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Comment by jncfhnb | Hacker News Reader

Generally incorrect. The critical thing here is that many player scores are correlated. Various configurations will have a lot more swing to their outcomes, and when you only care about being the number one spot, highest expected value probably won’t win it.

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Player value correlation is indeed a thing. So too match ups in the next game, player playing out of position, etc. I haven't experimented but you are probably right that a model that could use factors like these effectively would outperform what I described.

But they aren't critical to building a model that will beat your mates and land you on the single-digits top%.

This is something I’ve been trying to find a way to account for in my fantasy drafting the last few years, after trying to maximize expected value has not gotten me the desired results (probably also due in part to the delta between expected and actual value). It’s occurred to me that a “boom or bust” lineup that scores +2 st dev one week and -2 the next is way better than one that performs +1 every week (hand waving exact values).

Build a correlation matrix between player positions and teams. Add a constraint the forbids the solution from having negative correlations below X.

The way you would want to test it is take the top 500 solutions from each approach and see how many of them are winners under whatever framework you care about.

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