Diagnostic plots fail with LMMs

Question

I've been working on the following problem recently: We sent 18 people, 9 each, several times to two different clubs "N" and "O". These people arrived at the club either between 8 and 10 am (10) or between 10 and 12 pm (12). Each club consists of four sectors with ascending price classes. At the end of each test run, the subjects filled out a questionnaire reflecting a score for their satisfaction depending on the different parameters. The aim of the study is to find out how satisfaction can be modelled as a function of the club. You can download the data as csv for one week with this link (without spaces): https: // we.tl/t-I0UXKYclUk

After some try and error, I fitted the following model using the lme4 package in R (the other models were singular, had too strong internal correlations or higher AIC/BIC):

mod <- lmer(Score ~ Club + (1|Sector:Subject) + (1|Subject), data = dl)

Now I wanted to create some diagnostic plots as indicated here.

plot(resid(mod), dl$Score)
plot(mod, col=dl$Club)
library(lattice)
qqmath(mod, id=0.05)

Unfortunately, it turns out that there are still patterns in the residuals that can be attributed to the club but are not captured by the model. I have already tried to incorporate the club into the random effects, but this leads to singularities. Does anyone have a suggestion on how I can deal with these patterns in the residuals? Thank you!