It all gets back to sample size.
If I had to summarize the difference between evaluating college players and those in the NBA, "sample size" explains most of it in two words.
In the NBA world, we have the luxury of abundant points of observation. For pros, I have a sample of 82 games, of 48-minute length, over multiple years of a career, with virtually no difference in schedule between teams and relatively minor ones in terms of playing style.
College is a very different beast. For starters, we have 10 times as many teams, and even the most voracious viewer will be lucky to see a third of them play just once; contrast that with the NBA, where I can see every team play start to finish at least a dozen times and have a pretty clear idea how they, and all their players, operate.
The data is similarly constrained. At best, you'll get less than half as many college games as an NBA season, plus they're shorter and, at the highest levels, several will be against completely overmatched opponents. Cody Zeller of Indiana, for instance, had some very nice numbers this week, but I'm not sure we've learned much watching him and the rest of the Hoosiers stampede Stony Brook, Chattanooga, Evansville and Savannah State.
And what goes for individuals goes for teams as well. Not only do we have a shorter season in which to separate wheat from chaff, but we won't find any seven-game playoff series to serve as a proving ground. Instead the single-elimination format in conference tournaments and the NCAA tournament is pretty much a study in random number theory.
So, yes, we have some constraints here. Nonetheless, we still have a lot of information that, used intelligently, can point us to the players and teams that warrant our attention.
That's what I'll be doing in this space -- you may have noticed that things are a bit quiet on my regular beat -- and as I do with the pros, I'll be looking to point out the areas where the numbers collide with conventional wisdom. (Alternatively, I could just say, "Hey, this Kentucky team looks pretty good," but I figured you already knew that.)
The starting point for a lot of what I'll be talking about, as you might have guessed, is Player Efficiency Rating, or PER. It's a stat I developed for the NBA that we've ported over to the college side, and you can find all the leaders right here. How does it work? The short answer is that it's a per-minute rating of a player's statistical effectiveness; the actual formula takes up most of a page, but the idea is that it gives credits for good plays and subtracts for bad ones, and then adjusts for a team's pace, league averages and some other minor factors.
The most important thing to remember when looking at it is that it's a record of what a player did, rather than a statement about who is better than who; in that sense it's no different from field goal percentage or rebounding average or any other stat. The key difference is that it summarizes them into one number.
Two other items to keep in mind: PER is not weighted for either playing time or strength of schedule. So the leaderboard, especially this early in the season, will be cluttered with players who either played relatively few minutes or played against overmatched opponents.
PER has the same shortcomings in college that it does in the pro game -- it can only see what the numbers do. In practice, what this means is that it registers offense much more precisely than defense, so it can underrate good individual defenders and overrate bad ones.
With all that said, the numbers can still teach us plenty. And with that introduction out of the way, let's take a quick glance at what stood out from this week:
One early item to watch is that a lot of little guards are off to great starts. Some names on the PER leaderboard are Pitt's Travon Woodall, Texas' J'Covan Brown, St Joseph's' Carl Jones and Long Beach State's Casper Ware.
Brown is the only one of the bunch taller than 6-foot, at a lofty 6-1, and is the most surprising story of the bunch to boot. After two largely forgettable seasons at Texas in which he shot 35.4 percent and 40.6 percent, respectively, Brown has exploded for a 29.3-point scoring average in the Longhorns' first four games.