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

BMC Research Notes (2024) 17:52 Staunton et al. BMC Research Notes https://doi.org/10.1186/s13104-024-06714-x Open Access RESEARCH NOTE Streamlining performance prediction: data‑driven KPIs in all swimming strokes Craig A. Staunton1, Michael Romann2, Glenn Björklund1 and Dennis‑Peter Born2,3* Abstract Objective This study aimed to identify Key Performance Indicators (KPIs) for men’s swimming strokes using Prin‑ cipal 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 Alt‑ man 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 Introduction Competitive swimming encompasses four primary techniques: the front crawl or freestyle (FR), breaststroke (BR), backstroke (BA), and the butterfly (BU). Swimmers often specialize in specific strokes or distances, showcasing their expertise in the water [1]. Identifying Key Performance Indicators (KPIs) for each stroke *Correspondence: Dennis‑Peter Born 1 Swedish Winter Sports Research Centre, Department of Health Sciences, Mid Sweden University, Östersund, Sweden 2 Department for Elite Sport, Swiss Federal Institute of Sport, Hochschule Lärchenplatz, 2532 Magglingen, Switzerland 3 Section for High-Performance Sports, Swiss Swimming Federation, Bern, Switzerland becomes crucial for coaches and athletes to guide training strategies and optimize performance [2]. It’s evident that KPIs can vary significantly between strokes, given the distinct characteristics and techniques involved. For example, prior research has revealed different key somatic features of the 4 swimming strokes [3, 4]. Further, strokes with alternating arm movements, like freestyle and backstroke, may have different KPIs compared to those with continuous stroke actions, such as the butterfly and breaststroke [5]. Additionally, the nuances of turning, (i.e. tumble turn for alternating and touch turn for continuous swimming strokes), play a substantial role in influencing KPIs across different strokes [6]. With the ever-evolving landscape of competitive swimming and interdisciplinary experts involved in the support system, a wealth of performance data © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Staunton et al. BMC Research Notes (2024) 17:52 accompanies both training and competitions [7]. As advancements in technology continue to provide more sophisticated race analysis and greater accessibility to performance data, the challenges of managing ’big data’ in this field are growing. Despite this, some more recent research has used advanced statistical techniques in order to model swimming performance [8–10]. Furthermore, it is foreseeable that the future will bring increased prevalence of automated tracking systems and motion sensors integrated with swimmers. However, sifting through these data to discern its significance can be challenging for coaches and athletes. Data reduction techniques, such as Principal Component Analyses (PCA), provide a valuable means of extracting essential information that explain the most significant variances in performance and eliminate redundant variables that capture similar information (for more information about PCA please see the following reviews [11, 12]). For example, PCA has been utilised previously within sports such as swimming [13], skeleton [14] or rugby [15] to help with data reduction. When complemented by Multiple Regression Analysis, these techniques enable the identification and comparison of KPIs specific to each stroke. With these complexities in mind, this study’s primary objective is to explore the nuances of men’s swimming strokes. By employing data reduction techniques like PCA and Multiple Regression Analysis, we aim to achieve two key goals. Firstly, we seek to uncover KPIs across the four swimming strokes, offering deeper insights into each stroke’s unique intricacies. Secondly, our study aims to develop a performance prediction tool that can be used practically by coaches and athletes to monitor performance. Material and methods Participants Participants included all men’s individual 100 m races of the 2019 European Short-Course Swimming Championships in Glasgow, Scotland. Races included the FR, BA, BR and BU (FR: N = 74; swimming points = 782 ± 79; BA: N = 62; swimming points = 801 ± 84; BR: N = 47; swimming points = 826 ± 82; BU: N = 61; swimming points = 775 ± 78). All swimmers that participate at events hosted by the European Swimming Association LEN (Ligue Européenne de Natation) agree to be video monitored for television broadcasting and race analysis of the participating nations. The study was preapproved by the leading institution’s internal review board (registration number: 098-LSP-191119) and was in accordance to the latest version of the (...truncated)