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I, Christopher Negrich, am the sole author of this paper, but the tools described were designed in collaboration with Andrew Hoetker. ConstrictR (constrictor) and ConstrictPy are an R package and python tool designed together. ConstrictPy implements the functions and methods defined in ConstrictR and applies data handling, data parsing, input/output (I/O), and a user interface to increase usability. ConstrictR implements a variety of common data analysis methods used for statistical and subnetwork analysis. The majority of these methods are inspired by Lionel Guidi's 2016 paper, Plankton networks driving carbon export in the oligotrophic ocean. Additional methods were added to expand functionality, usability, and applicability to different areas of data science. Both ConstrictR and ConstrictPy are currently publicly available and usable, however, they are both ongoing projects. ConstrictR is available at github.com/cnegrich and ConstrictPy is available at github.com/ahoetker. Currently, ConstrictR has implemented functions for descriptive statistics, correlation, covariance, rank, sparsity, and weighted correlation network analysis with clustering, centrality, profiling, error handling, and data parsing methods to be released soon. ConstrictPy has fully implemented and integrated the features in ConstrictR as well as created functions for I/O and conversion between pandas and R data frames with a full feature user interface to be released soon. Both ConstrictR and ConstrictPy are designed to work with minimal dependencies and maximum available information on the algorithms implemented. As a result, ConstrictR is only dependent on base R (v3.4.4) functions with no libraries imported. ConstrictPy is dependent upon only pandas, Rpy2, and ConstrictR. This was done to increase longevity and independence of these tools. Additionally, all mathematical information is documented alongside the code, increasing the available information on how these tools function. Although neither tool is in its final version, this paper documents the code, mathematics, and instructions for use, in addition to plans for future work, for of the current versions of ConstrictR (v0.0.1) and ConstrictPy (v0.0.1).
ContributorsNegrich, Christopher Alec (Author) / Can, Huansheng (Thesis director) / Hansford, Dianne (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
“Social Sports is an application which facilitates the environment fans need to support their teams, in doing so our application aids hospitality businesses market their events and brings business during their downtime. Social Sports allows businesses to market their sports screening events to fans and supporters. Fans and supporters using Social Sports are able to see the percentage of supporters/fans on each side and decide which bar or restaurant to go watch the game. Social Sport’s mission is to connect sports fans with other like minded passionate fans and enable community formation and allow sports fans around the world to socialize with much ease.”
ContributorsWood, Alexander (Author) / Rodin, Dawson (Co-author) / Bhargana, Akshat (Co-author) / Cheshire, Ashley (Co-author) / Fuller, Sarah (Co-author) / Byrne, Jared (Thesis director) / Thomasson, Anna (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2023-05
Description
In baseball, a starting pitcher has historically been a more durable pitcher capable of lasting long into games without tiring. For the entire history of Major League Baseball, these pitchers have been expected to last 6 innings or more into a game before being replaced. However, with the advances in statistics and sabermetrics and their gradual acceptance by professional coaches, the role of the starting pitcher is beginning to change. Teams are experimenting with having starters being replaced quicker, challenging the traditional role of the starting pitcher. The goal of this study is to determine if there is an exact point at which a team would benefit from replacing a starting or relief pitcher with another pitcher using statistical analyses. We will use logistic stepwise regression to predict the likelihood of a team scoring a run if a substitution is made or not made given the current game situation.
ContributorsBuckley, Nicholas J (Author) / Samara, Marko (Thesis director) / Lanchier, Nicolas (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
Description
This honors thesis explores and models the flow of air around a cylindrical arrow that is rotating as it moves through the air. This model represents the airflow around an archery arrow after it is released from the bow and rotates while it flies through the air. This situation is important in archery because an understanding of the airflow allows archers to predict the flight of the arrow. As a result, archers can improve their accuracy and ability to hit targets. However, not many computational fluid dynamic simulations modeling the airflow around a rotating archery arrow exist. This thesis attempts to further the understanding of the airflow around a rotating archery arrow by creating a mathematical model to numerically simulate the airflow around the arrow in the presence of this rotation. This thesis uses a linearized approximation of the Navier Stokes equations to model the airflow around the arrow and explains the reasoning for using this simplification of the fully nonlinear Navier Stokes equations. This thesis continues to describe the discretization of these linearized equations using the finite difference method and the boundary conditions used for these equations. A MATLAB code solves the resulting system of equations in order to obtain a numerical simulation of this airflow around the rotating arrow. The results of the simulation for each velocity component and the pressure distribution are displayed. This thesis then discusses the results of the simulation, and the MATLAB code is analyzed to verify the convergence of the solution. Appendix A includes the full MATLAB code used for the flow simulation. Finally, this thesis explains potential future research topics, ideas, and improvements to the code that can help further the understanding and create more realistic simulations of the airflow around a flying archery arrow.
ContributorsCholinski, Christopher John (Author) / Tang, Wenbo (Thesis director) / Herrmann, Marcus (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Basketball has evolved and is continuing to evolve in parallel with media and communication. The 21st century bears witness to the digitization of basketball, media, and communication with the advent of social media. Arguably the most esteemed professional basketball league in the world, the National Basketball Association (NBA) observes fans and players alike conversing about the game through social media platforms available across the world. One of the most popular platforms, Twitter, enables anyone with a computer to write a textual post known as a “tweet” that can be made viewable to the public. The Twitter landscape holds a trove of data and information including “sentiment” for NBA teams to analyze with the goal of improving the success of their team from a managerial perspective. Two aspects this paper will examine are fan engagement and revenue generation from the perspective of several franchises in the NBA. The purpose of this research is to explore and discover if key measures of performance including both the number of points scored in a game and the game outcome either being a win or a loss, and the location of a game being won either at home or away on the road influence fan Twitter sentiment and if there is a correlation between fan Twitter sentiment and game attendance. The statistical computing tool RStudio in combination with data compiled from online databases and websites including Basketball Reference, Wikipedia, ESPN, and Statista are employed to execute two t-tests, two analysis of variance (ANOVA) tests, and one correlation test. The results indicate there is a significant difference in fan Twitter sentiment between high-scoring games and low-scoring games, between game wins and losses, among games being won at home versus away on the road, and there is no conclusion that can be made regarding any existing correlation between fan Twitter sentiment and game attendance.
ContributorsKwan, Matthew (Author) / McIntosh, Daniel (Thesis director) / Eaton, John (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2022-05
Description
In the basketball world, perhaps one of the most sought-after feelings is that of momentum. Basketball players, coaches, analysts, and fans alike are all too familiar with the idea that a “team has momentum” during a stretch of time, or that the team needs to do something to “generate their own momentum”. In a game that appears to be an accumulation of independent possessions, what exactly does momentum really mean? My goal was to see if there is a way to quantify momentum in an NBA game, particularly by looking at the Phoenix Suns 2021-2022 NBA season.
ContributorsRao, Ansh (Author) / Schneider, Laurence (Thesis director) / McIntosh, Daniel (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Information Systems (Contributor)
Created2022-05