Checking out Columbia’s plus/minus

I was reading Princeton Basketball and catching up on the Tigers when I came across his post about Princeton’s plus/minus numbers through the first 10 games of the season. Now, I think people read into these numbers a bit too much, and last season Ken Pomeroy wrote a good takedown of +/-. Still, there’s value in looking at lineup combinations and just having the numbers confirm what your eyes, or other stats, are seeing. Let’s look at some for Columbia through the Lions’ first 11 games of the season.

Most +/- stats are expressed as a rate state per 40 minutes. The length of a game just provides a simple baseline for the statistic. For reference Columbia has 717 points for, 620 points against in 440 minutes this season. That’s +8.8 points per 40 minutes.

Of course Columbia also has some other funny things going on. For one, the complexion of the roster changed dramatically  in the second game of the season. Also, the Lions lost four straight before winning their last seven. There’s also two non-Division I games thrown in there. Add it all up and you’ve got all sorts of funny things to deal with. When doing player analysis I subtracted the two games against Swarthmore and La Sierra. If you take those two games out the Lions have 535 points for, 522 points against and are +1.4 points per 40 minutes overall.

Here’s a few key players and their +/- per 40 for the nine Division I games.

Mark Cisco +11.7 / 40
Alex Rosenberg +6.1 / 40
John Daniels +6.0 / 40
Corey Osetkowski -16.6 / 40

What I think is interesting about those numbers is just how good the team has been when Cisco has been on the court. He’s played 225 minutes this season against Division I opponents, so that’s a pretty substantial sample size to go on too. Also, Rosenberg’s numbers are shown because I think it highlights how well he’s adjusted to the collegiate level. He seems comfortable out there and the +/- numbers appear to confirm it. Most of Osetkowski’s struggles were concentrated in a few games, Furman’s veteran frontline in particular gave him a lot of trouble. It’ll be interesting to check back with him at the end of the season and see if the trend has continued.

Because I know people will ask. Here are three other guys you might be interested in adjusted for Division I opponents only.

Meiko Lyles +3.6 / 40
Brian Barbour +2.3 / 40
Noruwa Agho -5.4 / 40

Barbour is doing better than the team overall, which is all you can ask from a guy that plays as much as he does. Also, Agho’s numbers are weighted pretty heavily with the Connecticut result, and are in just 67 minutes, so don’t read into them. But enough about individuals. Let’s look at lineups. Columbia has played 129 different combinations this season, ranging from 3 second to 55 minutes together. (For comparisons sake Princeton has played 102.)

The lineup that’s played 55 minutes together is Cisco, Daniels, Rosenberg, Meiko Lyles and Brian Barbour. You’ll notice that’s currently Columbia’s starting five. As a group they’re +26.8 / 40 minutes this season, with the non-Division I games included. The best of the eight lineup combinations that have played at least 10 minutes together though is Cisco, Barbour, Daniels, Lyles and Steve Egee. That lineup is +55.7 / 40 minutes in 10:46 worth of action. The worst of those eight lineups? Barbour, Cisco, Lyles, Blaise Staab and Chris Crockett at -50.5 / 40 minutes in 10:18.

What made the lineup so bad? Well, it was -6 in 2:11 against American, -4 in 2:54 against North Texas, and -3 in 1:55 against Long Island. This lineup just hasn’t meshed together in their limited minutes. That’s a little surprising, because it seems like a good mix of players who should be able to work off of each other.

Still, for every lineup we’re talking about except the recent starters the sample size is less than a quarter of a game total. It’s obviously early and luck and randomness are playing a huge part in these numbers.

Thankfully that will subside as the season goes along and hopefully we’ll be able to take a look at the end of the season and see just how much these numbers have, or haven’t, changed.

A big thanks to Pete McHugh at Columbia for providing me with the report that I used to create this post.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s