Sports Betting

NFL Predictions Explained: What Goes Into Each Pick

NFL predictions are built from team strength, matchup specifics, and market context, all compressed into a line or probability. Football is unique because small sample sizes (17 games per season) and high variance make every data point matter. Understanding what goes into NFL predictions helps you evaluate which picks to trust and which to fade.

February 18, 2026

Team Power Ratings

Typical ingredients start with team power ratings: offense and defense scores, often built from efficiency metrics like yards per play, success rate, EPA per play, and DVOA.

Core efficiency metrics:

EPA (Expected Points Added): How many points a team adds vs. average on each play
Success rate: Percentage of plays that gain meaningful yardage (50% of needed on 1st, 70% on 2nd, 100% on 3rd/4th)
DVOA (Defense-adjusted Value Over Average): Team efficiency compared to league average, adjusted for opponent strength
Yards per play: Simple but effective measure of offensive and defensive efficiency

These metrics get combined into overall team ratings. For example, a team might be rated +5.2 on offense (5.2 points better than average) and -2.8 on defense (2.8 points worse), giving them a net rating of +2.4.

Power ratings let you project spreads: if Team A is +4.5 and Team B is -1.5, the neutral-field spread would be Team A -6. Add home field (typically 1.5-2.5 points) and you get a predicted line.

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Situational Factors

Situational factors include:

Short weeks: Thursday games favor home teams and defenses (less prep time)
Cross-country travel: 3-hour time zone changes affect performance, especially early games
Bye-week rest: Teams coming off bye weeks historically perform better
Weather: Wind affects passing, cold affects ball handling, rain affects both
Surface: Some teams perform differently on turf vs. grass

These factors get layered onto base power ratings. A team rated -3 might move to -4.5 if they're home off a bye against an opponent on a short week traveling cross-country.

Injury Reports

QB and offensive line injuries, key corners and edge rushers, and cluster injuries at a position all dramatically impact predictions.

Quarterback impact:

Elite QB to backup: 6-10 point swing in spread
Average QB to backup: 3-5 point swing
Elite QB banged up: 1-3 point swing depending on injury severity

Other key positions:

Left tackle: 1-2 points (protects QB blindside)
#1 cornerback: 1-2 points (affects passing game strategy)
Edge rusher: 1-2 points (disrupts opponent QB)
Running back: 0.5-1.5 points (less impact in modern NFL)

Models should adjust for injuries, but often lag breaking news by 30-60 minutes. If you process injury reports faster than the market, you can find edges before lines adjust.

Looking for smarter picks without the guesswork? Check out Shurzy's Predictions tool for data-driven insights across NFL, NBA, NHL, MLB, and more.

Matchup Edges

Matchup edges matter: run vs. pass splits, how one team's strengths align with the other's weaknesses (e.g., strong deep passing vs. weak secondary).

Common matchup angles:

Elite pass rush vs. weak offensive line
Strong run defense vs. run-heavy offense
Man coverage corners vs. team that struggles vs. man
Zone-heavy defense vs. QB who excels vs. zone
Red zone efficiency gaps (one team elite in red zone, other terrible)

Good predictions identify 2-3 key matchup edges per game and quantify their impact. Not every matchup matters equally. Focus on the ones that actually determine outcomes.

Pace and Totals

Pace and totals predictions use plays per game, pass rate, red zone efficiency to project scoring environment.

Total prediction framework:

Project possessions: Team A averages 12 drives, Team B averages 11
Project points per drive: Team A scores 2.1 points per drive vs. Team B defense, Team B scores 1.8 vs. Team A defense
Calculate total: (12 × 2.1) + (11 × 1.8) = 25.2 + 19.8 = 45 points
Compare to posted total: If book has 48.5, model sees under value

Pace is often overlooked but critical. A game between two slow teams might have 22 total drives. A game between two fast teams might have 28. That's 6 extra scoring opportunities, which can easily swing a total by 10+ points.

Common Model Structures

Model-wise, common structures include:

Spread models:

Regress game margins or spreads on team stats and context to predict an "expected margin." Use thousands of historical games to learn which factors predict outcomes.

Totals models:

Project team scores separately, sum for over/under, then derive distribution around that sum. This allows for asymmetric scoring expectations (one team likely scores 28, other likely scores 17).

Market-anchored models:

Start from the consensus line/total and adjust for factors you believe are mispriced (weather, injuries, travel). This approach respects market efficiency while targeting specific edges.

Putting It All Together

A typical NFL pick like "Eagles -3 vs Cowboys" rests on something like:

Base power ratings: Model says Eagles approximately 4.5 points better on neutral field
Home field adjustment: Add 1.5-2 for home field → fair spread -6 to -6.5
Current market: Shows -3
Gap analysis: You see a 3-point discrepancy
Context check: Injury, weather, and matchup checks don't explain the gap
Conclusion: "Eagles -3" becomes a recommended pick

Public prediction sites then package this into ATS picks, moneyline recommendations, and totals, often with a short write-up summarizing the statistical edge and key narrative angles (e.g., pass rush vs. weak offensive line, red zone advantage).

The Bottom Line

NFL predictions combine team strength, situational factors, injuries, matchups, and market context. They're more art than science because sample sizes are small and variance is high, but structured predictions beat gut feel over a full season.

The key is understanding what goes into each pick so you can evaluate whether the prediction makes sense given current information.

FAQ

How accurate are NFL predictions?

Good NFL prediction models hit 52-55% against the spread long-term. Anything above 53% is excellent given market efficiency.

Do NFL predictions account for weather?

Yes, most models adjust for wind (affects passing), cold (affects ball handling), and precipitation. But human adjustment is often needed for extreme weather.