Optimization and comparison of machine learning algorithms for the prediction of the performance of football players

Morciano, Gianluca; Zingoni, Andrea; Calabrò, Giuseppe

doi:10.1007/s00521-024-10260-9

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-10260-9

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Optimization and comparison of machine learning algorithms for the prediction of the performance of football players

Gianluca Morciano,

Andrea Zingoni,

Giuseppe Calabrò

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Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics

Sanders,

Skodinski,

Peacock

2024

Sensors

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The purpose was to create a systematic approach for analyzing data to improve predictive models for fatigue and neuromuscular performance in volleyball, with potential applications in other sports. The study aimed to assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations and determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over a season, recording daily jump loads (total jumps, jump counts >38.1 cm (Jumps 38+), and >50.8 cm (Jumps 50+) in height) and comparing these with CMJ force metrics recorded the next day, both average and peak. Correlations and regressions were utilized to assess the relationship and predictive value for jump loads on CMJ test data. The findings revealed that the most significant (p < 0.001 for all) negative correlations (r ranged from −0.384 to −0.529) occurred between Jumps 50+ and the average CMJ test variables. Furthermore, there were no significant relationships between jump loads and peak-to-average ratios (p ≥ 0.233). Average CMJ force metrics and Jumps 50+ provide slightly more predictive (up to 28% of variability) potential for fatigue modeling of neuromuscular performance.

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Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics

Sanders,

Skodinski,

Peacock

2024

Sensors

View full text Add to dashboard Cite

show abstract

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Optimization and comparison of machine learning algorithms for the prediction of the performance of football players

Cited by 1 publication

References 62 publications

Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics

Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics

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