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Machine Learning and Data Mining for Sports Analytics 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings /

This book constitutes the refereed post-conference proceedings of the 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, colocated with ECML/PKDD 2020, in Ghent, Belgium, in September 2020. Due to the COVID-19 pandemic the conference was held online. The...

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Corporate Author: SpringerLink (Online service)
Other Authors: Brefeld, Ulf. (Editor, http://id.loc.gov/vocabulary/relators/edt), Davis, Jesse. (Editor, http://id.loc.gov/vocabulary/relators/edt), Van Haaren, Jan. (Editor, http://id.loc.gov/vocabulary/relators/edt), Zimmermann, Albrecht. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2020.
Edition:1st ed. 2020.
Series:Communications in Computer and Information Science, 1324
Subjects:
Artificial intelligence.
Computers.
Education—Data processing.
Application software.
Computer communication systems.
Artificial Intelligence.
Information Systems and Communication Service.
Computers and Education.
Computer Appl. in Social and Behavioral Sciences.
Computer Communication Networks.
Online Access:https://doi.org/10.1007/978-3-030-64912-8
Table of Contents:
  • Routine Inspection: A playbook for corner kicks
  • How data availability aects the ability to learngood xG models
  • Low-cost optical tracking of soccer players
  • An Autoencoder Based Approach to SimulateSports Games
  • Physical performance optimization in football
  • Predicting Player Trajectoriesin Shot Situations in Soccer
  • Stats Aren't Everything: Learning Strengths andWeaknesses of Cricket Players
  • Prediction of tiers in the rankingof ice hockey players
  • A Machine Learning Approach for Road CyclingRace Performance Prediction
  • Mining Marathon Training Data to GenerateUseful User Proles
  • Learning from partially labeled sequences forbehavioral signal annotation.

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