TY - JOUR
T1 - Importance of fundamental movement skills to predict technical skills in youth grassroots soccer
T2 - a machine learning approach
AU - Duncan, Michael J.
AU - Eyre, Emma L.J.
AU - Clarke, Neil
AU - Hamid, Abdul
AU - Jing, Yanguo
N1 - Publisher Copyright:
© The Author(s) 2023.
PY - 2024/6
Y1 - 2024/6
N2 - This study determined the contributors to soccer technical skills in grassroots youth soccer players using a machine learning approach. One hundred and sixty-two boys aged 7 to 14 (mean ± SD = 10.5 ± 2.1) years, who were regularly engaged in grassroots soccer undertook assessments of anthropometry and maturity offset (the time from age at peak height velocity (APHV)), fundamental movement skills (FMS), perceived physical competence, and physical fitness and technical soccer skill using the University of Ghent dribbling test. Coaches rated player's overall soccer skills for their age. Statistical analysis was undertaken, using machine learning models to predict technical skills from the other variables. A stepwise recursive feature elimination with a 5-fold cross-validation method was used to eliminate the worst-performing features and both L1 and L2 regularisation were evaluated during the process. Five models (linear, ridge, lasso, random forest, and boosted trees) were then used in a heuristic approach using a small subset of suitable algorithms to achieve a reasonable level of accuracy within a reasonable time frame to make predictions and compare them to a test set to understand the predictive capabilities of the models. Results from the machine learning analysis indicated that the total FMS score (0 to 50) was the most important feature in predicting technical soccer skills followed by coach rating of child skills for their age, years of playing experience and APHV. Using a random forest, technical skills could be predicted with 99% accuracy in boys who play grassroots soccer, with FMS being the most important contributor.
AB - This study determined the contributors to soccer technical skills in grassroots youth soccer players using a machine learning approach. One hundred and sixty-two boys aged 7 to 14 (mean ± SD = 10.5 ± 2.1) years, who were regularly engaged in grassroots soccer undertook assessments of anthropometry and maturity offset (the time from age at peak height velocity (APHV)), fundamental movement skills (FMS), perceived physical competence, and physical fitness and technical soccer skill using the University of Ghent dribbling test. Coaches rated player's overall soccer skills for their age. Statistical analysis was undertaken, using machine learning models to predict technical skills from the other variables. A stepwise recursive feature elimination with a 5-fold cross-validation method was used to eliminate the worst-performing features and both L1 and L2 regularisation were evaluated during the process. Five models (linear, ridge, lasso, random forest, and boosted trees) were then used in a heuristic approach using a small subset of suitable algorithms to achieve a reasonable level of accuracy within a reasonable time frame to make predictions and compare them to a test set to understand the predictive capabilities of the models. Results from the machine learning analysis indicated that the total FMS score (0 to 50) was the most important feature in predicting technical soccer skills followed by coach rating of child skills for their age, years of playing experience and APHV. Using a random forest, technical skills could be predicted with 99% accuracy in boys who play grassroots soccer, with FMS being the most important contributor.
KW - Motor Skill
KW - Talent Identification
KW - Children
KW - Football
KW - Motor Competence
KW - Machine learning
KW - analytics
UR - http://www.scopus.com/inward/record.url?scp=85171532436&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/a51a3af9-ad4b-3f14-9783-9d559d802347/
U2 - 10.1177/17479541231202015
DO - 10.1177/17479541231202015
M3 - Article
SN - 1747-9541
VL - 19
SP - 1042
EP - 1049
JO - International Journal of Sports Science and Coaching
JF - International Journal of Sports Science and Coaching
IS - 3
ER -