Continuous Soccer Pass Detection: A Comparison between Traditional and Streaming Machine Learning Methods.

Sports analytics has grown significantly through continuous data streams from wearable tracking devices. This article explores the performance of traditional Machine Learning methods (ML) compared to Streaming Machine Learning approaches (SML) in the context of sports analytics, specifically in identifying passes during a soccer match. The study utilizes leg movement data from wearable sensors on players’ shoes to differentiate between pass and nonpass actions. Balanced, imbalanced and rebalanced datasets are created and analyzed. Several traditional and streaming algorithms are tested, and statistical analyses are performed to assess their performance. The findings indicate that Streaming Machine Learning can achieve comparable or better performance than traditional methods, especially on larger rebalanced datasets. This research highlights the potential of Streaming Machine Learning for online sports analytics and suggests future directions for exploring player tendencies and edge device adaptability.