Nope. But I love the investigation. 🙂

xG is not even defined.

Right, based on my quick Googling it is a custom model, typically using shots on target but splicing location 20+ ways (versus just in box/out of box) and possibly other stats.

Perhaps a better question is Threat due for a fresh coat of paint, e.g. boosting the influence of shots in the box? 😉

None of the ICT metrics have great predictive ability unfortunately but yes if like what ICT is doing and maybe use it with other metrics such as fixtures then Threat is the best one to use

I think the big point here is the confusion of goals and assists (and Creativity/Threat). While we prefer our players to score goals, many will justify their selection based on their assist potential and since the relative points vary by position, we can't properly analyse the stats without separating forwards, mids and defenders.

Thanks for taking the time to provide such detailed feedback. I will attempt to address all your questions:

ICT stats for Gameweeks 1-20 correlation with GW21-30 points
1) Actually three questions on the ICT graph, are we looking at player level or team level?
Player level which as you say is more interesting for us. It maybe more susceptible to variation but it is also a far great sample size. Remember I also looked at the same stats as 11tengen11 and found similar correlations as him with shots on target for example using player data

2) Secondly did you have any minimum quantifier on minutes played?
I tested with setting this and it does not change the correlation (both at 950 mins which is half the time played and at 500, at 300 interestingly it reduces the correlation). This actually makes sense when you think about it, players that played low minutes would have a low index or threat score and also low goals and assists. I’m not using a per 90 metric in this work so leaving in low minutes does not matter and again provides a larger sample

3) Thirdly what positions did you filter it for as if defenders are in there, well then the ICT influence comparison is a heap of junk anyway
I did not filter defenders out mainly as there is no easy way to do that in the FFS custom stats tables without doubling the data collection effort (running it both for mids and forwards). It may make the influence comparison low as you said, however again it is the same as low time played both values should be lower for defenders so it does not adversely impact the correlation.

4) So why are we trying to compare metrics trying to explain different things each to "goals + assists"?
For two reasons. A) as fantasy players we are interested in a player scoring fantasy points so both goals and assists are relevant. Any metric that helps us predict either is valuable. B) When I just looked goals for example with Threat the correlation is actually lower so I presented to most favourable view as a judgement call.

5) Differences between 11tengen11 and my comparisons
That is a fair criticism, he is looking at teams and at the rest of the season. I was presenting his data as point of comparison and looked at what would be most interesting to fantasy mangers. If I also had a good historical repository of xG data I would provide that as a comparison. I have looked at shots on target across the whole season before, next time I run this I will do shots on target, and other shot data against the ICT metrics also from a predictive over next 10 games.

Last 4 gameweek stats
6) Second graph, comparing to "goals and assists" again
This is my bad. I explained what I was doing in the first article and did not repeat again for this one. This is what each stat is being correlated against:
Correlating GW17-20 stat to GW21-36 Goals + Assists (due to reasons explained above):
ICT Index
ICT Threat
ICT Influence
ICT Creativity

Correlating GW17-20 stat to GW21-36 Goals:
Shots on target
Shots in the box
Total shots

Correlating GW17-20 stat to GW21-36 Assists
Chances created

7) that said with SoS weightings form can do better
I have actually done a fair bit of work with fixtures, read some of my previous articles on this site, will continue to use fixtures strength in the future. One big conclusion of the data is that individual player stats e.g. shots on target has a far higher correlation that both opponent difficulty and team offensive strength

8) Also I think if you use a minimum minutes played in these comparisons (say 3 starts out of 4) they'll again boost your predictive abilities.
Again ran the data again with both min 90 mins and 190 (half the mins) and correlations are no different for the same reasons I mentioned above
Thanks again for the feedback, let me know if you have any further questions!

🙂 I look forward to it.

There are two problems to address. One is an historic analysis and one is more predictive. If you want to explain historic goals, SoT is the best explanation - remove all penalties and analyse forwards separately and variability is all explained by chance.

Where this fails is as a predictive tool is because SoT is subject to random variations so they don't really show how good the player is. Rather, and somewhat formulaically, they show PlayerThreat + Luckfactor. Over a 4 GW period, the random variation can easily contribute to over half of the 'Apparent threat'.

I follow at least 100 on twitter so I'll point you towards Danny Pages list which is pretty comprehensive then give you my top 5 outside of the names you've already mentioned.
List: https://twitter.com/DannyPage/lists/sport-quants-analysts/members

1) Statsbomb [ @Statsbomb @jair1970 @benjaminpugsley ]
2) Analytics FC [ @AnalyticsFC @Worville @GregorydSam @Torvaney @BobbyGardiner ]
3) DeepXG [ @DeepXG ]
4) North Yard Analytics [ @altmandaniel ]
5) Paul Riley [ @footballfactman ]

Why not just ignore the post rather than trying to be smart? He was polite...

Cheers Fitzy. Appreciate your help..