Universal Behavior of Opponent Statistics and Applications to the MLB

TL;DR

This paper introduces a universal distribution of opponent statistics in MLB and develops a renormalized pitcher statistic, aFIP, that accounts for opponent strength, improving player evaluation accuracy.

Contribution

It presents a novel algorithm to normalize pitcher statistics by opponent strength using a universal distribution, enhancing player performance assessment.

Findings

Opponent statistics follow a universal distribution across MLB teams.

aFIP differs significantly from FIP for some pitchers, indicating improved evaluation.

The method has potential applications in player valuation and sports analytics.

Abstract

In most popular sports leagues, like the MLB, NBA, and NFL, none of the commonly used statistics take into account the strengths of the opponents a player faces. One of the main reasons for this is the conventional belief that a player's luck tends to even out over the course of a season. The other main reason is the difficulties of finding a sensible algorithm to both quantify the strengths of the opponents and incorporate such quantifications into a renormalization of a player's statistics. In this paper, we first argue that certain statistics, such as Earned Run Average (ERA) or Fielding Independent Pitching (FIP) can be significantly skewed by opponents' strengths in the MLB. We then present an algorithm to renormalize such statistics, using FIP as the main example. This is achieved by observing that certain opponent statistics for all 30 teams in the MLB (e.g. the collection of each game's opponent FIP value over the course of a season) follow a universal distribution, up to scaling and shift. This enables us to establish a data set for a hypothetical average team and to develop a pitching statistic based on FIP which accounts for the strength of a pitcher's schedule through methods based on equipercentile equating. It is called aFIP, which measures what a pitcher's FIP would have been if he had faced a league-average offensive team every time he pitched. We find that there is a significant difference between aFIP and FIP for some pitchers during the 2019 season and other seasons as well, adding a new tool for player evaluation. This could make millions of dollars of difference in player contracts and in profits for teams as they enhance the accuracy with which they make player acquisitions. The universal distribution we observed also has many possible future applications throughout the sports world.