2014-15 Clemson Baskeball Season Preview: Advanced Stats Refresher

This is a refresher of our original KenPom advanced stats guide and will help you follow Clemson all year long.

Offensive Rating (Ortg): Offensive rating is a overall measure of offensive efficiency. We need to be careful when comparing feature players to complimentary players because coaches ask more of feature players, making it challenging for them to remain as efficient as role players who can be more selective about their shots. That said, this is a great shortcut to get a quick view and is best used in the same way that we use QBR for quarterbacks.

Last year, McDaniels had a team high Ortg of 111.4. This was driven by high percentages on 2-point shots and free throws as well as a reasonably low turnover rate. Sidy Djitte, who was extremely raw last year, especially early on, had an Ortg of 87.4.

It uses a complicated formula that credits made shots, assists, etc and penalizes turnovers missed shots, etc.

"A measure of personal offensive efficiency developed by Dean Oliver. The formula is very complicated, but accurate..." (KenPom.com)

Defensive Rebounding % (DR%): This stat is great because it is easy to interpret and helpful when comparing players with very different levels of playing time (e.g., McDaniels vs. Djitte). It is simply:

Defensive Rebounds / Defensive Rebounding Opportunities

Someone like Sidy Djitte didn't play enough minutes to attain a high rebounds per game number, but in his limited minutes he did get a large proportion of the rebounding opportunities. at 19.5, Djitte led the team in DR%. Of course, Nnoko and McDaniels had many more rebounds because they played many more minutes. This accounts for that.

Additionally, this, along with most of the other KenPom statistics, controls for tempo. Last year, Clemson often slowed games down and minimized total possessions. This means fewer shots, fewer missed shots, and thus fewer rebounding opportunities. If you compare someone on an up-tempo team like VMI to a Clemson player, that VMI player will have an advantage in their opportunity to accumulate traditional statistics.

Offensive Rebounding % (OR%): This is the same thing as DR% except on the offensive end. Comparing offensive rebounding totals isn't always fair because one team may have missed many more shots than the other had thus had more opportunities to get offensive rebounds. This stat can be especially useful in single game analysis. While others may fall into the trap of saying a team got beat on the offensive boards after looking at raw numbers, these adjusted numbers allow us to see if it was really just because one team missed more shots or if it was real.

Effective Field Goal % (eFG%): This multiplies 3P% by 1.5 because 50 (of 100) 3-pointers is worth the same number of points as 75 (of 100) 2-pointers. This makes it fair to compare post players with 3-point shooters. It’s also the only way to compare teams when one shoots a lot of 3’s and one does not.

For example, let's say we wanted to compare NC State and Boston College. Last year, NC State focused heavily on 2-point shots while Boston College relied on 3-point shots. NC State's FG% was 46.1. Boston College's FG% was 44.6. So NC State is the better shooting team, right? Wrong! NC State's eFG% is exactly 50.0 while BC's was 52.1. That's because a bigger chunk of BC's shots were three pointers and thus they needed to make fewer to get to the same amount of points. This gets us thinking in terms of points per shot instead of makes per shot. They don't count baskets to pick winners, they count points. Clemson has emphasized three point shooting over the offseason so let's hope we see improvement in this area.

Fouls Committed per 40 Minutes (FC/40): This is pretty self-explanatory. It shows how many fouls a player commits in an average 40 minute span of playing time. Since Sidy Djitte had a limited roles, comparing total fouls with someone like Landry Nnoko is not helpful. This stat allows us to see how quickly players are getting into foul trouble. Rod Hall committed only 1.2 fouls per 40, 6th lowest in the country. Sidy Djitte committed a team high 7.0 fouls per 40, on average he would foul out after less than 30 minutes if given the playing time.

Fouls Drawn per 40 Mins (FD/40): This is the same as above except it’s drawing fouls. This is a great measure of aggressiveness. McDaniels led the team last year at 4.8. Hall and Harrison lead returning players at 3.8 and 3.3, respectively.

Free Throw Rate (FT Rate): This is another metric that shows how good someone is at drawing fouls. It just divides FTs by FGs and multiplies by 100 so you can see how often they get to the line compared to how many shots they take. This normalizes for players who take more shots than others, so from our last example we saw that McDaniels drew more fouls per 40 minutes than Rod Hall, but here we see that Hall was foul more on a per shot basis with a FT Rate of 55.1 (he shot 55.1% as many FTs as FGs).

(Free Throws / Field Goals) * 100

Turnover Rate (TO Rate): This one is fairly complicated and hard to explain for individuals. It's easier to use on a team level when it is simply percentage of possessions that end in a turnover. On an player level, it is the percentage of personal possessions ending in a turnover. We need to be careful when using this to compare players that a coach asks to do very different things. For example, a spot up shooter will have much fewer turnovers than a point guard who is asked to penetrate and create their own shot.

Assist Rate (AST Rate): This is the same as TO rate only for ASTs. Again, this is a bit easier to use on a team level when it is percentage of possessions that end in an assist and basket.

Possessions: Finally, a game statistic that's especially useful is the number of possessions in the game. If this Clemson team is anything like last year's, controlling tempo will be key.In 2013-14 Clemson had the fourth fewest possessions per game at 60.2. Conversely Northwestern St. averaged 75.2. More possessions equals more opportunities to accumulate raw stats. This fluctuation is one reason why advanced stats are so useful.

FGA-OR+TO+0.475xFTA

For more, please visit this post from our friends over at Blogger So Dear or KenPom's definitions page.