Where the data comes from
The foundation is public, verifiable and regularly updated data on the inspection-station network and vehicle inspections. Other sources are used as an additional analytical layer to identify operators, locate stations, check data consistency, verify inspection validity and provide a more transparent view of vehicle history. Each source has a different role and is not automatically used in every calculation.
Source transparency matters to us as much as the result itself. For every metric we therefore distinguish between primary data, supporting sources and our own analytical calculation. If a source is not sufficiently informative for a particular metric, we do not include it in that calculation.
Czech Ministry of Transport · ARES · STKGuru.cz · Land Registry · KontrolaTachaku.cz
How we process the data
We first normalise source records to the level of a specific station and a consistent observation period. Only then do we create aggregates for station profiles and the nationwide overview. We do not rewrite source data based on calculated results; the published statistics are a separate analytical layer.
The most recent vehicle inspection included in the current summary is dated 25. 8. 2026. Some individual fields may be missing for particular stations when the source does not contain a usable record.
What the main indicators mean
Regular inspection pass rate
On this website, this means the share of regular vehicle inspections with a known standard result that ended with a “pass”.
Pass share = N(result 1) / N(result 1 + 2 + 3) × 100Records without a usable result 1/2/3 are excluded from the denominator. The indicator is therefore not the same as a share of all inspection types.
The current nationwide value for the observation period is 92,1 %. This does not mean a “first-attempt pass rate” and it is not a station quality rating.
Inspections with a recorded defect
We count regular inspections in which at least one defect was recorded. Each inspection is counted once in this share even if several defects were found.
Defect share = N(regular inspections with ≥ 1 defect) / N(all regular inspections) × 100Tables of specific defects use the frequency of individual recorded defects instead; that is a different metric from the number of inspections with a defect.
At least one defect was currently recorded in 44,8 % regular inspections in the nationwide summary.
Vehicle age
Vehicle age is derived from the first-registration date and the inspection date. We calculate the mean and median only from records where age can be determined reliably.
age ≈ (inspection date − first registration date) / 365.2425Alongside the value itself, we track vehicle-age data coverage so it is clear what share of regular inspections the statistic is based on.
Makes, models and vehicle types
The ordering is based on a simple count of occurrences in regular inspections. If a given attribute is missing from the source, that record is excluded from the distribution for that particular metric.
Item share = N(records with item) / N(records with a valid value for the metric) × 100For display, we only standardise the presentation of some names; the underlying analytical data is not changed.
How we compare individual inspection stations
- Same periodComparable station statistics use the same 12-month period rather than different-length intervals for individual stations.
- Minimum data basisFor comparisons based on shares, we use stations with at least 100 regular inspections and at least 100 known results. This reduces extreme values caused by very small samples.
- Description, not a ratingA higher or lower share of “pass” results does not by itself show that a station is better or worse. The result may be influenced, for example, by the mix and age of inspected vehicles.
- Unique vehiclesOn a station profile, unique VINs are counted within that specific station. The nationwide sum of these values is not the number of unique vehicles in Czechia, because the same VIN may appear at more than one station.
Limits and interpretation
STKMapa.cz describes what is recorded in the source data. We do not infer an inspector's motivation, the cause of a particular result or the quality of a station's work. If an attribute is missing or cannot be matched reliably, we leave it out of the relevant calculation.
- Statistics are not a verdictA difference between two inspection stations is not automatically evidence of different strictness or quality. The data shows inspection outcomes, not their causes.
- Data reflects a defined time periodThe figures relate to a specific observation period. Values may change as new records are added.
- Aggregation preserves the meaning of the dataThe public website shows aggregated statistics. We do not use individual inspections to publicly profile a specific vehicle.

