| 09:00 | Welcome |
| 09:30 |
Towards expected counter - Using comprehensible features to predict counterattacks
Henrik Rolf Biermann, Franz-Georg Wieland, Jens Timmer, Daniel Memmert, and Ashwin Phatak |
| 10:00 |
Shot analysis in different levels of German football using Expected Goals
Laurynas Raudonius and Thomas Seidl |
| 10:30 | Coffee break |
| 11:00 |
Model-based methods for high-performance analysis in sports
Invited talk by Stephanie Kovalchik Abstract: The how of what makes great athletes great is one of the major topics of sports analytics research. Value attribution is a fundamental tool for quantifying the specific actions and skills that improve outcomes in sport. With the growth in spatial temporal data in high-performance sport, methods for value attribution are becoming increasingly granular and sophisticated. In this talk, I will review several common types of model-based methods for value attribution and present applications in multiple pro sports. |
| 12:00 |
Predicting tennis serve directions with machine learning
Ying Zhu and Ruthuparna Naikar |
| 12:30 |
Discovering and visualizing tactics in table tennis games based on subgroup discovery
Pierre Duluard, Xinqing Li, Marc Plantevit, Céline Robardet, and Romain Vuillemot |
| 13:00 | Lunch break |
| 14:30 |
Athlete monitoring in professional road cycling using similarity search on time series data
Arie-Willem de Leeuw, Tobias Oberkofler, Mathieu Heijboer, and Arno Knobbe |
| 15:00 |
Modelling coach decisions in professional cycling teams
Maor Sagi, Paulo Saldanha, Guy Shani, and Robert Moskovitch |
| 15:30 |
Analysing basketball shots with graph embeddings
Marc Schmid |
| 16:00 |
Cost-efficient and bias-robust sports player tracking by integrating GPS and video
Hyunsung Kim, Chang Jo Kim, Minchul Jeong, Jaechan Lee, Jinsung Yoon, and Sang-Ki Ko |
| 16:30 | Coffee break |
| 17:00 |
Analyzing passing sequences for the prediction of goal-scoring opportunities
Conor McCarthy, Panagiotis Tampakis, Marco Chiarandini, Morten Bredsgaard Randers, Stefan Jänicke, and Arthur Zimek |
| 17:30 |
Let's penetrate the defense: A machine learning model for prediction and valuation of penetrative passes
Pegah Rahimian, Dayana Grayce da Silva Guerra Gomes, Fanni Berkovics, and Laszlo Toka |
| 18:00 |
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, and Keisuke Fujii |