A hybrid ensemble learning framework for basketball outcomes prediction

Cai, Wei; Yu, Ding; Wu, Ziyu; Du, Xin; Zhou, Teng

doi:10.1016/j.physa.2019.121461

Cited by 34 publications

(10 citation statements)

References 14 publications

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“…Our framework is beneficial to college student, which helps them to improve the physical fitness. In future, we plan to extend this method to the applications of other domains, such as basketball game prediction or time series analysis [13][14][15][16][17][18].…”

Section: Resultsmentioning

confidence: 99%

An enhanced learning framework for the classification of college student physical fitness

Wang¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

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Physical test (PF) is demonstrated by a variety of factors including body weight status, cardiorespiratory fitness, musculoskeletal fitness (muscular strength and endurance) and flexibility, which are related to college student's physical condition. The test helps them require an understanding of behavioral attributes and causative mechanisms that promote their physical fitness. Judging the level of college student's physical fitness quickly is very necessary. In this paper, we present an enhanced learning framework for the classification of college student physical fitness. To achieve this, we first design a selection strategy for the students by considering the features that have the greatest impact on. Then, we present an enhanced learning framework equipped with our custom loss function and boosting strategy to classify the level of students' physical fitness. Extensive experiments have demonstrated the outpeformance of our framework.

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Section: Resultsmentioning

confidence: 99%

An enhanced learning framework for the classification of college student physical fitness

Wang¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

Self Cite

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“…The results show that our method helps the athletes to achieve personal breakthroughs and create their own success. In future, we plan to extend this method to the applications of other domains, such as time series analysis [7,[20][21][22][23].…”

Section: Resultsmentioning

confidence: 99%

Towards the athletic psychological adjustment: an empirical study based on the Inverted-U-Type hypothesis

Jia-jun¹,

Xie²,

Yan³

et al. 2019

Proceedings of the 2nd International Conference on Humanities Education and Social Sciences (ICHESS 2019)

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The technical and emotional control of the javelin game plays an important role for the athlete's performance. However, previous researches show that many psychological methods do not consider the psychological state of the athletes, and take the corresponding measures for psychological adjustment. In this paper, we propose an improved Inverted-U-Type hypothesis method for athlete's psychological adjustment during the training time before competition. According to the athletes' psychological state, adjusting the factors, we carry the weak and strong interventions to adjust psychological and physiological state of the athletes to an optimal level. The results show that the adjustment of the athletes' psychological by our method can effectively eliminate the impact of internal and external factors to improve the stability, which is also help for the athletes' psychological training of other sports.

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“…The average accuracy obtained was >90%. In 2019, Cai et al [26] proposed a hybrid ensemble learning framework combining bagging and the random subspace method for predicting the outcome of the 2016-2017 Chinese Basketball Association season. The data set included 20 teams and 380 games.…”

Section: Predicting the Outcome Of A Matchmentioning

confidence: 99%

Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches

Huang

2021

Applied Sciences

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Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.

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A hybrid ensemble learning framework for basketball outcomes prediction

Cited by 34 publications

References 14 publications

An enhanced learning framework for the classification of college student physical fitness

An enhanced learning framework for the classification of college student physical fitness

Towards the athletic psychological adjustment: an empirical study based on the Inverted-U-Type hypothesis

Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches

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