Most soccer modeling was developed for competitions decided by accumulated points across a long season. American soccer decides its champion by a knockout, and the models do not transfer cleanly.
The two formats measure different things
A points table over many matches is close to a measurement of team strength, because repeated sampling averages out individual match variance.
A short knockout is a small sample by construction, so its outcome reflects strength and chance in a much less favorable ratio.
A model that predicts strength well can therefore predict champions poorly, and that is not a defect in the model. It is the format doing what it was designed to do.
Incentives change before the knockout starts
Once a team has secured qualification, the remaining regular season matches stop being about maximizing points. Rotation and load management take over.
Those matches still enter the dataset with full weight, so recent-form features absorb performances the team was not fully contesting.
Models built on rolling form are most affected, because the corrupted matches are exactly the most recent ones before the knockout begins.
Series structure creates dependence between matches
Where a round is decided over more than one match, the second is played with knowledge of the first, which changes approach and therefore the statistics generated.
Treating those matches as independent observations overstates the information they contain, because the second is partly a response to the first.
Home advantage also stops being symmetric across a series, since the order of venues interacts with which team needs to chase a result.
Small samples defeat most evaluation methods
Judging a model on knockout accuracy uses very few matches per season, so several years pass before the results distinguish a good model from a lucky one.
Analysts get more information by evaluating calibration on the regular season and treating knockout performance as a separate question about variance.
Simulation helps, because running the bracket many times produces a distribution of outcomes rather than a single prediction that will be judged against one result.
What actually transfers
Team strength estimation transfers fully, since it depends on match-level performance rather than on how the competition is settled.
Anything that converts strength into a season outcome does not transfer, because that conversion is entirely a function of the format's rules.
Keeping the two layers separate is the practical fix, and it lets one strength model serve competitions that decide their champions in completely different ways.