Climate change is an important issue for public policy. Unfortunately, although there are volumes of data about climate change, many members of the public are informed about the issue by politicized interpretations of the data. This is an impediment to planning policies and strategies to counter the impact of climate change, and identifies a need for climate awareness in the public. This thesis explores using gamification to motivate people to learn about long term trends in climate data. As a model for this edutainment activity, we choose a medium that engages millions of players to learn about large sets of data - Fantasy Sports. Fantasy sports have been shown to increase the player’s knowledge and understanding about the domain of the sport being played. With the huge amount of weather data available, we have designed and developed a fantasy weather game. People manage a team of cities with the goal of predicting weather better than other players in their league, and in the process gain an understanding of the weather patterns and climate change trends for those cities. We do a user-study to evaluate our application and prove its feasibility. An evaluation of the fantasy weather game indicates that the game had the desired effect of causing players to explore weather data in more detail. The evaluation also pointed out a number of potential improvements to the current prototype. Overall, the evaluation supports using the model of fantasy sports to motivate people to learn more about weather and climate data.

This study has examined the uses and gratifications, which fantasy sports users seek for their online participation. Several uses and gratifications were tested to demonstrate whether or not they were motivations for different groups of online fantasy users. A survey instrument was created and disseminated to online fantasy users through fantasy message boards on the Internet. Online fantasy users who are Beginners, have high levels of participation, and participate in monetary prize leagues were groups that were studied. The analyses identified seven motivations that are sought by online fantasy users of those three groups. Descriptive data indicates most of online fantasy users are full-time employees who spend time at work checking their fantasy leagues and teams. / Department of Journalism

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / The possibility of becoming a millionaire attracts over 200,000 daily fantasy sports (DFS) contest entries each Sunday of the NFL season. Millions of people play fantasy sports and the companies sponsoring daily fantasy sports are worth billions of dollars. This thesis develops optimization models for daily fantasy sports with an emphasis on tiered contests. A tiered contest has many different payout values, including the highly sought after million-dollar prize. The primary contribution of this thesis is the first model to optimize the expected payout of a tiered DFS contest. The stochastic integer program, MMIP, takes into account the possibility that selected athletes will earn a distribution of fantasy points, rather than a single predetermined value. The players are assumed to have a normal distribution and thus the team’s fantasy points is a normal distribution. The standard deviation of the team’s performance is approximated through a piecewise linear function, and the probabilities of earning cumulative payouts are calculated. MMIP solves quickly and easily fits the majority of daily fantasy sports contests. Additionally, daily fantasy sports have landed in a tense political climate due to contestants hopes of winning the million-dollar prize. Through two studies that compare the performance of randomly selected fantasy teams with teams chosen by strategy, this thesis conclusively determines that daily fantasy sports are not games of chance and should not be considered gambling. Besides creating the first optimization model for DFS tiered contests, this thesis also provides methods and techniques that can be applied to other stochastic integer programs. It is the author’s hope that this thesis not only opens the door for clever ways of modeling, but also inspires sports fans and teams to think more analytically about player selection.

Daily fantasy sports (DFS) has grown in popularity with millions of participants throughout the world. However, studies have shown that most profits from DFS contests are won by only a small percentage of players. This thesis addresses the challenges faced by DFS participants by evaluating sources that provide player projections for NBA DFS contests and by developing machine learning models that produce competitive player projections. External sources are evaluated by constructing daily lineups based on the projections offered and evaluating those lineups in the context of all potential lineups, as well as those submitted by participants in competitive FanDuel DFS tournaments. Lineups produced by the machine learning models are also evaluated in the same manner. This work experiments with several machine learning techniques including automated machine learning and notes the top model developed was successful in 48% of all FanDuel NBA DFS tournaments and 51% of single-entry tournaments over a two-month period, surpassing the top external source evaluated by 9 percentage points and 10 percentage points, respectively.

Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on January 31, 2008) Vita. Includes bibliographical references.

This study examines the different skills necessary for PGA tour players to succeed at specific annual tournaments, in order to create a predictive model for DraftKings PGA contests. The model takes into account data from the PGA Tour ShotLink Intelligence Program. The predictive model is created each week based on past results from the specific tournament in question, with the hope of predicting a group of twenty-five players who should be successful based on their statistical profile. The results of the model are detailed in this paper, which covers the first nine weeks of the 2017 PGA Tour season, with a net profit of $45,070. Despite a positive profit there is not enough information to prove significance, so the model would need to be carried out for many more weeks to be conclusive. Ultimately, the study shows that each PGA Tour course is slightly different, which means certain players should be more successful at certain courses, which is valuable information for predicting future outcomes.

Over the last two decades, digitization has been drastically shifting the way businesses operate and has provided access to high volume, variety, velocity, and veracity data. Naturally, access to such granular data has opened a wider range of possibilities than previously available. We leverage such data to develop application-driven models in order to evaluate current systems and make better decisions. We explore three application areas. In Chapter 1, we develop models and algorithms to optimize portfolios in daily fantasy sports (DFS). We use opponent-level data to predict behavior of fantasy players via a Dirichlet-multinomial process, and our predictions feed into a novel portfolio construction model. The model is solved via a sequence of binary quadratic programs, motivated by its connection to outperforming stochastic benchmarks, the submodularity of the objective function, and the theory of order statistics. In addition to providing theoretical guarantees, we demonstrate the value of our framework by participating in DFS contests. In Chapter 2, we develop an axiomatic framework for attribution in online advertising, i.e., assessing the contribution of individual ads to product purchase. Leveraging a user-level dataset, we propose a Markovian model to explain user behavior as a function of the ads she is exposed to. We use our model to illustrate limitations of existing heuristics and propose an original framework for attribution, which is motivated by causality and game theory. Furthermore, we establish that our framework coincides with an adjusted ``unique-uniform'' attribution scheme. This scheme is efficiently implementable and can be interpreted as a correction to the commonly used uniform attribution scheme. We supplement our theory with numerics using a real-world large-scale dataset. In Chapter 3, we propose a decision-making algorithm for personalized sequential marketing. As in attribution, using a user-level dataset, we propose a state-based model to capture user behavior as a function of the ad interventions. In contrast with existing approaches that model only the myopic value of an intervention, we also model the long-run value. The objective of the firm is to maximize the probability of purchase and a key challenge it faces is the lack of understanding of the state-specific effects of interventions. We propose a model-free learning algorithm for decision-making in such a setting. Our algorithm inherits the simplicity of Thompson sampling for a multi-armed bandit setting and we prove its asymptotic optimality. We supplement our theory with numerics on an email marketing dataset.

Since the U.S. Supreme Court lifted the federal ban on sports gambling, the popularity of sports gambling continues to increase. This has left the sport industry, including academics, interested in examining the drivers of sports gambling participation and their influence on consumers’ betting behavior. This dissertation includes three essays considering motivations to engage in sports gambling. While all focus on sports gambling, each of these three standalone essays embrace a different focus to explore sports gambling motivations and betting behavior. First, Essay One explores the differences in motivation and perception of skill versus luck between daily fantasy sports (DFS) and sports betting participants. Next, Essay Two investigates the interplay between motivations and game characteristics on betting intentions. Finally, Essay Three explores the effects of different marketing promotions and their fit with consumers’ regulatory focus on consumers’ betting behavior. Collectively, this research will provide insights and understandings of different drivers of sports gambling and their influence on consumer behavior regarding sports gambling. / Tourism and Sport

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