Lap waterfall: visualize the seven-term decomposition

A waterfall chart showing how fuel, compound, rubber, ambient, constructor, dirty air, and driver skill each contribute to a driver’s pace delta, with track noise shown as a diagnostic.

The Lap Waterfall chart makes the seven-term additive identity tangible. For any driver in any race, it shows you exactly how much of their pace delta their deviation from the field-pace baseline came from each of the seven identity terms. Alongside them it surfaces an eighth, non-closing bar track noise (track_unexplained_s) as a transparency diagnostic. When a driver looks slow, you can see whether the slowness belongs to the car, the tyre strategy, the conditions, or the driver. When a driver looks fast, you can tell whether they are genuinely overdelivering or simply running the lightest fuel and freshest rubber.

What the chart shows

Each driver gets a waterfall (stacked bar) chart where every segment is one additive component from fct_lap_residuals, averaged across all clean laps in the selected race. The segments stack horizontally:

Component	Column	Typical sign
Fuel	fuel_component_s	Positive early in race (heavy fuel slows the car)
Compound	compound_component_s	Negative on faster compounds vs season median
Rubber	rubber_component_s	Varies by stint position and track state
Ambient	ambient_component_s	Positive in high-temperature conditions
Constructor	constructor_component_s	Negative for front-running cars
Dirty Air	dirty_air_tax_s	Positive (running in wake always costs time)
Driver Skill	driver_skill_residual_s	Negative if driver outperforms predicted pace
Track Noise (diagnostic)	track_unexplained_s	Field-level residual not attributable to any modelled term not part of the identity

The six environmental terms sum to total_explained_s; add the driver-skill residual and you reconstruct pace_delta_s exactly. That is the seven-term identity, enforced in the warehouse by assert_additive_identity (see the Seven-Term Identity). Track noise (track_unexplained_s) is a separate field-level diagnostic the part of the smoothed field-pace curve the rubber and ambient fits cannot explain. It is shown for transparency but is not one of the closing terms; it is excluded from the CI-enforced identity.

How to read the bars

• Bars extending right (positive) are slowing contributions. A large positive fuel bar early in the race is expected the car is heavy. A large positive constructor bar is not a good sign for the team.
• Bars extending left (negative) are speeding contributions. A strongly negative driver skill bar means the driver is extracting more pace than their exact set of conditions predicts. A negative compound bar means they are on a faster compound than the season median.
• The total of the seven identity bars is pace_delta_s, the driver’s average deviation from field baseline across the race. The track-noise bar is appended as a diagnostic and is not part of that total.

How to navigate

1. Select a season in the filter bar. The chart defaults to a season average across all races, showing one bar stack per driver.
2. Select a race to narrow the view to a single event. Each driver’s bar stack now reflects that race’s average clean-lap conditions.
3. Click a driver name in the selector above the chart to highlight their waterfall and focus the view on their decomposition.
4. If no race is selected, the status label reads “season average select a race in the filter bar to narrow to one event” as a reminder.

To separate car and strategy effects from driver execution, find a lap where two drivers ran nearly identical stints same compound age, similar fuel load, similar track position and compare their waterfalls side by side. If their constructor bars are similar but one driver’s skill bar is significantly more negative, the pace difference is attributable to the driver, not the equipment.

Key use case: decomposing a confusing lap

Pick a driver who looked unusually slow (or fast) in a race. Open the Lap Waterfall for that race and driver. Work through the bars:

1. Is the constructor component large and positive? The car was off that weekend.
2. Is the compound component large and positive? They were on older or harder tyres than most of the field.
3. Is the dirty air tax high? They spent significant time in traffic.
4. Only after accounting for all of the above: what does the driver skill residual show? If it is still positive after all the above, there may be a genuine execution story.

The waterfall makes this decomposition explicit and arithmetic, not interpretive.

Neutralised laps (safety car, VSC, red flag) never reach this chart: fct_lap_residuals is built from green racing laps only, so the exclusion happens upstream in the warehouse. The filter applied in the query itself is NOT is_major_outlier_lap AND fuel_component_s IS NOT NULL. Laps removed by that filter are not shown in the n_laps provenance count.

The mart does carry is_safety_car_lap, is_vsc_lap and is_red_flag_lap columns, and they are FALSE on all 137,447 of its rows for the reason above. The table with live caution flags is int_stint_geometry, surfaced in the app as the Race Control Timeline.

Data source

The chart queries fct_lap_residuals (partitioned by season) via DuckDB-Wasm in the browser. The additive identity is enforced in the warehouse by assert_additive_identity, which runs against every lap on every pipeline build. The full table reference is at fct_lap_residuals. The data window covers 2018–2024.