Introduction
This guide goes deeper into how books price markets, why we remove the vig, how to evaluate your plays with %CLV, and how to build robust pricing with multiple devig methods, book weighting, and even machine learning.
What Is the Vig and Why Remove It?
The vig (or margin) is the fee books bake into prices to ensure long-term profit. Because of this margin, the implied probabilities from listed odds sum to more than 100%. To assess true value, we remove the vig to estimate fair odds/probabilities.
%CLV: Grading Your Process
Closing Line Value (CLV) measures your price vs. the market's closing price.
- %CLV = (Your Implied Probability – Closing Implied Probability) / Closing Implied Probability
Consistently positive %CLV suggests you're beating the market—a strong leading indicator for long-term profitability.
Understanding CLV with Examples
Example 1: Positive CLV
- You bet Lakers +6 at +110 (47.6% implied probability)
- Market closes at Lakers +4.5 at +110 (still 47.6% implied)
- Your +6 is now worth approximately +130 (43.5% implied)
- %CLV = (43.5% - 47.6%) / 47.6% = +9.5% CLV
Example 2: Negative CLV
- You bet Warriors -3 at -110 (52.4% implied probability)
- Market closes at Warriors -5.5 at -110 (still 52.4% implied)
- Your -3 is now worth approximately +105 (48.8% implied)
- %CLV = (48.8% - 52.4%) / 52.4% = -6.9% CLV
Why CLV Matters More Than Results
Short-term paradox: You can win a bet with negative CLV or lose a bet with positive CLV. Individual results don't validate your process.
Long-term truth: Studies show that bettors with consistently positive CLV are profitable over large samples, regardless of short-term variance.
Understanding statistical variance.
Tracking CLV Effectively
What to track:
- Average CLV by sport/market
- CLV distribution (what % of bets have >2% CLV?)
- CLV vs actual results correlation over time
Red flags:
- Consistently negative CLV across multiple sports
- CLV declining over time (market adapting to your edge)
- Wide variance in CLV (inconsistent process)
Benchmarks:
- Excellent: >3% average CLV
- Good: 1-3% average CLV
- Breakeven: 0-1% average CLV
- Problem: <0% average CLV
CLV Limitations
Market efficiency varies: CLV is most meaningful in liquid, efficient markets. In thin markets (obscure props), closing lines may not represent true fair value.
Timing matters: Early CLV from line shopping differs from CLV generated by new information (injuries, weather). Both are valuable but for different reasons.
Devigging Methods (Pros & Use Cases)
Let's examine each method with concrete examples. We'll use a market where the raw odds are Team A +110 and Team B -130.
Raw Implied Probabilities:
- Team A: 100/(110+100) = 47.62%
- Team B: 130/(130+100) = 56.52%
- Total: 104.14% (the 4.14% is the vig)
1. Equal Margin (Proportional)
How it works: Removes vig by scaling each probability proportionally to reach 100%.
Calculation:
- Scaling factor: 100% / 104.14% = 0.9602
- Team A fair probability: 47.62% × 0.9602 = 45.73%
- Team B fair probability: 56.52% × 0.9602 = 54.27%
Pros: Simple, stable, works well in balanced markets.
Cons: May underreact in heavily skewed markets where one side has extreme odds.
2. MPTO (Margin Proportional to Odds)
How it works: Distributes the vig equally across all outcomes, removing the same margin amount from each probability.
Calculation:
- Total overround: 4.14%
- Vig per outcome: 4.14% ÷ 2 outcomes = 2.07%
- Team A fair: 47.62% - 2.07% = 45.55%
- Team B fair: 56.52% - 2.07% = 54.45%
Pros: Simple, treats all outcomes equally regardless of probability.
Cons: May not be appropriate when one outcome is heavily favored, as it doesn't account for probability differences.
3. Logarithmic
How it works: Uses logarithmic scaling to distribute vig removal more smoothly across probability space.
Benefits: Performs well when dealing with heavy favorites or underdogs, maintaining probability relationships better than linear methods.
4. Shin Method
How it works: Assumes some bettors have insider information and accounts for this in the devigging process.
Key insight: If insiders bet one side more heavily, that side's odds may not reflect true probability due to this hidden information. The Shin method estimates what percentage of the betting volume comes from insiders.
Use case: Most valuable in markets where insider information exists (injury news, lineup changes, etc.).
5. Ratio Method
How it works: Distributes vig based on the ratio of probabilities between outcomes.
Practical Application: Works well when you want to preserve the original probability relationships while removing bookmaker margin.
Comparing Methods in Practice
Using our example, different methods produce:
- Equal Margin: A: 45.73%, B: 54.27%
- MPTO: A: 45.55%, B: 54.45%
- Difference: 0.18% variance
In balanced markets, methods converge. In skewed markets (heavy favorites), differences can exceed 1-2%, significantly affecting EV calculations.
Takeaway: No single method dominates every market. Use multiple and compare.
If you'd like to know more about the math behind these methods, consider reading this paper: Adjusting Bookmaker's Odds to Allow for Overround.
Method Blending
Blend outputs from several devig methods (e.g., a weighted average of Equal Margin, MPTO, and Shin).
- Why: Reduces model error in edge cases.
- How: Backtest weights by sport/market; adjust when market microstructure changes.
Book Weighting for True Odds
Not all books are equal. Some are sharper (more predictive) in specific sports/markets.
- Assign weights to each book's devigged probability by sport/market.
- Use time-aware weighting (e.g., higher weight near close for certain books).
Practical Book Weighting Example
Market: NFL Team Total Over/Under
Available Books: Circa, Pinnacle, DraftKings, FanDuel, BetMGM
Weighting Strategy:
- Circa: 35% weight (known for sharp NFL pricing)
- Pinnacle: 30% weight (generally efficient, high limits)
- DraftKings: 15% weight (sharp but can be influenced by public)
- FanDuel: 15% weight (similar to DraftKings)
- BetMGM: 5% weight (typically softer, follows market)
Calculation Process:
- Collect odds from all books
- Apply chosen devig method to each book's odds
- Weight each book's devigged probability
- Calculate consensus fair probability
- Compare individual book odds to consensus
Time-Based Weighting
Opening Lines (12+ hours before game):
- Pinnacle/Circa: Higher weight (less noise)
- Recreational books: Lower weight (may not reflect true opinion)
Closing Lines (final hour):
- All sharp money has moved, weights can be more equal
- Consider volume: books with higher limits get more weight
Sport-Specific Considerations
NBA Props:
-
Books specializing in player props may warrant higher weights
-
Consider which books take largest limits on specific prop types
Soccer Totals:
-
European books often more efficient than US books
-
Asian handicap specialists may provide better info for goal totals
College Sports:
-
Regional books may have information advantages for local teams
-
National books may be sharper for major conferences
Validating Your Weights
Backtest different weighting schemes:
- Track CLV using various weight combinations
- Measure which combinations produce best predictive accuracy
- Adjust weights seasonally if market structure changes
Dynamic weighting: As your sample size grows, let data guide weight adjustments rather than assumptions.
Your mileage will vary—validate with data.
Can We Go Further?
After mastering devigging methods and book weighting, you might wonder:
Can we be more accurate than these market-based approaches?
How Do Sportsbooks Really Price Lines?
The answer is yes—and this is exactly how professional sportsbooks create their lines. Modern books use machine learning models that consider hundreds of variables:
Model Inputs:
- Team/player strength ratings from multiple data sources
- Situational factors: rest, travel, weather, referee tendencies
- Market signals: steam moves, sharp action, public betting percentages
- Historical patterns: how similar matchups have played out
- Real-time adjustments: injuries, lineup changes, public information
The Process:
- Pre-game models generate opening lines
- In-game models adjust for live action and new information
- Risk management algorithms move lines based on betting volume and exposure
This is why major sportsbooks can open sharp lines and maintain them—they're not just guessing, they're running sophisticated prediction systems.
Can Individual Bettors Beat the Books?
The realistic answer: Maybe, in very niche markets with significant expertise and programming skills.
Where it might work:
- Obscure props where books allocate fewer modeling resources
- Local/regional markets where you have information advantages
- New bet types where books haven't optimized their models yet
What it requires:
- Substantial historical data collection and cleaning
- Advanced statistical modeling and machine learning expertise
- Faster information processing than professional books
- Significant time investment to maintain model accuracy
The bottom line: For 99% of bettors, proper application of devigging methods and book weighting will be more than enough to be profitable. The edge gained from sophisticated modeling is often smaller than the edge available from simply applying the techniques covered in this guide consistently and at scale.
Putting It Together with OddsTide
Using OddsTide's Advanced Features
The Positive Ev tool can automatically handle weighting multiple devigging methods and book portfolios, but understanding these concepts helps you:
Optimize your settings:
- Choose appropriate devig methods in your Tool Settings based on your market focus
- Understand why certain books appear more frequently in your opportunities
- Validate the tool's edge calculations with your own CLV tracking
Complement with other tools:
- Use the Odds Screen to monitor line movements and identify which books move first
- Apply Advanced Bankroll Management principles to size your +EV bets
- Track your results to validate the edge calculations over time
Building Your Advanced Workflow
- Start with OddsTide's Positive Ev tool for reliable devigging and edge calculation
- Track your CLV manually or with bet tracking software
- Analyze patterns in which books/markets provide the best opportunities
- Refine your approach based on your specific CLV and profitability data
- Scale up with confidence once you've validated your edge
Continuous Improvement
Monthly reviews:
- Analyze CLV by sport/market/book
- Identify which devig methods work best for your betting portfolio
- Adjust bankroll allocation based on observed edge accuracy
Market adaptation:
- Stay aware of which books are getting sharper/softer
- Notice when markets become more efficient (lower +EV opportunities)
- Evolve your strategy as the betting landscape changes
Final Word
The road to durable +EV isn't one trick. It's methodical: remove vig, validate with CLV, diversify devig methods, weight the right books, and evolve towards own-pricing with machine learning where it adds signal.
Start simple: Use OddsTide's Positive Ev tool to find edges, track your CLV, and build confidence in the process.
Scale advanced: As your sample size grows, implement the sophisticated techniques covered in this guide to refine and amplify your edge.
The mathematics works, but only with disciplined execution and continuous validation against market reality.
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