In basketball, efficiency is important. As in baseball, basketball teams have only a limited number of opportunities with which to score. It is therefore important that a team gets the most points out of each possession. More importantly, this concept should influence a team’s shot selection in order to maximize the number of points it receives from those possessions. Field Goal percentage, or FG% has long been used to compare team and player’s efficiency. However, FG% is simply too blunt a tool by which to measure a team or player’s efficiency effectively, because it overlooks two important facts. First, it doesn’t account for free throws, which are the most efficient way of scoring points for most players. Second, it isn’t weighted to include the fact that 3pt shots are worth 1.5 times as many points as a 2pt shot. To illustrate, let’s use an example: Corey Maggette of the LA Clippers and Shaquille O’Neal of the LA Lakers:
|
Corey
Maggette |
||||||
|
FGA |
FG% |
3pt FGA |
3pt FG% |
FTA |
FT% |
PPG |
|
13.9 |
44.7 |
3.2 |
32.9 |
8.5 |
84.8 |
20.7 |
|
Shaquille
O’Neal |
||||||
|
FGA |
FG% |
3pt FGA |
3pt FG% |
FTA |
FT% |
PPG |
|
14.2 |
58.4 |
0.0 |
0.0 |
10.1 |
49.0 |
21.5 |
It would be fairly easy to conclude that Shaq is much more efficient than Corey, converting an amazing 13.7% more of his field goals. That assumption, however, would be incorrect. As overall offensive efficiency is a measure of the number of total possessions a player or team used to get a quantity of points, we must include all offensive possessions. Let’s forget for a minute about possessions that don’t result in an attempt to score points i.e. turnover possessions. We can derive the number of offensive possessions by simply adding the possessions of each shot type: 3pt field goal possessions + 2pt field goal possessions + free throw possessions = total number of possessions. With both types of field goals (3pt and 2pt), one possession is used with each attempt. 3ptFGA and 2ptFGA are also both commonly combined as just FGA when keeping track of stats, so we can replace 3pt possessions and 2pt possessions with just FGA. With free throws, however, it’s not quite as simple. With each free throw possession, a player is usually awarded two attempts. However, depending on the situation, a player may be awarded only 1 free throw, or sometimes 3. To account for these situations, we divide FTA by 2.5. The general formula for offensive possessions then is FGA + (FTA/2.5). To determine the number of points per possession, or the offensive efficiency, take a players points per game average, and divide by the number of possessions, or, PPG / (FGA + (FTA/2.5)). Going back to our example, we can determine that Maggette’s efficiency is 1.197 (20.7/(13.9 + (8.5/2.5))). O’Neal’s efficiency by comparison is 1.179 (21.5 / (14.2 + (10.1/2.5))). What does this mean? It means that one possession in Maggette’s hands is worth 1.197 points on average, and 1.179 points in O’Neal’s hands. The reason for this is clearly evident when looking at Maggette’s free throw attempts and free throw percentage. Maggette spends about a quarter, or 4.25, of his total 18.15 possessions taking free throws, which he nets at a rate of about 84.8%. His efficiency is pulled up greatly by the high number of free throw attempts and his high percentage. O’Neal meanwhile sinks his free throws at just 49.0%, barely raising his efficiency.
Now that we’ve looked at offensive efficiency, or points per possession, it’s time to look at the number of possessions themselves. Let’s assume for a minute that we have two teams, A and B. Assume Team A and Team B both have offensive efficiencies of 1. That is, they score 1 point for each possession they have. Which team is more likely to win the game? The answer of course, is not that they would tie, it’s that we need more information. More specifically, we need to know how many possessions each team can expect to have during the game. If we know Team A had 100 possessions and Team B had 90, we can estimate that Team A will score 100 points and Team B 90. The number of points that a team scores then is OE (offensive efficiency) x the number of possessions. Working backwards, if we know the number of points a team scored and its OE, we can determine the number of possessions it had (Efficiency = points/possessions, so possessions = points x efficiency). Let’s take Houston for example:
|
Houston
Rockets |
||
|
FGA |
FTA |
PPG |
|
75.5 |
21.6 |
89.0 |
We find that Houston has an OE of about 1.058 and scores 89 points per game. This produces an expected number of possessions of about 84.1 (75.5 + (21.6/2.5)). This however does not tell the entire story. This includes only the possessions in which Houston attempted a shot. To find the total number of expected possessions per game for Houston, we need to add in those possessions that did not result in a shot attempt, or turnovers. Including TOs gives us:
|
Houston
Rockets |
|||
|
FGA |
FTA |
PPG |
TO |
|
75.6 |
21.4 |
89.0 |
15.8 |
This gives us a total of about 99.9 possessions per game. This isn’t exactly the number of possessions, but it’s very close. There are a few situations that aren’t accounted for, but they are fairly insignificant and we can overlook them. The total number of possessions however groups together two important types of possessions: offensive possessions and turnover possessions. We’ll see later why it is useful to keep them separate.
We’ve determined that two things are important for determining how many points a team will score: offensive efficiency and the number of offensive possessions. But this of course does not really tell us anything about how much a player contributes to his team or how likely a team is to win. To do that, we need to also consider a team or player’s defensive ability. But what is defense? The measure of a defense is really only the measure of your opponents’ offensive abilities over a period of games. We also know what is important in measuring their offensive abilities: offensive efficiency and the number of offensive possessions. With those two things, we are effectively able to determine teams’ defensive prowess. When looking at the top defensive efficiency teams, the usual suspects turn up: San Antonio, Detroit, and Minnesota are respectively the top 3. Slow, defensive minded teams with talented defensive frontcourts. When looking at the top teams at forcing turnovers, however, the explanation is not as obvious. The top 3 teams at forcing turnovers are: Denver, Boston and Memphis respectively. None are considered defensive powerhouses per se, though all are fairly well respected for their defensive merits. The answer of course, is that all 3 have excellent perimeter defenders. Turnovers are generally the result of steals or bad passes, both of which usually occur out on the perimeter. But the three top teams in forcing turnovers, again are not considered defensive powerhouses. Are they simply underrated? Perhaps, but more importantly the underlying message is that possessions simply aren’t as important as efficiency in determining great offenses or defenses. Possessions certainly are important, but not as important. Another example of this would be the Sacramento Kings. The Kings are not particularly careful with the ball, being about average at 13.5 turnovers per game. They are however tops in offensive efficiency at 1.129, and second in scoring, just .2ppg behind Dallas at 103.6ppg. Dallas also provides the counter example, however: that while not as important, possessions do make a difference. Despite having the fourth best efficiency at 1.067, Dallas leads the league in scoring. The difference in efficiency between Dallas and the Kings is about .062, a very large gap, but Dallas overcomes this fact by having far, far more offensive possessions. In fact, they average five more offensive possessions than Sacramento per game; at 97.2 offensive possessions, they have far and away the highest total in the league. The number of offensive possessions is mainly due to two reasons: they take care of the ball, with only 11.8 turnovers per game - best in the league - and the tempo of their games is quicker than any other team in the NBA.
So we know that Sacramento and Dallas are two of the top offensive teams, and San Antonio, Detroit, and Minnesota are top defensive teams. But are they any good? The answer, of course, comes from differentials: namely, efficiency differential and possession differential. Deriving these two stats is fairly simple. To derive efficiency differential, or ED, take a team’s offensive efficiency and subtract their defensive efficiency (their opponents offensive efficiency). A positive number indicates that they are more efficient than their opponents at scoring and a negative number indicates they are not. Finding possession differential is fairly simple as well. In going over expected possessions, I said that it is useful to keep offensive possessions and turnover possessions separate. This is why. Offensive possessions = base possessions – turnovers + offensive rebounds, roughly speaking. It’s not quite all of the offensive possessions but its very close. When looking for the differential possessions, we can throw out base possessions because they are roughly equal. The formula then becomes: OPA – OPB = - (TOA – TOB) + (ORA – ORB) where OP = offensive possessions, TO = turnovers, and OR = offensive rebounds. We can rewrite this to OPA – OPB = TOB – TOA + ORA – ORB. What we end up with, in words, is the difference in offensive possessions is equal to the number of turnovers a team forces minus the number of times you turn the ball over, plus your offensive rebounds minus their offensive rebounds. This makes sense. If you force more turnovers than you turn it over yourself, you gain more offensive possessions than the other team, and if you grab more offensive rebounds than the other team you gain more offensive possessions than the other team. So what we get are two ways of measuring possession differential: OPA – OPB, or TOB – TOA + ORA – ORB. Remember also that OP = (FGA + (FTA/2.5)) from earlier in the article. Using OPA – OPB is slightly more accurate because you’re looking at the actual number of shots each team is taking, but TOB – TOA + ORA – ORB is a good shorthand, and can allow you to quickly estimate differential based on changes to OR or TO.
This also means that the two stats that directly determine possession differential are offensive rebound differential and turnover differential, and this is important to remember as possession differential is one of the two important stats in determining how good a team is.
So, now we have the two main stats for determining how good a team is: efficiency differential and possession differential. But how do they translate to wins and losses?
We’ve looked at what makes one offense better than another offense, and one defense better than another defense. Of course, neither means that a team will be successful. The only requirement for being successful is of course outscoring your opponent. It can be done with a great offense, a great defense, or a combination of the two. The stat that is most important in determining this is of course PPG differential, or PPG – OPP PPG, where PPG = points per game and OPP PPG = opponents points per game. In predicting wins and losses, this of course makes logical sense. If a team outscores its opponents over the course of the season, it is likely to win. The larger the difference between the two, the more wins or losses the team is likely to incur. This means that the ratio of PPG to OPP PPG somehow directly relates to wins and losses. In baseball, Bill James came up with a formula for deriving win% based on this, which he named the baseball Pythagorean theorem. It is: (PPG^1.83)/((PPG^1.83)+(OPP PPG^1.83)). Another way of writing this formula is 1/(1+((OPP PPG)/(PPG))^1.83), or in words, the reciprocal of one plus the ratio of OPP PPG to PPG quantity to the 1.83 power. Unfortunately, this formula does not work for basketball. The main reason being that the margin of victory in basketball and baseball is not the same. Take for example the 2003 Oakland Athletics. They scored 768 runs, and allowed 643, a ratio of about 1.19. They also won 96 games and lost 66, for a 59.3 win%. In basketball, the New Jersey Nets have a 57.3% win%. The ratio of their points scored and points allowed however is just 1.01. This does not mean the Nets have just gotten lucky. Another example, the Dallas Mavericks, have a 63.4% win%. Their ratio of points scored to points allowed is about 1.04. So why is the margin of victory in baseball and basketball different? The answer is that it has to do with the relationship of one point, or whatever unit of scorekeeping the game uses, to the total number of points in each game. Consider for example, a game in which the average total of points scored was just over 1 and ties are not allowed. This means that in order to win, a team must have 1 more point than the other team. Because the average scoring is so low however, most games would likely end up 1-0, with some ending up 2-1, and anything higher than that rare. If most scores end up 1-0, a team with a 60% win% is going to score about 60 goals and allow about 40 goals out of every 100 games. This means the ratio of goals scored to goals allowed is about 1.5 for a team with the same win% as in baseball or basketball. This effect can also be seen if you were to take some very large number for the total number of points scored in a game. Say the average total score is 2000, and the average points scored for each team about 1000. Games on average would still likely be fairly close, and you might end up with something like 1000 points scored and 990 allowed for a 60% win%, which would put it around 1.01 margin of victory.
Therefore, to set the formula to work for basketball, you need to change some of the numbers around to reflect the difference in the average number of points scored per game. After playing with the numbers, I found the best-modified formula was when the effects of the Pythagorean theorem were amplified by about 6.45. That is: (.5 – (6.45*(.5-((PPG^1.83)/((PPG^1.83)+(OPP PPG^1.83)))). This can be rewritten as (3.725xPPG^1.83-2.725xOPP PPG^1.83)/(PPG^1.83 + OPP PPG^1.83). However, in playing with the numbers myself, I came across a simpler formula that I found to be slightly more accurate, and agreed with the above formula almost exactly (the largest variance between the two being .012). That formula is: ((PPG differential * 3.3 + 50) / 100). That is, each point of PPG differential is worth about 3.3% in win%. I’ve found this to be much easier shorthand for determining win% and just as, if not more accurate.
So if PPG differential is really all that is needed for determining win%, and thus predicting wins and losses, why are ED and PD (efficiency differential and possession differential) important? The answer is that while PPG differential is the big picture, ED and PD tell you what is happening with PPG differential. ED and PD are the two factors that directly effect PPG differential. We can see this clearly by looking back at the formulas for ED and PD. ED is (PPG / (FGA + (FTA/2)) – (OPP PPG / (OPP FGA + (OPP FTA/2)))). But FGA + (FTA/2) is offensive possessions as we found out earlier. Which means ED is (PPG / OP) – (OPP PPG/OPP OP). PD is of course just OP – OPP OP. PPG differential is (OP * OE) – (OPP OP * OPP OE). We can relate ED and PD to PPG differential by the approximate formula: ED*90 + PD = PPG differential. The reason for this is that there are approximately 90 offensive possessions in a game, and the average efficiency is about 1. To see why this is approximately correct, let’s assume for a minute that the efficiency for both teams is about 1, and each team got 90 possessions. This is then (PPG / 90 – OPP PPG/90) * 90 + (90-90). As you can see, it simplifies down to PPG – OPP PPG, which is PPG differential. The range on NBA efficiency is about .97 to 1.129 and the range on OP is about 84.1 to 97.2, so we can make these assumptions and stay pretty accurate. (It turns out the largest difference between ED*90 + PD for this year in the NBA and that team’s actual PPG is about .34, which would change the expected win% by about 1%). What this implies then is that changing ED by about .01 changes the PPG by about .9, and is worth about 3% in win%. Changing PD by about 1 changes PPG by about 1 and is worth about 3.3% in win%. You can also use it to predict PPG or OPP PPG: changing OE or OPP OE by about .01 changes PPG or OPP PPG by about .9 respectively, and changing OP or OPP OP by about 1 changes PPG or OPP PPG by about 1, respectively. In words, this means that a turnover or offensive rebound is worth about 1 point, and changing offensive efficiency by about a hundredth of a point is worth about a point. It also means that averaging 1 turnover less or 1 offensive rebound more for the entire season is worth about 3% in win%, and changing offensive efficiency by .01 for a season is worth about the same. Here’s an example to demonstrate these effects:
|
Houston
Rockets |
|||||
OE
|
OPP OE |
OP |
OPP OP |
W-L |
Win% |
|
1.058 |
1.000 |
84.1 |
88.0 |
45-37 |
54.9 |
From this we can see that Houston is absolutely a beast defensively and fairly adept offensively, holding opponents to 1.000 efficiency while averaging 1.058 offensive efficiency. This means that Houston has an astounding ED of about .058, worth about 5.2 PPG differential (about 17.2% in win%). However, we see that their PD is an abysmal negative 3.9, worth about 3.9 negative PPG differential (-12.87% win%). Combining the two gives them a total of about 1.3 PPG differential (4.29% win%). They in fact average 1.0 PPG differential (54.9% total win%). Were Houston able to take care of the ball a little better and force a few more turnovers, or if they could just get their PD to not be negative, (have as many possessions as their opponents average, which would mean PD = 0), they’d have a win% of about 67.2%, or about 55 wins. Their PD has cost them about 10 wins this year!
In the end, this means the three most important statistics for teams are offensive rebound differential, turnover differential, and efficiency differential. These three stats are the key to ultimately determining win%.
Written by Will Carr
March 27, 2004
Updated June 15, 2004
OFFENSIVE STATISTICS |
|||||||
|
Team Name |
FGA |
FTA |
PPG |
OE |
TO |
OR |
OP |
|
Atlanta |
79.6 |
24.1 |
90.5 |
1.014 |
15.6 |
12.1 |
89.2 |
|
Boston |
78.2 |
25.5 |
94.8 |
1.072 |
15.7 |
10.4 |
88.4 |
|
Chicago |
82.4 |
22.4 |
88.5 |
0.969 |
15.2 |
12.8 |
91.4 |
|
Cleveland |
82.4 |
24.8 |
93.1 |
1.008 |
14.2 |
13.6 |
92.3 |
|
Dallas |
88.2 |
22.6 |
103.8 |
1.067 |
11.8 |
14.3 |
97.2 |
|
Denver |
82.5 |
26.3 |
97.0 |
1.043 |
14.6 |
13.2 |
93.0 |
|
Detroit |
77.0 |
25.3 |
90.0 |
1.033 |
14.4 |
12.4 |
87.1 |
|
Golden State |
79.4 |
24.7 |
93.1 |
1.043 |
14.1 |
12.2 |
89.3 |
|
Houston |
75.5 |
21.6 |
89.0 |
1.058 |
15.8 |
10.3 |
84.1 |
|
Indiana |
77.1 |
24.6 |
91.0 |
1.047 |
13.5 |
11.8 |
86.9 |
|
LA Clippers |
80.2 |
28.1 |
95.5 |
1.044 |
15.6 |
14.0 |
91.4 |
|
LA Lakers |
81.4 |
28.7 |
98.6 |
1.062 |
13.4 |
12.2 |
92.9 |
|
Memphis |
81.2 |
26.1 |
96.6 |
1.054 |
14.4 |
12.8 |
91.6 |
|
Miami |
78.3 |
23.4 |
89.4 |
1.020 |
13.1 |
11.5 |
87.7 |
|
Milwaukee |
81.1 |
26.7 |
98.1 |
1.069 |
13.0 |
11.7 |
91.8 |
|
Minnesota |
80.1 |
21.3 |
94.7 |
1.069 |
12.2 |
10.7 |
88.6 |
|
New Jersey |
77.7 |
22.6 |
89.3 |
1.030 |
14.1 |
10.5 |
86.7 |
|
New Orleans |
80.5 |
23.6 |
92.3 |
1.026 |
14.1 |
13.3 |
89.9 |
|
New York |
79.4 |
21.1 |
91.8 |
1.045 |
15.3 |
11.6 |
87.8 |
|
Orlando |
82.5 |
24.4 |
94.6 |
1.025 |
13.0 |
12.2 |
92.3 |
|
Philadelphia |
76.3 |
24.5 |
88.5 |
1.028 |
14.7 |
11.5 |
86.1 |
|
Phoenix |
81.4 |
22.7 |
94.1 |
1.040 |
14.7 |
11.3 |
90.5 |
|
Portland |
78.9 |
21.1 |
90.7 |
1.038 |
13.8 |
12.7 |
87.3 |
|
Sacramento |
81.8 |
24.9 |
103.6 |
1.129 |
13.5 |
10.8 |
91.8 |
|
San Antonio |
78.5 |
25.2 |
91.2 |
1.030 |
14.1 |
12.5 |
88.6 |
|
Seattle |
80.4 |
21.7 |
96.4 |
1.082 |
13.8 |
11.1 |
89.1 |
|
Toronto |
77.4 |
20.1 |
85.4 |
1.000 |
13.3 |
10.1 |
85.4 |
|
Utah |
75.3 |
26.8 |
88.8 |
1.032 |
15.3 |
13.5 |
86.0 |
|
Washington |
80.0 |
27.0 |
91.9 |
1.012 |
16.7 |
13.6 |
90.8 |
|
Key: FGA
= Field Goal Attempts; FTA = Free Throw Attempts PPG =
Points Per Game; OE = Efficiency = (PPG/(FGA + (FTA/2))); TO =
Turnovers; OR = Offensive Rebounds OP =
Offensive Possessions = (FGA + (FTA/2)) |
|||||||
DEFENSIVE STATISTICS |
|||||||
|
Team Name |
OPP FGA |
OPP FTA |
OPP PPG |
OPP OE |
OPP TO |
OPP OR |
OPP OP |
|
Atlanta |
82.2 |
25.3 |
97.5 |
1.056 |
13.2 |
12.8 |
92.3 |
|
Boston |
80.8 |
26.3 |
96.7 |
1.059 |
16.3 |
13.0 |
91.3 |
|
Chicago |
80.6 |
27.3 |
96 |
1.049 |
14.5 |
12.3 |
91.5 |
|
Cleveland |
82.4 |
24.3 |
95.5 |
1.037 |
12.4 |
11.8 |
92.1 |
|
Dallas |
83.2 |
23.9 |
100.8 |
1.087 |
14.4 |
12.3 |
92.8 |
|
Denver |
81.2 |
24.6 |
96.1 |
1.056 |
16.4 |
12.9 |
91.0 |
|
Detroit |
77.8 |
21.1 |
84.3 |
0.978 |
14.7 |
12.0 |
86.2 |
|
Golden State |
80.9 |
22.9 |
94 |
1.044 |
13.4 |
12.0 |
90.1 |
|
Houston |
78.1 |
24.7 |
88 |
1.000 |
12.8 |
11.1 |
88.0 |
|
Indiana |
75.4 |
22 |
85.6 |
1.017 |
15.1 |
10.6 |
84.2 |
|
LA Clippers |
81.5 |
25.4 |
99.4 |
1.084 |
13.0 |
12.6 |
91.7 |
|
LA Lakers |
81 |
24 |
94.3 |
1.041 |
14.5 |
11.2 |
90.6 |
|
Memphis |
79.9 |
26.2 |
94.3 |
1.043 |
16.1 |
13.4 |
90.4 |
|
Miami |
75.7 |
25.6 |
89.7 |
1.044 |
14.2 |
10.9 |
85.9 |
|
Milwaukee |
81.9 |
23.6 |
97 |
1.062 |
13.6 |
12.0 |
91.3 |
|
Minnesota |
80.8 |
23.1 |
89.1 |
0.990 |
12.8 |
12.2 |
90.0 |
|
New Jersey |
76.9 |
22.3 |
87.8 |
1.023 |
15.2 |
10.6 |
85.8 |
|
New Orleans |
78.6 |
22.9 |
91.9 |
1.047 |
14.7 |
11.9 |
87.8 |
|
New York |
79.2 |
26.8 |
93.5 |
1.040 |
13.2 |
11.6 |
89.9 |
|
Orlando |
83 |
24 |
101.1 |
1.092 |
12.9 |
13.1 |
92.6 |
|
Philadelphia |
77.7 |
24.2 |
90.5 |
1.036 |
14.5 |
12.1 |
87.4 |
|
Phoenix |
81.4 |
25.5 |
97.9 |
1.069 |
15.0 |
12.7 |
91.6 |
|
Portland |
80.1 |
19.7 |
92 |
1.046 |
13.0 |
12.4 |
88.0 |
|
Sacramento |
84.4 |
21.7 |
97.8 |
1.051 |
14.3 |
13.2 |
93.1 |
|
San Antonio |
77.9 |
22.5 |
84.3 |
0.970 |
14.6 |
11.1 |
86.9 |
|
Seattle |
80.8 |
24.7 |
97.8 |
1.079 |
14.1 |
13.0 |
90.7 |
|
Toronto |
77.6 |
25.1 |
88.5 |
1.010 |
13.9 |
12.6 |
87.6 |
|
Utah |
71.7 |
29.6 |
89.9 |
1.076 |
14.8 |
10.6 |
83.5 |
|
Washington |
82.2 |
22.9 |
97.4 |
1.066 |
15.5 |
12.8 |
91.4 |
|
Key: OPP
FGA = Opponents’ Field Goal Attempts; OPP FTA = Opponents’ Free Throw
Attempts; OPP PPG =
Opponents’ Points Per Game; OPP OE =
Opponents’ Offensive Efficiency = (OPP PPG/(OPP FGA + (OPP FTA/2)); OPP TO =
Opponents Turnovers; OPP OR = Opponents Offensive Rebounds; OPP OP =
Opponents Offensive Possessions = (OPP FGA + (OPP FTA/2)) |
|||||||
|
DIFFERENTIAL STATISTICS |
|||||||||
|
Team Name |
FGA |
FTA |
TO |
OR |
TO + OR |
OP |
OE |
PPG |
Exp. PPG |
|
Atlanta |
(2.6) |
(1.2) |
(2.40) |
(0.70) |
(3.10) |
(3.08) |
(0.042) |
-7.000 |
-6.859 |
|
Boston |
(2.6) |
(0.8) |
0.60 |
(2.60) |
(2.00) |
(2.92) |
0.013 |
-1.900 |
-1.706 |
|
Chicago |
1.8 |
(4.9) |
(0.70) |
0.50 |
(0.20) |
(0.16) |
(0.080) |
-7.500 |
-7.383 |
|
Cleveland |
0.0 |
0.5 |
(1.80) |
1.80 |
0.00 |
0.20 |
(0.028) |
-2.400 |
-2.342 |
|
Dallas |
5.0 |
(1.3) |
2.60 |
2.00 |
4.60 |
4.48 |
(0.019) |
3.000 |
2.751 |
|
Denver |
1.3 |
1.7 |
1.80 |
0.30 |
2.10 |
1.98 |
(0.013) |
0.900 |
0.829 |
|
Detroit |
(0.8) |
4.2 |
0.30 |
0.40 |
0.70 |
0.88 |
0.056 |
5.700 |
5.880 |
|
Golden State |
(1.5) |
1.8 |
(0.70) |
0.20 |
(0.50) |
(0.78) |
(0.001) |
-0.900 |
-0.867 |
|
Houston |
(2.6) |
(3.1) |
(3.00) |
(0.80) |
(3.80) |
(3.84) |
0.058 |
1.000 |
1.338 |
|
Indiana |
1.7 |
2.6 |
1.60 |
1.20 |
2.80 |
2.74 |
0.030 |
5.400 |
5.446 |
|
LA Clippers |
(1.3) |
2.7 |
(2.60) |
1.40 |
(1.20) |
(0.22) |
(0.040) |
-3.900 |
-3.824 |
|
LA Lakers |
0.4 |
4.7 |
1.10 |
1.00 |
2.10 |
2.28 |
0.021 |
4.300 |
4.147 |
|
Memphis |
1.3 |
(0.1) |
1.70 |
(0.60) |
1.10 |
1.26 |
0.011 |
2.300 |
2.228 |
|
Miami |
2.6 |
(2.2) |
1.10 |
0.60 |
1.70 |
1.72 |
(0.024) |
-0.300 |
-0.431 |
|
Milwaukee |
(0.8) |
3.1 |
0.60 |
(0.30) |
0.30 |
0.44 |
0.007 |
1.100 |
1.060 |
|
Minnesota |
(0.7) |
(1.8) |
0.60 |
(1.50) |
(0.90) |
(1.42) |
0.079 |
5.600 |
5.694 |
|
New Jersey |
0.8 |
0.3 |
1.10 |
(0.10) |
1.00 |
0.92 |
0.006 |
1.500 |
1.500 |
|
New Orleans |
1.9 |
0.7 |
0.60 |
1.40 |
2.00 |
2.18 |
(0.021) |
0.400 |
0.296 |
|
New York |
0.2 |
(5.7) |
(2.10) |
0.00 |
(2.10) |
(2.08) |
0.005 |
-1.700 |
-1.606 |
|
Orlando |
(0.5) |
0.4 |
(0.10) |
(0.90) |
(1.00) |
(0.34) |
(0.066) |
-6.500 |
-6.319 |
|
Philadelphia |
(1.4) |
0.3 |
(0.20) |
(0.60) |
(0.80) |
(1.28) |
(0.008) |
-2.000 |
-1.985 |
|
Phoenix |
0.0 |
(2.8) |
0.30 |
(1.40) |
(1.10) |
(1.12) |
(0.029) |
-3.800 |
-3.709 |
|
Portland |
(1.2) |
1.4 |
(0.80) |
0.30 |
(0.50) |
(0.64) |
(0.007) |
-1.300 |
-1.290 |
|
Sacramento |
(2.6) |
3.2 |
0.80 |
(2.40) |
(1.60) |
(1.32) |
0.078 |
5.800 |
5.729 |
|
San Antonio |
0.6 |
2.7 |
0.50 |
1.40 |
1.90 |
1.68 |
0.059 |
6.900 |
7.035 |
|
Seattle |
(0.4) |
(3.0) |
0.30 |
(1.90) |
(1.60) |
(1.60) |
0.004 |
-1.400 |
-1.271 |
|
Toronto |
(0.2) |
(5.0) |
0.60 |
(2.50) |
(1.90) |
(2.20) |
(0.010) |
-3.100 |
-3.125 |
|
Utah |
3.6 |
(2.8) |
(0.50) |
2.90 |
2.40 |
2.48 |
(0.044) |
-1.100 |
-1.463 |
|
Washington |
(2.2) |
4.1 |
(1.20) |
0.80 |
(0.40) |
(0.56) |
(0.054) |
-5.500 |
-5.420 |
Note: All Differential Stats are in the form Offensive Stat – Defensive Stat unless otherwise noted;(Red) indicates a negative number Key: FGA = Field Goal Attempts; FTA =
Free Throw Attempts; TO = Turnovers; OR = Offensive Rebounds; TO + OR =
Turnover differential + Offensive Rebound differential; OP = Offensive Possessions; OE =
Offensive Efficiency; PPG = Points Per Game; Exp. PPG = Expected Points Per Game
differential (OE differential * 90 + OP differential) |
|||||||||
STANDINGS |
|||||||||
|
Team Name |
W |
L |
W% |
EWin% |
E Wins |
W% - EW% |
Py. Win% |
W% - PyW% |
PyW% - EW% |
|
Atlanta |
28 |
54 |
34.1% |
27.36% |
22 |
6.74% |
28.05% |
6.05% |
-0.68% |
|
Boston |
36 |
46 |
43.9% |
44.37% |
36 |
-0.47% |
44.14% |
-0.24% |
0.22% |
|
Chicago |
23 |
59 |
28.0% |
25.64% |
21 |
2.36% |
26.04% |
1.96% |
-0.40% |
|
Cleveland |
35 |
47 |
42.7% |
42.27% |
35 |
0.43% |
42.49% |
0.21% |
-0.22% |
|
Dallas |
52 |
30 |
63.4% |
59.08% |
48 |
4.32% |
58.65% |
4.75% |
0.43% |
|
Denver |
43 |
39 |
52.4% |
52.73% |
43 |
-0.33% |
52.75% |
-0.35% |
-0.02% |
|
Detroit |
54 |
28 |
65.9% |
69.40% |
57 |
-3.50% |
69.28% |
-3.38% |
0.12% |
|
Golden State |
37 |
45 |
45.1% |
47.14% |
39 |
-2.04% |
47.16% |
-2.06% |
-0.02% |
|
Houston |
45 |
37 |
54.9% |
54.42% |
45 |
0.48% |
53.33% |
1.57% |
1.08% |
|
Indiana |
61 |
21 |
74.4% |
67.97% |
56 |
6.43% |
68.03% |
6.37% |
-0.06% |
|
LA Clippers |
28 |
54 |
34.1% |
37.38% |
31 |
-3.28% |
38.19% |
-4.09% |
-0.81% |
|
LA Lakers |
56 |
26 |
68.3% |
63.69% |
52 |
4.61% |
63.15% |
5.15% |
0.53% |
|
Memphis |
50 |
32 |
61.0% |
57.35% |
47 |
3.65% |
57.11% |
3.89% |
0.24% |
|
Miami |
42 |
40 |
51.2% |
48.58% |
40 |
2.62% |
49.01% |
2.19% |
-0.43% |
|
Milwaukee |
41 |
41 |
50.0% |
53.50% |
44 |
-3.50% |
53.33% |
-3.33% |
0.17% |
|
Minnesota |
58 |
24 |
70.7% |
68.79% |
56 |
1.91% |
67.97% |
2.73% |
0.82% |
|
New Jersey |
47 |
35 |
57.3% |
54.95% |
45 |
2.35% |
55.00% |
2.30% |
-0.05% |
|
New Orleans |
41 |
41 |
50.0% |
50.98% |
42 |
-0.98% |
51.28% |
-1.28% |
-0.31% |
|
New York |
39 |
43 |
47.6% |
44.70% |
37 |
2.90% |
44.59% |
3.01% |
0.11% |
|
Orlando |
21 |
61 |
25.6% |
29.15% |
24 |
-3.55% |
30.41% |
-4.81% |
-1.27% |
|
Philadelphia |
33 |
49 |
40.2% |
43.45% |
36 |
-3.25% |
43.41% |
-3.21% |
0.04% |
|
Phoenix |
29 |
53 |
35.4% |
37.76% |
31 |
-2.36% |
38.32% |
-2.92% |
-0.56% |
|
Portland |
41 |
41 |
50.0% |
45.74% |
38 |
4.26% |
45.80% |
4.20% |
-0.06% |
|
Sacramento |
55 |
27 |
67.1% |
68.91% |
57 |
-1.81% |
66.99% |
0.11% |
1.92% |
|
San Antonio |
57 |
25 |
69.5% |
73.21% |
60 |
-3.71% |
73.18% |
-3.68% |
0.04% |
|
Seattle |
37 |
45 |
45.1% |
45.81% |
38 |
-0.71% |
45.75% |
-0.65% |
0.06% |
|
Toronto |
33 |
49 |
40.2% |
39.69% |
33 |
0.51% |
39.48% |
0.72% |
0.20% |
|
Utah |
42 |
40 |
51.2% |
45.17% |
37 |
6.03% |
46.37% |
4.83% |
-1.20% |
|
Washington |
25 |
57 |
30.5% |
32.11% |
26 |
-1.61% |
32.86% |
-2.36% |
-0.75% |
Key: W = Current Wins; L =
Current Losses; W% = Current Win% = (Wins/(Wins + Losses));
PWins = Current Projected Wins = (Current Win% * 82); EWin% = Expected Win% = ((50 + Exp. PPG differential*3.3)/100);EP Wins =
Expected Projected Wins = (Expected Win% * 82); W% - EW% = Current Win% -
Expected Win%; Py. Win%
= Basketball Pythagorean Theorem Win% =
((0.5-(6.45*(0.5-(PPG^1.83/(PPG^1.83+OPP PPG^1.83))))) W% - PyW%
= Current Win% - Pythagorean Win%; PyW% - EW% = Pythagorean Win% - Expected
Win% |
|||||||||