AI and IBM Watson Score to Make ESPN Fantasy Football Trades More Fair
Millions of ESPN Fantasy Football team “owners” are now able to get help from IBM and its Watson AI computing services to ensure that the player trades they make using ESPN’s mobile apps can be completed more fairly and equitably.
In the past, the ESPN fantasy football team owners negotiated trades using whatever information they could compile on their own about the NFL players they had on their rosters. Typically, that meant they used four sources to evaluate player trades, leaving the potential that they might not get a fair trade deal for a player they were sending to an opposing team owner.
The new feature, Trade Assistant with IBM Watson, was recently announced by IBM to bolster the capabilities of the online gaming platform. It is now available for use as part of the ESPN Fantasy Football Insights with IBM Watson services that have been available to players for the last four years, John J. Kent, the program manager for IBM sports and entertainment partnerships, told EnterpriseAI.
For fantasy team owners, this is a helpful addition that will provide data-driven recommendations about equitable trades, said Kent.
“What we’re told by ESPN is that player trading really does not occur much in their leagues,” he said. “This [new tool] can encourage trades to occur. Before, we speculated that people were being put out there with lopsided trades that weren’t fair,” causing them to hesitate from making additional trades.
“It’s fairy complex to go and look at making trades on your own,” said Kent, who himself enjoys fielding a fantasy NFL team using the ESPN apps. “We’re hoping it makes that a whole lot easier for team owners.”
The new trade assistant feature builds on the previous capabilities of IBM Watson’s involvement with ESPN by using advanced natural language processing capabilities to extract, translate and review inputs on football players and teams, said Kent. That includes player statistics, opinions from experts on ESPN.com, as well as a wide range of other news articles, blogs and podcast transcripts. By gathering all these sources and sifting through the data using AI and Watson, fantasy team owners can now go through a huge amount of previously unavailable structured and unstructured data to get the best insights to make their trades, he said.
During the 2019 ESPN Fantasy Football season, the initial IBM Watson services analyzed nearly 228 million articles and delivered 25 billion insights to team owners around the world, according to IBM. Before the new trade assistant feature, the Watson AI services were used for player insight, developing risk versus rewards scenarios, and helping team owners decide which players to acquire, said Kent.
So far, the new AI feature, which is only integrated into the ESPN fantasy football mobile apps, will be available to fantasy team owners throughout the 2020 season, according to IBM. It can’t be used on the ESPN Fantasy Football website.
“IBM Watson and the technology is going to look at your team, evaluate the cost of losing one of the players on your team and at upcoming matchups and other details, and it will give you recommendations,” said Kent. “It will use the AI to fairly balance those trades, to give team owners some balance to the deal.”
The trade assistant looks at a team owner’s players and team and can understand the market value of players in their leagues and the market value of players on their own teams, said Kent. “In a trade, there’s a cost of losing your player, so we calculate that, too. It’s giving a lot more information for team owners. It’s putting together what we think is a fair and balanced trade.”
For IBM, the Watson integration with ESPN Fantasy Football is just one example of how AI and natural language processing can help all kinds of businesses, said Kent.
“All businesses want to understand what customers are saying about them and that comes in with natural language processing,” he said. “It’s very much a demonstration of natural language processing that applies to every business and industry.”