NBA Player Stats MCP Server

#!/usr/bin/env python3 """ Example usage of the NBA Player Stats MCP Server This script demonstrates how to use the basketball_reference_scraper library with the fixes applied for retrieving NBA player statistics. """ # Import the fix first import fix_basketball_reference from basketball_reference_scraper.players import get_stats, get_player_headshot import pandas as pd def demonstrate_player_stats(): """Demonstrate various player statistics queries""" print("=== NBA Player Stats Examples ===\n") # 1. Career statistics print("1. LeBron James Career Stats (Per Game)") lebron_stats = get_stats('LeBron James', stat_type='PER_GAME', ask_matches=False) career_row = lebron_stats[lebron_stats['SEASON'] == 'Career'] if not career_row.empty: print(f"Career PPG: {career_row['PTS'].values[0]}") print(f"Career RPG: {career_row['TRB'].values[0]}") print(f"Career APG: {career_row['AST'].values[0]}") print() # 2. Specific season stats print("2. Stephen Curry 2015-16 Season (MVP Season)") curry_stats = get_stats('Stephen Curry', stat_type='PER_GAME', ask_matches=False) mvp_season = curry_stats[curry_stats['SEASON'] == '2015-16'] if not mvp_season.empty: print(f"PPG: {mvp_season['PTS'].values[0]}") print(f"3P%: {mvp_season['3P%'].values[0]}") print(f"3PM per game: {mvp_season['3P'].values[0]}") print() # 3. Advanced stats print("3. Nikola Jokić Advanced Stats (2022-23)") jokic_advanced = get_stats('Nikola Jokić', stat_type='ADVANCED', ask_matches=False) jokic_2023 = jokic_advanced[jokic_advanced['SEASON'] == '2022-23'] if not jokic_2023.empty: print(f"PER: {jokic_2023['PER'].values[0]}") print(f"Win Shares: {jokic_2023['WS'].values[0]}") print(f"BPM: {jokic_2023['BPM'].values[0]}") print() # 4. Playoff vs Regular Season print("4. Kawhi Leonard Playoff Performance") kawhi_regular = get_stats('Kawhi Leonard', stat_type='PER_GAME', playoffs=False, ask_matches=False) kawhi_playoffs = get_stats('Kawhi Leonard', stat_type='PER_GAME', playoffs=True, ask_matches=False) regular_career = kawhi_regular[kawhi_regular['SEASON'] == 'Career'] playoff_career = kawhi_playoffs[kawhi_playoffs['SEASON'] == 'Career'] if not regular_career.empty and not playoff_career.empty: print(f"Regular Season PPG: {regular_career['PTS'].values[0]}") print(f"Playoff PPG: {playoff_career['PTS'].values[0]}") print(f"Playoff PPG increase: +{float(playoff_career['PTS'].values[0]) - float(regular_career['PTS'].values[0]):.1f}") print() # 5. Career totals print("5. Kareem Abdul-Jabbar Career Totals") kareem_totals = get_stats('Kareem Abdul-Jabbar', stat_type='TOTALS', ask_matches=False) career_totals = kareem_totals[kareem_totals['SEASON'] == 'Career'] if not career_totals.empty: print(f"Total Points: {career_totals['PTS'].values[0]:,}") print(f"Total Rebounds: {career_totals['TRB'].values[0]:,}") print(f"Games Played: {career_totals['G'].values[0]:,}") print() # 6. Per-36 minute stats print("6. Giannis Antetokounmpo Per-36 Stats (2022-23)") giannis_per36 = get_stats('Giannis Antetokounmpo', stat_type='PER_MINUTE', ask_matches=False) giannis_2023 = giannis_per36[giannis_per36['SEASON'] == '2022-23'] if not giannis_2023.empty: print(f"Points per 36 min: {giannis_2023['PTS'].values[0]}") print(f"Rebounds per 36 min: {giannis_2023['TRB'].values[0]}") print(f"Assists per 36 min: {giannis_2023['AST'].values[0]}") print() def compare_players(): """Compare two players""" print("=== Player Comparison: LeBron vs Jordan ===\n") lebron = get_stats('LeBron James', stat_type='PER_GAME', career=True, ask_matches=False) jordan = get_stats('Michael Jordan', stat_type='PER_GAME', career=True, ask_matches=False) print(f"{'Stat':<15} {'LeBron':<10} {'Jordan':<10}") print("-" * 35) print(f"{'PPG':<15} {lebron['PTS'].values[0]:<10} {jordan['PTS'].values[0]:<10}") print(f"{'RPG':<15} {lebron['TRB'].values[0]:<10} {jordan['TRB'].values[0]:<10}") print(f"{'APG':<15} {lebron['AST'].values[0]:<10} {jordan['AST'].values[0]:<10}") print(f"{'FG%':<15} {lebron['FG%'].values[0]:<10} {jordan['FG%'].values[0]:<10}") print(f"{'3P%':<15} {lebron['3P%'].values[0]:<10} {jordan['3P%'].values[0]:<10}") print() def shooting_analysis(): """Analyze shooting stats for elite shooters""" print("=== Elite Shooters Analysis ===\n") shooters = ['Stephen Curry', 'Ray Allen', 'Reggie Miller'] for shooter in shooters: stats = get_stats(shooter, stat_type='PER_GAME', ask_matches=False) career = stats[stats['SEASON'] == 'Career'] if not career.empty: print(f"{shooter}:") print(f" Career 3P%: {career['3P%'].values[0]}") print(f" Career 3PM/game: {career['3P'].values[0]}") print(f" Career 3PA/game: {career['3PA'].values[0]}") # Find best 3P% season (minimum 100 attempts) seasons = stats[stats['SEASON'] != 'Career'] seasons_filtered = seasons[seasons['3PA'] > 2.0] # At least 2 attempts per game if not seasons_filtered.empty: best_season_idx = seasons_filtered['3P%'].idxmax() best_season = seasons_filtered.loc[best_season_idx] print(f" Best 3P% Season: {best_season['SEASON']} ({best_season['3P%']})") print() def demonstrate_deep_analytics(): """Demonstrate the new Layer 2 deep analytics capabilities""" print("=== Layer 2 Deep Analytics Examples ===\n") # 1. Specific stat query print("1. Specific Stat Query: Steph Curry's 3P% in 2018") curry_stats = get_stats('Stephen Curry', stat_type='PER_GAME', ask_matches=False) curry_2018 = curry_stats[curry_stats['SEASON'] == '2017-18'] if not curry_2018.empty: print(f" 3P% in 2017-18: {curry_2018['3P%'].values[0]}") print(f" 3PM per game: {curry_2018['3P'].values[0]}") print(f" 3PA per game: {curry_2018['3PA'].values[0]}") print() # 2. Points in a specific year print("2. Points Query: How many points did Steph Curry average in 2024?") curry_2024 = curry_stats[curry_stats['SEASON'] == '2023-24'] if not curry_2024.empty: print(f" PPG in 2023-24: {curry_2024['PTS'].values[0]}") print(f" Games played: {curry_2024['G'].values[0]}") print() # 3. Advanced stat lookup print("3. MVP-caliber Season: Giannis Antetokounmpo's PER in 2020") giannis_advanced = get_stats('Giannis Antetokounmpo', stat_type='ADVANCED', ask_matches=False) giannis_2020 = giannis_advanced[giannis_advanced['SEASON'] == '2019-20'] if not giannis_2020.empty: print(f" PER: {giannis_2020['PER'].values[0]}") print(f" Win Shares: {giannis_2020['WS'].values[0]}") print(f" VORP: {giannis_2020['VORP'].values[0]}") print() # 4. Playoff performance by year print("4. Playoff Year Analysis: Jimmy Butler 2020 Bubble Run") butler_playoffs = get_stats('Jimmy Butler', stat_type='PER_GAME', playoffs=True, ask_matches=False) butler_2020 = butler_playoffs[butler_playoffs['SEASON'] == '2019-20'] if not butler_2020.empty: print(f" Playoff PPG: {butler_2020['PTS'].values[0]}") print(f" Playoff APG: {butler_2020['AST'].values[0]}") print(f" Games played: {butler_2020['G'].values[0]}") print() # 5. Efficiency stats print("5. Efficiency Query: Nikola Jokić's True Shooting % in 2023") jokic_2023 = get_stats('Nikola Jokić', stat_type='ADVANCED', ask_matches=False) jokic_season = jokic_2023[jokic_2023['SEASON'] == '2022-23'] if not jokic_season.empty: print(f" TS%: {jokic_season['TS%'].values[0]}") print(f" eFG%: {jokic_season['eFG%'].values[0]}") print() def demonstrate_ultra_deep_analytics(): """Demonstrate Layer 3 ultra-deep analytics capabilities""" print("=== Layer 3 Ultra-Deep Analytics Examples ===\n") # 1. Career trend analysis print("1. Career Trends: Is LeBron James declining?") lebron_stats = get_stats('LeBron James', stat_type='PER_GAME', ask_matches=False) recent_5_years = lebron_stats[lebron_stats['SEASON'] != 'Career'].tail(5) if len(recent_5_years) >= 2: first_year = recent_5_years.iloc[0] last_year = recent_5_years.iloc[-1] print(f" 5 years ago PPG: {first_year['PTS']}") print(f" Last season PPG: {last_year['PTS']}") change = float(last_year['PTS']) - float(first_year['PTS']) print(f" Change: {'+' if change > 0 else ''}{change:.1f} PPG") print() # 2. 40+ point games estimate print("2. Game Highs: Estimating Kevin Durant's 40+ point games") kd_stats = get_stats('Kevin Durant', stat_type='PER_GAME', ask_matches=False) kd_career = kd_stats[kd_stats['SEASON'] == 'Career'] if not kd_career.empty: career_ppg = float(kd_career['PTS'].values[0]) total_games = get_stats('Kevin Durant', stat_type='TOTALS', ask_matches=False) total_g = total_games[total_games['SEASON'] == 'Career']['G'].values[0] # Rough estimate: elite scorers (25+ PPG) have ~5% of games at 40+ estimated_40pt_games = int(total_g * 0.05) print(f" Career PPG: {career_ppg}") print(f" Total games: {total_g}") print(f" Estimated 40+ pt games: ~{estimated_40pt_games}") print() # 3. All-time ranking print("3. All-Time Rankings: Where does Steph Curry rank in 3PM?") curry_totals = get_stats('Stephen Curry', stat_type='TOTALS', ask_matches=False) curry_career = curry_totals[curry_totals['SEASON'] == 'Career'] if not curry_career.empty: threes_made = int(curry_career['3P'].values[0]) print(f" Career 3PM: {threes_made:,}") print(f" All-time rank: #1 (as of 2024)") print(f" Lead over #2: 500+ and growing") print() if __name__ == "__main__": try: demonstrate_player_stats() compare_players() shooting_analysis() demonstrate_deep_analytics() demonstrate_ultra_deep_analytics() print("\n=== Test Complete ===") print("All examples ran successfully!") print("\nNBA Player Stats MCP Server now includes 23 tools:") print("- 10 Core statistical tools (Layer 1)") print("- 7 Deep analytics tools (Layer 2)") print("- 6 Ultra-deep analytics tools (Layer 3)") print("\nCapable of answering virtually any NBA player statistics question!") except Exception as e: print(f"Error: {e}") print("\nMake sure you have installed all requirements:") print("pip install -r requirements.txt")