Perfection is extremely difficult to achieve when playing team sports. This is especially true for lacrosse, a sport where dropped passes, missed shots and turnovers are prevalent even at the college and professional levels of the game. In order to improve on mistakes, teams must first recognize where the errors are being made. The purpose of this project is to implement the DMAIC process improvement method into lacrosse, with the goal of identifying and implementing improvements, leading to a more successful team.
In order to use DMAIC, lacrosse was expressed as a process that included five phases: offense, defense, riding, clearing and faceoffs. Data was gathered for each phase using game film from the Arizona State Men’s Club Lacrosse Team over the course of the 2019 and 2020 seasons. The data was then analyzed by comparing the output statistics of each phase to the goal differential, number of goals scored, and number of goals against. Once the areas of improvement were determined, additional analysis was done to determine why these certain areas needed improvement. The results provided what changes needed to be made in order to improve the team. In order to ensure the team sustained their success, control measures were put in place to determine what action needs to be taken and when.

Ultimate Frisbee or "Ultimate," is a fast growing field sport that is being played competitively at universities across the country. Many mid-tier college teams have the goal of winning as many games as possible, however they also need to grow their program by training and retaining new players. The purpose of this project was to create a prototype statistical tool that maximizes a player line-up's probability of scoring the next point, while having as equal playing time across all experienced and novice players as possible. Game, player, and team data was collected for 25 different games played over the course of 4 tournaments during Fall 2017 and early Spring 2018 using the UltiAnalytics iPad application. "Amount of Top 1/3 Players" was the measure of equal playing time, and "Line Efficiency" and "Line Interaction" represented a line's probability of scoring. After running a logistic regression, Line Efficiency was found to be the more accurate predictor of scoring outcome than Line Interaction. An "Equal PT Measure vs. Line Efficiency" graph was then created and the plot showed what the optimal lines were depending on what the user's preferences were at that point in time. Possible next steps include testing the model and refining it as needed.

Virtual Reality is being widely adapted for use in the consumer market. There are adaptations of the technology for every purpose, from education, to gaming, and even medical. There are businesses being formed worldwide that incorporate the gaming utility in an arcade/internet café style. However, there are other plausible business models. There is the preexisting model that companies are currently using, another option is to add this technology to preexisting physical arcades, and to create a new business with practices decided by consumer statistics. These three models were tested in this study to determine the profitability, feasibility, and best practices for each. Each business model appears to be incredibly profitable based on the assumptions used for this study.