Do AI models account for strength of schedule in NFL?

Posted Sept. 16, 2025, 11:48 a.m. by Luigi 1 min read

Strength of schedule is one of those things that sounds simple until you actually try to use it in sports betting or building models. On the surface, it just means looking at how tough a team’s opponents have been. But when you dig in, you realize that it completely changes how raw stats should be interpreted. A team that looks dominant in the box score can be exposed once you account for who they actually played, while another team with mediocre surface stats could secretly be playing elite football against brutal opponents. That’s why understanding strength of schedule (SoS) matters for bettors, analysts, and anyone who wants a realistic view of team quality.

This guide breaks down everything about SoS, how to measure it, and how modern AI models handle it. We’ll cover practical ways to build SoS into predictions, how to avoid common traps like data leakage, and how platforms like ATSwins use these ideas to sharpen their picks. By the end, you’ll see why this one adjustment can make the difference between a model that gets tricked by paper tigers and one that stays sharp all season long.

Table Of Contents

What Strength of Schedule Really Means
How AI Models Encode SoS
Data and Tools You’ll Need
Practical Recipes to Bake SoS Into Your Model
Evaluation and Sanity Checks
Common Pitfalls To Avoid
A Worked Example
Putting It All Together
Conclusion
Frequently Asked Questions (FAQs)

What Strength of Schedule Really Means

SoS is basically the adjustment layer for stats. Imagine two teams: one drops 30 points a game but only against bottom-ten defenses. Another averages 22 points per game but they did it against defenses ranked in the top five. Without SoS, the first team looks elite, and the second team looks average. Add SoS, and suddenly you realize the second team is actually tougher. That’s the whole point—stats aren’t telling you the whole story unless you adjust for who the opponent was.

There are two main ways SoS gets calculated. The first is win percentage based SoS. This one is simple: you take the average winning percentage of a team’s opponents. Sometimes you even go another level and look at the opponents of those opponents. It’s quick, but it’s blunt. It doesn’t factor in things like garbage-time points, weather, or key injuries. It also lags because early-season win percentages can be noisy.

The second approach is efficiency-based SoS. Instead of just looking at wins and losses, this digs into what teams actually did on the field. Think stats like expected points added per play (EPA) or Defense-adjusted Value Over Average (DVOA). These metrics try to capture how well a team performs per play or per drive and then adjust those results by opponent quality. That way, if you gain five yards a play against the 49ers, it means something totally different than gaining five yards against a bottom-tier defense.

Efficiency-based SoS is way more reliable for forecasting because it digs under the hood. That’s why most serious models lean on opponent-adjusted efficiency rather than raw win percentage. It’s also why bettors who ignore SoS often end up betting on “fake good” teams that fall apart once they face real competition.

How AI Models Encode SoS

AI models don’t all handle SoS the same way, but most of the competitive ones do it in some form. There are a few common feature-engineering tricks you’ll see:

First, opponent-adjusted EPA per play. You take a team’s offense or defense performance and compare it to what their opponents normally allow or produce. For example, if your offense racks up +0.15 EPA per play against a defense that normally gives up +0.10, you’re +0.05 above expectation. That’s a big deal.

Next is schedule-weighted rolling averages. Instead of just taking a team’s average over the last three games, you weigh those games by how good the opponents were. A solid game against a top defense counts more than an average game against a weak one. Add time decay so recent games count more and you’ve got a powerful feature.

Another trick is breaking out offense-defense unit splits. Strength of schedule isn’t one thing. A team might face elite run defenses but weak pass defenses. If you lump it all together, you miss those nuances. Splitting pass and run SoS separately on both sides of the ball gives the model way more precision.

You’ll also see clustering. Basically, you group opponents into tiers—elite, good, average, bad—based on efficiency metrics. Then you track how much time a team spent playing each tier. That helps reduce noise and makes features more interpretable.

For early in the season, models lean on priors like Elo ratings. Elo gives every team a starting score based on last season, regressed toward the mean, and then updates weekly. It keeps things from getting too wild in small samples.

Finally, AI models almost always add context like home field, rest days, travel distance, injuries, and especially quarterback status. Playing the Jets defense is not the same thing if Zach Wilson is starting versus Patrick Mahomes. These little contextual tweaks stop the model from drawing the wrong conclusions.

Data and Tools You’ll Need

If you want to actually build a model that adjusts for SoS, you don’t need to reinvent the wheel. Public play-by-play data, efficiency metrics, and even weather and travel info are all out there. For NFL, play-by-play datasets already have EPA built in. You can compute opponent adjustments by comparing each game to what those opponents normally allow. Schedules, rosters, and injury reports are all public. Stadium metadata like turf vs grass, altitude, or whether it’s a dome is easy to get too. Weather data can be pulled from free sources as well.

On the modeling side, Python and R both have tons of libraries. In Python you’ve got pandas, numpy, scikit-learn, and gradient boosting libraries like xgboost or lightgbm. R has similar options. If you’re into Bayesian stats, packages like PyMC or brms let you build models that directly encode uncertainty.

For travel, it’s as easy as plugging stadium coordinates into a distance function. That gives you miles traveled, time zones crossed, and whether a team is on a short week. These details may sound small, but they add up in predictions.

Practical Recipes to Bake SoS Into Your Model

One practical approach is rolling opponent-adjusted EPA per play. The idea is to adjust each team’s offense and defense by what their opponents normally do, then take rolling averages with decay. That gives you a week-by-week sense of how a team performs relative to expectation. Another method is using opponent tiers with clustering. Instead of tracking exact rankings, you just track how often a team plays elite or weak units. This smooths out noise.

Elo blending is another solid recipe. Start every team with an Elo prior regressed to the league average, update weekly with results, and use that rating as a single scalar feature. It won’t catch every detail, but it’s a great stabilizer.

For those who like Bayesian models, a hierarchical partial pooling model can separate offense and defense quality while automatically adjusting for schedule. That’s nerdy but powerful.

Market data can also be used cautiously. Closing betting lines reflect the wisdom of crowds. You can reverse engineer team ratings from past spreads and use those as priors. Just don’t cheat by using the line from the game you’re predicting—that would be leakage.

Evaluation and Sanity Checks

Building SoS features is one thing. Proving they work is another. The best way is forward-chaining cross-validation. Train your model on Weeks 1 through 8, then test on Week 9. Then repeat moving forward. That keeps you honest because you’re never using future info.

You can also run ablation tests. Build one model with SoS features and one without, then compare metrics like accuracy, Brier score, or log loss. If the SoS model wins, you know the adjustment is adding real signal.

Checking feature importance is another sanity test. If your model is relying heavily on opponent-adjusted metrics early in the season but less so later when samples stabilize, that makes sense. If the importance is flat, you might have engineered something wrong.

Common Pitfalls To Avoid

There are a bunch of traps when dealing with SoS. The most common one is using future information without realizing it. For example, if you build opponent ratings with full-season data when you’re predicting Week 6, you’re leaking the future. Always freeze your world state at prediction time.

Another trap is double-counting strength signals. If you use opponent-adjusted EPA, Elo, and market-implied ratings all at once, you could end up overweighting the same idea three times. Regularization helps, but you should also pick features carefully.

Overreacting to small samples is also a killer. Early in the season, two weeks of dominance against bad teams doesn’t mean much. That’s where priors and shrinkage save you.

Ignoring personnel context is another pitfall. Treating the Cowboys offense with a backup QB as equal to the Dak Prescott version will bias everything downstream. Quarterback status and injury reports matter.

A Worked Example

Let’s walk through a mini example. Say Team Alpha has an unadjusted offensive EPA per play of +0.12 after four games. Team Beta has +0.05. On the surface, Alpha looks better. But Alpha faced defenses ranked 25th, 28th, 30th, and 32nd. Those defenses usually allow +0.08 EPA per play. Beta faced defenses ranked 3rd, 5th, 7th, and 10th, which normally allow -0.05 EPA per play. Once you adjust, Alpha’s offense grades to +0.04 while Beta’s jumps to +0.10. Suddenly Beta is the better offense despite having worse raw numbers. That’s the power of SoS.

Putting It All Together

When you combine everything, a simple pipeline might look like this: gather play-by-play data, compute opponent-adjusted EPA per play, build rolling averages with time decay, add Elo priors, throw in rest and travel context, and annotate QB status. Use those as inputs to a machine learning model trained with forward validation. Compare versions with and without SoS adjustments, and keep tweaking until the SoS version consistently wins.

That’s essentially the secret sauce. You don’t have to overcomplicate it, but you do need to respect opponent context. AI models that do this consistently are the ones that avoid being fooled by fake-good teams or underrating solid squads that just had a brutal early schedule.

Conclusion

Strength of schedule is the hidden thread running through football stats. Raw numbers without opponent context can be misleading. Adjusted metrics, especially opponent-adjusted EPA and similar efficiency stats, make models sharper. Add in priors, recency weighting, and context like travel and quarterback status, and you’ve got a setup that stays grounded. The bottom line: serious models always account for SoS in some form. And platforms like ATSwins make that adjustment part of their core workflow, which is why their picks hold up across NFL, NBA, MLB, NHL, and NCAA action. Free and paid plans are available for anyone looking to make smarter, more informed betting decisions.

Frequently Asked Questions (FAQs)

What does “Do AI models account for NFL strength of schedule?” actually mean?

It means checking whether an AI prediction system adjusts team stats based on opponent quality. If a team racks up stats against weak defenses, a good model doesn’t treat that the same as doing it against elite defenses. SoS basically normalizes performance so you’re comparing apples to apples.

How do AI models account for NFL strength of schedule without leaking future info?

They use only data available before the game. That means computing opponent stats through the previous week only, locking datasets at prediction time, and never letting later outcomes sneak into earlier weeks. Models adjust performance by comparing it to what that opponent normally allowed up to that point in the season.

Why do picks improve when AI models account for NFL strength of schedule?

Because raw stats can lie. A team that looks like a powerhouse against weak opponents can be average once context is added. Adjusting for SoS gives cleaner projections, better calibration, and fewer surprises. That means sharper win probabilities, spreads, and player props.

How can I test if my own model really uses strength of schedule?

Run backtests with and without SoS features. Compare metrics like Brier score, log loss, and calibration. If the SoS version consistently performs better, then you know it’s adding real value. You can also check feature importance to see if opponent-adjusted stats are influencing predictions the way they should.

How does ATSwins handle this?

ATSwins uses time-aware opponent adjustments, rest and travel factors, and recency weighting in its modeling. They calibrate weekly, show transparent results, and track profits across major sports. That way, users can see what’s working and make informed decisions with confidence.

AI Football Betting Tools - How They Make Winning Easier

Bet Like a Pro in 2025 with Sports AI Prediction Tools

Sources

The Game Changer: How AI Is Transforming The World Of Sports Gambling

AI and the Bookie: How Artificial Intelligence is Helping Transform Sports Betting

How to Use AI for Sports Betting

Keywords:

MLB AI predictions atswins

ai mlb predictions atswins

NBA AI predictions atswins

basketball ai prediction atswins

NFL ai prediction atswins