Fantasy sports allow fans to manage a team of their favorite athletes and compete with friends and fellow managers. 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 spending a total of 7.7 billion-million minutes 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 cannot consume and summarize billions of bytes of natural language text and multimedia data to make a roster decision. Before our system, fantasy managers relied on expert projections and their analysis of, on average, 3.9 sources of information to make roster decisions. While these experts excel at evaluating players based on traditional statistics, they are omitting the majority of data that would inform their assessments. Our work discusses and shows the results of a novel (patent pending) machine learning pipeline to effectively manage an ESPN Fantasy Football team. The use of trained statistical entity detectors and document2vector models applied to over 50,000 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%. Next, deep learning feedforward neural networks that are 98 layers deep 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 with a 12% real world distribution. Finally, a multiple regression ensemble accepts 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 Root Mean Squared Error of 6.78 points. Next, the best out of the 24 probability density functions that were fit to the current projection and historical scores was 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. As a result, each user spent over 90 seconds using the evidence from our novel algorithms. The training data for our models was provided by a 2015 and 2016 web archive from Webhose, ESPN statistics, and Rotowire injury reports. We used 2017 fantasy football data as a test set.
[1]
Jeffrey Dean,et al.
Distributed Representations of Words and Phrases and their Compositionality
,
2013,
NIPS.
[2]
Jason Weston,et al.
A unified architecture for natural language processing: deep neural networks with multitask learning
,
2008,
ICML '08.
[3]
Jeffrey Dean,et al.
Efficient Estimation of Word Representations in Vector Space
,
2013,
ICLR.
[4]
Peng Wang,et al.
Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification
,
2016,
Neurocomputing.
[5]
Roman W. Lutz,et al.
Fantasy Football Prediction
,
2015,
ArXiv.
[6]
Jonathan Robert Landers,et al.
Machine Learning Approaches to Competing in Fantasy Leagues for the NFL
,
2019,
IEEE Transactions on Games.
[7]
Fantasy Football Projection Analysis
,
2015
.
[8]
Christine Anderson,et al.
The reality of fantasy: uncovering information-seeking behaviors and needs in online fantasy sports
,
2012,
CHI EA '12.
[9]
Jingping Bi,et al.
A word distributed representation based framework for large-scale short text classification
,
2015,
2015 International Joint Conference on Neural Networks (IJCNN).
[10]
Yoshua Bengio,et al.
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
,
2011,
ICML.
[11]
Frank M. Shipman,et al.
Data-driven web entertainment: the data collection and analysis practices of fantasy sports players
,
2014,
WebSci '14.
[12]
Stefan Jansen,et al.
Word and Phrase Translation with word2vec
,
2017,
ArXiv.
[13]
Sarvapali D. Ramchurn,et al.
Competing with Humans at Fantasy Football: Team Formation in Large Partially-Observable Domains
,
2012,
AAAI.