Middle and long distance athletics races viewed from the perspective of complexity

García-Manso, Juan Manuel; Martín-González, J.M.; Dávila, Nancy; Arriaza, Enrique

doi:10.1016/j.jtbi.2004.10.014

Cited by 13 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance prediction and modeling are cornerstones of sports medicine, essential in training and assessment of athletes with implications beyond sport, for example in the understanding of aging, muscle physiology, and the study of the cardiovascular system. Existing research on running performance focuses either on (A) explaining world records [ 1 – 6 ], (B) equivalent scoring [ 7 , 8 ], or (C) modelling of individual physiology [ 9 – 16 ]. Currently, however, there is no parsimonious model which simultaneously explains individual physiology (C) and collective performance (A,B) of runners.…”

Section: Introductionmentioning

confidence: 99%

“…This is equivalent to assuming a linear dependence log t = α log s + log c of log-time on log-distance. Power law models have been known to describe world record performances across sports for over a century [ 17 ], and have been applied extensively to running performance [ 1 – 6 ]. These power laws have been applied to prediction by practitioners: the Riegel formula [ 18 ] predicts performance by fitting c to each runner and fixing α = 1.06 (derived from world-record performances).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction and Quantification of Individual Athletic Performance of Runners

Blythe

Király

2016

PLoS ONE

View full text Add to dashboard Cite

We present a novel, quantitative view on the human athletic performance of individual runners. We obtain a predictor for running performance, a parsimonious model and a training state summary consisting of three numbers by application of modern validation techniques and recent advances in machine learning to the thepowerof10 database of British runners’ performances (164,746 individuals, 1,417,432 performances). Our predictor achieves an average prediction error (out-of-sample) of e.g. 3.6 min on elite Marathon performances and 0.3 seconds on 100 metres performances, and a lower error than the state-of-the-art in performance prediction (30% improvement, RMSE) over a range of distances. We are also the first to report on a systematic comparison of predictors for running performance. Our model has three parameters per runner, and three components which are the same for all runners. The first component of the model corresponds to a power law with exponent dependent on the runner which achieves a better goodness-of-fit than known power laws in the study of running. Many documented phenomena in quantitative sports science, such as the form of scoring tables, the success of existing prediction methods including Riegel’s formula, the Purdy points scheme, the power law for world records performances and the broken power law for world record speeds may be explained on the basis of our findings in a unified way. We provide strong evidence that the three parameters per runner are related to physiological and behavioural parameters, such as training state, event specialization and age, which allows us to derive novel physiological hypotheses relating to athletic performance. We conjecture on this basis that our findings will be vital in exercise physiology, race planning, the study of aging and training regime design.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction and Quantification of Individual Athletic Performance of Runners

Blythe

Király

2016

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…For the best positioned this decrease is much smaller, eventually showing almost imperceptible variations for the first position. An explanation for this phenomenon could be approached from the perspective provided by authors such as (Berthelot et al, 2015) who suggested that the highest athletic performance could already be achieved in many sports disciplines, while these small fluctuations in performance would be determined by contextual elements such as sociological periodicity or conditioning factors such as the use of new technologies or environmental influences (García-Manso, Martín-González, Dávila, & Arriaza, 2005;Haake, 2009).…”

Section: Discussionmentioning

confidence: 99%

Performance and ranking position evolution during 20 competitive seasons in elite 100 meter sprinters

Arroyo-Valencia

Rodríguez-Fernández

Castaño-Zambudio

et al. 2020

jhse

View full text Add to dashboard Cite

The literature contains several researches seeking to analyse and predict the behaviour of 100-meter dash performance through different mathematical models. Although when analysing the historical records in their entirety these simple models fit largely to their behaviour of the historical series, these approximations are not valid when the focus is on the analysis of accumulated times over the past 2 decades. For this reason, this work proposes new alternatives such as polynomial gradient or smooth models capable of explaining with greater accuracy what has happened during the last 20 years for this modality. Therefore, in order to analyse the distribution of competitive marks relative to the top five and bottom five in the ranking over a period of 20 seasons, a total amount of 428 records corresponding to the marks obtained by international level male athletes who conformed the IAAF world ranking in the 100 m race during the 20 indicated seasons were considered for this goal. The main findings of this research conclude with the lack of fitting between the simple approaches (linear or exponential models) and the reported decline in the records -therefore better performance-throughout the analysed period. In return, this work reveals the existence of a tendency towards overall reduction of time records, denoting a positive evolution of the "competitive health" of the discipline. These evolutions, however, seem to be influenced by the position in which athletes qualify, thus showing greater reductions for athletes classified in the bottom five than for those classified in the top five.

show abstract

“…Curiously, it has been recently described and extensively discussed by Garcia-Manso et al (45) that long and medium distance runners, when the mean velocity in a given distance or the time 301e spent in order to complete this distance are considered; power law of the following type is found: v = cd -α ; where the constant (c) or exponent value varies with the physical condition or training time of the athlete and d represents the completed distance. Phenomena of this kind could be observed in each group of athletes regardless their individual characteristics and did not demonstrate a defined scale; therefore, no statistical difference was observed in the response.…”

Section: Fractal Geometry and Auto-similar Organizationmentioning

confidence: 99%

Dinâmica não-linear e exercício físico: conceitos e aplicações

Pakenas

Souza

Pereira

2007

Rev Bras Med Esporte

View full text Add to dashboard Cite

Médicos, fisiologistas, bioquímicos, psicólogos e até profissionais envolvidos com exercício físico estão recentemente aumentando seus interesses pela dinâmica não-linear, uma teoria científica desenvolvida principalmente por matemáticos, que é genericamente conhecida por Teoria da Complexidade. Embora poucos trabalhos em Educação Física e Esporte utilizem esse paradigma para solucionar seus problemas, nota-se um crescente interesse por esse mesmo enfoque, principalmente em relação aos efeitos do exercício físico sobre mudanças na variabilidade e complexidade de séries temporais fisiológicas. Geralmente, tais mudanças se revelam na forma de queda em seu comportamento temporal, denotando diminuição na complexidade do organismo ou de componentes envolvidos especificamente na sua regulação. De acordo com a Teoria da Complexidade, por enfatizar interações não-lineares existentes em sistemas biológicos, verifica-se que não é importante apenas a elevação (supercompensação) de componentes do organismo com a prática de exercícios físicos, mas também aqueles que atrofiam (descompensação) paralelamente, porque podem contribuir para a ocorrência de perda de sincronia na funcionalidade desses sistemas. Assim, em oposição à ênfase que se dá no treinamento físico à repetição monótona de atividade física intensa e voltada para efeitos específicos positivos, que invariavelmente leva à simplificação do organismo, recomenda-se maior variação qualitativa e quantitativa nos exercícios praticados. O objetivo é preservar sua complexidade natural ou impedir que ocorra diminuição rápida com o envelhecimento. A presente revisão tem por objetivo, além de descrever a possível perda de complexidade com o treinamento físico, discutir alguns conceitos da Teoria da Complexidade de modo introdutório, com particular ênfase em tópicos envolvendo saúde e desempenho físico.

show abstract

Middle and long distance athletics races viewed from the perspective of complexity

Cited by 13 publications

References 16 publications

Prediction and Quantification of Individual Athletic Performance of Runners

Prediction and Quantification of Individual Athletic Performance of Runners

Performance and ranking position evolution during 20 competitive seasons in elite 100 meter sprinters

Dinâmica não-linear e exercício físico: conceitos e aplicações

Contact Info

Product

Resources

About