Performance prediction in major league baseball by long short-term memory networks

Sun, Hsuan-Cheng; Lin, Tsung-Nan; Tsai, Yen-Lung

doi:10.1007/s41060-022-00313-4

Cited by 12 publications

(9 citation statements)

References 29 publications

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“…Various datasets for sports analysis have been introduced to empower different ways to collect matches' information in a wide range of sports, such as basketball [24], [26], [27], rugby [22], badminton [14], baseball [6]- [9], and soccer [11], [23], [25], [28]. These datasets are each created with different variables based on their sport's unique characteristics and patterns to enable different analysis tasks.…”

Section: B Sport Datasetsmentioning

confidence: 99%

“…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%

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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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Section: B Sport Datasetsmentioning

confidence: 99%

Section: B Sport Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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VREN: Volleyball Rally Dataset with Expression Notation Language

Xia¹,

Tracy²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The work also considered modeling uncertainty quantification with evidential theory to assist the decision-making process in detecting chronic diseases. Also based on deep learning models, Sun et al [37] adopted the sequential Long Short-Term Memory (LSTM) models in the domain of sports analytics for the baseball industry. With the numbers of home runs as the predictive target, the authors applied their models on the data from Major league Baseball (MLB) to support important decisions in managing players and teams.…”

Section: Applied and Flexible Deep Learningmentioning

confidence: 99%

“…These formats now often supplement the tabular data structures of the past as shown by Nasir and Ezeife [33]. To accommodate using these new formats, data mining and machine learning models have adapted to support multichannel, multi-modal, and sequential inputs [33,37].…”

Section: New Trends From the Industry Perspectivementioning

confidence: 99%

Recent advances in domain-driven data mining

Liu

Fakharizadi

et al. 2022

Complex Intell. Syst.

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Data mining research has been significantly motivated by and benefited from real-world applications in novel domains. This special issue was proposed and edited to draw attention to domain-driven data mining and disseminate research in foundations, frameworks, and applications for data-driven and actionable knowledge discovery. Along with this special issue, we also organized a related workshop to continue the previous efforts on promoting advances in domain-driven data mining. This editorial report will first summarize the selected papers in the special issue, then discuss various industrial trends in the context of the selected papers, and finally document the keynote talks presented by the workshop. Although many scholars have made prominent contributions with the theme of domain-driven data mining, there are still various new research problems and challenges calling for more research investigations in the future. We hope this special issue is helpful for scholars working along this critically important line of research.

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