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Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers

Research output: Books and anthologies › Conference proceedings › Research

Authors

  • Ulf Brefeld (Editor)
  • Jesse Davis (Editor)
  • Jan Van Haaren (Editor)
  • Albrecht Zimmermann (Editor)

The proceedings contain 9 papers. The special focus in this conference is on Machine Learning and Data Mining for Sports Analytics. The topics include: Large Language Models on Race Commentary: Towards Granular Data in Cycling Analytics; GraphEIV: A Framework for Estimating the Expected Immediate Value in Basketball Using Graph Neural Networks; mathematical Models for Off-Ball Scoring Prediction in Basketball; an Analysis of the Influence of Game Context on Team Playing Style; Augmented Intelligence for FIFA Predictions; transformer-Based Framework for Versatile Analysis of Events Data in Soccer; Automated Detection of Shot Events in Game Phases Using GNSS Data from a Single Team.

Original languageEnglish
Place of PublicationCham
PublisherSpringer Verlag
Number of pages119
ISBN (print)978-3-031-86691-3
ISBN (electronic)978-3-031-86692-0
DOIs
Publication statusPublished - 2025
Event11th International Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2024 - Vilnius, Lithuania
Duration: 09.09.2024 → 09.09.2024
Conference number: 11

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Science and Business Media Deutschland GmbH
Volume2460
ISSN (Print)1865-0929
ISSN (electronic)1865-0937

Other publications by the same author(s)

Interactive sequential generative models for team sports

Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.

Research output: Journal contributions › Journal articles › Research › peer-review

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.

Research output: Journal contributions › Journal articles › Research › peer-review

The promise and challenges of computer mouse trajectories in DMHIs – A feasibility study on pre-treatment dropout predictions

Zantvoort, K., Matthiesen, J., Bjurner, P., Bendix, M., Brefeld, U., Funk, B. & Kaldo, V., 06.2025, In: Internet Interventions. 40, 40, 7 p., 100828.

Research output: Journal contributions › Journal articles › Research › peer-review

Toward Learning Distributions of Distributions

Wohlstein, M. & Brefeld, U., 2025, In: Proceedings of Machine Learning Research. 265, p. 269-275 7 p.

Research output: Journal contributions › Conference article in journal › Research › peer-review

Studying properties of water data using manifold-aware anomaly detectors

Paulsen, T. & Brefeld, U., 23.04.2024, (Submitted) Design of Cyber-Secure Water Plants. Mathur, A., Zhou, J. & Raman, G. (eds.). MDPI AG, (Water).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

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