Field depth matters: Comparing the valuation of passes in football
This study delves into the influence of missing spatial context information on the valuation of football actions through event data based metrics. Using actions from an entire Premier League season, we analyze successful passes originating from different field depths, considering the subsequent occurrence of goals. By comparing the value assignments by Valuing Actions by Estimating Probabilities (VAEP), we provide insights into the metric`s ability to recognize the quality of passes in the early stages of attacks.
© Copyright 2024 Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science. Published by Springer. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | sport games technical and natural sciences |
| Tagging: | Passspiel maschinelles Lernen |
| Published in: | Machine Learning and Data Mining for Sports Analytics. MLSA 2023. Communications in Computer and Information Science |
| Language: | English |
| Published: |
Cham
Springer
2024
|
| Series: | Communications in Computer and Information Science, 2035 |
| Online Access: | https://doi.org/10.1007/978-3-031-53833-9_6 |
| Pages: | 64-73 |
| Document types: | article |
| Level: | advanced |