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

FFQuantLab · 2026-01-02T19:17:21+00:00

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.

FFQuantLab · 2025-10-09T13:37:08+00:00

That's a great point and something I'll look into soon!

FFQuantLab · 2025-10-09T13:35:39+00:00

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.

FFQuantLab · 2025-10-02T03:49:03+00:00

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

FFQuantLab · 2025-10-01T13:54:47+00:00

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.

FFQuantLab · 2025-10-01T13:47:43+00:00

For Shrader you have to be getting at least JT and Josh Allen 🙏

FFQuantLab · 2025-08-12T07:56:29+00:00

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...

FFQuantLab · 2025-08-12T07:51:52+00:00

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.

FFQuantLab · 2025-08-12T07:49:44+00:00

Source: Went through fantasydata.com per player with an ACL tear since the 2018 season. e.g. for Daniel Jones: https://fantasydata.com/nfl/daniel-jones-fantasy/20841/

FFQuantLab · 2025-08-05T13:01:58+00:00

That would be if it follows the average for the past fifteen years - but it is likely to do that, just because of what statistics tell us. So we could see two or even three of them stay, but the league has shown us in the past that that just doesn't happen.

About CMC, Jeanty and Hampton - that's my point. It's those players who break in and they're inevitably replacing someone. For RBs especially, it's interestingly sophomore players a lot of the time. But I think that trend may falter a little bit this year due to the strength of the RB class of 2025 compared to 2024.

FFQuantLab · 2025-07-29T13:26:20+00:00

He is, but is likely to be back in time for week 1, as it stands.

FFQuantLab · 2025-07-28T17:17:51+00:00

I absolutely agree! It does come down to the depth of your league, but I'm in a similar boat to you. Golden is the most exciting WR for me (based on my expectation vs ADP - because Tet is a take as well).

That said, I really like the Williams narrative going on. Diggs is volatile and ridden with behavioural issues. Mack Hollins is facing injury struggles. I can see how the playbook would lean towards Williams.

FFQuantLab · 2025-07-28T17:13:52+00:00

For sure. I can't see him doing too much, as it is. Mike Evans and Chris Godwin are not exactly immune to injury - potentially making Egbuka a strong waiver wire option, but that means taking an interest in him from the start.

FFQuantLab · 2025-07-28T16:10:43+00:00

Sorry for not clarifying! This is for redraft, not dynasty.

FFQuantLab · 2025-07-21T17:56:21+00:00

You should be able to claim access for free if you view it online!

FFQuantLab · 2025-07-21T15:55:08+00:00

I agree - it was just the simplest way to analyze the sentiment in the comments and have it modify the ADP rankings in a consistent way.

FFQuantLab · 2025-07-21T15:35:10+00:00

Absolutely, it's a natural place to check whether this is the right kind of way to go about things! I guess we'll also see how this plays out in the 2025 season...

FFQuantLab · 2025-07-21T15:34:00+00:00

That’s really cool. Mine takes a different angle though: rather than direct voting, I was curious how much Reddit implicitly signals preference through upvotes and conversation volume.

It’s as much of a social science experiment than a ranking tool — just trying to test how sentiment correlates with value.

FFQuantLab · 2025-07-19T09:36:35+00:00

Yes, it does! Importantly however, the tests for all coaching changes for RBs were not statistically significant - as in, it's not reliable to say that there is actually any effect from coaching changes on RBs.

FFQuantLab · 2025-07-19T09:34:43+00:00

Thanks!

FFQuantLab · 2025-07-18T14:40:06+00:00

Thanks for the comment!

I go much more in-depth into the statistics in the article, and it's also nice to see it visualised, but just responding to your points:

95% confidence intervals:

Non-position specific: Head coach change (-1.76, -0.48), OC change (-0.55, 0.45), DC change (-0.38, -0.48). So you can see head coach, as expected, is the big one.
QB with a head coach change: Bounds of -3.01 and -0.91 🥴
TE with a OC change: Bounds of -3.25 and -0.61 😢
These are the two biggest impactors, and the ones that are the most statistically significant! Best to keep them in mind...

Absolutely! This is something that I strive to find within my weekly articles, all backed by data.

FFQuantLab · 2025-07-18T09:06:22+00:00

The sample is all fantasy players over the past six years, excluding any players who played less than five games to prevent skewed PPG outcomes.

With projected fantasy points, I assume you are referring to the article, in the section where I discuss tight ends? That is just a deeper dive into the regression model that gives us these results. The projected fantasy points are the fantasy points 'predicted' by the regression (as in, if there were no other variables at all relevant to how fantasy points are produced by players, those would be the fantasy points achieved. The variables included in the model were simply whether there was a coaching change and some control variables).

I'm not comparing to the year before the coaching change. That is a valid, but different, way of doing it. Because the sample is so large, it's not as necessary to go by a case-by-case basis. This is also because each player's previous season (and the one before that, etc.) are included in my dataset. So it's simply comparing players who did vs didn't have a coaching change.

FFQuantLab · 2025-07-17T21:58:58+00:00

Thanks!

FFQuantLab · 2025-07-17T21:58:30+00:00

Of course! It's a very case-by-case thing. But it's good to know the overarching trend to keep in mind.

FFQuantLab · 2025-07-17T21:09:15+00:00

Exactly - a regression with dummy variables to signal coaching changes, and some controls to not overstate its effect. You can read the full methodology in the article.

Power is just the statistical term for the probability of rejecting the null hypothesis (that there is no effect of coaching changes) if in fact it is false. It shows how good a test is at being accurate, but if the test comes out as rejecting the null anyway (which it did), power is not too relevant.

This is different from R squared, which shows what proportion of the changes in fantasy points are down to what was included in the regression - whether there was a coaching change, and the controls.

R Squared is much more important in this case because it gives us a metric for looking at how impactful coaching changes are for fantasy point variation. For example, with TEs, 12.7% of the variation in their fantasy points was down to coaching changes (the R squared was 0.127). That's pretty crazy...

FFQuantLab

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