Metrics & Evaluation Interactive

Closing Line Value (CLV)

Measure betting skill by comparing your bet price to the closing line. Positive CLV = you're beating the market. The #1 predictor of long-term success.

📊 What is CLV?

CLV = Closing Prob - Opening Prob

If you bet at -110 and the line closes at -115, you got a better price. The market moved to confirm your bet was sharp.

• Positive CLV = you bet before the line moved your way
• Negative CLV = line moved against you
• Consistent +CLV = sharp bettor

Why CLV Matters

• Predictive: +CLV bettors profit long-term
• Immediate: Don't need outcome to measure
• Skill signal: Better than win rate
• Detection: Books use CLV to identify sharps

Your Bet

-200 200

50 1000

📊 CLV Analysis

Opening Implied 52.4%

Closing Implied 53.5%

CLV +1.11%

Dollar Value +$1.22

Winning: Positive edge detected

Cumulative CLV (50 Bets)

Upward trend = you're consistently getting better prices than the market.

CLV Benchmarks

<-2%

Square

-2 to 0

Losing

~0%

Neutral

0 to 2

Winning

>2%

Sharp

💡 Practical Applications

For Bettors

→ Track CLV for every bet, not just outcomes
→ Bet early when you have information edge
→ Consistent +CLV matters more than win rate

For Books

→ CLV is the #1 sharp detection metric
→ +2% CLV over 100+ bets = definitely sharp
→ Use CLV to set limits and move lines

R Code Equivalent

# Calculate CLV
american_to_prob <- function(odds) { 
  if (odds < 0) return(-odds / (-odds + 100))
  return(100 / (odds + 100))
}

calculate_clv <- function(opening, closing) { 
  open_prob <- american_to_prob(opening)
  close_prob <- american_to_prob(closing)
  clv <- (close_prob - open_prob) * 100
  return(clv)
}

# Track bettor CLV
bettor_clv_analysis <- function(bets) { 
  bets$clv <- mapply(calculate_clv, bets$opening, bets$closing)
  
  list(
    avg_clv = mean(bets$clv),
    total_clv = sum(bets$clv),
    positive_rate = mean(bets$clv > 0),
    classification = ifelse(mean(bets$clv) > 2, "Sharp", 
                           ifelse(mean(bets$clv) > 0, "Winning", "Square"))
  )
}

# Example
clv <- calculate_clv(-110, -115)
cat(sprintf("CLV: %+.2f%%\n", clv))

✅ Key Takeaways

• CLV = Closing prob - Opening prob
• Positive CLV = you beat the closing line
• Best predictor of long-term betting success

• +2% avg CLV = sharp bettor
• Track CLV, not just win/loss
• Books use CLV to identify sharps