Prediction of elite athletes’ performance by analysis of peak‐performance age and age‐related performance progression

Gorzi, Ali; Khantan, Mina; Khademnoe, Omid; Eston, Roger G.

doi:10.1080/17461391.2020.1867240

Cited by 11 publications

(5 citation statements)

References 53 publications

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“…Segundo Hirsch et al 22 os lançadores têm mais massa corporal quando comparados com os velocistas e saltadores. Os resultados deste estudo estão de acordo com as observações de Gorzi et al 23 , que sugerem que os lançadores conseguem a sua idade de melhor desempenho numa idade mais avançada que os atletas de outros setores (velocidade / barreiras; meio-fundo e saltadores). Estes resultados são importantes para uma melhor gestão de expectativas em relação ao tempo que poderá demorar a alcançar o desempenho máximo de cada atleta.…”

Section: Discussionunclassified

Idades de referência dos atletas medalhados nos campeonatos de Portugal de atletismo: estudo de 30 anos (1990-2019)

Pinto,

Teles,

Massuça

2024

JPhysEduc

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Segundo a literatura, um inventário preciso da distribuição das idades de melhor desempenho nas diferentes disciplinas do atletismo pode proporcionar uma informação ímpar na altura de se produzirem planos e programas de treino de longo prazo para eventos ao mais alto nível. O presente estudo tem como objetivo identificar a idade de melhor desempenho dos atletas participantes nos campeonatos de Portugal de atletismo, com referência às disciplinas e setores que caracterizam a competição. Foram consideradas todas as participações medalhadas (pista coberta - PC, n = 2310; ar livre - AL, n = 3161). Os intervalos de confiança para a idade média dos atletas medalhados foram obtidos com recurso ao método de Equações de Estimação Generalizadas. Os resultados indicaram a média de idades, (i) PC, mais baixa no salto com vara feminino (21.60 anos) e no salto em comprimento masculino (24.42 anos), e mais elevada nos 3000 m marcha feminino (25.18 anos) e nos 5000 m marcha masculino (29.70 anos); (ii) AL, mais baixa no salto em comprimento feminino (22.90 anos) e nos 200 m masculino (23.46 anos), e mais elevada nos 5000 m feminino (27.55 anos) e 10000 m marcha masculino (32.23 anos). Os resultados sugerem que as idades de melhor desempenho ocorrem (i) PC, entre os 23 e os 24 anos nas atletas femininas, e os 24 e os 25 nos atletas masculinos; (ii) AL, entre os 23 e os 25 anos nas atletas femininas, e os 24 e 26 anos nos atletas masculinos. O conhecimento objetivo das idades de melhor desempenho nos campeonatos de Portugal de atletismo, por sexo e disciplina, poderá ser uma informação útil para uma gestão eficiente de talentos e das carreiras desportivas.

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Section: Discussionunclassified

Idades de referência dos atletas medalhados nos campeonatos de Portugal de atletismo: estudo de 30 anos (1990-2019)

Pinto,

Teles,

Massuça

2024

JPhysEduc

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“…운동 기술은 훈련으로 필요자원이 형성 (Krampe & Ericsson, 1996)되며, 이 과정에서 지도자의 영향으로 개발 (Fernandez, 2022) 되고 숙달되어 경기력 상승으로 이어진다 (Collins et al, 1999). 육상선수의 기록은 입문기에 빠르게 상승하다 점차 상승 속도가 완만해지고, 절정에 이르면 유지된다 (Gorzi et al, 2022;Hollings et al, 2014). 이처럼 경기력 영향 요인이 절정에서 조화롭게 발현 되는 절정기 육상선수의 경기력은 신체조건의 변화 (Berthelot et al, 2015)를 시작으로 쇠퇴기로 이어진다.…”

Section: Performance Development Stage Questionsunclassified

Performance Development Span and Psychological Experience by Stages of Track and Field Athletes

Kim¹,

YoungKil²

2023

Korean J Sport Sci

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PURPOSE The purpose of this study is to section the stages of performance development based on the track and field athletes' performance records, derive the performance development span, which was a continuum of the development stages, and extract the psychological experience of the performance development span.METHODS In this study, 56 retired track and field athletes were provided with competition records, and 10 athletes participated in in-depth interviews. With the stage of performance development partitioned using long and short-term moving averages and regression slope in PRR, a continuous of performance development span was derived. To extract psychological experiences in the performance development span, a subject analysis was conducted after an in-depth interview.RESULTS First, the track and field athletes' performance development stage calculated short and long-term moving averages in the PRR. Based on the average difference in the regression slope of the initial 20% CPR in which the long-term moving average was not calculated, it was divided into beginning, rising, peak, and decline periods. Second, the performance development span was a continuum of the stage was of performance development, and the beginning period was 0 < PRR ≤ 7, it was a time when the competition record rises sharply. The rising period was 7 < PRR ≤ 60, which was a virtuous cycle time of growth athlete. The peak period was 60 < PRR ≤ 74, which was a time when the peak record was maintained. The decline period was 74 < PRR ≤ 100, which was a time when the competition record was downward. Third, throughout the performance development span physical intelligence of track and field athletes was based on their natural physical superiority, the technical skills rises and remains at its peak and then enters a downward trend. Competitional Intelligence aims to become personalization as it matures gradually while its competition management capability and game knowledge are immature. Psychological intelligence overcomes the initial psychological atrophy to form confidence, and after experiencing psychological burden at the peak, confidence decreases. In the environmental context, the competition record rises in the early stages, continues to rise, peaks, and enters a downward trend.CONCLUSIONS Track and field athletes' performance development span was implemented as a continuum of beginning, rising, peak, and decline periods, and the psychological experience of the performance development span formed a span of physical intelligence, competitional intelligence, psychological intelligence, and environmental context.

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“…The modelling and prediction of sports performance can not only specify the exercise plan, but also increase the exercise efficiency. Generally speaking, the modelling and prediction of sports performance regard the sports performance data in a certain period of time as time series data, which can be modelled by multiple linear regression, and the sports performance of an individual can be estimated by calculating the prediction parameters [1]. Therefore, how to establish an accurate sports performance prediction model has attracted much attention from universities.…”

Section: Introductionmentioning

confidence: 99%

Sports performance prediction based on support vector machine

Zhang

Wu²

2022

International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)

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To deal with the problem of low accuracy of sports performance prediction, and to obtain ideal sports performance prediction results, this paper proposes a sports performance prediction model based on the selection of influencing factors and support vector machine. In this model, particle swarm optimization is introduced to determine the most relevant influencing factors related to the change characteristics of sports performance, which reduces the number of input vectors of sports performance prediction model and speeds up the modelling speed of sports performance. Then, the support vector machine is used to learn the historical data of sports performance, which overcomes the defects of traditional models such as artificial neural network and improves the prediction accuracy of sports performance. Experimental results are provided to verify the advantage of the proposed algorithm with respect to the traditional methods.

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Prediction of elite athletes’ performance by analysis of peak‐performance age and age‐related performance progression

Cited by 11 publications

References 53 publications

Idades de referência dos atletas medalhados nos campeonatos de Portugal de atletismo: estudo de 30 anos (1990-2019)

Idades de referência dos atletas medalhados nos campeonatos de Portugal de atletismo: estudo de 30 anos (1990-2019)

Performance Development Span and Psychological Experience by Stages of Track and Field Athletes

Sports performance prediction based on support vector machine

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