Fact or Fantasy? Analyzing Fantasy Football Player Valuations Through Different Ranking Systems

Abstract

ESPN’s Matthew Berry is the most prominent fantasy football analyst in the United States. When preparing for fantasy football drafts, owners conduct research so that they can make more well-informed decisions when it is time to select their players. During this research, owners flock to read articles that contain the highly regarded opinion of Matthew Berry. His annual “Love/Hate” article is one of the most popular fantasy football articles every year when it is released in mid-August. In this article, Berry lists several NFL players that he believes will outperform their average draft positions (or “ADP”) for the year’s fantasy football season (these players fall under the “Love” category) and several that he believes will underperform relative to their average draft positions (these players fall under the “Hate” category). This thesis first investigates whether Matthew Berry’s inflated values of his “Loves” and deflated values of his “Hates” compared to the ESPN experts’ consensus rankings were warranted in the past. This research is based on historical data related to the performance of past “Loves” and “Hates” during the season in which they were included in the article. The performance statistics of past “Loves” and “Hates” were taken and subsequently converted into fantasy points. These players were then ranked against their peers using metrics based on the actual fantasy points obtained by the players throughout the season. Berry’s rankings and the ESPN consensus rankings were then compared to these actual rankings to determine which ranking system was more successful at predicting the actual end-of-season rankings. This analysis will allow for the determination of whether Matthew Berry’s expertise compared to the ESPN consensus is fact or fantasy. iv This thesis then investigates which player statistics are most significant in predicting future per-game statistics for an upcoming season and defines equations for the expected per-game statistics using linear regression. After determining the significant player variables and defining equations to predict future player statistics, this thesis develops equations that represent a player’s expected fantasy points per game (or “PPG”) for the upcoming season. This thesis then ranks lists of players at each position based on the expected fantasy points per game equations and compares the performance of these rankings with that of Matthew Berry’s rankings. Lastly, based on the results of this analysis, conclusions are made as to the quality of the different approaches to valuing fantasy football players.