Optimaztion of Fantasy Basketball Lineups via Machine Learning

Machine learning is providing a way to glean never before known insights from the data that gets recorded every day. This paper examines the application of machine learning to the novel field of Daily Fantasy Basketball. The particularities of the fantasy basketball ruleset and playstyle are discussed, and then the results of a data science case study are reviewed. The data set consists of player performance statistics as well as Fantasy Points, implied team total, DvP, and player status. The end goal is to evaluate how accurately the computer can predict a player’s fantasy performance based off a chosen feature set, selection algorithm, and probabilistic methods

Competing in fantasy sports using machine learning

Cílem mojí bakalářské práce bylo navrhnout model na tvorbu portfolia fotbalových fantasy soupisek pro Daily fantasy turnaje s top-heavy výplatní strukturou. V těchto turnajích připadne většina zisku pouze nejlepším hráčům. Naším cílem tedy nebylo pouze maximalizovat očekáváný hráčský výkon, ale také varianci a kovarianci hráčů. Dalším cílem bylo minimalizovat korelaci mezi soupiskami portfolia. Vzorkováním pravděpodobnostních modelů jednotlivých fantasy statistik hráčů jsme odhadli distribuci fantasy bodů všech hráčů. Predikovaný průměr a kovarianci fantasy bodů hráčů jsme využili v následné námi navržené úloze smíšeného celočíselného kvadratického programování (MIQP). Vytvořený model jsme otestovali na reálných datech nacrawlovaných od poskytovatele fantasy sportů. Výsledky jsou velice slibné. Model skončil na konci sezóny v zisku.The goal of my bachelor thesis was to design a model for the creation of fantasy football lineup portfolios in Daily fantasy tournaments with top-heavy payoff structures. In these tournaments, most of the winnings go only to the top participants. Therefore, we not only aimed to maximize players expected performance but also their variance and covariance. Our objective was also to minimize the correlation between the portfolio’s lineups. By sampling probabilistic models of individual fantasy player statistics, we estimated fantasy point distribution for all players. We used players fantasy points mean and covariance prediction in our subsequent mixed-integer quadratic program (MIQP). We tested the created model on real fantasy data crawled from the fantasy sports provider. The results are very promising. The model finished in profit at the end of the season

Investigating Daily Fantasy Baseball: An Approach to Automated Lineup Generation

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

Optimizing daily fantasy sports contests through stochastic integer programming

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringTodd W. EastonThe 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

Modeling Daily Fantasy Basketball

Daily fantasy basketball presents interesting problems to researchers due to the extensive amounts of data that needs to be explored when trying to predict player performance. A large amount of this data can be noisy due to the variance within the sport of basketball. Because of this, a high degree of skill is required to consistently win in daily fantasy basketball contests. On any given day, users are challenged to predict how players will perform and create a lineup of the eight best players under fixed salary and positional requirements. In this thesis, we present a tool to assist daily fantasy basketball players with these tasks. We explore the use of several machine learning techniques to predict player performance and develop multiple approaches to approximate optimal lineups. We then compare each different heuristic and lineup creation combination, and show that our best combinations perform much better than random lineups. Although creating provably optimal lineups is computationally infeasible, by focusing on players in the Pareto front between performance and cost we can reduce the search space and compute near optimal lineups. Additionally, our greedy and evolutionary lineup search methods offer similar performance at a much smaller computational cost. Our analysis indicates that due to how player salaries are structured, it is generally preferred to construct a lineup consisting of a few stars and filling out the rest of the roster with average to mediocre players than to construct a lineup where all players are expected to perform about the same. Through these findings we hope that our research can serve as a future baseline towards developing an automated or semi-automated tool to optimize daily fantasy basketball

The Labor of Play: the Political Economy of Computer Game Culture

This dissertation questions the relationship between computer game culture and ideologies of neoliberalism and financialization. It questions the role computer games play in cultivating neoliberal practices and how the industry develops games and systems making play and work indistinguishable activities. Chapter 1 examines how computer game inculcate players into neoliberal practice through play. In chapter 2, the project shows Blizzard Entertainment systematically redevelops their games to encourage perpetual play aimed at increasing the consumption of digital commodities and currencies. Chapter 3 considers the role of esports, or professional competitive computer game play, to disperse neoliberal ideologies amongst nonprofessional players. Chapter 4 examines the streaming platform Twitch and the transformation of computer gameplay into a consumable commodity. This chapter examines Twitch’s systems designed at making production and consumption inseparable practices. The dissertation concludes by examining the economic, conceptual, and theoretical collapses threatening game culture and the field of game studies

Modern Day Bucket Shops? Fantasy Sports and Illegal Exchanges

The rapid emergence of online daily fantasy sports has raised questions as to why the contests are allowed, while other forms of gambling are restricted. Historically, “bucket shops” were banned enterprises where businesses would effectively accept wagers on whether companies’ stock prices would go up or down. There was never an underlying investment in companies themselves, only a deposit into a “bucket.” While bucket shops have largely faded, we examine whether they have disappeared in name only. Our analysis opens up another avenue for regulators beyond the antiquated skill-versus-chance evaluation typically applied to gambling activities and suggests that certain fantasy contests may run counter to Commodity Futures Trading Commission regulations. Applying this existing regulatory framework would likely enhance consumer protection and market integrity

Taxation of Electronic Gaming

At a doctrinal level, the subject of this Article is timely. During this time of the coronavirus pandemic, casinos have been closed and large populations have been subject to stay-home orders from local and state authorities. One can reasonably expect a large increase in electronic gaming and thus an increased need for proper consideration of its taxation. This Article argues for a cash-out rule of taxation. At a deeper level, the subject of this Article is timeless. Tax law is wickedly complex for a reason. This Article explores that complexity using the example of electronic gaming. It grapples with the source of that complexity: an inherent and unresolvable tension between economic theories of income and the practical needs of administering a system of taxation to a large population in a democracy. That tension led some scholars to argue for a standards-based approach to taxation. This Article considers and rejects that argument. Legal rules are necessary to mediate between theory and practice. Hence, this Article demonstrates the continued relevance and importance of doctrinal analysis in legal scholarship