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Advanced Bayesian Skill Ranking for NBA Players: A Case Study on Offensive and Defensive Skills

This paper presents a Bayesian skill ranking methodology applied to NBA players, focusing on analyzing offensive and defensive skills. Utilizing data from a complete game between the Cleveland Cavaliers and Detroit Pistons on March 7, 2007, and additional case studies from the Dallas Mavericks, we explore individual contributions through a new ranking framework. By incorporating Bayesian priors and smoothing techniques, we propose an innovative approach to evaluate player performance, paving the way for future research that may include even more comprehensive datasets and coaching impacts.

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Advanced Bayesian Skill Ranking for NBA Players: A Case Study on Offensive and Defensive Skills

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  1. NBA: Bayesian Skill Ranking Leland Chen, Joseph Huang, Ryan Thompson

  2. Proposed Network

  3. Gaussian Logistic

  4. Case Study: One Game Offensive/Defensive Skills Offensive Skills: Higher the better Defensive Skills: Lower the better 1 complete game; 183 total possessions • Traditional Box Score CLE @ DET, March 07, 2007. 101-97 OT.

  5. Southwest Division 2008-2009 Offensive Skills: Higher the better Defensive Skills: Lower the better 2pt Off./Def. Players (600+ possessions) 35 games; 3152 1st half possessions*

  6. Case Study: Dallas Mavericks Offensive/Defensive Skills Offensive Skills: Higher the better Defensive Skills: Lower the better 35 games; 3152 1st half possessions*

  7. Future Work Conclusion New way to rank players based on individual contributions • Smoothing/Bayesian Prior • Coach Skill • Even More Data

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