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
1. Auflage 2020
ISBN: 978-3-030-64911-1
Verlag: Springer International Publishing

Buch, Englisch, Band 1324, 141 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 242 g

Reihe: Communications in Computer and Information Science

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 11 papers presented were carefully reviewed and selected from 22 submissions. The papers present a variety of topics within the area of sports analytics, including tactical analysis, outcome predictions, data acquisition, performance optimization, and player evaluation.

Zielgruppe

Research

Autoren/Hrsg.

Brefeld, Ulf

Zimmermann, Albrecht

Davis, Jesse

Haaren, Jan van

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.