There's an increasing sense of inevitability of success associated with Kevin Love and whatever team he joins. I pulled monthly averages for his career from NBA Stats, and plotted the percent of three point field goal attempts (%FGA 3PT) and the percent of his made field goals off of an assist (FGM %AST). While he puts up phenomenal statistics in Minnesota, his game has progressed to that of a superior rebounder on defense and a set three point shooter on offense. The state of his offensive game suggests he would prove much more valuable in Cleveland where he could spread the floor for LeBron while also giving them some much needed long distance shooting than he would in Golden State where he would add yet another three point shooter without adding to their anemic post offense. Further, his increasing reliance on assists for made field goals indicates he needs to be set-up to score, thus making Cleveland and LeBron the preferable fit for Love.

# dem bammas straight fryin

## Monday, July 21, 2014

## Monday, June 30, 2014

### Why LeBron leaves

I pulled monthly split stats from Basketball-Reference.com on LeBron, Dwade and Bosh going back to the three's first season with the Heat. Below I charted their monthly offensive rating (an estimate of the points produced by a player per 100 possessions) and defensive rating (an estimate of the points allowed by a player per 100 possessions). Because these three are all starters and play the bulk of the Heat's minutes together, you would expect their ratings to mirror each other (the calculations takes into account several team statistics).

Offensively, over the last two years LeBron has really begun to distance himself from the other two. Further, DWade has exhibited a noticeable decline that began March 2013. Defensively, the three stars follow a much closer path, but again DWade appears to struggle recently more so than the LeBron and Bosh (in the bottom chart, as it is preferable to allow fewer points, the lower the line the better). Should LeBron stay in Miami these charts suggest he will be expected to carry an increasing load for the other Big Two as their performance wanes.

Offensively, over the last two years LeBron has really begun to distance himself from the other two. Further, DWade has exhibited a noticeable decline that began March 2013. Defensively, the three stars follow a much closer path, but again DWade appears to struggle recently more so than the LeBron and Bosh (in the bottom chart, as it is preferable to allow fewer points, the lower the line the better). Should LeBron stay in Miami these charts suggest he will be expected to carry an increasing load for the other Big Two as their performance wanes.

## Saturday, June 21, 2014

### Spurs, passing & distance

There was much emphasis--and a surprising number of highlights and GIFs--on the Spurs' exceptional passing during this season's championship run. For the first playoffs in history, the NBA used SportVU technology to report on spatial statistics that offer insight on statistics like passes per game, distance traveled, and other possession-based information not easily captured by conventional measures, like points, rebounds, etc.

The chart below breaks out passes per game for the 2013-14 NBA playoffs. The Spurs were third from the best, and clearly passes per game doesn't seem to determine success as the Bobcats are second and more successful playoff teams, like the Heat, Pacers, and Thunder are toward the bottom.

The following chart compares 2013-14 playoff win percentage with passes per game. The orange trend line actually goes down, which signals a negative correlation between win percentage and passes per game. In other words, more passes per game does not seem to equate with winning. (The R-squared number is a signal of model fit. The closer it is to 1 the better the data fit. In this chart the low R-squared signals that trend/ regression line does a poor job of approximating the data. If all the points hovered on or around the trend/ regression line then the R-squared number would be higher.)

When I looked at other spatial data that NBA Stats provided, there was one that the Spurs proved exceptional--distance traveled per game. The chart below shows the distance (in miles) traveled by the Spurs' players on the court per 48 minutes. The Spurs traveled almost a full mile more per 48 minutes than the next most traveled teams, the Trail Blazers, the Bulls and the Bobcats, and almost two miles per game more than the Pacers.

The chart below illustrates the relationship between distance traveled per 48 minutes and win percentage. The trend line indicates a slightly negative correlation and the R-squared shows even poorer model fit than the prior. The Spurs are an anomaly in the upper right hand corner of the chart. This chart simply shows that while distance traveled may have proved beneficial for the Spurs, it had no correlation on success in these playoffs for the other 15 teams.

While I did not find any spatial team measure that explained team success, these basic models are limited by the fact that there is a sample size of only one playoff. As the NBA continues to collect this data in future seasons, there will be more information so it will become easier to distinguish if meaningful relationships exist between measures of success, like winning, and spatial measures. Further, simple two variable models, like the two above, ignore a range of factors, like opponent, offensive and defensive schemes, etc that if accounted for would help clarify the actual relationship between passing or distance and success.

The chart below breaks out passes per game for the 2013-14 NBA playoffs. The Spurs were third from the best, and clearly passes per game doesn't seem to determine success as the Bobcats are second and more successful playoff teams, like the Heat, Pacers, and Thunder are toward the bottom.

The following chart compares 2013-14 playoff win percentage with passes per game. The orange trend line actually goes down, which signals a negative correlation between win percentage and passes per game. In other words, more passes per game does not seem to equate with winning. (The R-squared number is a signal of model fit. The closer it is to 1 the better the data fit. In this chart the low R-squared signals that trend/ regression line does a poor job of approximating the data. If all the points hovered on or around the trend/ regression line then the R-squared number would be higher.)

When I looked at other spatial data that NBA Stats provided, there was one that the Spurs proved exceptional--distance traveled per game. The chart below shows the distance (in miles) traveled by the Spurs' players on the court per 48 minutes. The Spurs traveled almost a full mile more per 48 minutes than the next most traveled teams, the Trail Blazers, the Bulls and the Bobcats, and almost two miles per game more than the Pacers.

The chart below illustrates the relationship between distance traveled per 48 minutes and win percentage. The trend line indicates a slightly negative correlation and the R-squared shows even poorer model fit than the prior. The Spurs are an anomaly in the upper right hand corner of the chart. This chart simply shows that while distance traveled may have proved beneficial for the Spurs, it had no correlation on success in these playoffs for the other 15 teams.

While I did not find any spatial team measure that explained team success, these basic models are limited by the fact that there is a sample size of only one playoff. As the NBA continues to collect this data in future seasons, there will be more information so it will become easier to distinguish if meaningful relationships exist between measures of success, like winning, and spatial measures. Further, simple two variable models, like the two above, ignore a range of factors, like opponent, offensive and defensive schemes, etc that if accounted for would help clarify the actual relationship between passing or distance and success.

## Friday, May 30, 2014

### Best & Worst Three-man Line-ups (2013/14 Playoffs)

Below are the best and worst three-man line-ups in the playoffs as of last night's game based on player impact estimate (PIE) and net rating (both standardized per 48 minutes of play). I pulled data from NBA stats (which limits each combination of three-man line-ups to 250).

As expected, three-man rotations from either the Spurs or the Heat take all of the top ten positions, with the exception that Lowry, Patterson and Vasquez, who had the second highest PIE. The ten worst PIEs are dominated by Bulls and Raptors, two first round exits. Surprisingly, the three worst PIEs are Heat rotations that include Udanis Haslem.

The net rating chart essentially mirrors the PIE chart with the exception that Lowry, Patterson and Vasquez dropped from second to sixth best. The fact that Udonis Haslem is the consistent piece (with otherwise extraordinary players) in the three worst PIEs and net rating for these playoffs signals the value of Chris Andersen's return (not necessarily because he is so valuable, but because Andersen is simply not Haslem).

As expected, three-man rotations from either the Spurs or the Heat take all of the top ten positions, with the exception that Lowry, Patterson and Vasquez, who had the second highest PIE. The ten worst PIEs are dominated by Bulls and Raptors, two first round exits. Surprisingly, the three worst PIEs are Heat rotations that include Udanis Haslem.

The net rating chart essentially mirrors the PIE chart with the exception that Lowry, Patterson and Vasquez dropped from second to sixth best. The fact that Udonis Haslem is the consistent piece (with otherwise extraordinary players) in the three worst PIEs and net rating for these playoffs signals the value of Chris Andersen's return (not necessarily because he is so valuable, but because Andersen is simply not Haslem).

## Friday, May 9, 2014

### 2013-14 Distance-Adjusted FG%

There are several advanced measures of field goal percentage (FG%), such as effective FG% (eFG%) and True Shooting % (TS%) that add value for made three point FGs (3FGMs), in the case of eFG%, or 3FGMs and free throws (FTs), in the case of TS%. But I have not encountered any measure that takes into account distance. Presumably the further the field goal attempt (FGA) from the basket the lower the likelihood that the attempt is made. I pulled 2013-14 regular season data from NBA Stats on FG% by distance and plotted the results below. The league FG% (orange line) is 45.6%. FG% is highest within 5 feet of the basket (59.0%) then drops 20 percentage points once a shot is attempted from 5-9 feet from the basket and hovers around that level for all attempts within 24 feet. Attempts from 25-29 feet drop to the lowest FG% by a five foot increment (34.5%).

I then applied the distance factor to each player's FGMs by five foot increment. I kept the FGA for each increment the same, thus the distance factors would inflate the FGMs and FG%. (In other words, for every play from 5-9 Feet I multiplied their FGMs by 1.51.) The chart below illustrates the effect this distance-weighting had on John Wall. As a guard he attempts more shots from further away from the basket so such a weighting system is expected to increase his FG% considerably (and, sure enough that is what happened). In weighting his FGMs based on the distance factors for each five foot increment (column DAFGM), Wall's Distance Adjusted FG% (DAFG%) increases for every five foot increment with the exception of less than five feet, which serves as the basis. Overall his FG% increases from his actual percentage of 43.7% to a DAFG% of 57.1%.

I wanted to standardize FG% based on distance so I used the FG% from within five feet of the basket as the base. Then I established a factor for each five foot increment based on the league-wide FG% within five feet divided by the league-wide FG% in a given five foot increment. The orange line in the chart below shows that in the 2013-14 NBA season, players were 1.51 times more likely to make a shot from less than five feet than they were from 5-9 feet from the basket. Likewise they were 1.71 times more likely to make a shot from less than five feet than they were from 25-29 feet from the basket.

I then applied the distance factor to each player's FGMs by five foot increment. I kept the FGA for each increment the same, thus the distance factors would inflate the FGMs and FG%. (In other words, for every play from 5-9 Feet I multiplied their FGMs by 1.51.) The chart below illustrates the effect this distance-weighting had on John Wall. As a guard he attempts more shots from further away from the basket so such a weighting system is expected to increase his FG% considerably (and, sure enough that is what happened). In weighting his FGMs based on the distance factors for each five foot increment (column DAFGM), Wall's Distance Adjusted FG% (DAFG%) increases for every five foot increment with the exception of less than five feet, which serves as the basis. Overall his FG% increases from his actual percentage of 43.7% to a DAFG% of 57.1%.

To give some scope on why it is worth considering a DAFG%, look at the chart below of the top ten and ten worst FG%s from 2013-14 (minimum 100 FGAs). All of the top FG% players are centers that only take shots close to the basket, and the worst FG%s are guards that rely more on longer jump shots.

The next chart shows the top ten and ten worst DAFG%s from 2013-14 (minimum 100 FGAs). While many of the poor shooting guards from the above chart remain, several guards and jump shooting forwards, like Korver, Nowitzki, Miller, Prigioni, Pargo and James, emerge as top distance-adjusted shooters.

Finally, it is important to understand the relationship between FG% and DAFG%. The chart below, which includes an orange linear regression line, illustrates that the two are highly correlated. Another way to interpret this, is that DAFG% does not revolutionize how we understand shooting efficiency. It largely reinforces what conventional measures already tell us. But, the chart immediately above shows that DAFG% also can grant insight on especially efficient guards and forward, who may not be recognized by conventional FG% measures simply because they take longer shots.

## Saturday, April 19, 2014

### 2013-14 Rookie Player Impact Estimates

Among the many measures aimed at capturing a player's overall value to a team (i.e., PER and WARP), NBA Stats uses Player Impact Estimate (PIE)*. The statistic combines a range of conventional stats and standardizes them per game. With the regular season complete I wanted to take a retrospective look at rookie performance and do some simple forecasting to see which players might excel in their sophomore season. I pulled PIE data from NBA Stats for all rookies that played at least 10 minutes per game. The first chart presents the results. Interestingly, based on PIE, top overall pick Anthony Bennett was actually the worst rookie in the 2013-14 season, and seventh overall pick Ben McLemore wasn't much better. The top performing rookies included Gorgui Dieng, Mason Plumlee, Michael Carter-Williams and Victor Oladipo. The latter two were effective consistently throughout their rookie years, Diegn and Plumlee came on strong during the last two months of the season.

Next, I wanted to get an idea of how PIE might predict performance among the 2013-14 rookies in their 2014-15 sophomore season. I collected data on the April PIE for 2012-13 rookies (i.e., the Anthony Davis class) and compared it with their full season PIE from their 2013-14 sophomore season. I captured only players from the 2012 draft class that played registered a PIE in April 2013 and registered a PIE for 2013-14, which resulted in 56 players (actually 57, but I dropped Robert Sacre and his -200% PIE for April 2013, which reflected 1.6 of the worst minutes of basketball in the 2012-13 season).

My thinking is that by April, the last month of the regular season, rookies have become accustomed to the NBA game. As a result, it represents a good focal point to project performance into their sophomore year. (I'm hesitant to project anything over the long-term based on rookie performance as their are players, like Steve Nash, that need a few years to adjust to the NBA game.) I included a linear regression line, which indicates a positive correlation between rookie year April PIE and sophomore season PIE. (The anomalous point that is about 15% for his rookie season and 16% for last April is, of course, Anthony Davis.)

Finally, I pulled April PIE data for this season's rookies. The top twenty are listed below. If April's performance holds, we can expect that Carter-Williams, Plumlee, Olynyk, Diegn and Oladipo to lead next year's sophomore class. (In April, Dieng averaged a double-double in points and rebounds plus two blocks per game; Olynyk averaged almost 17 points and 7 rebounds a game while shooting 55%.)

## Friday, April 4, 2014

### Conventional Stats that Matter

With a growing focus on advanced statistics and measures, some analysis disregards conventional playing statistics, like steals, points or field goal attempts. But many "advanced" stats are based largely on conventional measures. For example, effective field goal %; (eFG%) simply places greater weight on three point made baskets to account for their value. More complex advanced stats, like PER, combine a range of individual and team conventional statistics, like assists, rebounds, steals, FG%, etc.

Now this isn't to say that advanced stats are deficient in any way. In many cases they are superior measures of performance--because of their ability to account for more than a single measure of play, like just rebounds, or just points--but conventional statistics are of intrinsic value.

I collected team data from the last three full NBA seasons (2011-12 was cut short 16 games by a collective bargaining agreement work stoppage) and included all conventional team stats from basketball-reference.com (for example . . . ). Next, I cheated and ran a correlation matrix to determine those conventional statistics most correlated with winning and most correlated with losing. Below are charts that show the two conventional statistics most correlated with winning and the one conventional statistic most correlated with losing. (Wins over these three seasons are plotted on the horizontal axis for the first two charts; losses are plotted on the horizontal line for the third chart.)

Now this isn't to say that advanced stats are deficient in any way. In many cases they are superior measures of performance--because of their ability to account for more than a single measure of play, like just rebounds, or just points--but conventional statistics are of intrinsic value.

I collected team data from the last three full NBA seasons (2011-12 was cut short 16 games by a collective bargaining agreement work stoppage) and included all conventional team stats from basketball-reference.com (for example . . . ). Next, I cheated and ran a correlation matrix to determine those conventional statistics most correlated with winning and most correlated with losing. Below are charts that show the two conventional statistics most correlated with winning and the one conventional statistic most correlated with losing. (Wins over these three seasons are plotted on the horizontal axis for the first two charts; losses are plotted on the horizontal line for the third chart.)

For each chart I included a linear regression line (blue line) with a 95% confidence region (grey area). The line and grey area give some indication of the relationship between wins and each conventional statistic. The upward slant of the regression line indicates a positive correlation between blocks (first plot) and assists (second plot) with wins. In other words, blocks and assists, rather than FG%, points, or rebounds might represent the most important indicator of a team's success (as measured by regular season wins)

When I ran the correlation matrix, the conventional statistic most highly correlated with losing was fouls. While the association between blocks and assists with winning and fouls with losing does not necessarily hold with individual players, there is some evidence that at the team-level these are critical conventional statistics.

## Sunday, March 23, 2014

### 2013-14 Player Salary per Win Share (as of March 22)

I wanted to update the list of most cost-effective contracts in the NBA through so I pulled player advanced stats and salary data from Basketball-Reference.com on March 22. As a rough measure of player efficacy I used Win Shares (WS), which attempts to estimate the number of wins a player contributes to his team in a given season (read this for Basketball-Reference's detailed explanation of the measure). I divided the player's 2013-14 salary by WS to determine, which player contributes the greatest number of wins to his team at the lowest cost to the organization. For some reference, the average cost per WS was $1,551,542 and the median cost per WS was $1,427,214.

Typically, I standardize statistics as per minute or per 48 minutes of performance to take into account for the fact that players that play more minutes also are likely to accrue more statistics than players that play fewer minutes. (Basketball-Reference.com does provide WS per 48 minutes.) But taking salary into account changes the dynamics because a player receives his salary regardless if he plays 30 minutes a game or if he plays 2 minutes a game. As a result, the organization benefits from total WS (as opposed to WS/ 48) because the player receives his full salary and not some prorated amount based on performance.

I filtered the population of 2013-14 NBA players to those that have played 100 minutes or more, which left me with 422 players. The chart below shows the full spectrum of those 422 players: Ekpe Udoh is the most expensive per WS and Isaiah Thomas is the least expensive per WS. Among the 39 qualifying players with a negative WS, Kobe Bryant comes at the greatest cost to his team (i.e., his contract in 2013-14 is very burdensome and when he plays he has a negative effect on his team's chances of winning a game--this is a bad thing.)

First, the good--the table below lists the 10 most cost-effective contracts this season in reverse order. When taking salary into account, this table suggests that Isaiah Thomas, Lance Stephenson, and Chandler Parsons are the best players this season. To give you some context of cost/ WS of other notable players, consider: LeBron ($1.45M), Durant ($1.05M), Deron Williams ($4.01M), Kevin Love ($1.13M), and John Wall ($1.04M).

While the 5 most cost-ineffective players listed above prove burdensome on the financial well-being of their organizations, they all at least contribute to WS. Among players that have played at least 100 minutes in the 2013-14 NBA season, there are 39 players with a negative WS. In other words, when they are on the floor they actually hurt their team's chances of winning. It's one thing to be Jason Terry or Aaron Gray and to be paid a lot to help a little, it's another thing to be paid a lot and to hurt one's team. The table below shows that Kobe, Derrick Rose and OJ Mayo are the greatest offenders of this latter group. Ignoring injuries to players, like Bryant and Rose, from a cost-efficacy standpoint there is evidence to suggest that through March 22, OJ Mayo is the most burdensome player in the NBA.

Typically, I standardize statistics as per minute or per 48 minutes of performance to take into account for the fact that players that play more minutes also are likely to accrue more statistics than players that play fewer minutes. (Basketball-Reference.com does provide WS per 48 minutes.) But taking salary into account changes the dynamics because a player receives his salary regardless if he plays 30 minutes a game or if he plays 2 minutes a game. As a result, the organization benefits from total WS (as opposed to WS/ 48) because the player receives his full salary and not some prorated amount based on performance.

I filtered the population of 2013-14 NBA players to those that have played 100 minutes or more, which left me with 422 players. The chart below shows the full spectrum of those 422 players: Ekpe Udoh is the most expensive per WS and Isaiah Thomas is the least expensive per WS. Among the 39 qualifying players with a negative WS, Kobe Bryant comes at the greatest cost to his team (i.e., his contract in 2013-14 is very burdensome and when he plays he has a negative effect on his team's chances of winning a game--this is a bad thing.)

First, the good--the table below lists the 10 most cost-effective contracts this season in reverse order. When taking salary into account, this table suggests that Isaiah Thomas, Lance Stephenson, and Chandler Parsons are the best players this season. To give you some context of cost/ WS of other notable players, consider: LeBron ($1.45M), Durant ($1.05M), Deron Williams ($4.01M), Kevin Love ($1.13M), and John Wall ($1.04M).

The five least cost-effective contracts of players with a positive WS are listed below. Ekpe Udoh and Charlie Villanueva have the most cost-ineffective contracts. But they're not the worst . . .

While the 5 most cost-ineffective players listed above prove burdensome on the financial well-being of their organizations, they all at least contribute to WS. Among players that have played at least 100 minutes in the 2013-14 NBA season, there are 39 players with a negative WS. In other words, when they are on the floor they actually hurt their team's chances of winning. It's one thing to be Jason Terry or Aaron Gray and to be paid a lot to help a little, it's another thing to be paid a lot and to hurt one's team. The table below shows that Kobe, Derrick Rose and OJ Mayo are the greatest offenders of this latter group. Ignoring injuries to players, like Bryant and Rose, from a cost-efficacy standpoint there is evidence to suggest that through March 22, OJ Mayo is the most burdensome player in the NBA.

## Sunday, March 2, 2014

### Blocks, Defensive Rebounding & Defensive Rebounding %

I've always assumed that defenders--centers, in particular--that focus more on blocking shots sacrifice opportunities for defensive rebounds as an attempted block puts the defender out of position to box-out opponents and secure defensive rebounds. To measure this assumption, I pulled blocks (Blk), defensive rebounds (DRB), and defensive rebounding percentage (% of DRBs a player grabs while on the floor) for centers from NBA stats (games as of February 28)

I standardized blocks and DRBs both per minute played. DRB% was already standardized. Below are two charts. In the top one, I plotted Blks/ minute (vertical axis) against DRBs/minute (horizontal axis). In the bottom chart, I plotted Blks/ minute (vertical axis) against DRB%. For both I included a linear regression line (blue line) with a 95% confidence region (grey area). The line and grey area give some indication of the relationship between blocks and defensive rebounding. The anomalies in the upper right hand corner of each chart are Javale McGee in the top chart--indicating that he is exceptional as a defensive rebounder and shot blocker--and Cole Aldrich in the bottom chart--indicating that he is an exceptional shot blocker and grabs a disproportionate % of his team's DRBs. Both players present anecdotal evidence that shot blocking and defensive rebounding are unrelated.

If players with more blocks were poorer defensive rebounders then the blue line and grey area would start at the top left of the plot and slope down toward the bottom right of the plot. But that doesn't happen. In both plots the blue line is effectively horizontal, which indicates that there is no relationship between blocks and DRB or DRB%. In other words, blocks have no effect on a player's defensive rebounding.

There are some limitations to this approach to measuring the relationship between defensive rebounding and shot blocking. First, it would be better to have a measure of

Second, this approach only measures the relationship between shot blocking and two measures of defensive rebounding separately. It ignores intervening factors in the relationship, such as the DRB and shot blocking ability of teammates or a team's defensive scheme. (If there were a scheme where Kevin Love is only supposed to grab defensive rebounds and his teammates should focus on contesting shots--as often appears to be the case with the Timberwolves--then shot blocking could be indirectly related to DRB. As evidence of this Love is second to last in blocks per minute but second to best in DRB%.)

__.__I focused on centers that played at least 50 minutes and appeared in at least 5 games during the 2013-14 season (which resulted in a population of 103 centers).I standardized blocks and DRBs both per minute played. DRB% was already standardized. Below are two charts. In the top one, I plotted Blks/ minute (vertical axis) against DRBs/minute (horizontal axis). In the bottom chart, I plotted Blks/ minute (vertical axis) against DRB%. For both I included a linear regression line (blue line) with a 95% confidence region (grey area). The line and grey area give some indication of the relationship between blocks and defensive rebounding. The anomalies in the upper right hand corner of each chart are Javale McGee in the top chart--indicating that he is exceptional as a defensive rebounder and shot blocker--and Cole Aldrich in the bottom chart--indicating that he is an exceptional shot blocker and grabs a disproportionate % of his team's DRBs. Both players present anecdotal evidence that shot blocking and defensive rebounding are unrelated.

If players with more blocks were poorer defensive rebounders then the blue line and grey area would start at the top left of the plot and slope down toward the bottom right of the plot. But that doesn't happen. In both plots the blue line is effectively horizontal, which indicates that there is no relationship between blocks and DRB or DRB%. In other words, blocks have no effect on a player's defensive rebounding.

There are some limitations to this approach to measuring the relationship between defensive rebounding and shot blocking. First, it would be better to have a measure of

*attempted*shot blocks rather than just shot blocks. Unfortunately, the NBA does not collect this statistic (they do collect the number of shots a player has had blocked), and even if they did, it would be difficult to capture what qualifies as an "attempted shot block" as most field goal attempts are contested and, thus, could potentially be blocked. (In other words, does an*attempted*shot block only occur when a defender leaves his feet? Should the defender also have to have a hand raised in contesting the shot?)Second, this approach only measures the relationship between shot blocking and two measures of defensive rebounding separately. It ignores intervening factors in the relationship, such as the DRB and shot blocking ability of teammates or a team's defensive scheme. (If there were a scheme where Kevin Love is only supposed to grab defensive rebounds and his teammates should focus on contesting shots--as often appears to be the case with the Timberwolves--then shot blocking could be indirectly related to DRB. As evidence of this Love is second to last in blocks per minute but second to best in DRB%.)

## Sunday, February 16, 2014

### Net & Defensive Team Rating

This chart helps explain why the Pacers are so dominant--they strap. I collected team data on Feb 13 from NBA Stats on offensive rating (points scored per 100 possessions), defensive rating (points allowed per 100 possessions), and net rating (offensive rating minus defensive rating). I measured offensive rating with the size of the circle next to the team (larger circles equate to greater relative offensive efficiency), defensive rating on the horizontal axis, and net rating on the vertical axis.

With respect to net rating, a team wants to be above the horizontal bar because that signals they score more points than they allow per 100 possessions . With respect to defensive rating, the further left the team falls on the chart, the better as that signifies that the team allows fewer points per 100 possessions. As the chart shows, only four teams--Pacers, Thunder, Warriors and Bulls--allow fewer than 100 points per 100 possessions. The Pacers, however, are anomalous--and exceptional--in just how few points they allow per 100 possessions (93.6). To give some scope of this defensive efficacy, the Jazz, who have the worst defensive rating, allow 107.3 points per 100 possessions.

With respect to net rating, a team wants to be above the horizontal bar because that signals they score more points than they allow per 100 possessions . With respect to defensive rating, the further left the team falls on the chart, the better as that signifies that the team allows fewer points per 100 possessions. As the chart shows, only four teams--Pacers, Thunder, Warriors and Bulls--allow fewer than 100 points per 100 possessions. The Pacers, however, are anomalous--and exceptional--in just how few points they allow per 100 possessions (93.6). To give some scope of this defensive efficacy, the Jazz, who have the worst defensive rating, allow 107.3 points per 100 possessions.

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