Predicting Major League Baseball Championship Winners through Data Mining

Tolbert, Brandon; Trafali̇s, Theodore B.

doi:10.30958/ajspo.3.4.1

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…Accuracy: Tables 7 and 8 shows the accuracy found in every k-fold rounded to five digits of precision for proper display. When comparing the results of this proposal with the models in [9,15], the improvement is also shown. In [9], Tolbert used Support Vector Machines with different kernels for baseball players with accuracy values reaching 0.6.…”

Section: Testing-results In Annmentioning

confidence: 87%

“…When comparing the results of this proposal with the models in [9,15], the improvement is also shown. In [9], Tolbert used Support Vector Machines with different kernels for baseball players with accuracy values reaching 0.6. In the Pollack confusion matrix, specificity and sensitivity are considerably lower.…”

Section: Testing-results In Annmentioning

confidence: 87%

“…Soon thereafter James' ideas began being adopted by other sports. Since James has risen to relevance in the past decades, his ideas have been adopted, cited, and referenced by everyone from academia to various sports [9]. While this work has become widely influential, James' influence remains the heaviest within the sport of baseball [10].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

et al. 2021

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Throughout the modern age, sports have been a very important part of human existence. As our documentation of sports has become more advanced, so have the prediction capabilities. Presently, analysts keep track of a massive amount of information about each team, player, coach, and matchup. This collection has led to the development of unparalleled prediction systems with high levels of accuracy. The issue with these prediction systems is that they are proprietary and very costly to maintain. In other words, they are unusable by the average person. Sports, being one of the most heavily analyzed activities on the planet, should be accessible to everyone. In this paper, a preliminary system for using publicly available statistics and open-source methods for predicting NBA All-Stars is introduced and modified to improve the accuracy of the predictions, which reaches values close to 0.9 in raw accuracy, and higher than 0.9 in specificity.

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Section: Testing-results In Annmentioning

confidence: 87%

Section: Testing-results In Annmentioning

confidence: 87%

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A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

et al. 2021

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“…Related studies [8,9,19] have used competition files from Retrosheet and different feature selection methods to select the most relevant variables for input into the models for predicting the outcome of a match. Tolbert and Trafalis [20] used baseball data from FanGraphs, Sean Lahman's database, and mlb.com to predict the outcome of an MLB league championship. No studies have used data from Baseball-Reference to predict the outcome of a baseball game.…”

Section: Variables Selected In Baseball Researchmentioning

confidence: 99%

“…In 2016, Tolbert and Trafalis [20] used an SVM to develop an MLB championship prediction model. SVMs with a linear kernel, quadratic kernel, cubic kernel, and Gaussian radial basis function (RBF) kernel were employed to predict the American League champions, National League champions, and the World Series champions for the 2015 season.…”

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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Clustering Professional Baseball Players with SOM and Deciding Team Reinforcement Strategy with AHP

Kohara

Enomoto

2018

Lecture Notes in Computer Science

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Predicting Major League Baseball Championship Winners through Data Mining

Cited by 9 publications

References 2 publications

A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

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

Clustering Professional Baseball Players with SOM and Deciding Team Reinforcement Strategy with AHP

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