We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Streamlining performance prediction: data-driven KPIs in all swimming strokes.
- Authors
Staunton, Craig A.; Romann, Michael; Björklund, Glenn; Born, Dennis-Peter
- 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.
- Subjects
SWIMMING; KEY performance indicators (Management); BLAND-Altman plot; SWIMMING competitions; SWIMMING training; COACH-athlete relationships
- Publication
BMC Research Notes, 2024, Vol 17, p1
- ISSN
1756-0500
- Publication type
Article
- DOI
10.1186/s13104-024-06714-x