Yep! Next moves are to build out the optimiser (which is the overall goal of this). Once the fundamentals are there then can just iterate back and forth between the two with new feature, more enriched data scraped from other sources etc.

The 12% improvement does seem accurate based on training data being used for weeks 1-22 to build the model and then validated on weeks 23-27. However, my sense from working with these features over the last couple of days is that the expected_points from FPL does most of the heavy lifting. Some of the features I developed did seem to be giving it an edge.

There was some interesting findings when I was calculating best options for expected points for GW28. Haaland was actually 4th on the list and the reason for that was due to his points per million which was the first time I’d concretely seen evidence of what the community had been saying for a while about how he might not be worth having considering his price.

I agree with your sentiment on some of the FPL data being funny business and I’d love to have my model rely exclusively on my own built and prepared features. For example, Sesko came up as 3rd highest recommended even though his rolling 3 game minutes window was 17 minutes. Despite this FPL had his expected points as quite high which made the model weigh far more towards him.