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

arXiv: https://arxiv.org/abs/2603.25736 · HTML: https://mlsanity.com/paper/2603.25736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 26 Mar 2026.

## Builds on (most cited first, 20 of 20)

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- [Am I a Baller? Basketball Performance Assessment from First-Person Videos](https://mlsanity.com/paper/1611.05365/) (2016)
- [HITTER: A HumanoId Table TEnnis Robot via Hierarchical Planning and Learning](https://mlsanity.com/paper/2508.21043/) (2025)
- [Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment](https://mlsanity.com/paper/1702.07772/) (2017)
- [Achieving Human Level Competitive Robot Table Tennis](https://mlsanity.com/paper/2408.03906/) (2024)
- [Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation](https://mlsanity.com/paper/2104.09907/) (2021)
- [LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos](https://mlsanity.com/paper/2503.20936/) (2025)
- [TT3D: Table Tennis 3D Reconstruction](https://mlsanity.com/paper/2504.10035/) (2025)

## Abstract (arXiv)

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.