Mutually-Antagonistic Interactions in Baseball Networks

TL;DR

This paper models Major League Baseball matchups as bipartite networks, analyzing their structure over time and using biased random walks to evaluate player performance and the impact of rule changes.

Contribution

It introduces a network-based approach to analyze baseball matchups and player performance, incorporating temporal changes and rule modifications.

Findings

Network structure varies over time with rule changes.

Player network position influences ranking sensitivity.

Random walk rankings do not correlate with traditional success metrics.

Abstract

We formulate the head-to-head matchups between Major League Baseball pitchers and batters from 1954 to 2008 as a bipartite network of mutually-antagonistic interactions. We consider both the full network and single-season networks, which exhibit interesting structural changes over time. We find interesting structure in the network and examine their sensitivity to baseball's rule changes. We then study a biased random walk on the matchup networks as a simple and transparent way to compare the performance of players who competed under different conditions and to include information about which particular players a given player has faced. We find that a player's position in the network does not correlate with his success in the random walker ranking but instead has a substantial effect on its sensitivity to changes in his own aggregate performance.