| Session 1: Football |
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Routine Inspection: A playbook for corner kicks
Laurie Shaw and and Sudarshan Gopaladesikan |
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How data availability affects the ability to learn good xG models
Pieter Robberechts and Jesse Davis |
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Pass Annotation from Soccer Video Streams via Deep Learning
Danilo Sorano, Fabio Carrara, Fabrizio Falchi, Paolo Cintia and Luca Pappalardo |
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Low-cost optical tracking of soccer players
Gabor Csanalosi, Gergely Dobreff, Alija Pašić, Marton Molnar and Laszlo Toka |
| Session 2: Physical performance |
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A Machine Learning Approach for Road Cycling Race Performance Prediction
Leonid Kholkine, Tom De Schepper, Tim Verdonck and Steven Latré |
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Mining Marathon Training Data to Generate Useful User Profiles
Jakim Berndsen, Barry Smyth and Aonghus Lawlor |
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Physical performance optimization in football
Gergely Dobreff, Péter Revisnyei, Gábor Schuth, György Szigeti, László Toka and Alija Pašić |
| Session 3: Other sports |
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Invited talk: "Using collective motion models to help players improve their game"
David Sumpter (University of Uppsala) |
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Stats Aren't Everything: Learning Strengths and Weaknesses of Cricket Players
Swarup Ranjan Behera and Saradhi Vijaya |
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Prediction of tiers in the ranking of ice hockey players
Timmy Lehmus Persson, Haris Kozlica, Niklas Carlsson and Patrick Lambrix |
| Session 4 |
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An Autoencoder Based Approach to Simulate Sports Games
Ashwin Vaswani, Rijul Ganguly, Het Shah, Sharan Ranjit, Shrey Pandit and Samruddhi Bothara |
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A Behavioral Pattern Mining Approach to ModelPlayer Skills in Rocket League
Romain Mathonat, Jean-Francois Boulicaut and Mehdi Kaytoue |
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Learning from partially labeled sequencesfor behavioral signal annotation
Anna Aniszewska-Stepien, Romain Herault, Guillaume Hacques, Ludovic Seifert and Gilles Gasso |