PREDICTIVE MODELING AND ANALYTICS FOR PROFESSIONAL BASEBALL: AN ANALYSIS OF INJURIES, PLAYER PERFORMANCE, AND MEDICAL STAFF OPTIMIZATION
Open Access
- Author:
- Scheri, Patrick
- Area of Honors:
- Industrial Engineering
- Degree:
- Bachelor of Science
- Document Type:
- Thesis
- Thesis Supervisors:
- Guodong Pang, Thesis Supervisor
Catherine Mary Harmonosky, Thesis Honors Advisor - Keywords:
- Predicting Future Injury
Medical Staff Modeling
Professional Baseball - Abstract:
- The research in this paper aims to help Major League Baseball (MLB) teams find the next big competitive advantages within baseball – injury modeling and medical staff optimization. The objective of this research is to create models to predict future injury, and evaluate medical staffing so that teams and players can increase their future performance. The game of baseball is quickly shifting towards analytics and as teams strive to find every advantage possible, they must consider evaluating their medical departments. The research in this paper utilizes a predictive model to indicate the odds of a pitcher requiring Tommy John surgery (a common baseball injury). The model used a variety of variables ranging from basic statistics, pitch selections and velocities, and pitching mechanics to generate an equation for the likelihood a player will require surgery. The model showed that a pitcher’s pitch selection is one of the largest indicators of surgery. Additionally, the number of appearances that a pitcher has over his career, and the duration of the appearances can be predictors of surgery. Following the creation of the predictive model, the staffing of team medical departments was evaluated. A baseline scenario was created using one teams known combination of physicians, team consultants, athletic trainers, physical therapists, occupational therapists, chiropractors, massage therapists, strength coaches, and nutritionists. Four additional scenarios were then created to demonstrate a low budget team, a team that highly values preventative action, a team looking to maximize player satisfaction, and a team looking to minimize overall injury. The tradeoffs between each model came from changes in budget, injury time, and risk. This research is just the beginning for what can be done in analyzing medicine in professional sports. As analytics continue to become a larger part of sports, teams can save millions of dollars and increase performance by analyzing every aspect of their operation.