The F1 race
intelligence layer.

A Kalman filter provides online state estimation — updating the tire health estimate and, critically, the estimated degradation rate on every incoming lap time. Early degradation is detected before the lap time cliff fully materializes. A second stage models the full degradation curve per compound, including non-linear acceleration near the thermal cliff, and provides uncertainty bounds alongside the point estimate.

Tire strategy determines ~40% of race outcomes. A model that catches the cliff 3–5 laps early — before any on-screen indication — is the most valuable pre-pit signal available. For fantasy: transfer decisions. For betting: an independent pit-window read a trading desk can weigh against its own pricing.

Win probability is the share of simulated races a driver finishes first. On each update we run the remaining laps forward hundreds of times: every car carries its own pace and lap-to-lap uncertainty, its tire degrades on its current compound, and its remaining mandatory pit stops are sampled — so a fresher car yet to stop is correctly weighed against the car ahead on track. A shared safety-car draw, per-driver retirement hazard, and dirty-air overtaking (a faster car has to complete the pass, gated by how hard the circuit is to overtake on) play out across every simulation. Early-race pace is shrunk toward a pre-race prior built from the weekend's practice and qualifying and recent form, then handed over to live pace as clean laps accumulate. The finishing-order distribution across all simulations gives win, podium, and points-finish probability directly.

A 15–25 percentage point swing in win probability during a safety car period is routine. For a sportsbook carrying $200K–$500K in open race positions during a race, that is material risk exposure moving faster than most odds refresh pipelines. It is an independent, real-time probability read your traders can weigh against their primary feed and in-house models.

ECP is the expectation of the same race-to-finish simulation that drives win probability. Each lap the simulation produces P(finish = k) for every driver; ECP is the dot product of that finishing distribution with the F1 points vector [25, 18, 15, 12, 10, 8, 6, 4, 2, 1, 0…]. Because ECP and win probability come from one simulation, they always agree. The result is championship-granular: a leader holding station maps to roughly 18–22 ECP; a P12 driver just outside the points has ECP ≈ 0.3. A safety car neutralises the field and can move ECP by 10+ points across multiple drivers at once. ECPA — the signed delta from the prior lap — is the fastest single-number summary of which driver gained or lost championship value this lap. SC deployments, pit stops, and overtakes produce the largest single-lap ECPA swings.

Championship title races have 3–4 decisive weekends per season. Live ECP converts real-time race data into the metric championships are actually decided on. For sports media covering a title fight, ECP arc visualisations are the highest-signal graphic available. For DFS/fantasy scoring: ECP directly models what points finishes cost and gain in the context of the current race.

A gradient-boosted ensemble reads a rolling window of recent contextual features per driver and assesses how far through the tire lifecycle a driver is sitting relative to the expected end-of-stint pattern. The output is a probability per driver per lap, not a binary window signal. This means downstream consumers can track the gradient — a probability climbing lap over lap is a very different signal than one holding steady.

The pit stop market is F1's most actively traded in-play event. A probability signal with 3–5 laps of advance notice — before the team has made the call, before any on-screen hint — represents a genuinely tradeable edge. No other commercial F1 data API provides it.

A gradient-boosted ensemble handles the mix of continuous timing features and categorical circuit/weather features efficiently, with inference under 5ms — fast enough to update on every lap. Key predictive signals: recent incident frequency, how tightly the pack has bunched, per-circuit historical SC probability, session phase (the opening laps are statistically higher risk), and weather severity. An elevated 2026 safety-car rate was anticipated from the regulation change and applied as a base-rate adjustment before the season started.

Safety cars cause the largest single-lap swings in race outcome probability — and trigger the largest volume spikes across every in-play market simultaneously. Knowing before the flag drops is the most operationally valuable advance signal in live F1 data.

A gradient-boosted classifier trained on driver-pair laps across multiple seasons predicts whether driver A overtakes driver B within the next 3 laps. The critical design decision: a per-circuit overtake index trained on historical position-change rates per circuit-sector pair. 2026 transition: the DRS feature becomes Overtake Mode — a similar activation concept with different physics. Overtake Mode provides electric boost anywhere on track — not just designated straights. The circuit index was recalibrated for the broader activation envelope.

Not all close gaps produce overtakes. A per-pair model that distinguishes real threat from traffic management gives broadcast graphics, fantasy scoring engines, and in-play market signals something genuinely predictive — not a replay of what the timing screen already shows.

LTOE — Lap Time on Expected — is the residual between actual lap time and what the model predicts, given all context. Trained on lap-level data across the full historical archive. Core features: tyre age and compound (compound-specific degradation per circuit), fuel-corrected lap time, track evolution, a clean-air / traffic indicator, and a constructor baseline that absorbs car performance relative to the field for that specific constructor and season. LTOE = actual − predicted. Negative = faster than expected. That is real pace, not fresh tire advantage.

Tire age alone accounts for 0.8–1.2 seconds of lap time degradation in the final stint. Without correcting for this, 'fast lap times' just means 'driver on fresh rubber in clear air.' LTOE removes all of it. It is the only commercially available normalized pace metric for F1.