Setting Up Your Data Stack

How much greyhound data will you actually store?

Start by being honest about scale. If you are looking at one meeting format and a handful of tracks, a spreadsheet will serve you for a while. Once you want every UK and Irish meeting across several years, you have outgrown it. UK afternoon racing alone produces tens of thousands of races a year, each with around six runners carrying attributes such as finishing time, sectional splits, trap, weight, grade and going.

A spreadsheet handles a few thousand rows without complaint but starts to struggle somewhere between 50,000 and 200,000 rows, depending on how many formulas and lookups sit on top. A couple of years of UK and Irish data with derived columns can reach that range, and a rolling five-year history is firmly database territory. So the first decision is whether you are building a weekend hobby model or a multi-year archive.

Spreadsheets versus simple databases for racing data

The two are not rivals; each does a different job. Spreadsheets are ideal for exploring a small slice of data, manually reviewing a few races to sanity check a model, sharing a file with someone who does not code, and keeping readable notes and overrides. They are also prone to accidental edits breaking long formulas, and calculations can drift over time.

A simple database is better for storing tens of thousands of rows you want to filter, join and summarise, for rerunning the same saved queries, and for keeping data in one place instead of ten monthly files. The sensible pattern is to use both: keep the master data in a database file or a folder of CSV files, and export filtered slices to a spreadsheet when you want to look at them. For a one-person setup, SQLite and DuckDB are free, serverless options.

Choosing a tool you will open three nights a week

The best tool is the one you will actually use for a year. For most readers the options narrow to three: spreadsheet-only with a tidy folder of CSV files, a database file plus a little SQL, or Python or R working alongside a database. SQL is a small language, and the handful of statements you need can be learned in an afternoon.

There is no shame in staying with a spreadsheet, but there is no future in it if you want years of history run through a model. Move up when the data demands it, not before. Pick the tool that suits your weakest habit, avoid exotic cloud setups, keep your data file portable enough to email to yourself, and test the whole stack with a single meeting before pulling in months of history.

Why raw data must stay untouched

A clear folder layout pays off. The chapter suggests a raw folder holding files exactly as they arrived from your source, a processed folder for cleaned and merged data, a folder for scripts and saved queries, an exports folder for disposable pulls, and a backups folder for dated snapshots kept somewhere physically separate.

The most important rule is that raw data is never edited in place. Every cleaning step, whether renaming a column, fixing a date format or dropping a stray row, happens in a script that writes to the processed folder. That way, when you improve your cleaning logic months later, you can rerun everything from scratch and get a consistent result. Edit the raw file by hand and that ability is gone for good.

File naming conventions that save you later

Naming sounds trivial until you have hundreds of files. Use ISO dates in the form year, month, day, which sort correctly and cannot be misread. Put the source and content in the file name, so a glance tells you where a file came from and what it holds. Stick to letters, numbers, underscores and hyphens, avoiding spaces and special characters.

Version rather than overwrite. If a rerun of a query gives a meaningfully different result, save the new file alongside the old one with a version tag. It is also worth writing a short README explaining the folder layout and how to rebuild the processed data from the raw files, so that you, months from now, can follow what you did without guesswork.

A backup routine for your betting data that actually works

The chapter applies the classic 3-2-1 rule: three copies, on two types of storage, with one kept offsite. In practice that means your working folder, a local backup on an external drive or separate disk updated weekly, and an offsite copy in a trusted cloud folder or at another location, updated weekly or monthly.

Keeping your only copy of years of cleaned data on a laptop desktop is a serious risk, since drives fail and machines get lost. Automation helps only if it runs, so check that files really sync and that you can open them on a fresh device. A backup you have never restored from is untested. Finally, make sure your stack answers one useful question in its first week, or you will drift back to scribbled notes.