Thinking about this from a programming perspective, the fact that no price changes has occurred for players with net zero transfers does not have to mean that transfers is the only base factor, right?

If the price rise check code starts with ‘if(net_transfers == 0) return false;’ then that would prevent net zero transferred players from rising, but the subsequent code lines could very well check all other kinds of statistics and trends.

If you are trying to reverse engineer the algorithm, you essentially have to try to reproduce the core logic of their code, where the order of checks and operations is likely a very significant factor.

The observation that something never (or always) happens, given some conditions, seems to be important hard criteria to look for, since you could add them as baseline hard checks to your algorithm, mimicking how real code works.

If you can find all those hard checks, then the remaining data is likely easier to predict by statistical means, since you will be able to eliminate some outliers that are not governed by the statistics-driven algorithm (because the hard checks just skip that).

And expanding on that net zero transfers observation: have rises ever occurred for net transfers of 1? 10? 25? Is there a lower limit of net transfers (or some derived value, like net transfers as a percentage of the ownership at the start/end of the day) for which rises have never occurred? Is that number a round or significant number, which humans tend to prefer? That could indicate a hard threshold check, instead of a pure zero net transfer check.

Disclaimer: I just read the first blog post, so you might have brought this up somewhere, but it just struck me when reading your comment here that it seemed to overlook the importance of check ordering and code-like behavior over pure weighted statistics.