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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

Publikation: Bücher und Anthologien › Konferenzbände und -dokumentationen › Forschung

Authors

  • Ulf Brefeld (Herausgeber*in)
  • Jesse Davis (Herausgeber*in)
  • Jan Van Haaren (Herausgeber*in)
  • Albrecht Zimmermann (Herausgeber*in)

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.

OriginalspracheEnglisch
ErscheinungsortCham
VerlagSpringer Verlag
Anzahl der Seiten119
ISBN (Print)978-3-031-86691-3
ISBN (elektronisch)978-3-031-86692-0
DOIs
PublikationsstatusErschienen - 2025
Veranstaltung11th International Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2024 - Vilnius, Litauen
Dauer: 09.09.2024 → 09.09.2024
Konferenznummer: 11

Publikationsreihe

NameCommunications in Computer and Information Science
VerlagSpringer Science and Business Media Deutschland GmbH
Band2460
ISSN (Print)1865-0929
ISSN (elektronisch)1865-0937

Weitere Publikationen dieser Person(en)

Interactive sequential generative models for team sports

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

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items

Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Angenommen/Im Druck) in: Psychometrika.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, in: Machine Learning. 114, 2, 18 S., 44.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Self-improvement for Computerized Adaptive Testing

Rudolph, Y., Neubauer, K. & Brefeld, U., 2026, Machine Learning and Knowledge Discovery in Databases - Research Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings. Ribeiro, R. P., Jorge, A. M., Soares, C., Gama, J., Pfahringer, B., Japkowicz, N., Larrañaga, P. & Abreu, P. H. (Hrsg.). Cham: Springer International Publishing, Band 2. S. 70-86 17 S. (Lecture Notes in Computer Science; Band 16014 LNCS).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

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, 7 S., 100828.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

DOI

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  7. The Relation of Children's Performances in Spatial Tasks at Two Different Scales of Space
  8. Passive Rotation of Rotational Joints and Its Computation Method
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