KDD 2023 Workshop on Data Science and AI for Sports

Even though sports analytics is moving fast, a big research gap still exists because sports science and artificial intelligence draw on many different fields. Most current methods stay locked inside a single sport or a narrow technical area, so ideas don’t spread easily. This research direction aims to close that gap by building unified frameworks that bring together sport‑specific knowledge—like biomechanics and physiological limits—with modern machine learning models. PhD students could work on multi‑modal data‑fusion methods that merge spatiotemporal tracking, sensor telemetry, and event data. A core focus would be creating transfer‑learning architectures that let tactical or physiological models shift from one sport to another with only a small amount

of new data. The goal is to produce general, easy‑to‑interpret AI systems that turn complex athletic data into useful insights for sports scientists and coaches. By creating standard cross‑sport benchmarks, this work would push forward both machine‑learning theory and real‑world performance analysis, encouraging collaboration between data scientists and sports practitioners.

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