Micro-Betting Will Change Sports, but Tech Challenges Remain

David Nugent

In the same way fantasy sports changed the sports business by engaging fans in player performance outside of their favorite teams, U.S. sports betting will engage a new generation of fans, generate billions in betting revenue, and millions in additional ad and sponsorship revenue driven by a massive amount of new fan engagement. But traditional “outcome-based” betting (i.e. betting winners/losers, spreads, over/under, etc.) is only the beginning of the story.

“Micro-betting” is loosely defined as betting during the event, on things that are about to happen in real time, and do not rely on the final outcome of the event. J.P. Morgan estimates there will be more than $9 billion per year spent on U.S. sports betting by 2025, with almost $7 billion of that on in-play betting and micro-bets. Micro-betting will usher in a new era of sports betting and have a profound impact on the sports ecosystem more broadly.

Although micro-betting already exists, there are challenges to opening the floodgates to billions of dollars in micro-betting revenue. Simply stated, those challenges are speedscale and smarts.

Let’s start with the challenges of speed, specifically the speed that live data and video can be delivered to fans. All digital sports experiences rely on the delivery of data from either a league or a data provider. The speed at which that data can be delivered, parsed and presented to a fan, in a way that helps them understand what is happening in the event, is critical. Timing gaps between the live event and a bettor’s ability to wager on what is about to happen present integrity issues.

Although sports betting will put stress on firms’ technology, professional leagues and data providers like Sportradar and Genius Sports have generally solved the real-time data issue in the past year or so. Delivering real-time video and synchronizing what a fan sees with the data needed to bet is another challenge. Video delivery at scale has traditionally leveraged caching, which makes video delivery cheaper and more stable, but the real-time video needed for betting will be more expensive and less stable, at least in the short term. Again, some providers are promoting solutions for this with varying degrees of accuracy.

The second challenge with speed for micro-betting takes place after the live data is received by the bettor. Bettors will want bets to be resolved quickly, as it may impact how much or what they bet on next. For example, there is an average of about 24 seconds between pitches in Major League Baseball. If a sportsbook were to surface a betting opportunity about the next pitch to a batter, it would need to decide what opportunity to surface (i.e. “Will the next pitch be a ball or strike?” or “Will the next pitch be put in play?”), deliver odds on that wagering opportunity to a user, have the bettor react/wager, and receive and record the wager before the pitcher delivers the pitch. While sportsbooks have found ways to offer certain versions of this (e.g. “Will an NFL kicker make this field goal?”) with limited up-time for some high-profile games, currently partnering with B2B product company Simplebet is the only way to offer these types of markets at scale (for every drive and every play of every game).

This leads us to the second challenge, scale.

Executives at sportsbooks agree that they see the momentum and the upside to micro-betting, but are challenged to do it accurately, and at scale. They also see micro-bets as a great path to pull more casual sports fans into the sports betting ecosystem once the tech exists.

Kelly Pracht thinks her startup, nVenue, has solved both the speed and the scale problems in micro-betting. (Full disclosure: The author is part of a firm that has invested in nVenue.) “Machines are very good at solving problems at scale, but surfacing a proper betting opportunity quickly enough while also creating right probabilities and lines, and doing it across many key moments in many sports requires a massive amount of compute cycles,” Pracht said. “It’s only recently that we could get access to the data we need in real time. Now that we have that we are really excited about what we can do.”

The third challenge is probably the most important when it comes to the broad adoption of sports betting, and that’s “smarts.” Can the ML/AI required to quickly deliver betting opportunities surface smart betting opportunities that make sense to sports fans?

“As fans we don’t bet the way the gaming industry does,” said Pracht. “We like to predict plays and enjoy being proved right.” She calls these “Romo-like predictions,” in honor of NFL analyst Tony Romo’s knack for letting fans in on what the next play will look like. The goal is reproducing that with machine learning and offering micro-bets.

There’s also always the possibility that because micro-bets happen so quickly, they become more difficult to monitor from an integrity standpoint. This could lead to the type of issues called out this week as the FA looked into suspicious betting patterns around a yellow card in an Arsenal match. By and large, though, the current hurdles seem to be more closely tied to tech than cheating.

As is now the case across all sports, technology will play a critical role in the path toward billions of dollars in new revenue. Micro-betting is poised to change the size of the sports betting ecosystem, drawing in legions of fans who were not traditional gamblers, and generating billions in new revenue.

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