Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

Lu, Chi-Jie; Lee, Tian–Shyug; Wang, Chien-Chih; Chen, Wei-Jen

doi:10.3390/pr9091563

Cited by 9 publications

(3 citation statements)

References 86 publications

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“…Different learning algorithms achieve different prediction accuracies in a specific application. Therefore, multiple learners can be used to complement each other to enhance the learning effects [8]. The most representative ensemble learning algorithms are bagging, boosting, and stacking.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Wang

2022

Sustainability

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Many dyeing and finishing factories generally use old-fashioned dyeing machines. A key issue when using these machines is that the dyeing tank cannot detect entanglement problems, which may result in a lower dyeing quality. In this paper, imbalanced data with ensemble machine learning, such as Extreme Gradient Boosting (XGBoost) and random forest (RF), are integrated to predict the possible states of a dyeing machine, including normal operation, entanglement warning, and entanglement occurrence. To verify the results obtained using the proposed method, we worked with industry−academia collaborators. We collected 1,750,977 pieces of data from 1848 batches. The results obtained from the analysis show that after employing the Borderline synthetic minority oversampling technique and the Tomek link to deal with the data imbalance, combined with the model established by XGBoost, the prediction accuracy of the normal operation states, entanglement warning, and entanglement occurrence were 100%, 94%, and 96%, respectively. Finally, the proposed entanglement detection system was connected with the factory’s central control system using a web application programming interface and machine real-time operational parameter data. Thus, a real-time tangle anomaly warning and monitoring system was developed for the actual operating conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Wang

2022

Sustainability

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“…Because basically playing basketball requires some special skills, both physical qualities and creative and innovative game development (D'Elia, D'Andrea, Esposito, Altavilla, & Raiola, 2021). In addition, beginner athletes also need a proper and effective training program model to improve their basketball shooting skills (Lu, Lee, Wang, & Chen, 2021). Because if the coach is wrong in choosing a training program, it will have a negative impact on novice athletes, one of which is the occurrence of a relatively monotonous training process (Li, Wang, Liu, Zhang, & Hastie, 2022).…”

Section: Introductionmentioning

confidence: 99%

The effect of animal name and wall shoot training on the accuracy of shooting free throw in terms of hand eye coordination in beginner athletes

Munir,

Sumaryanti,

Nasrulloh

et al. 2024

Retos

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Providing training program treatment to athletes is the most important thing in the success of a coach to get more optimal results, one of which is using modified exercise animals name and wall shoots. This study aims to analyze: 1) the difference in the effect of animals name and wall shoot training on the accuracy of free throw shooting techniques, 2) the difference in the influence between athletes who have high and low hand eye coordination on the accuracy of shooting free throw, 3) the interaction between animals name and wall shoot training and high and low hand eye coordination on the accuracy of shooting free throw in beginner class athletes. This study is a type of factorial 2x2 experimental research with a population of 38 athletes and then the basis for determining samples using purposive sampling techniques based on inclusion criteria set by researchers, so that the sample in this study amounted to 20 athletes who would later be divided into each group using ordinal pairing techniques. The instrument used is to measure the accuracy of shooting free throw, namely drill shoot free throw, while hand eye coordination uses a one-pint image. The data analysis technique used is ANOVA two way. The results of this study show that: 1) there is a significant difference in the effect between animal name and wall shoot exercises on the accuracy of shooting free throw, as evidenced by the results of Fhitung's analysis of 6.741 with sig values. amounted to 0.001. 2) there is a significant difference in the influence on the ability of the hand eye coordination system on the free throw shooting accuracy component, as evidenced by the result of a Fcalculate value of 32.463 with a sig value. amounted to 0.000. 3) there is a significant interaction between the name of the animal and the wall shooting exercise in terms of free throw accuracy, as evidenced by the results of the Fcalculate value of 2.294 with the sig value. 0,003. So it can be concluded that the two exercises significantly affect the accuracy of the free throw shooting itself. These factors need to be considered in planning and implementing training programs to improve athlete performance, especially in terms of optimizing the accuracy of free throw shooting techniques in basketball athletes in the beginner class. Keywords: Animals Name, Wall Shoot, Precision, Shooting Free Throw, Hand Eye Coordination, Athletes.

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“…While sports outcome prediction has received widespread attention [7][8][9][10][11][12], the existing research has mainly focused on basketball, particularly NBA games, using data mining methods or traditional machine learning models [13][14][15][16][17][18][19][20]. However, there has been less research on applying graph neural networks (GNN) to predict basketball game outcomes.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

Zhao

Chun-jie

Guang-xin

2023

Entropy

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Basketball is a popular sport worldwide, and many researchers have utilized various machine learning models to predict the outcome of basketball games. However, prior research has primarily focused on traditional machine learning models. Furthermore, models that rely on vector inputs tend to ignore the intricate interactions between teams and the spatial structure of the league. Therefore, this study aimed to apply graph neural networks to basketball game outcome prediction, by transforming structured data into unstructured graphs, to represent the interactions between teams in the 2012–2018 NBA season dataset. Initially, the study used a homogeneous network and undirected graph to build a team representation graph. The constructed graph was fed into a graph convolutional network, which yielded an average success rate of 66.90% in predicting the outcome of games. To improve the prediction success rate, feature extraction based on the random forest algorithm was combined with the model. The fused model yielded the best results, and the prediction accuracy was improved to 71.54%. Additionally, the study compared the results of the developed model with previous studies and the baseline model. Our proposed method considers the spatial structure of teams and the interaction between teams, resulting in superior performance in basketball game outcome prediction. The results of this study provide valuable insights for basketball performance prediction research.

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Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

Cited by 9 publications

References 86 publications

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

Machine-Learning-Based System for the Detection of Entanglement in Dyeing and Finishing Processes

The effect of animal name and wall shoot training on the accuracy of shooting free throw in terms of hand eye coordination in beginner athletes

Enhancing Basketball Game Outcome Prediction through Fused Graph Convolutional Networks and Random Forest Algorithm

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