10th International Workshop, MLSA 2023, Turin, Italy, September 18, 2023, Revised Selected Papers Brefeld, Ulf. editor. edt http://id.loc.gov/vocabulary/relators/edt Davis, Jesse. editor. edt http://id.loc.gov/vocabulary/relators/edt Van Haaren, Jan. editor. edt http://id.loc.gov/vocabulary/relators/edt Zimmermann, Albrecht. editor. edt http://id.loc.gov/vocabulary/relators/edt SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

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access X, 204 p. 56 illus., 49 illus. in color. online resource. This book constitutes the refereed proceedings of the 10th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2023, held in Turin, Italy, in September 2023. The 16 full papers included in this book were carefully reviewed and selected from 31 submissions. They were organized in topical sections as follows: Football/Soccer, Basketball, Other team sports, Individual sports. Sports Data Analytics: An Art and a Science -- Foot ball/ soccer -- ETSY: A rule-based approach to Event and Tracking data Synchronization -- Masked Autoencoder Pretraining for Event Classi cation in Elite Soccer -- Quanti cation of Turnover Danger with xCounter -- Pass Receiver and Outcome Prediction in Soccer Using Temporal -- Graph Networks -- Field Depth Matters: Comparing the Valuation of Passes in Football -- Basket ball -- Momentum matters: investigating high-pressure situations in the NBA through scoring probability -- Are Sports Awards About Sports? Using AI to Find the Answer -- The Big Three: a practical framework for designing Decision Support -- Systems in Sports and an application for basketball -- Ot her t eam sp ort s -- What data should be collected for a good handball Expected Goal model? .-Identifying Player Roles in Ice Hockey -- Position Prediction -- Boat speed prediction in SailGP -- Individual sp ort s -- Exploring Table Tennis Analytics: Domination, Expected Score and Shot Diversity -- Specialization Evaluation -- Exploiting Clustering for Sports Data Analysis: A Study of Public and Real-world Datasets. edited by Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann. Artificial intelligence Computer networks  Application software Database management Software engineering Artificial Intelligence Computer Communication Networks Computer and Information Systems Applications Database Management System Software Engineering Q334-342 TA347.A78 9783031538339 https://doi.org/10.1007/978-3-031-53833-9 https://doi.org/10.1007/978-3-031-53833-9 240225 20251030111443.0 978-3-031-53833-9