Do Returning Possession Minutes (RPMs) Explain Anything?

When looking at preseason projections one thing we’ve tended to use here at Big Apple Buckets is returning possession minutes (RPMs). The idea of the statistic, which is calculated by adding up the total of each player’s minutes percentage and possession percentage, is that it really tells you what’s coming back.

There are definitely holes. I gathered data from College Basketball Reference and KenPom for the past four seasons and did regression analysis against Pythagorean Ratings versus a team’s RPMs and their Pythagorean Rating the previous two seasons. I found something I think is quite interesting: RPMs matter more in some conferences, though it’s not all intuitive.

I collected historical RPM data for 15 conferences. When I create a linear regression based on those factors here’s what I found:

Conference r^2
CUSA 0.735
America East 0.723
Ivy League 0.717
OVC 0.597
MAC 0.571
WAC 0.556
Big West 0.552
Overall 0.536
Horizon League 0.472
Summit League 0.469
MAAC 0.467
Southland 0.441
Sun Belt 0.418
CAA 0.406
NEC 0.388
Patriot League 0.360

As you can see, for some conference’s RPMs can add a great deal of confidence to your predictions for the following season. The projections for America East teams explain 72% of the actual pythagorean performance year-to-year.

What this really might be speaking to though isn’t necessarily how seniority affects a league’s dynamics, but rather stability. Even though Vermont has only 25% possession minutes returning next season, the model still thinks the Catamounts are going to be the best team in America East — just not as good as they were last season. For the past three seasons either Stony Brook or Vermont has finished atop America East. Similarly, Harvard and Princeton have been near the top of the Ivy League each of the past four seasons. (As an interesting aside, I thought the CUSA model might get worse now that Memphis is gone, but it actually held up quite well in 2014.)

This might seem obvious, but projecting stability it much easier than projecting random injuries (such as Julian Boyd’s knee injury), suspensions, or mid-season transfers.

Thus it’s important to remember when we talk about RPMs that they aren’t a perfect window into the future. (Of course, no projection model is.) It’s great that Siena, Monmouth, St. Peter’s, Saint Francis U. and Central Connecticut are bringing back a ton of experience from last season, but it doesn’t guarantee that any of those teams are going to take a huge leap forward. (Two of them probably will, but good luck picking which two.)

The Ivy League is a place where seniority matters. Expectations are high in Morningside Heights this season. And they should be. Columbia was good last season and, considering all of the talent they have returning, the Lions should take another step forward. On the flip side, with just a 44% RPM — last in the Ivy League — there really doesn’t seem to be a way for Penn to turn things around in a hurry.

RPM isn’t a silver bullet, but it’s a start. Being able to explain 53% of where a mid-major team might end up (the overall average) is way better than 0%. We’ve left square one. Now let’s sprinkle in some knowledge and a few guesses and see where the season takes us.

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