Contextualized backtesting infrastructure that works with 5-10 real events. Three innovations that no existing PM analytics tool has.
Hashdive shows you whale trades. Oddpool shows you cross-platform odds. Verso builds a terminal. Nobody lets you TEST whether a PM trading idea actually works — with proper out-of-sample validation and small-N compensation. That's the gap we fill.
The most interesting prediction markets have the fewest data points. The markets with abundant data have the least institutional interest.
Data points since PM mainstream (~2024). The paradox: the most tradeable events have the least testable data. Our infrastructure solves this.
Don't group events by topic. Group them by how prediction markets behave around them. A tariff announcement and a coaching dismissal are structurally identical — both are surprise-binary-fast events.
Scheduled (Fed, CPI, Matchday) vs. Surprise (Tariffs, Injuries, Sanctions)
Clean Yes/No (Win/Lose, Rate Cut) vs. Continuous (S&P level, vote share)
Fast (injury: minutes) vs. Slow (championship race: months)
Deep (US Election: $3B+) vs. Thin (Bundesliga transfer: <$50K)
Oracle-automated (match result, CPI) vs. Human judgment (ambiguous politics)
A "tariff announcement" with only 3-5 historical data points becomes backtestable when clustered with 30-50 structurally similar surprise-binary-fast events. The clustering turns untestable into testable.
Traditional synthetic data generates artificial price paths. We generate artificial event contexts — "what if the tariff was 25% instead of 10%?" — and model PM reactions.
Traditional synthetic data methods (GANs, Monte Carlo) operate on numbers. They generate plausible-looking price paths but cannot generate plausible-looking event contexts. A language model can understand that a 25% tariff is qualitatively different from 10% — not just numerically higher. It models second-order effects (retaliation chains), media reactions, and institutional positioning.
This is not hallucination. It is parametric scenario design with an LLM as domain expert, calibrated against real data points.
Synthetic scenarios are only valuable if they're calibrated against reality. For each cluster with 5+ real events: generate 50 counterfactuals per event, hold out 20% of real events as test set, train on 80% real + all synthetics, validate against held-out events. Track prediction error and publish calibration scores alongside all backtest results.
If the synthetic expansion doesn't improve predictions on held-out real events, it gets discarded. Radical transparency — the system earns trust through measurable calibration.
Certain PM behavioral patterns are domain-agnostic. Football (306 matches/season) calibrates finance models (8 Fed meetings/year). The high-N domain trains the low-N domain.
PM spreads narrow 2-4h before kickoff as late info arrives. Same pattern before FOMC?
Measurable in footballInjury news: 15-45min to full pricing. Tariff tweet: similar latency adjusted for liquidity?
Measurable in footballRed card causes 15-20% overshoot that mean-reverts. Surprise tariff: same overshoot?
HypothesisLow-liquidity transfer markets drift 5-8% on one large order over 24h. Niche politics: same?
Hypothesiskicker-Tipps vs Polymarket on Bundesliga. Superforecasters vs Polymarket on macro. Same gap structure?
Measurable both sidesEvery football prediction that runs through the system improves the accuracy of finance backtesting, and vice versa. This creates a compounding data asset that no single-vertical competitor can match.
The PM infrastructure stack has three layers. Layer 1 (execution) and Layer 2 (data) are being built. Layer 3 (strategy) is wide open.
1. Domain intelligence — 22 Bundesliga club dossiers, AI infrastructure research (31 entries), Soccer Economics. Years of accumulated content that powers meaningful event clustering and counterfactual generation.
2. Live prediction data — Finance and Football verticals with Brier Score tracking provide the real calibration data.
3. Cross-vertical bridge — No competitor operates across both finance and sports with prediction data in both.
4. The Strategy Factory — Working methodology since February 2026. Not an idea — a system.
We sell the Factory. We keep the recipes.