Determining Factors of Risk Tolerance: Evidence from Fantasy Football Snake Drafts

This paper utilizes fantasy football snake drafts to analyze risk tolerance of individuals who are trying to maximize their present and future utility, but are faced with unknown factors and only have limited resources. Fantasy football provides a unique perspective on risk tolerance, different than the commonly researched fields of auctions, financial portfolios, and lotteries. I examine mock draft data from Fantasy Football Calculator as well as rankings data from Fantasy Pros to gauge the amount of risk associated with each draft pick. I find that the more perceived uncertainty that is connected to an individual selection, the more likely the selection will exhibit risk averse characteristics

Applications of Artificial Intelligence to the NHL Entry Draft

This thesis investigates the application of various fields of artificial intelligence to the domain of sports management and analysis. The research in this thesis is primarily focussed on the entry draft for the National Hockey League, though many of the models proposed may be applied to other sports and leagues with minimal adjustments. A utility model is proposed to define which players are preferred by which teams for a given draft. This model allows for the consideration of how teams acting in a multiagent system may reason about each other's preferences, as well as how they might strategize and interact with one another through trades. A trading scheme where agents may trade picks with each other to change the picking order is established and an algorithm is proposed to find optimal trade offers to propose under an imperfect knowledge setting. Through simulations based on the National Hockey League Entry Draft data, the algorithms provide mutually beneficial trades that also increase the social utility of the league over the course of the draft. Machine learning classifiers are proposed to suggest which prospects will be successful at the highest level of the sport over various metrics using statistics and scouting reports from their draft year as features. The classifiers out-perform conventional draft selections in the NHL and provide insights into which attributes of a player are important in development. Clustering techniques are used to determine playstyles in the NHL and these clusters are fed as annotations into additional classifiers to project which prospects will fall into certain clusters later in their careers. These latter classifiers demonstrated promising results but were ultimately limited by the availability of data. A discussion of future avenues of artificial intelligence research in the young but growing field of sports analytics is carried throughout this thesis

Allocation of Public Resources: Bringing Order to Chaos

Science Olympiad (SO) is a team-based academic competition involving multiple subject areas (Events) with arcane rules governing the team composition. Add to the mix parental contention over which student(s) get on the “All-Star” team, and you have a potentially explosive situation. This project brings order and logic to school-based SO programs and defuses tense milestones through the implementation of an institutional structure that: assigns students to Events based on solicited student preferences for the Events, collects objective student performance data, composes competitive teams based on student performance (aka “Moneyball”), and brings transparency to the Team Selection process through crowdsourcing. The Event Assignment mechanism is simple, fast, easy to understand, and yields Pareto-optimal results based on student preferences, without the exchange of money or tokens, and with effectively no incentive to game the system. The Team Selection mechanism optimizes student performance data from teachers (Event Coaches) and competitions to compose a tiered series of teams with the greatest potential performance. And the Crowdsource Tool allows any stakeholder to compose a candidate team for advancing to the State competition, where the team with the highest potential performance score advances to State whether the team was composed with the Crowdsource Tool or by the Team Selection algorithm. The end result is that students get more of the Events that they want; Team Selection is transparent and far less contentious; teams are higher quality; and managing the SO program for a school takes considerably less time and effort

Bidding Strategies for Fantasy-Sports Auctions

Fantasy sports is a fast-growing, multi-billion dollar industry [10] in which competitors assemble virtual teams of athletes from real professional sports leagues and obtain points based on the statistical performance of those athletes in actual games. Users (team managers) can add, drop, and trade players throughout the season, but the pivotal event is the player draft that initiates the competition. One common drafting mechanism is the so-called auction draft: managers bid on athletes in rounds until all positions on each roster have been filled. Managers start with the same initial virtual budget and take turns successively nominating athletes to be auctioned, with the winner of each round making a virtual payment that diminishes his budget for future rounds. Each manager tries to obtain players that maximize the expected performance of his own team. In this paper we initiate the study of bidding strategies for fantasy sports auction drafts, focusing on the design and analysis of simple strategies that achieve good worst-case performance, obtaining a constant fraction of the best value possible, regardless of competing managers’ bids. Our findings may be useful in guiding bidding behavior of fantasy sports participants, and perhaps more importantly may provide the basis for a competitive auto-draft mechanism to be used as a bidding proxy for participants who are absent from their league’s draft. © Springer-Verlag GmbH Germany 201