FEATURED STORY
How Sports Teams Should Solve For ‘The Invisible Fan’ - Using AI
Photo: Ramp Economist Research Report of Corporate Spend on Sports Teams.
Hands down, the biggest structural issue in all of sports is “the invisible fan.”
The most daunting statistic is that SeatGeek told Sports Business Journal that venues know only about 40% of attendees.
This is no secret; sports teams are notoriously bad about knowing who their fans actually are.
Our take: the industry does not just need more fan data. It needs a unified fan layer that can actually be queried and used by commercial teams.
Despite having so many unique touch points spanning digital and live events, a team may know the original ticket buyer, but doesn’t fully know:
Who actually attended that game
Whether that person was a friend, client, or resale buyer
What they bought in the stadium (concessions or merchandise)
How that fan engages with the team on social media
To add fuel to the fire, arguably the hottest company in tech right now highlighted another invisible customer: the corporate buyer.
The preconceived thought might be that businesses spend on “autopilot” for sports teams tickets & luxury suites to wow clients and close deals. Data suggests that in football and basketball, companies often keep spending regardless of how the team is performing.
Baseball is the exception. There, corporate demand behaves much more like a real market, responding to wins, timing, and momentum.
Ramp, the $32B AI-powered financial operations startup, just dropped an economic research report revealing how corporations spend on sports teams tickets vs. the team’s performance.
In-house economist, Ara Kharazian, tracked $57M in corporate spend on sports teams and venues across 68 teams in the NFL, NBA, and MLB - from 2022 to 2025.
When the Kansas City Chiefs went 6-11 last season, companies spent 2x as much on their tickets vs. when they did in the Chiefs’ Super Bowl-winning 2024 season.
The Las Vegas Raiders won three games, and companies still spent $2.1M taking clients and prospects.
The Washington Wizards are currently 17-55 with a few weeks left in the season and have amassed $700K+ in corporate spend.
However:
“That’s not the case in baseball. When the Cleveland Guardians’ win rate dropped 10 percentage points in 2023, corporate spend fell 78%. The next year, they improved by 10 points, and spend came roaring back, beating their previous record.”
The key finding: When an MLB team’s win rate improves 1%, it’s associated with a 4% lift in corporate spend on tickets. When a franchise has a 10% improvement in win rate, it translates to 37% more corporate dollars funneling in.
While the baseball angle is compelling, the bigger takeaway is that Ramp, a third-party company outside the traditional sports ecosystem, was able to uncover a customer-spend insight that many teams still may not have a unified view into themselves.
Fret not, this isn’t another pessimistic piece on what’s wrong.
We’re going to cover:
How AI & MCP (Model Context Protocol) Can Fix This Gap For Sports Teams
Why Emerging League & Team Operators Should Use This As A Cautionary Guide
Teams Are Racing To Build With AI, But Here’s Where To Start: MCP
Caption: Sports Leagues are racing to hire AI Staff - this job alone paying up to $220K.
Four weeks ago, we wrote a preliminary piece on The AI Tech Stack We’d Use If We Owned A Sports Team.
In that, we tried to remove the noise and highlight a few pieces of AI we’d use for various workflows to automate things like: ticket sales, sponsorship sales, and social media.
But we failed to explain an even more integral piece that teams should be focused on where to start: MCP or Model Context Protocol.
Put simply, MCP is not a database. It is the connective tissue that lets AI tools talk to the systems where the data already lives.
The first fix has already started:
More digital ticketing tech
More account-linked entries
Better group-ticket identity capture
More centralized league/team data models
But the next problem is usability.
Because even when the data exists, it often still sits in too many tools and silos.
If a team builds the right data foundation, MCP-style architecture can make ticketing, CRM, concessions, parking, merch, sponsorship, and marketing systems far more queryable through a common interface.
Instead of Ticket Sales or Corporate Partnerships bugging Analytics or rummaging through Tableau dashboards, a non-technical team member themselves could ask things like:
Which guests brought by suite holders have the highest return rate?
Which corporate-hosted guests later convert into ticket buyers?
Which sponsors over-index with our known fans?
By stitching previously siloed data into one operational layer, sales, partnerships, ticketing, and marketing teams can query the fan graph directly in plain English instead of waiting on analysts.
And this is just the beginning. The next step is hyper personalized & targeted marketing based off of connecting all the previously fragmented fan touchpoints.
So if you operate a team, one of the key hires I’d make in 2026 is someone who can help with AI.
What To Focus On If You Invest in or Run an Emerging Team or League:
At the earliest stages, emerging sports leagues and teams focus on optimizing for eyeballs. With a strong social following or number of viral videos, emerging team & league execs & investors can try to justify proof of demand.
But if there’s any cautionary wisdom we can pull from the established leagues, avoid falling into the trap of being content rich, but data poor.
The trap is easy to fall into:
You launch the league.
Crowds show up.
Tickets move.
Sponsors sign.
Hospitality sells.
And everyone assumes the business is building real fan relationships.
But if ticketing, group sales, sponsor activations, merch, concessions, parking, and CRM all live in separate systems, the league ends up with fragments of fans instead of customer knowledge.
Instead:
Emerging leagues should build a unified customer graph before they build a bigger audience.
The NFL did not centralize and expand customer-data sharing because it was ‘a nice to have’. It did it because fragmented fan data was limiting engagement and sales.
If you do not know the fan, you cannot fully know the sponsor outcome either.
A fragmented fan graph weakens targeting, personalization, retention, and sponsor measurement. This is key data that’s directly correlated with justifying larger (or smaller) sponsorship investments.
More data is not the same as usable data.
Even if the league collects ticketing, app, concessions, merch and sponsor data, that still does not help much if the systems cannot talk to each other.
Because once the league scales without these foundational pillars, it inherits the same problems incumbents are now spending years untangling:
anonymous attendees
weak sponsor attribution
limited upsell visibility
poor re-engagement
and too much dependence on analysts to answer basic commercial questions
Our Final Thoughts:
The next winners in sports won’t just have bigger audiences. They’ll have a clearer view of who those audiences actually are, and a faster way to act on that knowledge.
As new AI products are shipped every day, most people are falling into analysis paralysis. The amount of options to pick from makes it intimidating to find a place to start from.
If any of this was interesting to you and want help around how your org thinks about AI, feel free to reach out! We’re more than happy to help.