A theoretical-experimental model of races for evaluation of athletes through graphics
DOI:
https://doi.org/10.11606/issn.1981-4690.2025e39194403Keywords:
Biomechanics, Derivatives, Mathematical modeling, Sports trainingAbstract
This study aims to mathematically model short-term races of athletes and people who do not play sports, in order to raise the time equation of the two runners. This is an observational case study of a quantitative nature with the participation of a sedentary individual and an athlete of the gaucho futsal gold series, both practitioners of short-term races. A Sony film camera and the Sony Vegas 8.0 program were used for the analysis. Also, two series of 10 and 20 meter runs of each participant were filmed and analyzed. From this, the derivatives of the curves were calculated, verifying the athlete's complete performance throughout the race course. Thus, it was possible to model the performance of short-term test participants using a first principles method. The results indicate that the performance of the participants is similar due to the fact that the person who does not practice sports has aptitude for short-term races. It is concluded that for the athlete, in the 10m race, the athlete's deceleration demonstrates that the modeling is correct, since the negative acceleration indicates that he has decreased his speed. In the 20m race, it is observed that the speed as a function of time always increases and we always find a positive acceleration.
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1. Enright E, Gard M. Media, digital technology and learning in sport: a critical response to Hodkinson, Biesta and James. Phys Educ Sport Pedagog. 2016;21(1):40-54.
2. Mucedola MS. Applying Health and Physical Education Teacher Pedagogy in a Community Setting. J Phys Sports Educ. 2018;31(5):45-47.
3. Sareen R. US Patent 10,628,729. 2020. Disponível em: https://patents.google.com/patent/US10628729B2/en.
4. Faubert J. Professional athletes have extraordinary skills for rapidly learning complex and neutral dynamic visual scenes. Sci Rep. 2013;3:1154.
5. Eils E, et al. Characteristic plantar pressure distribution patterns during soccer-specific movements. Am J Sports Med. 2004;32(1):140-145.
6. Orange ST, et al. Test-Retest reliability of a commercial linear position transducer (gym aware power tool) to measure velocity and power in the back squat and bench press. J Strength Cond Res. 2020;34(3):728-737.
7. Kerdok AE, et al. Energetics and mechanics of human running on surfaces of different stiffnesses. J Appl Physiol. 2002;92(2):469-478.
8. Neptune RR, Sasaki K. Ankle plantar flexor force production is an important determinant of the preferred walk-to-run transition speed. J Exp Biol. 2005;208(5):799-808.
9. Yeadon MR. The biomechanics of the human in flight. Am J Sports Med. 1997;24(4):575-580.
10. Cavanagh PR. Biomechanics of distance running. São Paulo: Human Kinetics; 1990.
11. Faubert J. Professional athletes have extraordinary skills for rapidly learning complex and neutral dynamic visual scenes. Sci Rep. 2013;3:1154.
12. Oliveira PRM. Contribuição da biomecânica para a melhoria do rendimento desportivo, em atletismo: desenvolvimento e validação de uma unidade de medição inercial dedicada [tese]. Evora (PT): Instituto de Investigação e Formação Avançada, Universidade de Evora; 2020.
13. Yildiz S, et al. The relationship between start speed, acceleration and speed performances in soccer. Universal J Educ Res. 2018;6(8):1697-1700.
14. Roschel H, Tricoli V, Ugrinowistsch C. Treinamento físico: considerações práticas e científicas. Rev Bras Educ Fís Esporte. 2011;25:53-65.
15. Shinabargar AJ, Hellrich M, Baker B. What makes Usain Bolt unique as a sprinter? Phys Teach. 2010;48(6):365.
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