Cleaning and Validating Data

Why cleaning greyhound data protects your model

Cleaning is not about making data look tidy. Its purpose is to remove every plausible way your future model can be misled. The chapter separates two goals. Consistency means the same thing is always written the same way, so a track spelt two ways is recognised as one venue. Trustworthiness means every record describes what you think it describes, so a finishing position of zero is read as a non-runner rather than as a real result.

Many bettors jump straight from having a spreadsheet to having a model. The step in between is where an edge is either protected or destroyed, so the chapter asks you to treat cleaning as part of the model itself, not as a separate chore to rush through before the interesting work begins.

First-pass checks for duplicates, typos and non-runners

Before writing clever logic, run blunt checks on every new batch. Scheduled pulls with retries can deliver the same race twice, so define what makes a race unique: track, race identifier, date and off-time together are usually enough. Trim whitespace and convert names to title case before matching, which catches the cheap typos and stray trailing spaces that databases treat as different strings.

Non-runners disguised as runners are among the most damaging errors. Race cards list every entered dog, including withdrawals, and results may show a zero position, an empty time or a status tucked into comments. Left in, these rows teach the model that dogs with no form finish last. A common rule is that a runner needs a real position between 1 and the field size and a non-empty time. Reserves that ran count as runners; reserves that did not, and rows with a missing trap, are dropped.

Standardising track names, grades, distances and dates

Build a small lookup table that maps every spelling of a venue to one canonical name, and decide once whether labels include words like Park or Stadium. Be careful with fuzzy matching, because two genuinely different venues can have similar names. When a track rebrands, treating it as a continuation is usually fine for quantitative work, but record the decision in your cleaning log.

Grade labels are some of the messiest fields you will meet. Convert them to upper case, strip whitespace, and consider keeping both the full grade and a coarser band as separate features. For distances, strip the unit and cast to a number; a distance in yards usually signals an unfamiliar feed. Normalise off times to a single time zone such as UTC, and convert ambiguous date strings to a proper date type at import so nothing later has to guess.

Three principled ways to handle missing values

Missing values are not all alike, and treating them as one thing causes silent errors. Drop the row when the missing value is the outcome you want to predict, such as a finishing position. Impute a sensible default for input variables, for example the median sectional at that track and distance. Never impute zero for a time, because that tells the model something impossible and distorts every calculation it touches.

The third strategy is to flag the absence as its own value. A dog with no recent form may be returning from injury or new to racing, and that absence carries meaning, so a column such as sectional_missing lets the model decide. As a rule of thumb, imputation suits rare, random gaps under about 5% of rows, while flagging is safer when gaps are common or linked to something interesting.

Validation checks that expose hidden bugs

Cleaning without validation just rearranges the same mistakes. Ask the cleaned data to prove it is consistent and plausible. The chapter gives a worked benchmark: a trainer winning 14% of races in six-runner fields is below the random baseline of about 16.7%. One such figure might be real, but low strike rates across many trainers and tracks point to a data problem rather than poor training.

Other checks include plotting field sizes, which should peak at a few familiar values, and looking for a long tail of odd counts. Trap 1 winning, say, 60% of races suggests positions are misaligned with traps. Finishing times at a track and distance should look bell-shaped, non-runner rates should stay stable, and race totals should match the scale you expect. Run these checks on every new batch and treat sudden shifts as red flags.

Keeping a cleaning log and a fixed order of operations

The chapter calls documentation its most important task. Every rule, from non-runners to track mappings to missing values, belongs in one log kept next to the data, recording fields standardised, keep or drop rules, validation checks and what changed since the last version. When something breaks months later, the log shows which assumptions are baked in, and it makes the whole dataset reproducible. Never tweak these rules mid-backtest because results look poor; that is curating the answer.

A sensible default sequence is to import raw data into a staging table, run the blunt checks, standardise messy text fields, cast types properly, handle missing inputs, validate against benchmarks, write to a clean table for all downstream analysis, and update the log. Each step is small, but together they produce a dataset you can build on with confidence.