Analytics

analytics

16 independently-subscribable analytics.* feeds — the real-time outputs of 14 production ML models and the algorithmic layer on top of them. Each feed answers one question and is delivered as its own stream, so you subscribe to exactly what your product needs. For how each model is built and calibrated, see the Models reference →

bash

# SSE — stream two analytics feeds together
curl -H "Authorization: Bearer $TOKEN" \
  "https://api.racehooks.io/v1/stream?feeds=analytics.strategy,analytics.race-odds"

# Webhook — register analytics.race-odds for a driver filter
curl -X POST "https://api.racehooks.io/v1/webhooks" \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"feedId":"analytics.race-odds","webhookUrl":"https://example.com/hook","filters":{"driverNumbers":["1","4"]}}'

✦

The driverNumbers filter works on analytics.strategy, analytics.race-odds, analytics.gap-projection, analytics.tire-strategy, and analytics.qualifying. For analytics.team-points the payload is small (10 constructors) — no filter needed.

Feed ID	Tier	Cadence	Description
analytics.strategy	analytics	Per lap	Per-driver strategy signals: pit stop probability, undercut threat, pace mode, LTOE, clean (fuel-corrected) pace, tire health, DNF risk; plus session-level safety car probability.
analytics.race-odds	analytics	Per lap	CTMC position distribution, podium/points odds, expected points, fastest-lap probability, H2H vs teammate, plus same-team pairs.
analytics.true-pace	analytics	Per lap	Who's genuinely fastest: each car's session pace stripped of fuel load, tyre age, and traffic (dirty air), ranked — the public-data proxy for hidden long-run pace.
analytics.gap-projection	analytics	Per lap	Projected gap to the race leader at race end for every driver: five percentile bands (p10–p90) plus P(lapped) from Monte Carlo simulation.
analytics.winning-margin	analytics	Per lap	Session-level race outlook: projected winning margin (P1–P2) with percentile bands, the probability the margin lands above 5s / 10s / below 3s, and the safety-car collapse risk that resets the lead.
analytics.race-preview	analytics	Twice per race weekend (post-final-practice, post-qualifying)	Pre-race projections in two stages: post-final-practice (long-run pace ranking, circuit strategy priors, weather outlook) and post-qualifying (classification with measured pole margin, pace-vs-grid deltas, derived race distance, pre-race pit-stop-count distributions).
analytics.race-duration	analytics	Per lap	Projected total race time with p10/p90 bands, expected winner lap time, red-flag probability, and P(race exceeds the 2-hour FIA limit).
analytics.tire-strategy	analytics	Per pit approach	Next-compound probabilities for drivers approaching a pit window (pitStopProbability > 0.25). Sparse — fires only when relevant.
analytics.team-points	analytics	Per lap	Constructor-level expected points from discrete joint position-distribution convolution, with P(both drivers score) and full points distribution.
analytics.championship-probability	analytics	Once, post-race	Post-race championship win probability for every driver (WDC) and constructor (WCC). afterRaceId identifies which race the data reflects.
analytics.qualifying	analytics	Per lap improvement	Per-sector fastest probabilities and per-driver pace deltas during qualifying, with pole-margin probability. Fires after each personal best.
analytics.sector-pace	analytics	On sector anomaly	Per-sector pace deltas with anomaly detection; fires when a sector-pace anomaly is detected.
analytics.battle	analytics	On battle state change	On-track battle status between a driver and the car directly ahead (closing, holding, defending).
analytics.pit-window	analytics	On window open/urgent	Pit-window status (OPEN / URGENT) combining pit probability, undercut viability, tyre cliff risk, and gap context.
analytics.track-conditions	analytics	Per lap	Track evolution and grip-condition analytics derived from field pace trends.
analytics.pit-quality	analytics	On pit completion	Pit-stop execution quality assessment (stationary time vs. expected) after a stop completes.

Subscribe with the feedId via webhook or SSE. Tier shown is the minimum plan required.

`analytics.strategy`Race Strategy Analyticsanalytics

Per lap · Race, Sprint

Per-driver strategy signals: pit stop probability, undercut threat, pace mode, LTOE, clean (fuel-corrected) pace, tire health, DNF risk; plus session-level safety car probability.

Backed by: pit stop probability, overtake probability, undercut threat, pace normalization (LTOE), safety car probability, pit-stop count, DNF risk, pace mode, EKF tire health, competitor intelligence. How the models work →

Field	Type	Description
`regulationsEra`	`string`	"2026" or "pre-2026". All 2026 models have era-specific calibration applied.
`safetyCarProbability`	`number`	P(any SC/VSC deployment next lap). Session-level — same value for all drivers. Gradient-boosted classifier with a self-exciting component over historical incident data.
`vscProbability`	`number\|null`	P(virtual SC specifically). null when the 3-class SC model is not loaded.
`fullScProbability`	`number\|null`	P(full safety car). null when the 3-class SC model is not loaded.
`competitorIntelligence`	`object`	Field-level pit-window estimates. Omitted for the first few laps before the engine has data.
`drivers[].pitStopProbability`	`number`	0–1. Gradient-boosted-ensemble probability of a pit stop this lap, over a rolling multi-lap feature window.
`drivers[].pitRecommended`	`boolean`	true when pitStopProbability clears the session's era-adjusted decision threshold.
`drivers[].overtakeProbability`	`number`	0–1. Gradient-boosted-ensemble probability of a position gain via overtake within 3 laps. Circuit overtake index applied.
`drivers[].undercutThreat`	`object`	Present when the car ahead is within the strategic gap threshold: score, viable, gapToCarAheadSec, estimatedDeltaSec, probability.
`drivers[].undercutThreat.call`	`object`	Calibrated undercut-timing call (race only, target tyre age known): recommend ("UNDERCUT_NOW" \| "WAIT" \| "HOLD"), strikeInLaps, pSuccess, pNow. The recommendation gates on the worst case (target covers next lap); pSuccess/pNow are calibrated against realized outcomes with the target's reaction marginalized.
`drivers[].paceMode`	`string`	"PUSH" \| "HOLD" \| "MANAGE" \| "WARM_UP" \| "DELTA". Lap-time variance and LTOE-based pace classification.
`drivers[].ltoe`	`object`	Lap Time Over Expected: ltoeSec (negative = faster than expected), expectedLapTimeSec, actualLapTimeSec, confidenceScale (1.0 once 3+ races of the season's data exist).
`drivers[].tireHealth`	`object`	tireHealth (0–1), degRateSecPerLap, stintLap. Derived from a Kalman filter on the lap-delta series.
`drivers[].dnfRisk`	`object`	dnfRiskScore (0–1 retirement hazard), constructorHazardRate, lapsTo50PctSurvival.
`drivers[].pitStopCount`	`object`	Race-level stop-count distribution: pOneStop, pTwoStop, pThreePlusStop (sum to 1.0) and expectedStops. Multiclass gradient-boosted model over circuit, grid, weather, and starting compound.

Sample payload

json

{
  "feed": "analytics.strategy",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 35,
  "utc": "2026-06-08T14:32:18.441Z",
  "regulationsEra": "2026",
  "safetyCarProbability": 0.08,
  "vscProbability": 0.04,
  "fullScProbability": 0.04,
  "drivers": [
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "pitStopProbability": 0.79,
      "pitRecommended": true,
      "overtakeProbability": 0.12,
      "paceMode": "PUSH",
      "ltoe": {
        "ltoeSec": -0.182,
        "expectedLapTimeSec": 87.594,
        "actualLapTimeSec": 87.412,
        "confidenceScale": 0.75,
        "expectedLapTimeP10Sec": 87.262,
        "expectedLapTimeP90Sec": 88.041,
        "paceUncertaintySec": 0.779
      },
      "tyreCliff": {
        "cliffProbability": 0.63,
        "cliffWithinLaps": 5,
        "cliffDetected": true,
        "lapsToCliff": 3.2
      },
      "ltoeConfidenceFlag": "2026_early",
      "tireHealth": {
        "tireHealth": 0.58,
        "degRateSecPerLap": 0.041,
        "stintLap": 14
      },
      "dnfRisk": {
        "dnfRiskScore": 0.012,
        "lapsTo50PctSurvival": null,
        "constructorHazardRate": 0.018
      }
    },
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "pitStopProbability": 0.06,
      "pitRecommended": false,
      "overtakeProbability": 0.34,
      "undercutThreat": {
        "score": 0.71,
        "viable": true,
        "gapToCarAheadSec": 4.218,
        "estimatedDeltaSec": 1.8,
        "probability": 0.62,
        "call": {
          "recommend": "WAIT",
          "strikeInLaps": 3,
          "pSuccess": 0.58,
          "pNow": 0.54
        }
      },
      "pitEquilibrium": {
        "action": "MEDIUM",
        "rivalResponse": "HARD",
        "equilibriumGapSec": 1.9,
        "rivalPosition": 5,
        "why": {
          "label": "PIT WINDOW OPTIMAL",
          "detail": "pitting for medium now is the equilibrium call; the car behind's best response is to cover (hard); projected duel gap at the flag +1.9s",
          "tone": "info"
        }
      },
      "paceMode": "HOLD",
      "ltoe": {
        "ltoeSec": 0.048,
        "expectedLapTimeSec": 88.055,
        "actualLapTimeSec": 88.103,
        "confidenceScale": 0.75
      },
      "ltoeConfidenceFlag": "2026_early",
      "tireHealth": {
        "tireHealth": 0.91,
        "degRateSecPerLap": 0.018,
        "stintLap": 8
      },
      "dnfRisk": {
        "dnfRiskScore": 0.009,
        "lapsTo50PctSurvival": null,
        "constructorHazardRate": 0.021
      }
    }
  ]
}

`analytics.race-odds`Race Outcome Probabilitiesanalytics

Per lap · Race, Sprint

CTMC position distribution, podium/points odds, expected points, fastest-lap probability, H2H vs teammate, plus same-team pairs.

Backed by: CTMC win probability, fastest lap probability. How the models work →

Field	Type	Description
`drivers[].positionDistribution`	`number[20]`	20-element CTMC probability vector. Index 0 = P(finish 1st). Sums to ≤1.0 (lower for retired drivers).
`drivers[].mostLikelyPosition`	`number`	1-based index of the highest-probability position slot.
`drivers[].podiumProbability`	`number`	P(finish P1–P3). Sum of positionDistribution[0..2].
`drivers[].top10Probability`	`number`	Sum of positionDistribution[0..9]. Equivalent to P(score points).
`drivers[].expectedPoints`	`number`	Expected F1 points from the distribution × points structure, plus fastest-lap bonus weighted by fastestLapProbability.
`drivers[].lapsLedExpected`	`number`	Expected race laps led from the current position. CTMC forward integration.
`drivers[].fastestLapProbability`	`number`	L1-normalised P(set the session fastest lap). Field sums to 1.0.
`drivers[].positionChange`	`object`	CTMC forward projection at next3Laps/next5Laps/next10Laps: gainAtLeast1, loseAtLeast1, holdPosition.
`drivers[].h2hVsTeammate`	`number\|null`	P(this driver finishes ahead of team-mate). null when team-mate has no distribution.
`drivers[].gridPosition`	`number`	Starting grid slot. Present once real qualifying data is loaded — never a fallback value.
`drivers[].expectedPlaceDifferential`	`number`	Expected places gained vs. grid (positive = gaining): grid − E[finish] from the position distribution. The fantasy place-differential projection.
`pairs[]`	`object[]`	Same-team H2H pairs: constructorId, driverA/B, pABeatsB, pBBeatsA, pTie.

Sample payload

json

{
  "feed": "analytics.race-odds",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 35,
  "utc": "2026-06-08T14:32:18.441Z",
  "drivers": [
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "position": 1,
      "positionDistribution": [
        0.47,
        0.25,
        0.16,
        0.07,
        0.03,
        0.01,
        0.01,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0
      ],
      "mostLikelyPosition": 1,
      "podiumProbability": 0.88,
      "top6Probability": 0.97,
      "top10Probability": 0.99,
      "expectedPoints": 18.9,
      "pointsFinishProbability": 0.99,
      "lapsLedExpected": 24.1,
      "fastestLapProbability": 0.28,
      "positionChange": {
        "next3Laps": {
          "gainAtLeast1": 0.03,
          "loseAtLeast1": 0.08,
          "holdPosition": 0.89
        },
        "next5Laps": {
          "gainAtLeast1": 0.05,
          "loseAtLeast1": 0.13,
          "holdPosition": 0.82
        },
        "next10Laps": {
          "gainAtLeast1": 0.08,
          "loseAtLeast1": 0.21,
          "holdPosition": 0.71
        }
      },
      "h2hVsTeammate": 0.61
    },
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "position": 2,
      "positionDistribution": [
        0.38,
        0.28,
        0.19,
        0.09,
        0.04,
        0.01,
        0.01,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        0
      ],
      "mostLikelyPosition": 1,
      "podiumProbability": 0.85,
      "top6Probability": 0.96,
      "top10Probability": 0.99,
      "expectedPoints": 17.1,
      "pointsFinishProbability": 0.99,
      "lapsLedExpected": 18.4,
      "fastestLapProbability": 0.41,
      "positionChange": {
        "next3Laps": {
          "gainAtLeast1": 0.11,
          "loseAtLeast1": 0.06,
          "holdPosition": 0.83
        },
        "next5Laps": {
          "gainAtLeast1": 0.17,
          "loseAtLeast1": 0.1,
          "holdPosition": 0.73
        },
        "next10Laps": {
          "gainAtLeast1": 0.26,
          "loseAtLeast1": 0.16,
          "holdPosition": 0.58
        }
      },
      "h2hVsTeammate": 0.39
    }
  ],
  "pairs": [
    {
      "constructorId": "mclaren",
      "driverA": {
        "driverId": "norris-lando",
        "constructorId": "mclaren",
        "number": "4",
        "tla": "NOR",
        "name": "Lando Norris",
        "team": "McLaren F1 Team"
      },
      "driverB": {
        "driverId": "piastri-oscar",
        "constructorId": "mclaren",
        "number": "81",
        "tla": "PIA",
        "name": "Oscar Piastri",
        "team": "McLaren F1 Team"
      },
      "pABeatsB": 0.61,
      "pBBeatsA": 0.38,
      "pTie": 0.01
    }
  ]
}

`analytics.true-pace`True Pace Rankinganalytics

Per lap · Race, Sprint

Who's genuinely fastest: each car's session pace stripped of fuel load, tyre age, and traffic (dirty air), ranked — the public-data proxy for hidden long-run pace.

Backed by: True-pace deconvolution. How the models work →

Field	Type	Description
`drivers[].truePaceSec`	`number`	Deconvolved lap pace (s): the session's trailing clean-lap mean with fuel load, tyre age and traffic (dirty air) stripped — the car's fresh-tyre clean-air pace.
`drivers[].truePaceRelSec`	`number`	True pace relative to the FIELD MEDIAN (s/lap); negative = faster than the field. Comparable across sessions. Compute gap-to-fastest from truePaceSec if needed.
`drivers[].rank`	`number`	1 = genuinely fastest. Field-relative true pace predicts finish position ~2× better than raw fuel-corrected pace.

Sample payload

json

{
  "feed": "analytics.true-pace",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 35,
  "utc": "2026-06-08T14:32:18.441Z",
  "drivers": [
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "truePaceSec": 87.412,
      "truePaceRelSec": -0.31,
      "rank": 1
    },
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "truePaceSec": 87.523,
      "truePaceRelSec": -0.199,
      "rank": 2
    },
    {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari",
      "truePaceSec": 87.706,
      "truePaceRelSec": -0.016,
      "rank": 3
    }
  ]
}

`analytics.gap-projection`Gap to Leader Projectionanalytics

Per lap · Race, Sprint

Projected gap to the race leader at race end for every driver: five percentile bands (p10–p90) plus P(lapped) from Monte Carlo simulation.

Backed by: Monte Carlo gap projection. How the models work →

Field	Type	Description
`drivers[].currentGapToLeaderSec`	`number`	Current gap to the race leader (seconds).
`drivers[].expectedGapToLeaderSec`	`number`	Monte Carlo mean projected gap at race end.
`drivers[].p10GapSec`	`number`	10th-percentile projected gap — optimistic scenario.
`drivers[].p50GapSec`	`number`	Median projected gap.
`drivers[].p90GapSec`	`number`	90th-percentile projected gap — pessimistic scenario.
`drivers[].probabilityLapped`	`number`	P(driver is lapped by race end). Typically < 0.05 for active drivers.

Sample payload

json

{
  "feed": "analytics.gap-projection",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 35,
  "utc": "2026-06-08T14:32:18.441Z",
  "drivers": [
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "currentGapToLeaderSec": 0,
      "expectedGapToLeaderSec": 0,
      "p10GapSec": 0,
      "p25GapSec": 0,
      "p50GapSec": 0,
      "p75GapSec": 0,
      "p90GapSec": 0,
      "probabilityLapped": 0
    },
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "currentGapToLeaderSec": 3.6,
      "expectedGapToLeaderSec": 5.8,
      "p10GapSec": 2.1,
      "p25GapSec": 3.9,
      "p50GapSec": 5.6,
      "p75GapSec": 7.8,
      "p90GapSec": 10.2,
      "probabilityLapped": 0
    },
    {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari",
      "currentGapToLeaderSec": 12.4,
      "expectedGapToLeaderSec": 18.1,
      "p10GapSec": 12.7,
      "p25GapSec": 15.2,
      "p50GapSec": 17.9,
      "p75GapSec": 21,
      "p90GapSec": 24.6,
      "probabilityLapped": 0.02
    },
    {
      "driverId": "russell-george",
      "constructorId": "mercedes",
      "number": "63",
      "tla": "RUS",
      "name": "George Russell",
      "team": "Mercedes-AMG Petronas F1 Team",
      "currentGapToLeaderSec": 58.3,
      "expectedGapToLeaderSec": 81.5,
      "p10GapSec": 66.4,
      "p25GapSec": 74.1,
      "p50GapSec": 81,
      "p75GapSec": 88.9,
      "p90GapSec": 97.2,
      "probabilityLapped": 0.41
    }
  ]
}

`analytics.winning-margin`Winning Margin Projectionanalytics

Per lap · Race, Sprint

Session-level race outlook: projected winning margin (P1–P2) with percentile bands, the probability the margin lands above 5s / 10s / below 3s, and the safety-car collapse risk that resets the lead.

Backed by: Monte Carlo winning margin. How the models work →

Field	Type	Description
`leadBattle.leader / leadBattle.runnerUp`	`object`	Current P1 and P2 (each a full DriverRef: number, driverId, tla, name, team).
`leadBattle.currentMarginSec`	`number`	Current P1–P2 gap in seconds.
`leadBattle.expectedFinalMarginSec`	`number`	Expected P1–P2 gap at race end. Mixture of the no-SC linear drift and the post-SC reset distribution.
`leadBattle.p10MarginSec / p50MarginSec / p90MarginSec`	`number`	Percentile bands from 5,000 Monte Carlo samples.
`leadBattle.probabilityMarginAbove5 / Above10`	`number`	P(final margin > 5s) / P(> 10s).
`leadBattle.probabilityMarginBelow3`	`number`	P(final margin < 3s) — close-finish probability.
`leadBattle.scCollapseRisk`	`number`	P(safety car before race end) — the dominant driver of margin collapse. Gaps reset toward ~1.5s at an SC restart.

Sample payload

json

{
  "feed": "analytics.winning-margin",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 40,
  "utc": "2026-06-08T14:32:18.441Z",
  "leadBattle": {
    "leader": {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing"
    },
    "runnerUp": {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team"
    },
    "currentMarginSec": 4.8,
    "expectedFinalMarginSec": 7.2,
    "p10MarginSec": 1.9,
    "p50MarginSec": 6.8,
    "p90MarginSec": 13.4,
    "probabilityMarginAbove5": 0.71,
    "probabilityMarginAbove10": 0.28,
    "probabilityMarginBelow3": 0.14,
    "scCollapseRisk": 0.22
  }
}

`analytics.race-preview`Pre-Race Previewanalytics

Twice per race weekend (post-final-practice, post-qualifying) · Practice, Qualifying

Pre-race projections in two stages: post-final-practice (long-run pace ranking, circuit strategy priors, weather outlook) and post-qualifying (classification with measured pole margin, pace-vs-grid deltas, derived race distance, pre-race pit-stop-count distributions).

Backed by: Practice long-run pace summary, Circuit priors, pit-stop count. How the models work →

Field	Type	Description
`stage`	`string`	"practice" (final practice ended) or "grid" (qualifying ended).
`circuitId`	`string`	Canonical RaceHooks circuit slug (e.g. "monaco").
`drivers[]`	`object`	Each entry is a full DriverRef (number, driverId, tla, name, team) plus the stage-specific fields below.
`drivers[].practicePace`	`object`	relPacePct (lower = faster), longRunDone, primaryCompound, lapsCompleted, sessionType, paceRank. Long-run drivers rank ahead of short-run-only drivers — long-run pace is the race-pace signal.
`strategy.compoundDeg[]`	`object`	Per-compound degradation prior (sec/lap) for this circuit, from the post-weekend prior pipeline.
`strategy.pitLaneDeltaSec`	`number`	Circuit pit-lane time loss in seconds.
`strategy.scTimeCostSecPerLap`	`number`	Time cost of a safety-car lap at this circuit vs green pace.
`strategy.redFlagBaseRate`	`number`	Fraction of sessions at this circuit with at least one red flag (2018–2025).
`qualifying`	`object`	Grid stage: session classification (order with segments), pole lap, and the MEASURED pole margin in ms. The official grid may differ after penalties.
`drivers[].paceVsGridDelta`	`number`	Grid stage: practice pace rank minus qualifying position — positive = raced faster in practice than qualified (a classic recovery-drive signal).
`drivers[].pitStopCount`	`object`	Grid stage: pre-race stop-count distribution (pOneStop/pTwoStop/pThreePlusStop/expectedStops) assuming a medium-compound start; race distance derived from the FIA 305 km rule.
`scheduledRaceLaps`	`number\|null`	Grid stage: derived race distance in laps (FIA 305 km rule; Monaco 260 km). null when circuit length is unknown.

Sample payload

json

{
  "feed": "analytics.race-preview",
  "stage": "grid",
  "eventId": "2026-monaco",
  "raceId": "2026-monaco-r1",
  "circuitId": "monaco",
  "utc": "2026-06-08T14:32:18.441Z",
  "scheduledRaceLaps": 78,
  "qualifying": {
    "poleLapMs": 70123,
    "poleMarginMs": 84,
    "order": [
      {
        "driverId": "norris-lando",
        "constructorId": "mclaren",
        "number": "4",
        "tla": "NOR",
        "name": "Lando Norris",
        "team": "McLaren F1 Team",
        "position": 1,
        "lapTimeMs": 70123,
        "segment": "Q3"
      },
      {
        "driverId": "verstappen-max",
        "constructorId": "red-bull-racing",
        "number": "1",
        "tla": "VER",
        "name": "Max Verstappen",
        "team": "Red Bull Racing",
        "position": 2,
        "lapTimeMs": 70207,
        "segment": "Q3"
      },
      {
        "driverId": "leclerc-charles",
        "constructorId": "ferrari",
        "number": "16",
        "tla": "LEC",
        "name": "Charles Leclerc",
        "team": "Scuderia Ferrari",
        "position": 3,
        "lapTimeMs": 70298,
        "segment": "Q3"
      }
    ]
  },
  "drivers": [
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "qualifyingPosition": 1,
      "practicePaceRank": 1,
      "paceVsGridDelta": 0,
      "pitStopCount": {
        "pOneStop": 0.72,
        "pTwoStop": 0.26,
        "pThreePlusStop": 0.02,
        "expectedStops": 1.3
      }
    },
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "qualifyingPosition": 2,
      "practicePaceRank": 3,
      "paceVsGridDelta": -1,
      "pitStopCount": {
        "pOneStop": 0.68,
        "pTwoStop": 0.3,
        "pThreePlusStop": 0.02,
        "expectedStops": 1.34
      }
    },
    {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari",
      "qualifyingPosition": 3,
      "practicePaceRank": 2,
      "paceVsGridDelta": 1
    }
  ]
}

`analytics.race-duration`Race Duration Projectionanalytics

Per lap · Race, Sprint

Projected total race time with p10/p90 bands, expected winner lap time, red-flag probability, and P(race exceeds the 2-hour FIA limit).

Backed by: Race-finish-time formula (LTOE + SC cost + red-flag base rates). How the models work →

Field	Type	Description
`expectedRaceTimeMinutes`	`number`	Expected total race duration in minutes: elapsed + remaining racing time + expected SC and red-flag time loss.
`p10RaceTimeMinutes / p90RaceTimeMinutes`	`number`	10th/90th percentile race-duration band.
`expectedWinnerLapTimeSec`	`number`	Expected winner average lap time over the remaining laps (degradation-adjusted).
`probabilityRedFlag`	`number`	Rule-based P(red flag before race end): circuit base rate boosted by wet running, lap-1 chaos, SC history, and retirements.
`probabilityOvertimeSession`	`number`	P(race exceeds the 2-hour FIA time limit).
`scDeployedCount`	`number`	Safety-car deployments so far this race.

Sample payload

json

{
  "feed": "analytics.race-duration",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 40,
  "utc": "2026-06-08T14:32:18.441Z",
  "expectedRaceTimeMinutes": 98.4,
  "p10RaceTimeMinutes": 95.1,
  "p90RaceTimeMinutes": 104.7,
  "expectedWinnerLapTimeSec": 74.812,
  "probabilityRedFlag": 0.11,
  "probabilityOvertimeSession": 0.03,
  "scDeployedCount": 1
}

`analytics.tire-strategy`Tire Strategy Probabilityanalytics

Per pit approach · Race, Sprint

Next-compound probabilities for drivers approaching a pit window (pitStopProbability > 0.25). Sparse — fires only when relevant.

Backed by: tyre compound. How the models work →

Field	Type	Description
`drivers[].pitProbability`	`number`	Current pit-stop probability — why the driver is in the sparse set.
`drivers[].nextCompoundProbability.soft`	`number`	P(next compound = soft).
`drivers[].nextCompoundProbability.medium`	`number`	P(next compound = medium).
`drivers[].nextCompoundProbability.hard`	`number`	P(next compound = hard).
`drivers[].nextCompoundProbability.wetWeather`	`number`	High-recall merged Inter+Wet class. Zeroed on a dry track; elevated when a weather event is approaching.
`drivers[].nextCompoundProbability.dryCompoundProbabilities`	`object`	Dry-only renormalised view { soft, medium, hard } summing to 1 — use on a dry track where wetWeather is 0.

Sample payload

json

{
  "feed": "analytics.tire-strategy",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 33,
  "utc": "2026-06-08T14:32:18.441Z",
  "drivers": [
    {
      "driverId": "hamilton-lewis",
      "constructorId": "ferrari",
      "number": "44",
      "tla": "HAM",
      "name": "Lewis Hamilton",
      "team": "Scuderia Ferrari",
      "pitProbability": 0.68,
      "nextCompoundProbability": {
        "soft": 0.72,
        "medium": 0.24,
        "hard": 0.04,
        "wetWeather": 0,
        "dryCompoundProbabilities": {
          "soft": 0.72,
          "medium": 0.24,
          "hard": 0.04
        }
      }
    }
  ]
}

`analytics.team-points`Team Points Analyticsanalytics

Per lap · Race, Sprint

Constructor-level expected points from discrete joint position-distribution convolution, with P(both drivers score) and full points distribution.

Backed by: CTMC win probability, constructor points convolution. How the models work →

Field	Type	Description
`constructors[].constructorId`	`string`	Team identifier (e.g. "red-bull", "ferrari", "mclaren").
`constructors[].expectedConstructorPoints`	`number`	Expected combined points from both drivers, via discrete convolution of joint position distributions — analytically exact.
`constructors[].scoringBothDriversProbability`	`number`	P(both drivers finish top 10). Joint probability from the distribution product.
`constructors[].podiumBothDriversProbability`	`number`	P(both drivers finish on the podium). Same independence product over P1–P3.
`constructors[].pointsDistribution`	`number[]`	P(team scores exactly K combined points) for K = 0…44.

Sample payload

json

{
  "feed": "analytics.team-points",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1",
  "lap": 40,
  "utc": "2026-06-08T14:32:18.441Z",
  "constructors": [
    {
      "constructorId": "red-bull",
      "expectedConstructorPoints": 33.1,
      "scoringBothDriversProbability": 0.88,
      "podiumBothDriversProbability": 0.19,
      "pointsDistribution": [
        0,
        0,
        0.01,
        0.02,
        0.04,
        0.07
      ]
    },
    {
      "constructorId": "mclaren",
      "expectedConstructorPoints": 27.6,
      "scoringBothDriversProbability": 0.82,
      "podiumBothDriversProbability": 0.11,
      "pointsDistribution": [
        0,
        0.01,
        0.02,
        0.03,
        0.05,
        0.08
      ]
    }
  ]
}

`analytics.championship-probability`Championship Win Probabilityanalytics

Once, post-race · Season

Post-race championship win probability for every driver (WDC) and constructor (WCC). afterRaceId identifies which race the data reflects.

Backed by: season simulator (infer_scwp). How the models work →

Field	Type	Description
`seasonYear`	`number`	Championship season the projection covers.
`afterRaceId`	`string`	The most recent official race the probabilities are computed through.
`simulationsRun`	`number`	Monte Carlo iterations behind the projection.
`drivers[]`	`object`	WDC row — a full DriverRef (number, driverId, tla, name, team) plus the fields below.
`drivers[].scwp`	`number`	P(this driver wins the WDC). Field sums to ≤1.0.
`drivers[].scwpTop3`	`number`	P(this driver finishes top-3 in the championship).
`drivers[].expectedFinalPoints`	`number`	Expected end-of-season points total.
`drivers[].championshipDeficit`	`number`	Points behind the current championship leader (0 for the leader).
`constructors[].constructorId`	`string`	Canonical team slug (e.g. "mclaren").
`constructors[].ccwp`	`number`	P(this team wins the WCC). Field sums to ≤1.0.
`constructors[].ccwpTop3`	`number`	P(this team finishes top-3 in the constructors' championship).
`constructors[].expectedFinalPoints`	`number`	Expected end-of-season combined points.
`constructors[].championshipDeficit`	`number`	Points behind the leading constructor.

Sample payload

json

{
  "feed": "analytics.championship-probability",
  "seasonYear": 2026,
  "afterRaceId": "2026-monaco-r1",
  "simulationsRun": 10000,
  "utc": "2026-06-08T14:32:18.441Z",
  "drivers": [
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "scwp": 0.41,
      "scwpTop3": 0.93,
      "expectedFinalPoints": 421.5,
      "championshipDeficit": 0
    },
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "scwp": 0.36,
      "scwpTop3": 0.9,
      "expectedFinalPoints": 409.2,
      "championshipDeficit": 12
    },
    {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari",
      "scwp": 0.14,
      "scwpTop3": 0.71,
      "expectedFinalPoints": 372.8,
      "championshipDeficit": 41
    }
  ],
  "constructors": [
    {
      "constructorId": "mclaren",
      "ccwp": 0.58,
      "ccwpTop3": 0.98,
      "expectedFinalPoints": 742,
      "championshipDeficit": 0
    },
    {
      "constructorId": "red-bull",
      "ccwp": 0.31,
      "ccwpTop3": 0.94,
      "expectedFinalPoints": 705.4,
      "championshipDeficit": 36
    }
  ]
}

`analytics.qualifying`Qualifying Analyticsanalytics

Per lap improvement · Qualifying

Per-sector fastest probabilities and per-driver pace deltas during qualifying, with pole-margin probability. Fires after each personal best.

Backed by: qualifying pace, calibrated qualifying distribution. How the models work →

Field	Type	Description
`segment`	`string`	Active segment: "Q1", "Q2", or "Q3".
`poleLeader`	`string`	Driver number currently projected to take pole (lowest sum of sector deltas).
`poleMarginAbove100ms`	`number`	P(gap between pole and P2 > 0.1s). From the calibrated margin model.
`sectors.s1.drivers[].pFastest`	`number`	P(this driver sets fastest S1). L1-normalised across drivers with pace data.
`sectors.s1.drivers[].predictedDeltaMs`	`number`	Predicted S1 delta vs. session median (ms). Negative = faster.
`drivers[].sector1DeltaMs`	`number\|null`	Predicted S1 delta vs. session median (ms). null when the qualifying pace model is not loaded.
`drivers[].elimination`	`object`	Q1/Q2 only, once most of the field has predictions: pEliminatedQ1, pEliminatedQ2, pReachesQ3, projectedFinalQualifyingPosition — rank-based order statistics over the predicted field.

Sample payload

json

{
  "feed": "analytics.qualifying",
  "sessionId": "9560",
  "raceId": "2026-monaco-r1-q",
  "utc": "2026-06-08T14:32:18.441Z",
  "segment": "Q3",
  "circuitId": "monaco",
  "poleMarginAbove100ms": 0.71,
  "poleMarginAbove300ms": 0.31,
  "poleLeader": {
    "driverId": "verstappen-max",
    "constructorId": "red-bull-racing",
    "number": "1",
    "tla": "VER",
    "name": "Max Verstappen",
    "team": "Red Bull Racing"
  },
  "sectors": {
    "s1": {
      "drivers": [
        {
          "driverId": "verstappen-max",
          "constructorId": "red-bull-racing",
          "number": "1",
          "tla": "VER",
          "name": "Max Verstappen",
          "team": "Red Bull Racing",
          "pFastest": 0.43,
          "predictedDeltaMs": -12.4,
          "sigma": 0.032
        },
        {
          "driverId": "norris-lando",
          "constructorId": "mclaren",
          "number": "4",
          "tla": "NOR",
          "name": "Lando Norris",
          "team": "McLaren F1 Team",
          "pFastest": 0.31,
          "predictedDeltaMs": -8.1,
          "sigma": 0.032
        }
      ]
    },
    "s2": {
      "drivers": [
        {
          "driverId": "norris-lando",
          "constructorId": "mclaren",
          "number": "4",
          "tla": "NOR",
          "name": "Lando Norris",
          "team": "McLaren F1 Team",
          "pFastest": 0.38,
          "predictedDeltaMs": -10.2,
          "sigma": 0.029
        },
        {
          "driverId": "verstappen-max",
          "constructorId": "red-bull-racing",
          "number": "1",
          "tla": "VER",
          "name": "Max Verstappen",
          "team": "Red Bull Racing",
          "pFastest": 0.36,
          "predictedDeltaMs": -9.8,
          "sigma": 0.029
        }
      ]
    },
    "s3": {
      "drivers": [
        {
          "driverId": "verstappen-max",
          "constructorId": "red-bull-racing",
          "number": "1",
          "tla": "VER",
          "name": "Max Verstappen",
          "team": "Red Bull Racing",
          "pFastest": 0.49,
          "predictedDeltaMs": -14.1,
          "sigma": 0.034
        },
        {
          "driverId": "norris-lando",
          "constructorId": "mclaren",
          "number": "4",
          "tla": "NOR",
          "name": "Lando Norris",
          "team": "McLaren F1 Team",
          "pFastest": 0.29,
          "predictedDeltaMs": -8.3,
          "sigma": 0.034
        }
      ]
    }
  },
  "drivers": [
    {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing",
      "sector1DeltaMs": -12.4,
      "sector2DeltaMs": -9.8,
      "sector3DeltaMs": -14.1
    },
    {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team",
      "sector1DeltaMs": -8.1,
      "sector2DeltaMs": -10.2,
      "sector3DeltaMs": -8.3
    }
  ]
}

`analytics.sector-pace`Sector Pace Analyticsanalytics

On sector anomaly · Race, Sprint

Per-sector pace deltas with anomaly detection; fires when a sector-pace anomaly is detected.

Backed by: sector pace analyzer. How the models work →

Field	Type	Description
`data.driver`	`object`	The driver the anomaly is for (full DriverRef: number, driverId, tla, name, team).
`data.s1 / s2 / s3`	`object\|null`	Per-sector reading: deltaSec vs the rolling median baseline, baselineMs, currentMs, cleanLapCount.
`data.anomalyDetected`	`boolean`	true when a sector departs its baseline beyond the warning threshold.
`data.anomalySector / anomalyLevel`	`number\|string`	Which sector (1–3) and severity ("WARNING" \| "CRITICAL").
`data.classifiedCause`	`string\|null`	"RAIN" \| "TIRE_CLIFF" \| "INCIDENT" \| "UNKNOWN" — rule-based cause classification.
`data.rainConfidence`	`number`	0–1 confidence the anomaly is rain-driven.
`data.crossDriverCorrelated`	`boolean`	true when ≥3 cars show the same-sector anomaly on the same lap (weather, not car-specific).
`data.driversAffectedSameSector`	`object[]`	Other cars flagged in the same sector this lap — a full DriverRef each.

Sample payload

json

{
  "feed": "analytics.sector-pace",
  "sessionId": "9560",
  "lap": 33,
  "utc": "2026-06-08T14:32:18.441Z",
  "data": {
    "driver": {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team"
    },
    "raceLap": 33,
    "s1": {
      "deltaSec": 0.08,
      "baselineMs": 28450,
      "cleanLapCount": 6,
      "currentMs": 28530
    },
    "s2": {
      "deltaSec": 2.13,
      "baselineMs": 31210,
      "cleanLapCount": 6,
      "currentMs": 33340
    },
    "s3": null,
    "anomalyDetected": true,
    "anomalySector": 2,
    "anomalyLevel": "WARNING",
    "classifiedCause": "RAIN",
    "rainConfidence": 0.78,
    "crossDriverCorrelated": true,
    "driversAffectedSameSector": [
      {
        "driverId": "leclerc-charles",
        "constructorId": "ferrari",
        "number": "16",
        "tla": "LEC",
        "name": "Charles Leclerc",
        "team": "Scuderia Ferrari"
      },
      {
        "driverId": "hamilton-lewis",
        "constructorId": "ferrari",
        "number": "44",
        "tla": "HAM",
        "name": "Lewis Hamilton",
        "team": "Scuderia Ferrari"
      }
    ]
  }
}

`analytics.battle`Strategy Battle Alertanalytics

On battle state change · Race, Sprint

On-track battle status between a driver and the car directly ahead (closing, holding, defending).

Backed by: strategy battle tracker. How the models work →

Field	Type	Description
`data.attacker / defender`	`object`	The two cars in the on-track fight (each a full DriverRef: number, driverId, tla, name, team).
`data.currentGapSec`	`number`	Current gap between the pair (seconds).
`data.catchRateSecPerLap`	`number`	Positive = attacker closing; negative = defender pulling away.
`data.lapsToStrikingDistance`	`number\|null`	Projected laps until the attacker is in range; null when not catching or already there.
`data.battleStatus`	`string`	Lifecycle of the battle (forming, in-range, resolved…).

Sample payload

json

{
  "feed": "analytics.battle",
  "sessionId": "9560",
  "lap": 41,
  "utc": "2026-06-08T14:32:18.441Z",
  "data": {
    "attacker": {
      "driverId": "verstappen-max",
      "constructorId": "red-bull-racing",
      "number": "1",
      "tla": "VER",
      "name": "Max Verstappen",
      "team": "Red Bull Racing"
    },
    "defender": {
      "driverId": "norris-lando",
      "constructorId": "mclaren",
      "number": "4",
      "tla": "NOR",
      "name": "Lando Norris",
      "team": "McLaren F1 Team"
    },
    "currentGapSec": 1.62,
    "catchRateSecPerLap": 0.31,
    "lapsToStrikingDistance": 2,
    "battleStatus": "APPROACHING",
    "forecast": {
      "passProbabilityPerLap": 0.22,
      "pPassWithinHorizon": 0.526,
      "horizonLaps": 5
    }
  }
}

`analytics.pit-window`Pit Window Alertanalytics

On window open/urgent · Race, Sprint

Pit-window status (OPEN / URGENT) combining pit probability, undercut viability, tyre cliff risk, and gap context.

Backed by: pit window alert, pit stop probability, EKF tire health. How the models work →

Field	Type	Description
`data.driver`	`object`	The driver the alert is for (full DriverRef: number, driverId, tla, name, team).
`data.status`	`string`	Pit-window state for the driver (survival-model window plus overrides).
`data.pitProbability`	`number`	0–1 pit-stop probability from the pit stop model.
`data.undercutViable`	`boolean`	Whether an undercut on the car ahead is currently viable.
`data.cliffRisk`	`string`	Tyre-cliff proximity classification feeding the window call.
`data.overrideReason`	`string\|null`	"VSC_WINDOW" \| "SC_WINDOW" \| "LEADER_PITTED" — event overrides that open a window early.
`data.vscNetDeltaSec`	`number\|null`	Estimated time saved by pitting under the current VSC/SC, when applicable.
`data.pitCount`	`number`	The driver's current pit-stop count.

Sample payload

json

{
  "feed": "analytics.pit-window",
  "sessionId": "9560",
  "lap": 34,
  "utc": "2026-06-08T14:32:18.441Z",
  "data": {
    "driver": {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari"
    },
    "raceLap": 34,
    "status": "URGENT",
    "pitProbability": 0.81,
    "undercutViable": true,
    "cliffRisk": "BLISTERING_RISK",
    "overrideReason": null,
    "vscNetDeltaSec": null,
    "pitCount": 1
  }
}

`analytics.track-conditions`Track Conditions Analyticsanalytics

Per lap · Race, Sprint

Track evolution and grip-condition analytics derived from field pace trends.

Backed by: track conditions model. How the models work →

Field	Type	Description
`data.sector1 / sector2 / sector3`	`string`	Per-sector surface condition classification.
`data.fullCircuitWet`	`boolean`	All three sectors classified wet.
`data.intermediateWindowOpen`	`boolean`	Any sector wet AND slick runners losing more than ~2s — the inter window.
`data.crossoverRecommended`	`boolean`	Slick cars now faster than intermediates in at least one sector — the crossover call.
`data.lastUpdatedLap`	`number`	Race lap this classification was last updated.

Sample payload

json

{
  "feed": "analytics.track-conditions",
  "sessionId": "9560",
  "lap": 28,
  "utc": "2026-06-08T14:32:18.441Z",
  "data": {
    "sector1": "DRYING",
    "sector2": "WET",
    "sector3": "DRYING",
    "fullCircuitWet": false,
    "intermediateWindowOpen": true,
    "crossoverRecommended": false,
    "lastUpdatedLap": 28
  }
}

`analytics.pit-quality`Pit Quality Alertanalytics

On pit completion · Race, Sprint

Pit-stop execution quality assessment (stationary time vs. expected) after a stop completes.

Backed by: pit quality model. How the models work →

Field	Type	Description
`data.driver`	`object`	The driver whose stop this grades (full DriverRef: number, driverId, tla, name, team).
`data.stationaryTimeSec`	`number`	Measured stationary time for the completed stop.
`data.teamSessionAverageSec`	`number`	The team's rolling average stationary time this session (before this stop).
`data.vsTeamSessionAverageSec`	`number`	This stop minus the team average — positive = slower than the crew's norm.
`data.quality`	`string`	Grade for the stop relative to session norms.
`data.stopNumber`	`number`	Nth stop this session for this driver.

Sample payload

json

{
  "feed": "analytics.pit-quality",
  "sessionId": "9560",
  "lap": 24,
  "utc": "2026-06-08T14:32:18.441Z",
  "data": {
    "driver": {
      "driverId": "leclerc-charles",
      "constructorId": "ferrari",
      "number": "16",
      "tla": "LEC",
      "name": "Charles Leclerc",
      "team": "Scuderia Ferrari"
    },
    "raceLap": 24,
    "stationaryTimeSec": 4.1,
    "teamSessionAverageSec": 2.7,
    "vsTeamSessionAverageSec": 1.4,
    "fieldSessionAverageSec": 3,
    "vsFieldSessionAverageSec": 1.1,
    "fieldRank": 14,
    "totalFieldStopsSoFar": 16,
    "quality": "SLOW",
    "stopNumber": 2
  }
}

← Telemetry Results & Standings →

Analytics

analytics.strategyRace Strategy Analyticsanalytics

analytics.race-oddsRace Outcome Probabilitiesanalytics

analytics.true-paceTrue Pace Rankinganalytics

analytics.gap-projectionGap to Leader Projectionanalytics

analytics.winning-marginWinning Margin Projectionanalytics

analytics.race-previewPre-Race Previewanalytics

analytics.race-durationRace Duration Projectionanalytics

analytics.tire-strategyTire Strategy Probabilityanalytics

analytics.team-pointsTeam Points Analyticsanalytics

analytics.championship-probabilityChampionship Win Probabilityanalytics

analytics.qualifyingQualifying Analyticsanalytics

analytics.sector-paceSector Pace Analyticsanalytics

analytics.battleStrategy Battle Alertanalytics

analytics.pit-windowPit Window Alertanalytics

analytics.track-conditionsTrack Conditions Analyticsanalytics

analytics.pit-qualityPit Quality Alertanalytics

`analytics.strategy`Race Strategy Analyticsanalytics

`analytics.race-odds`Race Outcome Probabilitiesanalytics

`analytics.true-pace`True Pace Rankinganalytics

`analytics.gap-projection`Gap to Leader Projectionanalytics

`analytics.winning-margin`Winning Margin Projectionanalytics

`analytics.race-preview`Pre-Race Previewanalytics

`analytics.race-duration`Race Duration Projectionanalytics

`analytics.tire-strategy`Tire Strategy Probabilityanalytics

`analytics.team-points`Team Points Analyticsanalytics

`analytics.championship-probability`Championship Win Probabilityanalytics

`analytics.qualifying`Qualifying Analyticsanalytics

`analytics.sector-pace`Sector Pace Analyticsanalytics

`analytics.battle`Strategy Battle Alertanalytics

`analytics.pit-window`Pit Window Alertanalytics

`analytics.track-conditions`Track Conditions Analyticsanalytics

`analytics.pit-quality`Pit Quality Alertanalytics