A network-based dynamical ranking system for competitive sports

TL;DR

This paper introduces a dynamic network-based ranking system for sports that accounts for players' fluctuating performance over time, improving prediction accuracy over static models.

Contribution

It proposes a novel dynamic ranking method with online update equations, enhancing prediction accuracy by considering temporal performance variations.

Findings

Outperforms static network-based rankings in predicting match outcomes.

Effectively captures temporal fluctuations in player performance.

Demonstrated on professional men's tennis data.

Abstract

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.