Michael Bogaty

Summer 2017

Effect of Base Runners on MLB Pitch Selection: Michael Bogaty ‘19, Tyler Duncan ‘18, and Luke Benz ‘19 examine how having runners on baser–particularly base stealing threats–influences pitch selection in MLB.


BY THE NUMEBRS: Baseball/Softball MVPs: Luke Benz ‘19 analyzes the most valuable Ivy League Baseball/Softball players using Wins Above Replacement (WAR) and other advanced MLB stats.

BY THE NUMBERS: Ivy League Baseball Park Factors: Michael Bogaty ’19 explores which Ivy League baseball stadiums can be classified as hitter-friendly and pitcher-friendly parks.

BY THE NUMBERS: Ivy League Baseball Pythagorean Win Percentages: Michael Bogaty ’19 calculates Pythagorean Win Percentages for each of the Ancient Eight teams and analyzes which teams under/over-performed last season in hopes of better predicting this season’s results.

BY THE NUMBERS: Math To The Palestra: Luke Benz ‘19 and Michael Bogaty ’19 break down odds and scenarios for the Yale Men’s and Women’s Basketball teams for make the Ivy League tournament.

BY THE NUMBERS: Hoops Analysis: Luke Benz ‘19 and Michael Bogaty ’19 examine updated playoff odd for the inaugural Ivy League Tournament after the first full weekend of Ancient Eight play.

BY THE NUMBERS: ECAC Hockey Predictions: Michael Bogaty ’19 crunches playoff odds for the men’s and women’s ECAC hockey teams and analyzes the Yale men’s teams chances vs. rival Quinnipiac University in the “Battle for New Haven”.

BY THE NUMBERS: Women’s Hockey Predictions: Michael Bogaty ’19 updates his ELO model and uses it to predict results for the Yale Women’s Hockey Team.

BY THE NUMBERS: Men’s Hockey Prediction’s: Michael Bogaty ’19 begins a new series in our collaboration with the Yale Daily News, in which we make predictions for several Yale Sports Teams. In this first installment, Bogaty covers Yale Men’s Hockey.


Player Clustering in the NBA: As a result of position, physical attributes, or basketball ability players in the NBA naturally gravitate toward certain roles within a team. We attempt to formalize this notion by clustering NBA players based on a combination of traditional and advanced statistics. These clusters can then be applied to examine how teams construct lineups.