Search Results (35)

The primary aim of this study was to examine pitching performance metrics before and after ulnar collateral ligament reconstruction (UCLR) among Major League Baseball (MLB) starting and relief pitchers. The following information was extracted from the “Tommy John Surgery List” database regarding UCLR surgeries among MLB pitchers: UCLR date, pitcher type (starting pitcher, relief pitcher), and age at time of UCLR. Pitching performance metrics were extracted from the Baseball Savant online platform for the two seasons immediately prior to UCLR (pre) and the two seasons immediately following returning to pitching after UCLR (post). Fifty-nine pitchers were included in this study (29 starting pitchers, 30 relief pitchers). The outcome measures included the number of pitches thrown, earned run average (ERA), walks plus hits per inning pitched (WHIP), strikeout percentage, whiff percentage, walk percentage, batting average against, ground ball percentage, and fastball velocity. There was a pitcher type-by-time interaction effect for ERA (p = 0.01; η² = 0.12) and WHIP (p = 0.01; η² = 0.12). Starting pitcher ERA increased from 3.68 to 4.40 from pre- to post-surgery, while relief pitcher ERA decreased from 4.47 to 3.90. In addition, starting pitcher WHIP increased from 1.22 to 1.29 from pre- to post-surgery, while relief pitcher WHIP decreased from 1.38 to 1.28. There was a main effect of time for pitches thrown (p = 0.04; η² = 0.07). Significant differences between starting and relief pitchers were observed for changes in ERA and WHIP following UCLR, whereas no pitcher type-by-time interactions were observed for the remaining performance metrics. Both starting and relief pitchers threw fewer pitches following UCLR, while fastball velocity and other key pitching performance metrics remained largely unchanged. Full article

Background/Objectives: Baseball pitching injuries associated with fatigue-induced mechanisms may be attributed to change in lower-body power. In this study, a stretch-resistant belt (theorized to increase intra-abdominal pressure) was studied to determine if it influenced countermovement jump (CMJ) power pre- and post-pitching. Methods: Thirteen college athletes participated in three separate, randomized pitching sessions of forty pitches to evaluate the CMJ performance impacts owed to wearing a team-issued baseball belt versus a belt that was configured with the intent to raise intra-abdominal pressure (IAP). The three belt conditions were; (1) the team-issued belt, standard belt (SB), (2) the IAP-configured belt worn at regular length (RIAP), and the IAP-configured belt fastened two inches with the tightest cinch (2IN). Maximum jump heights were measured on a Jumpmat and captured with hands on hips. Data was integrated to compute jump power and the eccentric utilization ratio, being the ratio of a full stretch CMJ to a static CMJ biased to concentric power. Static CMJ testing had pitchers hold the bottom position for 5 s before takeoff. Repeated measures ANOVA with a post hoc Bonferroni correction determined significant differences; subject-specific interactions were identified. Results: Most athletes maintained or improved performance post-pitching with the RIAP with less variability in coordinating stretch-shortening responses. On a group level, RIAP had greater post-pitching CMJ height and power versus 2IN (p < 0.03) and had less CMJ power loss compared to SB and 2IN belt conditions (p < 0.02). IAP was not directly measured, yet this exploratory study provides preliminary evidence that a 5 mm, theoretical IAP design, via a stretch-resistant belt can influence pre- and post-pitching lower-body neuromuscular performance in collegiate pitchers. Conclusions: The RIAP condition showed less performance decline inferring fatigue resistance, preserved max CMJ height, and lessened post-pitch CMJ power loss. Maximal cinching tended to compromise post-pitch lower-body power and inferred the need to individualize the stretch-resistant belt, designed to increase intra-abdominal pressure, for performance and injury protection benefits. Full article

Background/Objectives: Due to their positions, professional baseball catchers are at elevated risk of concussion, which can impair visual processing. There is a need for sensitive sensorimotor monitoring tools to track concussion-related neurophysiological changes more accurately. We investigated whether oculometrics can address this need. Methods: Four Major League Baseball catchers completed an oculometric assessment shortly after suffering a concussion (Time 1) and again after completing vision rehabilitation (Time 2). The assessment produces 10 z-scored measures, including a summary score. Results: Players’ Time 1 summary score tended to be typical of a normal healthy adult (Mean = 0.07 z-scored units). On average, players improved by 1.3 z-score units from their Time 1 summary score (SD = 1.07). Exploratory analyses revealed that sensorimotor recovery was driven by smooth pursuit latency, proportion of tracking comprising smooth pursuit, and the amplitude of catch-up saccades. Conclusions: Our analysis was based on a very small sample of concussion cases, each of which was unique. Despite this limitation, our data show how oculometrics can measure improvements in visual processing following a concussion among baseball players with exceptional perceptual-motor skills. Our data highlight the risk that brain injuries in high-performing individuals go undetected due to standard-of-care tools normed to behavior from healthy control populations; for these athletes, “normal” scores cannot be interpreted as neurologically “healthy”. Full article

This paper introduces fairer measures of individual pitcher performance in baseball using the Shapley Value from coalitional game theory. The paper’s key conceptual innovation is a novel two-stage procedure for constructing the coalitionary game value functions for runs allowed and outs recorded by a baseball team’s defense. This procedure enables the Shapley Value calculation to fairly divide credit for runs and out between different pitchers and between pitchers and fielders. It also results in two new statistics—Shapley Pitcher Runs (SPR) and Shapley Pitcher Outs (SPO)—that, unlike traditional pitching statistics, consistently satisfy several mathematical fairness axioms. A third statistic, called Shapley Run Average, provides a fairer measure of pitcher efficiency. I calculate these statistics for the 2022 Major League Baseball regular season and the 1955–2022 World Series championships. Using SPR and SPO as the standard for fairness, empirical analysis reveals that the traditional pitching statistics systematically and unfairly overcredit pitchers by 40–50%, with starting pitchers miscredited more severely than relievers. Analysis of SRA identifies efficient pitchers whose performance is obscured by conventional statistics and enables a reassessment of historic World Series performances. Overall, this work demonstrates another application of the Shapley Value to creating new performance measures in team sports. Full article

This study develops and evaluates machine learning models for classifying Korean Baseball Organization (KBO) playoff advancement using pitching metrics from 2015 to 2024 (N = 100 team-seasons), focusing specifically on pitching’s contribution to playoff qualification to address the ERA-FIP paradox at the team level. Five algorithms were compared: Random Forest, Support Vector Machines, Logistic Regression, Neural Networks, and Decision Trees. Independent variables included ten pitching statistics: Earned Run Average (ERA), Walks and Hits per Inning Pitched (WHIP), Fielding Independent Pitching (FIP), Strikeouts per 9 Innings (K/9), Walks per 9 Innings (BB/9), Strikeout-to-Walk Ratio (K/BB), Home Runs per 9 Innings (HR/9), batting average against (BAA), and opponent On-Base Percentage (OBP) and On-Base Plus Slugging (OPS). Logistic Regression achieved the highest classification performance with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.804 and classification accuracy of 73.0%, followed by Neural Network (AUC = 0.799, CA = 72.0). Feature importance analysis showed ERA and WHIP, both defense-dependent metrics, as the dominant predictors of postseason qualification, collectively accounting for 33.7% of information gain, while FIP ranks fifth, indicating that defense-dependent metrics are more informative for team success than defense-independent measures. The findings highlight the strategic importance of pitching–defense synergy, demonstrate the applicability of machine learning-based playoff classification beyond Major League Baseball, and provide empirical evidence that defense-dependent metrics (ERA, WHIP) exhibit superior discriminatory power compared to defense-independent metrics (FIP) for team playoff qualification. Findings reflect pitching’s contribution to playoff success; comprehensive models integrating hitting, defense, and managerial factors would provide more complete classification frameworks. Full article

The COVID-19 pandemic disrupted traditional patterns of sport consumption, raising questions about whether fans would return to stadiums and how sensitive they would be to ticket prices in the recovery period. This study reconceptualizes ticket price elasticity as a market-based indicator of fan loyalty and applies it to Major League Baseball (MLB) during 2021–2023. Using team–season attendance data from Baseball-Reference, primary-market ticket prices from the Team Marketing Report Fan Cost Index, and secondary-market prices from TicketIQ, we estimate log–log fixed-effects panel models to separate causal price responses from popularity-driven correlations. The results show a strongly negative elasticity of attendance with respect to primary-market prices (β ≈ −7.93, p < 0.001), indicating that higher ticket prices substantially reduce attendance, while secondary-market prices are positively associated with attendance, reflecting demand shocks rather than causal effects. Heterogeneity analyses reveal that brand strength, team performance, and game salience significantly moderate elasticity, supporting the interpretation of inelastic demand as revealed loyalty. These findings highlight the potential of elasticity as a Fan Loyalty Index, providing a replicable framework for measuring consumer resilience. The study offers practical insights for pricing strategy, fan segmentation, and engagement, while emphasizing the broader social role of sport in restoring community identity during post-pandemic recovery. Full article

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player valuation; (4) predict future player injuries; and (5) project future game outcomes. Second, we identify a set of data repositories for baseball analytics studies. The most popular data repositories are Baseball Savant and Baseball Reference. Third, we conduct an in-depth analysis of the machine learning models applied in baseball analytics. The most popular machine learning models are random forest and support vector machine. Furthermore, only a small fraction of studies have rigorously followed the best practices in data preprocessing, machine learning model training, testing, and prediction outcome interpretation. Full article

In this study, we analyze the winning percentage of 16 teams that have participated in Major League Baseball since 1901. First, 69 variables for each team are classified into pitching, batting, and fielding using factor analysis, and then the effect of the newly classified variables on the winning percentage is analyzed. In addition, after expressing each team’s winning rate as a fuzzy number using a fuzzy partition, the linear relationship between the previous year and the next year using the fuzzy probability is investigated, and we estimate the fuzzy regression model and Markov regression model using the Double Least Absolute Deviation (DLAD) method. The proposed fuzzy model describes variables that affect the winning percentage of the next year according to the winning rate of the previous year. The estimated fuzzy regression model showed that the on-base percentage allowed by the pitcher and the on-base percentage of the batter had the greatest effect on the winning percentage. Full article

The impact of sporting events on local economies and their spatial distribution is a topic of active policy debate. This study adds to the discussion by examining granular cellphone location data to assess the spillover effects of Major League Baseball (MLB) games in a major US city. Focusing on the 2019 season, we explore granular geospatial patterns in mobility and consumer spending on game days versus non-game days in the Saint Louis region. Through density-based clustering and hotspot analysis, we uncover distinct spatiotemporal signatures and variations in visitor affluence across different teams. This study uses features like game day characteristics, location data (latitude and longitude), business types, and spending data. A significant finding is that specific spatial clusters of economic activity are formed around the stadium, particularly on game days, with multiple clusters identified. These clusters reveal a marked increase in spending at businesses such as restaurants, bars, and liquor stores, with revenue surges of up to 38% in certain areas. We identified a significant change in spending patterns in the local economy during games, with results varying greatly across teams. Notably, the XGBoost model performs best, achieving a test R² of 0.80. The framework presented enhances the literature at the intersection of urban economics, sports analytics, and spatial modeling while providing data-driven actionable insights for businesses and policymakers. Full article

Major League Baseball (MLB) is particularly vulnerable to climate change due to its season duration, geographic footprint, and largely outdoor nature. Therefore, the purposes of this study were to investigate whether U.S.-based MLB fans’ climate change skepticism and experiential processing influenced their climate change risk perceptions and adaptation willingness, and to determine if those relationships were further influenced by fans’ sport identification with MLB. A cross-sectional survey design tested the study’s purposes using a sample (n = 540) of self-identified MLB fans. Data were analyzed using structural equation modeling on the Mplus 8 statistical package to test the hypothesized model. The results indicated consistencies across low and highly identified MLB fans on their climate change risk perceptions and willingness to adapt, but revealed group differences between the factors influencing fans’ risk perceptions of climate change. The findings provide early empirical evidence to support the United Nations’ (UN) Sport for Climate Action Framework, and managerial implications regarding the nexus of climate change and sport consumer behavior research. Full article

The COVID-19 pandemic has led to a dramatic increase in baseball viewership, thereby providing an opportunity to comprehensively explore the determinants of the new audience. To this end, we analyze the preferences of the Taiwanese audience in 2019 and 2020, both before and after the COVID-19 outbreak, through TV ratings based on the effect of outcome uncertainty, tournament factors, consumer availability, and game quality. The empirical findings show that the behavior of the small-scale Chinese Professional Baseball League (CPBL) sports television viewing market differs from that of large-scale markets such as Major League Baseball. Additionally, the effect of the outcome uncertainty of the game is inconsistent before and after the COVID-19 pandemic. New audiences, unlike existing audiences, have been affected by team quality and consumer availability that are statistically significant, but tournament factors are not significant. This study provides the first empirical analysis of the factors driving TV ratings of CPBL games as well as the impact before and after the COVID-19 outbreak as a contribution to filling the gap in sports communication research. The observations can be used by strategic departments of professional teams for their marketing target, to identify potential fans, and to direct their marketing resources towards sustaining or even growing during the pandemic events. Full article

Air quality is a growing environmental concern that has implications for human physical and mental health. While air pollution has been linked to cognitive disease progression and declines in overall health, the impacts of air quality on athletic performance have not been extensively investigated. Much of the previous research focused on endurance sports indicates that air quality negatively impacts athletic performance; however, the effects of air quality on non-endurance elite team performance remains largely unknown. The purpose of this study was to examine the impact of air quality on errors committed by Major League Baseball (MLB) teams, interceptions thrown by quarterbacks in the National Football League (NFL), and overall quarterback performance in the NFL. Linear regression analysis was used to determine the impact of the median air quality index (AQI) of counties with MLB and NFL teams on errors, interceptions, and overall quarterback performance of players on those MLB and NFL teams. AQI was a significant positive predictor of errors and interceptions, indicating increased errors and interceptions with decreased air quality. Similarly, quarterback performance was significantly reduced for quarterbacks from teams in counties with worse air quality. These findings suggest that air quality has a significant impact on performance in the MLB and NFL, indicating impairments in physical and cognitive performance in professional athletes when competing in areas with poorer air quality. Hence, it is likely that air quality impacts athletic performance in numerous sports that have not yet been investigated. Full article

Baseball is a sport that involves a large number of statistics, which are often displayed during broadcast events to show the players’ performance levels. With the advent of big data, the amount and types of data used in broadcasts have increased yearly. However, the use of complex information challenges the audience’s ability to process it. This study considered data types used during broadcasts as the basis for an in-depth exploration of audiences’ experience resulting from the application of visualization. The study also examined the relationship between the contents of broadcast information and audiences’ sports participation, entertainment experience, and cognitive load. Baseball fans with varying levels of experience with handling different types of information were surveyed to understand the variations in their entertainment experiences and cognitive load levels when they watched a baseball game. The results indicated that fans with low participation levels had insufficient viewing experience, such that the use of visualized statistical information did not facilitate their understanding of the game, nor did they gain more pleasure or meaning from the game through the visualized information. Fans with high participation levels already possessed a wealth of baseball knowledge and experience, so providing visualized information did not significantly elevate their viewing experiences either. Moreover, the visualized information caused them to experience varying amounts of additional cognitive load. These results provide a reference that can be used to design sports broadcasts tailored to different information types and fan characteristics, thus improving fans’ viewing experience of sports broadcasts. Full article

There has been a considerable amount of literature on binomial regression models that utilize well-known link functions, such as logistic, probit, and complementary log-log functions. The conventional binomial model is focused only on a single parameter representing one probability of success. However, we often encounter data for which two different success probabilities are of interest simultaneously. For instance, there are several offensive measures in baseball to predict the future performance of batters. Under these circumstances, it would be meaningful to consider more than one success probability. In this article, we employ a bivariate binomial distribution that possesses two success probabilities to conduct a regression analysis with random effects being incorporated under a Bayesian framework. Major League Baseball data are analyzed to demonstrate our methodologies. Extensive simulation studies are conducted to investigate model performances. Full article

This study analyzed the characteristics of pitched baseballs from TV broadcast videos to understand the effects of the Magnus force on a pitched-baseball trajectory using aerodynamic theory. Furthermore, an automatic measurement and analysis system for pitched-baseball trajectories, ball speeds, and spin rates was established, capturing the trajectory of the baseball thrown by the pitcher before the catcher catches it and analyzing its related dynamic parameters. The system consists of two parts: (1) capturing and detecting the pitched baseball in all frames of the video using the YOLOv3-tiny deep learning algorithm and automatically recording the coordinates of each detected baseball position; (2) automatically calculating the average speed and spin rate of the pitched baseball using aerodynamic theory. As the baseball thrown by the pitcher is fast, and live-action TV videos like sports and concerts are typically at least 24 fps or more, this study used YOLOv3-tiny algorithm to speed up the calculation. Finally, the system automatically presented pitching data on the screen, and the pitching information in the baseball game was easily obtained and recorded for further discussion. The system was tested using 30 videos of pitched baseballs and could effectively capture the baseball trajectories, throw points, catch points, and vertical displacements. Compared with the values from the TV broadcast, the average errors on the calculated ball speed and spin rate were 1.88% and 7.51%, respectively. Using the ratio of the spin rate and ball speed as a parameter to analyze the pitching state of the pitcher’s four-seam fastball in the Nippon Professional Baseball and Major League Baseball matches, it was observed that when this ratio increased, the Magnus displacement of the ball increased, thereby decreasing its late break. Therefore, the developed system provides scientific pitching data to improve the performance of baseball pitchers. Full article