Use predictive analytics and big data technologies to enhance badminton game strategy development and performance indicators

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

The increasing integration of data-driven approaches and machine learning (ML) in sports presents a significant opportunity to optimize performance, predict outcomes, refine strategies, especially badminton. Despite its promise, challenges such as the lack comprehensive datasets, limited use advanced ML techniques, insufficient focus on tactical decision-making, underutilization predictive analytics training remain prevalent. To develop model that analyzes technical decisions badminton, enhancing strategy development performance evaluation for competitive gameplay, proposed method, Puffer Fish Algorithm-tuned Intelligent Support Vector Machine (PFA-INT-SVM), combines benefits Pufferfish mutation with INT-SVM improve prediction accuracy classification tasks. utilizes data encompasses player metrics, shot types, match context, opponent behavior, physical conditions, environmental factors, decisions. One-hot encoding is applied categorical features, while normalization standardizes numerical data, Linear Discriminant Analysis (LDA) employed dimensionality reduction feature extraction. Experimental results demonstrate PFA-INT-SVM significantly outperforms traditional methods terms efficiency. This effectively predicts showing promising potential badminton analysis. findings highlight future integrating techniques practical applications.

Language: Английский

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Developing a Target Games-Based Long Service Training Model in Badminton for Beginner Athletes

Physical Education Theory and Methodology, Journal Year: 2025, Volume and Issue: 25(2), P. 322 - 330

Published: March 30, 2025

Background. The development of long service skills in badminton is one the important components to improve game performance. low variety training models considered as a problem beginner players, which has an impact on mastery this technique. Objectives. This study aimed develop and determine effectiveness model based target games enhancing players. Materials methods. research design used was method by following Borg & Gall model, involving ten stages ranging from needs analysis testing. subjects were players at Faculty Sport Sciences, Medan State University, with sample 42 divided into experimental control groups. Data collection pre-experimental pretest-posttest design. assisted SPSS application paired-sample t-test independent samples t-test. Results. Based upon validation results, 18 variations exercise deemed feasible for large-scale trials average feasibility percentage 77%. These outcomes obtained after minor revisions suggestions increase intensity duration exercises. However, overall without significant changes. results showed substantial difference between group using games-based conventional (p < 0.05). proved be more effective improving ability Conclusions. According findings, it can concluded that provides efficacy also applied widely coaching

Language: Английский

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Analyze the impact of complex scheduling algorithms on injury rates and athletic performance in a collegiate sports environment

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Efficiently managing sports schedules in collegiate environments is a challenging and crucial task. With multiple teams, events, facilities, diverse stakeholder needs, traditional scheduling methods often fail to meet the dynamic complex requirements of modern programs. Complex algorithms offer promising solution by optimizing allocation resources, time slots, facilities while minimizing conflicts maximizing participation. The research analyzes impact on injury rates athletic performance environment, with particular focus ACL injuries basketball players. gathers history, performance, demographic data from athletes. was preprocessed using cleansing normalizing data, handling missing values. employs an ICO-MLPN predict risk improve environments. explores application DLB algorithm create tailored that account for individual requirements, training intensity, recovery periods reduce ACL. findings suggest also significantly incidence injuries, offering framework programs recall 95.33%, accuracy 98.70%, precision 98.2%, F1-score 96.20%. After implementing DLB, incident rate decreased 50% 30%, treatment costs 20%, physical health satisfaction improved 65% 85%, mental increased 60% 80%. Recovery 2.5 days 2 days, minimum severity 65%. approach underscores potential combining optimization innovative personalized prioritize both reduction.

Language: Английский

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Implementing machine learning algorithms to optimize sprint performance and biomechanical analysis of track and field athletes

Journal of Computational Methods in Sciences and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Sprint performance is a crucial component of athletic performance, especially in sports like track and field, football, rugby, which require quick bursts peak effort over short durations. Understanding the biomechanics sprinting essential for enhancing preventing injuries, creating effective training plans. Traditional research on sprint evaluation often focuses discrete measures while neglecting intricate interactions between variables that evolve throughout sprint. This study addresses these challenges by applying machine learning (ML) algorithm, specifically Polar Bear-tuned Multi-Source Kernel Support Vector Machine (PB-MKSVM), to predict optimize field athletes. The system analyzes biomechanical characteristics such as muscle activation patterns, joint angles, ground reaction forces, stride length. Data were collected using wearable sensors motion capture systems during standardized trials, various parameters recorded. Standard preprocessing steps including noise removal outlier detection applied data. Power Spectral Density (PSD) was employed extract features from preprocessed results demonstrate proposed method outperforms traditional algorithms predicting efficiency identifies complex, phase-specific changes movement patterns. model effectively sprinters’ movements differentiate skill levels. Using Python software, achieved impressive metrics, accuracy (94.5%), precision (92.7%), recall (93.6%), F1-score (92.1%), R 2 (0.92), AUC (0.91), highlighting its robust predictive ability. illustrates how models can advance mechanics provide insightful information athletes coaches seeking improve performance.

Language: Английский

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Biomechanical analysis and tactical awareness cultivation of badminton players’ variable speed running training

Molecular & cellular biomechanics, Journal Year: 2024, Volume and Issue: 21(4), P. 458 - 458

Published: Dec. 24, 2024

In recent years, the combination of machine learning (ML) and computer vision has influenced sports training approaches, notably for monitoring player performance. This research gives a detailed biomechanical analysis badminton players during speed-running training, using insights from ML techniques. Key metrics such as gait, speed, acceleration are assessed by tracking players’ motions dynamics their running patterns The stroke video dataset was collected Kaggle source. To ensure high-quality input analysis, data preprocessing stages include stabilization with Kalman filter, noise reduction Gaussian smoothing, frame extraction temporal sampling. Feature approaches like histogram oriented gradients (HOG) used shape recognition optical flow motion tracking. study provides use simulation environment built on Modified Ant Lion Optimized Decision Trees (MALO+DT) model trained historical data, which allows prediction movement adjustments based contextual features variations fatigue. findings demonstrate that speed improves tactical awareness decision-making in dynamic environments. performance suggested approach evaluated Python platform. achieves good accuracy (98.3%), recall (97.4%), F1-score (98%), precision (97.5%), demonstrating model's abilities effect biomechanics. Furthermore, significance this is development, providing coaches analysts actionable to enhance practices increase show combining significantly adaptability responsiveness matches, resulting more strategic teaching.

Language: Английский

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