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This paper develops a regression framework for histogram-valued data using unbalanced optimal transport. By optimally mapping input histograms to targets while preserving distributional structures, the approach quantifies individual contributions in team dynamics. Applied to football analytics with Serie A data (2023–2024), it reveals patterns distinguishing wins, losses, and draws.

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Unbalanced Optimal Transport for Histogram Regression: Applications in Sports Analytics

  • 会议论文

  • 发表日期：

    2025年

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  • Alessandro Spelta

摘要

This paper develops a regression framework for histogram-valued data using unbalanced optimal transport. By optimally mapping input histograms to targets while preserving distributional structures, the approach quantifies individual contributions in team dynamics. Applied to football analytics with Serie A data (2023–2024), it reveals patterns distinguishing wins, losses, and draws.