In response to the data-first point, I also personally feel that models should be used as a 2nd layer after planning a strategy (risk appetite, positional weighting, double/triple ups, fixture runs), instead of as an optimisation tool - but that’s a separate debate around the usage of it

Yep I’d agree with that for sure

But like I say, my main gripe is the attitude that not following the models favourite picks makes you a “bad” player

I agree it’s lack of data literacy

But the models have a habit of pretty extreme predictions to start of the season, so I’d say a healthy level of skepticism of those is a good place to start

The alternative is either applying tactical knowledge or reducing your personal value of players with risk

Personally, no, but I see no issue in people doing that

It’s the shock online when a model isn’t perfect, and the assumption that a data-first approach works across seasons, that I have an issue with

And don’t get me wrong I don’t think there’s a problem with following Review/models, I just think the discourse around “good” managers being inherently low ranked currently is…. interesting

Two options imo:

a) play it super safe and don’t go for players with massive uncertainty (e.g. Bruno with a full new team around him)

b) take big differential risks early with the aim to get ahead of the pack and WC early (eg GW4) once we have more information

Yep agreed! Definitely means some players have basically a baseline of 3-4pts, which is a totally different distribution to someone who just scores/assists - even if the eV is the same

Will give that a listen, cheers!

Yeah defo agree that the language is a massive barrier when most people are actually applying very similar logic and processes!

So essentially, that gives FH15 +4 to your score? Assuming you ever take a hit during the season

Yep 100000%

It was refreshing to see people finally take distributions into account for the AM chip.

Even data/model-heavy players would be like “yeah but it’s +0.1 eV” like that’s statistical noise, the distribution & your motivations (ie risk profile) are way more important

Yeah very excited to see what they can come out with - hadn’t spotted them talking about ranges that’s pretty exciting, hopefully shake things up a bit with a bit more of a realistic grounding of the prediction

Also feels like it should be somewhat straightforward to include, as most are based off some combination of Poisson distributions (and sometimes a few other features)

Yeah in a 12 first (or 2nd) is huge, getting salah/haaland and then getting the double pick on the turnaround (eg if you get salah then you guarantee two ok fwds in your 2nd/3rd pick)

GW2 BB club 🎉

I’d agree however I think because it’s a much more prevalent stat (~0-20, unlike 0-1 for CS) I think you could definitely fit an actual distribution to a player imo

My logic being that some players are likely to have higher variation than others. So a distribution + PDF helps with that

Ok interesting that makes sense. So essentially you are sampling from the distribution of that player’s CBIT. Is that distinction being done per player or would 1.5 xDC be the same CBIT/90 for each player, if that makes sense?

Yeah I’d probably go back and count (obviously not manually). Hard to estimate the distribution without seeing the full dataset.

Then for predicting I’d either model CBIT against opponent stats (e.g. attacking power/rating, possession etc), or sample from the distribution for DefCon % chance

Then following on, if it's a predictive model trained/modelled on 24/25 data, have you tested it on 23/24 data just to see how it does on unseen?