Current State of Data and Analytics Research in Baseball

Mizels, Joshua; Erickson, Brandon J.; Chalmers, Peter N.

doi:10.1007/s12178-022-09763-6

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…This study further reaffirms that moneyball statistics remain relevant because of their ability to accurately indicate success in terms of a team making the playoffs (Mizels, Erickson, and Chalmers 2022). While baseball will continue a transition with "Statcast" statistics such as excepted ERA (xERA), exit velocity, and more, the effect moneyball statistics have had on the game should always be remembered.…”

Section: Discussionsupporting

confidence: 61%

“…This new method initially was not well received but was eventually adopted by Major League Baseball (MLB) teams. More recently, studies have shown that moneyball statistics such as fielding independent pitching (FIP), on-base percentage (OBP), and slugging percentage (SLG) continue to accurately quantify defensive and offensive performance amid new statistics from the "Statcast" era (Mizels, Erickson, and Chalmers 2022). In this paper, we further explore the boundaries of moneyball statistics and other common baseball statistics to predict if a team makes the playoffs through different tests and machine learning models.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Baseball Statistics by Predicting Playoff Teams

Nakra,

Kimes

2023

J Stud Res

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In this paper, we explore how different baseball statistics correlate to an entry to the playoffs. We use LogisticRegression and XGBoost to evaluate if a baseball statistic has a high correlation with whether or not a team makes the playoffs. We set up three models: the Moneyball model (which uses moneyball statistics), the All Stats Model (which uses moneyball statistics and additional common statistics), and the XGBoost model (which uses the same dataset of the All Stats model, but the structure of the model is different). We compare these models, evaluating their accuracy, variable coefficients, and confusion matrix. From these tests, we find that the Moneyball Model has similar accuracies to the All Stats Model, revealing that moneyball statistics are still a relevant and accurate way to predict if a team makes the playoffs. The variable coefficients test highlights that moneyball statistics have the highest importance in the model's ability to predict if a team makes the playoffs. While the tests provide a foundation for the evaluation of moneyball and common baseball statistics, there remains future opportunities to use different models and a larger dataset.

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Section: Discussionsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

Evaluating Baseball Statistics by Predicting Playoff Teams

Nakra,

Kimes

2023

J Stud Res

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“…WT in sports is not only used for performance tracking or improvement but also for determining and preventing athlete injury incidence [151][152][153][154]. By providing real-time physiological data to athletes and coaches (compared to laboratorybased standard evaluations), WT allows for the development of accurate treatment plans and customized training programs to potentially reduce and mitigate injuries [155][156][157].…”

Section: Injury Preventionmentioning

confidence: 99%

Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities

Seçkin,

Ateş,

Seçkin

2023

Applied Sciences

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Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry.

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“…An alternative method used in tracking overhead sporting movements that is cheaper and easier to set up is based on wearable sensors such as IMU. These are a small and lightweight system embedded with 3D accelerometers, gyroscopes, and magnetometers, which make IMU more accessible and quicker in terms of gathering and processing the kinetic and kinematic data [63,64]. Despite IMU proving comparable to the gold standard accuracy of motion capture in certain circumstances, there is a phenomenon called IMU drift that stems from continuously integrated noise errors, and in turn could cause absolute IMU measurement error to increase over time [65,66].…”

Section: Data Collection and Statistical Analysis Approachesmentioning

confidence: 99%

A Systematic Review of the Biomechanical Studies on Shoulder Kinematics in Overhead Sporting Motions: Types of Analysis and Approaches

Ramasamy,

Usman,

Razman

et al. 2023

Applied Sciences

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The shoulder is a unique and complex joint in the human body with three bones and four joints, which makes it the most unstable joint in the body due to the amount of motion. To improve approaches toward understanding the performance of overhead throwing movements, this systematic review summarizes the type of analysis related to shoulder biomechanics involving overhead sporting motions. A search of seven databases identified 33 eligible studies, which were subsequently scored using the Modified Coleman Methodology Score scale. A total of nine articles from badminton, seven from baseball, five from volleyball, five from tennis, three from cricket, and one from softball were reviewed. All 33 studies evaluated shoulder kinematics and 12 of them also investigated the forces and torques (kinetics). The most common methods used were 3D motion analysis (76%), digital video cameras (15%), electromagnetic tracking system (6%), and finally 3% used IMU sensors. Overall, shoulder external rotation during the back swing, internal rotation, and elbow extension during the forward acceleration phase were the strongest predictors of high velocity overhead throwing movement. The findings provide some useful insights and guidance to researchers in their future contribution to the existing body of literature on shoulder overhead throwing movement biomechanics.

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Current State of Data and Analytics Research in Baseball

Cited by 7 publications

References 37 publications

Evaluating Baseball Statistics by Predicting Playoff Teams

Evaluating Baseball Statistics by Predicting Playoff Teams

Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities

A Systematic Review of the Biomechanical Studies on Shoulder Kinematics in Overhead Sporting Motions: Types of Analysis and Approaches

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