Relationship between Physical Training and Tactical Training in Sports Training Relying on Boosting and Bagging Algorithms

Liu, Kaining; Li, Xinwen; Rochester, Christine A.

doi:10.1155/2022/8429597

Cited by 4 publications

(2 citation statements)

References 18 publications

(17 reference statements)

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“…Reinforcement learning algorithms can analyze vast amounts of historical game data to identify successful strategies for offense, defense, and transitions between the two [11]. By simulating various game scenarios and adjusting tactics based on observed outcomes, these algorithms can suggest optimal formations, player positioning, passing patterns, and defensive structures that maximize the team's chances of scoring goals while minimizing the risk of conceding [12].…”

Section: Introductionmentioning

confidence: 99%

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

Chong Xu

2024

jes

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Tactical intelligence refers to the ability to gather, analyze, and apply information swiftly and effectively to achieve specific goals or objectives in dynamic and often challenging situations. It involves understanding the current environment, assessing available resources, and making informed decisions to gain a competitive advantage or mitigate risks. Tactical Intelligent Decision Modelling in Sports Competitions, grounded in Reinforcement Learning (RL) algorithms, represents a cutting-edge approach to enhancing strategic decision-making processes within athletic domains. This innovative methodology leverages RL's ability to learn optimal actions through trial and error, adapting strategies based on feedback received from the environment. By applying RL algorithms to sports competitions, teams can develop intelligent decision models that dynamically adjust tactics and strategies in response to changing game conditions and opponent behaviors. Such models enable teams to optimize their performance, exploit opponent weaknesses, and capitalize on opportunities during competitions. This paper introduces Predictive Weighted Big Data Reinforcement Learning (PWBDRL), an innovative approach aimed at optimizing decision-making processes and performance outcomes in sports competitions. Leveraging predictive analytics, big data techniques, and reinforcement learning, PWBDRL offers a comprehensive framework for athlete prediction and strategy optimization. The results show a remarkable increase in win rates, with the RL-based decision models achieving an average win rate of 80.5% compared to 65.2% with traditional methods. Additionally, we observe a substantial enhancement in average scores, with the RL-based models achieving an average score of 95.6 compared to 78.3 with baseline approaches. Moreover, the RL-based models exhibit superior adaptability, requiring fewer iterations to converge to optimal strategies, with an average convergence time of 200 episodes compared to 500 episodes for traditional methods.

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Section: Introductionmentioning

confidence: 99%

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

Chong Xu

2024

jes

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“…One of the factors that plays an essential role in determining an athlete's sporting performance is physical ability (Baxter-Jones, 2019;Söğüt et al, 2019). Physical ability is vital because it contributes to improving competition performance and the level of tactics and techniques of an athlete (Liu et al, 2022). Apart from that, physical abilities can also be used to predict performance and assess the potential of an athlete (Corluka et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

High-Intensity Interval Training (HIIT) Progressive Sprint-Release Model: Its Effect in Increasing Speed, Aerobic Capacity, and Anaerobic Capacity of Athletes

Jatmiko,

Kusnanik,

Sidik

2024

Retos

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Physical abilities is one of the most important aspects influencing an athlete's competitive success. This research aims to determine the effect of the High-Intensity Interval Training (HIIT) Progressive Sprint-Release model in increasing athletes' speed, aerobic capacity, and anaerobic capacity. The research method used is quasi-experimental quantitative research with a research design, namely a randomized pretest-posttest group design. Purposive sampling technique was used to determine research subjects with the criteria of female rugby athletes aged 18–22 years, exercising regularly six times per week, healthy, and without injuries. There were 20 athletes who were willing to be research subjects and were divided into two groups randomly, namely 10 athletes in the group that underwent the High Intensity Interval Training (HIIT) treatment with the Progressive Sprint-Release (PSRG) model and 10 other athletes in the control group (CG). Progressive Sprint-Release model HIIT treatment for 8 weeks with training frequency 3 times a week. The research instruments used were a 20-meter sprint to measure speed, an MFT to measure aerobic capacity, and a 300-meter sprint to measure anaerobic capacity. Data analysis technique used was the paired sample t-test and the independent sample t-test, and SPSS software version 25 was used. The results of the paired sample t-test, which compared the pre-test with the post-test in the PSRG group (pre-test vs. post-test), showed a significant increase in speed (3.99 ± 0.231 vs. 3.87 ± 0.253 seconds, p<0.05), aerobic capacity (29.23 ± 4.026 vs. 31.81 ± 4.346 ml/kg/min, p<0.05), and anaerobic capacity (75 ± 9.821 vs. 63.6 ± 6.736 seconds, p<0.05). The results of the independent sample t-test showed that there were significant differences between PSRG and CG in speed (p<0.05), aerobic capacity (p<0.05), and anaerobic capacity (p<0.05). In conclusion, the HIIT Progressive Sprint-Release model can increase speed, aerobic capacity, and anaerobic capacity. Keywords: Progressive sprint-release exercise; high-intensity interval training; anaerobic capacity; aerobic capacity; speed

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Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training

She

2024

Applied Mathematics and Nonlinear Sciences

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Sports injury is a hot issue in the field of exercise science and sports medicine and a practical problem that needs to be solved urgently. Based on big data analysis, this paper proposes an Ada-RF prediction model that integrates the fusion of the Adaboost algorithm and Random Forest algorithm by studying five machine learning algorithms: logistic regression, k-nearest neighbor, plain Bayes, Adaboost algorithm, and Random Forest and evaluates experiments through the model evaluation criteria. The influence factors of injury risk in adolescent male basketball players were explored in terms of demographic information, training load, subjective perceived health, and assessment of athletic quality. By screening the important factors as independent variables, the risk of athlete injury and illness as dependent variables were applied to the model constructed in this paper and the models built by five traditional machine learning classification algorithms. The prediction effects of multiple models are compared. The experimental results show that the accuracy, recall, specificity, F1 score, sensitivity, and AUC value of the Ada-RF model constructed in this paper are 0.869, 0.885, 0.755, 0.683, 0.754, and 0.789, respectively, which indicate that the Ada-RF integrated model outperforms the single prediction model, and can be used for the early prevention and early treatment of athletes’ injuries and illnesses. It shows that the Ada-RF integrated model outperforms the single prediction model and can provide scientific and accurate auxiliary information for the early prevention and treatment of injuries and diseases in athletes.

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Relationship between Physical Training and Tactical Training in Sports Training Relying on Boosting and Bagging Algorithms

Cited by 4 publications

References 18 publications

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

High-Intensity Interval Training (HIIT) Progressive Sprint-Release Model: Its Effect in Increasing Speed, Aerobic Capacity, and Anaerobic Capacity of Athletes

Application of Big Data Analysis in Model Construction to Prevent Athlete Injury in Training

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