NBA Stats API Deep Dive

# NBA Stats API Deep Dive ## Introduction The NBA Stats API provides programmatic access to comprehensive basketball statistics directly from NBA.com. This guide covers authentication, endpoints, and data extraction techniques. ## Python Implementation ```python import requests import pandas as pd from time import sleep class NBAStatsAPI: def __init__(self): self.base_url = "https://stats.nba.com/stats" self.headers = { 'User-Agent': 'Mozilla/5.0', 'Referer': 'https://stats.nba.com/', 'Accept': 'application/json' } def get_player_stats(self, season='2023-24', per_mode='PerGame'): """Fetch player statistics for a given season""" endpoint = f"{self.base_url}/leaguedashplayerstats" params = { 'Season': season, 'SeasonType': 'Regular Season', 'PerMode': per_mode, 'MeasureType': 'Base' } response = requests.get(endpoint, headers=self.headers, params=params) data = response.json() # Convert to DataFrame headers = data['resultSets'][0]['headers'] rows = data['resultSets'][0]['rowSet'] return pd.DataFrame(rows, columns=headers) def get_team_stats(self, season='2023-24'): """Fetch team statistics""" endpoint = f"{self.base_url}/leaguedashteamstats" params = { 'Season': season, 'SeasonType': 'Regular Season' } sleep(0.6) # Rate limiting response = requests.get(endpoint, headers=self.headers, params=params) data = response.json() headers = data['resultSets'][0]['headers'] rows = data['resultSets'][0]['rowSet'] return pd.DataFrame(rows, columns=headers) # Usage api = NBAStatsAPI() player_stats = api.get_player_stats(season='2023-24') print(player_stats.head()) ``` ## R Implementation ```r library(httr) library(jsonlite) library(dplyr) get_nba_player_stats <- function(season = "2023-24", per_mode = "PerGame") { base_url <- "https://stats.nba.com/stats/leaguedashplayerstats" headers <- c( `User-Agent` = "Mozilla/5.0", `Referer` = "https://stats.nba.com/", `Accept` = "application/json" ) params <- list( Season = season, SeasonType = "Regular Season", PerMode = per_mode, MeasureType = "Base" ) response <- GET(base_url, query = params, add_headers(.headers = headers)) data <- content(response, as = "parsed") # Extract and convert to data frame headers <- unlist(data$resultSets[[1]]$headers) rows <- data$resultSets[[1]]$rowSet df <- do.call(rbind, lapply(rows, function(x) as.data.frame(t(unlist(x))))) colnames(df) <- headers return(df) } # Usage player_stats <- get_nba_player_stats(season = "2023-24") head(player_stats) ``` ## Key Endpoints - `/leaguedashplayerstats` - Player statistics - `/leaguedashteamstats` - Team statistics - `/playergamelog` - Game-by-game logs - `/shotchartdetail` - Shot location data - `/boxscoretraditionalv2` - Box scores ## Best Practices 1. Always include proper headers (User-Agent, Referer) 2. Implement rate limiting (0.6s between requests) 3. Handle API errors gracefully 4. Cache responses when possible 5. Respect NBA.com terms of service