Fantasy Football League Invokes IBM Watson APIs to Improve Fan Experience

In a move that could have broad implications for how APIs get used within the context of advanced analytics applications, Edge Up Sports, an organizer of a fantasy football league, revealed today that it plans to make use of IBM Watson cloud services to make it simpler for more fantasy football players to participate in the league.

Edge Up Sports CEO Ilya Tabakh told ProgrammableWeb that the fantasy football league organization will initially make use of the APIs that IBM gained when it acquired AlchemyAPI earlier this year. Specifically, Edge Up Sports will invoke text analytics and sentiment analysis APIs to make it easier for fans to aggregate various media reports about specific players they may be tracking.

The challenge that fantasy football league players face today is that it takes a lot of time and effort to track media reports concerning specific players. The APIs developed by AlchemyAPI make it simpler to aggregate all the content and then invoke sentiment analysis tools to determine to what degree those reports are positive, neutral, or negative.

Tabakh says that Edge Up Sports has plans to invoke additional IBM Watson APIs, such as image recognition, once it masters this first wave of IBM Watson APIs within its offerings. The goal, said Tabakh, is to not only reduce the amount of time participants in the league have to spend researching players, but also make the fantasy football league more accessible to a larger number of players.

In theory, that same approach can be taken to almost any activity that requires continuous tracking of information. For example, there’s not much difference from a content perspective between tracking reports about football players and public companies. In the latter case, those reports could be included within a stock trading service.

Thanks to the rise of APIs, coupled with advanced analytics and Machine Learning algorithms, the amount of data that can be exposed to developers is increasing in terms of both volume and quality. Rather than simply gaining access to raw data via API, developers will be able to access large amounts of data which, from an analytics perspective, have already been sorted and aggregated.

The challenge facing developers now is having the patience to wait for providers of APIs to develop the technical expertise required to implement advanced analytics and advanced machine learning algorithms at a time when there isn’t much expertise with those technologies generally available.

Be sure to read the next Analytics article: Twitter to Provide New Data via Gnip Insights APIs