Learning to Describe Player Form in the MLB

Connor Heaton, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Major League Baseball (MLB) has a storied history of using statistics to better understand and discuss the game of baseball, with an entire discipline of statistics dedicated to the craft, known as sabermetrics. At their core, all sabermetrics seek to quantify some aspect of the game, often a specific aspect of a player’s skill set - such as a batter’s ability to drive in runs (RBI) or a pitcher’s ability to keep batters from reaching base (WHIP). While useful, such statistics are fundamentally limited by the fact that they are derived from an account of what happened on the field, not how it happened. As a first step towards alleviating this shortcoming, we present a novel, contrastive learning-based framework for describing player form in the MLB. We use form to refer to the way in which a player has impacted the course of play in their recent appearances. Concretely, a player’s form is described by a 72-dimensional vector. By comparing clusters of players resulting from our form representations and those resulting from traditional sabermetrics, we demonstrate that our form representations contain information about how players impact the course of play, not present in traditional, publicly available statistics. We believe these embeddings could be utilized to predict both in-game and game-level events, such as the result of an at-bat or the winner of a game.

Original languageEnglish (US)
Title of host publicationMachine Learning and Data Mining for Sports Analytics - 8th International Workshop, MLSA 2021, Revised Selected Papers
EditorsUlf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783031020438
StatePublished - 2022
Event8th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2021 - Virtual, Online
Duration: Sep 13 2021Sep 13 2021

Publication series

NameCommunications in Computer and Information Science
Volume1571 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference8th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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