Improving predictor selection for injury modelling methods in male footballers
Authors: Philp, F., Al-Shallawi, A., Kyriacou, T., Blana, D. and Pandyan, A.
Journal: BMJ Open Sport and Exercise Medicine
Volume: 6
Issue: 1
ISSN: 2055-7647
DOI: 10.1136/bmjsem-2019-000634
Abstract:Objectives This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. Methods Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. Results The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of a € no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. Conclusion For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.
https://eprints.bournemouth.ac.uk/36623/
Source: Scopus
Improving predictor selection for injury modelling methods in male footballers.
Authors: Philp, F., Al-Shallawi, A., Kyriacou, T., Blana, D. and Pandyan, A.
Journal: BMJ Open Sport Exerc Med
Volume: 6
Issue: 1
Pages: e000634
ISSN: 2055-7647
DOI: 10.1136/bmjsem-2019-000634
Abstract:OBJECTIVES: This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. METHODS: Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. RESULTS: The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. CONCLUSION: For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.
https://eprints.bournemouth.ac.uk/36623/
Source: PubMed
Improving predictor selection for injury modelling methods in male footballers
Authors: Philp, F., Al-shallawi, A., Kyriacou, T., Blana, D. and Pandyan, A.
Journal: BMJ OPEN SPORT & EXERCISE MEDICINE
Volume: 6
Issue: 1
eISSN: 2055-7647
ISSN: 2398-9459
DOI: 10.1136/bmjsem-2019-000634
https://eprints.bournemouth.ac.uk/36623/
Source: Web of Science (Lite)
Improving predictor selection for injury modelling methods in male footballers.
Authors: Philp, F., Al-Shallawi, A., Kyriacou, T., Blana, D. and Pandyan, A.
Journal: BMJ open sport & exercise medicine
Volume: 6
Issue: 1
Pages: e000634
eISSN: 2055-7647
ISSN: 2055-7647
DOI: 10.1136/bmjsem-2019-000634
Abstract:Objectives
This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football.Methods
Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season.Results
The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury.Conclusion
For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.https://eprints.bournemouth.ac.uk/36623/
Source: Europe PubMed Central
Improving predictor selection for injury modelling methods in male footballers.
Authors: Philp, F., Al-shallawi, A., Kyriacou, T., Blana, D. and Pandyan, A.
Journal: BMJ Open Sport and Exercise Medicine
Volume: 6
Issue: 1
ISSN: 2055-7647
Abstract:Objectives: This objective of this study was to evaluate whether combining existing methods of elastic net for zero-inflated Poisson and zero-inflated Poisson regression methods could improve real-life applicability of injury prediction models in football. Methods: Predictor selection and model development was conducted on a pre-existing dataset of 24 male participants from a single English football team's 2015/2016 season. Results: The elastic net for zero-inflated Poisson penalty method was successful in shrinking the total number of predictors in the presence of high levels of multicollinearity. It was additionally identified that easily measurable data, that is, mass and body fat content, training type, duration and surface, fitness levels, normalised period of 'no-play' and time in competition could contribute to the probability of acquiring a time-loss injury. Furthermore, prolonged series of match-play and increased in-season injury reduced the probability of not sustaining an injury. Conclusion: For predictor selection, the elastic net for zero-inflated Poisson penalised method in combination with the use of ZIP regression modelling for predicting time-loss injuries have been identified appropriate methods for improving real-life applicability of injury prediction models. These methods are more appropriate for datasets subject to multicollinearity, smaller sample sizes and zero-inflation known to affect the performance of traditional statistical methods. Further validation work is now required.
https://eprints.bournemouth.ac.uk/36623/
Source: BURO EPrints