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While former New York Yankees pitcher Goose Gossage unleashed his tirade on the deterioration of the unwritten rules of baseball and nerds ruining the sport about halfway through my writing of the paper, sentiments like his were inspiration for my topic: the evolution of statistics and data in baseball. By telling the story of how baseball data and statistics have evolved, my goal was to also demonstrate how they have been intertwined since the beginning—which would essentially mean that nerds have always been ruining the sport (if you subscribe to that kind of thought).
In the quest to showcase this, it was necessary to document how baseball prospers from numbers and numbers prosper from baseball. The relationship between the two is mutualistic. Furthermore, an all-encompassing historical look at how data and statistics in baseball have matured was a critical portion of the paper. With a metric such as batting average going from a radical new measure that posed a threat to the status quo, to a fiercely cherished statistic that was suddenly being unseated by advanced analytics, it shows the creation of new and destruction of old has been incessant. Innovators like Pete Palmer, Dick Cramer and Bill James played a large role in this process in the 1980s. Computers aided their effort and when paired with the Internet, unleashed the ability to crunch data to an even larger sector of the population. The unveiling of Statcast at the commencement of the 2015 season showed just how much potential there is for measuring previously unquantifiable baseball acts.
Essentially, there will always be people who mourn the presence of data and statistics in baseball. Despite this, the evolution story indicates baseball and numbers will be intertwined into the future, likely to an even greater extent than ever before, as technology and new philosophies become increasingly integrated into front offices and clubhouses.
In the quest to showcase this, it was necessary to document how baseball prospers from numbers and numbers prosper from baseball. The relationship between the two is mutualistic. Furthermore, an all-encompassing historical look at how data and statistics in baseball have matured was a critical portion of the paper. With a metric such as batting average going from a radical new measure that posed a threat to the status quo, to a fiercely cherished statistic that was suddenly being unseated by advanced analytics, it shows the creation of new and destruction of old has been incessant. Innovators like Pete Palmer, Dick Cramer and Bill James played a large role in this process in the 1980s. Computers aided their effort and when paired with the Internet, unleashed the ability to crunch data to an even larger sector of the population. The unveiling of Statcast at the commencement of the 2015 season showed just how much potential there is for measuring previously unquantifiable baseball acts.
Essentially, there will always be people who mourn the presence of data and statistics in baseball. Despite this, the evolution story indicates baseball and numbers will be intertwined into the future, likely to an even greater extent than ever before, as technology and new philosophies become increasingly integrated into front offices and clubhouses.
ContributorsGarcia, Jacob Michael (Author) / Kurland, Brett (Thesis director) / Doig, Stephen (Committee member) / Jackson, Victoria (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Beginning with the publication of Moneyball by Michael Lewis in 2003, the use of sabermetrics \u2014 the application of statistical analysis to baseball records - has exploded in major league front offices. Executives Billy Beane, Paul DePoedesta, and Theo Epstein are notable figures that have been successful in incorporating sabermetrics to their team's philosophy, resulting in playoff appearances and championship success. The competitive market of baseball, once dominated by the collusion of owners, now promotes innovative thought to analytically develop competitive advantages. The tiered economic payrolls of Major League Baseball (MLB) has created an environment in which large-market teams are capable of "buying" championships through the acquisition of the best available talent in free agency, and small-market teams are pushed to "build" championships through the drafting and systematic farming of high-school and college level players. The use of sabermetrics promotes both models of success \u2014 buying and building \u2014 by unbiasedly determining a player's productivity. The objective of this paper is to develop a regression-based predictive model that can be used by Majors League Baseball teams to forecast the MLB career average offensive performance of college baseball players from specific conferences. The development of this model required multiple tasks: I. Data was obtained from The Baseball Cube, a baseball records database providing both College and MLB data. II. Modifications to the data were applied to adjust for year-to-year formatting, a missing variable for seasons played, the presence of missing values, and to correct league identifiers. III. Evaluation of multiple offensive productivity models capable of handling the obtained dataset and regression forecasting technique. IV. SAS software was used to create the regression models and analyze the residuals for any irregularities or normality violations. The results of this paper find that there is a relationship between Division 1 collegiate baseball conferences and average career offensive productivity in Major Leagues Baseball, with the SEC having the most accurate reflection of performance.
ContributorsBadger, Mathew Bernard (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / Department of Economics (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Over the past several decades, analytics have become more and more prevalent in the game of baseball. Statistics are used in nearly every facet of the game. Each team develops its own processes, hoping to gain a competitive advantage over the rest of the league. One area of the game that has struggled to produce definitive analytics is amateur scouting. This project seeks to resolve this problem through the creation of a new statistic, Valued Plate Appearance Index (VPI). The problem is identified through analysis that was performed to determine whether any correlation exists between performances at the country's top amateur baseball league, the Cape Cod League, and performances in Major League Baseball. After several stats were analyzed, almost no correlation was determined between the two. This essentially means that teams have no way to statistically analyze Cape Cod League performance and project future statistics. An inherent contextual error in these amateur statistics prevents them from correlating. The project seeks to close that contextual gap and create concrete, encompassing values to illustrate a player's offensive performance in the Cape League. To solve for this problem, data was collected from the 2017 CCBL season. In addition to VPI, Valued Plate Appearance Approach (VPA) and Valued Plate Appearance Result (VPR) were created to better depict a player's all-around performance in each plate appearance. VPA values the quality of a player's approach in each plate appearance. VPR values the quality of the contact result, excluding factors out of the hitter's control. This statistic isolates player performance as well as eliminates luck that cannot normally be taken into account. This paper results in the segmentation of players from the 2017 CCBL into four different groups, which project how they will perform as they transition into professional baseball. These groups and the creation of these statistics could be essential tools in the evaluation and projection of amateur players by Major League clubs for years to come.
ContributorsLothrop, Joseph Kent (Author) / Eaton, John (Thesis director) / McIntosh, Daniel (Committee member) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The widespread use of statistical analysis in sports-particularly Baseball- has made it increasingly necessary for small and mid-market teams to find ways to maintain their analytical advantages over large market clubs. In baseball, an opportunity for exists for teams with limited financial resources to sign players under team control to long-term contracts before other teams can bid for their services in free agency. If small and mid-market clubs can successfully identify talented players early, clubs can save money, achieve cost certainty and remain competitive for longer periods of time. These deals are also advantageous to players since they receive job security and greater financial dividends earlier in their career. The objective of this paper is to develop a regression-based predictive model that teams can use to forecast the performance of young baseball players with limited Major League experience. There were several tasks conducted to achieve this goal: (1) Data was obtained from Major League Baseball and Lahman's Baseball Database and sorted using Excel macros for easier analysis. (2) Players were separated into three positional groups depending on similar fielding requirements and offensive profiles: Group I was comprised of first and third basemen, Group II contains second basemen, shortstops, and center fielders and Group III contains left and right fielders. (3) Based on the context of baseball and the nature of offensive performance metrics, only players who achieve greater than 200 plate appearances within the first two years of their major league debut are included in this analysis. (4) The statistical software package JMP was used to create regression models of each group and analyze the residuals for any irregularities or normality violations. Once the models were developed, slight adjustments were made to improve the accuracy of the forecasts and identify opportunities for future work. It was discovered that Group I and Group III were the easiest player groupings to forecast while Group II required several attempts to improve the model.
ContributorsJack, Nathan Scott (Author) / Shunk, Dan (Thesis director) / Montgomery, Douglas (Committee member) / Borror, Connie (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
Description
The story of Moneyball is an informative tale. It is the true story of the Oakland Athletics baseball team in the 2002 season who managed to not only compete with teams who had nearly three times the payroll size and all the star players, but also won an American League record 20 games in a row. Their manager, Billy Beane, was able to achieve this by using sabermetrics, a newly invented term that describes the advanced statistics and metrics used to judge a player's contribution to the success of the team over traditional statistics and gut feeling, to draft and trade for undervalued players to create a competitive team under his small-market budget. This story is well known as a best-selling novel by Michael Lewis and later a film by the same name. Clearly it was successful in the field of baseball, but can it be used in other business industries? The idea of sabermetrics, or finding more information to predict the future of a player is very similar to the ideas of Information Measurement Theory (IMT) as theorized by Dr. Dean Kashiwagi, a professor at Arizona State University. The goal of this paper is to use Moneyball as a narrative to show how applying the concepts of IMT to businesses could allow them to better predict their performance and the future of their industry. Moreover, these same ideas can predict if the leadership of the company will be successful by analyzing their personal characteristics. This paper will act as a guide for businesses to start following the concepts of IMT and to better analyze themselves and their industry to increase performance and reduce stressful decision-making.
ContributorsKent, Austin (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This project looks at the change in strikeout patterns over the past 19 years of Major League Baseball. New research in 2001 revolutionized the pitching statistics field, and non-coincidentally, the number of strikeouts has ballooned since then. I first detail the statistical nature of the increase, looking at where the additional strikeouts are coming from. Then, a discussion of why this has happened, referencing changes in baseball strategy and talent usage optimization follows. The changes in the ways MLB teams use their pitching staffs are largely the cause of this increase. Similar research is cited to confirm that these strategy changes are valid and are having the effect of increasing strikeouts in the game. Strikeout numbers are then compared to other pitching statistics over the years to determine whether the increase has had any effect on other pitching metrics. Lastly, overall team success is looked at as a verification method as to whether the increased focus on increasing strikeouts has created positive results for major league teams. Teams making the MLB playoffs consistently ranked much higher than non-qualifying teams in terms of strikeout rates. Also included in the project are the details of data acquisition and manipulation, to ensure the figures used are valid. Ideas for future research and further work on the topic are included, as the amount of data available in this field is quite staggering. Further analysis could dive into the ways pitches themselves are changing, rather than looking at pitching outcomes. Overall, the project details and explains a major shift in the way baseball has been played over the last 19 years, complete with both pure data analysis and supplementary commentary and explanation
ContributorsCasalena, Jontito (Author) / Doig, Stephen (Thesis director) / Pomrenke, Jacob (Committee member) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05