A systematic review of load control in football using a Global Navigation Satellite System (GNSS)

Principe, Vitor Ayres; Vale, Rodrigo Gomes de Souza; Nunes, Rogean Rodrigues

doi:10.1590/s1980-65742020000400059

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…The receiver manufacturer used in our study (Fieldwiz V1, CH) did not store any spatial accuracy factor such as horizontal dilution of precision. Therefore, we recommend the manufacturer to report signal quality details for practical use and research purposes 53,54 . Nevertheless, using features not based on expert hypotheses but fully extracted from signal processing methods appeared to be as valuable as the commercial ones.…”

Section: Discussionmentioning

confidence: 99%

Using Global Navigation Satellite Systems for modeling Athletic Performances in elite football players

Imbach

Ragheb

Leveau

et al. 2022

Preprint

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This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

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Section: Discussionmentioning

confidence: 99%

Using Global Navigation Satellite Systems for modeling Athletic Performances in elite football players

Imbach

Ragheb

Leveau

et al. 2022

Preprint

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show abstract

“…Advancements in technology and analytical techniques have ushered in new opportunities within the applied sports environment. Nowadays, sports practitioners have the capability to monitor TL through the utilization of global navigation satellite systems and other microtechnology [ 5 , 6 ]. In the past, elite and professional teams might have been hesitant to share their methods, but recent research indicates their increasing willingness to participate in and publish applied research [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Arabic translation and cultural adaptation of a training load and player monitoring in high-level football questionnaire: A cognitive interview study

Bursais

2024

PLoS ONE

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Background Understanding the current practice and the associated challenges in applying monitoring tools is essential to improving football performance in the Middle East, thus the purpose was to translate and culturally adapt a published questionnaire that assessed the practice and perception of High-Level football teams toward Training Load and Player Monitoring to be used in the Arabic context, aiming to contribute to the enhancement of football performance, player welfare, and training quality in the region. Method A total of 15 Arabic-speaking coaches (mean age 42.6 ± 9.9 years; mean experience 10.9 ± 5.7 years; 53.3% football coaches and 46.7% strength & conditioning coaches) were conveniently selected to participate in this study. The current research followed a systematic cross-cultural adaptation process, which included forward translation, synthesis, back-translation, expert panel review, and pre-testing through cognitive interviewing. Three rounds of cognitive interviews were conducted with the 15 participants. Descriptive statistics, including means with standard deviations and frequencies with percentages, were reported for the participants’ characteristics. Result With some minor linguistic modifications to the questionnaire by the expert committee (i.e., adjustments such as Sport Scientist to Sport Science Specialist), the instrument was translated and culturally adapted into Arabic. All participants confirmed that the resulting Arabic versions of the training load and player monitoring in high-level football questionnaires were appropriate and fully understandable for Arabic speakers in conveying the intended meanings of the items in each. Conclusion The training load and player monitoring in the high-level football questionnaire was successfully translated and culturally adapted into Arabic and are now ready for use in the Arabic context, offering an opportunity for comprehensive research and enabling tailored performance optimization strategies, which could ultimately lead to advancements in player development and welfare within Arabic-speaking football communities.

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Using global navigation satellite systems for modeling athletic performances in elite football players

Imbach¹,

Ragheb²,

Leveau³

et al. 2022

Sci Rep

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This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019–2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

show abstract

A systematic review of load control in football using a Global Navigation Satellite System (GNSS)

Cited by 3 publications

References 39 publications

Using Global Navigation Satellite Systems for modeling Athletic Performances in elite football players

Using Global Navigation Satellite Systems for modeling Athletic Performances in elite football players

Arabic translation and cultural adaptation of a training load and player monitoring in high-level football questionnaire: A cognitive interview study

Using global navigation satellite systems for modeling athletic performances in elite football players

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