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Projecting a Quarterback's Fantasy Football Point Output for Daily Fantasy Sports using Statistical Models

by Nicholas King, Aera LeBoulluec
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 4
Year of Publication: 2017
Authors: Nicholas King, Aera LeBoulluec
10.5120/ijca2017913614

Nicholas King, Aera LeBoulluec . Projecting a Quarterback's Fantasy Football Point Output for Daily Fantasy Sports using Statistical Models. International Journal of Computer Applications. 164, 4 ( Apr 2017), 22-27. DOI=10.5120/ijca2017913614

@article{ 10.5120/ijca2017913614,
author = { Nicholas King, Aera LeBoulluec },
title = { Projecting a Quarterback's Fantasy Football Point Output for Daily Fantasy Sports using Statistical Models },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 4 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number4/27471-2017913614/ },
doi = { 10.5120/ijca2017913614 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:21.793439+05:30
%A Nicholas King
%A Aera LeBoulluec
%T Projecting a Quarterback's Fantasy Football Point Output for Daily Fantasy Sports using Statistical Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 4
%P 22-27
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the new age of daily fantasy sports (DFS), fantasy football has become an enormous revenue generator for DFS sites, such as DraftKings and FanDuel. Both companies are valued over $1 billion. However, previous analysis done by popular DFS site Rotogrinders, has shown that only the top players are consistently winning, the top 10 players much more frequently than the remaining 20,000 players. Using complex statistical models they're able to identify top athletes and value picks (based on an athlete's draft 'salary') that the average player might not be aware of. There is a need to evaluate which methods and algorithms are best at predicting fantasy football point output. These methods could then be applied to future DFS contests to see if they can predict other fantasy sports as well. There are few resources available on this subject, as DFS are still relatively new and few people publish their work, since they generally develop these models for their own financial gain. This research will attempt to find some effective statistical models to predict the weekly fantasy point output of a quarterback.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fantasy football daily fantasy sports statistical models machine learning predictive analytics DraftKings FanDuel.

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