Video-based Skill Assessment for Golf: Estimating Golf Handicap

Christian Keilstrup Ingwersen, Artur Xarles Esparraguera, Albert Clapés, Meysam Madadi, Janus Nørtoft Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Sergio Escalera

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Automated skill assessment in sports using video-based analysis holds great potential for revolutionizing coaching methodologies. This paper focuses on the problem of skill determination in golfers by leveraging deep learning models applied to a large database of video recordings of golf swings. We investigate different regression, ranking and classification based methods and compare to a simple baseline approach. The performance is evaluated using mean squared error (MSE) as well as computing the percentages of correctly ranked pairs based on the Kendall correlation. Our results demonstrate an improvement over the baseline, with a 35% lower mean squared error and 68% correctly ranked pairs. However, achieving fine-grained skill assessment remains challenging. This work contributes to the development of AI-driven coaching systems and advances the understanding of video-based skill determination in the context of golf
Original languageEnglish
Title of host publicationProceedings of the 6th International ACM Workshop on Multimedia Content Analysis in Sports
PublisherAssociation for Computing Machinery
Publication date2023
ISBN (Electronic)979-8-4007-0269-3
Publication statusPublished - 2023
Event6th International Workshop on Multimedia Content Analysis in Sports - Ottawa, Canada
Duration: 29 Oct 202329 Oct 2023


Conference6th International Workshop on Multimedia Content Analysis in Sports


  • Datasets
  • Neural networks
  • Action quality assessment
  • Golf
  • Action understanding


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