Research projects in sports analytics allow our students to advance their understanding of statistics, dive into interesting datasets, and solve real business problems. WSABI aims to use insights from faculty and community to power the sports industry, both off and on the field of play.
A hierarchical Bayesian model of pitch framing
Sameer K. Deshpande* and Abraham Wyner
By Shane T. Jensen, Kenneth E. Shirley and Abraham J. Wyner
BAYESBALL: A BAYESIAN HIERARCHICAL MODEL FOR EVALUATING FIELDING IN MAJOR LEAGUE BASEBALL
Research focus: Spatial models, Bayesian shrinkage, baseball fielding.
Matt Popowitz (C20) completed “RE+: Factoring Player and Team Hitting Ability into Run Expectancy and the True Value of a given Stolen Base”
For years, run expectancy in a given inning was determined according to the base-out state. Now, we take into account both the current batter and the rest of the team’s offensive prowess. Using our findings, we are able to determine how valuable (or costly) a given stolen base attempt truly is!
Jake Flancer (W21) presented his work showcasing hockey analytics at the RIT Sports Analytics Conference
A Bayesian Model for Estimating NHL Team Scoring
Jacob Richey (C21) presented his work at the Carnegie Mellon Sports Analytics Conference,
Factoring Strength of Schedule into Player Analysis, Player elo, done in conjunction with Professor Adi Wyner
Jack Soslow (W19), Jake Flancer (W21), Eric Dong (ENG19), Andrew Castle (W21) were finalists in the inaugural Big Data Bowl hosted by the National Football League
Research focus: Using autoencoded receiver routes to optimize yardage