As a fellow data professional, I cringe a bit every time I see a blog post from Evan Miya.
* Ignoring the 1st point since having good players is obvious.
* The 2nd point is derived solely from this graph and his "case study" of 10 teams:
However, that's definitely not enough information to come to a conclusion. The alternative explanation of this chart is: More players transfer out of bad schools than they transfer out of good schools. He's mixing up correlation and causation.
Additionally, there's 360-some teams, so what's true for the bottom 300 teams is actually irrelevant when talking about building a national championship roster. If you chop off the graph and only look at the top teams (i.e. the only ones with a shot of a national championship), you'll see that there's no trend at all for good teams, and maybe even a negative correlation.
The rest of his proof is showing that ~10 teams have had the same success because they've had similar roster constructions to what he recommends.
But again argue that the explanation is the opposite. These teams were all preseason contenders with big enough NIL pockets to retain talent. If you're a starter on UConn, Kansas, Gonzaga, etc, you're not going to transfer. So of course teams that are elite have a high number of returning minutes, because unless a player graduates or goes pro, there's not really much motivation to transfer out.
* The 3rd point is the exact same as the second point, just restated (a team that has a lot of 1-and-done players will have less returning minutes).
