Super Bowl champion Nick Foles told Fox News Digital in a recent interview that he credits AI for getting him into fantasy football since he retired from the NFL.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Now that he's a former NFL player, Donald Driver is allowed to play fantasy football, and he's loving every second of it. The Green Bay Packers legend played 14 seasons, all while calling Lambeau Field his home, and if he could, he probably would have picked himself plenty of times for his fantasy team. Driver had seven seasons of at least 1,000 yards, including six straight from 2004 to 2009.

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.

Fantasy Premier League (FPL) performance predictors tend to base their algorithms purely on historical statistical data. The main problems with this approach is that external factors such as injuries, managerial decisions and other tournament match statistics can never be factored into the final predictions. In this paper, we present a new method for predicting future player performances by automatically incorporating human feedback into our model. Through statistical data analysis such as previous performances, upcoming fixture difficulty ratings, betting market analysis, opinions of the general-public and experts alike via social media and web articles, we can improve our understanding of who is likely to perform well in upcoming matches. When tested on the English Premier League 2018/19 season, the model outperformed regular statistical predictors by over 300 points, an average of 11 points per week, ranking within the top 0.5% of players rank 30,000 out of over 6.5 million players.

Millions of ESPN Fantasy Football players are rolling into week 13 of the NFL hoping to survive injuries and bye weeks on their way to the playoffs. But starting last season, players could also count on the trusty advice of Watson, IBM's artificial intelligence platform, in times of need. ESPN's Daniel Dopp, co-host of "The Fantasy Show with Matthew Berry" on ESPN, sat down with IBM Master Inventor Aaron Baughman at the IBM Innovation Lab in New York City to discuss Watson's foray into the arcane science of fantasy football. "It helps to have a compromise between the heart and the brain. We trained Watson on millions of fantasy football stories, blog posts and videos. We taught it to develop a scoring range for thousands of players with their upsides and their downsides. And we taught it to estimate the chances a player will exceed their upside or fall below the downside," Baughman said.

I don't do Fantasy Football but if I did, I would probably look to #AI to give me an unfair advantage. Winning in Fantasy Football with help of AI? See how the experts @IBM built ESPN's Fantasy Football Application at the Digital Developer Conference: AI & Cloud https://lnkd.in/eh4nY4a

For many, the term "analytics" conjures up images of highly trained, bookish professionals poring over multiple screens of incomprehensible symbols and text that hold the secrets to data-driven insights. But the real power of analytics is unlocked when it can be used by everyone. To showcase just how easy using analytics can be, and how powerful, Adobe is providing Fantasy Football players with a chance to see for themselves. Its "Hack the Huddle" site gives all who use it the power of Adobe Analytics and Adobe Sensei, the company's AI and machine learning tool, to improve their Fantasy team performance--and the chance to win $5,000 in the on-site contest. Adobe Analytics democratizes the ability to use powerful analytics, whether you're seeking business insight or simply want to pick a third running back for your Fantasy Football team.

Adobe Analytics announced today that it will use its Adobe Sensei AI platform to help fans with their fantasy football teams during the 2019 NFL Season. Of all the technological advancements in the sports world, it can be argued that the most impactful innovations in the foreseeable future will be automated insights (AI) and machine learning (ML), changing the way front offices make player decisions and business operations departments make fan experience decisions. Data is also creeping into other facets of sports, especially with the steady march into the mainstream of gambling and daily fantasy sports. As applications of AI and ML expand into the sports world, a number of companies are looking to take advantage of its burgeoning impact. One of these companies, Adobe, has moved into the sports world and is, today, announcing a new initiative, just as the NFL season is beginning.

Football is back and fans across the country are sporting their favorite player jerseys and cheering on their top teams. There are a number of ways to get ready for kick-off--prepping the delicious tailgate food and upgrading to a big-screen TV to name a few--but did you know that your Amazon Echo can help you do even more to celebrate the return of football season? Whether you have the ever-popular Echo Dot or the screen-enabled Echo Show, there are plenty of ways that the Alexa-enabled speakers can help you out this season. Planning a watch party and need to find out when the game is on? Or maybe you want to check the latest stats on your hometown team?

We present the first real-world benchmark for sequentially-optimal team formation, working within the framework of a class of online football prediction games known as Fantasy Football. We model the problem as a Bayesian reinforcement learning one, where the action space is exponential in the number of players and where the decision maker's beliefs are over multiple characteristics of each footballer. We then exploit domain knowledge to construct computationally tractable solution techniques in order to build a competitive automated Fantasy Football manager. Thus, we are able to establish the baseline performance in this domain, even without complete information on footballers' performances (accessible to human managers), showing that our agent is able to rank at around the top percentile when pitched against 2.5M human players.