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

Huang, Meiling; Li, Yunzhi

doi:10.3390/app11104499

Cited by 23 publications

(16 citation statements)

References 27 publications

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“…The core of convolutional neural network (CNN) is the convolutional layer, which is a neural network model that specializes in processing two-dimensional images, but it is also widely used in one-dimensional and three-dimensional data and has obtained favorable results. This study used Python’s Keras to construct the 1DCNN model and referred to the model architecture of Huang and Li [ 4 ]. There are 8 layers in total; the order is 1D convolutional layer, maximum pooling layer, 1D convolutional layer, maximum pooling layer, dropout layer, fully connected layer, dropout layer, and output layer, using Sigmoid activation function.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the AHP model predicts that the winning probability for Kansas City Royals (KCR) is 0.6106, and this team is the most likely team to become the 2017 season champion. Huang and Li [ 4 ] collected 2019 MLB game data, including hitting, pitcher, and home/away, to compare the prediction accuracies between using the data of the starting pitcher or the entire pitcher before and after the feature selection. The best prediction accuracy (94.18%) was obtained from an artificial neural network (ANN) after feature selection from the entire pitcher database.…”

Section: Introductionmentioning

confidence: 99%

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Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Huang

2022

Entropy

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(1) Background and Objective: Major League Baseball (MLB) is one of the most popular international sport events worldwide. Many people are very interest in the related activities, and they are also curious about the outcome of the next game. There are many factors that affect the outcome of a baseball game, and it is very difficult to predict the outcome of the game precisely. At present, relevant research predicts the accuracy of the next game falls between 55% and 62%. (2) Methods: This research collected MLB game data from 2015 to 2019 and organized a total of 30 datasets for each team to predict the outcome of the next game. The prediction method used includes one-dimensional convolutional neural network (1DCNN) and three machine-learning methods, namely an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). (3) Results: The prediction results show that, among the four prediction models, SVM obtains the highest prediction accuracies of 64.25% and 65.75% without feature selection and with feature selection, respectively; and the best AUCs are 0.6495 and 0.6501, respectively. (4) Conclusions: This study used feature selection and optimized parameter combination to increase the prediction performance to around 65%, which surpasses the prediction accuracies when compared to the state-of-the-art works in the literature.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Huang

2022

Entropy

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“…Studying football networks enhances our understanding of players' performance and the real-time game situation, which is helpful to estimate the outcome of the game. In recent years, deep learning methods such as convolutional neural networks have been used to predict the outcomes of different sports games [66][67][68]. Their models outperformed traditional approaches like Bayesian Networks and SVMs.…”

Section: Network-based Ranking Of Clubsmentioning

confidence: 99%

A survey on football network analysis

Li,

Xu,

Zhou

2023

EPL

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Being the world's most popular sport, football research has traditionally concentrated on empirical summaries or statistics, with only limited data available in the past. In recent years, social network analysis has been applied to a variety of fields, which also brings new perspectives to the study of football spots. In this paper, we survey the literature related to football networks and discuss the use of network measures to analyze the performance of footballers and teams in different types of football networks. We aim to find out how to construct appropriate football networks based on different perspectives on football research. Various studies on football network analysis, including team performance, player interactions, and club behavior, are reviewed. The findings provide insights into team performance, player roles, and social dynamics within football teams and clubs.

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“…These datasets are each created with different variables based on their sport's unique characteristics and patterns to enable different analysis tasks. Generally, sports analysis tasks can be divided into two areas: match outcome predictions (e.g., [7], [9], [14], [22]- [28]) and player performance analysis (e.g., [6], [8], [11], [14]). Two of our tasks similarly focus on outcome predictions.…”

Section: B Sport Datasetsmentioning

confidence: 99%

“…Because of its unique nature, baseball is one of the sports where these computer-assisted studies and analyses have been widely implemented [4]. For example, areas of study include performance analysis on a specific posture [5], player performance and lineup predictions [6], match outcome predictions [7], [9], tactical preparation aids [8], and similar motion retrieval [10]. Computer-assisted research on other sports that have been recently introduced include evaluating player actions in soccer [11] and movement pattern recognition in basketball [12].…”

Section: Introductionmentioning

confidence: 99%

VREN: Volleyball Rally Dataset with Expression Notation Language

Xia¹,

Tracy²,

Zhao³

et al. 2022

Preprint

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This research is intended to accomplish two goals: The first goal is to curate a large and information rich dataset that contains crucial and succinct summaries on the players' actions and positions and the back-and-forth travel patterns of the volleyball in professional and NCAA Div-I indoor volleyball games. While several prior studies have aimed to create similar datasets for other sports (e.g. badminton and soccer), creating such a dataset for indoor volleyball is not yet realized. The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset. Based on the curated dataset and our descriptive sports language, we introduce three tasks for automated volleyball action and tactic analysis using our dataset:(1) Volleyball Rally Prediction, aimed at predicting the outcome of a rally and helping players and coaches improve decisionmaking in practice, (2) Setting Type and Hitting Type Prediction, to help coaches and players prepare more effectively for the game, and (3) Volleyball Tactics and Attacking Zone Statistics, to provide advanced volleyball statistics and help coaches understand the game and opponent's tactics better. We conducted case studies to show how experimental results can provide insights to the volleyball analysis community. Furthermore, experimental evaluation based on real-world data establishes a baseline for future studies and applications of our dataset and language. This study bridges the gap between the indoor volleyball field and computer science.

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

Cited by 23 publications

References 27 publications

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

Exploring and Selecting Features to Predict the Next Outcomes of MLB Games

A survey on football network analysis

VREN: Volleyball Rally Dataset with Expression Notation Language

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