By The Sideline
A data file can have a release date that differs from the date of its observations. A download time is a third timestamp. Treating them as interchangeable can make a dashboard appear fresher than its inputs.
The Sideline's public five-row CSV sample provides a small example. It has sample_version and value_as_of fields, plus value_type to identify the modeled quantity. In the sample checked on November 2, 2026, the version is 2026-10-11 and the value-as-of date is 2026-10-05. Those are sample dates, not a claim that the file describes today's complete athlete dataset.
You can inspect the distinct values before joining or aggregating the file:
import csv with open('sample.csv', newline='', encoding='utf-8-sig') as source: rows = list(csv.DictReader(source)) for field in ('sample_version', 'value_as_of', 'value_type'): present = sorted({row[field] for row in rows if row[field]}) missing = sum(not row[field] for row in rows) print(field, present, 'missing:', missing) The point is to expose the distinction before analysis. Keep a file's version as provenance for the artifact. Use the observation date when stating the age of a value. Record the download time separately in your workflow. If future files contain multiple observation dates, do not flatten them into the newest one without explaining which rows are older.
Preserve the value_type label as well. An estimate remains an estimate after conversion to JSON or insertion into a database. A blank field should remain missing rather than silently becoming zero. A five-row sample demonstrates a format; it does not establish population coverage or grant rights to redistribute a full dataset.
Download the example: https://thesideline.co/samples/sideline-college-sports-data-sample.csv
Field-use and license context: https://thesideline.co/data/usage
Disclosure: The Sideline publishes the linked sample. This tutorial was prepared with AI assistance; its field names and distinct date/type values were checked against the public sample.
