team_id category_name category_display category_abbrev rank
#> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 nba 401283399 29 points Points Pts 1
#> 2 nba 401283399 29 points Points Pts 2
#> 3 nba 401283399 29 points Points Pts 3
#> 4 nba 401283399 29 points Points Pts 4
#> 5 nba 401283399 29 points Points Pts 5
#> 6 nba 401283399 29 points Points Pts 6
#> 7 nba 401283399 29 points Points Pts 7
#> 8 nba 401283399 29 points Points Pts 8
#> 9 nba 401283399 29 points Points Pts 9
#> 10 nba 401283399 29 assists Assists Ast 1
#> # ℹ 38 more rows
#> # ℹ 4 more variables: athlete_id <chr>, display_value <chr>, value <dbl>,
#> # athlete_ref <chr>
# Game-day roster index. Returns athlete ids + core-v2 $refs.
espn_nba_event_competitor_roster(event_id = EID, team_id = TID)
#> # A tibble: 0 × 5
#> # ℹ 5 variables: league <chr>, event_id <chr>, team_id <chr>, athlete_id <chr>,
#> # ref <chr>
# Full team-game statistics in long format.
espn_nba_event_competitor_statistics(event_id = EID, team_id = TID)
#> # A tibble: 98 × 10
#> league event_id team_id category_name category_display stat_name stat_abbrev
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 29 defensive Defensive blocks BLK
#> 2 nba 401283399 29 defensive Defensive defensiv… DR
#> 3 nba 401283399 29 defensive Defensive steals STL
#> 4 nba 401283399 29 defensive Defensive turnover… Points Con…
#> 5 nba 401283399 29 defensive Defensive avgDefen… DR
#> 6 nba 401283399 29 defensive Defensive avgBlocks BLK
#> 7 nba 401283399 29 defensive Defensive avgSteals STL
#> 8 nba 401283399 29 defensive Defensive avg48Def… DR
#> 9 nba 401283399 29 defensive Defensive avg48Blo… BLK
#> 10 nba 401283399 29 defensive Defensive avg48Ste… STL
#> # ℹ 88 more rows
#> # ℹ 3 more variables: stat_display <chr>, value <dbl>, display_value <chr>
# Team records as of the event: overall / home / away / conf / div.
espn_nba_event_competitor_records(event_id = EID, team_id = TID)
#> # A tibble: 5 × 11
#> league event_id team_id record_id name abbreviation display_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 29 900 overall Game Record Year To Date
#> 2 nba 401283399 29 33 Home NA Home
#> 3 nba 401283399 29 34 Road NA Road
#> 4 nba 401283399 29 60 vs. Div. NA DIV
#> 5 nba 401283399 29 61 vs. Conf. NA CONF
#> # ℹ 4 more variables: short_display_name <chr>, type <chr>, summary <chr>,
#> # value <dbl>
# One-row final score, quick lookup.
espn_nba_event_competitor_score(event_id = EID, team_id = TID)
#> # A tibble: 1 × 8
#> league event_id team_id value display_value winner source_id
#> <chr> <chr> <chr> <dbl> <chr> <lgl> <chr>
#> 1 nba 401283399 29 122 122 TRUE 1
#> # ℹ 1 more variable: source_description <chr>Per-game player box score, play detail, and on-court lineups
This is the headline 2E.1 batch – the per-(game x player) box score in long format plus single-play deep dives.
EID <- "401283399"; TID <- 29; AID <- 6440 # event, team, athlete
# Per-game box score for one athlete -- one row per (category x stat).
# Same shape as event_competitor_statistics() but scoped to an athlete.
espn_nba_event_player_box(event_id = EID, team_id = TID,
athlete_id = AID)
#> NULL
# Per-athlete game-day row: starter flag, did_not_play + reason,
# ejected flag, period of entry, for_player_id (substitution slot).
espn_nba_event_competitor_roster_entry(event_id = EID, team_id = TID,
athlete_id = AID)
#> NULL
# Single-play detail. play_id comes from espn_nba_pbp() output.
PID <- "4012833997" # example play id
espn_nba_event_play(event_id = EID, play_id = PID)
#> # A tibble: 1 × 19
#> league event_id play_id sequence_number type_id type_text text short_text
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 4012833997 7 137 Turnarou… Jona… J. Valanc…
#> # ℹ 11 more variables: period <int>, clock <chr>, scoring_play <lgl>,
#> # score_value <dbl>, away_score <int>, home_score <int>, shooting_play <lgl>,
#> # coordinate_x <dbl>, coordinate_y <dbl>, team_ref <chr>, wallclock <chr>
# Players on court at a specific play (long format). Coverage is
# sparse -- many plays return zero rows.
espn_nba_event_play_personnel(event_id = EID, play_id = PID)
#> # A tibble: 0 × 7
#> # ℹ 7 variables: league <chr>, event_id <chr>, play_id <chr>, team_id <chr>,
#> # athlete_id <chr>, athlete_ref <chr>, competitor_ref <chr>Officials, team records, and the draft
# Single official detail. The URL segment is the CREW ORDER, not the
# ESPN-stable official_id -- so pair this with the `order` column from
# espn_nba_event_officials() rather than `official_id`.
espn_nba_event_official_detail(event_id = "401283399", order = 1)
#> # A tibble: 1 × 10
#> league event_id official_id first_name last_name full_name display_name
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 401283399 6856 Eric Lewis Eric Lewis Eric Lewis
#> # ℹ 3 more variables: position_id <chr>, position_name <chr>, order <int>
# Per-record stat array. Use espn_nba_team_record() to discover
# valid record_id values for a (team_id, season) pair.
espn_nba_team_record_detail(team_id = 29, season = 2024, record_id = 0)
#> # A tibble: 21 × 15
#> league team_id season season_type record_id record_name record_abbrev
#> <chr> <chr> <int> <int> <chr> <chr> <chr>
#> 1 nba 29 2024 2 0 overall Total
#> 2 nba 29 2024 2 0 overall Total
#> 3 nba 29 2024 2 0 overall Total
#> 4 nba 29 2024 2 0 overall Total
#> 5 nba 29 2024 2 0 overall Total
#> 6 nba 29 2024 2 0 overall Total
#> 7 nba 29 2024 2 0 overall Total
#> 8 nba 29 2024 2 0 overall Total
#> 9 nba 29 2024 2 0 overall Total
#> 10 nba 29 2024 2 0 overall Total
#> # ℹ 11 more rows
#> # ℹ 8 more variables: record_display <chr>, record_type <chr>,
#> # record_summary <chr>, stat_name <chr>, stat_abbrev <chr>,
#> # stat_display <chr>, value <dbl>, stat_display_value <chr>
# Draft year top-level metadata + rich single-pick record.
espn_nba_season_draft(season = 2024)
#> # A tibble: 1 × 10
#> league season year uid number_of_rounds display_name short_display_name
#> <chr> <int> <int> <chr> <int> <chr> <chr>
#> 1 nba 2024 2024 s:40~l:4… 2 2024 Nation… 2024 NBA Draft
#> # ℹ 3 more variables: status_ref <chr>, athletes_ref <chr>, rounds_ref <chr>
espn_nba_draft_athlete_detail(season = 2024, athlete_id = 108206)
#> # A tibble: 1 × 19
#> league season draftee_id athlete_id first_name last_name full_name
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 nba 2024 108206 5211175 Zaccharie Risacher Zaccharie Risacher
#> # ℹ 12 more variables: display_name <chr>, height <dbl>, display_height <chr>,
#> # weight <dbl>, display_weight <chr>, position_name <chr>,
#> # position_abbrev <chr>, pick_overall <int>, pick_round <int>,
#> # pick_team_id <chr>, athlete_ref <chr>, headshot <chr>One thing to know about default season types
Most of the new wrappers that take a season_type
parameter default to c(2L, 3L) (regular season +
postseason) and bind the two results into a single tibble keyed by a
season_type column. This is more useful than the strict
season_type = 2 default for end-of-season analysis, and
pass season_type = 2 to limit to regular season only.
# Regular season only.
espn_nba_season_leaders(season = 2024, season_type = 2)
#> # A tibble: 400 × 15
#> league season season_type category_name category_display category_short
#> <chr> <int> <int> <chr> <chr> <chr>
#> 1 nba 2024 2 pointsPerGame Points Per Game PPG
#> 2 nba 2024 2 pointsPerGame Points Per Game PPG
#> 3 nba 2024 2 pointsPerGame Points Per Game PPG
#> 4 nba 2024 2 pointsPerGame Points Per Game PPG
#> 5 nba 2024 2 pointsPerGame Points Per Game PPG
#> 6 nba 2024 2 pointsPerGame Points Per Game PPG
#> 7 nba 2024 2 pointsPerGame Points Per Game PPG
#> 8 nba 2024 2 pointsPerGame Points Per Game PPG
#> 9 nba 2024 2 pointsPerGame Points Per Game PPG
#> 10 nba 2024 2 pointsPerGame Points Per Game PPG
#> # ℹ 390 more rows
#> # ℹ 9 more variables: category_abbrev <chr>, rank <int>, athlete_id <chr>,
#> # team_id <chr>, display_value <chr>, value <dbl>, rel <chr>,
#> # athlete_ref <chr>, team_ref <chr>
# Default — RS + post, bound, keyed by season_type column.
espn_nba_season_leaders(season = 2024)
#> # A tibble: 800 × 15
#> league season season_type category_name category_display category_short
#> <chr> <int> <int> <chr> <chr> <chr>
#> 1 nba 2024 2 pointsPerGame Points Per Game PPG
#> 2 nba 2024 2 pointsPerGame Points Per Game PPG
#> 3 nba 2024 2 pointsPerGame Points Per Game PPG
#> 4 nba 2024 2 pointsPerGame Points Per Game PPG
#> 5 nba 2024 2 pointsPerGame Points Per Game PPG
#> 6 nba 2024 2 pointsPerGame Points Per Game PPG
#> 7 nba 2024 2 pointsPerGame Points Per Game PPG
#> 8 nba 2024 2 pointsPerGame Points Per Game PPG
#> 9 nba 2024 2 pointsPerGame Points Per Game PPG
#> 10 nba 2024 2 pointsPerGame Points Per Game PPG
#> # ℹ 790 more rows
#> # ℹ 9 more variables: category_abbrev <chr>, rank <int>, athlete_id <chr>,
#> # team_id <chr>, display_value <chr>, value <dbl>, rel <chr>,
#> # athlete_ref <chr>, team_ref <chr>What’s not here
A few endpoints exist on ESPN but are deliberately not wrapped:
| Endpoint | Why we skip it |
|---|---|
/teams/{id}/depth-charts |
ESPN basketball doesn’t carry depth charts |
cdn.espn.com/core game summary |
Fully redundant with espn_*_game_all()
|
core-v2 /events/{id}/plays
|
Strict downgrade of espn_*_pbp(); needs per-play
$ref resolution and returns less data |
espn_nba_rankings (would-be) |
The NBA has no poll; only MBB has
espn_mbb_rankings()
|
| MBB transfer portal | Not on ESPN’s basketball API |
And a handful of endpoints we do wrap that have known data quirks:
-
espn_mbb_injuries()andespn_mbb_team_injuries()– ESPN rarely populates injury data for college basketball. -
espn_mbb_event_odds()– always empty; ESPN doesn’t carry NCAA lines. -
espn_nba_freeagents()– empty outside the FA window. -
espn_*_athlete_awards()– sparse for most athletes. -
espn_*_athlete_eventlog()– returns$refURLs, not parsed stats. Use_athlete_gamelog()instead. -
espn_*_season_info()– mostly$refURLs. - web-common-v3 endpoints – some legacy seasons return HTTP 404.
When something looks off, an empty tibble is far more often the
correct, expected return than a bug. ESPN’s basketball coverage is
uneven across leagues, seasons, and franchise tenures, and
hoopR mirrors that unevenness honestly rather than papering
over it.