Matching Items (3)
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- All Subjects: Sabermetrics
- Creators: Shunk, Dan
- Creators: Casalena, Jontito
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program acquisition completion time were not explicitly identified in the simulation study. This research seeks to determine the acquisition processes that contribute significantly to total simulated program time in the acquisition system for all programs reaching Milestone C. Specifically, this research examines the effect of increased scope management, technology maturity, and decreased variation and mean process times in post-Design Readiness Review contractor activities by performing additional simulation analyses. Potential policies are formulated from the results to further improve program acquisition completion time.
ContributorsWorger, Danielle Marie (Author) / Wu, Teresa (Thesis director) / Shunk, Dan (Committee member) / Wirthlin, J. Robert (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
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
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