Sports partnership deals are bundles. Four social posts, an email blast, a banner campaign, an in-stadium activation — all wrapped into a single negotiated dollar figure. The league publishes recommended valuations for each component, but those numbers are blunt instruments: tier-based, fixed-price, and only loosely tied to the audience a team can deliver.
For a Major League franchise, the disconnect was creating real friction. League guidance suggested the team's social inventory alone was worth several times what partners were actually paying for equivalent packages. Were partners getting a deal? Was the league wrong? Was the team underpricing? The team needed an answer that would hold up in negotiation.
Reverse-Engineering Value from Real Deals
Rather than start from the league's published rate card, we started from the team's actual partnership ledger — 35 active partners and the eight common deal attributes that appeared across most contracts. The question we wanted the data to answer: when partners pay for a bundle, how much value is implicitly being assigned to each component?
The league's rate card tells you what a deal should be worth. The signed contracts tell you what partners are actually willing to pay. Those are very different numbers.
For each partner we calculated the league valuation given the components delivered, and compared to what was actually paid. The pattern was striking: 18 of 35 partners were paying more than the league's number, eight were paying less, the rest were close. The team wasn't systematically under- or overpricing — it was negotiating each deal somewhat independently, with the rate card as a loose anchor.
With the dataset assembled, we ran a regression relating total deal value to the volume of each component. The coefficients have a useful interpretation: they're the average dollar value the market is willing to pay for one additional unit, holding the rest of the bundle constant.
What the Numbers Said
post (vs. $7.5–12.5K rate card)
against the league rate card
on a typical social post
From the regression-implied per-post value, we backed out an implied value per impression. Given the median post received ~37,000 impressions, that worked out to roughly $0.14 per eyeball — net of the standard revenue split with the league, that's a useful number both as a floor for follower value and as a benchmark across deal types.
The Audience-Size Anomaly
One mathematical wrinkle: because the league's rate card uses fixed prices for social rather than CPMs, every additional follower the team adds lowers its effective CPM under the league framework. A team with one million followers and a team with five million followers are valued the same on social — plainly wrong, but it's how the rate card works. Partners with sophisticated buyers tend to push back on those numbers, and the team's social inventory in real deals works out to implied CPMs that are extraordinarily high relative to typical paid social benchmarks.
What the Framework Enables
The output wasn't a fixed answer — it was a repeatable framework. As the team signs more deals, the same regression can be re-run to update the implied component values. Deal mix evolves. Audience composition shifts. A model that gets re-estimated each year captures that drift; a static rate card doesn't. For the partnership team, the immediate value was negotiation leverage. For finance, it was a more accurate way to value the audience as an asset.
Thinking about how to value your sponsorship inventory, partnership assets, or audience? Say hello.