With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to reduce recidivism. Attempts have been made, but they ultimately failed. Recently, however, there has been an increase in experimentation with the concept of teaching inmates basic computer skills to reduce recidivism. As labor becomes increasingly digitized, it becomes more difficult for inmates who spent a certain period away from technology to adapt and find employment. At the bare minimum, anybody entering the workforce must know how to use a computer and other technological appliances, even in the lowest-paid positions. By incorporating basic computer skills and coding educational programs within prisons, this issue can be addressed, since inmates would be better equipped to take on a more technologically advanced labor market.<br/>Additionally, thoroughly preparing inmates for employment is a necessity because it has been proven to reduce recidivism. Prisons typically have some work programs; however, these programs are typically outdated and prepare inmates for fields that may represent a difficult employment market moving forward. On the other hand, preparing inmates for tech-related fields of work is proving to be successful in the early stages of experimentation. A reason for this success is the growing demand. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent between 2019 and 2029. This is noteworthy considering the national average for growth of all other jobs is only 4 percent. It also warrants the exploration of educating coders because software developers, in particular, have an expected growth rate of 22 percent between 2019 and 2029. <br/>Despite the security risks of giving inmates access to computers, the implementation of basic computer skills and coding in prisons should be explored further. Programs that give inmates access to a computing education already exist. The only issue with these programs is their scarcity. However, this is to no fault of their own, considering the complex nature and costs of running such a program. Accordingly, this leaves the opportunity for public universities to get involved. Public universities serve as perfect hosts because they are fully capable of leveraging the resources already available to them. Arizona State University, in particular, is a more than ideal candidate to spearhead such a program and serve as a model for other public universities to follow. Arizona State University (ASU) is already educating inmates in local Arizona prisons on subjects such as math and English through their PEP (Prison Education Programming) program.<br/>This thesis will focus on Arizona specifically and why this would benefit the state. It will also explain why Arizona State University is the perfect candidate to spearhead this kind of program. Additionally, it will also discuss why recidivism is detrimental and the reasons why formerly incarcerated individuals re-offend. Furthermore, it will also explore the current measures being taken in Arizona and their limitations. Finally, it will provide evidence for why programs like these tend to succeed and serve as a proposal to Arizona State University to create its own program using the provided framework in this thesis.

In the early years of the National Football League, scouting and roster development resembled the wild west. Drafts were held in hotel ballrooms the day after the last game of regular season college football was played. There was no combine, limited scouting, and no salary cap. Over time, these aspects have changed dramatically, in part due to key figures from Pete Rozelle to Gil Brandt to Bill Belichick. The development and learning from this time period have laid the foundational infrastructure that modern roster construction is based upon. In this modern day, managing a team and putting together a roster involves numerous people, intense scouting, layers of technology, and, critically, the management of the salary cap. Since it was first put into place in 1994, managing the cap has become an essential element of building and sustaining a successful team. The New England Patriots’ mastery of the cap is a large part of what enabled their dynastic run over the past twenty years. While their model has undoubtedly proven to be successful, an opposing model has become increasingly popular and yielded results of its own. Both models center around different distributions of the salary cap, starting with the portion paid to the starting quarterback. The Patriots dynasty was, in part, made possible due to their use of both models over the course of their dominance. Drafting, organizational culture, and coaching are all among the numerous critical factors in determining a team’s success and it becomes difficult to pinpoint the true source of success for any given team. Ultimately, however, effective management of the cap proves to be a force multiplier; it does not guarantee that a team will be successful, but it helps teams that handle the other variables well sustain their success.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.

College athletics are a multi-billion dollar industry featuring hard-working student-athletes competing at a high level for national championships across a variety of different sports. Across the college sports landscape, coaches and players are always seeking an edge they can gain in order to obtain a competitive advantage over their opponents. While this may sound nefarious, the vast amounts of data about these games and student-athletes can be used to glean insights about the sports themselves in order to help student-athletes be more successful. Data analytics can be used to make sense of the available data by creating models and using other tools available that can predict how student-athletes and their teams will do in the future based on the data gathered from how they have performed in the past. Colleges and universities across the country compete in a vast array of sports. As a result of these differences, the sports with the largest amounts of data available will be the more popular college sports, such as football, men’s and women’s basketball, baseball and softball. Arizona State University, as a member of the Pac-12 conference, has a storied athletic tradition and decades of history in all of these sports, providing a large amount of data that can be used to analyze student-athlete success in these sports and help predict future success. However, data is available from numerous other college athletic programs that could provide a much larger sample to help predict with greater accuracy why certain teams and student-athletes are more successful than others. The explosion of analytics across the sports world has resulted in a new focus on utilizing statistical techniques to improve all aspects of different sports. Sports science has influenced medical departments, and model-building has been used to determine optimal in-game strategy and predict the outcomes of future games based on team strength. It is this latter approach that has become the focus of this paper, with football being used as a subject due to its vast popularity and massive supply of easily accessible data.