ReddRadar
Agent skill
betting-strategy
Details the core betting strategy, including edge calculation, sport-specific thresholds, Kelly Criterion sizing, and portfolio optimization.
Stars
163
Forks
31
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/betting-strategy
SKILL.md
Betting Strategy
Edge Calculation
python
edge = elo_probability - market_probability
A bet is recommended when:
elo_prob > threshold(high confidence)edge > 0.05(5%+ edge over market)
Sport Thresholds
| Sport | Threshold | Min Edge | Notes |
|---|---|---|---|
| NBA | 73% | 5% | High-scoring, predictable |
| NHL | 66% | 5% | High variance sport |
| MLB | 67% | 5% | Lower home advantage |
| NFL | 70% | 5% | Small sample size |
Confidence Levels
python
def get_confidence(elo_prob, threshold):
if elo_prob > threshold + 0.10:
return "HIGH"
elif elo_prob > threshold:
return "MEDIUM"
return None # Don't bet
Lift/Gain Analysis
Validate model quality by decile:
python
from lift_gain_analysis import analyze_sport
overall, current = analyze_sport('nba')
# Key metrics:
# - Lift > 1.0: Better than random
# - Top decile lift > 1.3: Strong signal
# - Gain %: Cumulative wins captured
Interpretation:
- Decile 10 (highest prob): Should have highest lift
- If lift < 1.0 in high deciles, model is miscalibrated
- Set threshold where lift starts exceeding 1.2-1.3
Kelly Criterion
Optimal bet sizing:
python
def kelly_fraction(win_prob, odds):
"""Calculate Kelly bet fraction."""
# odds in decimal format (2.0 = even money)
q = 1 - win_prob
b = odds - 1
return (win_prob * b - q) / b
# Use fractional Kelly (25-50%) to reduce variance
bet_fraction = kelly_fraction(prob, odds) * 0.25
Portfolio Optimization
python
from portfolio_optimizer import optimize_bets
# Optimize across all opportunities
optimized = optimize_bets(
opportunities=bets,
bankroll=1000,
max_exposure=0.10, # 10% max per bet
correlation_penalty=0.5 # Reduce correlated bets
)
Bet Tracking
All bets saved to placed_bets table:
python
default_db.execute("""
INSERT INTO placed_bets
(bet_id, game_id, sport, ticker, side, amount, price, elo_prob, edge, placed_at)
VALUES (...)
""")
CLV (Closing Line Value)
Track if we beat the closing line:
python
from clv_tracker import calculate_clv
clv = calculate_clv(bet_id)
# Positive CLV = consistently beating market
Files to Reference
plugins/portfolio_optimizer.py- Kelly and optimizationplugins/lift_gain_analysis.py- Model validationplugins/clv_tracker.py- CLV trackingdocs/VALUE_BETTING_THRESHOLDS.md- Threshold analysis
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