发表机构
Sabancı University; Carnegie Mellon University（萨班哲大学; 卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Graph-to-Grid连续坐标特征绘制方法，将球员特征按坐标双线性散布到网格，在2022世界杯53,628次传球上提升传球选择似然约四分之一纳特，优于栅格输入及多种替代方案。
AI 中文摘要
密集传球曲面为每个球场单元格提供信息：在该处传出的球是否会到达，持球者是否会选择该传球，以及随后控球的价值。绘制这些曲面的网络将状态读取为按单元格计数的栅格，从而丢失了每个球员在单元格内的具体位置。激光雷达检测器、鸟瞰感知和图天气模型将实体特征移动到网格上，将每个实体分箱到单元格或学习转移过程。我们评估插值形式：每个球员的特征按球员实测坐标双线性散布到网格上，因此曲面损失端到端地训练逐球员编码器。这些系统采用了一种接口；本文衡量了该接口。在2022年世界杯的53,628次传球中，绘制特征相比仅输入栅格的同一核心，将选择似然提高了约四分之一纳特：在八折交叉验证的每场比赛中，每个臂在五个种子上调参，并在来自另一提供商的七场德甲和德乙比赛上重新训练后均如此。十三项预先指定的研究定位了增益：绘制九个原始球员特征且无编码器时承载了其中四分之三的增益，而学习到的编码器和消息传递增加了较小但明确的增量。绘制特征也有助于原始SoccerMap和经典U-Net，而偏移通道、更细栅格、注意力绘制器和无栅格解码器则无此效果。跨提供商边界冻结时，似然优势消失；注入的跟踪误差压缩了该优势。这些结果涉及观测端点的预测，而非假设传球经过校准的评估。
英文摘要
Dense pass surfaces give, for every pitch cell, whether a pass played there would arrive, whether the carrier would choose it, and what the possession would then be worth. The networks that draw them read the state as a raster of per-cell counts, losing where inside a cell each player stands. LiDAR detectors, bird's-eye-view perception and graph weather models move entity features onto a grid, binning each entity to a cell or learning the transfer. We evaluate the interpolated form: each player's features are scattered bilinearly onto the grid at the player's measured coordinates, so the surface loss trains the per-player encoder end to end. Those systems adopt an interface; this paper measures one. On 53,628 passes from the 2022 World Cup, painting improves selection likelihood over the same core fed rasters alone by about a quarter of a nat: in every match of an eight-fold cross-validation, with every arm tuned over five seeds, and after retraining on seven Bundesliga and 2. Bundesliga matches from another provider. Thirteen pre-specified studies locate the gain: painting the nine raw player features with no encoder carries three quarters of it, and the learned encoder and message passing add a smaller, resolved increment. Painting also helps the original SoccerMap and a canonical U-Net, whereas offset channels, a finer raster, an attention painter and a raster-free decoder do not. Frozen across the provider boundary the likelihood advantage is lost; injected tracking error compresses it. These results concern observed-endpoint prediction, not calibrated evaluation of hypothetical passes.
Comments39 pages, 5 figures, 18 tables
