NFL 2nd Half Lines: How to Model Halftime Markets and Find Edges
- Ronald Lockington
- Nov 10
- 4 min read
Updated: 2 days ago
TLDR
Pregame markets are tougher. Edges are thinner than in weaker derivatives like NFL 2nd-half (live) lines.
Data you need: pregame closing spread/total, 2nd-half lines at halftime, and scoring by half (often stored as by-quarter).
Goal: learn the mapping: given pregame line X and halftime margin Y, what is a fair 2H line?
Method: de-vig the market, fit a simple model, backtest, iterate.
Sample size: Target multiple recent seasons (exclude 2020 only).
Use our No-Vig Calculator, Kelly Bet Sizer, Odds Screen, and NFL 2nd Half Lines Model Template to speed this up.
Why This Is Failing You
Most recreational bettors live in pregame markets. Those are the most efficient and the fastest to correct, so edges are rare and fleeting. 2nd-half lines are live markets posted at halftime. They’re derivatives of the pregame number conditioned on the actual halftime score and game state. Because they’re conditional and posted under time pressure, they’re weaker than pregame—that’s the opportunity.
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Data For Your 2nd Half Model
Minimum fields:
Pregame closing lines (spread & total).
Halftime (2nd-half) lines — the posted 2H spread & 2H total at halftime.
Scores by half (often stored by quarter; sum Q1+Q2 for HT, Q3+Q4 for 2H).
Metadata (home/away, date, team IDs, possibly closing moneyline, pace proxies if available).
Coverage:
Include multiple recent seasons to capture current styles and totals environments.
Exclude 2020 only due to COVID anomalies.
Halftime derivatives are noisy; you want many games (>500) across seasons if possible. At ~250–280 games per full NFL season, 4–6 seasons is a healthy start. That gives you a good number of games without going too far in the past when the game was played differently.
Building the Model the Right Way
Problem Statement
We want fair 2H lines given (a) the pregame expectation and (b) what actually happened for 30 minutes.
Formally:
Given pregame spread S0 and halftime margin HM (home points − away points at halftime), what is the fair 2H spread S2H*?
De-Vig (for inputs/targets)
Convert market lines to no-vig equivalents before using them as inputs or comparison targets. For regression you’ll predict line levels (points), but de-vigging helps ensure you’re learning market signal instead of margin.
Targets (points, not probabilities)
Spread target: S2H* (fair 2H spread in points).
Total target: T2H* (fair 2H total in points).
Feature Set (start simple)
Core features that travel well:
S0, |S0|, and a favorite/dog indicator.
T0.
HM (halftime margin).
FH (first-half points).
Interactions: S0×HM, T0×FH.
Optional (v2+): crude pace proxies (drives, plays). Skip if unavailable.
Baseline Specifications (transparent ≈ robust)
Spreads (OLS to points): S2H* = β0 + β1·S0 + β2·HM + β3·(S0×HM) + ε
Totals (OLS to points): T2H* = γ0 + γ1·T0 + γ2·FH + γ3·(T0×FH) + η
Notes:
Fit with OLS first; add regularization (Ridge/Lasso) only if you see instability or multicollinearity.
Keep specs minimal at v1; expand features only when out-of-sample error justifies it.
Convert Fair Lines to Prices (for EV)
Your regression outputs fair lines in points. To compare to the live market and compute EV, map fair lines to implied cover/over probabilities via an assumed 2H scoring distribution:
Estimate historical 2H scoring variance by state (e.g., halftime margin buckets).
Use a discrete empirical distribution margin/2H points to translate S2H*/T2H* into probabilities at each book’s posted 2H number.
Then compute expected value vs. current (de-vig) prices.
Backtest & Stress Test (regression metrics)
Walk-forward by season to avoid leakage.
Evaluate MAE/RMSE on S2H* and T2H*.
Residual diagnostics by halftime-margin buckets (tied, ±1–3, ±4–7, ±8–13, ≥14) and by favorite strength.
Track CLV vs. 2H close using prices derived from your fair lines.
Iterate cautiously: one feature/change at a time, keep a version log, and prefer improvements that lift out-of-sample RMSE/MAE and CLV consistency.
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What to Do Next
Download the NFL 2nd Half Lines Model Template for an easy, repeatable guide.
Feed your data (pregame close, halftime score, posted 2H line).
Line shop with the Odds Screen.
Size with the Kelly Bet Sizer.
Track with SlipSync or your spreadsheet.
Start small, log everything, and scale what proves out in backtests and live fire.
FAQ
Q: Are NFL 2nd-half lines “live” or “pregame”?
A: They’re live markets posted at halftime. You’re betting only quarters three and four based on the halftime state.
Q: Why do 2nd-half lines offer value?
A: They’re weaker, derivative markets posted under time pressure and conditioned on a unique state (the actual halftime score and context). That combination can produce temporary mispricing versus a sound model.
Q: How much data do I need?
A: More than 200 if you can. Target multiple seasons, excluding 2020 only. Don’t filter out blowouts; the model must learn those tails.
Q: What if I can’t find 2H lines historically?
A: Build your own archive going forward and/or source from third-party databases/APIs that store halftime markets. Always store pregame close, 2H lines at halftime, and by-quarter scoring.
Q: Can this approach generalize to other sports?
A: Yes. The exact features change (pace, possession, foul trouble in basketball, etc.), but the method—de-vig → conditional model → fair price → EV → backtest—stays the same.
Q: How do I know my model works?
A: Track CLV vs. 2H close, calibration (Brier/log loss), and profitability by state buckets (halftime margin, favorite strength). Edges should be consistent out-of-sample.
Final Word
Don’t try to out-sharp the pregame market if you’re not running enterprise-grade process. Attack 2nd-half (live) lines with a clean, conditional model, honest backtests, and disciplined sizing. That’s where realistic, repeatable edge lives.
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