Closing Line Value (CLV): Measuring AI Model Performance in Sports Betting

What is Closing Line Value (CLV)?

Closing Line Value (CLV) measures how the odds you took compare to the market's final (closing) odds just before kickoff/first pitch. If your average entry odds are better (higher for back bets) than the close, the market moved toward your model – strong evidence of a genuine predictive edge.

Why it matters: Liquid markets aggregate distributed information. Consistently beating the close (positive effective CLV) is a leading indicator of long‑term profitability (see Value Betting).

Formula & Calculation

Decimal odds back bet raw CLV% = (Closing Odds / Placed Odds) - 1. A negative result means you beat the close (good).

Example: Placed 2.60, Closing 2.45 ⇒ 2.45/2.60 - 1 = -5.77% (interpreted as +5.77% value captured).

Goal: Sustain a negative average raw CLV% (effective positive edge) over large samples (> 1–2% in major markets is strong).

CLV vs. Realized ROI

Short-term variance: You can show positive CLV yet negative ROI over small samples; ROI is noisier.

If long‑run CLV ≈ 0 but ROI is positive, you're likely running hot — scale stake sizes down (see Bankroll Management).

Tracking Pipeline

1. Immediately log each bet: timestamp, league, market, placed odds, model probability, edge %, stake.

2. Near start time capture closing odds (single sharp book OR trimmed mean of top books).

3. Compute per-bet CLV% then aggregate: mean, median, distribution histogram, rolling windows (250 / 1,000 bets).

4. Visualize trend & volatility; alert on regime shifts (e.g. 3 rolling windows deteriorating).

Interpreting Deviations

Consistently beating the close: Model has transferable information edge.

Early strong then flattening: Market assimilated your features — enrich feature space / recalibrate.

Losing to the close: Investigate data latency, scraping accuracy, overfitting, illiquidity / spoofed moves.

Common Pitfalls

• Using off-market / limited books as closing proxy (bias).

• Mixing market types (AH vs. 1X2) without normalization to implied probabilities.

• Not excluding void / push bets from aggregates.

• Drawing conclusions from tiny sample (< 500 bets).

Improvement Loop

1. Segment CLV by league, market, time-to-start bucket.

2. Diagnose negative cohorts → feature recalibration (injuries, pace, travel, lineups).

3. Dynamically adjust minimum edge thresholds to volatility & calibration uncertainty.

4. Tie stake multipliers to sustained positive segmented CLV (not headline ROI).

5. Monthly calibration audit; archive snapshots for drift analysis & rollback.

Conclusion

CLV is your variance‑resilient quality signal confirming your model anticipates fair price movement.

Combine rigorous CLV tracking with edge filtering (Value Betting) and disciplined staking (Bankroll Guide) for durable compounding.