Training load prior to injury in professional rugby league players

C Cummins; D King; H Thornton; J Delaney; G Duthie; M Welch; A Murphy

Abstract

INTRODUCTION:
Injury data analysis methods have focused predominately on univariate or simple multivariate correlations between training loads (TL) and injuries. These approaches result in limited insight into the overall effect of external TL variables and a lack of reliable predictive power for injury risk. Conversely, machine learning (ML) algorithms are useful for modeling phenomena described by multidimensional data with complex (usually non-linear) relationships. The application of ML to predicting injury risk in high-performance sport is relatively limited to date. Consequently, this study examined the efficacy of applying ML to multidimensional TL in predicting injury risk in rugby league.

Original language	English
Pages	1-1
Publication status	Published - Jul 2018
Event	ECSS 2018: 23rd Annual Congress of the European College of Sport Science - University College Dublin and Ulster University, Dublin, Ireland Duration: 4 Jul 2018 → 7 Jul 2018

Conference

Conference	ECSS 2018: 23rd Annual Congress of the European College of Sport Science
City	Dublin, Ireland
Period	4/07/18 → 7/07/18

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Cite this

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title = "Training load prior to injury in professional rugby league players",

abstract = "INTRODUCTION: Injury data analysis methods have focused predominately on univariate or simple multivariate correlations between training loads (TL) and injuries. These approaches result in limited insight into the overall effect of external TL variables and a lack of reliable predictive power for injury risk. Conversely, machine learning (ML) algorithms are useful for modeling phenomena described by multidimensional data with complex (usually non-linear) relationships. The application of ML to predicting injury risk in high-performance sport is relatively limited to date. Consequently, this study examined the efficacy of applying ML to multidimensional TL in predicting injury risk in rugby league. ",

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year = "2018",

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AU - Cummins, C

AU - King, D

AU - Thornton, H

AU - Delaney, J

AU - Duthie, G

AU - Welch, M

AU - Murphy, A

PY - 2018/7

Y1 - 2018/7

N2 - INTRODUCTION: Injury data analysis methods have focused predominately on univariate or simple multivariate correlations between training loads (TL) and injuries. These approaches result in limited insight into the overall effect of external TL variables and a lack of reliable predictive power for injury risk. Conversely, machine learning (ML) algorithms are useful for modeling phenomena described by multidimensional data with complex (usually non-linear) relationships. The application of ML to predicting injury risk in high-performance sport is relatively limited to date. Consequently, this study examined the efficacy of applying ML to multidimensional TL in predicting injury risk in rugby league.

AB - INTRODUCTION: Injury data analysis methods have focused predominately on univariate or simple multivariate correlations between training loads (TL) and injuries. These approaches result in limited insight into the overall effect of external TL variables and a lack of reliable predictive power for injury risk. Conversely, machine learning (ML) algorithms are useful for modeling phenomena described by multidimensional data with complex (usually non-linear) relationships. The application of ML to predicting injury risk in high-performance sport is relatively limited to date. Consequently, this study examined the efficacy of applying ML to multidimensional TL in predicting injury risk in rugby league.

UR - https://sport-science.org/

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T2 - ECSS 2018: 23rd Annual Congress of the European College of Sport Science

Y2 - 4 July 2018 through 7 July 2018

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