Council Post: The Role Of AI In Sports Fan Engagement

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Nancy Hensley is the Chief Product & Marketing Officer (CPMO) atStats Perform, bringing great products to market in Sportstech.

When Moneyball first entered the public conversation, the baseball community largely met it with skepticism. The concepts that drove Moneyball have been documented for some time but didn’t get put to the test until recently. Scouts had been using the same processes since these sports began. No one understood these new methods, and many felt threatened by them. Expected goals (xG) in soccer was another metric people didn’t understand at first but that is now widely embraced.

Today, advanced analytics are widely embraced. Journalists share insights around exit velocity and curvature of pitches. Soccer broadcasts can measure momentum swings and their impact on matches.

Although deep-dive analysis isn’t a new concept, how we consume it—and how we educate others using it—is changing. As the chief product and marketing officer at a company that offers AI capabilities for the sports industry, I’ve found that fans crave more insights into their favorite teams and players and more platforms to watch sports via over-the-top media services and streaming providers. With the introduction of big tech into sports media, the experience looks very different than it did on a linear TV 10 years ago. For example, Amazon Prime boasts “Next Gen Stats” to differentiate their NFL broadcasts, and the NBA has teamed up with Microsoft to “up their game” in a more technology-infused fan experience.

What Fans Really Want

Many fans want to dive in deep when watching sports. They love chatting with friends and family about their favorite teams, dissecting certain plays and engaging with other fans around shared interests. Beyond that, I believe they can benefit from personalized data and content to create their own experiences.

But not every fan chooses to sit and watch a full soccer match or spend time driving to and from a football game.

In short, fans should be able to experience and celebrate the moments that matter most to them. AI can play a major role in capturing and predicting those moments.

Nothing engages a fan more than insights into their favorite team and player. The challenge is serving up those insights at scale at the right time. AI can help with key insights and visualizations. AI and machine learning can automatically detect formation patterns that could take humans hours to do, allowing sports broadcasters, analysts and fans to find the hidden story faster than ever.

Computer vision and optical tracking can offer an extra set of “eyes” to fill in the blanks and create performance data that previously didn’t exist. Predictive analytics can assess player growth and development potential.

Content is king. Technology such as natural language generation (NLG) can deliver relevant content at scale, including match previews, recaps and bios, bringing us closer to players both on and off the field.

An AI Toward The Future

Although AI has become more prevalent in sports, there’s still room for growth. Two key areas to watch are betting and safety.

The cost of TV ads for online gambling and gambling services more than doubled from 2020 to 2021 but only accounted for about 1% of the total TV advertising market, according to Nielsen data reported byBarron’s. Tech companies should work to support leagues and betting services in driving clients to integrated experiences in ways that make it easier to watch, bet and get the stats and predictions they need, when they need them.

Sportsbooks of old didn’t focus on content. Today, sportsbooks understand that bettor engagement is critical to their retention and share of wallet. Therefore, sports tech companies should work on developing advanced statistics, predictions and even interactive experiences for sportsbooks and guide them in integrating that type of content into their customer experience.

For example, they could develop advanced filters for betting services to help fans make more informed bets. They could also develop AI for reengaging the bettor, sending automated messages with predictions and integrated odds, or letting the bettor review auto-generated highlights of key match moments.

They should also find ways for teams and leagues to use data to improve player and fan safety. For example, the NFL collects information from kickoffs and punt returns to determine what changes could make these wild plays safer for everyone involved.

They should also develop ways for brands to track social media posts to analyze abuse trends, tactics and networks—aiming to create inclusive, secure platforms for fans across the globe. That safety already extends to games: The Dutch government has partnered with soccer stakeholders to address discrimination using technology.

We’re scratching the surface of how fans and sports can engage together. Yet there’s one constant I’ve seen: Fans want personalized content and experiences that immerse them in the action.

However, making AI consumable, scalable and relevant is easier said than done. It takes different skill sets to accomplish. If you are a tech company starting to take the journey toward leveraging AI, it’s important to understand that you need more than just a few data scientists to make it all work. I believe the biggest challenge has always been and will remain the data.

Data is food for AI, so ensuring the data flows properly through the right pipelines is the only way your models can perform their magic. In my experience, the reason so many data science projects take longer is because we tend to rely on the data scientist to also do the data engineering. Data science is a team sport. That team should include data engineers and ML engineers alongside data scientists and software engineers to “productionalize” AI. Having the right players on the team is a winning strategy.

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