Bayesian modelling of elite sporting performance with large databases

Griffin, Jim E.; Hinoveanu, Laurentiu; Hopker, James G.

doi:10.1515/jqas-2021-0112

Cited by 3 publications

(8 citation statements)

References 34 publications

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“…Overall, these findings support the hypothesis that skewness is a signature of constraint. It is common knowledge that constraint can generate skew, and previous work has shown that athletic performance skews towards low performance [78]. Our conclusion that motor constraints cause performance distributions to skew away from the direction of constraint in a wide variety of athletic activities, however, appears to be novel.…”

Section: Discussion: Performance Constraints In Athletes and Songbirdsmentioning

confidence: 63%

Skewed performance distributions as evidence of motor constraint in sports and animal displays

Logue,

Bonnell

2023

R. Soc. open sci.

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Animal displays (i.e. movement-based signals) often involve extreme behaviours that seem to push signallers to the limits of their abilities. If motor constraints limit display performance, signal evolution will be constrained, and displays can function as honest signals of quality. Existing approaches for measuring constraint, however, require multiple kinds of behavioural data. A method that requires only one kind could open up new research directions. We propose a conceptual model of performance under constraint, which predicts that the distribution of constrained performance will skew away from the constraint. We tested this prediction with sports data, because we know a priori that athletic performance is constrained and that athletes attempt to maximize performance. Performance consistently skewed in the predicted direction in a variety of sports. We then used statistical models based on the skew normal distribution to estimate the constraints on athletes and displaying animals while controlling for potential confounds and clustered data. We concluded that motor constraints tend to generate skewed behaviour and that skew normal models are useful tools to estimate constraints from a single axis of behavioural data. This study expands the toolkit for identifying, characterizing, and comparing performance constraints for applications in animal behaviour, physiology and sports.

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Section: Discussion: Performance Constraints In Athletes and Songbirdsmentioning

confidence: 63%

Skewed performance distributions as evidence of motor constraint in sports and animal displays

Logue,

Bonnell

2023

R. Soc. open sci.

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“…We have previously developed a Bayesian hierarchical model to investigate both population-and individual-level longitudinal performance trajectories over time adjusted for age-related changes. 15 Our work illustrated how individual performance progression could be modelled while allowing for confounders, such as atmospheric conditions, and could be fitted using Markov chain Monte Carlo. We calculate a term called excess performance by subtracting the population performance trajectory from the individual performance trajectory to show whether an athlete is performing better or worse than their age-matched counterparts.…”

Section: The Main Objective Of What We Have Previously Termedmentioning

confidence: 99%

Section: Modelling Performancementioning

confidence: 99%

“…Our methodology for modelling performance has been developed over several years (see previous studies [14][15][16][17] ). We use the specification of a Bayesian spline model documented in Griffin et al 15 to construct performance trajectories for individual athletes. In brief, our model assumes individual performances can be represented as the sum of an individual performance trajectory, the effects of sport/ discipline specific confounders and an observation error.…”

Section: Modelling Performancementioning

confidence: 99%

“…Run the Markov chain Monte Carlo algorithm in Griffin et al,15 we will use θ ðsÞ to represent the sth posterior sample of a parameter θ and assume that there are S samples. We define a and b to be smallest and largest integer ages in database respectively.…”

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confidence: 99%

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Competitive performance as a discriminator of doping status in elite athletes

Hopker,

Griffin,

Hinoveanu

et al. 2023

Drug Testing and Analysis

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As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti‐doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age‐matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78–0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for anti‐doping purposes.

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Competitive performance as a discriminator of doping status in elite athletes

Hopker¹,

Griffin²,

Hinoveanu³

et al. 2023

Preprint

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As the aim of any doping regime is to improve sporting performance, it has been suggested that analysis of athlete competitive results might be informative in identifying those at greater risk of doping. This research study aimed to investigate the utility of a statistical performance model to discriminate between athletes who have a previous anti-doping rule violation (ADRV) and those who do not. We analysed performances of male and female 100 and 800 m runners obtained from the World Athletics database using a Bayesian spline model. Measures of unusual improvement in performance were quantified by comparing the yearly change in athlete's performance (delta excess performance) to quantiles of performance in their age-matched peers from the database population. The discriminative ability of these measures was investigated using the area under the ROC curve (AUC) with the 55%, 75% and 90% quantiles of the population performance. The highest AUC values across age were identified for the model with a 75% quantile (AUC = 0.78-0.80). The results of this study demonstrate that delta excess performance was able to discriminate between athletes with and without ADRVs and therefore could be used to assist in the risk stratification of athletes for antidoping purposes.

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Bayesian modelling of elite sporting performance with large databases

Cited by 3 publications

References 34 publications

Skewed performance distributions as evidence of motor constraint in sports and animal displays

Skewed performance distributions as evidence of motor constraint in sports and animal displays

Competitive performance as a discriminator of doping status in elite athletes

Competitive performance as a discriminator of doping status in elite athletes

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