AI models account for strength of schedule in NFL? - 101

Posted Sept. 16, 2025, 12:56 p.m. by Dave 1 min read

Strength of schedule, usually shortened to SoS, has a massive impact on how teams look when you glance at raw stats. If you’re only staring at the numbers without asking who those numbers came against, you’re basically reading a story without context. And in sports betting, context is everything. Stats can fool you if you don’t check the quality of opponents.

This blog is all about breaking down what strength of schedule really means, how it’s measured, and more importantly, how AI models handle it without falling into traps like data leakage. We’ll also walk through how you can actually apply these ideas yourself, what tools and data sources you’d want to lean on, and how to validate that your adjustments actually work in practice. If you’re serious about getting an edge, especially with the help of AI, you’ll want to stick around for the full breakdown.

Table of Contents

What “strength of schedule” means in NFL models
How AI models actually encode SoS
Avoiding leakage and circularity
Data and tools you’ll need
Practical recipes to bake SoS into your model
Evaluation and sanity checks
Common pitfalls to avoid
A worked micro-example
Putting it all together: a minimalist pipeline
Frequently asked clarifications
Bottom line
Conclusion
Related Posts
Frequently Asked Questions (FAQs)

What “strength of schedule” means in NFL models

Strength of schedule, or SoS, is just a way of answering the question: how tough has a team’s slate of opponents actually been? When you watch highlights or scan ESPN box scores, you’ll see numbers like points per game, yards per play, or straight-up wins and losses. But without knowing who those numbers came against, those stats can be really misleading.

Think about it. Let’s say a team is averaging 27 points per game after six weeks. Sounds elite, right? But then you dig in and realize they’ve only faced defenses ranked near the bottom of the league. Suddenly that scoring average doesn’t look as impressive. On the flip side, maybe another team is only averaging 21 points per game, but they’ve gone up against top-five defenses every week. That context flips the story.

There are two broad ways analysts usually calculate SoS.

First, the basic win percentage method. You just average the winning percentages of a team’s opponents. It’s easy and gives you a rough snapshot, but it’s super blunt. It doesn’t tell you if those wins came against weak opponents, if the game turned on fluky turnovers, or if weather totally shifted the outcome.

Second, the more advanced method: opponent-adjusted efficiency. This one looks under the hood. Metrics like Expected Points Added (EPA) per play and DVOA (Defense-adjusted Value Over Average) try to measure not just outcomes, but how efficient teams were given down, distance, and situation. Then they adjust for opponent strength. For example, gaining 5.5 yards per play against the 49ers is not the same as doing it against one of the worst defenses in the league. These opponent-adjusted approaches smooth out the noise and give a clearer picture.

That’s why most serious analytics-driven models don’t just look at raw stats. They bake in strength of schedule either directly or indirectly. Otherwise, you’re just betting off vibes, and that doesn’t cut it when money’s on the line.

How AI models actually encode SoS

When you look under the hood of AI-powered prediction systems, you’ll see a ton of clever ways strength of schedule gets folded in. Let’s walk through a few of the most common approaches.

One of the most popular is opponent-adjusted EPA/play. Basically, you look at how a team’s offense performs compared to what that opponent usually allows. If a team puts up 0.15 EPA/play against an elite defense, that’s way more impressive than the same stat against a bottom-tier defense. You can also do the same for defense: how well did a team limit opponents compared to what those offenses normally generate?

Another method is schedule-weighted rolling averages. Instead of just averaging the last three or five games, you weight those performances by how strong the opponents were. A strong showing against a top-five unit should count more than a blowout against a weak one.

Then there’s the idea of breaking it down by unit. Not all schedules are equally tough across the board. A team might have faced great run defenses but below-average pass defenses. Splitting SoS by run and pass helps capture that nuance.

Some models even cluster opponents into tiers. You might group defenses into elite, good, average, below-average, and bad. Then you can track how many snaps or drives a team had against each tier. That avoids overreacting to outliers.

There are also prior-based systems like Elo ratings. These ratings evolve weekly and give you a baseline strength measure that’s more stable than just raw stats. Early in the season, models often lean on Elo or Bayesian priors to avoid wild swings from small sample sizes.

Finally, you have situational factors like home-field advantage, travel, rest days, and even altitude or turf type. All of that interacts with strength of schedule and gives you a more realistic picture of team performance.

Avoiding leakage and circularity

One of the biggest traps in modeling SoS is data leakage. That’s when you accidentally use information from the future to predict the past. It sounds silly, but it happens more often than you’d think.

For example, if you’re predicting Week 10, you can’t use an opponent’s end-of-season stats. At that point in the year, you only know what they’ve done through Week 9. Similarly, if you’re adjusting for quarterback performance, you can’t let the model know about a midseason injury before it actually happened.

Another pitfall is double-counting. Let’s say you include both opponent-adjusted EPA and Elo ratings in your feature set. If you’re not careful, those two signals might overlap so much that you’re basically feeding the same info twice. That can make your model overconfident.

The rule of thumb here is simple: only use information that would have been available before kickoff. Nothing from the future sneaks in.

Data and tools you’ll need

If you want to build your own SoS-aware model, most of what you need is already out there. Play-by-play data is available through public projects like nflverse, and it comes with EPA pre-calculated. That gives you the building blocks for efficiency metrics.

You’ll also want schedule data, rosters, and injuries. Even something as simple as tracking the starting quarterback can make a huge difference. Weather and venue metadata, like whether a stadium is open or closed, grass or turf, or at altitude, also add valuable context.

For rating systems, you can either build Elo yourself or borrow a public version and update it as the season goes on. On the modeling side, libraries like pandas, scikit-learn, XGBoost, or PyMC in Python will cover almost everything you need.

Practical recipes to bake SoS into your model

Now let’s get into some hands-on approaches.

One classic recipe is rolling opponent-adjusted EPA/play. The idea is simple: compare what a team actually did in a game against what that opponent normally allows. Then take a rolling average of those adjusted numbers across recent weeks, weighting more recent games higher.

Another recipe is clustering. Group opponents into talent tiers, then track how often a team faces each tier. For example, maybe 40 percent of their offensive snaps came against elite defenses. That’s a clean, interpretable feature you can feed into your model.

You can also blend Elo ratings into the mix. Elo is stable and low-noise, so using it alongside more volatile efficiency stats gives your model balance.

For more advanced setups, you might build hierarchical models that automatically shrink small-sample ratings toward league averages. That way, you’re not overreacting early in the season when sample sizes are tiny.

And finally, you can use market-implied ratings. Betting markets are basically the wisdom of the crowd, and they reflect a lot of hidden information like injuries. As long as you avoid leakage, they’re some of the most predictive features you can use.

Evaluation and sanity checks

When you’re testing if your SoS adjustments actually work, you want to run forward-chaining validation. That means training your model up through Week X and then testing it on Week X+1, repeating as you go. It simulates how things would work in real life.

You should also run ablation tests. Build one model with SoS features and one without. If the version with SoS consistently wins on accuracy, log loss, and calibration, you know it’s adding real value.

Other sanity checks include checking early-season stability, looking at residuals, and making sure your features aren’t collinear or overlapping too much.

Common pitfalls to avoid

The biggest mistake is overfitting to tiny samples. Two weeks of data doesn’t tell you much, but if your model reacts too strongly, it’ll just chase noise.

Another common error is treating backup QBs the same as starters in your opponent adjustments. A defense facing Patrick Mahomes is not the same as facing Zach Wilson, and your model needs to know the difference.

And of course, don’t use stale priors. Last season’s stats should fade quickly once real games start.

A worked micro-example

Here’s a small example to show how big SoS adjustments can be.

Suppose Team Alpha has a raw offensive EPA/play of +0.12 after four games, while Team Beta sits at +0.05. Looks like Alpha is better, right?

But check their opponents. Alpha faced defenses ranked near the bottom of the league. On average, those defenses allow +0.08 EPA/play to everybody. Beta, on the other hand, faced defenses ranked in the top ten. Those defenses usually allow -0.05 EPA/play.

So once you adjust:

Alpha’s adjusted offense is +0.12 minus +0.08, which equals +0.04.

Beta’s adjusted offense is +0.05 minus -0.05, which equals +0.10.

Suddenly Beta looks better, even though their raw stat was lower. That’s the power of opponent adjustment.

Putting it all together: a minimalist pipeline

Here’s how a simple SoS-aware pipeline might look. You start by pulling play-by-play data and building weekly aggregates for offense and defense. Then you build opponent-adjusted features by comparing what teams did against what opponents usually allow.

Next, you maintain Elo ratings as a stable prior. You layer in situational context like travel and rest days, plus QB status. Then you build your feature set and train models like gradient boosting for win probability or point spreads.

Finally, you validate with forward-chaining, run ablations, and keep refining your priors early in the season.

Frequently asked clarifications

One question people always ask: can’t AI models just learn strength of schedule implicitly if you feed them enough raw stats? Sometimes, but it’s messy. Explicitly modeling SoS is more efficient and way less noisy.

Another common one: is win percentage-based SoS ever enough? For quick conversations, sure. But for actual betting models, opponent-adjusted efficiency is way stronger.

People also ask how many weeks of prior data to use. A good rule of thumb is three to five games with exponential decay, plus season-to-date features with heavier shrinkage early on.

And yes, you should split pass and run separately. That granularity helps.

Bottom line

So do AI models account for strength of schedule? The good ones definitely do. They use opponent-adjusted stats, priors, and situational context to make sure raw numbers don’t mislead. They build rolling features, apply shrinkage early in the season, and carefully avoid leakage.

The analytics world agrees: if you’re not adjusting for schedule, you’re flying blind.

Conclusion

At the end of the day, strength of schedule is the thread that ties everything together. Raw stats will always mislead you without context. Adjusting for opponents, weighting recent games, accounting for injuries, and validating carefully is the only way to get a true read on team strength.

That’s exactly why platforms like ATSwins exist. ATSwins is an AI-powered sports prediction platform built for bettors who want sharper insights. It delivers data-driven picks, player props, betting splits, and profit tracking across NFL, NBA, MLB, NHL, and NCAA. Whether you’re on a free plan or a paid one, the goal is simple: give you practical tools to make smarter, more informed decisions.

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Frequently Asked Questions (FAQs)

What does it mean when we say AI models account for strength of schedule?

It means the model adjusts team stats based on how tough their opponents were. If a team racks up yards against weak defenses, the model doesn’t treat it the same as yards gained against elite defenses.

How do AI models do this without leaking future info?

They only use stats available before kickoff. That means computing opponent averages through the previous week, not using end-of-season numbers. They also lock in injury and roster data only when it’s confirmed.

Why do predictions improve when SoS is factored in?

Because raw stats can create illusions. A team that looks elite on paper might only be beating up on weak opponents. Once you adjust, those paper tigers get exposed, and your predictions get sharper.

How can I test if my own model is actually using SoS?

Run an A/B test: one version with SoS features, one without. Compare accuracy, calibration, and log loss. If SoS helps, you’ll see the difference.

How does ATSwins handle this across sports?

ATSwins builds opponent strength directly into its models using opponent-adjusted efficiency, travel and rest context, and rolling recency weights. Predictions are calibrated weekly, profits are tracked transparently, and both free and paid plans are designed for bettors who actually want results, not fluff.

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