RaceHooks Insights — F1 Model Validation Dashboard

June 14, 2026

Data quality 77/100

360 km/hBORtop speed

43%avg full throttle

24%avg braking

64pit stops

Every prediction below was produced during the race and validated against ground truth after. ML model validation — what the models saw vs. what actually happened.

🔵 Pit windows: 9/64 flagged

14% of stops had a pre-lap recommendation. Industry baseline ~30%.

🟢 Overtakes: 51 of 62 called

82% of high-conviction laps had an on-track pass within 1 lap.

⚡ Fastest pit: 2.1s (BOR)

L33 stop. All 63 stops timed and logged.

Tyre strategy

S Soft

M Medium

H Hard

I Intermediate

W Wet

Tire health over race — Barcelona-Catalunya Grand Prix

ALB

ALO

ANT

Each driver falls toward 0 through the stint and resets to 1.0 after a pit. Pit events shown as vertical markers.

Tire Degradation

Live

Tracks how much performance budget a tire has left on a 0–1 scale. Warns 3+ laps before the cliff that causes sudden lap-time loss — earlier than any broadcast graphic.

Algorithm

Extended Kalman Filter (EKF) state-space model with Gaussian Process Regression (GPR) for cliff-lap prediction. Calibrated per compound and circuit from 2020–2025 data.

Key metrics

0.0 – 1.0Output scale

2020–2025Calibration

Per lapUpdate cadence

~0.5msInference

In your webhook payload

analytics.tireHealth = {
  tireHealth: 0.38,           // 0–1 performance index
  cliffLapPredicted: 27,      // GPR cliff prediction
  cliffRisk: "THERMAL",       // NONE | GRAINING | THERMAL | BLISTERING
  degModeActive: "DEGRADING"  // WARM_UP | STABLE | DEGRADING | CLIFF
}

Driver analytics summary

Pos	Driver	Compounds	Stops	Peak pit P	Avg LTOE	Peak ECP
P1	HAMLewis Hamilton	SHM	4	49%	-0.19s	25.0
P2	ANTKimi Antonelli	MH	3	58%	+0.50s	21.9
P2	RUSGeorge Russell	MH	3	58%	+0.43s	24.9
P3	NORLando Norris	MH	2	58%	+0.61s	18.8
P4	VERMax Verstappen	SMH	4	41%	-0.03s	22.4
P5	PIAOscar Piastri	MH	2	58%	+0.69s	12.8
P6	HADIsack Hadjar	MHS	4	58%	-0.36s	8.1
P6	LECCharles Leclerc	MH	3	58%	+0.45s	21.6
P7	GASPierre Gasly	MH	3	58%	-0.79s	6.0
P8	COLFranco Colapinto	SH	3	40%	+1.04s	6.1
P9	LAWLiam Lawson	MH	3	40%	+1.23s	5.0
P10	LINArvid Lindblad	MH	2	58%	+0.91s	4.0
P11	BORGabriel Bortoleto	MHS	4	41%	+0.05s	2.6
P12	HULNico Hulkenberg	SH	2	13%	+0.13s	4.8
P12	SAICarlos Sainz Jr.	SHM	3	100%	+1.02s	0.6
P13	OCOEsteban Ocon	SHM	3	74%	-0.31s	0.0
P14	PERSergio Perez	SHM	3	28%	+0.05s	4.1
P15	BEAOliver Bearman	MHS	3	39%	-0.73s	8.0
P17	ALOFernando Alonso	H	2	58%	-0.28s	1.6
P18	ALBAlexander Albon	MHS	5	31%	+0.78s	12.5
P21	BOTValtteri Bottas	M	1	13%	-0.22s	—
P22	STRLance Stroll	H	0	6%	+0.24s	—
P–	5555		2	58%	+0.23s	8.6
P–	SAIsainz-carlos		0	6%	+1.90s	0.6

Your product could know all of this before the pit wall does.

Everything shown above — tire health, pit windows, safety car probability, championship points — delivered as structured JSON during the live race. Per driver, per lap, via webhook.

Live webhook delivery from $499/mo · Full analytics from $6,999/mo

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