Mining the training load metrics in basketball: a systematic review

This systematic review aimed to analyze the internal and external training load metrics used in basketball training and competition. A systematic search following PRISMA guidelines was conducted in the PubMed database from the earliest record to May 2022, and methodological quality was assessed using the Physiotherapy Evidence Database (PEDro) scale. A systematic search of the PubMed database identified 95 records, from which seven studies met the inclusion criteria. Internal load was primarily quantified using peak heart rate (HR) during 3×3 matches (%HRmax, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), HR monitoring ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 3), training momentum ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), and rating of perceived exertion (RPE, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> = 4). External load metrics included changes of direction and peripheral fatigue ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), vertical jumps ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 2), motion analysis, and GPS tracking ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1). The most commonly applied training load measures were RPE, HR-based monitoring, and time-motion analysis. These findings provide valuable insights for optimizing workload management in basketball. Coaches can use individualized training approaches considering positional demands, integrate small-sided games to manipulate load, and apply wearable technology for real-time monitoring. Additionally, injury prevention strategies should incorporate acute-to-chronic workload ratios. However, it must be acknowledged that the restricted use of PubMed as the sole database severely limited the comprehensiveness of the review, and therefore the conclusion that only seven studies exist should be interpreted with caution. Further research is needed on female and youth players to enhance evidence-based monitoring strategies across different populations.

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Publication Details

Journal
Sports, Performance and Wellbeing.
Published
2026-02-21
DOI
https://doi.org/10.21595/spw.2025.25236
Primary Topic
Sports Performance and Training
Type
article
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article

Mining the training load metrics in basketball: a systematic review

Rafael Araújo Rocha, José E. Teixeira, Luís Branquinho, João Couto et al.
Sports, Performance and Wellbeing.
Sports Performance and Training
article

Mining the training load metrics in basketball: a systematic review

Rafael Araújo Rocha, José E. Teixeira, Luís Branquinho, João Couto, João Barros, Filipe Nogueira, Luciano Bernardes Leite, Ana Sousa, Luís Melo, Pedro Rodrigues, Alexandra Malheiro
article en

Abstract

This systematic review aimed to analyze the internal and external training load metrics used in basketball training and competition. A systematic search following PRISMA guidelines was conducted in the PubMed database from the earliest record to May 2022, and methodological quality was assessed using the Physiotherapy Evidence Database (PEDro) scale. A systematic search of the PubMed database identified 95 records, from which seven studies met the inclusion criteria. Internal load was primarily quantified using peak heart rate (HR) during 3×3 matches (%HRmax, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), HR monitoring ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 3), training momentum ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), and rating of perceived exertion (RPE, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> </mml:math> = 4). External load metrics included changes of direction and peripheral fatigue ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1), vertical jumps ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 2), motion analysis, and GPS tracking ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> </mml:math> 1). The most commonly applied training load measures were RPE, HR-based monitoring, and time-motion analysis. These findings provide valuable insights for optimizing workload management in basketball. Coaches can use individualized training approaches considering positional demands, integrate small-sided games to manipulate load, and apply wearable technology for real-time monitoring. Additionally, injury prevention strategies should incorporate acute-to-chronic workload ratios. However, it must be acknowledged that the restricted use of PubMed as the sole database severely limited the comprehensiveness of the review, and therefore the conclusion that only seven studies exist should be interpreted with caution. Further research is needed on female and youth players to enhance evidence-based monitoring strategies across different populations.

Sports, Performance and Wellbeing.Vol. 1(1)
Quality Education
Openalex Percentile: Top 49%
Sports Performance and Training
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