Who's the GOAT? Sports Rankings and Data-Driven Random Walks on the Symmetric Group

TL;DR

This paper introduces a data-driven random walk approach on the symmetric group to analyze historical sports rankings, identifying the GOATs in tennis and providing a formal partial order of player greatness.

Contribution

It develops a novel method combining random walks and stochastic dominance to compare players over time, capturing evolution and uncertainty in rankings.

Findings

Steffi Graf and Serena Williams are identified as the GOATs of WTA.

Djokovic, Federer, and Nadal are identified as the GOATs of ATP.

The method reveals differences in career longevity and dominance among top players.

Abstract

Given a collection of historical sports rankings, can one tell which player is the greatest of all time (i.e., the GOAT)? In this work, we design a data-driven random walk on the symmetric group to obtain a stationary distribution over player rankings, spanning across different time periods in sports history. We combine this distribution with a notion of stochastic dominance to obtain a partial order over the players. Compared to existing methods, our approach is distinct in that i) using historical rankings captures the evolution of value systems and facilitates player comparisons when head-to-head data is unavailable, and i) aggregating into a partial order formally comes to terms with the possibility that, while some player comparisons can be established conclusively, others are "too close to call." We implement our methods using publicly available data from the Association of Tennis Professionals (ATP) and the Women's Tennis Association (WTA). Our main findings indicate that Steffi Graf and Serena Williams are the ones that come ahead as the GOATs of the WTA. Likewise, the "Big Three," that is Novak Djokovic, Roger Federer, and Rafael Nadal, are the ones that come ahead as the GOATs of the ATP. As a secondary finding, we note major differences in terms of career and dominance longevity for top players across the associations. While initially motivated by this application in sports analytics, our methods can also be applied to other practical domains where deriving rankings from historical data can inform operational decisions, such as route planning logistics.