Analysis on the construction of sports match prediction model using neural network

Li, Hang

doi:10.1007/s00500-020-04823-w

Cited by 35 publications

(16 citation statements)

References 30 publications

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“…Finally, there is variation, that is, gene variation in biological evolution, as shown in formulas (17) and (18):…”

Section: Ga Optimization Of Bp Neural Network Sports Performance Prediction Modelmentioning

confidence: 99%

“…But it needs the result of sports performance prediction to have smaller errors and fit the actual situation better. e prediction model of sports performance based on neural network algorithm not only has strong data processing and prediction ability but also has small prediction error, which meets the demand of sports performance prediction [18]. erefore, this paper is based on BP neural network algorithm to build sports performance prediction model and on the BP neural network to add a genetic algorithm to optimize the algorithm, in order to make up for the shortcomings of the BP neural network in sports performance prediction.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Analysis of Sports Performance Prediction Model Based on GA‐BP Neural Network Algorithm

Jin-juan

2021

Computational Intelligence and Neuroscience

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There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.

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“…Finally, there is variation, that is, gene variation in biological evolution, as shown in formulas (17) and (18):…”

Section: Ga Optimization Of Bp Neural Network Sports Performance Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Analysis of Sports Performance Prediction Model Based on GA‐BP Neural Network Algorithm

Jin-juan

2021

Computational Intelligence and Neuroscience

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“…In sports events, due to the complexity of the activities and the interest of the construction unit, the internal environment is more complicated, which is very risky [1].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Risk Prediction of Sports Events Based on Gray Neural Network Model

Wang

2021

Complexity

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In this paper, neural network is used as a predictive network modeling method, with the support of MATLAB Neural Toolbox, based on the implementation of predictive research. A risk warning model is designed for sports events relying on neural network s to reduce the losses caused by risk accidents. First, the article introduces a literature review of sports event risk warning, combined with the sports event risk warning index system; summarizes the main advantages of using neural network and fuzzy theory; and establishes a sports event risk warning model relied on neural network. The article starts with the application of gray network in sports risk warning design, starting from the necessity of applying gray network in sports event risk warning; analyzes the risk warning model and operation process; and conducts sample data verification to verify this power of the model. Practice has proved that the application of gray neural network in sports events can play a role in risk warning.

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“…The prediction accuracies of the model for all NBA team data from 2014 to 2017 were 91.78% and 91.64%, respectively. Li [27] proposed an improved back-propagation neural network (BPNN) in 2020 to predict the outcome of the main tournament of the Union of European Football Associations, which is the Champions League, and compared the results with those obtained using multiple linear regression (MLR) and detrended cross-correlation analysis. The improved BPNN had a prediction error of almost zero with high accuracy and reliability.…”

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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Analysis on the construction of sports match prediction model using neural network

Cited by 35 publications

References 30 publications

[Retracted] Analysis of Sports Performance Prediction Model Based on GA‐BP Neural Network Algorithm

[Retracted] Analysis of Sports Performance Prediction Model Based on GA‐BP Neural Network Algorithm

[Retracted] Risk Prediction of Sports Events Based on Gray Neural Network Model

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

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