Video-based Skill Assessment for Golf: Estimating Golf Handicap

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

Abstract: 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.