Relevant bibliographies by topics / Sabermetrics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Sabermetrics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Sabermetrics"

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Burroughs, Benjamin. "Statistics and Baseball Fandom: Sabermetric Infrastructure of Expertise." Games and Culture 15, no. 3 (June 19, 2018): 248–65. http://dx.doi.org/10.1177/1555412018783319.

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Baseball is a rich mélange of tradition, spectatorship, evaluation, and fandom. Statistical fandom is presented as a cultural infrastructure, which influences how all fans perceive the game including what is valued in the game, how the game itself is played, and Major League Baseball as an industry. In building off of Halverson’s conception of a fantasy plane of baseball fandom, this research theorizes an additional statistical plane. Sabermetrics serve as a microcosm for a larger statistical turn in sports and reporting. The labor of saberfans builds a cultural algorithm through statistical analysis that shapes all fan engagement. Sabermetric inputs become an infrastructure of expertise through which the larger sporting public understands and evaluates baseball and culture.
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Abisaid, Joseph L., and William P. Cassidy. "Traditional baseball statistics still dominate news stories." Newspaper Research Journal 38, no. 2 (June 2017): 158–71. http://dx.doi.org/10.1177/0739532917716170.

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This study investigates whether journalists have adopted sabermetrics, the use of advanced baseball statistics for making player projections and objectively measuring player performance, in their reporting of Major League Baseball stories since the print and theatrical release of Moneyball. Findings suggest that traditional baseball statistics still dominate baseball news reporting but the use of sabermetrics has significantly increased after Moneyball was published.
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Teodoro, Manuel P., and Jon R. Bond. "Presidents, Baseball, and Wins above Expectations: What Can Sabermetrics Tell Us about Presidential Success?" PS: Political Science & Politics 50, no. 02 (March 31, 2017): 339–46. http://dx.doi.org/10.1017/s1049096516002778.

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ABSTRACT Presidential scholars and baseball writers debate who were the greatest. While baseball analysis evolved from qualitative impressions of “experts” to rigorous, data-driven “sabermetrics,” analysis of presidential greatness continues to rely on “old-school” reputational rankings based on surveys of scholars’ qualitative assessments. Presidential-congressional relations and baseball are all about winning, but what fans (of sports and politics) find most intriguing is Wins Above Expectations (WAE)—did the team do better or worse than expected? This paper adapts the Pythagorean Expectations (PE) formula developed to analyze baseball to assess legislative success of presidents from Eisenhower to Obama. A parsimonious regression model and the PE formula predict annual success rates with 90% accuracy. The estimates of WAE from the two approaches, however, are uncorrelated. Regression analysis does not identify any president who systematically exceeded expectations, but sabermetric analysis indicates that Republican presidents outperform Democrats. Neither approach correlates with recent presidential greatness rankings.
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Soto Valero, César, and Mabel González Castellanos. "Sabermetría y nuevas tendencias en el análisis estadístico del juego de béisbol (Sabermetrics and new trends in statistical analysis of baseball)." Retos, no. 28 (March 23, 2015): 122–27. http://dx.doi.org/10.47197/retos.v0i28.34826.

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La sabermetría es reconocida actualmente como una tendencia novedosa en el estudio del juego de béisbol. Con mucho auge y utilización en el análisis empírico, esta se basa en el estudio estadístico riguroso de la evidencia objetiva obtenida durante el juego. Teniendo en cuenta tanto sus aportes teóricos como prácticos, la sabermetría se fundamenta en una constante búsqueda por comprender cómo jugar mejor y más eficientemente al béisbol, lo cual se expresa y soporta mediante un tipo de análisis de actuación único entre todos los deportes colectivos. El presente trabajo aborda los aspectos esenciales de la sabermetría, fundamentando la necesidad de su surgimiento y utilización, como una forma de perfeccionar la manera en que tradicionalmente se ha llevado a cabo el análisis estadístico en el béisbol. Además, se brinda un resumen de los estadísticos sabermétricos más utilizados, tanto de bateo y picheo como otros de valor individual para el equipo, con el propósito de hacer más clara su comprensión, estudio y posterior utilización entre los seguidores de este deporte.Abstract. Sabermetrics is recognized as a new trend in the study of baseball game. This is based on the rigorous statistical study of the objective evidence obtained and has been used extensively in its empirical analysis. Considering both theoretical and practical contributions, sabermetrics involves the constant quest of understanding how to play baseball better and more efficiently, which is expressed and supported by an exceptional type of analysis performance unique among all team sports. This paper describes the essential aspects of sabermetrics, pointing in the necessity of its emergence and use, as a way to improve the traditional statistical analysis of baseball. Moreover, a summary of the sabermetrics statistics most widely used is given. Both batting and pitching, as well as others of individual value for the team are stated throughout this work in order to make sabermetrics understanding, study and further use clearer among followers of this sport.
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Soto Valero, C. "Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods." International Journal of Computer Science in Sport 15, no. 2 (December 1, 2016): 91–112. http://dx.doi.org/10.1515/ijcss-2016-0007.

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Abstract Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.
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Costa, Gabriel B., and John T. Saccoman. "THE KEYSTONE COMBINATION: TEAM TEACHING A SABERMETRICS COURSE." PRIMUS 7, no. 3 (January 1997): 213–21. http://dx.doi.org/10.1080/10511979708965862.

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Kwon, Soon-Gyu, Kyu-Won Lee, and Hyong-Jun Choi. "2016~2018 Korean professional baseball Sabermetrics Index Analysis." Korean Journal of Sports Science 28, no. 3 (June 30, 2019): 1015–23. http://dx.doi.org/10.35159/kjss.2019.06.28.3.1015.

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Talsma, Gary. "Data Analysis and Baseball." Mathematics Teacher 92, no. 8 (November 1999): 738–42. http://dx.doi.org/10.5951/mt.92.8.0738.

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An investigation that leads to one of the significant contributions that sabermetrics has made to our understanding of baseball. Along the way, we illustrate the application of several principles of data analysis (Moore 1995, 95) in a context that is familiar to, and motivating for, many of our students.
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Yule, Steven, Allison Janda, and Donald S. Likosky. "Surgical Sabermetrics: Applying Athletics Data Science to Enhance Operative Performance." Annals of Surgery Open 2, no. 2 (March 29, 2021): e054. http://dx.doi.org/10.1097/as9.0000000000000054.

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Middleton, Justin, Emerson Murphy-Hill, and Kathryn T. Stolee. "Data Analysts and Their Software Practices: A Profile of the Sabermetrics Community and Beyond." Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (May 28, 2020): 1–27. http://dx.doi.org/10.1145/3392859.

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Dissertations / Theses on the topic "Sabermetrics"

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Chernoff, Parker. "Sabermetrics - Statistical Modeling of Run Creation and Prevention in Baseball." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3663.

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The focus of this thesis was to investigate which baseball metrics are most conducive to run creation and prevention. Stepwise regression and Liu estimation were used to formulate two models for the dependent variables and also used for cross validation. Finally, the predicted values were fed into the Pythagorean Expectation formula to predict a team’s most important goal: winning. Each model fit strongly and collinearity amongst offensive predictors was considered using variance inflation factors. Hits, walks, and home runs allowed, infield putouts, errors, defense-independent earned run average ratio, defensive efficiency ratio, saves, runners left on base, shutouts, and walks per nine innings were significant defensive predictors. Doubles, home runs, walks, batting average, and runners left on base were significant offensive regressors. Both models produced error rates below 3% for run prediction and together they did an excellent job of estimating a team’s per-season win ratio.
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Johnson, Kyle. "Growing Acceptance of Sabermetrics in Newspapers: A Look into How Advanced Statistics Enter the Baseball Parlance." Thesis, The University of Arizona, 2014. http://hdl.handle.net/10150/320188.

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Libsch, Anton I. "Predicting Average Annual Value of Free Agent Contracts in Major League Baseball." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1239.

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This project uses multiple linear regression to predict the value of Major League Baseball free agent contracts, inspired by the low volume of published research on this topic. I found one published paper that shared my research goal but its predictive power needed improvement. An in depth comparison of our models is carried out with k-fold cross validation mean square prediction error being used as the main standard. The predictor variables considered in my models were related to performance evaluation and position, and the response variable was inflation-adjusted average annual value of the contract. The result of the project is two linear regression models, one for hitters and one for pitchers.
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Yeager, Joshua. "Height, Weight, and Durability in Major League Baseball." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1684.

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Using data from the 2000-2016 Major League Baseball seasons, this paper looks at the determinants of durability amongst baseball athletes, durability is measured in games played for batters and innings pitched for pitchers, with a particular focus on height and weight. This paper finds evidence that lighter, shorter batters play significantly more games than taller, heavier batters. Additionally, amongst pitchers, there is only circumstantial evidence that height and weight are important determining factors of player durability. Finally, I find that starting pitchers increase the likelihood of becoming injured in the regular season by 10.3% and 21.8% if their hits per nine innings and home runs per nine inning totals, respectively increase by one.
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Nehm, Eric. "WHIP, BABIP, and FIP: the role of radio broadcasters in the diffusion of advanced statistics in Major League Baseball broadcasts." Thesis, Kansas State University, 2015. http://hdl.handle.net/2097/20545.

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Master of Science
Department of Journalism and Mass Communications
Barbara DeSanto
The purpose of this study was to take a closer look at the use of advanced statistics in Major League Baseball (MLB) radio broadcasts and the possible effects usage could have on the roles of MLB broadcasters. This study used a theoretical framework supported by role theory and diffusion of innovations theory. The study employed in-depth interviews of eight MLB radio broadcasters to get a better understanding of the broadcasters’ thoughts on the value of advanced statistics, how using advanced statistics could affect their role as a broadcaster, the factors that influence the potential inclusion of advanced statistics in their broadcasts, and ultimately, whether broadcasters perceived advanced statistics as a trend or a fad. The interviews revealed a number of things about the baseball broadcasting industry with disagreements appearing in regards to techniques in using advanced statistics in broadcasts and the value of advanced statistics to listeners. Despite the disagreements, the most important finding was the unanimous belief that advanced statistics are not a fad, but rather something that will remain a part of baseball broadcasts going forward.
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Estes, Brent Cullen James Jeffrey D. "Slugger or slacker a sabermetric assessment of free agency on major league baseball player performance /." Diss., 2006. http://etd.lib.fsu.edu/theses/available/04062006-125546.

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Thesis (Ph. D.)--Florida State University, 2006.
Advisor: Jeff James, Florida State University, College of Education, Dept. of Sport Management, Recreation Management and Physical Education. Title and description from dissertation home page (viewed June 13, 2006). Document formatted into pages; contains ix, 131pages. Includes bibliographical references.
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Books on the topic "Sabermetrics"

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1960-, Huber Michael R., and Saccoman John T. 1964-, eds. Reasoning with sabermetrics: Applying statistical science to baseball's tough questions. Jefferson, N.C: McFarland & Co., 2012.

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Costa, Gabriel B. Practicing sabermetrics: Putting the science of baseball statistics to work. Jefferson, N.C: McFarland & Company, Inc., Publishers, 2009.

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1960-, Huber Michael R., and Saccoman John T. 1964-, eds. Understanding sabermetrics: An introduction to the science of baseball statistics. Jefferson, N.C: McFarland & Co., 2008.

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Costa, Gabriel B. Practicing sabermetrics: Putting the science of baseball statistics to work. Jefferson, N.C: McFarland & Company, Inc., Publishers, 2009.

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1960-, Huber Michael R., and Saccoman John T. 1964-, eds. Practicing sabermetrics: Putting the science of baseball statistics to work. Jefferson, N.C: McFarland & Company, Inc., Publishers, 2009.

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All-stars for all time: A sabermetric ranking of the major league best, 1876-2007. Jefferson, N.C: McFarland & Co., 2008.

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Backstop: A history of the catcher and a sabermetric ranking of 50 all-time greats. Jefferson, N.C: McFarland & Co., 2006.

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Hole Truth: Determining the Greatest Players in Golf Using Sabermetrics. University of Nebraska Press, 2019.

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Costa, Gabriel B. Sabermetrics: Baseball, Steroids, and How the Game Has Changed over the Past Two Generations. Elsevier Science & Technology Books, 2021.

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Castrovince, Anthony. Fan's Guide to Baseball Analytics: Why WAR, WHIP, WOBA, and Other Advanced Sabermetrics Are Essential to Understanding Modern Baseball. Sports Publishing, 2020.

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Book chapters on the topic "Sabermetrics"

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Bronson, Richard, and Gabriel B. Costa. "Sabermetrics – An introduction." In Matrix Methods, 365–74. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-818419-6.00011-3.

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Bronson, Richard, and Gabriel B. Costa. "Sabermetrics – A module." In Matrix Methods, 375–80. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-12-818419-6.00012-5.

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"7. Estimating the Impact of Sabermetrics." In The Sabermetric Revolution, 115–36. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.115.

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"7. Evaluating Fielders Sabermetrics’ Last Frontier." In Mathletics, 64–70. Princeton: Princeton University Press, 2012. http://dx.doi.org/10.1515/9781400842070.64.

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Cassuto, Leonard, and David Grant. "Babe Ruth, sabermetrics, and baseball’s politics of greatness." In The Cambridge Companion to Baseball, 33–48. Cambridge University Press, 2011. http://dx.doi.org/10.1017/ccol9780521761826.004.

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"1. Revisiting Moneyball." In The Sabermetric Revolution, 1–22. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.1.

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"6. Analytics and the Business of Baseball." In The Sabermetric Revolution, 102–14. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.102.

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"The Expected run Matrix." In The Sabermetric Revolution, 139–40. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.139.

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"Modeling the Effectiveness of Sabermetric Statistics." In The Sabermetric Revolution, 141–54. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.141.

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"Notes." In The Sabermetric Revolution, 155–78. University of Pennsylvania Press, 2014. http://dx.doi.org/10.9783/9780812209129.155.

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Conference papers on the topic "Sabermetrics"

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Jang, Wu-In, and Young-Ho Park. "A Database Design for Selecting a Golden Glove Winner using Sabermetrics." In Information Science and Industrial Applications 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.110.08.

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Okuda, Takashi. "Assessing knowledge worker outcome: Performance assessment using a method inspired by baseball and sabermetrics." In 2010 IEEE International Professional Communication Conference (IPCC 2010). IEEE, 2010. http://dx.doi.org/10.1109/ipcc.2010.5530015.

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