Spending Data Needs A Sports Lens: Why Transaction Analysis Should Incorporate Sports Expertise

By Adam Grossman

The Ramp Economics Lab recently examined credit card transaction data to measure the economic impact of sports events on local economies. This ability to track how spending in the zip codes surrounding stadiums spikes on game days, or how a single marquee matchup like the Subway Series can double daily card spending near Yankee Stadium, gives sports properties, rights holders, and their partners a powerful tool for demonstrating value.

But the same column also demonstrates a broader challenge: credit card transaction data, however powerful, is only as useful as the contextual lens through which it is applied. For sports specifically, that means any large-scale data analysis of fan spending behavior must incorporate current, on-the-ground sports expertise. Without it, the analysis risks being driven by assumptions and stereotypes that may no longer reflect reality or may never have reflected it in the first place.

Consider Ramp’s baseball spend analysis. Ramp found that corporate spending on football and basketball is large winning independent while spending on baseball is largely winning dependent. Ara Kharazian, the study’s author, concludes “Baseball is slower, and I think a harder sell for clients and prospects who may find the game…potentially boring.”

The problem is that the MLB recently has taken direct steps to reverse the sport’s perception has been characterized as slow and increasingly out of step with modern entertainment preferences. The introduction of the pitch clock for the 2023 season reduced the average nine-inning game time from 3 hours and 3 minutes in 2022 to 2 hours and 40 minutes — a 24-minute reduction and the sport’s shortest average since 1985. In 2024, games shortened further still to an average of 2 hours and 37 minutes, the fastest pace since the mid-1980s. That represents a 27-minute reduction in under two years. To put it another way, the average MLB game today is nearly as much faster relative to 2022 as a half hour of your time.

This shift has real implications for how spending data around baseball should be interpreted and acted upon. Faster games mean different concession windows, different transportation patterns, different pre- and post-game spending behavior, and different partnership activation opportunities. An analysis built on pre-2023 assumptions about the rhythm and duration of a baseball game is, quite simply, analyzing a sport that no longer really exists.

The NFL presents a different but equally instructive example. A Wall Street Journal study of NFL broadcasts found that the ball is in play for approximately 11 minutes across a game that lasts more than three hours. The remaining broadcast time is consumed by huddles, line formations, replays, and other non-game action with more than 60 percent of broadcast airtime is occurring between plays.

That does not mean the NFL is "potentially boring". But it does mean that what drives NFL fan spending is not amount of live in-game action or long breaks between action. Applying a generic “more action equals more engagement” framework to NFL spending data would be drawing the wrong conclusions from the right numbers.

These two examples illuminate a broader principle. Credit card transaction data can tell you what fans are spending. Sports expertise tells you why. The former without the latter can lead to misdiagnoses that result in mispriced partnerships, poorly timed activations, and missed revenue opportunities.

This is particularly important as large-scale data analysis becomes more central to how sports properties, agencies, and brands make decisions. The analytical tools are improving rapidly. But the quality of the insight those tools produce depends entirely on the quality of the assumptions baked into the analysis. Assumptions derived from outdated stereotypes, baseball is slow, football is action-packed, fans behave consistently across sports, will produce outputs that are precise but wrong.

The emergence of credit card transaction data as a serious tool for measuring sports’ economic footprint is a meaningful development for the industry. Getting the most out of it requires pairing the rigor of large-scale data analysis with the kind of current, granular sports knowledge that prevents the data from being filtered through the wrong lens.