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arXiv Machine Learning By Kaan G\"unay, Orhun Gun

Graph-to-Grid (G2G): Continuous-Coordinate Feature Painting for Soccer Pass Surfaces

Read the original on arXiv Machine Learning →

The paper introduces Graph-to-Grid (G2G), a method that paints continuous‑coordinate player features onto a grid using bilinear interpolation, enabling end‑to‑end training of per‑player encoders for pass surface prediction. On 53,628 World Cup passes, this painting approach improves selection likelihood by about a quarter of a nat compared to raster‑only inputs, with further gains from learned encoders and message passing. The study also shows that painting benefits existing models like SoccerMap and U‑Net, while other variants such as offset channels or raster‑free decoders do not provide similar improvements.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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