Predicting the receivers of football passes

Heng Li, Zhiying Zhang

Research output: Journal article publicationConference articleAcademic researchpeer-review

Abstract

Football (or association football) is a highly-collaborative team sport. Passing the ball to the right player is essential for winning a football game. Anticipating the receiver of a pass can help football players build better collaborations and help coaches make informed tactical decisions. In this work, we analyze a public dataset that contains 12,124 passes performed by professional football players. We extract five dimensions of features from the dataset and build a learning to rank model to predict the receiver of a pass. Our model’s first, top-3 and top-5 guesses find the correct receiver of a pass with an accuracy of 50%, 84%, and 94%, respectively, when we exclude false passes, which outperforms three baseline models that we use to rank the candidate receivers of a pass. The features that capture the positions of the candidate receivers play the most important roles in explaining the receiver of a pass.

Original languageEnglish
Pages (from-to)170-179
Number of pages10
JournalCEUR Workshop Proceedings
Volume2284
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event5th Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2018 - Dublin, Ireland
Duration: 10 Sep 2018 → …

Keywords

  • Football pass prediction
  • Gradient boosting decision tree
  • LambdaMART
  • Learning to rank
  • LightGBM

ASJC Scopus subject areas

  • Computer Science(all)

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