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2026
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Folder: Grand Prix
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  • View: Pace, accumulated race time

    Normalization: Fuel & Tire use

    Population: Top 6 drivers

    X-Axis: Lap number in race order

    Y-Axis: Time delta to reference

    Reference: Race winner’s average lap

  • Pace Timeline

    The Pace Timeline reconstructs the race using pace-normalized lap times. A linear-regression manifold equalizes the effects of fuel consumption and tire wear, making every lap directly comparable with every other lap as a measure of driver–car pace.

    The original race timeline and total race time are preserved, so the finishing order and gaps remain unchanged. As in the Race Timeline, the chart is measured against the leader’s average pace; the slope shows relative pace, while discontinuities reveal time lost to pit stops and race interruptions.

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  • View: Pace, lap time distributions

    Normalization: Fuel & Tire use

    Population: Top 6 drivers

    X-Axis: Percentiles and Quartiles

    Y-Axis: Lap time in seconds

    Filter: 5%,10% excluded for scaling

  • PPace Distributions

    Pace Distributions plot each driver’s pace-normalized lap-time percentiles. A linear-regression manifold equalizes fuel consumption and tire wear, making every lap directly comparable as a measure of driver–car pace, while preserving the original total race time and finishing order.

    The slower side of the distribution reflects time lost to race entropy—traffic, pit stops and interruptions. Toward the upper quartile, those effects fall away, revealing pure pace. The shape and separation of the curves show both outright speed and the consistency with which it was delivered.

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  • View: Pace, accumulated sorted time

    Normalization: Fuel & Tire use

    Population: Top 6 drivers

    X-Axis: Percentiles and Quartiles

    Y-Axis: Time delta to reference

    Reference: Winner average pace

  • Pace Sorted Time

    Pace Sorted Time reintegrates the pace-normalized lap distributions in percentile order—as if every driver completed the slowest laps first and the fastest laps last. A linear-regression manifold equalizes fuel consumption and tire wear, making every lap directly comparable as a measure of driver–car pace, while preserving the original total race time and finishing order.

    On the left, the height of each curve shows time lost to race entropy. On the right, the slope shows how that time was recovered through pace. All curves converge at the finish, preserving the actual result and gaps.

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