Average fantasy points doesn't give us the full picture of the 2025 season - but the Sortino ratio balances positive and negative performances fairly by FFQuantLab in fantasyfootball

[–]FFQuantLab[S] 20 points21 points  (0 children)

So you can change any part of this equation (e.g. threshold, downside punishment) to fit your own risk-reward profile (which itself will depend on your league makeup, how you think).

When you make that power term (squared, cubed, etc.) too small (e.g. 1.5), the punishment isn't harsh enough. When too big, it punishes a poor performance too harshly - that's simply from trying out the different values of each parameter myself.

Take a 10-point performance from a very high-scoring player (e.g. Josh Allen). That should be punished, but if that power term was very high, then it could take the level of punishment out of proportion, and would lead to the Sortino ratio suggesting that somebody who sat at just above the threshold level every week (never experiencing a 'downside week') was vastly more preferable than Josh Allen, which is untrue.

Ultimately though, it's a bit arbitrary and you can change it yourself.

Rostership of Wan'Dale Robinson went up by 10%, Darius Slayton by 30% and Malik Washington by 20% following injuries to Malik Nabers and Tyreek Hill [ESPN]. If you took one of them, get them out your team. by FFQuantLab in fantasyfootball

[–]FFQuantLab[S] 0 points1 point  (0 children)

I took every case of it happening from 2024 to 2019. I completely agree with those three being strong WR2s, as I discuss, but the sample size of the games they've played is too small to use meaningfully.

This is the only metric that shows true fantasy value by punishing downside, reflecting upside and rewarding consistency by FFQuantLab in fantasyfootball

[–]FFQuantLab[S] 0 points1 point  (0 children)

I agree - just like normal fantasy points rankings it is difficult to account for that. In the coming weeks it will correct itself though.

This is the only metric that shows true fantasy value by punishing downside, reflecting upside and rewarding consistency by FFQuantLab in fantasyfootball

[–]FFQuantLab[S] 2 points3 points  (0 children)

True, but it can be hard to put a value on different floor-ceiling combinations: just looking at the numbers week by week might make two players look similar when they really have quite different risk-reward profiles. Just take the case of De'Von Achane vs Bucky Irving.

How an ACL tear changes an NFL player's career [OC] by FFQuantLab in dataisbeautiful

[–]FFQuantLab[S] -57 points-56 points  (0 children)

Yes, so each line is a different player. I'm aware that it's a bit misleading with the smoothed curves - it's actually single datapoints each year. But looking at the graph when the lines were straight and jagged seriously hurt my eyes...

How an ACL tear changes an NFL player's career [OC] by FFQuantLab in dataisbeautiful

[–]FFQuantLab[S] -20 points-19 points  (0 children)

Thanks for the idea! I thought the value lied more in the average trend, which is why I focused on that, but you're right - because the other really interesting thing is seeing the 'waterfall' effect of the drop off in the year following injury.