DSC198-PASS-Basketball-TalentID-Project

DSC198 Performance Analysis and Sports Science Basketball Talent Id and recruiting project

DSC198 PASS Basketball Project:

Advancing High School Player Evaluation With Data

Project Members

• Student: Andrew Li (ahli@ucsd.edu{.email})
• Student: Jeffrey Lau (j8lau@ucsd.edu{.email})
• Student: Pravar Bhandari (psbhanda@ucsd.edu{.email})
• Student: Ted Feng (zef007@ucsd.edu{.email})
• Domain Expert: Clint Allard (callard@ucsd.edu{.email})

Project Description:

Are there diamonds in the rough?

The UC Sand Diego basketball coaches have been trying to figure out how to get an advantage in recruiting. They know that being the newest D1 institution leaves them with a large disadvantage when it comes to notoriety and brand appeal. Like all college sports, talent wins. They talked as a group and figured their best chance for success is to find the best developmental players, knowing that the more established teams will overlook them or not want to take the time to develop them. But how do we know which players will project a higher probability of success greater than the most recent scouting rankings? They want you to investigate what correlations exist between high school stats and success in our conference. They also want to know how they can give value to these players to best understand how to allocate their scholarships. Is that player worth more than another because of a recruiting service ranking, or might another player of a lower rank have more value to us in the long run?

References:

• Statistics from Max Preps
• University websites for current college players
• Synergy Statistics for advanced metrics
• HoopsIQ for advanced metrics

Key Learning Objectives

1. Analyze current conference team’s rosters for normative data regarding anthropometrics, recruiting regions, playing time through college career, successful recruits, etc
2. Identify current recruiting practices of coaches and nuance of recruiting process.
3. Assess high school statistics of current Big West players for correlation to success in college.

Secondary Objectives

1. Identify KPIs for recruiting efficiency.
2. Create strength of opponent model to make high school stats relative to competition level.