IBM's AI Finds Its Ideal Testing Ground in Fantasy Football
As another American football season gets fully underway, vast numbers of players are returning to ESPN's Fantasy Football Draft, which stands as the sport's leading strategy game.
IBM recently extended its collaboration with ESPN for yet another season, with the company providing players with more extensive offerings than previously available.
For this year, IBM has incorporated its watsonx platform into ESPN's system to provide users with guidance and strategies that could assist them in refining their approaches to the game.
The latest Fantasy Insights feature produces tailored recommendations for the platform's 14 million users drawing from what IBM states are 36 billion insights analysed across the season.
Can AI create the perfect fantasy draft?
During the 1990s, IBM's supercomputer Deep Blue notably defeated world champion Garry Kasparov at chess.
Players seeking an advantage will be anticipating that IBM's technology might provide them with a comparable edge over their competitors in this season's draft.
The latest feature displays on the ESPN Fantasy Football homepage and provides users with AI-produced summaries of the players who are pertinent to each week's matches.
It functions in conjunction with IBM's current collection of AI-driven tools, which include Waiver Grades, Trade Grades, Boom and Bust probabilities and the Trade Analyzer.
"Fantasy football is all about optimising decision making, and AI is helping fans make them with deeper insights," says Jonathan Adashek, Senior Vice President of Marketing and Communications at IBM.
Jonathan regards the technology as an excellent demonstration of AI, introducing millions of individuals to the capabilities of modern AI technologies.
"With Fantasy Insights Built With IBM watsonx, we're putting the power of watsonx into the hands of millions of people on the ESPN Fantasy Football app, helping them make smarter, more informed decisions while also showcasing the same capabilities businesses around the world use to scale AI," he says.
What can gamers expect from IBM's watsonx?
So, what precisely does IBM's technology involve?
IBM's platform categorises players through data-driven criteria spanning numerous dimensions.
Fresh categories for 2025 include Buy Low Sell High, which pinpoints players who have thus far encountered strong defences but who will compete against less challenging opponents in the weeks ahead.
Additionally, there's the Diamond in the Rough function. This focuses on the footballers who have been rostered by less than 85% of Fantasy Football players, but who have scored in the 85th percentile for two consecutive weeks.
Trade Bait is an insight that spotlights the players that are frequently chosen in drafts, but have been delivering subpar performances for two successive weeks.
Additional categories include predictive analytics, media sentiment analysis and risk assessment for NFL players, which demonstrates the scope and thoroughness of IBM's analysis.
Predicted Top Boom and Predicted Top Bust examines player performance for future weeks, whilst Hot in the Media monitors popularity in media coverage.
Media Darlings combines a tenfold rise in positive sentiment, but who have been selected by less than 50% of players.
Lastly, the Risky Business category examines high-projection players coming back from injury, while Sure Thing pinpoints players with dependable scoring floors and Game Breaker spotlights those likely to surpass projections.
A fruitful partnership
Watsonx.data serves as the engine powering ESPN's latest data-driven offering, gathering information from an extensive array of sources before processing it with AI.
It extends beyond just Fantasy Football for ESPN, though, and it isn't exclusively about that for IBM either.
The sports media corporation believes that these latest AI features might assist in boosting user engagement throughout its platforms, while simultaneously providing its game with a USP in a crowded and competitive marketplace.
For IBM, possessing such a prominent customer use case provides its technology with substantial visibility, demonstrating to consumers and businesses its AI credentials.
With millions of simultaneous users utilising IBM's AI and processing recommendations in real time, this collaboration serves as the ideal proving ground.
