ArticleBMC research notes2024
Streamlining performance prediction: data-driven KPIs in all swimming strokes.
Article in BMC research notes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Personalized skill transfer optimization in swimming training through multi-agent reinforcement learning driven digital twin environments.Scientific reports · 2026Article
- Stroke rate-stroke length dynamics in elite freestyle swimming: application of kernel density estimation.Frontiers in sports and active living · 2025Article
- Determining Validity and Reliability of an In-Field Performance Analysis System for Swimming.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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.
resultsDuration 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.