Super Bowl LX: How Have Analytics Helped the Patriots? A UNH Expert Weighs In
From two consecutive last-place finishes to the Super Bowl, the New England Patriots have completed a remarkable turnaround. Some reasons are easy to spot — a new head coach in Mike Vrabel, the emergence of second-year quarterback Drake Maye, and a significantly improved roster. Others are less visible, rooted in the smallest details, such as the growing role of analytics.
With a Super Bowl matchup against the Seattle Seahawks on the horizon, we spoke with Peter Zaimes, senior lecturer of decision sciences and founder of the UNH Sports Analytics Lab, about how the Patriots have used analytics to gain an edge — and how it could factor into the championship game.
“Analytics is decision support under uncertainty. It doesn’t replace coaching, it helps quantify tradeoffs and improve odds over time,” Zaimes says. “It shifts the conversation from what feels right in one moment to what is most likely to win across thousands of similar situations while still accounting for context like opponent, weather, personnel, and game state.”
What’s one way the Patriots used analytics to their advantage this season?
The way they developed Drake Maye. The Patriots built a system around Maye’s data profile rather than asking him to fit a traditional mold, maximizing his strengths and allowing him to put up massive numbers.
Peter Zaimes.
Offensive Coordinator Josh McDaniels realized Maye is most dangerous when playing at a high tempo with simplified "half-field" reads. By analyzing his fastest release times, they built more RPOs (run-pass options) and play-action designs that allowed him to use his speed to stress defenders. As a result, he finished the year with a 72% completion rate (leading the NFL) because the scheme ensured he was never holding the ball long enough for his mechanics to break down.
The analytics team also used "tight window" tracking to see exactly where Maye was most accurate. The data showed that Maye excels at layering the ball into intermediate "seams" — tight spaces between defenders — rather than just checking it down. Instead of asking him to be a "game manager," McDaniels called plays that targeted these specific high-probability windows.
How do New England and Seattle differ in how they use analytics?
The Patriots lean into situational and opponent-specific decision support using data to sharpen fourth-down choices, clock management, and matchup planning so they can steal hidden edges in high-leverage moments. That approach fits a “win the possession” mindset: minimize mistakes, maximize field position, and exploit opponent tendencies as the game tightens.
Seattle’s analytics edge shows up in consistency. The Seahawks focus on staying ahead of the chains, avoiding negative plays, and running a system that works week after week. Defensively, data helps them disguise coverages and confuse quarterbacks, relying less on star power and more on structure to force bad decisions.
In a Super Bowl setting, the contrast is clear: New England looks to win key moments, while Seattle aims to control the game through steady efficiency and defensive pressure.
What do the analytics suggest will matter most in this matchup?
This game is likely to swing on early-down efficiency and avoiding negative plays, not just highlight-reel explosives. The team that consistently stays “on schedule” (2nd-and-6 instead of 2nd-and-12) usually wins the late-game leverage battle because it keeps the whole playbook open. It also puts a spotlight on pressure without blitzing, so if one defense can generate disruption while keeping coverage numbers intact, it can quietly tilt the game.
Another key is third-down distance management: converting 3rd-and-3 is more manageable than 3rd-and-9. I strongly favor New England for having to convert a key third-and-long based on Maye’s legs.
If the game is close late, what’s a situation where analytics could influence a key decision?
A classic analytics moment is 4th-and-2 (or 4th-and-3) near midfield, like the opponent’s 43–to-47 yard line, with 4–to-6 minutes left in a one-score game. Coaches are choosing between:
- Go for it (higher win probability if you convert),
- Punt (trust your defense and pin them back), or
- Try a long field goal (low make rate + huge field-position risk if you miss).
Analytics often favor going for it there because the conversion rate is strong and a first down can be worth far more than the expected value of a punt — especially if you’re down late and possessions are limited.
Where might the Patriots try to exploit an edge?
The Patriots’ clearest “analytics edge” in this matchup is leaning into what they do best: throwing the ball. The numbers strongly suggest New England should put the game in Maye’s hands with quick game, play-action, and spread-out passing concepts.
One vulnerability analytics would help them target is that Seattle is prone to turnovers, especially if the Patriots can create pressure and force the Seahawks into longer down-and-distance situations. Seattle committed 28 turnovers in the regular season, and quarterback Sam Darnold had 20 himself.
The Patriots' plan on paper is simple: win early downs through the air, avoid negative plays, and make Seattle’s offense earn every possession without giving away free field position. If the game comes down to a few plays, the Patriots will likely be hunting the biggest analytical swing point of all: stealing one extra possession via a takeaway or a high-leverage stop.
Quick Hits
How analytics is impacting football
Shift from tradition-based decisions to expected-value decision-making.
More aggressive fourth-down choices, turning former punt territory into scoring opportunities.
Offensive focus on staying “on schedule” with efficient early-down plays.
Routine short completions driven by coverage tendencies and down-and-distance optimization.
Player evaluation centered on efficiency, decision-making, and handling pressure — not just raw stats.
Greater mathematical optimization of special teams and clock management to gain hidden edges.
Analytics in this year's playoffs
- Bears vs. Rams (Divisional Round): Chicago faced a short fourth down near midfield in a tight game and chose to punt. Analytics favored going for it, and NFL Next Gen Stats estimated the decision cost the Bears about 3% in win probability. The Rams responded with a momentum-shifting scoring drive.
- Patriots vs. Texans (Divisional Round): New England faced a 4th-and-1 and went for it instead of attempting a long field goal. Analytics labeled it a clear “go” decision, increasing win probability by more than 3% just by attempting the conversion. The Patriots converted and went on to score.
- Broncos vs. Patriots (AFC Championship): Denver went for it on fourth down deep in Patriots territory and failed. A field goal would have tied the game, and with heavy snow later affecting play, the decision reshaped late-game strategy and ultimately the outcome.
Did you know?
Super Bowl LII between the Eagles and the Patriots — and the “Philly Special” — is an example of analytics in action.
The Eagles were already one of the league’s most analytics-aggressive teams on fourth down, and Head Coach Doug Pederson used game-day decision support (including analytics input in his headset) to treat certain situations as “green light” go-for-it territory rather than automatic field goals.
In that moment — 4th-and-goal at the 1 just before halftime — the data favored a touchdown attempt over settling for three points. The play itself was a well-practiced trick design: a direct snap, a pitch, and a pass to quarterback Nick Foles. Analytics didn’t replace coaching instinct — it supported it, reducing the fear of the moment by aligning the decision with the highest-value outcome.