AI Bankroll Management Strategies: Kelly, Fractional Sizing & Drawdown Control

Why Bankroll Management Matters More Than Raw Edge

Even a strong predictive model (Value Betting, CLV positive) can still go broke through oversized staking. Bankroll management translates probabilistic edge into controlled geometric growth by balancing three forces: expected value (EV), volatility (variance of returns) and tail risk (probability of large drawdowns).

Goal hierarchy: 1) Survival (avoid ruin), 2) Variance discipline (keep emotional + capital stability), 3) Compounding efficiency (maximize long‑run log growth).

Kelly Criterion Fundamentals

For a single binary outcome with decimal odds O and model probability p, Kelly fraction f* = (p*O - 1) / (O - 1) assuming fair payout otherwise. Simplified for even odds: f* = 2p - 1.

Advantages: Maximizes asymptotic log utility; penalizes overconfidence. Risks: Extremely sensitive to probability miscalibration (see Calibration).

Multi-bet portfolio: Solve convex optimization with constraints Σ f_i * L_i ≤ VaR_limit where L_i is worst-case loss (stake). Approximate by summing fractional Kelly across independent bets but cap total exposure per time bucket.

Fractional Kelly & Practical Adjustments

Because model edge & probabilities are uncertain, practitioners scale to 0.25–0.50 Kelly (Fractional Kelly) to reduce variance drag and estimation error amplification.

Rule of thumb: If your calibration or CLV tracking shows instability (rolling 1,000 bet CLV deteriorating), cut fraction further until stability recovers.

Fractional Kelly reduces growth only modestly when uncertainty is high, while dramatically lowering maximum drawdown magnitude.

Edge Filtering & Minimum Thresholds

Do not allocate to every positive expected value estimate; enforce a minimum edge filter (e.g. ≥3%) like in Profit Report.

Rationale: Micro-edges near zero are most vulnerable to calibration error & fees (commission / spread) eroding EV.

Adaptive thresholding: Raise edge threshold during drawdown to concentrate risk on higher-confidence signals; relax slightly when equity at ATH (all-time high) but monitor variance metrics.

Volatility Targeting & Exposure Caps

Track realized daily / weekly PnL standard deviation. If vol > target (e.g. 4% of bankroll weekly), proportionally scale all stake sizes by target_vol / realized_vol factor.

Set per-market & per-league exposure caps (e.g. max 10% aggregate risk in a single league round) to avoid hidden correlation spikes (synchronized outcomes).

In-play & pre-match mixing? Use risk buckets; pre-match commitments reduce allowable in-play Kelly fractions to preserve capital buffer.

Drawdown Monitoring & Recovery Protocols

Define hard drawdown triggers (e.g. -15%, -25%). At -15%: halve Kelly fraction; at -25%: freeze new bet types, run calibration & CLV diagnostics (see CLV Guide).

Track MAR (CAGR / Max Drawdown) and Calmar-like ratios to evaluate risk-adjusted performance; optimize for smoother equity growth, not just headline ROI.

Use rolling skew & kurtosis of returns; abrupt shift may signal model regime change (injury news modelling lag, data feed quality).

Bankroll Segmentation & Liquidity Logistics

Segment capital: 1) Active betting float (hot wallet), 2) Reserve buffer (2–3 expected worst-month losses), 3) Strategic expansion fund (model R&D).

Replenish active float only after independent variance assessment; avoid emotional top-ups mid downswing without diagnostic confirmation.

Use a standardized stake ticket schema: bet_id, timestamp, league, market, raw_prob, calib_prob, edge%, stake_fraction, stake_amount, implied_vol. Enables later forensic audits.

Putting It All Together

Workflow: (1) Generate calibrated probabilities → (2) Filter by adaptive edge threshold → (3) Compute Kelly fractions → (4) Apply volatility + exposure caps → (5) Scale by fractional factor → (6) Execute & log → (7) Track CLV + calibration + drawdown dashboards.

Monthly review: Adjust fractional factor based on stability metrics (ECE, Brier delta, mean CLV).

Bankroll growth becomes a byproduct of disciplined process, not impulsive stake escalation.

Conclusion

Effective bankroll management converts fragile raw edge into resilient compounding. Combining calibrated probabilities (Calibration), verified market respect (CLV) and disciplined fractional Kelly produces sustainable, lower-volatility ROI.

Continue exploring foundational pillars: Value Betting for edge identification and Profit Reports for empirical performance benchmarks.