Over the past year, I’ve been working on AccaPlanner, a UK-focused football betting insights platform built on WordPress. The goal was to create a fast, scalable system capable of processing large volumes of football data while generating useful, AI-driven content for users looking for betting angles and match analysis.
This wasn’t a typical brochure-style WordPress project – it involved API integrations, background processing, caching strategies and a fair amount of performance optimisation to make everything run smoothly under load.
The Idea Behind AccaPlanner
AccaPlanner was built to provide:
- Match previews based on real data
- Betting angles derived from odds and form
- Personalised picks tailored to user preferences
Unlike many content-heavy betting sites, the focus here was on automating insight generation at scale, rather than manually writing previews for every fixture.
👉 You can view the live platform here: https://www.accaplanner.com/
Working with Football Data at Scale
At the core of the platform is integration with the SportMonks football API, which provides:
- Fixtures across global leagues
- Betting odds (1X2, BTTS, Over/Under)
- Team form and head-to-head data
One of the first challenges was structuring this data efficiently within WordPress. Instead of repeatedly calling the API, I built a lightweight data layer that:
- Normalises fixture data into reusable objects
- Caches responses using WordPress transients
- Minimises repeated external requests
This significantly reduced load times and API usage.
Introducing AI-Generated Content
To scale content production, I implemented AI-generated:
- Match previews
- Betting angles
- “For you” personalised picks
Each piece of content is generated using structured prompts built from real match data – ensuring the output stays relevant and grounded in actual statistics.
However, the initial implementation had a major flaw.
The Performance Problem
Originally, AI content was generated on page load, which meant:
- Slow response times
- Increased server load
- Poor user experience under traffic spikes
This is a common trap when integrating AI into web applications.
The Fix: Asynchronous Processing & Caching
To solve this, I redesigned the system to use a background processing model:
- AI requests are never triggered on frontend page load
- A queue system stores fixtures that need content
- WP-Cron and WP-CLI handle processing in the background
- Results are cached using transients
I also implemented a prewarming system, which generates content for upcoming fixtures before users visit the page.
The result:
- Faster page loads
- Reduced server strain
- Scalable content generation
Handling Traffic & Scalability
One of the more interesting challenges has been balancing:
- Traffic spikes (especially around matchdays)
- Queue processing limits
- Server resources
The system now prioritises:
- High-demand fixtures
- Recently viewed matches
- Core betting markets
This ensures the most valuable pages are always populated first.
Multi-language & UK Focus
AccaPlanner is primarily targeted at a UK audience, so the platform is designed with that in mind:
- Uses UK English across content
- Focuses on football markets relevant to UK bettors
- Supports multiple languages (EN, ES, FR, PT) via WPML
From an SEO perspective, keeping a strong UK signal helps improve visibility in UK search results, particularly in a competitive niche like football betting.
Final Thoughts
AccaPlanner has been an interesting project because it sits somewhere between:
- A traditional WordPress site
- A data-driven application
- An AI-powered content system
It’s a good example of how WordPress can still be used as a foundation for more complex, scalable platforms – as long as performance and architecture are handled properly.
If you’re interested in building something similar – whether that’s a data-driven platform, API-heavy WordPress site or AI-powered content system – feel free to get in touch.