Research
RESEARCH PROJECTS
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.
Published Research
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
Published Research
By Sameer K. Deshpande* and Abraham Wyner
A hierarchical Bayesian model of pitch framing
Research focus: Baseball; Bayesian modeling; uncertainty
quantification.
Published Research
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
Research
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
March 2021
Research focus: Creating a relative skill rating system based upon success in coverage matchups to measure defender skill
Research
Jake Flancer (W21) completed “NCAA Plus Minus”
January 2021
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.
Research
Matt Popowitz (C20) completed “RE+: Factoring Player and Team Hitting Ability into Run Expectancy and the True Value of a given Stolen Base”
September 2020
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!
Research
Jake Flancer (W21) presented his work showcasing hockey analytics at the RIT Sports Analytics Conference
September 2019
A Bayesian Model for Estimating NHL Team Scoring
Research
Jacob Richey (C21) presented his work at the Carnegie Mellon Sports Analytics Conference,
November 2019
Factoring Strength of Schedule into Player Analysis, Player elo, done in conjunction with Professor Adi Wyner
Research
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