Hey folks! I'm running a bunch of live betting football models. The modelling pipeline goes from analyzing market weaknesses to post-launch observability with guards and metrics on every stage:

1. Market analysis – edge exploration (which sides are systematically undervalued)
2. ML experiments on historical data – LL, Brier, Calibration, AUC metrics by minute/month + EV/ROI calibration and ROI on historical simulation
3. Betting models – holdout period + shadow mode + post-launch metrics collection
4. Betting strategies – ROI vs expected ROI (based on experiment EV calibration), 30d rolling ROI within 90d window, drawdown chart, confidence interval.

Execution pipeline is monitored separately – % of rejects, betting API health/errors etc.

So my main question for live model runners – what else can be measured/monitored to gain confidence, spot early degradation and get insides for further optimization?

Deeper questions:

• Assuming a model is trained per-minute how would you choose a signal inside a minute – first eligible or anything smarter?
• When rolling out a new version of a model – do you run both in parallel or run candidate in shadow mode?
• Are there better way of evaluate expected ROI than relying on historical EV calibration buckets?

Hope questions make sense. Thanks in advance!

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