Glossary

On-Time Performance data

On-Time Performance data is a data product category; Datadory exemplifies it with 2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with and BTS TranStats - Air Carrier Statistics (T-100) & Airline On-Time Performance: monthly on-time records for certificated carriers, granularity.

What is On-Time Performance data?

On-Time Performance data reports each scheduled flight against its schedule: departure and arrival delays in minutes plus cancellation and diversion flags, one row per flight.

BTS has published it monthly since October 1987, and the Kaggle mirror packages one full calendar year of it.

In this catalog it appears concretely: - 2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with — "scheduled and actual times, delay minutes, cancellation and diversion flags". - BTS TranStats - Air Carrier Statistics (T-100) & Airline On-Time Performance: monthly on-time records for certificated carriers, granularity — "On-time data: one row per flight". - BTS record tags include.

Why does On-Time Performance data matter when choosing a dataset?

A label on a listing is not a deliverable. Teams that license on the strength of a product-name match routinely find the shipped files cover a narrower slice than they assumed, and backfilling history afterwards costs more than the license ever did.

The failure mode is concrete: the label appears in a listing, the delivered files tell a different story, and the gap surfaces mid-project when fixing it is most expensive.

You rarely have to take a vendor's word for it. 82.4% of the 1,744 datasets Datadory catalogs are free to access, and 2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with lets you inspect the real artifact before any budget is committed.

How do you evaluate On-Time Performance data in a data source?

Treat every claim of this attribute as testable:

  1. Open 2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with and confirm its record — "scheduled and actual times, delay minutes, cancellation and diversion flags" — against the files you actually receive. 2. Open BTS TranStats - Air Carrier Statistics (T-100) & Airline On-Time Performance: monthly on-time records for certificated carriers, granularity and confirm its record — "On-time data: one row per flight" — against the files you actually receive. 3.

Adjacent concepts worth reading next: - cancellation and diversion flags - flight delay minutes - form 234 on time reporting - t100 air carrier statistics

Frequently asked questions

What is an example of On-Time Performance data?

2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with is the clearest example in this catalog. Its record states: "scheduled and actual times, delay minutes, cancellation and diversion flags". Across all 1,744 datasets Datadory averages a quality score of 7.81 out of 10, so a named example can be weighed rather than trusted blindly.

Is data described as "On-Time Performance data" free to use?

Treat access and permission separately. 82.4% of the 1,744 datasets in this catalog are free to access, but 235 are freemium and 61 are paid outright, so confirm both the price and the license on the exact distribution before building on it.

How do I verify a source really provides On-Time Performance data?

Open 2015 Flight Delays and Cancellations (5.8M U.S. Flights): every domestic 2015 flight with next to BTS TranStats - Air Carrier Statistics (T-100) & Airline On-Time Performance: monthly on-time records for certificated carriers, granularity and compare the promise with the download. Field definitions are verified for 1495 of 1,744 datasets (85.7%), which makes that check fast inside the catalog and manual outside.

Datasets containing this field

Datasets containing On-Time Performance data

5 datasets carry on-time performance data in the catalog. Open one, count the fields, judge for yourself.

Every listing shows the field dictionary, sample rows, and coverage before you commit. API, files, or your warehouse. Daily, weekly, or hourly.

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