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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

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
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Social media is used by people every day to discuss the nuances of their lives. Major League Baseball (MLB) is a popular sport in the United States, and as such has generated a great deal of activity on Twitter. As fantasy baseball continues to grow in popularity, so does the

Social media is used by people every day to discuss the nuances of their lives. Major League Baseball (MLB) is a popular sport in the United States, and as such has generated a great deal of activity on Twitter. As fantasy baseball continues to grow in popularity, so does the research into better algorithms for picking players. Most of the research done in this area focuses on improving the prediction of a player's individual performance. However, the crowd-sourcing power afforded by social media may enable more informed predictions about players' performances. Players are chosen by popularity and personal preferences by most amateur gamblers. While some of these trends (particularly the long-term ones) are captured by ranking systems, this research was focused on predicting the daily spikes in popularity (and therefore price or draft order) by comparing the number of mentions that the player received on Twitter compared to their previous mentions. In doing so, it was demonstrated that improved fantasy baseball predictions can be made through leveraging social media data.
ContributorsRuskin, Lewis John (Author) / Liu, Huan (Thesis director) / Montgomery, Douglas (Committee member) / Morstatter, Fred (Committee member) / Industrial, Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description

For my creative project/thesis, I gave a fully rehearsed, fully performed hour long recital using rare baseball music I researched, hunted down, studied, practiced, and then performed in a recital setting. I used my long history with and personal knowledge of Baseball, as well as my newly studied knowledge of

For my creative project/thesis, I gave a fully rehearsed, fully performed hour long recital using rare baseball music I researched, hunted down, studied, practiced, and then performed in a recital setting. I used my long history with and personal knowledge of Baseball, as well as my newly studied knowledge of and newly acquired skills with Musical Theater, Opera, and Voice to make a project that celebrated both my past achievements and what I learned with my performance degree these last 4 years. I, in total, learned 16 new songs and performed each of them back to back to back, with breaks in between each set and an intermission, as well as brief histories and summaries of each song or each song set. I then performed the recital on February 25th in the ASU School of Music Recital Hall, and invited as many friends, peers, colleagues, and family members as I could to attend, while also sharing the streaming and subsequent recording online as well. I was accompanied by pianist Stephen Kuebelbeck on piano, and the two of us spent hours upon hours rehearsing in addition to performing the recital itself. My thesis director, Carole FitzPatrick, helped me with all the vocal technique, song selection, memorization, recital approach, and planning out the logistics of my recital, while Dr. Kay Norton helped me with research such as song selection, history of the pieces, history of the composers, and historical context of the pieces. While this is an unconventional project, I feel like it best reflects my unconventional major. It gives me both advanced knowledge on a niche in my field of performance, provides me with rehearsed music that I love and can use and carry forward into most any concert or performance setting, and provides me with personal artistic satisfaction by combining together two worlds I dearly love and am a part of, in a creative way. It also gives me the irreplaceable experience of putting together my own recital (completely outside of class and on my own time), as recital performances will hopefully become a regular part of my life as a singing performer.

ContributorsLadley, Edward (Author) / FitzPatrick, Carole (Thesis director) / Norton, Kay (Committee member) / Barrett, The Honors College (Contributor) / Performance (Music Theatre) (Contributor) / Kuebelbeck, Stephen (Musician)
Created2022-05
Description
In 2015, a new way to track baseball games was introduced to MLB, marking the beginning of the Statcast Revolution. This new way to track the game brought about a number of new statistics, including the use of expected statistics. Expected statistics provide an estimate of what a player’s statistics

In 2015, a new way to track baseball games was introduced to MLB, marking the beginning of the Statcast Revolution. This new way to track the game brought about a number of new statistics, including the use of expected statistics. Expected statistics provide an estimate of what a player’s statistics should be on average with their same actions. This will be explored more in the upcoming paper. While expected statistics are not intended to predict the future performance of players, I theorized that there may be some relation, particularly on younger players. There is not any research on this topic yet, and if there does exist a correlation between expected statistics and future performance, it would allow teams to have a new way to predict data on their players. Research to find a correlation between the two was carried out by taking predictive accuracies of expected batting average and slugging of 12 MLB players throughout their rookie to 8th year seasons and combining them together to find an interval in which I could be confident the correlation lay. Overall, I found that I could not be certain that there was a correlation between the predictive accuracy of expected statistics and the length of time a player has played in MLB. While this conclusion does not offer any insights of how to better predict a player’s future performance, the methodology and findings still present opportunities to gain a better understanding of the predictive measures of expected statistics.
ContributorsEdmiston, Alexander (Author) / Pavlic, Theodore (Thesis director) / Montgomery, Douglas (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor)
Created2024-05