Quantitative Edge in Prop Betting
KRYTEX delivers Monte Carlo simulation-backed prop analyses with 10,000+ trials. Statistical methodology for high-confidence betting edges.
LIVE SIMULATION RESULTS (10,000 TRIALS)
Statistical Models
Bayesian updating with regression models on player performance
Calibrated Outputs
Validated with Brier score & calibration plots for accuracy
Expected Value
EV = P × payout − (1 − P) × stake for true edge calculation
Quantitative Methodology
Statistical rigor in every prediction – our prop-betting analysis framework
Historical Performance Analysis
Our AI analyzes the last 5-10 games of player data, fits statistical distributions, and detects mean-reversion patterns for accurate predictions.
- Fits normal, log-normal, and negative-binomial distributions
- Adjusts for opponent defensive efficiency and home/road splits
- Detects outliers and mean-reversion patterns in performance
Contextual Factor Integration
Comprehensive adjustment for all situational factors that impact performance, from injury reports to coaching tendencies.
- Injury reports, lineup changes, rest days, and travel impact
- Team pace adjustments and coaching tendency analysis
- Implied probabilities extracted from current market lines
Monte Carlo Simulation Engine
Sophisticated statistical engine running thousands of simulations to generate true probabilities and confidence intervals.
- 1,000-10,000 trial Monte Carlo simulations for true P(over) and P(under)
- Regression + Bayesian updating methodologies for reliability
- Confidence intervals based on simulated distribution with market noise adjustments
What Data-Driven Bettors Say
Join quantitative bettors who embrace statistical edge over gut feeling
"The Monte Carlo simulations and confidence intervals give me real statistical edge. I can make truly informed decisions with actual probabilities."
Mike R.
Quantitative Bettor
"I love having expected value calculations based on real statistical methods. The Bayesian updating approach catches market inefficiencies others miss."
Jason L.
Former Hedge Fund Analyst
"Their contextual factor integration is impressive. The way they adjust for defensive efficiency and coaching tendencies shows in the 95% confidence intervals."
Sarah K.
Data Scientist & Bettor
Ready for Statistical Edge?
Join quantitative bettors who use Monte Carlo simulations and Bayesian methods to find true value in prop markets.