All season long, NBADLeague.com w'll be looking at its top prospects and teams through the lens of advanced stats to get a better idea of who's destined for a call-up.

Not all paces of play are created equal. But how to we put everyone on a level playing field?

**Otto Kitsinger/NBAE via Getty Images**
Human beings have made some pretty astute discoveries and observations in their time…a heliocentric universe…a round Earth…and the fact that per game statistics do not accurately reflect *true *value of basketball teams and players (just to name a few). As I’m not much of a scientist, let’s focus on the third one.

Let’s start by answering the following question: **Why aren’t per game statistics adequate? What’s wrong with them? **

The answer is multifaceted but ultimately centers around one word: __opportunities__. While all games are 48 minutes long, teams and players do not have the same number of opportunities to acquire statistics. Of course, the most important opportunity factor in basketball is the possession, defined as “the period of time between when one team gains control of the ball and when the opposing team gains control of the ball.”

Another essential factor is “**pace**,” which refers to the number of possessions a team has in a game.

Thus, to illustrate the shortcomings of per-game stats, and to put our three new shiny vocabulary words to good use, let’s take a look at a simple example. Last season, the Iowa Energy played at a blistering pace of **103.86** while the Idaho Stampede defied their name and trudged along at **96.06**. Therefore, any given player on the Energy (and the team itself) was given roughly **8 more possessions** on offense and had to defend its opponents for 8 more possessions a game.

This discrepancy would clearly influence standard per game statistics. We’d assume, for example, that the Energy would both score and yield more points than the Stampede. That doesn’t not mean, however, that they Energy are necessarily better than the Stampede offensively or worse than them defensively.

And here we are: the point of all of this. When you level the playing field, you’re left with one question:

...if given the same number of opportunities, how would these two teams compare?

It is now that I expect you are having your **“AHA! Moment”** and realize that it is most useful to measure how efficiently teams convert the opportunities they are given. Lucky for us, this is precisely what rate statistics measure by controlling for pace.

It is with these core concepts in mind that I will attempt guide you through the basketball wilderness and provide some order to the chaotic sea of box scores and stats out there.

Below, I have briefly outlined some of the specific APBRmetrics (Association for Professional Basketball Research) that I will be focusing on this season to whet your statistical appetite:

Definition: Adjusted FG% accounting for fact that 3-pointers are a lower percentage, but worth an extra point.

eFG% = (FGM + 0.5 x 3PM) / FGA

Definition: Turnovers per 100 possessions designed to control for pace.

TORatio = (TO x 100) / POSS

Definition: The percentage of available offensive rebounds converted.

OREB% = OREB / (OREB + OppDReb)

Definition: A measure of made free throws per 100 field goal attempts to gauge which teams shoot and make free throws most effectively.

FTM Rate = FTM x 100 / FGA

Definition: Pace controlled statistic measuring points produced per 100 possessions for individuals and points scored per 100 possessions for teams.

**INDIVIDUAL**

(Pts produced / Indiv. Possessions) x 100

**TEAM**

Definition: Pace controlled statistic measuring points allowed per 100 possessions for both players and teams.

(Opp Pts Allows/ Opp Possessions) x 100

Definition: A pace controlled statistic that measures point differential (+/-) per 100 possessions.

NetRtg = OffRtg – DefRtg

Definition: A measure of shooting efficiency that accounts for three pointers, free throws and two point field goals.

PTS / (2 x (FGA + 0.44 x FTA))

Note: None of these incredible metrics would be possible without the groundbreaking work of Dean Oliver and his book “Basketball on Paper.” If you are so inclined and would like to take a deep dive into the derivation of these metrics, I suggest you pick up a copy.

For more information before next week's first course about the 2011-12 season, check out three pieces on this very subject from last season, which can be found here:
ADVANCED STATS, PART 1 | PART 2 |
REBOUNDING RATE