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German Sport University Cologne

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Representative Papers

A Universal Dense Football Event Representation Based on TabTransformer

Jun 08, 2026

This work addresses the challenge of modeling heterogeneous features in soccer event data, where traditional one-hot or ordinal encodings fail to capture semantic relationships among categorical variables. The study proposes the first application of TabTransformer to soccer event representation learning, leveraging learnable dense embeddings and self-attention mechanisms to capture sport-specific action semantics during pretraining and generate general-purpose event representations. By effectively modeling latent dependencies among categorical features, the approach significantly outperforms baseline models on downstream tasks such as action value estimation and playing style recognition. Moreover, improved Brier scores demonstrate that the model yields better-calibrated predictive probabilities.

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Latest Papers

A Universal Dense Football Event Representation Based on TabTransformer

Jun 08, 2026

This work addresses the challenge of modeling heterogeneous features in soccer event data, where traditional one-hot or ordinal encodings fail to capture semantic relationships among categorical variables. The study proposes the first application of TabTransformer to soccer event representation learning, leveraging learnable dense embeddings and self-attention mechanisms to capture sport-specific action semantics during pretraining and generate general-purpose event representations. By effectively modeling latent dependencies among categorical features, the approach significantly outperforms baseline models on downstream tasks such as action value estimation and playing style recognition. Moreover, improved Brier scores demonstrate that the model yields better-calibrated predictive probabilities.

0 citationsRead paper