In‑Play AI Betting: Real-Time Models, Latency & Risk Control

Live betting provides dynamic price movement—ideal for monetizing model edge faster than pre‑match markets. But speed, latency and risk over‑exposure threaten returns. This guide shows how to build, evaluate and integrate real-time models into a robust pipeline. Read Value Betting, CLV and Bankroll Management first for fundamentals.

1. Data & Feature Pipeline

Sources: (a) Live odds (multiple books for consensus + outlier detection), (b) Play-by-play / event feed (shots, fouls, possession), (c) Tracking / positional data (if available), (d) Context metadata (weather, referee).

Feature layers: State features (current score, remaining time), momentum features (xG since last update, sequence length without attempt), volatility features (odds delta velocity), regime flags (red card, injury, timeout).

Log latency metrics: arrival_timestamp_feed, model_update_timestamp, bet_dispatch_timestamp → compute age / market drift risk.

2. Bayesian / Incremental Updating

Use a pre-trained pre‑match base model (see Deep Learning) as prior. Update probabilities after each new event via: Posterior ∝ Prior * Likelihood(Event | Hidden State).

Alternative: Recurrent / Transformer model in streaming mode processing a sliding window of the last N events and propagating hidden state.

Recalibrate posteriors periodically (Calibration)—live sequences create systematic overconfidence shortly before critical phases.

3. Latenz als Edge & Risiko

Edge sources: (1) Faster event ingestion vs book delay, (2) Better state representation (granular xG), (3) Superior regression logic under scoreline bias.

Measurement: Track real_market_odds_snapshot at dispatch + next_tick_odds → impact ratio. Negative slippage > threshold? Throttle quote-chasing frequency.

Anti-latency failsafe: If age > 1500ms during high volatility state (goal / red card phase) auto-abort bet.

4. Edge-Erkennung & Filter

Raw edge = (Odds * p_live) - 1. Set dynamic minimum edge: baseline 2% + volatility_adjustment. In high in‑play variance raise threshold to avoid noise trades.

Cluster similar markets (match winner, Asian handicap adjacent lines) → allow at most one position per cluster per time slice to avoid correlation blowups.

Discard window: Ignore edge immediately after major event (5–10s) until market re-equilibrates (prevents overreaction chasing).

5. Staking & Risiko Governance

Use fractional Kelly on live calibrated probability (see Bankroll Guide) plus hard cap: max 0.5% bankroll per in‑play ticket.

Session exposure cap: Sum of open in‑play EV worst-case loss <= 6% bankroll; above that pause until settlements free capital.

Volatility targeting: If 1h rolling PnL std > target, scale all stakes by factor target_vol / realized_vol.

6. CLV & Post-Mortem für Live Wetten

In live betting there's no classic pre‑match close. Use proxy: mid‑odds 3 seconds after execution; compute Live Execution Value (LEV) analogous to CLV.

Segment LEV by game phase (early / mid / crunch time) & event regime (post goal vs quiet period).

Goal: Persistent negative LEV% (you beat adjustment). Positive LEV% → investigate latency / overreaction hypotheses.

7. Qualitäts- & Drift-Monitoring

Realtime dashboards: (a) Edge distribution heatmap, (b) LEV rolling mean, (c) missed edge count (edge > threshold but not placed due to caps), (d) calibration ECE live window.

Drift trigger: Three consecutive match days with increasing overround sensitivity → review model features (tempo, possession normalization).

Automatic kill switch: If session drawdown > 5 * median session loss or ECE > 2x target (see Calibration Guide), halt execution.

8. Compliance & Logging

Persistiere jeden Bet: bet_id, league, timestamp_feed, ts_model, ts_dispatch, raw_prob, calib_prob, odds, edge%, stake_frac, expected_value, latency_ms, cluster_id.

Hash-basierte Integrität: Schreibe fortlaufenden Hash über Logs zur nachträglichen Auditierbarkeit.

Privacy: Entferne personenbezogene Daten; nur technische Telemetrie & Marktdaten gespeichert.

Conclusion

In‑play AI betting increases edge density—without strict latency and risk controls that advantage turns into a volatility trap. Combine: (1) Calibrated realtime models (Calibration), (2) Disciplined staking (Bankroll), (3) Continuous LEV / CLV monitoring and (4) Adaptive edge filters to build sustainable live returns.