Matching Items (4)
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- All Subjects: Renaissance
- All Subjects: Statistical Analysis
- Creators: Davulcu, Hasan
- Creators: Bryan, Chris
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
DescriptionA look into the historical significance of clothing and clothing construction to self-fashioning.
ContributorsLee, Elizabeth Kristina (Author) / Facinelli, Diane (Thesis director) / Ryner, Bradley (Committee member) / TerBeek, Kendra (Committee member) / Barrett, The Honors College (Contributor) / Department of English (Contributor)
Created2013-05
ContributorsMattson, Arron Phillip (Author) / Adams, Valerie (Thesis director) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Barrett, The Honors College (Contributor)
Created2013-05
Description
Current popular NBA mobile applications do little to provide information about the NBA's players, usually providing limited statistical information or news and completely ignoring players' presence on social media. For fans, especially fans who are unfamiliar with the NBA, finding this information by themselves can be a daunting task, one which requires extensive knowledge about how the NBA provides media related to its players. NBA PlayerTrack has been designed to centralize player information from a variety of media streams, making it easier for fans to learn about and stay up-to-date with players and enabling fan discussion about those players and the NBA in general. By providing a variety of references to the locations of player information, NBA PlayerTrack also serves as a tool for learning about how and where the NBA presents player-related media, allowing fans to more easily locate information they desire as they become more invested in the NBA.
ContributorsSethia, Sumbhav (Author) / Davulcu, Hasan (Thesis director) / Faucon, Philippe (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
This thesis serves as a baseline for the potential for prediction through machine learning (ML) in baseball. Hopefully, it also will serve as motivation for future work to expand and reach the potential of sabermetrics, advanced Statcast data and machine learning. The problem this thesis attempts to solve is predicting the outcome of a pitch. Given proper pitch data and situational data, is it possible to predict the result or outcome of a pitch? The result or outcome refers to the specific outcome of a pitch, beyond ball or strike, but if the hitter puts the ball in play for a double, this thesis shows how I attempted to predict that type of outcome. Before diving into my methods, I take a deep look into sabermetrics, advanced statistics and the history of the two in Major League Baseball. After this, I describe my implemented machine learning experiment. First, I found a dataset that is suitable for training a pitch prediction model, I then analyzed the features and used some feature engineering to select a set of 16 features, and finally, I trained and tested a pair of ML models on the data. I used a decision tree classifier and random forest classifier to test the data. I attempted to us a long short-term memory to improve my score, but came up short. Each classifier performed at around 60% accuracy. I also experimented using a neural network approach with a long short-term memory (LSTM) model, but this approach requires more feature engineering to beat the simpler classifiers. In this thesis, I show examples of five hitters that I test the models on and the accuracy for each hitter. This work shows promise that advanced classification models (likely requiring more feature engineering) can provide even better prediction outcomes, perhaps with 70% accuracy or higher! There is much potential for future work and to improve on this thesis, mainly through the proper construction of a neural network, more in-depth feature analysis/selection/extraction, and data visualization.
ContributorsGoodman, Avi (Author) / Bryan, Chris (Thesis director) / Hsiao, Sharon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05