A Machine Learning Analysis of Technical Skills and Tactical Awareness as Performance Predictors for Goalkeepers in European Football League

Rabiu Muazu Musa*, Anwar P.P.Abdul Majeed, Aina Munirah Ab Rasid, Mohamad Razali Abdullah

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

Abstract

This chapter highlights the tactical and technical performance indicators that could distinguish between highly skilled and low-skilled goalkeepers (GKs). It has been demonstrated from the study findings that a set of indicators encompassing accurate and key passes, fouls, accurate foot passes from open play, accurate, hand passes, accurate passes from set pieces, short passes accurate, medium passes accurate, accurate long passes, and goals conceded could be essential in identifying the performance level of the GKs. It has also been demonstrated from the current finding that the application of the machine learning model is non-trivial in predicting the performance levels of the GKs. The decision tree model is found to yield an excellent prediction of the level of GKs levels with respect to the investigated indicators. It is then inferred that GKs’ training programmes should include aspects related to said indicators.

Original languageEnglish
Title of host publicationSpringerBriefs in Applied Sciences and Technology
PublisherSpringer Science and Business Media Deutschland GmbH
Pages29-34
Number of pages6
DOIs
Publication statusPublished - 2024

Publication series

NameSpringerBriefs in Applied Sciences and Technology
VolumePart F2301
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

Keywords

  • European football leagues
  • Goalkeepers’ performance
  • Highly skilled goalkeepers
  • Machine learning models
  • Tactical awareness

Fingerprint

Dive into the research topics of 'A Machine Learning Analysis of Technical Skills and Tactical Awareness as Performance Predictors for Goalkeepers in European Football League'. Together they form a unique fingerprint.

Cite this