A recent trend among sports fans along both sides of the letterman jacket is that of Daily Fantasy Sports (DFS). The DFS industry has been under legal scrutiny recently, due to the view that daily sports data is too random to make its prediction skillful. Therefore, a common view is that it constitutes online gambling. This thesis proves that DFS, as it pertains to Baseball, is significantly more predictable than random chance, and thus does not constitute gambling. We propose a system which generates daily lists of lineups for Fanduel Daily Fantasy Baseball contests. The system consists of two components: one for predicting player scores for every player on a given day, and one for generating lists of the best combinations of players (lineups) using the predicted player scores. The player score prediction component makes use of deep neural network models, including a Long Short-Term Memory recurrent neural network, to model daily player performance over the 2016 and 2017 MLB seasons. Our results indicate this to be a useful prediction tool, even when not paired with the lineup generation component of our system. We build off of previous work to develop two models for lineup generation, one completely novel, dependent on a set of player predictions. Our evaluations show that these lineup generation models paired with player predictions are significantly better than random, and analysis shows insights into key aspects of the lineup generation process.

Addressing fairness among users and risk mitigation in the context of resource allocation in transportation systems under uncertainties poses a crucial challenge yet to be satisfactorily resolved. This dissertation attempts to address this challenge, focusing on achieving a balance between system-wide efficiency and individual fairness in stochastic transportation resource allocation problems. To study complicated fair and risk-averse resource allocation problems - from public transit to urban air mobility and multi-stage infrastructure maintenance - we develop three models: DrFRAM, FairUAM, and FCMDP. Each of these models, despite being proven NP-hard even in a simplistic case, inspires us to develop efficient solution algorithms. We derive mixed-integer linear programming (MILP) formulations for these models, leveraging the unique properties of each model and linearizing non-linear terms. Additionally, we strengthen these models with valid inequalities. To efficiently solve these models, we design exact algorithms and approximation algorithms capable of obtaining near-optimal solutions. We numerically validate the effectiveness of our proposed models and demonstrate their capability to be applied to real-world case studies to adeptly address the uncertainties and risks arising from transportation systems. This dissertation provides a foundational platform for future inquiries of risk-averse resource allocation strategies under uncertainties for more efficient, equitable, and resilient decision-making. Our adaptable framework can address a variety of transportation-related challenges and can be extended beyond the transportation domain to tackle resource allocation problems in a broader setting. / Doctor of Philosophy / In transportation systems, decision-makers constantly strive to devise the optimal plan for the most beneficial outcomes when facing future uncertainties. When optimizing overall efficiency, individual fairness has often been overlooked. Besides, the uncertainties in the transportation systems raise serious questions about the adaptability of the allocation plan. In response to these issues, we introduce the concept of fair and risk-averse resource allocation under uncertainties in this dissertation. Our goal is to formulate the optimal allocation plan that is both fair and risk-averse amid uncertainties. To tackle the complexities of fair and risk-averse resource allocation problems, we propose innovative methods and practical algorithms, including creating novel formulations as well as deriving super-fast algorithms. These solution approaches are designed to accommodate the fairness, uncertainties, and risks typically in transportation systems. Beyond theoretical results, we apply our frameworks and algorithms to real-world case studies, thus demonstrating our approaches' adaptability to various transportation systems and ability to achieve various optimization goals. Ultimately, this dissertation aims to contribute to fairer, more efficient, and more robust transportation systems. We believe our research findings can help decision-makers with well-informed choices about resource allocation in transportation systems, which, in turn, lead to the development of more equitable and reliable systems, benefiting all the stakeholders.

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