用KNN算法构建足球场地控制模型，快速灵活且可调参。
A Neighbor-based Approach to Pitch Ownership Models in Soccer
- 基于KNN算法，根据球员位置动态计算场地控制权。
- 仅需三个超参数，适配不同水平球员与不确定性场景。
- 支持多种现有方法模拟，适合战术分析与后续研究。
场地控制模型为足球分析提供了重要支持，其优势在于利用追踪数据包含的上下文信息（如球员位置），而事件数据无法捕捉这些细节。本文提出一种基于K近邻（KNN）算法的新方法，用于构建足球比赛中的场地控制模型。该方法具备快速推理能力，可通过同一算法实现多种控制策略建模。尽管灵活性高，模型仅需三个超参数即可调整，便于适配不同球员技能水平。通过微调少量参数，该方法能模拟文献中多种现有方法，包括对不确定性的处理。我们通过多组可视化案例展示了模型的优势与局限性。代码已开源于github.com/nvsclub/KNNPitchControl。
原文摘要 · Abstract (English)
Pitch ownership models allow many types of analysis in soccer and provide valuable assistance to tactical analysts in understanding the game's dynamics. The novelty they provide over event-based analysis is that tracking data incorporates context that event-based data does not possess, like player positioning. This paper proposes a novel approach to building pitch ownership models in soccer games using the K-Nearest Neighbors (KNN) algorithm. Our approach provides a fast inference mechanism that can model different approaches to pitch control using the same algorithm. Despite its flexibility, it uses only three hyperparameters to tune the model, facilitating the tuning process for different player skill levels. The flexibility of the approach allows for the emulation of different methods available in the literature by adjusting a small number of parameters, including adjusting for different levels of uncertainty. In summary, the proposed model provides a new and more flexible strategy for building pitch ownership models, extending beyond just replicating existing algorithms, and can provide valuable insights for tactical analysts and open up new avenues for future research. We thoroughly visualize several examples demonstrating the presented models' strengths and weaknesses. The code is available at github.com/nvsclub/KNNPitchControl.
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