My suggestion: Compare to normal parameters.
This means you get two lists of AUC scores
Your params: [0.80,0.75,0.73,....,0.95]
Pyod params: [0.82,0.71,0.48,....,0.95]
look at two values
$\sum_i your_i-pyod_i$
<l2st>
Total improvment. If positive, then your parameters help;)
But hard to see if this is significant    
</l2st>
Fraction of $your_i>pyod_i$
<l2st>
Quantised, so does not care about improving your parameters further
But easy to see if this is significant
<l3st>
0.5->Probably just random
0.9->Probably quite significant
</l3st>
</l2st>


