Search Results (13)

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

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

(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. Full article

For a large ensemble of complex systems, a Many-System Problem (MSP) studies how heterogeneity constrains and hides structural mechanisms, and how to uncover and reveal hidden major factors from homogeneous parts. All member systems in an MSP share common governing principles of dynamics, but differ in idiosyncratic characteristics. A typical dynamic is found underlying response features with respect to covariate features of quantitative or qualitative data types. Neither all-system-as-one-whole nor individual system-specific functional structures are assumed in such response-vs-covariate (Re–Co) dynamics. We developed a computational protocol for identifying various collections of major factors of various orders underlying Re–Co dynamics. We first demonstrate the immanent effects of heterogeneity among member systems, which constrain compositions of major factors and even hide essential ones. Secondly, we show that fuller collections of major factors are discovered by breaking heterogeneity into many homogeneous parts. This process further realizes Anderson’s “More is Different” phenomenon. We employ the categorical nature of all features and develop a Categorical Exploratory Data Analysis (CEDA)-based major factor selection protocol. Information theoretical measurements—conditional mutual information and entropy—are heavily used in two selection criteria: C1—confirmable and C2—irreplaceable. All conditional entropies are evaluated through contingency tables with algorithmically computed reliability against the finite sample phenomenon. We study one artificially designed MSP and then two real collectives of Major League Baseball (MLB) pitching dynamics with 62 slider pitchers and 199 fastball pitchers, respectively. Finally, our MSP data analyzing techniques are applied to resolve a scientific issue related to the Rosenberg Self-Esteem Scale. Full article

Without assuming any functional or distributional structure, we select collections of major factors embedded within response-versus-covariate (Re-Co) dynamics via selection criteria [C1: confirmable] and [C2: irrepaceable], which are based on information theoretic measurements. The two criteria are constructed based on the computing paradigm called Categorical Exploratory Data Analysis (CEDA) and linked to Wiener–Granger causality. All the information theoretical measurements, including conditional mutual information and entropy, are evaluated through the contingency table platform, which primarily rests on the categorical nature within all involved features of any data types: quantitative or qualitative. Our selection task identifies one chief collection, together with several secondary collections of major factors of various orders underlying the targeted Re-Co dynamics. Each selected collection is checked with algorithmically computed reliability against the finite sample phenomenon, and so is each member’s major factor individually. The developments of our selection protocol are illustrated in detail through two experimental examples: a simple one and a complex one. We then apply this protocol on two data sets pertaining to two somewhat related but distinct pitching dynamics of two pitch types: slider and fastball. In particular, we refer to a specific Major League Baseball (MLB) pitcher and we consider data of multiple seasons. Full article

In 2017, Sports Illustrated (SI) made headlines when their remarkable prediction from 2014 that the Houston Astros (a team in one of the lowest Major League Baseball divisional rankings) would win the World Series, came true. The less-publicised story was that in 2017, SI predicted the Los Angeles Dodgers to win the Major League Baseball (MLB) title. Assessing the forecasting accuracy of experts is critical as it explores the difficulty and limitations of forecasts and can help illuminate how predictions may shape sociocultural notions of sport in society. To thoroughly investigate SI’s forecasting record, predictions were collected from the four major North American sporting leagues (the National Football League, National Basketball Association, Major League Baseball, and National Hockey League) over the last 30 years (1988–2018). Kruskal–Wallis H Tests and Mann–Whitney U Tests were used to evaluate the absolute and relative accuracy of predictions. Results indicated that SI had the greatest predictive accuracy in the National Basketball Association and was significantly more likely to predict divisional winners compared to conference and league champions. Future work in this area may seek to examine multiple media outlets to gain a more comprehensive perspective on forecasting accuracy in sport. Full article

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. Full article

Modern society pays more and more attention to leisure activities, and watching sports is one of the most popular activities for people. In professional leagues, sports scheduling plays a very critical role. To efficiently arrange a schedule while complying with the relevant rules in a sports league has become a challenge for schedule planners. This research uses Major League Baseball (MLB) of the year 2016 as a case study. The study proposed the Variable Neighborhood Search (VNS) algorithm with different coding structures to optimize the objective function—minimize the total travelling distance of all teams in the league. We have compared the algorithmic schedules with the 2016 and 2019 MLB regular-season schedules in the real-world case for its performance evaluation. The results have confirmed success in reducing the total travelling distances by 2.48% for 2016 and 6.02% in 2019 while lowering the standard deviation of total travelling distances by 7.06% for 2016. Full article

The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher. Full article

This case study entailed a Twitter content analysis to address the pandemic-delayed start to Major League Baseball (MLB) in the shortened 2020 season. This case study helps address the overarching objective to investigate how the sports world, especially fans, responded to MLB played during the 2020 COVID-19 pandemic. The methods investigated the common themes and determined who used predetermined Twitter hashtags. We recorded how many times external links, photos, emojis, and the 30 MLB teams were mentioned in the 779 tweets obtained during 39 days of data retrieval. Results showed that the most common category of tweeted content concerned news reports. Comparable numbers of positive and negative responses to the start of the MLB season were recognized, with a fraction of tweets highlighting COVID-19 impacts on health and modification of play (e.g., cardboard fans). The majority of Twitter users were from media and layperson categories. More inferred males tweeted using the selected hashtags. In exploratory analyses, results indicated that 50.2% of the sample included a link or a photo, and 2.2% of the sample used an emoji. The three most mentioned teams were the Cardinals (N = 51), Marlins (N = 49), and the Yankees (N = 48). The results confirmed the value of social media analysis as a research approach and revealed patterns emerging during a unique pandemic sports and media era. Full article

During the 2008 Major League Baseball (MLB) season, there was a perception that the rate at which wood bats were breaking was on the rise. MLB responded by implementing changes to the wood bat regulations that were essentially transparent to the players, e.g., changing the orientation for the hitting surface on maple bats, setting a lower bound on wood density, and reducing the allowable range for the slope of grain (SoG) of the wood used to make bats. These new regulations resulted in a 65% reduction in the wood-bat breakage rate. It is proposed that a further reduction to the multi-piece failure (MPF) rate can be realized by accounting for the role that bat profile plays with respect to bat durability. Durability is defined here as the relative bat/ball speed that results in crack initiation, i.e., the higher the breaking speed, the better the durability. The aim of the current work is to complete a parametric study to investigate if bat profile influences bat durability with respect to SoG. Three bat profiles with very different geometries and volumes are analyzed using the finite element software, LSDYNA^®. The mechanical behavior of the wood is modeled using the *MAT_WOOD material model in combination with the *MAT_ADD_EROSION option. The effective wood material properties are varied as a function of wood density and SoG. Results include how varying bat profile and SoG influences bat durability. The study is limited to maple wood bats. Full article

Winning one or two games during a Major League Baseball (MLB) season is often the difference between a team advancing to post-season play, or “waiting until next year”. Technology advances have made it feasible to augment historical data with in-game contextual data to provide managers immediate insights regarding an opponent’s next move, thereby providing a competitive edge. We developed statistical models of pitcher behavior using pitch sequences thrown during three recent MLB seasons (2011–2013). The purpose of these models was to predict the next pitch type, for each pitcher, based on data available at the immediate moment, in each at-bat. Independent models were developed for each player’s most frequent four pitches. The overall predictability of next pitch type is 74:5%. Additional analyses on pitcher predictability within specific game situations are discussed. Finally, using linear regression analysis, we show that an index of pitch sequence predictability may be used to project player performance in terms of Earned Run Average (ERA) and Fielding Independent Pitching (FIP) over a longer term. On a restricted range of the independent variable, reducing complexity in selection of pitches is correlated with higher values of both FIP and ERA for the players represented in the sample. Both models were significant at the α = 0.05 level (ERA: p = 0.022; FIP: p = 0.0114). With further development, such models may reduce risk faced by management in evaluation of potential trades, or to scouts assessing unproven emerging talent. Pitchers themselves might benefit from awareness of their individual statistical tendencies, and adapt their behavior on the mound accordingly. To our knowledge, the predictive model relating pitch-wise complexity and long-term performance appears to be novel. Full article