Learning To Describe Player Form in The MLB
Connor Heaton, Prasenjit Mitra

TL;DR
This paper introduces a contrastive learning framework to create player form representations in MLB, capturing impact on play beyond traditional statistics, with potential for improved game outcome predictions.
Contribution
It presents a novel contrastive learning approach to describe player form in MLB, capturing impact on play not reflected in traditional sabermetrics.
Findings
Form representations contain unique impact information
Clusters of player forms differ from traditional statistics
Potential to predict game outcomes using embeddings
Abstract
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…
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Taxonomy
TopicsSports Analytics and Performance · Sports Dynamics and Biomechanics · Video Analysis and Summarization
