cs.CVMar 26, 2026

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

Authors: Akihiro Kubota, Tomoya Hasegawa, Ryo Kawahara, Ko Nishino

Organizations: Graduate School of Informatics, Kyoto University, Kyoto, Japan

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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.

Figures & tables

Figure 1: We demonstrate skill level quantification using dyadic sports as a primary example. From match videos, we jointly learn player-specific generative models and a player space defined by their latent embeddings. We show that this learned player space contains rich information regarding individual proficiency, enabling the quantification of skill scores and both relative and absolute ranking predictions.
Figure 2: We model player semantics including their skill levels from observation of their plays. Our main model, HitFlow, jointly learns player embeddings and the distribution of racket hit vectors governing the ball trajectories in a play conditioned on the game context. The player embeddings encode semantics of each player including their skill levels. We demonstrate the extraction of such deeply latent characteristics of the players through probing and ranking of players and show their accuracy in match prediction.
Figure 3: The architecture of HitFlow. The velocity field estimation by Flow Matching is conditioned on the game context and player identity through FiLM [ 25 ] . Player embeddings are jointly learned with the model resulting in a player space encoding their latent characteristics including skill levels.
methodsideobliqueback
TT3D12.417.129.8
HitFormer (w/o opt.)10.316.318.8
HitFormer (w/ opt.)10.116.218.5
Table 1: 3D ball trajectory reconstruction accuracy in Mean Absolute Error (MAE) in centimeters for various viewpoints. HitFormer consistently outperforms TT3D, particularly from the back viewpoint. Hit-vector optimization further refines accuracy.
methodHitFlowLATTE-MV (i)LATTE-MV (ii)
energy score (↓)0.370.400.40
Table 2: Ball trajectory prediction accuracy comparison. HitFlow outperforms LATTE-MV for both ensemble settings.
Figure 4: Hit vector (ball trajectory) prediction. For each pair, the left shows LATTE-MV prediction (seed ensemble) and the right shows HitFlow prediction. The GT trajectories are shown in blue and the trajectories where the MAE takes median in the samples are shown in red as representatives. HitFlow outputs diverse trajectories and covers GT within the distribution.
Figure 5: Matthew Correlation Coefficient (MCC) ( left ) and t-SNE result for the player space by cosine distance. (a) MCC is sufficiently large even with a small number of labels, and tend to increase as the number of labels grow for sex, handedness, and height. Age appears to be unrelated to the player embeddings. (b) Player 14 , 15 and 43 , who are all female choppers, are particularly close to each other. Sex and handedness are reflected in the distributional bias simultaneously, showing that the embeddings encode sex, handedness and play style reflected in the observed plays.
Figure 8
sexoverallrank gap >5>5
Male77.3 %87.4 %
Female73.5 %79.8 %
Table 3: Accuracy of rank prediction between unknown players. Overall accuracy for both male and female is over 70 %, and when the rank gap becomes larger than 5, the accuracy becomes higher.
Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: t-SNE visualization of hit vectors in SynthHit. Predefined shot types form distinct clusters, while random samples bridge the transition regions between them.
Figure 9: Number of rally samples available per player in the JTTA dataset.
Figure 10: Trajectory estimation from the observation viewpoint: (a) before and (b) after optimization. Green points denote ground truth, and orange points represent estimates. In the pre-optimized state, the estimation fails to capture the bounce discontinuity, resulting in a large reprojection error. Post-optimization, this deviation is resolved, accurately capturing the impact on the table.
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