An experience sampling study of organizational stress processes and future playing time in professional sport

Rumbold, James L.; Fletcher, David; Daniels, Kevin

doi:10.1080/02640414.2020.1717302

Cited by 25 publications

(34 citation statements)

References 42 publications

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“…But as an important extension, this evidence might also be enlarged as the measure of success in the present study may also be even more valid since game time is commonly used 34 as it is achievable for players of all positions compared to goals and assists, which is more challenging to realize for defenders compared to strikers. Similarly to the correlation with game intelligence, it is remarkable that the unique relationship between executive functions and game time seems to exist across all teams as age had no meaningful effect on the correlation.…”

Section: Discussionmentioning

confidence: 89%

“…Firstly, the interrelation of coach-rated game intelligence and objective assessments of executive functions is analyzed. Secondly, the prognostic capabilities of multidisciplinary and multiphasic performance data, including physiological and cognitive (objective and coach-rated) parameters, are examined in two regards: 1) in terms of future success de ned as the playing time per player in games which is a commonly used performance indicator 34 and 2) in terms of future injuries (i.e. contact and non-contact).…”

Section: Introductionmentioning

confidence: 99%

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Fundamental Relationships of Executive Functions and Physiological Abilities with Game Intelligence, Game Time and Injuries in Elite Soccer Players

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Memmert

2021

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Objectives: The study’s aim was threefold: examine 1) the relation between executive functions and coach-rated evaluation of game intelligence; 2) the predictive value of cognitive and athletic performance parameters concerning playing time, and 3) injuries to analyze fundamental associations of soccer expertise across all ages.Design/Methods: A sample of 172 elite soccer players (12-34 years of age) performed tests assessing multiple-object tracking, working memory capacity, cognitive flexibility, and inhibition. Endurance-performance at the individual anaerobic threshold (IAT), ability to repeatedly perform intense exercises (RIEA) and maximal anaerobic performance parameters (squat jump, countermovement jump, drop jump, sprint) were also measured. Game intelligence game time and injury incidence were tracked.Results: Working memory capacity, cognitive flexibility, and a total cognition score showed correlations with game intelligence, and the same parameter, along with selective attention and game intelligence, were also correlated with game time. Sprint and RIEA were connected with game time, whereas contact injuries only correlated with sprint, and noncontact injuries with sprint and performance-IAT.Conclusion: Especially executive functions represent fundamental associations with game intelligence and game time across all age groups but not with injury incidences, whereas certain physiological abilities may contribute to more game time and less non-contact injuries depending on age.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Fundamental Relationships of Executive Functions and Physiological Abilities with Game Intelligence, Game Time and Injuries in Elite Soccer Players

Scharfen

Memmert

2021

Preprint

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“…Quasi-binomial was used to test the association between smoking and periodontitis (Zeng et al, 2014 ). In professional rugby players, coping related to eliciting support was linked to minutes played at a professional level five years later using zero-inflated binomial regression (Rumbold, Fletcher, & Daniels, 2020 ).…”

Section: Example Use Of Count Distributionsmentioning

confidence: 99%

Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression

Green

2021

Health Psychology and Behavioral Medicine

103

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Background: Dependent variables in health psychology are often counts, for example, of a behaviour or number of engagements with an intervention. These counts can be very strongly skewed, and/or contain large numbers of zeros as well as extreme outliers. For example, ‘How many cigarettes do you smoke on an average day?’ The modal answer may be zero but may range from 0 to 40+. The same can be true for minutes of moderate-to-vigorous physical activity. For some people, this may be near zero, but take on extreme values for someone training for a marathon. Typical analytical strategies for this data involve explicit (or implied) transformations (smoker v. non-smoker, log transformations). However, these data types are ‘counts’ (i.e. non-negative whole numbers) or quasi-counts (time is ratio but discrete minutes of activity could be analysed as a count), and can be modelled using count distributions – including the Poisson and negative binomial distribution (and their zero-inflated and hurdle extensions, which alloweven more zeros). Methods: In this tutorial paper I demonstrate (in R, Jamovi, and SPSS) the easy application of these models to health psychology data, and their advantages over alternative ways of analysing this type of data using two datasets – one highly dispersed dependent variable (number of views on YouTube, and another with a large number of zeros (number of days on which symptoms were reported over a month). Results: The negative binomial distribution had the best fit for the overdispersed number of views on YouTube. Negative binomial, and zero-inflated negative binomial were both good fits for the symptom data with over-abundant zeros. Conclusions: In both cases, count distributions provided not just a better fit but would lead to different conclusions compared to the poorly fitting traditional regression/linear models.

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“…Quasi-binomial was used to test the association between smoking and periodontitis (Zeng et al, 2014). In professional rugby players, coping related to eliciting support was linked to minutes played at a professional level five years later using zero-inflated binomial regression (Rumbold, Fletcher, & Daniels, 2020).…”

Section: Example Use Of Count Distributionsmentioning

confidence: 99%

Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression

Green¹

2020

Preprint

View full text Add to dashboard Cite

Dependent variables in health psychology can be very strongly skewed, and/or contain large numbers of zeros as well as extreme outliers. For example, “How many cigarettes do you smoke on an average day?” The modal answer may be zero, but may range from 0 to 40+. The same can be true for minutes of moderate to vigorous physical activity. For some people this may be near zero, but take on extreme values for someone training for a marathon. The measures could be counts of behaviour or number of engagements with an intervention. Typical analytical strategies for this data involve explicit (or implied) transformations (smoker v. non-smoker, log transformations). However, this data can be more accurately thought of as ‘counts’ (ie positive whole numbers) or quasi-counts (time is ratio, but discrete minutes of activity could be analysed as a count), and modelled using count distributions — including the Poisson and negative binomial distribution, and their zero-inflated and hurdle extensions, which model even more zeros. This paper demonstrates the easy application of these models to health psychology data, and their advantages over alternative ways of analysing this type of data.

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An experience sampling study of organizational stress processes and future playing time in professional sport

Cited by 25 publications

References 42 publications

Fundamental Relationships of Executive Functions and Physiological Abilities with Game Intelligence, Game Time and Injuries in Elite Soccer Players

Fundamental Relationships of Executive Functions and Physiological Abilities with Game Intelligence, Game Time and Injuries in Elite Soccer Players

Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression

Too many zeros and/or highly skewed? A tutorial on modelling health behaviour as count data with Poisson and negative binomial regression

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