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Advanced Stats 101: Part Two

by Steve Weinman, NBA

Part I of this discussion of statistical analysis focused on the value of accounting for pace through use of possession-based rate stats. While offensive and defensive efficiency provide high-level assessments of a team’s play at each end of the floor, examining their components offers further insight into strengths and weaknesses.

In his landmark book Basketball On Paper, Dean Oliver identified what he termed the Four Factors of Basketball Success. Listed in order of the importance he assigned to each (see Oliver’s work here for full details), they are: shooting, ball control, rebounding and getting to and converting at the foul line.


Shooting refers to how well a team uses its attempts from the field to put the ball in the hoop. The problem with traditional field-goal percentage is that it doesn’t account for the added value of three-pointers. The three-pointer is a low-percentage shot, but it carries the reward of the extra point. According to traditional field-goal percentage, if big man Courtney Sims scores three times in five tries from the low post and guard Richard Roby hits two threes on five attempts, Sims shoots 60 percent, while Roby shoots 40 percent. That is not a clear representation of the fact that each player used five shots to score six points.

To correct this problem, effective field goal percentage (eFG%) counts each made three-point basket as one and one-half made baskets to mirror the ratio of value between a three-pointer and a two-pointer. In 2009-10, the D-League averaged a 51.2 percent eFG mark. For those who are calculation-inclined, here’s the formula:

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

Ball control

Every time a team turns the basketball over, it forfeits a chance to shoot on that possession, guaranteeing no points for that offensive set. Clearly, the less frequently a team turns the ball over and the more it forces turnovers, the better. As seen with scoring, raw turnover figures fail to account for pace and penalize uptempo teams simply because more chances with the ball will likely lead to more turnovers.

Turnover ratio accounts for this by measuring turnovers per 100 possessions. The Iowa Energy dominated both ends of the ball control spectrum in 2009-10, turning the ball over a league-low 11.8 times per 100 possessions and taking it away from opponents a league-high 14.8 times per 100 possessions.

Looking at the formula provides a chance to discuss the calculation of possessions as well, which can end with a field goal attempt, a free throw attempt or a turnover or be extended by an offensive rebound. So:

Possessions = FGA – OREB + (0.44 * FTA) + TO

TORatio = (TO * 100) / POSS


Offensive rebounding allows a team to correct for its missed shots by creating extra opportunities to convert on those possessions. Raw offensive rebounds totals provide a poor indicator of a team’s prowess on the boards because they fail to account for either pace or shooting. Fast-paced teams will take more shots and thus have more rebound opportunities. Teams that shoot well will miss fewer shots and have fewer chances to grab caroms.

Every missed shot is an offensive rebound opportunity, and the only two potential results are an offensive rebound or a defensive rebound. Assessing a team on the percentage of offensive rebound opportunities it converts provides an accurate read on its work on the boards; the stat for this is called offensive rebound rate (OREB% or ORR). In 2009-10, D-League teams retrieved 29.1 percent of their missed shots, led by the Idaho Stampede at 33.8 percent.

OREB% = OREB / (OREB + OppDReb)

Getting to the foul line – and converting

Reaching the foul line is important because it offers what tend to be easy points. Free throw percentage in the NBA and D-League normalizes around 75 percent. This means that on average, a possession that ends in a two-shot foul will have an expected value of 1.5 points – or 150 points per 100 possessions, which dwarves last year’s D-League offensive rating of 105.7 and demonstrates the value of getting to the foul line.

Thus, teams that spend more time at the line rather than shooting from the field are more likely to be successful offensively. One can measure this simply by comparing the number of free throw attempts to the number of field goal attempts (or ,for the sake of prettiness on paper, multiplying by 100 to see how many free throw attempts a team takes per 100 field goal attempts).

That said, some teams shoot markedly better at the foul line than others, and some high-volume scorers (particularly big men) struggle at the foul line to the point that sending them there can be an asset for the defense. To account for this, I prefer to use a free throw rate calculation that measures made free throws per 100 field goal attempts. The better a team shoots from the foul line, the closer this figure will be to the attempts-based calculation. Last year, the Dakota Wizards led the way in free throws made rate (30.1 per 100 field goal attempts) but finished third in free throws attempted rate behind Utah and Austin. Across the league, teams made 26 free throws for every 100 shots they took from the field.

FTA Rate = FTA * 100 / FGA

FTM Rate = FTM * 100 / FGA

In the interest of accounting for conversion at the foul line, the use of free throw rate or FTR in this space will refer to free throws made rate unless otherwise noted.

Shooting, turnovers, rebounding and reaching and utilizing the foul line: Success or failure in those four areas determines a team’s proficiency at the offensive and defensive ends, which will determine how often that team will score more points than its opponents. Which is the end goal: winning games.

None of this is atom-splitting news. Whatever your level of statistical inclination, as an observer of the game, you likely understand that shooting the ball in the hoop, collecting loose balls and making free throws are all good, and giving the ball to the other team is bad. All we’re doing here is providing an effective way to quantify those actions. The use of the four factors and associated rate stats informs the evaluation of strengths and weaknesses not only at the team level but at the player level as well. Particularly in the NBA Development League, where individual development merits primary emphasis, this is crucial. A future follow-up will examine the four factors and several related statistics as applied to players.

Below, you will find the D-League’s 2009-10 team offensive and defensive efficiency figures as well as each team’s ranking in each of the four factors. The statistics presented in this article were compiled using the NBA's StatsCube data warehouse.

Team Offense: 2009-10

Rank Team OE eFG% TO Ratio OREB% FTR
1 Rio Grande Valley Vipers 111.3 1 6 8 11
2 Austin Toros 109.9 4 7 2 3
3 Idaho Stampede 108.5 11 2 1 16
4 Sioux Falls Skyforce 108.0 2 11 13 5
5 Dakota Wizards 107.1 9 14 3 1
6 Maine Red Claws 106.2 6 10 12 8
7 Reno Bighorns 106.1 8 12 4 6
8 Iowa Energy 106.0 14 1 6 9
9 Bakersfield Jam 105.5 6 8 14 7
10 Utah Flash 105.4 3 15 9 2
11 Tulsa 66ers 105.1 5 13 10 13
12 Los Angeles D-Fenders 104.6 13 4 5 12
13 New Mexico Thunderbirds 104.3 10 9 16 4
14 Fort Wayne Mad Ants 103.7 12 3 15 4
15 Erie BayHawks 100.9 16 5 11 15
16 Springfield Armor 98.6 15 16 7 10

Team Defense: 2009-10

Rank Team DE eFG% TO Ratio OREB% FTR
1 Iowa Energy 101.3 2 1 14 3
2 Erie BayHawks 102.1 1 13 1 10
3 Reno Bighorns 103.3 t-4th 9 12 1
4 Tulsa 66ers 103.3 t-4th 4 3 11
5 Maine Red Claws 103.5 3 5 7 8
6 Utah Flash 104.0 11 2 5 13
7 Dakota Wizards 104.8 10 3 10 6
8 Fort Wayne Mad Ants 105.2 6 7 13 2
9 Sioux Falls Skyforce 105.9 8 6 11 5
10 Austin Toros 106.0 9 8 6 9
11 Rio Grande Valley Vipers 106.5 7 12 9 15
12 Idaho Stampede 107.6 13 10 4 16
13 Bakersfield Jam 108.1 12 16 8 12
14 Los Angeles D-Fenders 109.2 15 11 15 4
15 New Mexico Thunderbirds 110.1 14 14 16 7
16 Springfield Armor 110.7 15 15 2 14