Streamlining performance prediction: data-driven KPIs in all swimming strokes

Abstract

Abstract Objective This study aimed to identify Key Performance Indicators (KPIs) for men’s swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men’s individual 100 m races of the 2019 European Short-Course Swimming Championships. Results Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs − 2.3% to + 2.3%), Breaststroke (0% bias, LOAs − 0.9% to + 0.9%), Butterfly (0% bias, LOAs − 1.2% to + 1.2%), and Freestyle (0% bias, LOAs − 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.

Keywords

• Competitive swimming
• Data analysis
• Key performance indicators
• Performance prediction
• Training strategies