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- Creators: W. P. Carey School of Business
- Status: Published
Description
Monocular is a user engagement application that offers a website owner the opportunity to track user behavior and use the data to better understand the site's strengths and weaknesses in terms of user satisfaction and motivation. This data allows the customer to make improvements to a website, resulting in a better user experience and potential for an improved bottom line.
ContributorsHooke, Wade (Co-author) / Ortiz-Monasterio, Diego (Co-author) / Clark, Joseph (Thesis director) / Prince, Linda (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / W. P. Carey School of Business (Contributor)
Created2014-05
Description
The beautiful game is unpredictable. Arguably half of soccer is entirely out of our control, instead being determined by a simple concept: luck. But what of the other 50%? Ultimately, the goal of the rapidly-advancing technologies and analytics in on-field sports performance is to maximize the elements that we \u2014 the coaches, players, decision-makers, and analysts \u2014 truly control. Once perceived as too mathematical and systemized, contradicting coaches' intuitions, sports sciences are burgeoning in the sports arena both in applied and mainstream popularity. While the industry has its critics and is far shy of its pinnacle, its advancements and successes cannot be ignored. From the training ground to match day decision-making, analytics are embedded in soccer and sport. Technology and analytics are vastly utilized throughout sporting organizations across a myriad of sports and purposes: scouting and drafting, fan experience, ticketing, etc. However, while these areas must be addressed in discussing the success of analytics in assessing situations and reducing uncertainty, my central thesis relates to the technological capabilities and corresponding analytical tools utilized to identify, assess, and improve on-field soccer performance: match analysis. This paper's core focuses on optimizing performance in soccer players in three specific areas of performance: technical abilities and tactics, physiology, and neuroscience.
ContributorsHeckendorn, Jason Farrell (Author) / Eaton, John (Thesis director) / Ostrom, Amy (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor) / W. P. Carey School of Business (Contributor) / Department of Management (Contributor)
Created2014-05
Description
We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.
ContributorsBarolli, Adeiron (Author) / Jimenez Arista, Laura (Thesis director) / Wilson, Jeffrey (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The sport of football has become one of the most widely loved and watched sports in the United States. Fans of football are extremely dedicated to the sport and form very personal, emotional attachments to teams within the National Football League. Through studying these fans, three main analysis topics will be addressed in this thesis: the established fan knowledge that creates rules within the fandom on how a fan participates and the exceptions to those rules, the need that fan's who do not fit into the normative patterns of the fandom feel to explain their motivations, and the degree of fanship that can be measured through a fan's explanation for their deviant behavior. For this fandom analysis, two sets of data will be referenced throughout the discussion. The first is anecdotal data pulled from various reddit threads on the personal experiences of each fan as well as some opinion sharing about the National Football League fandom. The second set of data is primary data that I have compiled via a survey. The established fan knowledge within this fandom is two fold: First, fans choose their team for a specific reason (usually based off of the team their parent's are a fan of or the state they grew up in), which typically takes place by age twelve. Secondly, once a fan chooses a team, they should stick with that team through their entire fanship. This second piece of fan knowledge is the most important rule within the fandom. This idea of loyalty is what guides fan participation. Identifying this rule led me to my main question about this fandom \u2014 if fans are so dedicated to their chosen team, what does it mean when someone switches to a new team. I feel that this breaking of a bond that seems so personal to fans is important and should be researched. This brought me to researching these fans that have switched teams, learning why, as well as what this group of fans can say about the National Football League fandom as a whole.
ContributorsGutierrez, Brooke Kathleen (Author) / Eaton, John (Thesis director) / Lee, Christopher (Committee member) / W. P. Carey School of Business (Contributor) / Department of Marketing (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis project was a response to the NFL's struggles with domestic violence in the recent past. To contextualize the issue, this paper examines key domestic violence statistics and rates in the United States. Also discussed are the issues that United States law enforcement has with domestic violence cases, namely a problem with under-reporting and inconsistent arrest policies from state to state. Controversy surrounding NFL players and domestic violence was sparked by Ray Rice's arrest in 2014 and exacerbated by the league's light discipline of Rice. This paper attempts to uncover the root causes of the NFL's domestic violence problem and identifies issues the league has in terms of its inconsistent punishment history for domestic violence offenses, recent arrest trends for NFL players and the efforts the league and its members have made to combat the issue. Also considered are the ways the NFL has attempted to resolve the issue of domestic violence. The paper examines the policies set forth in the NFL's personal conduct policy as well as partnerships the league has made with nationwide organizations dedicated to education and prevention of domestic violence, including NO MORE and the National Domestic Violence Hotline. This thesis also compares the NFL's policies regarding domestic violence to that of its peers, Major League Baseball and the National Basketball Association. Lastly, the thesis offers several recommendations to the NFL for how it can rectify its prior shortcomings. These recommendations include revamped education programs for players, heightened disciplinary standards and more open communication with fans and outsiders.
ContributorsBeck, Ian Christopher (Author) / DeCarolis, Claudine (Thesis director) / Brumfield, Kenya (Committee member) / School of Criminology and Criminal Justice (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
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.
ContributorsLindstrom, Trent (Author) / Schneider, Laurence (Thesis director) / Wilson, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Politics and Global Studies (Contributor)
Created2022-05