💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于大数据的NBA美国职业篮球联赛数据分析可视化系统介绍
基于大数据的NBA美国职业篮球联赛数据分析可视化系统是一套采用Hadoop+Spark大数据处理框架构建的专业体育数据分析平台，该系统通过HDFS分布式文件系统存储海量NBA比赛数据，利用Spark SQL进行高效的数据查询与处理，结合Pandas和NumPy进行深度数据挖掘与统计分析，为用户提供全方位的篮球数据洞察服务。系统采用前后端分离的架构设计，后端基于Django框架或Spring Boot框架构建RESTful API接口，前端运用Vue.js+ElementUI组件库开发响应式用户界面，集成Echarts图表库实现丰富的数据可视化效果，通过HTML、CSS、JavaScript和jQuery技术栈确保良好的用户交互体验。系统核心功能涵盖球员表现分析模块，深入挖掘球员各项技术统计数据及表现趋势；比赛阶段分析模块，对常规赛、季后赛等不同阶段进行对比分析；发展趋势分析模块，基于历史数据预测球员及球队未来发展走向；团队实力分析模块，综合评估各支球队的整体实力水平；数据关系分析模块，探索各项数据指标之间的内在关联性；同时配备大屏可视化展示功能，以直观震撼的图表形式呈现分析结果，另外系统还提供完善的用户管理功能包括个人信息维护、密码修改等基础服务，整个系统以MySQL作为关系型数据库存储用户信息和处理结果，充分展现了大数据技术在体育数据分析领域的强大应用价值。
基于大数据的NBA美国职业篮球联赛数据分析可视化系统演示视频
基于大数据的NBA美国职业篮球联赛数据分析可视化系统演示图片
基于大数据的NBA美国职业篮球联赛数据分析可视化系统代码展示
spark = SparkSession.builder.appName("NBA_Data_Analysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_player_performance(request):
player_id = request.GET.get('player_id')
season = request.GET.get('season', '2023-24')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/nba_db").option("dbtable", "player_stats").option("user", "root").option("password", "password").load()
player_df = df.filter(df.player_id == player_id).filter(df.season == season)
collected_data = player_df.collect()
if not collected_data:
return JsonResponse({'error': 'Player data not found'}, status=404)
stats_pd = pd.DataFrame([row.asDict() for row in collected_data])
avg_points = float(stats_pd['points'].mean())
avg_rebounds = float(stats_pd['rebounds'].mean())
avg_assists = float(stats_pd['assists'].mean())
shooting_percentage = float(stats_pd['field_goals_made'].sum() / stats_pd['field_goals_attempted'].sum() * 100)
performance_trend = []
for i in range(len(stats_pd)):
recent_games = stats_pd.iloc[max(0, i-4):i+1]
trend_score = recent_games['points'].mean() + recent_games['rebounds'].mean() + recent_games['assists'].mean()
performance_trend.append(float(trend_score))
consistency_score = 100 - (np.std(stats_pd['points']) / np.mean(stats_pd['points']) * 100)
peak_performance = stats_pd.loc[stats_pd['points'].idxmax()]
efficiency_rating = (avg_points + avg_rebounds + avg_assists) / (stats_pd['turnovers'].mean() + 1)
monthly_performance = stats_pd.groupby(pd.to_datetime(stats_pd['game_date']).dt.month).agg({'points': 'mean', 'rebounds': 'mean', 'assists': 'mean'}).to_dict('index')
home_away_split = stats_pd.groupby('home_away').agg({'points': 'mean', 'shooting_percentage': 'mean'}).to_dict('index')
clutch_performance = stats_pd[stats_pd['game_time_remaining'] <= 300].agg({'points': 'mean', 'shooting_percentage': 'mean'}).to_dict()
return JsonResponse({'avg_points': avg_points, 'avg_rebounds': avg_rebounds, 'avg_assists': avg_assists, 'shooting_percentage': shooting_percentage, 'performance_trend': performance_trend, 'consistency_score': consistency_score, 'peak_performance': peak_performance.to_dict(), 'efficiency_rating': efficiency_rating, 'monthly_performance': monthly_performance, 'home_away_split': home_away_split, 'clutch_performance': clutch_performance})
def analyze_game_stage_performance(request):
season = request.GET.get('season', '2023-24')
team_id = request.GET.get('team_id')
games_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/nba_db").option("dbtable", "game_stats").option("user", "root").option("password", "password").load()
team_games = games_df.filter(games_df.season == season)
if team_id:
team_games = team_games.filter((games_df.home_team_id == team_id) | (games_df.away_team_id == team_id))
regular_season = team_games.filter(team_games.game_type == 'regular')
playoffs = team_games.filter(team_games.game_type == 'playoff')
regular_pd = pd.DataFrame([row.asDict() for row in regular_season.collect()])
playoff_pd = pd.DataFrame([row.asDict() for row in playoffs.collect()])
regular_stats = {'avg_points': float(regular_pd['points'].mean()), 'avg_rebounds': float(regular_pd['rebounds'].mean()), 'avg_assists': float(regular_pd['assists'].mean()), 'win_rate': float(regular_pd['result'].value_counts().get('W', 0) / len(regular_pd))}
playoff_stats = {'avg_points': float(playoff_pd['points'].mean()) if len(playoff_pd) > 0 else 0, 'avg_rebounds': float(playoff_pd['rebounds'].mean()) if len(playoff_pd) > 0 else 0, 'avg_assists': float(playoff_pd['assists'].mean()) if len(playoff_pd) > 0 else 0, 'win_rate': float(playoff_pd['result'].value_counts().get('W', 0) / len(playoff_pd)) if len(playoff_pd) > 0 else 0}
stage_comparison = {'points_diff': playoff_stats['avg_points'] - regular_stats['avg_points'], 'rebounds_diff': playoff_stats['avg_rebounds'] - regular_stats['avg_rebounds'], 'assists_diff': playoff_stats['avg_assists'] - regular_stats['avg_assists'], 'win_rate_diff': playoff_stats['win_rate'] - regular_stats['win_rate']}
monthly_progression = regular_pd.groupby(pd.to_datetime(regular_pd['game_date']).dt.month).agg({'points': 'mean', 'win_rate': lambda x: (x == 'W').mean()}).to_dict('index')
quarter_performance = regular_pd.groupby('quarter').agg({'points': 'mean', 'field_goal_percentage': 'mean'}).to_dict('index')
clutch_games = regular_pd[regular_pd['point_differential'].abs() <= 5]
clutch_performance = {'games_count': len(clutch_games), 'win_rate': float((clutch_games['result'] == 'W').mean()), 'avg_scoring': float(clutch_games['points'].mean())}
back_to_back = regular_pd[regular_pd['days_rest'] == 0]
rest_impact = {'back_to_back_win_rate': float((back_to_back['result'] == 'W').mean()), 'rested_win_rate': float((regular_pd[regular_pd['days_rest'] >= 2]['result'] == 'W').mean()), 'performance_drop': float(regular_pd['points'].mean() - back_to_back['points'].mean())}
return JsonResponse({'regular_season': regular_stats, 'playoffs': playoff_stats, 'stage_comparison': stage_comparison, 'monthly_progression': monthly_progression, 'quarter_performance': quarter_performance, 'clutch_performance': clutch_performance, 'rest_impact': rest_impact})
def analyze_team_strength(request):
season = request.GET.get('season', '2023-24')
teams_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/nba_db").option("dbtable", "team_stats").option("user", "root").option("password", "password").load()
season_teams = teams_df.filter(teams_df.season == season)
teams_pd = pd.DataFrame([row.asDict() for row in season_teams.collect()])
teams_pd['offensive_rating'] = (teams_pd['points_per_game'] / teams_pd['possessions_per_game']) * 100
teams_pd['defensive_rating'] = (teams_pd['opponent_points_per_game'] / teams_pd['opponent_possessions_per_game']) * 100
teams_pd['net_rating'] = teams_pd['offensive_rating'] - teams_pd['defensive_rating']
teams_pd['pace'] = teams_pd['possessions_per_game']
teams_pd['true_shooting'] = teams_pd['points'] / (2 * (teams_pd['field_goals_attempted'] + 0.44 * teams_pd['free_throws_attempted']))
teams_pd['effective_fg_pct'] = (teams_pd['field_goals_made'] + 0.5 * teams_pd['three_pointers_made']) / teams_pd['field_goals_attempted']
teams_pd['turnover_rate'] = teams_pd['turnovers'] / teams_pd['possessions_per_game']
teams_pd['rebounding_rate'] = (teams_pd['offensive_rebounds'] + teams_pd['defensive_rebounds']) / (teams_pd['offensive_rebounds'] + teams_pd['defensive_rebounds'] + teams_pd['opponent_offensive_rebounds'] + teams_pd['opponent_defensive_rebounds'])
teams_pd['strength_score'] = (teams_pd['net_rating'] * 0.4) + (teams_pd['win_percentage'] * 100 * 0.3) + (teams_pd['true_shooting'] * 100 * 0.2) + (teams_pd['rebounding_rate'] * 100 * 0.1)
team_rankings = teams_pd.nlargest(30, 'strength_score')[['team_name', 'strength_score', 'net_rating', 'offensive_rating', 'defensive_rating']].to_dict('records')
offensive_leaders = teams_pd.nlargest(10, 'offensive_rating')[['team_name', 'offensive_rating', 'points_per_game']].to_dict('records')
defensive_leaders = teams_pd.nsmallest(10, 'defensive_rating')[['team_name', 'defensive_rating', 'opponent_points_per_game']].to_dict('records')
correlation_matrix = teams_pd[['win_percentage', 'net_rating', 'true_shooting', 'turnover_rate', 'rebounding_rate']].corr().to_dict()
strength_distribution = {'elite': len(teams_pd[teams_pd['strength_score'] >= 70]), 'good': len(teams_pd[(teams_pd['strength_score'] >= 50) & (teams_pd['strength_score'] < 70)]), 'average': len(teams_pd[(teams_pd['strength_score'] >= 30) & (teams_pd['strength_score'] < 50)]), 'below_average': len(teams_pd[teams_pd['strength_score'] < 30])}
conference_comparison = teams_pd.groupby('conference').agg({'strength_score': 'mean', 'net_rating': 'mean', 'win_percentage': 'mean'}).to_dict('index')
home_away_advantage = teams_pd.groupby('team_name').agg({'home_win_pct': 'mean', 'away_win_pct': 'mean'}).mean().to_dict()
return JsonResponse({'team_rankings': team_rankings, 'offensive_leaders': offensive_leaders, 'defensive_leaders': defensive_leaders, 'correlation_matrix': correlation_matrix, 'strength_distribution': strength_distribution, 'conference_comparison': conference_comparison, 'home_away_advantage': home_away_advantage})
基于大数据的NBA美国职业篮球联赛数据分析可视化系统文档展示
💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
