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Spatiotemporal Data Analysis in Soccer Using Machine Learning Approaches
From Raw Data to Validated Performance Indicators.DE
English
235 b
Coming soon
New
Expected 19. 04. 2027
Up to 30 days for returns
This book presents a unified framework for automating performance analysis in soccer through machine learning. Rather than treating match segmentation, event detection, and performance indicator validation in isolation, it develops a sequential pipeline that transforms raw spatiotemporal tracking data into structured, analysis-ready representations. The work integrates three empirical studies with broader conceptual contributions including a rigorous framework for performance indicator validity, a systematic clarification of Match Momentum distinct from psychological momentum, and a structured taxonomy of ML application domains in soccer. Embedded within a broader argument for democratizing soccer analytics, the book demonstrates how algorithmic automation can reduce reliance on costly manual annotation and proprietary data products, thereby lowering analytical barriers for resource-constrained clubs.
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