Analytics in Football - Official Thread

On that table above though, I'd always be looking at a more long term sample in addition.

For context, I'd imagine Schick and Benz were well above xG last season and may be just cooling off.

It's always best to look at things from a 5/10/20/38 (and maybe more) game perspective rather than just going by this season to date.
 
I did some quick number churning, here's the opposite end of the scale

Players in top 5 leagues who have scored 2+ more than their xG

btw -
never heard of Sheraldo Becker. Sounds like a composite striker from a counterfeit version of FIFA

1665507408290.png

Similarly for assists...

Top 20 who are racking up way more assist than their xA would suggest

Note: The cluster of Man City players are surely linked in no small part to the overperforming viking cyborg


1665507624073.png

Top 20 who can blame their teammates for not converting their chances


1665507781710.png
 
FBRef are changing data providers, from Statsbomb to Opta.

On the whole it looks like bad news. Broader coverage of leagues traded off for inferior xG data and loss of some useful data points below.


Data Removed

passes_pressure, passes_by_foot, passes_by_body_part, passes_by_height, passes_by_specific_result, pass_targets, nutmegs, players_dribbled_past
carries, carries_completed, carry_distance, carry_progressive_distance, progressive_carries, carries_into_penalty_area, carries_into_final_third,
blocked_shots_saves, blocked_carries
pressures, pressures_def_3rd, pressures_mid_3rd, pressures_att_3rd, pressure_regains

This one hurts in particular, as I was tweaking a fun Python visualisation around the top nutmeggers in the top 5 leagues
 
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