Determinant biomechanical variables for each sprint phase performance in track and field: A systematic review

Moura, Túlio Bernardo Macedo Alfano; Leme, Juliane Cristine; Nakamura, Fábio Yuzo; Cardoso, Jefferson Rosa; Moura, Felipe Arruda

doi:10.1177/17479541231200526

Cited by 4 publications

(1 citation statement)

References 107 publications

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“…These models, while innovative, presented notable challenges, including the requirement for highly accurate data collection and the complex measurement of specific constants. Conceptually, a velocity curve is expected to depict three distinct stages: the acceleration, peak speed, and deceleration phases, as described in prior studies [15][16][17][18][19] (Fig 1). However, these conventional models demonstrated several shortcomings.…”

Section: Introductionmentioning

confidence: 98%

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

Tam,

Yao

2024

PLoS ONE

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This study presents a novel approach to modeling the velocity-time curve in 100m sprinting by integrating machine learning algorithms. It critically addresses the limitations of traditional speed models, which often require extensive and intricate data collection, by proposing a more accessible and accurate method using fewer variables. The research utilized data from various international track events from 1987 to 2019. Two machine learning models, Random Forest (RF) and Neural Network (NN), were employed to predict the velocity-time curve, focusing on the acceleration phase of the sprint. The models were evaluated against the traditional exponential speed model using Mean Squared Error (MSE), with the NN model demonstrating superior performance. Additionally, the study explored the correlation between maximum velocity, the time of maximum velocity occurrence, the duration of the maximum speed phase, and the overall 100m sprint time. The findings indicate a strong negative correlation between maximum velocity and final time, offering new insights into the dynamics of sprinting performance. This research contributes significantly to the field of sports science, particularly in optimizing training and performance analysis in sprinting.

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Section: Introductionmentioning

confidence: 98%

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

Tam,

Yao

2024

PLoS ONE

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Sprint and jump performance are determined by localized BIA - an ecological study in track and field adolescent athletes

Oliveira,

Lázari,

Matias

et al. 2024

BMC Sports Sci Med Rehabil

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Background Raw data obtained through bioelectrical impedance analysis (BIA) have been applied in different populations to assess body fluids and cell integrity. Assessing raw BIA parameters in specific muscles is an emerging method for evaluating muscle function. We investigated the associations of the BIA-derived variables of resistance (R), reactance (Xc) and phase angle (PhA) measured through whole-body (WB) and muscle-localized (ML) methods with performance in the countermovement jump (CMJ) and 50-meter (m) sprint. Methods Thirty-one male track and field athletes (16.5 ± 1.6 years) were assessed. Fat-free mass (FFM) and Fat mass percentage (%FM) were determined by skinfold thickness. BIA at 50 kHz was employed to obtain the WB and ML (right thigh) parameters. The WB and ML-BIA parameters were adjusted by height (R/H, Xc/H) and segment length (R/L, Xc/L). The CMJ assessment was conducted via a contact mat; the software recorded the jump height. The 50-m sprint time was measured via two sets of photocells. Pearson’s correlation and linear multiple regression were performed. Results ML-PhA was inversely related to the 50-m sprint (β=-0.56) and by itself explained 29% of the sprint time variation. It remained a significant predictor even after adjusting for age, height, FFM and peak height velocity (PHV). ML-R/L was directly related to 50-m sprint (β = 0.48) and inversely related to CMJ performance (β=-0.54), explaining 20% and 27% of the variation in 50-m sprint and CMJ performance, respectively. Similarly, it remained a significant predictor in the adjusted models. Correlations between WB-BIA (PhA, R/H) and performance tests were found to be dependent on covariates. Conclusions In this sample, the ML-BIA parameters of R/L and PhA were significantly associated with performance independent of age, height, FFM and PHV. Higher ML-PhA values were associated with better sprint times, whereas higher ML-R/L values were associated with worse sprint times and CMJ performance. Supplementary Information The online version contains supplementary material available at 10.1186/s13102-024-01023-z.

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The Mechanics of Speed: A Systematic Literature Review on Athletic Sprint Techniques

Rumini,

Adi S,

Kusuma

2024

Teor. metod. fiz. vihov.

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Objectives. This study aimed to collect and analyze findings from research on athletic sprinting techniques through a biomechanics approach to provide an in-depth understanding of effective technique and biomechanical factors that influence sprint performance. Materials and methods. This review inquiry adhered to the PRISMA guidelines for systematic reviews and meta-analyses. The studies should have been published between January 2020 and August 2025, which encompassed the previous five years. The following keywords were used in the search process: Techniques for Athletic Sprinting. The search was conducted using the ScienceDirect database. Results. The search process showed the following results: set position — 2 articles, hip angle — 3 articles, shorter time — 2 articles, fascicle length — 1 article, knee flexor — 3 articles, and hamstring length — 2 articles. Conclusions. The center of mass and contact time along with force application to the back block are critical toacceleration. Muscle properties, such as fascicle length, and joint motion (ankle, hip, and BCM), in addition to trunk, knee, and hip angles, define sprint mechanics.

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Determinant biomechanical variables for each sprint phase performance in track and field: A systematic review

Cited by 4 publications

References 107 publications

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

Advancing 100m sprint performance prediction: A machine learning approach to velocity curve modeling and performance correlation

Sprint and jump performance are determined by localized BIA - an ecological study in track and field adolescent athletes

The Mechanics of Speed: A Systematic Literature Review on Athletic Sprint Techniques

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