Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon
PurposeThis study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots, alongside traditional training metrics, might provide a more detailed understanding of hear...
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| Format: | Article |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1546909/full |
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| author | Craig A. Staunton Craig A. Staunton Andreas Kårström Andreas Kårström Hannes Kock Hannes Kock Marko S. Laaksonen Glenn Björklund |
| author_facet | Craig A. Staunton Craig A. Staunton Andreas Kårström Andreas Kårström Hannes Kock Hannes Kock Marko S. Laaksonen Glenn Björklund |
| author_sort | Craig A. Staunton |
| collection | DOAJ |
| description | PurposeThis study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots, alongside traditional training metrics, might provide a more detailed understanding of heart rate (HR) intensity patterns, aiding in the evaluation of training quality and compliance.MethodsFifteen elite-level youth biathletes from two national academy programmes were monitored over 5–6 weeks using HR monitors. Training sessions were measured via time-in-zone (TIZ) within a five-zone HR model with any time accumulated below the threshold for Zone 1, considered Zone 0. Sessions were dichotomised into those planned as low-intensity training (LIT) or those planned with high-intensity training (HIT). KDE analyses were conducted in MATLAB (Version R2020b) using the “ksdensity” function to create 2D KDE plots that visualise HR intensity accumulation across each programme, session type (e.g., Low-intensity training: LIT; High-intensity training: HIT), and individual athlete responses. Traditional histogram plots and grouped bar charts were also used for comparison.ResultsFor LIT sessions, athletes performed less time in Zone 1 than planned, while performed time exceeded planned time in Zone 2. For HIT sessions, performed time in Zone 5 was lower than planned. All sessions contained unplanned time in Zone 0. The 2D KDE plots provided a continuous and detailed representation of HR intensity accumulation throughout training sessions, revealing patterns and intensity fluctuations that complement traditional TIZ analyses.Conclusions2D KDE plots might serve as a valuable complementary tool for assessing TID in biathlon, offering a more nuanced and continuous view of HR intensity. By identifying discrepancies between planned and performed training intensity, coaches can refine strategies and provide individualised feedback. Incorporating KDE plots into training monitoring could improve training alignment, helping reduce overtraining or undertraining risks and optimising athlete development. |
| format | Article |
| id | doaj-art-f6772ee4babc418c8e80dcaf04dc82c7 |
| institution | Kabale University |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-f6772ee4babc418c8e80dcaf04dc82c72025-08-20T03:31:27ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-06-01710.3389/fspor.2025.15469091546909Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlonCraig A. Staunton0Craig A. Staunton1Andreas Kårström2Andreas Kårström3Hannes Kock4Hannes Kock5Marko S. Laaksonen6Glenn Björklund7Department of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, SwedenDepartment of Environmental and Bioscience, School of Business, Innovation and Sustainability, Halmstad University, Halmstad, SwedenDepartment of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, SwedenSwedish Biathlon Federation, Östersund, SwedenDepartment of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, SwedenDepartment of Endurance Sports, Institute for Applied Training Science, Leipzig, GermanyDepartment of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, SwedenDepartment of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, SwedenPurposeThis study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots, alongside traditional training metrics, might provide a more detailed understanding of heart rate (HR) intensity patterns, aiding in the evaluation of training quality and compliance.MethodsFifteen elite-level youth biathletes from two national academy programmes were monitored over 5–6 weeks using HR monitors. Training sessions were measured via time-in-zone (TIZ) within a five-zone HR model with any time accumulated below the threshold for Zone 1, considered Zone 0. Sessions were dichotomised into those planned as low-intensity training (LIT) or those planned with high-intensity training (HIT). KDE analyses were conducted in MATLAB (Version R2020b) using the “ksdensity” function to create 2D KDE plots that visualise HR intensity accumulation across each programme, session type (e.g., Low-intensity training: LIT; High-intensity training: HIT), and individual athlete responses. Traditional histogram plots and grouped bar charts were also used for comparison.ResultsFor LIT sessions, athletes performed less time in Zone 1 than planned, while performed time exceeded planned time in Zone 2. For HIT sessions, performed time in Zone 5 was lower than planned. All sessions contained unplanned time in Zone 0. The 2D KDE plots provided a continuous and detailed representation of HR intensity accumulation throughout training sessions, revealing patterns and intensity fluctuations that complement traditional TIZ analyses.Conclusions2D KDE plots might serve as a valuable complementary tool for assessing TID in biathlon, offering a more nuanced and continuous view of HR intensity. By identifying discrepancies between planned and performed training intensity, coaches can refine strategies and provide individualised feedback. Incorporating KDE plots into training monitoring could improve training alignment, helping reduce overtraining or undertraining risks and optimising athlete development.https://www.frontiersin.org/articles/10.3389/fspor.2025.1546909/fullbig datadata sciencedensity estimationheart rateNordic skiingtraining load |
| spellingShingle | Craig A. Staunton Craig A. Staunton Andreas Kårström Andreas Kårström Hannes Kock Hannes Kock Marko S. Laaksonen Glenn Björklund Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon Frontiers in Sports and Active Living big data data science density estimation heart rate Nordic skiing training load |
| title | Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon |
| title_full | Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon |
| title_fullStr | Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon |
| title_full_unstemmed | Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon |
| title_short | Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon |
| title_sort | kernel density estimation a novel tool for visualising training intensity distribution in biathlon |
| topic | big data data science density estimation heart rate Nordic skiing training load |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1546909/full |
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