Quantifying and Optimizing Baseball Decision-Making
In-game decision-making in baseball is a complex art. We break it down into quantifiable components and then optimize them.
The optimal play,
every time.
Walk-Off Labs builds tools that help baseball teams turn data into actionable insights. We combine statistics, machine learning, and game theory to give you the edge.
What we do
In the age of big data and artificial intelligence, every team in baseball has access to advanced models capable of quantifying and predicting the abilities of their players. Knowing how to use these models is an entirely different challenge.
Our platform, Perfect Game, uses game theory and advanced optimization algorithms to translate player models into explainable, actionable insights that maximize expected wins over the course of a season.
Our team
Meet the team →
Tristan Mott
PhD Student
BYU
Caleb Bradshaw
Research Engineer
Walk-Off Labs
David Grimsman
Professor of CS
BYU
Chris Archibald
Professor of CS
BYU