How Players Deal with a Bad Shooting Night
July 21, 2008
Every player reacts differently to having an off night shooting. Some will let it get to their heads and their entire game falls apart while others are able to focus on getting their teammates involved or hitting the boards. We decided to take a look at how players reacted last season to having a game where they shot under 30% from the field. How were their assist, rebound, and turnover numbers affected? To keep everything relevant, we only looked at players who averaged at least 10 points per game during the season and who had at least 10 games where they shot under 30% from the field.
On a per minute basis, we found that rebounding averages were the same in bad games as during the season, but assists dropped 3% and turnovers increased 4%. So, that wasn’t very interesting, but some of the individual players stood out. Linas Kleiza of the Denver Nuggets must have pleased coach George Karl with his ability to tune out bad shooting performances and increase his rebounding by 29%, his assists by 38%, and decrease his turnovers by 36%! Although he doesn’t record many assists to begin with (1.2 per game), the increase in rebounding is interesting.
If you’re not impressed by Kleiza because he isn’t enough of an impact player, how about Hedo Turkoglu? Hedo increased his rebounds by 24%, his assists by 18%, and decreased his turnovers by 6% in bad shooting performances.
Ok, so now you’re wondering, who are the guys who completely stink up the joint when their shot isn’t falling. Sam Cassell falls into that category with his turnovers going up 22%, rebounds down 37%, and assists down 14%. There’s not much trash to talk when Sam I Am is having a bad game, apparently. Grant Hill, surprisingly, isn’t much better with his turnovers going up 16%, rebounds down 14%, and assists down 34% in bad games. Josh Smith is pretty consistent with his rebounding and assist numbers in bad games, but he turns the ball over, pathetically, 51% more often when he’s having a bad shooting night! (Sorry for posting this while you’re on the free agent market, Josh)
The turnover numbers are the most interesting here so let’s look at the increase in turnovers for the 10 biggest culprits (of letting their poor shooting get to their head):
| J.R. Smith | 61% |
| Josh Smith | 51% |
| Al Horford | 50% |
| Luis Scola | 46% |
| Kirk Hinrich | 44% |
| Paul Pierce | 43% |
| Lamar Odom | 40% |
| Wally Szczerbiak | 39% |
| John Salmons | 35% |
| Andres Nocioni | 31% |
Coach, you might want to bench these guys when their shot isn’t falling (except Pierce, he might get hot)!
The 10 Most Consistent Shooters in the NBA
July 14, 2008
A few weeks back we looked at the most consistent scorers in the NBA and found that Corey Maggette of the LA Clippers topped the list. But, what about the most consistent shooters? Everyone has an off night every now and then, but which players are the most reliable to hit their shots? Once again, we used the coefficient of variation (CV) statistic to determine the answer to that question. We took 3 point shots out of the picture and just looked at 2 pointers. Using a minimum of 50 games played, here’s what we found:
The 10 Most Consistent 2-Point Shooters
| Lebron James | 0.21 |
| Al Jefferson | 0.22 |
| Hakim Warrick | 0.23 |
| Carmelo Anthony | 0.23 |
| Monta Ellis | 0.23 |
| Amare Stoudemire | 0.23 |
| Kevin Garnett | 0.24 |
| Andre Miller | 0.24 |
| Kobe Bryant | 0.25 |
| Dirk Nowitzki | 0.25 |
Surprised not to see more big guys on the list? Overall, bigs post higher shooting percentages than guards, but apparently they aren’t as consistent, although Amare Stoudemire and Kevin Garnett are the notable exceptions here. Hakim Warrick and Monta Ellis are the guys who really stand out on this list though. Warrick was highly consistent at hitting 50% of his shots despite putting up less than 10 shots per game. That’s a difficult thing to do, but it shows that Warrick was ready when his opportunities came. Ellis, though, might even be more impressive when you look at his stats as a whole. He’s only 6′3″, but is able to shoot 53% and shoots it consistently. Of course, being able to dunk like he can has to help your shooting percentage:
Most of the other guys on the list shoot high volumes so it’s a bit easier to get in rhythm and get close to your shooting percentage.
Oh, and the most inconsistent shooter? Louis Williams of the 76ers. Right behind him? Not surprisingly, Jason Kidd.
Who are really the best rebounders and shot blockers?
July 8, 2008
If I were to ask you to name the best rebounders and shotblockers in the NBA, you’d probably rattle off some names like Dwight Howard, Marcus Camby, and other players on the league leaders list, and rightfully so…but, what if they didn’t have their height advantage? What if Dwight Howard was 6′5″? Would he still be a good rebounder? Better than a 6′5″ Nate Robinson? Of course we will never know the actual answer to this, but we can make some guesstimates. I took a look at the height of all the players in the league, their rebounding abilities, and shotblocking prowess to come up with two prediction models. Basically, you tell the model how tall a guy is and it’ll tell you how many rebounds he should get and how many blocks he should get. If you’re Nate Robinson, expectations are low, but if you’re Dwight Howard you’re gonna have to put up some pretty big numbers.
So who did better than expected according to the model? Here are the top 10 rebounders with the percentages indicating how many rebounds they got per 40 minutes compared to how many they were expected to pull down:
| Chuck Hayes | 169% |
| Reggie Evans | 169% |
| Dwight Howard | 167% |
| Jason Kidd | 161% |
| Shawn Marion | 157% |
| Nick Collison | 157% |
| Marcus Camby | 155% |
| Kurt Thomas | 153% |
| Bonzi Wells | 147% |
| David Lee | 147% |
So you can see Dwight Howard is a great rebounder no matter how you look at it. Here are the numbers for blocks:
| Josh Smith | 311% |
| Marcus Camby | 286% |
| Jason Maxiell | 246% |
| Samuel Dalembert | 192% |
| Chris Kaman | 187% |
| Amare Stoudemire | 186% |
| Andray Blatche | 186% |
| Ben Wallace | 183% |
| Dwyane Wade | 176% |
| Delonte West | 176% |
Mostly big guys who you’d expect there, but Jason Maxiell at 6′7″ is impressive. Ben Wallace is still looking good and almost certainly would have topped the list a few years ago. And, don’t underestimate the shotblocking abilities of Dwyane Wade and Delonte West, both whom just qualified for the list with 0.76 Blocks per 40 minutes. The minimum requirements were 0.75 Blocks per 40 minutes and 1500 minutes. For rebounds, they were 5 Rebounds per 40 minutes and 1500 minutes.
Perhaps the most impressive player from these top 10 lists is Marcus Camby, ranking on both lists despite that fact that the model has high expectations for a 6′11″ player such as himself. I’d guess he would drop off the lists if we adjusted for wingspan, though.
If you’re interested, a similar analysis was done for the 2004-05 season at lowpost.net. The analysis over there factored in height as well as body mass index (BMI) and even devotes some special attention to the Knicks. It’s worth checking out.
How good are NBA GMs at drafting players?
June 30, 2008
Gilbert Arenas. Carlos Boozer. Michael Redd.
What do all those players have in common? Well, in case you had forgotten (which would be hard for a serious hoops fan to do considering how constantly we are reminded), they were all selected in the 2nd round of the NBA Draft and are now bonafide stars. Compare those three with the likes of number one overall selections Kwame Brown and Michael Olowokandi and you might think NBA GMs don’t know what the heck they are doing. We agree that some don’t, but after analyzing the drafts since 1995, we have to say they are doing a pretty good job overall.
We took a look at the 2007-08 PER (Player Efficiency Rating) for each player drafted since 1995 and this is what we saw:
That trend line you see is the logarithmic regression. We won’t get into all the details, but basically that just shows you the overall trend as the draft progresses. As you can see, the top picks have paid great dividends despite the occasional busts that we pointed out earlier. The PER drops off considerably throughout the first 10 picks or so, then it levels off. By the time you get out of the draft lottery (past pick #14 or #13 depending on the season), it’s becomes more of a crapshoot. Players picked 15th will do better on average than players picked 30th, but not by much. Considering this, it makes a lot of sense to package a few lower picks and try to trade for a top pick, if possible. What are the odds your GM is really able to acquire a few solid players late in the draft? Doesn’t look likely.
Drafting is not a science, though, and I’m sure a lot of you have noticed some potential flaws in our chart by now. Let’s point them out:
- Looking at 2007-08 PER only is misleading. It’s true we are only looking at the most recent season’s PER so we are discounting the value of players who have seen better days. This makes our chart also a measure of longevity in some respects, but, to be fair, we did look at a chart for players drafted recently (since 2004) and the same pattern emerged. Also, we didn’t look at 1991-1994 because there are few players from those drafts still in the league.
- PER is not an accurate measure of a player’s value. True, there is no one statistic that can tell us the value of every player in the league, but we think PER is pretty good and while some players will be overrated and some underrated, it will average out in the long run.
There are probably some more flaws, but we don’t think the overall pattern would change much no matter how you look at it.
If you were a GM, what would your drafting strategy be?
Are the most efficient players the most consistent?
June 23, 2008
Last week, I asked “Do Consistent Minutes make a Player Consistent?” and the stats revealed an interesting, but not surprising conclusion.
“Looking at those numbers above, one can make a good argument that guys who get the most consistent minutes score more consistently. Even for players who play less than 10 minutes per game, there is a moderate correlation. It’s really intriguing when you look at the 30 or more category. We might assume that guys who are playing 30 or more minutes must be pretty consistent as a group to earn that kind of time, but the numbers show that the more consistent their burn the most consistent their scoring is.”
The topic generated an interesting discussion over at ClutchCity and actually created more questions than answers, prompting me to follow up on it this week. A lot of people were interested in how consistency correlated with efficiency. It would make sense that more efficient players were more consistent, but is this actually the case? I decided to check the correlation between consistency and a few other stats:
- TS% - True Shooting Percentage. This formula adjusts for three-point shots and free throw attempts to give us a more accurate shooting percentage. It is often used as a measure of a player’s efficiency. The formula is PTS / (2 * (FGA + 0.44 * FTA)).
- eFG% - Effective Field Goal Percentage. Like TS%, this metric adjusts for three-point shots, however it does not consider free throw attempts. It is also used to measure a player’s efficiency. The formula is (FG + 0.5 * 3P) / FGA
- FT Rate - Free Throw Rate. This is a measure of a player’s ability to draw fouls. The formula is FTA/FGA.
So, basically we are asking if efficiency and/or the ability to draw fouls help make a player consistent.
After putting together all the numbers, the first thing I did was immediately drop all players who played less than 40 games last season. It just wouldn’t make sense to include players who possibly had very limited playing time. Then, I worked out the correlations dividing the players up into 4 groups based on their Points Per 48 Minutes averages. Note that a lower coefficient of variation indicates a more consistent player so that’s why we get negative correlation numbers. Here’s the data:
| TS% | eFG% | FT Rate | |
| Bottom 25% Pts/48 | -0.46 | -0.41 | 0.06 |
| 25-50% Pts/48 | -0.24 | -0.3 | 0.16 |
| 50-75% Pts/48 | -0.33 | -0.29 | -0.28 |
| Top 25% Pts/48 | -0.11 | 0.07 | -0.21 |
The main thing that stands out here are the trends. Both efficiency measures become less and less correlated with consistency as we look at higher scorers. The opposite happens with FT Rate although neither the correlations nor the trend is as strong. So, what does this mean?
Well, what I see here are two different ways players are consistent. For the top scorers, it is less important that they be efficient in order to be consistent, but more important they draw fouls and get to the line. For the lower scorers, they need to be efficient to be consistent players. This makes sense intuitively. All top scorers have off-nights and the difference between struggling to get 20 points and building a 3-18 brick house with 7 points has a lot to do with the player’s ability to get to the line. The smart, consistent player will put his head down, go to the basket, and draw a foul while the inexperienced scorer will keep heaving jumpers or stop playing altogether. It’s a little bit different for the players who don’t score a whole lot because they don’t get many touches. These guys need to find their own shots by rebounding and being in the right spots to get easy buckets. Not surprisingly then, the players with high efficiency ratings who usually get a lot of easy buckets are also the most consistent among lower scoring players.
The data above doesn’t lead us to an obvious conclusion so this is up for debate. Agree? Disagree?
Do consistent minutes make a player consistent?
June 16, 2008
“He’s their most consistent player.”
It’s a quote you hear rather often watching NBA telecasts. Usually it seems pretty accurate and its hard to argue with, but do our eyes deceive us? I decided to take a look at who the most consistent players in the league really are. And, then I’ll analyze whether or not coaches can make their team more consistent by playing their guys consistent minutes.
First, a word on the methodology. If you ever took statistics, you’ve heard of the standard deviation (SD), and, everyone knows what an average is. Well, if you took even more statistics, you might have heard of the coefficient of variation (CV), which is simply the SD divided by the average. The CV tells us how widely dispersed a sample is, or, on the flip side, how consistent it is. A low CV means the sample is consistent. The useful property of the CV, for our purposes, is that it can be compared between players even when their averages aren’t close (ie. Lebron James‘ scoring consistency can be compared to Kwame Brown’s), and, thus, we can use it to rank every player in the league in terms of consistency.
So, without further ado, here are the 10 most consistent scorers in the 2007-08 regular season:
| Player | PP48 | SD | CV | |
| Corey | Maggette | 29.71 | 6.61 | 0.22 |
| Amare | Stoudemire | 35.58 | 8.23 | 0.23 |
| LeBron | James | 35.76 | 8.44 | 0.24 |
| Andre | Iguodala | 24.11 | 6.13 | 0.25 |
| Carmelo | Anthony | 34.13 | 8.83 | 0.26 |
| Kevin | Garnett | 27.69 | 7.35 | 0.27 |
| Allen | Iverson | 30.62 | 8.14 | 0.27 |
| Kobe | Bryant | 34.89 | 9.32 | 0.27 |
| Dwyane | Wade | 30.18 | 8.10 | 0.27 |
| Yao | Ming | 28.03 | 7.66 | 0.27 |
As you probably would have guessed, the highest scorers are generally the most consistent scorers as they can count on their shot attempts from game to game while role players take what they can get. It would be very unlikely a player could average, say 10 points per game, and consistently do that every game. Kevin Garnett and Andre Iguodala, though, stand out a bit given their lower averages, relatively speaking. If a longer list were listed, you would see that Chris Kaman is the only guy in the top 30 who averages less than 15 PPG.
And, now here are the 10 players who get the most consistent minutes:
| Player | MPG | SD | CV | |
| Richard | Jefferson | 38.98 | 3.81 | 0.10 |
| Al | Jefferson | 35.52 | 3.63 | 0.10 |
| Andre | Iguodala | 39.52 | 4.40 | 0.11 |
| LeBron | James | 40.27 | 4.86 | 0.12 |
| Lamar | Odom | 38.07 | 4.76 | 0.13 |
| Rashard | Lewis | 38.06 | 4.77 | 0.13 |
| Tony | Parker | 33.49 | 4.19 | 0.13 |
| Antawn | Jamison | 38.75 | 4.85 | 0.13 |
| Dirk | Nowitzki | 35.97 | 4.57 | 0.13 |
| Caron | Butler | 39.76 | 5.08 | 0.13 |
So, interestingly, there is an overlap of two players who are on both top 10 lists (Andre Iguodala and Lebron James). Of course these players are both stars and you would expect them to rank in at least the top 30, but it’s curious why other stars aren’t ranking. Kevin Martin, for example, was the 7th leading scorer this season, but ranked only 44th in scoring consistency. Could this possibly have something do to with the fact that he got only the 29th most consistent minutes in the league? Would Kings coach Reggie Theus get more consistency out of his top scorer if he were to give him more consistent minutes? Not surprisingly, the injury-prone Tracy McGrady ranked the least consistent scorer of anyone averaging over 20 PPG.
This all leads us to the question of whether or not coaches would be wise to play their players on a more consistent basis to get more consistent scoring output. What is the correlation between consistency of minutes and consistency of scoring? Correlation, to refresh your memory, is a number between -1 and 1, indicating how closely related two variables are in either a negative or positive direction. A correlation over 0.5 is considered strong in the positive direction.
It is probably not surprising, then, that if we take all the players in the league we get a very strong correlation of 0.79 between consistency of scoring and consistency of minutes. This is a very strong correlation, but not all that useful since we already know that players who don’t play much are less consistent than those who play regularly (in other words, if a guy is so consistent, he’d probably be playing more anyways). It’s a lot more useful to break the players down into groups based on their MPG. So here is what we get:
| MPG | Correlation |
| Less than 10 | 0.37 |
| 10 to 19.99 | 0.60 |
| 20 to 29.99 | 0.51 |
| 30 or more | 0.53 |
Looking at those numbers above, one can make a good argument that guys who get the most consistent minutes score more consistently. Even for players who play less than 10 minutes per game, there is a moderate correlation. It’s really intriguing when you look at the 30 or more category. We might assume that guys who are playing 30 or more minutes must be pretty consistent as a group to earn that kind of time, but the numbers show that the more consistent their burn the most consistent their scoring is.
So, are you surprised by any of the names on the list or the results?
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