The State of Analytics in Women's Basketball

WNBA analytics has experienced tremendous growth over the past decade, evolving from basic box score analysis to sophisticated player tracking and advanced metrics. While the league initially lagged behind the NBA in terms of publicly available data and analytical infrastructure, recent years have seen significant improvements:

• Player Tracking Data: Second Spectrum tracking technology was implemented league-wide, providing detailed spatial and movement data
• Advanced Metrics: Teams increasingly employ data scientists and analysts to develop proprietary models
• Public Interest: Growing community of researchers, journalists, and fans analyzing WNBA data
• Academic Research: More scholarly studies examining performance, equity, and competitive dynamics in women's basketball

The analytical landscape continues to mature as teams recognize the competitive advantages that data-driven decision making provides in roster construction, game strategy, and player development.

Available Data Sources

Several data sources are available for WNBA analytics, each with different levels of detail and accessibility:

Official WNBA Sources

• WNBA Stats API: Official statistics API providing play-by-play, box scores, and advanced metrics
• WNBA.com: Official website with game logs, player profiles, and season statistics
• Second Spectrum: Tracking data (limited public access, primarily for teams)

Third-Party Data Providers

• Her Hoop Stats: Comprehensive WNBA statistics, play-by-play data, and analytical content
• Basketball Reference (WNBA): Historical statistics, advanced metrics, and player comparisons
• Synergy Sports: Video analysis and play-type data (subscription required)
• wehoop Package (R): Direct access to WNBA play-by-play and box score data
• wnba_api (Python): Community-developed Python wrapper for WNBA Stats API

Community Resources

• GitHub Repositories: Open-source datasets and analytical tools
• Kaggle: WNBA datasets for machine learning and analysis
• Social Media: Analysts and journalists sharing insights and data visualizations

Python Analysis: WNBA Player Performance

Python is widely used for WNBA analytics due to its rich ecosystem of data science libraries. Here's an example analyzing player efficiency and shot selection:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime

# Example: Analyzing WNBA player efficiency and shot distribution
# Note: This uses simulated data structure similar to WNBA API responses

class WNBAPlayerAnalyzer:
    """Analyze WNBA player performance metrics."""

    def __init__(self, player_data):
        """
        Initialize with player data.

        Parameters:
        -----------
        player_data : pd.DataFrame
            DataFrame with columns: player_name, team, minutes, points,
            rebounds, assists, steals, blocks, turnovers, fg_made, fg_attempted,
            three_made, three_attempted, ft_made, ft_attempted
        """
        self.data = player_data
        self.calculate_advanced_metrics()

    def calculate_advanced_metrics(self):
        """Calculate advanced efficiency metrics."""
        df = self.data

        # True Shooting Percentage
        df['ts_pct'] = (df['points'] /
                       (2 * (df['fg_attempted'] + 0.44 * df['ft_attempted']))) * 100

        # Effective Field Goal Percentage
        df['efg_pct'] = ((df['fg_made'] + 0.5 * df['three_made']) /
                         df['fg_attempted']) * 100

        # Player Efficiency Rating (simplified version)
        df['per'] = ((df['points'] + df['rebounds'] + df['assists'] +
                     df['steals'] + df['blocks'] -
                     (df['fg_attempted'] - df['fg_made']) -
                     (df['ft_attempted'] - df['ft_made']) -
                     df['turnovers']) / df['minutes']) * 36

        # Usage Rate (simplified - actual calculation requires team totals)
        total_possessions = df['fg_attempted'] + 0.44 * df['ft_attempted'] + df['turnovers']
        df['usage_rate'] = (total_possessions / df['minutes']) * 40

        # Assist to Turnover Ratio
        df['ast_to_ratio'] = df['assists'] / df['turnovers'].replace(0, 1)

        self.data = df

    def plot_efficiency_vs_usage(self, min_minutes=200):
        """Plot True Shooting % vs Usage Rate."""
        df = self.data[self.data['minutes'] >= min_minutes]

        plt.figure(figsize=(12, 8))
        scatter = plt.scatter(df['usage_rate'], df['ts_pct'],
                            s=df['minutes']/10, alpha=0.6,
                            c=df['per'], cmap='viridis')

        # Add player labels for top performers
        for idx, row in df.nlargest(10, 'per').iterrows():
            plt.annotate(row['player_name'],
                        (row['usage_rate'], row['ts_pct']),
                        fontsize=8, alpha=0.7)

        plt.xlabel('Usage Rate (%)', fontsize=12)
        plt.ylabel('True Shooting %', fontsize=12)
        plt.title('WNBA Player Efficiency vs Usage Rate\n(Size = Minutes Played, Color = PER)',
                 fontsize=14)
        plt.colorbar(scatter, label='Player Efficiency Rating')
        plt.grid(True, alpha=0.3)
        plt.tight_layout()
        return plt

    def analyze_shot_distribution(self, player_name):
        """Analyze shot selection for a specific player."""
        player = self.data[self.data['player_name'] == player_name].iloc[0]

        # Calculate shot distribution
        two_pt_attempted = player['fg_attempted'] - player['three_attempted']
        two_pt_made = player['fg_made'] - player['three_made']

        shot_data = {
            '3PT': {
                'made': player['three_made'],
                'attempted': player['three_attempted'],
                'pct': (player['three_made'] / player['three_attempted'] * 100
                       if player['three_attempted'] > 0 else 0)
            },
            '2PT': {
                'made': two_pt_made,
                'attempted': two_pt_attempted,
                'pct': (two_pt_made / two_pt_attempted * 100
                       if two_pt_attempted > 0 else 0)
            },
            'FT': {
                'made': player['ft_made'],
                'attempted': player['ft_attempted'],
                'pct': (player['ft_made'] / player['ft_attempted'] * 100
                       if player['ft_attempted'] > 0 else 0)
            }
        }

        return shot_data

    def get_top_performers(self, metric='per', n=10):
        """Get top N players by specified metric."""
        return self.data.nlargest(n, metric)[
            ['player_name', 'team', 'minutes', 'points',
             'ts_pct', 'per', 'usage_rate']
        ]

# Example usage with simulated data
def create_sample_wnba_data():
    """Create sample WNBA player data for demonstration."""
    np.random.seed(42)
    n_players = 50

    data = {
        'player_name': [f'Player_{i}' for i in range(n_players)],
        'team': np.random.choice(['LVA', 'NYL', 'SEA', 'CHI', 'PHX', 'MIN'], n_players),
        'minutes': np.random.randint(200, 1200, n_players),
        'points': np.random.randint(100, 800, n_players),
        'rebounds': np.random.randint(50, 400, n_players),
        'assists': np.random.randint(30, 300, n_players),
        'steals': np.random.randint(10, 80, n_players),
        'blocks': np.random.randint(5, 60, n_players),
        'turnovers': np.random.randint(20, 120, n_players),
        'fg_made': np.random.randint(80, 350, n_players),
        'fg_attempted': np.random.randint(180, 750, n_players),
        'three_made': np.random.randint(10, 100, n_players),
        'three_attempted': np.random.randint(30, 280, n_players),
        'ft_made': np.random.randint(20, 180, n_players),
        'ft_attempted': np.random.randint(25, 220, n_players),
    }

    return pd.DataFrame(data)

# Run analysis
if __name__ == "__main__":
    # Load or create data
    player_data = create_sample_wnba_data()

    # Initialize analyzer
    analyzer = WNBAPlayerAnalyzer(player_data)

    # Get top performers
    print("Top 10 Players by PER:")
    print(analyzer.get_top_performers(metric='per', n=10))

    # Plot efficiency vs usage
    plt = analyzer.plot_efficiency_vs_usage(min_minutes=300)
    plt.savefig('wnba_efficiency_usage.png', dpi=300, bbox_inches='tight')

    # Analyze specific player
    shot_dist = analyzer.analyze_shot_distribution('Player_0')
    print(f"\nShot Distribution for Player_0:")
    for shot_type, stats in shot_dist.items():
        print(f"{shot_type}: {stats['made']}/{stats['attempted']} ({stats['pct']:.1f}%)")

Key Analysis Components:

• Advanced efficiency metrics (TS%, eFG%, PER)
• Usage rate calculations to understand offensive load
• Shot distribution analysis across different shot types
• Visualization of efficiency vs volume trade-offs
• Comparative analysis across players and teams

R Analysis: WNBA Statistics with wehoop

The wehoop package provides direct access to WNBA data in R. Here's a comprehensive example analyzing team performance and player statistics:

# Install and load required packages
# install.packages("wehoop")
# install.packages("tidyverse")
# install.packages("ggplot2")

library(wehoop)
library(tidyverse)
library(ggplot2)

# Load WNBA play-by-play data for a season
wnba_pbp <- load_wnba_pbp(seasons = 2023)

# Load WNBA team box scores
wnba_team_box <- load_wnba_team_box(seasons = 2023)

# Load player box scores
wnba_player_box <- load_wnba_player_box(seasons = 2023)

# ============================================
# Analysis 1: Team Offensive Efficiency
# ============================================

team_efficiency <- wnba_team_box %>%
  group_by(team_display_name) %>%
  summarise(
    games = n(),
    total_points = sum(field_goals_made * 2 +
                      field_goals_made3_pt +
                      free_throws_made, na.rm = TRUE),
    total_possessions = sum(field_goals_attempted +
                           0.44 * free_throws_attempted +
                           turnovers -
                           offensive_rebounds, na.rm = TRUE),
    offensive_rating = (total_points / total_possessions) * 100,
    pace = total_possessions / games,
    fg_pct = mean(field_goals_pct, na.rm = TRUE),
    three_pt_rate = sum(field_goals_attempted3_pt, na.rm = TRUE) /
                    sum(field_goals_attempted, na.rm = TRUE),
    three_pt_pct = mean(field_goals_pct3_pt, na.rm = TRUE),
    ft_rate = sum(free_throws_attempted, na.rm = TRUE) /
              sum(field_goals_attempted, na.rm = TRUE),
    ast_pct = sum(assists, na.rm = TRUE) /
              sum(field_goals_made, na.rm = TRUE),
    tov_pct = sum(turnovers, na.rm = TRUE) / total_possessions * 100
  ) %>%
  arrange(desc(offensive_rating))

# Visualize team offensive ratings
ggplot(team_efficiency, aes(x = reorder(team_display_name, offensive_rating),
                            y = offensive_rating, fill = offensive_rating)) +
  geom_col() +
  geom_text(aes(label = round(offensive_rating, 1)),
            hjust = -0.2, size = 3) +
  coord_flip() +
  scale_fill_gradient(low = "#FF6B6B", high = "#4ECDC4") +
  labs(title = "WNBA Team Offensive Ratings (2023)",
       subtitle = "Points per 100 possessions",
       x = "Team",
       y = "Offensive Rating") +
  theme_minimal() +
  theme(legend.position = "none",
        plot.title = element_text(face = "bold", size = 14))

# ============================================
# Analysis 2: Player Performance Metrics
# ============================================

player_stats <- wnba_player_box %>%
  group_by(athlete_display_name, team_display_name) %>%
  summarise(
    games = n(),
    minutes = sum(minutes, na.rm = TRUE),
    points = sum(points, na.rm = TRUE),
    rebounds = sum(rebounds_total, na.rm = TRUE),
    assists = sum(assists, na.rm = TRUE),
    steals = sum(steals, na.rm = TRUE),
    blocks = sum(blocks, na.rm = TRUE),
    turnovers = sum(turnovers, na.rm = TRUE),
    fg_made = sum(field_goals_made, na.rm = TRUE),
    fg_att = sum(field_goals_attempted, na.rm = TRUE),
    three_made = sum(field_goals_made3_pt, na.rm = TRUE),
    three_att = sum(field_goals_attempted3_pt, na.rm = TRUE),
    ft_made = sum(free_throws_made, na.rm = TRUE),
    ft_att = sum(free_throws_attempted, na.rm = TRUE),
    .groups = 'drop'
  ) %>%
  filter(games >= 10) %>%  # Minimum games threshold
  mutate(
    # Advanced metrics
    ppg = points / games,
    rpg = rebounds / games,
    apg = assists / games,
    mpg = minutes / games,
    ts_pct = points / (2 * (fg_att + 0.44 * ft_att)),
    efg_pct = (fg_made + 0.5 * three_made) / fg_att,
    ast_to_ratio = assists / ifelse(turnovers == 0, 1, turnovers),
    # Per 36 minutes stats
    pts_per36 = (points / minutes) * 36,
    reb_per36 = (rebounds / minutes) * 36,
    ast_per36 = (assists / minutes) * 36
  )

# Top scorers
top_scorers <- player_stats %>%
  arrange(desc(ppg)) %>%
  select(athlete_display_name, team_display_name, games, ppg,
         ts_pct, efg_pct) %>%
  head(10)

print("Top 10 Scorers:")
print(top_scorers)

# ============================================
# Analysis 3: Shot Distribution Analysis
# ============================================

shot_analysis <- player_stats %>%
  mutate(
    two_pt_att = fg_att - three_att,
    two_pt_made = fg_made - three_made,
    two_pt_pct = ifelse(two_pt_att > 0, two_pt_made / two_pt_att, 0),
    three_pt_pct = ifelse(three_att > 0, three_made / three_att, 0),
    three_pt_rate = three_att / fg_att,
    three_pt_freq = three_att / games,
    two_pt_freq = two_pt_att / games
  ) %>%
  filter(minutes >= 300)  # Minimum minutes threshold

# Visualize 3PT shooting efficiency vs volume
ggplot(shot_analysis, aes(x = three_pt_freq, y = three_pt_pct)) +
  geom_point(aes(size = points, color = ts_pct), alpha = 0.6) +
  geom_hline(yintercept = mean(shot_analysis$three_pt_pct, na.rm = TRUE),
             linetype = "dashed", color = "red", alpha = 0.5) +
  geom_vline(xintercept = mean(shot_analysis$three_pt_freq, na.rm = TRUE),
             linetype = "dashed", color = "blue", alpha = 0.5) +
  scale_color_gradient(low = "#FFA500", high = "#4169E1",
                       name = "True Shooting %") +
  labs(title = "WNBA 3-Point Shooting: Efficiency vs Volume",
       subtitle = "2023 Season (min 300 minutes)",
       x = "3-Point Attempts per Game",
       y = "3-Point Field Goal %",
       size = "Total Points") +
  theme_minimal() +
  theme(plot.title = element_text(face = "bold", size = 14))

# ============================================
# Analysis 4: Play-by-Play Shot Chart Analysis
# ============================================

# Filter for shot attempts from play-by-play data
shots <- wnba_pbp %>%
  filter(!is.na(shooting_play) & shooting_play == TRUE) %>%
  mutate(
    shot_made = scoring_play == TRUE,
    shot_value = case_when(
      type_text %in% c("Three Point Jumper", "3pt Fadeaway Jumper") ~ 3,
      type_text == "Free Throw" ~ 1,
      TRUE ~ 2
    ),
    distance_category = case_when(
      shot_value == 3 ~ "Three Point",
      coordinate_x < 10 ~ "At Rim",
      coordinate_x < 20 ~ "Paint",
      TRUE ~ "Mid-Range"
    )
  )

# Shot efficiency by distance
shot_efficiency <- shots %>%
  group_by(distance_category) %>%
  summarise(
    attempts = n(),
    makes = sum(shot_made, na.rm = TRUE),
    fg_pct = makes / attempts,
    points = sum(ifelse(shot_made, shot_value, 0), na.rm = TRUE),
    pps = points / attempts  # Points per shot
  ) %>%
  arrange(desc(pps))

print("Shot Efficiency by Distance:")
print(shot_efficiency)

# ============================================
# Analysis 5: Team Four Factors
# ============================================

four_factors <- wnba_team_box %>%
  group_by(team_display_name) %>%
  summarise(
    # Shooting (eFG%)
    efg_pct = mean((field_goals_made + 0.5 * field_goals_made3_pt) /
                   field_goals_attempted, na.rm = TRUE),
    # Turnovers (TOV%)
    tov_pct = mean(turnovers / (field_goals_attempted +
                                 0.44 * free_throws_attempted +
                                 turnovers), na.rm = TRUE),
    # Rebounding (ORB%)
    orb_pct = mean(offensive_rebounds / (offensive_rebounds +
                   opponent_rebounds_defensive), na.rm = TRUE),
    # Free Throws (FT Rate)
    ft_rate = mean(free_throws_made / field_goals_attempted, na.rm = TRUE)
  ) %>%
  arrange(desc(efg_pct))

print("Team Four Factors:")
print(four_factors)

# ============================================
# Analysis 6: Player Consistency Analysis
# ============================================

player_consistency <- wnba_player_box %>%
  filter(!is.na(points)) %>%
  group_by(athlete_display_name) %>%
  filter(n() >= 15) %>%  # Minimum games
  summarise(
    games = n(),
    avg_points = mean(points, na.rm = TRUE),
    sd_points = sd(points, na.rm = TRUE),
    cv = sd_points / avg_points,  # Coefficient of variation
    max_points = max(points, na.rm = TRUE),
    min_points = min(points, na.rm = TRUE),
    games_over_20 = sum(points >= 20),
    consistency_score = avg_points / cv  # Higher = more consistent
  ) %>%
  filter(avg_points >= 10) %>%
  arrange(desc(consistency_score))

print("Most Consistent High-Volume Scorers:")
print(head(player_consistency, 10))

Analysis Highlights:

• Team offensive efficiency using possession-based metrics
• Player performance with advanced statistics (TS%, eFG%, per-36 rates)
• Shot distribution and efficiency by distance
• Four Factors analysis (shooting, turnovers, rebounding, free throws)
• Player consistency metrics using coefficient of variation
• Play-by-play shot chart analysis with spatial data

Key Differences from NBA Analytics

While WNBA analytics borrows many concepts from NBA analysis, there are important differences to consider:

1. Data Availability and Access

• Limited Historical Data: WNBA has fewer seasons of comprehensive data compared to the NBA
• Tracking Data: Less publicly available player tracking and SportVU-style data
• Smaller API Ecosystem: Fewer third-party tools and libraries for WNBA data access
• Video Resources: Less extensive video archives for advanced scouting analysis

2. Game Dynamics

• Shorter Games: 40-minute games (vs. 48 for NBA) affects pace and per-game statistics
• Ball Size: Smaller ball (28.5" vs. 29.5") impacts shooting percentages and comparisons
• Three-Point Line: Same distance but different shooting profiles and volume
• Pace of Play: Generally higher-paced games with different strategic emphasis

3. Statistical Norms

• Shooting Percentages: Different baseline percentages require adjusted benchmarks
• Physical Play: Different foul rates and free throw attempt patterns
• Position Definitions: More fluid position-less basketball in many systems
• Statistical Outliers: Smaller league size means individual dominance can be more pronounced

4. League Structure

• Shorter Season: 40-game regular season (vs. 82 for NBA) impacts sample sizes
• Roster Size: 12-player rosters create different rotation patterns
• Salary Cap Structure: Different economic constraints affect roster construction
• International Play: Many players compete overseas, affecting availability and fatigue

5. Analytical Adjustments

• Per-Game vs. Per-Minute: Shorter games require different normalization approaches
• Small Sample Sizes: More volatility in season-long statistics due to fewer games
• Playoff Format: Different playoff structure affects predictive modeling
• Benchmark Metrics: Need WNBA-specific baselines for "league average" comparisons

Growth of WNBA Analytics

The WNBA analytics community has grown substantially, driven by multiple factors:

Increased Investment in Analytics

• Team Analytics Departments: Most teams now employ dedicated analysts and data scientists
• Technology Partnerships: Collaborations with Second Spectrum and other tech providers
• Data Infrastructure: Improved data collection, storage, and access systems
• Coaching Integration: Growing acceptance of analytics in coaching decisions

Media and Public Engagement

• Analytical Journalism: More journalists incorporating advanced metrics in coverage
• Social Media Presence: Active community of analysts sharing insights on Twitter/X
• Podcasts and Content: Growing number of analytics-focused WNBA podcasts and articles
• Visualization: Creative data visualizations highlighting WNBA player and team performance

Academic and Research Interest

• Gender Equity Research: Studies examining pay, media coverage, and performance metrics
• Sports Science: Research on women's basketball physiology and performance
• Machine Learning Applications: Predictive models for player development and game outcomes
• Conference Presentations: WNBA analytics featured at Sloan, SABR, and other conferences

Open Source Tools and Community

• wehoop Package: R package providing easy access to WNBA data
• Python Libraries: Community-developed tools for WNBA data analysis
• GitHub Repositories: Shared code and datasets for collaborative analysis
• Her Hoop Stats: Comprehensive public resource for WNBA analytics

Notable Milestones

• 2017: Second Spectrum player tracking installed in all WNBA arenas
• 2018: WNBA launches enhanced stats API with play-by-play data
• 2020: Increased media coverage of advanced WNBA metrics during pandemic season
• 2022: wehoop package released, dramatically improving R data access
• 2023: Multiple WNBA analytics panels at major sports analytics conferences

Future Directions

The future of WNBA analytics is promising, with several exciting developments on the horizon:

Technology and Data Collection

• Enhanced Tracking Data: More granular player and ball tracking with skeletal tracking
• Wearable Technology: Biometric data for load management and injury prevention
• Computer Vision: Automated video analysis for play classification and scouting
• Shot Quality Models: WNBA-specific expected field goal percentage models
• Defensive Metrics: Better quantification of individual and team defensive impact

Advanced Modeling

• Player Projection Systems: Improved models for rookie performance and player aging curves
• Win Probability Models: Real-time game prediction incorporating WNBA-specific factors
• Lineup Optimization: Data-driven approaches to rotation and substitution patterns
• Draft Analytics: Predictive models for college-to-WNBA transition
• Contract Value Analysis: Quantifying player value relative to salary cap constraints

Integration with Strategy

• Play Calling Analytics: Data-driven play selection based on defensive matchups
• Opponent Scouting: Automated opponent tendency reports and weakness identification
• In-Game Adjustments: Real-time analytics dashboards for coaching staffs
• Player Development: Personalized training programs based on performance data
• Fatigue Management: Load monitoring for players competing year-round

Equity and Representation

• Pay Equity Analysis: Data-driven advocacy for fair compensation based on performance
• Media Coverage Metrics: Quantifying media exposure and advocating for increased coverage
• Fan Engagement: Analytics demonstrating WNBA's growing popularity and value
• Comparative Analysis: Fair comparisons between women's and men's basketball metrics
• Youth Development: Analytics informing girls' basketball training and development programs

Accessibility and Education

• Open Data Initiatives: More publicly available WNBA datasets for research and analysis
• Educational Resources: Tutorials and courses focused on WNBA analytics
• Community Building: Networks connecting WNBA analysts, researchers, and practitioners
• Diversity in Analytics: Encouraging more women and underrepresented groups in sports analytics
• Fan-Facing Analytics: Making advanced metrics more accessible to casual fans

Research Opportunities

• Cross-Sport Analysis: Comparing strategies and metrics across women's basketball globally
• Longitudinal Studies: Tracking player development and league evolution over time
• Psychological Factors: Integrating mental performance data with physical metrics
• International Comparisons: Analyzing how international play affects WNBA performance
• Historical Analysis: Digitizing and analyzing historical WNBA data for trend analysis

Getting Started with WNBA Analytics

For those interested in exploring WNBA analytics, here are recommended steps:

1. Learn the Fundamentals

• Understand basic basketball statistics and their limitations
• Study advanced metrics like TS%, eFG%, PER, and BPM
• Learn about possession-based statistics and four factors
• Familiarize yourself with WNBA rules and game structure

2. Access Data Sources

• Explore the WNBA Stats API for official data
• Use wehoop (R) or Python libraries for easy data access
• Visit Her Hoop Stats for comprehensive statistics and analysis
• Check Basketball Reference for historical data

3. Develop Technical Skills

• Learn R or Python for data analysis and visualization
• Practice cleaning and manipulating sports data
• Experiment with statistical modeling and machine learning
• Create compelling data visualizations to communicate insights

4. Engage with the Community

• Follow WNBA analysts on social media
• Participate in online discussions and forums
• Share your own analyses and visualizations
• Attend sports analytics conferences and presentations

5. Contribute to the Field

• Publish your analyses on blogs or Medium
• Contribute to open-source WNBA analytics projects
• Conduct original research on women's basketball
• Advocate for better data access and analytical resources

Conclusion

WNBA analytics has evolved from a nascent field into a sophisticated discipline that combines statistical rigor, technological innovation, and basketball expertise. While challenges remain in terms of data availability and historical depth, the community continues to grow and produce valuable insights.

The unique characteristics of the WNBA—shorter games, different pace, distinct shooting profiles—require analysts to adapt NBA methodologies while developing WNBA-specific approaches. As technology improves and more resources are dedicated to women's basketball analytics, we can expect even more sophisticated analysis and deeper understanding of the game.

Whether you're a team analyst, academic researcher, journalist, or passionate fan, there are abundant opportunities to contribute to WNBA analytics. The field welcomes diverse perspectives and innovative approaches, ensuring continued growth and impact for years to come.