How good was my shot? Quantifying Player Skill Level in Table Tennis

TL;DR

This paper introduces a generative model that embeds table tennis players' tactical strokes into a latent space, enabling accurate quantification and prediction of individual skill levels based on detailed game context and player behavior.

Contribution

It presents a novel approach to quantifying skill by learning a latent space of player tactics from 3D-reconstructed match data, capturing individual styles and skill attributes.

Findings

Latent space reflects distinct play styles and skill attributes.

The model accurately predicts relative and absolute skill levels.

Skill quantification is effective even in complex, interactive sports behaviors.

Abstract

Gauging an individual's skill level is crucial, as it inherently shapes their behavior. Quantifying skill, however, is challenging because it is latent to the observed actions. To explore skill understanding in human behavior, we focus on dyadic sports -- specifically table tennis -- where skill manifests not just in complex movements, but in the subtle nuances of execution conditioned on game context. Our key idea is to learn a generative model of each player's tactical racket strokes and jointly embed them in a common latent space that encodes individual characteristics, including those pertaining to skill levels. By training these player models on a large-scale dataset of 3D-reconstructed professional matches and conditioning them on comprehensive game context -- including player positioning and opponent behaviors -- the models capture individual tactical identities within their latent space. We probe this learned player space and find that it reflects distinct play styles and attributes that collectively represent skill. By training a simple relative ranking network on these embeddings, we demonstrate that both relative and absolute skill predictions can be achieved. These results demonstrate that the learned player space effectively quantifies skill levels, providing a foundation for automated skill assessment in complex, interactive behaviors.