Fantasy Football Prediction
This work addresses the need for enhanced, unbiased prediction tools for Fantasy Football users who pay to play, though it is incremental in nature.
The paper tackled predicting Fantasy Football scores for NFL quarterbacks using limited game data from the last six seasons, employing Support Vector Regression and Neural Networks, with results described as promising given the data constraints.
The ubiquity of professional sports and specifically the NFL have lead to an increase in popularity for Fantasy Football. Users have many tools at their disposal: statistics, predictions, rankings of experts and even recommendations of peers. There are issues with all of these, though. Especially since many people pay money to play, the prediction tools should be enhanced as they provide unbiased and easy-to-use assistance for users. This paper provides and discusses approaches to predict Fantasy Football scores of Quarterbacks with relatively limited data. In addition to that, it includes several suggestions on how the data could be enhanced to achieve better results. The dataset consists only of game data from the last six NFL seasons. I used two different methods to predict the Fantasy Football scores of NFL players: Support Vector Regression (SVR) and Neural Networks. The results of both are promising given the limited data that was used.