int_sc_hazard_history

Safety-Car / Virtual-Safety-Car base rate per circuit (venue slug) as a hazard per racing lap, POINT-IN-TIME AS OF THE START OF EACH SEASON.

Part of the Strategy family.

Safety-Car / Virtual-Safety-Car base rate per circuit (venue slug) as a hazard per racing lap, POINT-IN-TIME AS OF THE START OF EACH SEASON. Grain is one row per (circuit_slug, season); the rate on a row is estimated from races at that circuit in seasons STRICTLY BEFORE it, so a 2019 row never sees 2019 or later. Estimated from SC/VSC deployment onsets in stg_track_status with racing-lap exposure from stg_laps. Raw and empirical-Bayes-shrunk rates are provided; the EB prior is itself season-lagged (pooled over all circuits, seasons < S), because shrinking toward an all-time pooled rate would leak the future through the prior.

REBUILT 2026-09-11 (work item 02d). The previous version pooled every season into ONE row per circuit, so a 2018 consumer read a hazard partly estimated from 2024 races. Any consumer must now join on (circuit_slug, season), not circuit_slug alone — a circuit-only join silently fans out 5-7x and reinstates the leak. int_pit_strategy_cost_curve is the one dbt consumer and was updated with it.

TRACK-STATUS COVERAGE, measured against data/dev.duckdb on 2026-09-11. The old header warned ingestion was “incomplete for some seasons”; that caveat was stale. Every race with laps has a track_status timeline: 2018 21/21, 2019 21/21, 2020 17/17, 2021 22/22, 2022 22/22, 2023 22/22, 2024 24/24 — 148/148, 100% in every season. Timelines are thin on incident-free races (19 races carry <= 3 messages, the minimum being a single AllClear), which is an absence of events rather than an absence of ingestion, and the hazard numerator counts onsets so it reads those correctly as zero.

MISSINGNESS, deterministic and declarable. The raw rates are NULL where a circuit has no prior exposure — its debut season — because “unknowable” and “measured, no onsets” are different statements: 36 rows of 149, one per circuit. The shrunk rates survive a circuit debut (zero prior exposure shrinks fully to the pooled prior, the correct EB answer) and are NULL only where the pooled prior is itself unknowable: all 21 rows of season 2018, the earliest season in the warehouse. Both NULL sets are deterministic on (circuit_slug, season) and readable off the companion prior_seasons_n / prior_racing_laps columns, which are never NULL.

ENTERED THE FEATURE LINEAGE 2026-09-20 (02d). Five columns are read by fct_cliff_prediction_features on the (circuit_key, race_year) join. That moves this model out of features.py’s report-only survey and into audit_aggregation_scope’s enforced scope, which is why the four exemptions below now exist: they were the advance notice the survey was written to give, and this is the ruling arriving. The columns are mart-only and are NOT in ml/src/schema.py’s FEATURE_COLUMNS pending 02d’s arms.

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Lineage

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Overview

Property	Value
Layer	intermediate
Family	Strategy
Contract enforced	No
Upstream	stg_track_status, stg_laps
Model-level tests	unique_combination_of_columns

19 tests guard this model - 17 generic, 2 singular.

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Columns

Column	Type	Nullable	Tests	Description
circuit_slug		Yes	not_null	Venue identifier (race name slug, e.g. monaco_grand_prix); recurs across seasons.
season		Yes	not_null	The season this estimate is “as of the start of”. The estimate uses seasons < this value only; the current season’s own races are deliberately excluded, so the value is constant across a season.
season_races_n		Yes	not_null	Races at this circuit in THIS season — exposure the season lag deliberately excludes from the estimate. Carried so a consumer can see how much is being withheld, not as an input to any rate.
prior_seasons_n		Yes	not_null	Distinct earlier seasons contributing to the estimate. 0 on a circuit’s debut season; never NULL. This is the companion count that makes the rate NULLs explainable from the data.
prior_races_n		Yes	not_null	Races in the estimate (this circuit, seasons < season). 0 on a debut; never NULL.
prior_racing_laps		Yes	not_null, expect_column_values_to_be_between	Racing-lap exposure in the estimate (sum of per-race leader lap counts over seasons < season) — the hazard denominator. 0 on a debut season; never NULL.
prior_sc_onsets		Yes	not_null	Safety-Car onsets in the estimate window. Never NULL.
prior_vsc_onsets		Yes	not_null	Virtual-Safety-Car onsets in the estimate window. Never NULL.
prior_any_onsets		Yes	not_null	Combined SC+VSC onsets in the estimate window. Never NULL.
sc_hazard_per_lap		Yes	expect_column_values_to_be_between	Raw Safety-Car onsets per racing lap over seasons < season. NULL on a circuit’s debut season (no prior exposure) — see the model description’s missingness note.
vsc_hazard_per_lap		Yes	expect_column_values_to_be_between	Raw Virtual-Safety-Car onsets per racing lap over seasons < season. NULL on a circuit’s debut season.
any_hazard_per_lap		Yes	expect_column_values_to_be_between	Raw combined SC+VSC onsets per racing lap over seasons < season. NULL on a circuit’s debut season.
sc_hazard_per_lap_shrunk		Yes	expect_column_values_to_be_between	Empirical-Bayes SC hazard shrunk toward the season-lagged pooled rate (pseudo-count sc_hazard_prior_laps). NULL only for season 2018, which has no prior season to pool.
vsc_hazard_per_lap_shrunk		Yes	expect_column_values_to_be_between	Empirical-Bayes VSC hazard shrunk toward the season-lagged pooled rate. NULL only for season 2018.
any_hazard_per_lap_shrunk		Yes	expect_column_values_to_be_between	Empirical-Bayes combined SC+VSC hazard shrunk toward the season-lagged pooled rate. NULL only for season 2018.