Accessing WNBA Data Sources

WNBA Data Landscape

Accessing quality WNBA data has historically been more challenging than NBA data, but the landscape has improved significantly in recent years. Modern tools and packages now provide programmatic access to play-by-play, box scores, player tracking, and advanced statistics.

Python: Accessing WNBA Data

import pandas as pd
import requests
import json
from datetime import datetime, timedelta

class WNBADataCollector:
    """Comprehensive WNBA data collection class"""

    def __init__(self):
        self.base_url = "https://site.api.espn.com/apis/site/v2/sports"
        self.wnba_endpoint = f"{self.base_url}/basketball/wnba"

    def get_season_scoreboard(self, season=2024):
        """Get all games for a WNBA season"""
        url = f"{self.wnba_endpoint}/scoreboard"
        params = {
            'limit': 1000,
            'dates': season
        }

        response = requests.get(url, params=params)
        if response.status_code == 200:
            return response.json()
        return None

    def get_team_stats(self, season=2024):
        """Fetch team statistics for season"""
        url = f"{self.wnba_endpoint}/teams"

        response = requests.get(url)
        if response.status_code != 200:
            return None

        teams_data = response.json()

        teams_list = []
        for team in teams_data.get('sports', [{}])[0].get('leagues', [{}])[0].get('teams', []):
            team_info = team.get('team', {})
            teams_list.append({
                'team_id': team_info.get('id'),
                'name': team_info.get('displayName'),
                'abbreviation': team_info.get('abbreviation'),
                'location': team_info.get('location')
            })

        return pd.DataFrame(teams_list)

    def get_player_stats(self, season=2024):
        """Fetch player statistics"""
        # ESPN doesn't have direct player stats endpoint for WNBA
        # Alternative: scrape from games or use Basketball Reference

        url = f"{self.wnba_endpoint}/athletes"
        response = requests.get(url)

        if response.status_code != 200:
            return None

        data = response.json()

        players_list = []
        for athlete in data.get('athletes', []):
            players_list.append({
                'player_id': athlete.get('id'),
                'name': athlete.get('fullName'),
                'position': athlete.get('position', {}).get('abbreviation'),
                'team': athlete.get('team', {}).get('abbreviation')
            })

        return pd.DataFrame(players_list)

    def get_game_details(self, game_id):
        """Get detailed play-by-play for specific game"""
        url = f"{self.wnba_endpoint}/summary"
        params = {'event': game_id}

        response = requests.get(url, params=params)
        if response.status_code == 200:
            return response.json()
        return None

    def extract_box_score(self, game_id):
        """Extract box score from game"""
        game_data = self.get_game_details(game_id)

        if not game_data:
            return None

        box_score = game_data.get('boxscore', {})
        players = box_score.get('players', [])

        all_player_stats = []

        for team in players:
            team_name = team.get('team', {}).get('displayName')
            team_abbr = team.get('team', {}).get('abbreviation')

            for player in team.get('statistics', [{}])[0].get('athletes', []):
                stats = {
                    'game_id': game_id,
                    'team': team_abbr,
                    'player': player.get('athlete', {}).get('displayName'),
                    'player_id': player.get('athlete', {}).get('id')
                }

                # Extract statistics
                for stat in player.get('stats', []):
                    stats[stat] = player['stats'][stat]

                all_player_stats.append(stats)

        return pd.DataFrame(all_player_stats)

# Example usage
collector = WNBADataCollector()

# Get team data
teams = collector.get_team_stats(season=2024)
print("WNBA Teams:")
print(teams)

# Get scoreboard for specific date
scoreboard = collector.get_season_scoreboard(season=2024)
if scoreboard and 'events' in scoreboard:
    games = scoreboard['events']
    print(f"\nFound {len(games)} games in 2024 season")

    # Get box score for first game
    if games:
        first_game_id = games[0]['id']
        box_score = collector.extract_box_score(first_game_id)
        print(f"\nBox Score Sample:")
        print(box_score.head())

# Advanced: Calculate team efficiency metrics
def calculate_team_efficiency(box_scores_df):
    """Calculate offensive and defensive efficiency from box scores"""

    team_stats = box_scores_df.groupby('team').agg({
        'PTS': 'sum',
        'FGM': 'sum',
        'FGA': 'sum',
        'FG3M': 'sum',
        'FTM': 'sum',
        'FTA': 'sum',
        'REB': 'sum',
        'AST': 'sum',
        'TOV': 'sum'
    }).reset_index()

    # Calculate efficiency metrics
    team_stats['FG_PCT'] = team_stats['FGM'] / team_stats['FGA']
    team_stats['TS_PCT'] = (
        team_stats['PTS'] /
        (2 * (team_stats['FGA'] + 0.44 * team_stats['FTA']))
    )
    team_stats['AST_TO_RATIO'] = team_stats['AST'] / team_stats['TOV']

    return team_stats

print("\n=== WNBA Data Access Methods ===")
print("1. ESPN API - Play-by-play and box scores")
print("2. Direct web scraping - Basketball Reference")
print("3. Manual data collection - WNBA Stats website")
print("4. R wehoop package - Most comprehensive for WNBA")

library(wehoop)
library(tidyverse)
library(lubridate)

# The wehoop package provides the most comprehensive WNBA data access in R

# =============================================================================
# 1. Get WNBA Team Information
# =============================================================================

# Load team data
wnba_teams <- wehoop::wnba_teams()

cat("WNBA Teams:\n")
print(wnba_teams %>%
  select(display_name, abbreviation, location, color))

# =============================================================================
# 2. Load Play-by-Play Data
# =============================================================================

# Load WNBA play-by-play for 2024 season
pbp_2024 <- wehoop::load_wnba_pbp(seasons = 2024)

cat("\nPlay-by-Play Data Structure:\n")
print(glimpse(pbp_2024))

# Sample of play-by-play data
cat("\nSample Plays:\n")
print(pbp_2024 %>%
  select(game_date, home_team, away_team, type_text,
         score_value, home_score, away_score) %>%
  head(10))

# =============================================================================
# 3. Load Team Box Scores
# =============================================================================

# Load team box scores for season
team_box <- wehoop::load_wnba_team_box(seasons = 2024)

cat("\nTeam Box Score Summary:\n")
print(team_box %>%
  select(game_date, team_display_name, team_winner,
         field_goals_made, field_goals_attempted,
         three_point_field_goals_made, total_rebounds,
         assists, turnovers) %>%
  head(10))

# =============================================================================
# 4. Load Player Box Scores
# =============================================================================

# Load player box scores
player_box <- wehoop::load_wnba_player_box(seasons = 2024)

cat("\nPlayer Box Score Sample:\n")
print(player_box %>%
  select(athlete_display_name, team_display_name,
         minutes, points, rebounds, assists, steals, blocks) %>%
  arrange(desc(points)) %>%
  head(10))

# =============================================================================
# 5. Advanced Data Processing
# =============================================================================

# Calculate team efficiency metrics
team_efficiency <- team_box %>%
  group_by(team_display_name) %>%
  summarise(
    games = n(),
    ppg = mean(team_score, na.rm = TRUE),
    fg_pct = sum(field_goals_made) / sum(field_goals_attempted),
    three_pt_pct = sum(three_point_field_goals_made) /
                   sum(three_point_field_goals_attempted),
    ft_pct = sum(free_throws_made) / sum(free_throws_attempted),
    rpg = mean(total_rebounds, na.rm = TRUE),
    apg = mean(assists, na.rm = TRUE),
    tov_pg = mean(turnovers, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  mutate(
    ts_pct = team_score / (2 * (field_goals_attempted +
                                 0.44 * free_throws_attempted))
  ) %>%
  arrange(desc(ppg))

cat("\nTeam Efficiency Rankings:\n")
print(team_efficiency)

# =============================================================================
# 6. Player Performance Analysis
# =============================================================================

# Top scorers
top_scorers <- player_box %>%
  group_by(athlete_display_name, team_display_name) %>%
  summarise(
    games = n(),
    total_points = sum(points, na.rm = TRUE),
    ppg = mean(points, na.rm = TRUE),
    rpg = mean(rebounds, na.rm = TRUE),
    apg = mean(assists, na.rm = TRUE),
    fg_pct = sum(field_goals_made) / sum(field_goals_attempted),
    .groups = "drop"
  ) %>%
  filter(games >= 10) %>%
  arrange(desc(ppg)) %>%
  head(20)

cat("\nTop Scorers (min 10 games):\n")
print(top_scorers)

# =============================================================================
# 7. Game Schedule and Results
# =============================================================================

# Load schedule
schedule <- wehoop::wnba_schedule(season = 2024)

cat("\nSchedule Information:\n")
print(schedule %>%
  select(game_date, home_display_name, away_display_name,
         home_score, away_score, status_type_completed) %>%
  head(10))

# =============================================================================
# 8. Visualization: Team Shooting Efficiency
# =============================================================================

ggplot(team_efficiency,
       aes(x = fg_pct, y = three_pt_pct,
           size = ppg, label = team_display_name)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  geom_text(size = 3, hjust = -0.1) +
  scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(
    title = "WNBA Team Shooting Efficiency 2024",
    subtitle = "Field Goal % vs Three-Point %, sized by PPG",
    x = "Field Goal %",
    y = "Three-Point %",
    size = "Points Per Game"
  ) +
  theme_minimal()

cat("\n=== wehoop Package Capabilities ===\n")
cat("✓ Play-by-play data\n")
cat("✓ Team and player box scores\n")
cat("✓ Schedule and standings\n")
cat("✓ Historical data (multiple seasons)\n")
cat("✓ NCAA Women's Basketball data\n")

Data Quality Considerations

WNBA data availability varies by source. ESPN and wehoop provide the most reliable access to play-by-play and box score data. For advanced tracking metrics (shot locations, defensive matchups), data is more limited compared to the NBA.