4054860
Predicting pass receiver in football using distance based features
This paper presents our approach to the football pass prediction challenge of the Machine Learning and Data Mining for Sport Analytics workshop at ECML/PKDD 2018. Our solution uses distance based features to predict the receiver of a pass. We show that our model is able to improve prediction results obtained on a similar dataset. One particularity of our approach is the use of failed passes to improve the predictions.
© Copyright 2019 Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330. Published by Springer. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | technical and natural sciences sport games |
| Tagging: | maschinelles Lernen Passspiel |
| Published in: | Machine Learning and Data Mining for Sports Analytics. MLSA 2018. Lecture Notes in Computer Science, vol 11330 |
| Language: | English |
| Published: |
Cham
Springer
2019
|
| Online Access: | https://doi.org/10.1007/978-3-030-17274-9_12 |
| Pages: | 145-151 |
| Document types: | article |
| Level: | advanced |