How to Run Geo Experiments During Sporting Events

If you run marketing or analytics for a streaming platform, broadcast network, or sportsbook — or really any brand with significant advertising or sponsorship tied to sports — you don't need anyone to explain how big games move your numbers. You can feel it in real time.

When there’s an important divisional matchup or a game featuring a heated rivalry (not the HBO show), you know there’s going to be a spike in performance. For streamers, that means more sign‑ups to watch the game. For broadcasters, more tune‑ins. For sportsbooks, more first‑time deposits and bigger handle.

All generally great things… unless you’re running a geo-lift experiment at the same time.

If you don’t design that experiment to account for the sports calendar, you can end up “measuring” the impact of a can't-miss matchup instead of the effectiveness of your media. Fortunately, these mixed signals are what rigorous incrementality testing is designed to solve, preventing you from wasting ad dollars on conversions that would have happened anyway. 

The problem: When the sports schedule hijacks your geo test

This isn't a hypothetical concern. It's a recurring pattern as Haus has partnered with enterprise streaming platforms, broadcasters, and sportsbooks. As Lenny Levin, Analytical Lead for Media and Entertainment at Haus, puts it: "Almost all of our features and improvements came because we were just listening to our customers. They came to us saying, ‘We have this problem’— and our team of scientists leaned in to figure out a solution. Covariate stratification for sports is one of the clearest examples of that in practice.

"Not all sports are created equal when designing an experiment," Lenny notes. "When we think about sports testing in America, the sports calendar that causes the most chaos is the football season because of its concentrated, high-stakes schedule. Each NFL team plays only 17 games, and college football plays even fewer, and those games are only on a few select days. NBA and NHL teams have 82 games, and MLB has a whopping 162 games in a season, spread across the week. For football in the US, you have to think of it as a completely separate experiment from testing outside of sports season."

To see why, consider this scenario: You're running a geo experiment on a major streaming platform that carries a full Sunday slate of NFL games plus a marquee divisional matchup. You split US DMAs 50/50 into test and control, hit Launch, and wait for clean, causal results.

Then Sunday happens.

One group ends up with most of the markets where that divisional game actually matters — hometown teams, long‑suffering fan bases, decades of rivalry baked into local behavior. Sign‑ups, viewing hours, handle, and deposits all spike in those cities. The other group gets a quieter slate. Maybe a couple of solid games, but nothing that sets the region on fire.

From your experiment readout, it looks like the first group responded much better to your campaign. But nothing in your media plan explains that gap — it's the sports calendar doing the heavy lifting. 

In this example, the sports DMAs are concentrated in the treatment cells, potentially leading to an unlucky draw producing false lift results. But the opposite is equally impactful when they're in control. That can mask a lot of the treatment effect, leading to a false negative.

Streaming, broadcast TV, and sports betting all run into the same pattern: Real‑world sports schedules can quietly overpower otherwise good test designs. If you don't plan for that up front, your experiment doesn't know any better. It just sees a divergence between test and control and happily attributes it to "treatment." When you interpret the results, it's very easy to draw the wrong conclusion.

The "obvious" fix (and why it isn't the right one)

Another layer to this challenge is that many sports DMAs don't typically have such high volumes. For example, when Ohio State plays, the Columbus DMA can actually have higher volumes than a major city like New York. That's why they get flagged as outliers. So when you see a spike that looks like an outlier, the natural instinct is to treat it as one: Cut it out of the data or winsorize it so that extreme values are dampened.

There’s only one issue: A sports‑driven surge usually isn’t an outlier.

A sell‑out Sunday, a rivalry game, or a deep tournament run is completely real behavior, totally organic demand, and exactly how your audience is supposed to behave in those markets. The spike isn’t “bad data.” It’s your customers being fans.

As Lenny describes, "When trying to address this issue, we asked ourselves, ‘Is this actually an outlier? Or is this just the organic impact of media — exacerbated by the fact that you have a sports game?’ And after working with our customers, we realized it’s critical data that shapes the understanding of their media’s performance, and shouldn’t be removed or dampened."

So instead of editing the data after the fact, we asked a different question:

What if we could keep the sports spike in the data, but design the experiment so it doesn’t distort the lift estimate?

That’s where covariate stratification comes in. 

What is covariate stratification? 

Covariate stratification is, at its core, a smarter way to build your test and control groups for complex geo‑experiments. It works by balancing on two variables instead of one. 

Here’s how it works. First, we stratify on your primary KPI — the business metric you care most about measuring. In a standard GeoLift experiment, Haus uses stratified random sampling to divide markets (DMAs) into test and control in a way that keeps expected KPI volume balanced between the two groups. Conceptually, you can think of it as ranking DMAs by historical performance, grouping similar ones into strata, and then randomly assigning within each group. This protects you from unlucky splits where, say, all your highest‑volume DMAs accidentally land in-test.

The second stratification layer is the sports calendar, treated as a covariate. Before the experiment launches, we work with our customers to identify which markets have relevant games scheduled during the treatment window and flag those DMAs as a covariate — essentially a second dimension to balance on. At a minimum, that covariate is binary: sports‑affected market vs. not sports‑affected. The design then ensures that sports‑affected DMAs are distributed proportionally across test and control, rather than clustering on one side.

Think of it like this: When Ohio State squares off against rival Michigan on a Saturday, having Columbus, OH, in one group and Detroit, MI, in the other creates a balanced experiment design.

The result is that both groups experience the sports effect in roughly equal measure. When tournament games or big matchups drive a surge in sports‑heavy markets, that surge shows up in both test and control, and the divergence that’s left over is much more likely to be driven by your marketing, not the schedule. Your lift estimate reflects what your campaigns actually did — not which side accidentally got all the die‑hard fans.

KPI volume is still the primary balancing factor. Sports is a secondary lens applied after we’ve done the usual KPI‑based stratification. Your design doesn’t get contorted around the game schedule; it just gets smarter about it.

From uncertainty to Madness: The unique measurement challenges of March Madness 

March Madness deserves its own mention because it creates a concentrated, high-stakes sports schedule — 67 games across 3 weeks, mostly on the weekend. This condensed, weekend-heavy schedule is a particularly tricky case for geo-experiments. 

The chaos isn't about volume; it's about the concentration and unpredictability of which markets get activated each week. At the start of the tournament, there are 68 teams. You can flag their home DMAs as sports‑affected and balance them across test and control. But as the bracket progresses, some teams go home early, and others become Cinderella stories that inspire a national obsession. The importance of different markets shifts over time.

This year, most of our customers have decided that the best call for their business is to sit this one out and hold off running any holdout experiments. For these brands, the tournament represents a short, high-value window, and the opportunity cost of pulling back spend even on a fraction of the country to run a test wasn’t aligned with their business goals. 

However, if you're a brand that does want to test during this period, we’d suggest tiering the field based on historical data to reduce the likelihood of an unlucky draw and a noisy result. We’d use past tournaments to group teams by likelihood of deep runs — programs that consistently reach late rounds, then solid but less predictable programs, and then the rest of the field.

Lenny explains, "You're never going to get it perfect — but Duke, UConn, Kansas, Kentucky: there are teams that historically always make it through. Those are your tier one teams."

Then, at design time, those tiers become part of the covariate structure: Higher‑tier teams, mid-tier teams, and low-tier teams would be evenly balanced across the test and control groups. On the back end, we add a safety net after the experiment runs with leave‑one‑out (LOO) analysis. As part of our standard diagnostics, we iteratively drop individual markets and re‑estimate lift. If a late‑tournament run in one DMA is driving outlier behavior, LOO will flag it. The better the upfront design, the less work that safety net has to do — but it’s there when you need it.

What covariate stratification unlocks for streaming, broadcast, and sports betting teams

For growth marketers, media buyers, and analytics leaders at streamers, broadcasters, and sportsbooks, covariate stratification isn’t just a neat statistical flourish. It functions as an enterprise‑grade guardrail that makes your whole measurement program more trustworthy.

You get cleaner reads during tentpole events because you can run GeoLift experiments during NFL season, playoffs, or March Madness without treating the calendar as an unfixable nuisance. Tests stay interpretable even when your fans are losing their minds over questionable playcalls, missed shots, and last-minute wins. You also spend less time firefighting in analysis — fewer debates about whether to throw out certain days or DMAs, more time answering the actual business question.

It also strengthens trust with finance and leadership. When you can explain that the design is explicitly balanced on both business scale (primary KPI volume) and sports exposure (covariates), the results are much easier to defend in rooms where stakes are measured in millions, not CPMs. And it gives you a measurement stack that looks like the real world: Sports aren’t noise; they’re one of the most powerful forces driving sign‑ups, tune‑in, and wagering behavior. Your experiments should acknowledge that reality instead of dampening or removing these spikes.

The bigger point

Sports are not a bug in your measurement environment. They’re a feature of the world your customers actually live in.

The right response isn’t to pretend big games didn’t happen or to sand down every spike until your data looks flat. It’s to design experiments that can hold their shape when things get loud.

The more you do up front at the design stage — using tools like covariate stratification for sports DMAs — the less firefighting you’ll be doing on the back end. That way, you can trust what your results are actually telling you about your media, your fans, and where the next ad dollars should really go.