General
Converting PER to Statistical Offensive Adjusted Plus-Minus
Last year, I followed Eli Witus’ instructions and Aaron Barzilai’s data-and-method to run 2006-2009 adjusted plus-minus (APM) numbers, split into offensive and defensive APM.
My results are a little different than others’ because I removed garbage time (defined as point spread > 10 +minutes left in game) and left each season unweighted in order to calculate a Statistical more easily.
Here’s the output, split into 2006-2009 and 2007-2009. combining0609-and-0709.xlsx
FROM APM TO STATISTICAL APM
I’ve always been interested in figuring out what made an offense work, but the main reason I tried making a Statistical is because I didn’t like PER. I didn’t understand it, I didn’t like how hard it was to calculate, and I figured a stat had to be organic to have meaning. That was before I tried to beat it.
Awhile back, I created a Statistical called EOPM by regressing Ilardi/Barzilai’s 5-year weighted APM numbers against rate data from basketball-reference: TS%, AST%, OREB%, TOV%, and USG%. I figured the ease of use (copying and pasting the whole row of advanced stats into an excel calculator) would outweigh any inaccuracies, as long is it was close.
But when I ran the r^2, it showed just .52, lower than Rosenbaum’s .57 from years back. There were a few obvious problems with my method:
1) I used unweighed rates while Ilardi/Barzilai calcuated their APM numbers by giving extra weight to recent years. I wasn’t sure how to quickly and accurately weight rate stats, so I lazily just left them unweighted.
2) I just used 5 rates. Obviously that left a lot out.
3) I used TOV%, a stat I’ve come to dislike since. Jason Collins and Dennis Rodman had enormous TOV%’s despite low Per36 turnovers because they rarely shot the ball.
EOPM didn’t work how I wanted. But with my new unweighted APM data in hand, I decided to create a new EOPM using Rosenbaum’s Per40 stats idea. I tried to improve it further by adjusting for estimated Pace and including TS%. In all, I made the inputs TS%, and Per36 pace adjusted (Per36 * 100/estimatedpace) OREB, DREB, AST, STL, BLK, TOV, PF and PTS.
The Formula is: EOPM = -17.173 + 18.131 * TS% + 0.317 * OREB + .190 * DREB + .716 *AST + .334 * STL - .324 * BLK - 1.447* TOV - .280 * PF + .461* PTS
with an r^2 of .723. Pretty good! Next step, comparing it to PER. (Here’s my EOPM work: eopmdata.xlsx.)
COMPARING EOPM WITH PER
I figured, for EOPM to be useful, it should accurately find Team Offensive Ratings for past years given player EOPMs and minutes played. By multiplying each player’s EOPM by his % of minutes played during that season, then summing those products for each team, I hoped to nail each team’s Offensive Rating within a few tenths of a point.
I performed the calculations for every 2009 NBA team, regressed my estimated O-Ratings against the actual O-Ratings and got an r^2 I liked: .800. Then I did the same using PER: .914. Wait, what?
Whoa. (Here’s my work: per-vs-eopm.xlsx)
After a bit of headscratching, I found that Fit is a big reason why adding EOPMs doesn’t work as well as PER. For example, if a team is extremely good at offensive rebounding, the effect on each player’s EOPM is tiny, but for the team it leads to more shots and a higher ORating. (When including offensive rebound rate in the team regression, EOPM’s r^2 jumped to .893.)
PER though, already considers team rebounding in its calculation, so its r^2 is unaffected when including team offensive rebound rate (jumps to .915).
So Hollinger beat me to a pulp. And as a result, I’ve started using PER a lot more. But sometimes I like to balance it out a bit…
COMPARING PER TO EOPM
Even though PER was a more accurate determinant of Team Offensive Rating, I still had reason to think EOPM fairly judged performance. After all, the numbers are organic, and when adjusted for fit, its correlation nearly matches PER’s.
So my next step was seeing how the stats differed. I regressed PER against EOPM, and the result was this formula. EOPM = -8.4+.56* PER, with an r^2 of .736.
First off, that r^2 seemed oddly low for two stats that estimated the same thing, and I figured out why once I calculated ExpectedEOPM for each player using that formula. Here were the players (who played >5000 minutes from 2006-2009) whose ExpectedEOPM differed most from their actual EOPM.
Biggest Overestimates:
| Name | PER | ExpectedEOPM | EOPM | Difference |
|---|---|---|---|---|
| DeSagana Diop | 11.1 | -2.2 | -6.6 | 4.4 |
| Ben Wallace | 14.5 | -0.3 | -4.0 | 3.7 |
| Samuel Dalembert | 14.9 | -0.1 | -3.5 | 3.4 |
| Darko Milicic | 13.1 | -1.1 | -4.4 | 3.3 |
| Marcus Camby | 18.6 | 2.0 | -1.2 | 3.2 |
| Dwight Howard | 22.1 | 3.9 | 0.8 | 3.1 |
| Joel Przybilla | 13.5 | -0.9 | -3.8 | 2.9 |
| Kendrick Perkins | 12.4 | -1.5 | -4.3 | 2.8 |
| Josh Smith | 17.6 | 1.4 | -1.4 | 2.8 |
| Yao Ming | 24.1 | 5.0 | 2.3 | 2.7 |
| Chris Kaman | 15.0 | 0.0 | -2.7 | 2.6 |
| Jason Collins | 4.0 | -6.2 | -8.8 | 2.6 |
Biggest Underestimates:
| Name | PER | ExpectedEOPM | EOPM | Difference |
|---|---|---|---|---|
| Jose Calderon | 18.0 | 1.6 | 4.1 | -2.4 |
| Deron Williams | 18.0 | 1.6 | 4.0 | -2.4 |
| Steve Nash | 22.0 | 3.9 | 6.2 | -2.3 |
| Chauncey Billups | 21.8 | 3.7 | 5.7 | -1.9 |
| Mike Bibby | 16.4 | 0.7 | 2.5 | -1.7 |
| Michael Redd | 20.3 | 2.9 | 4.6 | -1.7 |
| Raja Bell | 11.6 | -1.9 | -0.4 | -1.6 |
| Eddie House | 14.6 | -0.3 | 1.3 | -1.6 |
| Antonio Daniels | 13.8 | -0.7 | 0.8 | -1.5 |
| Steve Blake | 12.6 | -1.4 | 0.1 | -1.5 |
| Leandro Barbosa | 17.2 | 1.2 | 2.7 | -1.5 |
| Mike James | 14.6 | -0.3 | 1.2 | -1.5 |
See a pattern? If EOPM is to be believed, Hollinger overestimated big man stats (blocks and rebounds) to increase the PER of frontcourt players. For a possible answer why, take a look at this chart.
Average APMs by position among players who played >5000 minutes between 2006-2009:
| Off | Def | |
|---|---|---|
| PG | +1.2 | -0.5 |
| SG | +1.0 | -0.8 |
| SF | +0.5 | -0.2 |
| PF | +0.4 | +0.9 |
| C | -1.6 | +1.5 |
One axiom of the NBA game is that guards generally make a bigger impact offensively because they have the ball in their hands the most, and PFs/Cs make a bigger impact defensively because they defend more shots by playing in the paint, and the chart seems to support that.
Now look at the EOPM and PER of the same sample of players:
| EOPM | PER | |
|---|---|---|
| PG | +1.4 | 15.6 |
| SG | +1.1 | 15.5 |
| SF | +0.3 | 15.1 |
| PF | +0.2 | 16.6 |
| C | -1.4 | 15.9 |
While EOPM shows the same positional correlation as APM, PER shows the positions as equal, possibly even with a slight offensive edge to inside players.
I’m guessing Hollinger did this in an attempt to sell PER as an all-around player evaluator. In his articles, he often analyzes transcations using only PER. He created his Expected Wins Added statistic based purely off PER. There’s even a Hollinger Analysis feature on the ESPN Trade Machine that estimates wins added using only PER.
But PER’s .70 correlation with Team Net Rating (Offensive Rating minus Defensive Rating) seems largely attributed to its ridiculous .914 correlation with offense. For comparison, EOPM correlates at .69 with Team Net Rating.
By overrating Big Man categories (blocks and rebounds), Hollinger levels the positional playing field by accounting for the defensive impact of power forwards and centers. Interestingly, that change doesn’t affect PER’s correlation with Team Offensive Rating because NBA lineups are generally built the same way, with two definite guards and two definite big men. Therefore, each team receives roughly the same amount of over- and under-estimation.
CONVERTING PER TO EOPM
In spite of that oddity, I think PER’s pretty great. Not only does it correlate impressively with Team Offensive Rating, but it’s widely available, and it’s easy to find PER across seasons at basketball-reference.com.
But what if you wanted to quickly estimate a player’s Offensive Statistical APM, and filter out Hollinger’s big man overestimates?
Remember that .736 correlation between PER and EOPM? Insert Rebounds Per 36 and Blocks Per 36 into the regression, and the correlation skyrockets to .963, with the following formula:
EOPM (estimated) = -7.5 + .63 * PER - .19 * Rebounds36 - 1.22 * Blocks36
I wish it were a little easier to calculate, but it’s not bad.
General
Vinny Del Negro’s Offense
VDN was an elite midrange jumpshooter as a player. With Tim Duncan and David Robinson clogging the lane, VDN kept defenses honest with a career 49.5 eFG% over 12 seasons. But despite his shooting ability, he made just 0.3 threes and 1.3 free throws per game, leaving his career TS% at a mediocre 53.4% . Perhaps he isn’t aware of that last part, because as coach of the Bulls, he’s brought midrange shooting to his offense. Blah.
Hoopdata.com tracks shot selection like NBA HotSpots used to. According to them, NBA teams averaged 40.0 two-point jumpers per 100 possessions last year, accounting for 40.1% of total scoring attempts. The Bulls’ 42.2 and 41.9% ranked 10th and 11th highest respectively. This season, without 3pt shooters Ben Gordon, Andres Nocioni and Larry Hughes, VDN has increased 2pt jumpshots to 50.6 per 100 possessions and 50.5% of scoring attempts, both leading the league.
Chicago media has justified it by saying Vinny is working with what he’s got. It’s true in a way: Luol Deng, Derrick Rose, Brad Miller and Taj Gibson take few 3s or FTs. Vinny tried the three early on, but with the Bulls making just 26.2%, he’s scaled it back to 10.7 attempts over the past six games. The league average is 17.7.
It’s nothing new to argue that an excess of 2pt jumpshots is a bad idea. Just 40.5% of 2pt jumpers shot last year went in, falling well short of the league’s 54.4 TS%. Advanced game charting presents more bad news: 82games says just 7% of all shooting fouls occur on 2pt jumpshots and the Rockets’ Eli Witus says that the 2pt jumpshot is the shot least likely to be offensively rebounded. The data shows that offenses relying heavily on the 2pt shot would be better off penetrating, or even easier, taking a couple steps back to shoot a 3. I want to focus on the jumpshots here, because it’s easier to shift 2pt jumpshots to 3s than to layups.
For the Bulls, there’s little incentive to keep doing what they’re doing. Their shot selection is so bad that even if they shot league average from each location (60.5% inside, 40.1% on 2pt jumpers, 34.7% on 3s), their TS% would be just 51.3%, ranking them 27th in the NBA. Add in their poor shooting, and an ugly 96.9 Offensive Rating results.
But if you’re Del Negro, what can you do? Your team is shooting poorly from 2s, but they’re making just 26.2% 3s. Is there another option?
To try to answer this, I’ll define Percentage of Jumpers as the percentage of your team’s jumpshots that are two-pointers. The league average this year and last was 67%. The Bulls are currently at 80%. What would happen if the Bulls moved to 67%, and the field goal percentages stayed the same?
Using Excel, I ran 100,000 simulations of the following matchup: Team A takes 80 two-pointers and 20 threes each game, and Team B takes 67 two-pointers and 33 threes each game. To start, each 2pt shot has a 39.1% chance of going in, and each 3pt shot has a 26.2% chance of going in - the current rates for the Bulls.
The results:
| 100,000 Simulations, 80/20 ratio vs. 67/33 ratio 2pt jumpshot FG% = 39.1%, 3pt jumpshot FG% = 26.2% |
|||
| Team | Win% | PPG | StDev |
| Team A (80 2s) | 50.0% | 78.30 | 10.50 |
| Team B (67 2s) | 50.0% | 78.32 | 11.02 |
Conventional wisdom says that taking more 3s at a 26.2% rate is a bad idea. At the very least, the game to game variability would be chaotic. But the math shows differently. Even with a 26.2 3pt%, the Bulls can maintain their winning% by taking more threes, average the same PPG, and barely affect their StDev. With the offensive rebounding increase and the potential for improved floor spacing, this is likely at least a wash scenario for a team in need of a spark offensively.
But the added benefit is, due to practice or simply having more chances to regain their shooting stroke, their 3pt% should improve over time. After all, the lowest team 3pt% in the past five years was 31.2% by the ‘05 Hawks, so 26.2% is probably an anomaly. Here’s how the simulations play out if the Bulls make 29% of threes:
| 100,000 Simulations, 80/20 ratio vs. 67/33 ratio 2pt jumpshot FG% = 39.1%, 3pt jumpshot FG% = 29.0% |
|||
| Team | Win% | PPG | StDev |
| Team A (80 2s) | 47.0% | 79.94 | 10.65 |
| Team B (67 2s) | 53.0% | 81.08 | 11.18 |
Here we see a distinct advantage in Win% and in PPG. Moving to the 31.2% the lowly Hawks shot a few years back:
| 100,000 Simulations, 80/20 ratio vs. 67/33 ratio 2pt jumpshot FG% = 39.1%, 3pt jumpshot FG% = 31.2% |
|||
| Team | Win% | PPG | StDev |
| Team A (80 2s) | 44.9% | 81.29 | 10.70 |
| Team B (67 2s) | 55.1% | 83.26 | 11.34 |
The PPG difference of 1.97 per 100 jumpshots translates to about 1.25 per 63 jumpshots, which is the Bulls’ per game average. 55.1% of the time they’d score more points than they would taking 80% 2pt jumpers. Over the course of the season, that shift should add about 3 extra wins.
Personally, I think the Bulls might have better 3pt shooters than the media gives them credit for. Kirk Hinrich and John Salmons both made over 40% last year. Luol Deng leads the league with 8.8 jumpshots per game from 16-23 feet, and makes 38%. Derrick Rose makes 41% of his 6.4 per game, and both their percentages are consistent with last year. If Deng and Rose step back, can they hit 30% of their 3s? It still wouldn’t be efficient, but the numbers say it would improve the Bulls offense. If their shooting more 3s also helps them improve, all the better.
Fun With Numbers
EOPM - Estimating APM with rates
(This is an old, crappier version of EOPM kept for nostalgia. Click this link for the updated EOPM.)
Last year, Steve Ilardi and Aaron Barzilai created the most accurate Adjusted Plus-Minus (APM) numbers to date. For those unfamiliar with Adjusted Plus-Minus, it takes as input every 5-man lineup in the NBA, the opponent lineups they faced, and the point differential when those 10 players are on the court - for every rotation in the NBA for a given season. By employing multivariable regressions that are well above my abilites, Iladri and Barzilai used that data to separate each player from the effects of his teammates and opponents. What we’re left with is every player’s net effect, offensively and defensively, on the game’s score per 100 possessions.
I really like APM. It’s the only statistic I know of that can account for intangibles - elements of a player’s game that affect the score but don’t show up in a box score - such as tipping rebounds to teammates, forcing your opponent to take bad shots, boxing out your opponent’s rebounder, setting a good pick, and hustle plays.
That’s not to say APM is perfect. Read through Ilardi’s summary of his method, and you’ll see that APM isn’t exact. Because players play in a limited number of different lineups each year, it’s impossible to completely separate a player from every other factor. So each player’s APM comes with a level of possible error. Its other flaw is it doesn’t account for style of play. We saw last year that Shaquille O’Neal might be best suited for a slow offense (he struggled with the Suns, and the Suns are struggling again this year), so his APM might underestimate his value on a slower paced team.
As a result of those flaws, the APM for each player can vary uncomfortably from year to year. Back in 2005, Steve Rosenbaum (the creator of the first version of APM) attempted to minimize that variation by trying to find a relationship between Box Score Statistics and APM. Rosenbaum used a linear regression to fit per-40 minute statistics to his 2005 APM data, and he wrote his findings here.
EOPM (Expected Offensive Adjusted Plus Minus) - My Attempt at a Statistical Plus/Minus
I think I can improve on Rosenbaum’s Statistical APM in two ways. First, he fit his data to 2005 APM numbers, which had higher error levels across the board than the 2007 Ilardi APM numbers I’m using. Second, he used Per 40 Minute boxscore totals - rebounds per 40, assists per 40, steals per 40, blocks per 40 - which 1) don’t account for pace, or number of available rebounds, or number of available assists, and 2) leave for some illogical conclusions, such as “Players who go to the line more, holding the other variables constant, tend to be more effective on offense and defense. In fact, the effect is larger on defense.” While I don’t doubt Rosenbaum found a correlation between FTA and defensive APM, I don’t see how to apply it in projections. (Devin Harris dramatically increased his FTA per 40 this season, does that mean he became a better defender?)
I’ve fixed those issues in two ways. First, I’ve decided not to estimate defensive ability - there’s not enough data in a boxscore (steals, blocks, fouls, and that’s it) to logically rate defense. To me, it doesn’t make sense that a higher free throw rate would cause a higher defensive APM. And (another Rosenbaum conclusion) it doesn’t make sense to me that a higher steal rate would cause a higher offensive APM. So I’ve only used categories that logically correlate to offense. Second, I’ve used rates - TS%, AST%, OREB%, TOV%, USG% - instead of per 40 numbers to account for available opportunities and the size of the impact a player makes on his team’s offense (AST% and USG% especially). With those changes, EOPM has a big benefit to me that Statistical Plus-Minus doesn’t have: 1) It focuses more on what a player means to his team, based on his role on the team, rather than a player’s effect in a vacuum, and 2) because it uses just five logical inputs, EOPM is easy to understand, calculate, and use in deeper analysis.
But does it mean anything? Using every player who played at least half his team’s minus last year, I ran a regression using Ilardi’s 2007-2008 Offensive APM data as the dependent variable and a player’s TS%, OREB%, AST%, TOV%, and USG% (copied from basketball-reference, so TS% is between 0 and 1, the others are between 0 and 100) as the independent variables. The formula my Excel spreadsheet spit back is as follows.
Expected OPM = -18.2057+30.2201*TS%+0.128564*OREB%+0.183697*AST%-0.31078*TOV%+0.136568*USG%
Here are the EOPMs (click to view html spreadsheet) for every qualified player in the 2007-2008 season along with his actual Offensive APM and the difference between the two numbers. You can download the Excel spreadsheet below where you can tinker with the data.
EOPM Spreadsheet (click the link and save as)
A few notes:
1) The average per-player difference between Expected OPM and actual Offensive APM was 1.693, meaning if a player’s Expected OPM is +3.0, you can be fairly certain that his actual Offensive APM is between +1.3 and +4.7 (APM error rate aside). For a 5-variable system, I think that’s surprisingly accurate.
2) Some players were off by as much as 4 to 5 points, which on the surface isn’t good. But a closer look suggests that Expected OPM may have identified which players had an inflated or deflated Offensive APM because of APM error. Below are the players with the highest differential.
| Highest Differential: Offensive APM - EOPM | |||
| Player | Offensive APM | EOPM | Difference |
| Jason Kidd | +5.98 | +0.59 | -5.39 |
| Jarrett Jack | -5.48 | -0.45 | +5.03 |
| Rajon Rondo | -4.20 | +0.68 | +4.88 |
| Jamario Moon | +3.88 | -0.53 | -4.41 |
| David West | -3.14 | +1.26 | +4.39 |
| Lamar Odom | -3.53 | +0.76 | +4.29 |
| Paul Pierce | +7.33 | +3.10 | -4.22 |
| Kobe Bryant | +8.96 | +4.80 | -4.15 |
| Tony Parker | +0.67 | +4.73 | +4.06 |
| Devin Harris | +6.89 | +3.05 | -3.84 |
| Steve Nash | +10.01 | +6.28 | -3.73 |
| Luol Deng | +4.52 | +1.00 | -3.52 |
| Andrei Kirilenko | +4.28 | +0.95 | -3.33 |
| Peja Stojakovic | +5.05 | +1.77 | -3.28 |
| Andre Iguodala | -1.56 | +1.69 | +3.25 |
Jamario Moon, Luol Deng, Andrei Kirilenko, and Peja Stojakovic all ranked among the Top 25 Offensive players in basketball last year according to Offensive APM, yet none were their team’s 1st or 2nd offensive option. And maybe their rankings are accurate, but they sure seem off to me. And while Steve Nash and Devin Harris played well in ‘07-’08, I don’t think many would’ve ranked them as the #1 and #6 offensive players in basketball. EOPM adjusted accoringly.
On the flip side, Offensive APM rated Jarrett Jack, Chris Kaman, Rajon Rondo, and David West as four of the worst offensive players in basketball. None of them were offensive stars last year, but they certainly didn’t seem to be in Bruce Bowen / Ben Wallace territory. David West’s Offensive APM rated 8.19 points below immoble teammate Peja Stojakovic - that especially didn’t seem right. Again, EOPM adjusted accordingly.
Maybe I’m (very) biased, but EOPM seems to pass the validity test moreso than Offensive APM, or PER, or ORtg, or any other standalone stat I’ve seen. 27 of the top 31 players on the list have been all-stars, and the other four (Jose Calderon, Deron Williams, Jason Terry, Andre Miller) had years deserving of consideration on playoff teams. Similarly, the names at the bottom - Ben Wallace, Jeff Green, Bruce Bowen, Sam Dalembert, Marcus Camby, Drew Gooden, Brendan Haywood - have gained a reputation as non-scorers, inefficient shooters and/or turnover machines.
3) To get a sense of which factors have the strongest effect on EOPM, I took the 1st and 3rd quartile rates of the players I analyzed (there are 130 players in my database, so I found the 32nd and 98th ranked players for each category), found the difference between them, then multiplied that difference by the rate’s coefficient in the EOPM formula.
| Which Rates Impact EOPM Most? | |||||
| Statistic | Coefficient | 25th Percent Rate | 75th Percent Rate | Spread | Spread * EOPM |
| TS% | +30.2201 | .576 | .529 | .047 | 1.42 |
| OREB% | +0.1286 | 7.3 | 2.2 | 5.1 | 0.66 |
| AST% | +.18369 | 22.0 | 8.9 | 13.1 | 2.41 |
| TOV% | -0.3108 | 10.3 | 14.5 | 4.1 | 1.27 |
| USG% | +.1366 | 25.6 | 18.4 | 7.2 | 0.98 |
That’s not a great way of showing how much a rate can affect EOPM - AST% can get to over 50% (Chris Paul, John Stockton), and four players had a USG% over 30% last season (LeBron, Kobe, McGrady, Carmelo). But either way, AST% stands above the others, and I find that interesting. For decades, the adage was “You need a big man to win a championship”, and I figured part of the reason was that a quality post scorer means more to an offense than a quality wing player or point guard. But Ilardi’s Offensive APM data shows otherwise. Several all-star caliber big men had surprisingly low Offensive APM numbers last season - Dwight Howard (+2.78), Shaquille O’Neal (+1.12), Andruw Bynum (+1.00), Yao Ming (+0.21), Al Jefferson (-0.07).
On the other hand, the best passing big men in the league had strong Offensive APMs - Kevin Garnett (+6.88), Tim Duncan (+4.39), Brad Miller (+2.08). That makes me rethink what a interior-scorer actually does to impact an offense, and “ability to pass” and “what does an assist actually mean” are two things I’ll be exploring in the coming weeks.
I also want to take a look at using EOPM to project team success, both in the regular season and playoffs, in much the same way Neil Paine did at the basketball-reference.com blog using Rosenbaum’s method. And I’m going to look at breaking EOPM down by position. The difference between Offensive APM and EOPM for point guards is higher than I want it to be (the error is partially due to the huge range in PG Offensive APM totals, from Kidd’s +10.01 to Jack’s -5.48), and perhaps there’s a way to lower that difference by accounting more for how a player contributes to his team.
For now, feel free to download the spreadsheet I used EOPM Spreadsheet (click the link and save as) if you’d like to play around with the numbers and knock the crap out of my method :) And if you’re just curious about a specific player’s EOPM, you can find the EOPM of any player in history by typing their rate data (straight from their basketball-reference page in the “Advanced” section) into the EOPM calculator below. Make sure TS% is between 0 and 1 (example: .552), and the other rates are between 0 and 100 (example 20.6).
Player of the Day: Chris Gatling, -0.15 career EOPM
Fun With Numbers
What’s a Good Shot? Part I of II
NBA.com’s HotSpots breaks down field goal shooting by location of shot attempt. Here’s the HotSpots for my Bulls this season:
Chicago Bulls HotSpots Shot Chart, 2008-2009 Season
For those seeing HotSpots for the first time, the inner semi-circle statistic represents shots that were scored around the basket. The stats outside the biggest arc represent 3 pointers. And the Bulls are suprisingly really good at those, especially the straight-on 3.
The numbers aren’t perfect. As of today basketball-reference has the Bulls shooting 367/974 from 3-point range, while HotSpots has them at 365/956. But the numbers are close enough across the board to get a sense of what’s what. So let’s take a look at league-wide data, which I’ve converted into eFG% (FG% that awards an extra point for 3’s) by zone. Below is the data for every shot taken in the NBA this season.
Combined Shot Chart of Every NBA Team, 2008-2009 Season
Organizing that data by distance from the hoop:
| 3-pointers: | 12398/33430 | 57.1% eFG |
| Long-twos: | 13946/34278 | 40.1% eFG |
| Short-jumpers: | 8699/22132 | 39.3% eFG |
| Close-range: | 34030/60670 | 56.1% eFG |
(Note: basketball-reference has league-wide 3PT% at 36.6%, which correlates to a 54.9% eFG. I’ll be using that 54.9% in the next post.)
37.5% of NBA field goal attempts are from “mid-range” (long-twos + short jumpers), yet eFG% on those shots is under 40%. And you’d be hard-pressed to find anybody scoring efficiently from that range - Ray Allen is the best I could find, and he’s hitting at a 52.2% clip.
What about drawing fouls from that range? According to 82games.com, shooting fouls occur on only 2% of mid-range field goal attempts compared to about 17% on close-range attempts.
Makes you wonder why offenses would ever settle for a mid-range shot, much less settle 37.5% of the time. It would make sense that the best offensive teams take a high ratio of shots from efficient zones compared to inefficient zones. We’ll test it out next Monday.
Player of the day: Uwe Blab, career high 46.8 eFG% in 1986