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use to query historical data on the IMSA Weathertech seasons
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| name | IMSA Analyst |
| description | use to query historical data on the IMSA Weathertech seasons |
Analyze racing data from the DuckDB database covering IMSA WeatherTech (2021-2026), WEC (2025), and ELMS (2025). Provides insights into lap times, driver performance, team comparisons, weather impacts, chassis performance, and race strategies.
Every lap includes chassis, homologation, and manufacturer columns:
LMDh, LMH, DPi, LMP2, LMP3, GTE, GT3Query with the skill included ./query.sh "SELECT 1". Schema can be found in ./schema.md
Output Formats:
-markdown flag)./query.sh --csv "SELECT ..." for token-efficient output with large result setsYou may have to use --remote parameter to access the database, and that may require the use of INSTALL httpfs if its not already there. You will figure it out.
WHERE event = 'X' AND session = 'race' → get session_idWHERE session_id = X AND class = 'Y' AND flags = 'GF' AND lap_time_driver_quartile IN (1, 2)session = 'race' and top 50% of laps unless asked otherwiseAlways compare within a single session_id. A session_id uniquely identifies one specific session (e.g., "2025 Sebring Race"). Laps from different sessions should NOT be compared directly.
✅ DEFAULT: Filter by session_id (captures year, event, session, start_date)
❌ AVOID: Comparing across multiple session_ids without explicit reason
It's a good idea to start with querying the seasons table at the beginning. Example:
./query "SELECT * FROM seasons WHERE session = 'race' AND season in (2024,2025) ORDER BY date"
Default to session = 'race' unless specifically asked otherwise. Practice and qualifying have different objectives, tire strategies, and fuel loads.
✅ ALWAYS: Start with race sessions
⚠️ ONLY IF ASKED: Look at practice or qualifying data
GTP ≠ LMP2 ≠ GTD even in the same session. Different classes have completely different car specs and performance.
❌ NEVER: Compare GTP times to GTD times
✅ ALWAYS: Analyze each class separately within the session
When calculating average lap times, ALWAYS filter to a single session_id AND class. Averaging across sessions or classes produces meaningless numbers.
The Golden Filtering Rule:
WHERE session_id = X -- Single session
AND class = 'Y' -- Single class
AND bpillar_quartile IN (1, 2) -- BPillar top 50% (race sessions only)
For performance analysis, use BPillar filtering - automatically excludes pit laps, first lap, slow laps, and traffic.
✅ ALWAYS: Filter to bpillar_quartile IN (1, 2) for pace analysis in races
✅ ALTERNATIVE: Use lap_time_driver_quartile IN (1, 2) for non-race sessions
❌ AVOID: Including quartiles 3 and 4 when analyzing true pace
The bpillar_quartile column (race sessions only) intelligently filters laps:
By filtering to bpillar_quartile IN (1, 2), you get only clean, representative racing pace.
While lap times aren't comparable across sessions, these analyses ARE valid:
⚠️ Still not valid: Averaging lap times across different sessions, even with quartile filtering. Each session must be analyzed independently first.
Example valid cross-session query:
-- Races participated by driver (not comparing lap times!)
SELECT
driver_name,
COUNT(DISTINCT session_id) as race_count,
COUNT(DISTINCT event) as unique_venues,
STRING_AGG(DISTINCT event, ', ' ORDER BY event) as events_raced
FROM laps
WHERE year = '2025'
AND session = 'race'
GROUP BY driver_name
ORDER BY race_count DESC;
driver_id (VARCHAR) for stable identification across name variants-- First, identify the session_id you want to analyze
SELECT
session_id,
year,
event,
session,
start_date,
COUNT(*) as total_laps,
COUNT(DISTINCT class) as classes
FROM laps
WHERE year = '2025'
AND event = 'Sebring'
AND session = 'race' -- almost always race
GROUP BY session_id, year, event, session, start_date
ORDER BY start_date;
-- driver_id is a string like 'firstname lastname' - look up by name first
SELECT DISTINCT driver_id, driver_name
FROM laps
WHERE driver_name LIKE '%Beche%' -- partial match
ORDER BY driver_name;
Always use this macro for human-readable times:
CREATE OR REPLACE MACRO format_time(t) AS (
CASE
WHEN t > 3600 THEN STRFTIME('%H:%M:%S', MAKE_TIMESTAMP(CAST(t * 1000000 AS BIGINT))) || '.' || LPAD(CAST(FLOOR((t * 1000) % 1000) AS VARCHAR), 3, '0')
ELSE STRFTIME('%M:%S', MAKE_TIMESTAMP(CAST(t * 1000000 AS BIGINT))) || '.' || LPAD(CAST(FLOOR((t * 1000) % 1000) AS VARCHAR), 3, '0')
END
);
-- Get fastest laps per class in a specific race session
SELECT
driver_name,
team_name,
car,
class,
format_time(lap_time) AS lap_time,
lap AS lap_number
FROM laps
WHERE session_id = 12345 -- ← Use the session_id from query above
AND class = 'GTP' -- ← Analyze each class separately
AND bpillar_quartile IN (1, 2) -- BPillar top 50% (auto-excludes pit/slow laps)
ORDER BY lap_time ASC
LIMIT 10;
-- Compare drivers within a single race session using their best laps
SELECT
driver_name,
COUNT(*) AS total_laps,
format_time(MIN(lap_time)) AS fastest,
format_time(AVG(lap_time)) AS average,
format_time(STDDEV(lap_time)) AS std_dev,
ROUND(STDDEV(lap_time) / AVG(lap_time) * 100, 2) AS cv_percent
FROM laps
WHERE session_id = 12345 -- ← Single session only
AND class = 'GTP' -- ← Single class only
AND bpillar_quartile IN (1, 2) -- BPillar top 50% representative pace
GROUP BY driver_name
HAVING COUNT(*) >= 5 -- Minimum lap sample for bpillar top 50%
ORDER BY cv_percent ASC;
-- Analyze pit stops in a specific race
SELECT
driver_name,
team_name,
car,
lap,
format_time(pit_time) AS pit_duration,
session_time_lap_number
FROM laps
WHERE session_id = 12345 -- ← Single race session
AND pit_time IS NOT NULL
ORDER BY pit_time ASC
LIMIT 20;
-- Weather effects within a single race session and class
SELECT
CAST(track_temp_f / 10 AS INT) * 10 AS temp_bucket,
COUNT(*) AS laps,
format_time(AVG(lap_time)) AS avg_lap_time,
format_time(MIN(lap_time)) AS fastest_lap
FROM laps
WHERE session_id = 12345 -- ← Single race session
AND class = 'GTP' -- ← Single class
AND bpillar_quartile IN (1, 2) -- BPillar representative performance
AND track_temp_f IS NOT NULL
GROUP BY temp_bucket
ORDER BY temp_bucket;
-- Track tire degradation for a specific driver in a race
-- NOTE: Using ALL laps here to see full degradation curve
SELECT
driver_name,
stint_number,
stint_lap,
format_time(lap_time) AS lap_time,
lap_time_driver_quartile,
session_time_lap_number
FROM laps
WHERE session_id = 12345 -- ← Single race session
AND driver_id = 'tobi lutke' -- ← Use driver_id string (e.g., 'firstname lastname')
AND lap_time IS NOT NULL
AND flags = 'GF' -- Green flag only to exclude cautions
ORDER BY stint_number, stint_lap;
-- Alternative: Focus only on clean, representative laps
-- WHERE ... AND lap_time_driver_quartile IN (1, 2)
-- Compare teammates in a single race session using representative pace
WITH teammate_stats AS (
SELECT
driver_name,
team_name,
COUNT(*) AS laps,
MIN(lap_time) AS fastest,
AVG(lap_time) AS average
FROM laps
WHERE session_id = 12345 -- ← Single race session
AND team_name = 'Porsche Penske Motorsport'
AND bpillar_quartile IN (1, 2) -- BPillar top 50% pace
GROUP BY driver_name, team_name
)
SELECT
driver_name,
laps,
format_time(fastest) AS fastest_lap,
format_time(average) AS avg_lap,
format_time(average - (SELECT MIN(average) FROM teammate_stats)) AS gap_to_fastest,
ROUND((average - (SELECT MIN(average) FROM teammate_stats)), 3) AS gap_seconds
FROM teammate_stats
ORDER BY average;
session = 'race' unless specifically asked for practice/qualifyinglap_time_driver_quartile IN (1, 2) for representative paceWHERE session_id = X AND class = 'Y' AND lap_time_driver_quartile IN (1, 2)flags = 'GF' for clean lap comparisonsIS NOT NULL filtersflags = 'GF' to exclude caution lapslaps_2025 instead of WHERE year = '2025' if availablesession = 'race' unless explicitly asked otherwisebpillar_quartile IN (1, 2) for race pace analysisdriver_id = 'tobi lutke', not numeric IDs'01' ≠ '1' - use exact matchesdriver_id (VARCHAR) for joins/filters, driver_name for displaylap_time_driver_quartile for practice/qualifying✅ Have I identified the specific session_id?
✅ Have I specified a single class?
✅ Am I using session = 'race' (unless specifically asked for practice/qualifying)?
✅ Have I filtered to bpillar_quartile IN (1, 2) for race analysis?
✅ Am I only comparing lap times within these boundaries?
✅ For averages, am I filtering to one session_id + one class + bpillar quartiles 1-2?
SELECT session_id, start_date, COUNT(*) as laps
FROM laps
WHERE event = 'Sebring' AND year = '2025' AND session = 'race'
GROUP BY session_id, start_date;
SELECT DISTINCT class FROM laps WHERE session_id = X;
SELECT DISTINCT session_id, event, year, class
FROM laps
WHERE driver_id = 'tobi lutke' AND session = 'race' -- driver_id is VARCHAR
ORDER BY year, event;
flags = 'FCY' or similar)raining = TRUE) during the sessionformat_time(lap_time)ROUND(x, 2)AS fastest_lap, not AS fl"Who's fastest at [track]?" → Find the race session_id, then query fastest laps per class (use top 50% filter)
"Who won the [event] race?" → Valid question - find the race session_id, analyze by class
"Who's fastest overall in 2025?" → ❌ INVALID - lap times aren't comparable across different tracks
"What's [driver]'s average pace?"
→ Must specify session_id and class, filter to bpillar_quartile IN (1, 2)
"How did [driver] do in the race?" → Get all laps for driver_id at specific session_id, show pace relative to class using bpillar quartiles
"Compare [team A] vs [team B]"
→ Only valid within same session_id and same class, filter to bpillar_quartile IN (1, 2)
"What was the pit strategy at Sebring?" → Filter to the Sebring race session_id, show pit_time entries per class
"How does weather affect pace at Road America?" → Choose the Road America race session_id, group by temperature within each class (top laps only)
"Who's most consistent in GTP?" → Calculate CV for a specific race session_id, filtered to GTP class and top 50% laps
"Show me tire degradation for [driver]" → Use ALL laps for specific session_id to see full wear curve (exception to quartile rule)
"Best lap times for [driver]?"
→ Specify session_id + class, show their fastest laps using bpillar_quartile IN (1, 2)
session = 'race' unless specifically asked for practice/qualifyingbpillar_quartile IN (1, 2) for race pace analysisflags = 'GF' or pit lap exclusions'tobi lutke' for filtering/joins, driver_name for display--csv flag for token efficiency with large resultsRemember: The goal is actionable insights within the context of a specific race session. Always filter to session_id + class + top laps first, then analyze. Present findings clearly and suggest follow-up questions when appropriate.
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