If I assume that’s your feedback from week 9 submission then I’d conclude that it’s not ideal.
In many ways it’s similar to my week 8 submission when I had a similar looking chart, a similar high market share and a low leaderboard position. The difference is my week 8 RMSE model was 500.1 ish and your week 9 is 499.46 ish.
Now you’d hope that a model with a much lower RMSE should be better at differentiating risk. If that was the case though you’d expect to write a smaller market share of the the policies with higher actual claims. Your chart though doesn’t show such behaviour. That may be because your good performance on the public RMSE leaderboard is not generalising to the unseen policies in the profit leaderboard. This could be a result of placing too much emphasis on RMSE leaderboard feedback and not enough on good local cross validation results. It can lead to unwittingly overfitting to the RMSE leaderboard at the expense of a good fit to unseen data.
Now I think I have a similar issue, ie a poor fitting model as my week 8 and week 9 charts are less that ideal… (I’d rather have a chart like @davidlkl but with greater market share). But the cause of my issue is that in seeking not to overfit too much I haven’t fit well enough relative to others.
Another point to investigate is your market share. A 30% market share, suggests your profit margin is set lower than competitors. In a pool of 10 people a 10% market share would be a a reasonable target to go for.
The only difference between my week 8 and week 9 submission was that I increased my rates by a fixed single digit percentage. I’ve learnt that I perhaps increased rates too much as my market share has fallen further than I’d like.
So for my final submission I’ll be refining my model and making a minor tweak to my profit margin. (And no doubt subsequently regretting doing so as I see my profit leaderboard position plummet!)