What Is Good Fantasy Play, Anyway?
Musings.
If you want the football data, it is all in one place here - for free.
Fantasy games are about managing resources under constraints.
Finding the ratio between that resource allocation and expected return is the formula for success.
If you want to do this particularly well, try to locate a high output from investment in assets that diverge from crowd consensus. Taking a risk by ‘chasing differentials’ every week is the basic version of this. A more advanced version might be obtaining those same assets through strategic means at times when others cannot (e.g. patient utilisation of transfers/wildcards).
A good way to conceptualise your risk appetite is by the amount of investment you allocate to strategies (players or even chip usage) that diverge from the market. This is a useful definition in a fantasy context because it ties risk-taking to your distribution of outcomes. You understand allocating resources outside of the most-owned assets increases your chances of both benefit and harm.
The above is good fantasy play and is generally known to most people. We just aren’t very good at executing it. Indeed, most players lean too hard into the above, to the point that their play worsens because of it. Risk-seeking to their detriment. Harm-seeking.
So if all the above is what we know about good play - what is unknown? I feel like there is a lot more to learn. I am quite interested in working out what questions to ask, so I can learn what I need to know.
Expected minutes and chip plans seem to garner attention as a source of edge. I think there is a seductive mask to granularity in forecasting these. It isn’t obvious to me that the level of granularity has a strong causal link to good fantasy decisions. Or, at least not to the extent that the outcome is a net positive for fantasy decisions, considering the negatives being drunk on this granularity can bring. I don’t think we handle time horizons well in a fantasy context, nor do we adequately appreciate the switching costs that make many decisions almost irreversible.
How do we deal with that? My best guess is that the answer lies in something we might think of as robustness. I am never fully sure what that looks like, but in my head, I generally associate it with principles that look like (1) selecting assets that fare well no matter what the future holds; or (2) exploring a little less than you might think.
Optimisation models are probably going to develop better approaches to the above - but they probably won’t be helpful for us using them if we are not asking the right questions.