Advanced Point-Spread Tactics: Bayesian Ratings & Distribution Modeling

Master point spreads using hierarchical ratings, distributional models and algorithmic middling.

1. Hierarchical Bayesian Power Ratings

• League-Level Priors: model team strengths with shared priors and team-specific deviations.
• Posterior Ratings: update ratings using MCMC or variational inference after each game.
• Shrinkage Effects: borrow strength from league data to stabilize small-sample ratings.

2. Margin Distribution via Convolution

• Offense/Defense PDFs: fit Gaussian or Skellam distributions to points for and against.
• Convolution: derive margin PDF

  fM(m) = ∫fO(x) fD(x−m) dx
  

• Cover Probability: integrate tail of margin PDF beyond the spread.

3. Algorithmic Middling & Hedge Detection

• Middle Window Scan: detect when early spread < late spread – 1.
• Proportional Stakes: allocate to capture middle when final margin lands in that range.
• Exposure Monitoring: track open positions until both legs settle.

4. Cross-Market Synthetic Spreads

• Inter-Book Discrepancies: monitor spreads and alternative lines across sportsbooks.
• Synthetic Constructs: pair opposite spreads or overlay moneyline/total to create hedged outcomes.
• Correlation Control: size bets to neutralize directional bias.

5. Dynamic Kelly with Utility Maximization

• Utility Functions: define U(W) penalizing drawdowns.
• Fractional Kelly: apply a fraction of the Kelly stake based on risk aversion.
• Continuous Re-Optimization: recalc stakes as model P and market odds update.

Pro Tip: Automate your entire pipeline—data collection, model fitting, stake calculation, execution and performance logging—to eliminate emotional error and capture every edge.