Soccer is bar none, the most popular sport in the entire world. It is played, followed, and loved by virtually every single country on Earth. Despite this massive support for the sport which houses some of the world’s biggest names in the world, its shortcomings when dealing with issues of racial injustice and incidents of racist behavior have become more pronounced in recent years. Although this open discussion regarding racism within the sport has recently begun to sprout, its roots can be tied back to decades ago while continuing to the present day, with players, referees, coaches, fans, commentators, and more all involved on both sides of the issue. <br/> We found this topic to be most prevalent in today’s society after witnessing multiple shameful racist incidents that have occurred to some of the world’s biggest players throughout European football in 2019, as well as the recent ongoing fight for racial reform and increased awareness regarding racial injustice in the United States. By doing comprehensive research and<br/>analysis on such incidents that have occurred throughout the years we hope to raise more<br/>awareness regarding this subject that has plagued the beautiful game. In addition, we hope to<br/>offer ways to remedy the problem one step at a time, all while answering the tough, but necessary questions regarding what specifically should be done in the sport, that others have been afraid to talk about for far too long. Specifically, we wanted to mainly highlight the experience of black players, with a further discussion on other minority groups, in English and Italian football as these two leagues have been a part of the largest debate between how club traditions, player-fan interactions, league policies, and staff management have all affected the way we view the game as the endemic of racism within the sport is exacerbated.

Soccer is bar none, the most popular sport in the entire world. It is played, followed, and loved by virtually every single country on Earth. Despite this massive support for the sport which houses some of the world’s biggest names in the world, its shortcomings when dealing with issues of racial injustice and incidents of racist behavior have become more pronounced in recent years. Although this open discussion regarding racism within the sport has recently begun to sprout, its roots can be tied back to decades ago while continuing to the present day, with players, referees, coaches, fans, commentators, and more all involved on both sides of the issue. <br/> We found this topic to be most prevalent in today’s society after witnessing multiple shameful racist incidents that have occurred to some of the world’s biggest players throughout European football in 2019, as well as the recent ongoing fight for racial reform and increased awareness regarding racial injustice in the United States. By doing comprehensive research and analysis on such incidents that have occurred throughout the years we hope to raise more awareness regarding this subject that has plagued the beautiful game. In addition, we hope to offer ways to remedy the problem one step at a time, all while answering the tough, but necessary questions regarding what specifically should be done in the sport, that others have been afraid to talk about for far too long. Specifically, we wanted to mainly highlight the experience of black players, with a further discussion on other minority groups, in English and Italian football as these two leagues have been a part of the largest debate between how club traditions, player-fan interactions, league policies, and staff management have all affected the way we view the game as the endemic of racism within the sport is exacerbated.

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%.

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.