A good prediction doesn't always make a good decision

A developer built FPL Decision Engine, a tool that combines football data, AI reasoning, and rule-based checks to evaluate fantasy Premier League transfer decisions rather than simply ranking players. The system separates AI recommendations from what the rules permit, rejecting suggestions that break budget or squad constraints, and the developer says research into whether the engine actually improves decision-making remains ongoing with no demonstrated performance gains.

I started building an FPL tool because I kept coming back to one question: what's the point of predicting the best player if signing him isn't actually the best move? It gets complicated pretty quickly. You have a budget, transfer limits, players you already own, upcoming fixtures, and the occasional terrible decision you made three gameweeks ago. So I started building FPL Decision Engine. The idea was to combine football data, AI reasoning, and rule-based checks to compare actual decisions rather than simply rank players. One part I found particularly interesting was separating what AI suggests from what the system allows. An AI model might recommend a great player. But if the transfer exceeds the budget or breaks the three-players-per-club rule, the recommendation should be rejected. Confidence doesn't make an invalid decision valid. I also began exploring something else: what happens when my judgment disagrees with AI? Does it actually improve my decisions, or just make me more confident in them? That research is still developing, and I haven't established that the engine improves FPL performance. There's plenty left to test. The public repository contains a curated version of the code, architecture, and a worked example. What started as a football project has made me much more interested in decision systems generally. Predictions are useful. Figuring out what to do with them is the harder part.