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.
By Ryan S. Brill (W’23), Sameer K. Deshpande and Abraham J. Wyner
A Bayesian analysis of the time through the order penalty in baseball
Research focus: baseball, Bayesian statistics, mathematical modeling, pitching, time through order penalty
By Namita Nandakumar (W’18)
What Does It Mean To Draft Perfectly? An Evaluation Of Draft Strategy In The National Hockey League
Research focus: National Hockey League, amateur draft, game theory
By Sameer K. Deshpande* and Abraham Wyner
A hierarchical Bayesian model of pitch framing
Research focus: Baseball; Bayesian modeling; uncertainty
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
Wharton Moneyball Academy students Jake Federman, Eshan Mehere, Aidan Resnick, and Maxwell Resnick named 2021 Diamond Dollars Case Competition winners at the SABR Virtual Analytics Conference.
Research focus: Devise an improved metric for Game Score to evaluate the performance of starting pitchers.
Zach Bradlow (W22), Zach Drapkin (W22), Ryan Gross (PhD), Sarah Hu (W23) were finalists in the 2021 Big Data Bowl hosted by the National Football League
Research focus: Creating a relative skill rating system based upon success in coverage matchups to measure defender skill
Jake Flancer (W21) completed “NCAA Plus Minus”
Using compiled data from college basketball R package bigballR, this project presents open source college basketball player +/- models, most notably the prior-informed sRAPM metric.
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