  Fantasy sports allow fans to manage a team of their favorite athletes and
compete with friends. The fantasy platform aligns the real-world statistical
performance of athletes to fantasy scoring and has steadily risen in popularity
to an estimated 9.1 million players per month with 4.4 billion player card
views on the ESPN Fantasy Football platform from 2018-2019. In parallel, the
sports media community produces news stories, blogs, forum posts, tweets,
videos, podcasts and opinion pieces that are both within and outside the
context of fantasy sports. However, human fantasy football players can only
analyze an average of 3.9 sources of information. Our work discusses the
results of a machine learning pipeline to manage an ESPN Fantasy Football team.
The use of trained statistical entity detectors and document2vector models
applied to over 100,000 news sources and 2.3 million articles, videos and
podcasts each day enables the system to comprehend natural language with an
analogy test accuracy of 100% and keyword test accuracy of 80%. Deep learning
feedforward neural networks provide player classifications such as if a player
will be a bust, boom, play with a hidden injury or play meaningful touches with
a cumulative 72% accuracy. Finally, a multiple regression ensemble uses the
deep learning output and ESPN projection data to provide a point projection for
each of the top 500+ fantasy football players in 2018. The point projection
maintained a RMSE of 6.78 points. The best fit probability density function
from a set of 24 is selected to visualize score spreads. Within the first 6
weeks of the product launch, the total number of users spent a cumulative time
of over 4.6 years viewing our AI insights. The training data for our models was
provided by a 2015 to 2016 web archive from Webhose, ESPN statistics, and
Rotowire injury reports. We used 2017 fantasy football data as a test set.
