  There seems to be an upper limit to predicting the outcome of matches in
(semi-)professional sports. Recent work has proposed that this is due to chance
and attempts have been made to simulate the distribution of win percentages to
identify the most likely proportion of matches decided by chance. We argue that
the approach that has been chosen so far makes some simplifying assumptions
that cause its result to be of limited practical value. Instead, we propose to
use clustering of statistical team profiles and observed scheduling information
to derive limits on the predictive accuracy for particular seasons, which can
be used to assess the performance of predictive models on those seasons. We
show that the resulting simulated distributions are much closer to the observed
distributions and give higher assessments of chance and tighter limits on
predictive accuracy.
