Greyhound Racing Data and APIs: Tools for Data-Driven Bettors

Why Spreadsheets Beat Gut Feeling in Greyhound Betting

For three years I kept my greyhound betting records in my head. I “knew” which tracks suited my approach, which trainers were in form, which trap draws I preferred. Then I started a spreadsheet and discovered that half of what I “knew” was wrong. My favourite track for betting was actually my worst for ROI. The trainer I trusted most had a 14% strike rate at my target venue — below the random baseline for six-runner fields.

Mobile betting accounts for more than 70% of all greyhound wagers in modern markets, yet the vast majority of those bets are placed without any systematic data analysis. The bettors who track their results, model trap bias, and compare sectional times across meetings operate with an informational advantage that is entirely self-created. The data exists; most people simply do not collect it.

Greyhound racing produces approximately 25,000 BAGS races per year across the UK alone, each generating six runners’ worth of finishing times, sectional splits, trap positions, weights and grades. That is 150,000 individual data points annually from BAGS racing alone, before adding open and evening fixtures. The raw material for serious analysis is abundant — the challenge is capturing it in a format you can actually use.

Free and Paid Data Sources for UK Greyhound Racing Results

When I first went looking for greyhound data, I expected something like the horse racing ecosystem — centralised databases, clean APIs, readily downloadable datasets. What I found was more fragmented, but workable once you know where to look.

GBGB’s official results service is the starting point. Licensed track results are published with finishing positions, times, weights, trainers and grades. The data is reliable because it comes from the governing body, but it is not always presented in a format that lends itself to bulk analysis. You may need to scrape or manually record the information you need.

Third-party results websites aggregate data from multiple tracks and often add features the official source lacks — historical form records, trainer statistics, trap bias breakdowns by venue, and race-by-race archives going back several years. The quality varies between services. Some are maintained by dedicated greyhound enthusiasts who update results daily; others are commercial operations that charge subscription fees for premium data access. The paid services typically offer downloadable CSV or Excel files, which saves considerable time compared to manual data entry.

Betting exchange data is another valuable source. Historical starting prices, market movements and matched volumes on greyhound races are available through exchange APIs and data services. This data allows you to track how the market priced each dog versus its actual finishing position — essential for identifying systematic mispricings that a value betting approach can exploit.

For bettors focused on one or two tracks, the most effective data source might be the simplest: the race card and result from each meeting, recorded in a personal spreadsheet. This manual approach lacks the breadth of a commercial database, but it forces you to engage with every data point and builds an intimate knowledge of your target venues that no third-party service can replicate.

Available APIs: What You Can Automate and What You Cannot

The greyhound racing data landscape has improved significantly in recent years, though it remains less developed than the equivalent infrastructure for horse racing or football. NSoft launched virtual greyhound races with AI-driven behaviour and animation in 2025, signalling growing technological investment in the sector — but the data tools available to individual bettors are still maturing.

Several commercial providers offer APIs that deliver greyhound race cards, results and historical data in structured formats (typically JSON or XML). These services allow you to build automated systems that pull race data, calculate metrics and flag selections based on your criteria. The typical setup involves querying the API for upcoming race cards, running your analysis algorithms against the data, and outputting a list of potential bets that meet your value thresholds.

What APIs handle well includes race cards (runner details, trap draws, grades, weights), results (finishing positions, times, starting prices) and historical archives. What they handle less well — or not at all — includes real-time sectional times during races, live odds movements across multiple bookmakers, and trainer-specific performance breakdowns. For these data points, you typically need to combine API data with manual collection or screen-scraping tools.

The cost of API access ranges from free tiers with limited data and rate limits to professional subscriptions costing several hundred pounds annually. For a bettor analysing two or three meetings per week, a mid-tier subscription usually provides sufficient data. For someone building a fully automated system that scans every meeting, the higher tiers are necessary.

A word of practical advice: do not build an automated system until you have a proven manual method. I have seen bettors spend months coding elaborate data pipelines that automate a selection process which has never been tested on paper. Automate what works; do not automate what you hope might work.

Setting Up a Simple Greyhound Results Tracker

You do not need programming skills or an API subscription to start tracking greyhound data effectively. A spreadsheet is enough, and the discipline of maintaining it teaches you more about the sport than any pre-built tool.

My tracker contains one row per bet with the following columns: date, track, race number, dog name, trap, grade, weight, recent form figures, first sectional time (when available), odds taken, bet type, stake, result and profit/loss. That is fourteen columns, and each row takes about 90 seconds to complete after a race finishes.

From this basic structure, I calculate rolling metrics: strike rate by track, ROI by grade, average winning odds, and — most usefully — trap bias data for my target venues built from my own observations rather than a third-party estimate. After 200 recorded races at a single track, the patterns become clear enough to inform selections. After 500, they become statistically reliable.

The tracker also serves as a behavioural check. Reviewing a month’s bets reveals patterns you did not notice in real time. Overbetting on certain meetings. Consistently poor results at specific grades. A tendency to back certain trap positions regardless of form. These behavioural insights are as valuable as the statistical ones, and they only emerge from honest, comprehensive record-keeping.

The transition from manual tracking to a more automated setup happens naturally once your data volume grows. After six months of manual records, you will know exactly which metrics matter to your approach and which are noise. That knowledge makes any subsequent investment in data tools or form analysis software targeted and efficient, rather than a speculative purchase driven by marketing promises.