An Ensemble of Long Short‐Term Memory Models to Automatically Detect End‐Range Movement Patterns in Men's Professional Hard Court Grand Slam Tennis

ABSTRACT Evaluating end‐range movements during tennis match‐play can quantify high intensity load exposure and facilitate specific analysis of players' high‐end physical capabilities. Currently, the process to evaluate such movement is labour intensive, with an established and efficient process to identify end‐range movements lacking. Using three‐dimensional pose model data for male competitors in the 2024 Australian Open, we evaluated an ensemble of long short‐term memory (LSTM) models to correctly classify coach‐identified end‐range movement patterns. An ensemble of 10 LSTM models that took the average prediction value and applied a class prediction threshold of 0.63 was the best performing approach, producing an F1‐score of 0.944, overall accuracy of 95.9%, precision of 97.8% and recall of 91.2%. From these results, we provide a novel and practical way of using real‐world pose model data and machine learning to automatically detect one of the most physically demanding movement tasks in professional men's tennis. This work enhances post‐match analysis via an automated analytical pipeline that can quantify high intensity movement exposures and produce descriptive statistics of end‐range movement to assist with the load monitoring and management of professional players.