Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods

Football is a complex game that requires combined technical, tactical, and psychological skills. The effect of training methods on players is crucial to improve their performance significantly. Different training methods can improve certain performance; however, the effect of combined training methodologies has not been sufficiently investigated. This study aimed to investigate the differential effects of small-sided games (SSGs), strength training (ST), and a combined training model (CTM) on the physical performance of soccer players. In this study, we analyzed 60 players in three groups. Two groups were trained independently with two single-training methods, small-sided games and a strength training model, and one group was trained independently with a double-training method, a combination of single-training methods. Before each training session, each group was given theoretical education specific to the training program they would perform. Eighteen physical measurements of the players were obtained using sensitive devices before and after they were completed. Four tree-based machine learning models, decision tree, random forest, gradient boosting, and extreme gradient boosting, were applied to solve the complex pattern of training strategies using the measurements. Extensive and comparative experiments were conducted to distinguish the groups of players. The distinction between the initial and final measurements was analyzed separately, and the extreme gradient boosting model achieved 0.73–0.80 and 1.00 accuracy for initial and final tests by outperforming other models. A superior model, XGBoost, was used to analyze the effective decisive factors that improved after the training sessions. The results showed that players in the double-training group demonstrated significantly greater improvements in skill performance compared to both single-training groups. In contrast, the skill gains observed in the single-training groups were modest and partially overlapping, indicating limited differentiation between them. These results suggest that integrated training programs may offer more comprehensive benefits and can inform evidence-based decision-making for coaches seeking to optimize physical development in soccer players.

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Publication Details

Journal
Applied Sciences
Published
2025-05-19
DOI
https://doi.org/10.3390/app15105672
Citations
4
Primary Topic
Sports Performance and Training
Type
article
Field-Weighted Citation Impact
6.89
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article

Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods

Musa Oytun, Cevdet Tınazcı, Boran Şekeroğlu, Hasan Ulaş Yavuz et al.
4 citations
Applied Sciences
Sports Performance and Training
6.89
article

Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods

Musa Oytun, Cevdet Tınazcı, Boran Şekeroğlu, Hasan Ulaş Yavuz, Hüseyin Güneralp
article en
4 citations

Abstract

Football is a complex game that requires combined technical, tactical, and psychological skills. The effect of training methods on players is crucial to improve their performance significantly. Different training methods can improve certain performance; however, the effect of combined training methodologies has not been sufficiently investigated. This study aimed to investigate the differential effects of small-sided games (SSGs), strength training (ST), and a combined training model (CTM) on the physical performance of soccer players. In this study, we analyzed 60 players in three groups. Two groups were trained independently with two single-training methods, small-sided games and a strength training model, and one group was trained independently with a double-training method, a combination of single-training methods. Before each training session, each group was given theoretical education specific to the training program they would perform. Eighteen physical measurements of the players were obtained using sensitive devices before and after they were completed. Four tree-based machine learning models, decision tree, random forest, gradient boosting, and extreme gradient boosting, were applied to solve the complex pattern of training strategies using the measurements. Extensive and comparative experiments were conducted to distinguish the groups of players. The distinction between the initial and final measurements was analyzed separately, and the extreme gradient boosting model achieved 0.73–0.80 and 1.00 accuracy for initial and final tests by outperforming other models. A superior model, XGBoost, was used to analyze the effective decisive factors that improved after the training sessions. The results showed that players in the double-training group demonstrated significantly greater improvements in skill performance compared to both single-training groups. In contrast, the skill gains observed in the single-training groups were modest and partially overlapping, indicating limited differentiation between them. These results suggest that integrated training programs may offer more comprehensive benefits and can inform evidence-based decision-making for coaches seeking to optimize physical development in soccer players.

Applied SciencesVol. 15(10)
Near East University (CY)
Peace, Justice and strong institutions
Openalex Percentile: Top 3%
Sports Performance and Training
6.89
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