Matching Items (5)
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- All Subjects: Analytics
- Creators: Department of Management
- Creators: Goegan, Brian
Description
Social entrepreneurship has received a great deal of attention in recent years. Scholars constantly debate of the meaning of the term and the direction of the field. This paper explores literature written between the years 2010 \u2014 2015 in an effort to understand the current state of social entrepreneurship and gain insight as to the direction it is headed. This paper looks at definitions, characteristics, geographical differences, legal designations, and major themes such as social enterprise, social innovation, & social value as well as the implications for performance measures in an attempt to understand the broad concept that is social entrepreneurship.
ContributorsTalarico, Anthony (Author) / Shockley, Gordon (Thesis director) / Hayter, Christopher (Committee member) / Department of Management (Contributor) / School of Public Affairs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Investment real estate is unique among similar financial instruments by nature of each property's internal complexities and interaction with the external economy. Where a majority of tradable assets are static goods within a dynamic market, real estate investments are dynamic goods within a dynamic market. Furthermore, investment real estate, particularly commercial properties, not only interacts with the surrounding economy, it reflects it. Alive with tenancy, each and every commercial investment property provides a microeconomic view of businesses that make up the local economy. Management of commercial investment real estate captures this economic snapshot in a unique abundance of untapped statistical data. While analysis of such data is undeniably valuable, the efforts involved with this process are time consuming. Given this unutilized potential our team has develop proprietary software to analyze this data and communicate the results automatically though and easy to use interface. We have worked with a local real estate property management and ownership firm, Reliance Management, to develop this system through the use of their current, historical, and future data. Our team has also built a relationship with the executives of Reliance Management to review functionality and pertinence of the system we have dubbed, Reliance Dashboard.
ContributorsBurton, Daryl (Co-author) / Workman, Jack (Co-author) / LePine, Marcie (Thesis director) / Atkinson, Robert (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Management (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
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
This paper attempts to introduce analytics and regression techniques into the National Hockey League. Hockey as a sport has been a slow adapter of analytics, and this can be attributed to poor data collection methods. Using data collected for hockeyreference.com, and R statistical software, the number of wins a team experiences will be predicted using Goals For and Goals Against statistics from 2005-2017. The model showed statistical significance and strong normality throughout the data. The number of wins each team was expected to experience in 2016-2017 was predicted using the model and then compared to the actual number of games each team won. To further analyze the validity of the model, the expected playoff outcome for 2016-2017 was compared to the observed playoff outcome. The discussion focused on team's that did not fit the model or traditional analytics and expected forecasts. The possible discrepancies were analyzed using the Las Vegas Golden Knights as a case study. Possible next steps for data analysis are presented and the role of future technology and innovation in hockey analytics is discussed and predicted.
ContributorsVermeer, Brandon Elliot (Author) / Goegan, Brian (Thesis director) / Eaton, John (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Our research encompassed the prospect draft in baseball and looked at what type of player teams drafted to maximize value. We wanted to know which position returned the best value to the team that drafted them, and which level is safer to draft players from, college or high school. We decided to look at draft data from 2006-2010 for the first ten rounds of players selected. Because there is only a monetary cap on players drafted in the first ten rounds we restricted our data to these players. Once we set up the parameters we compiled a spreadsheet of these players with both their signing bonuses and their wins above replacement (WAR). This allowed us to see how much a team was spending per win at the major league level. After the data was compiled we made pivot tables and graphs to visually represent our data and better understand the numbers. We found that the worst position that MLB teams could draft would be high school second baseman. They returned the lowest WAR of any player that we looked at. In general though high school players were more costly to sign and had lower WARs than their college counterparts making them, on average, a worse pick value wise. The best position you could pick was college shortstops. They had the trifecta of the best signability of all players, along with one of the highest WARs and lowest signing bonuses. These were three of the main factors that you want with your draft pick and they ranked near the top in all three categories. This research can help give guidelines to Major League teams as they go to select players in the draft. While there are always going to be exceptions to trends, by following the enclosed research teams can minimize risk in the draft.
ContributorsValentine, Robert (Co-author) / Johnson, Ben (Co-author) / Eaton, John (Thesis director) / Goegan, Brian (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05