Predicting race time in male amateur marathon runners

Salinero, Juan José; Ml, Soriano; Lara, Beatriz; Gallo-Salazar, César; Areces, Francisco; Ruiz-Vicente, Diana; Abián-Vicén, Javier; Gonzalez-Millán, Cristina; Coso, Juan Del

doi:10.23736/s0022-4707.16.06503-8

Cited by 32 publications

(9 citation statements)

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“…The regression model obtained based on pacing characteristics explain 19% of the variance in performance. This percentage is logical since, although pacing may be related to performance, many other factors also play an important role such as training, cardiovascular and anthropometric characteristics (Barandun et al, 2012; Salinero et al, 2017; Keogh et al, 2019). At the same time, pacing characteristics could be affected by factors such as age, sex, experience, collective behaviour, psychological characteristics, cardiovascular (Billat, Palacin, Correa, & Pycke, 2020; Breen et al, 2018; March et al, 2011; Rapoport, 2010; Renfree, CrivoidoCarmo, Martin, & Peters, 2015 Swain et al, 2019) and metabolic factors (Rapoport, 2010), among others.…”

Section: Discussionmentioning

confidence: 99%

Effect of marathon characteristics and runners’ time category on pacing profile

Oficial-Casado

Uriel

Pérez-Soriano

et al. 2020

European Journal of Sport Science

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This study aimed to analyse differences in pacing profiles in four marathon competitions and to explore that pacing per time category. A database of 91,493 runners gathered from 4 different races was analysed (Valencia, Chicago, London and Tokyo Marathon). Participants were categorized in accordance with their completion time. The relative speed of each section for each runner was calculated as a percentage of the average speed for the entire race. In the four marathons studied, the first 5 km differed widely, presenting London the highest relative speeds (5 km: CI95% London vs. Valencia [12.1, 13.6%], p < 0.001 and ES = 2.1; London vs. Chicago [5.5, 7.1%], p < 0.001 and ES = 1.1; London vs. Tokyo [15.2, 16.8%], p < 0.001 and ES = 2.3). Races did not differ at each section for high-performance runners (sub-2:30), but differences between races increased as the time category increases (e.g. 35 km and sub-3:00: CI95% London vs. Tokyo [−3.1, −1.8%], p < 0.001 and ES = 0.7; 35 km and sub-5:00: London vs. Tokyo [−9.8, −9.2%], p < 0.001 and ES = 1.3). The difference in relative speed between the first and second half of the marathon was higher in London than in the other marathons (e.g. CI95% London vs. Valencia [10.3, 10.8%], p < 0.001 and ES = 1.3). In conclusion, although race characteristics affect pacing, this effect was higher as the category time increases. Race pacing characteristics should be taken into consideration for runners and coaches choosing the race and working on pacing strategies, for researches to extrapolate or interpret results, or for race organizations to improve its pacing characteristics.

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Section: Discussionmentioning

confidence: 99%

Effect of marathon characteristics and runners’ time category on pacing profile

Oficial-Casado

Uriel

Pérez-Soriano

et al. 2020

European Journal of Sport Science

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“…Using physical metrics to predict on-field performance in sports has not been limited to baseball. Sullivan et al 18 published that “proficiency in pole vaulting is best predicted by grip height, which is strongly correlated to stature and simple field measures of leg speed and power, and upper body muscular endurance.” In male amateur marathon runners, Salinero et al 19 tried to predict their race times based on anthropometry, training characteristics, muscular strength, and effort-related cardiovascular response. They concluded that “marathon performance could be partially predicted by two different equations, including body fat percentage, recovery heart rate in the Ruffier test and a half-marathon or 10-km performance.” In addition to physical characteristics, Martin et al 20 investigated the possibility that hormone levels could predict the physical performance in adolescent team sport athletes.…”

Section: Discussionmentioning

confidence: 99%

The Uses of Vision Data in Selecting Players for the Major League Baseball Draft

Kirschen¹,

Laby²

2021

Optom Vis Sci

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“…In general, in healthy subjects, respiratory function is not usually decisive or limiting the performance in aerobic endurance sports. 11 Training programs that include exercising leg muscles as running or cycling are commonly used both in sedentary populations as well in patients with diverse pathology (chronic obstructive pulmonary disease, chronic heart failure, arterial hypertension, etc.) to improve their performance, reduce perception of symptoms and improve the quality of life.…”

Section: Spirometry and Volume Flow Curvementioning

confidence: 99%