Park factor estimation improvement using pairwise comparison method

TL;DR

This paper introduces a pairwise comparison method using logistic regression to improve the estimation of park factors in baseball, accounting for environmental effects and team matchups, validated on extensive MLB data.

Contribution

It proposes a novel pairwise comparison approach with logistic regression for more accurate park factor estimation, outperforming traditional methods.

Findings

Proposed method outperforms conventional park factor estimation techniques.

Validated on over 1.5 million MLB plate appearances from 2010-2017.

Demonstrates improved accuracy in quantifying ballpark effects.

Abstract

Each ballpark has a different size in baseball. It could be easily imagined that there would be many home runs in a small ballpark. Moreover, the environment of the ballpark, such as altitude, humidity, air pressure, and wind strength, affects the trajectory of batted balls. Park Factors (PF) are introduced in baseball to quantify the effect of each ballpark on the results (e.g., home runs). In this paper, I assume that each plate appearance can be modeled as a match-up between a batter's team and a pitcher's team plus a ballpark. The effects of each ballpark will be distilled by using a logistic regression method. Numerical verification shows that the proposed method performs better than conventional PF. The verification is based on the results of more than 1.5 million plate appearances from the 2010 to 2017 Major League Baseball (MLB) seasons.