Pulling Data Through APIs

The core endpoints of a greyhound racing API

An API is essentially a door: you send a request, the server checks you are allowed in, and it returns a parcel of data, usually JSON and sometimes CSV. Field names vary between APIs, but the underlying entities are remarkably consistent, and learning their shape is most of the battle.

The endpoints you will lean on most are meetings or fixtures (your calendar of tracks and race times), races (distance, grade, going and race type), runners (trap, weight, rating and a form summary), results (finishing order, times, sectional splits and judges' comments), odds (price snapshots through the day), dog profiles (a single dog's career record) and tracks (direction, length, surface and bend layout). Not every API offers all of these, and some nest extra fields inside one object.

Which fields to capture from every API response

Capture more than you need today. It is far easier to ignore an extra column than to discover two years later that you never recorded trap number and face a painful historical fix. If a field is optional in the response, store it as missing rather than pretending it does not exist.

The chapter lists the essentials: a stable race identifier, the date and off-time stored in UTC so daylight-saving changes do not trip you up, distance, grade and going as separate fields rather than one string, trap number, weight and rating for each runner, finishing position, time and sectional splits, one or more timestamped odds snapshots per runner, and a note of the source and response schema version so you can spot changes later.

API keys, rate limits and caching

Most racing APIs authenticate with a simple key sent as a header or query parameter. Keep it secret: store it in an environment variable or a secrets file excluded from version control, never hard-code it into your script, and never let it appear in a public notebook or a screenshot of your code.

Every API also imposes rate limits, whether per minute, hour, day or endpoint, so read the terms before writing any code. Repeatedly hitting the limit can get your key suspended. Two habits help. Cache aggressively, so a restarted script does not fetch the same meetings again, and batch requests by whole date or meeting rather than firing one call per race. A sensible rule is to keep your peak rate at about a third of the published ceiling.

Scheduling pulls by how fast the data changes

Racing data ages at different speeds, and your schedule should reflect that. Results never change once the judge has called them, so they can be pulled overnight. Pre-off odds refresh every few seconds before the off and are better handled by a separate streaming process. Form and runner profiles drift slowly, so once a day or less is plenty.

The chapter sets out a weekly rhythm covering fixtures and declarations, odds snapshots at several points in the day, results, sectionals and weight changes, settled starting prices, refreshed dog profiles, and a final sweep to backfill gaps, retry failures and archive raw responses. A simple scheduled job can run all of this. Write the schedule down, even as a comment in the script, and throttle big historical backfills so you are not locked out.

Logging every call and planning for gaps

APIs change: fields get added or renamed, endpoints retire, and servers have bad afternoons. Log every call with the timestamp, endpoint, parameters, status code, response size and a hash of the payload, ideally in a small database table. Log every failure with the URL, parameters, status and error body, so that a missing meeting can be rebuilt from a saved request rather than guesswork.

Expect gaps and decide in advance how to handle them. The chapter offers three options: flag and keep the row with a missing-value marker, drop the race and record the reason in a rejections table, or retry at increasing intervals before flagging it for attention. Keeping a simple data issues note of oddities, such as a trap number of zero or a strangely timed odds snapshot, makes the cleaning stage far easier.

Why the market is the benchmark for an API-fed model

Collecting data is only half the story; you are competing with a market that sees the same information and more, often faster. The chapter notes that a simple logistic regression built on API greyhound form data has been shown to pick winners around 33% of the time, while the starting-price favourite wins roughly 41.7% of races. The market is the bar to measure against, not an afterthought.

By the end of this stage you should have an inventory of the endpoints you rely on and the fields each returns, a pull schedule that runs without you, and logging that tells you exactly what worked, what failed and what changed. With data arriving in your own system on your own terms, the next job is making sure it is trustworthy before you rely on it.