Due to some recent developments, I will not be posting updates to Basketball-Statistics.com for the indefinite future. Everything that is on the site will remain. It’s been a blast providing research for you, the readers, to enjoy. However, I have some other commitments now that will prevent me from writing articles. I apologize for leaving just as I was starting to get the NCAA stats rolling, but it is something I must do.
Thank you for reading!
Like I did for last season, I recalculated a few common advanced stats for this season using the play-by-play data. The article is here:
Recently I started contributing to the New York Times’ NBA blog “Off the Dribble.” My first article, which compares Kobe Bryant and LeBron James’s abilities to “make their teammates better,” can be found here:
My apologies for the relative silence at Basketball-Statistics.com during the last few days. I’d like to blame it on the holidays, but that’s actually not the case. I’ve been working on a couple of big things recently, including populating a database of the play-by-play data for every NCAA Division I game played this year. Because of the massive amounts of errors present in the official data, this process has been painfully slow and required a lot of tweaks. I’m just about done, though. The first thing I’d like to do with the data is calculate net plus-minus for every player.
There has been some wonderful work on statistical plus-minus, which estimates plus-minus using box score statistics. However, with the play-by-play data, we can calculate the real thing. Those stats should be released in the next few days (all that is left is to run the numbers). Once that is complete, I can work on creating adjusted plus-minus. So be on the lookout for NCAA plus-minus and other advanced research on college basketball!
The following is part of a weekly series at the Orlando Magic blog, Third Quarter Collapse.
For my previous piece on the Magic, I charted hustle stats such as deflections, loose balls, missed blockouts, etc. While these things are all important, perhaps the area in which hustle is most important is defense. Although it takes more than just good hustle to be a good defender (as a certain Mr. Howard will show us later), effort is one of the keys to being a good defensive team. Therefore, I decided to track defensive plays in last night’s contest between the Magic and the Pacers (this time, I only kept track of the Magic’s stats). I imagine most (if not all) NBA teams track these on their own, as well as companies such as Synergy Sports.
To see the numbers, click the link below:
The rest of this article will explain what those numbers mean. I will also provide a few observations and notes about the contest.
The first column is “Forced Misses.” This is pretty self-explanatory, although I should explain a few things. First, forced misses don’t only occur on an individual’s man-to-man assignment. A help defender that forces a missed shot would receive the credit. Second, I conservatively rewarded a few forced turnovers as forced misses. These were situations in which a player caused his opponent to turn the ball over (through traveling, bobbling the ball, etc.) by applying good pressure and staying in good position.
The second column is “Baskets Allowed.” This is also self-explanatory, with one caveat. If a player made a bad defensive play that eventually led to someone from the opposing team scoring, he was the one credited with an allowed basket. For example, on one play, Jason Williams allowed his man to penetrate into the lane with ease, forcing the Magic to help and rotate. The Pacers swung the ball and ended up with an easy three-pointer. Although Williams’ man did not receive any points or assists for the play, Williams was penalized for allowing the basket.
The third column is “Good Help D.” This occurred when a player came off his man to either help a beaten teammate or to make a good play such as causing a turnover. When Dwight Howard met the opponent in the lane and forced a miss, he was credited with a “Good Help D.” When Ryan Anderson strayed from his man for a second and reached in and knocked the ball loose from another player, he also received credit for good help defense. As you can see by now, many of these statistics are subjective (which is both a great thing and a bad thing).
The fourth column is titled “BB/MD.” This stands for blow-bys/middle drives. This occurred any time a Magic player allowed his man to drive right past him without the use of a screen (in certain cases, when there was a switch on a screen and the new defender allowed the opponent receiving the screen to drive by, a BB/MD was assessed). A BB/MD did not have to result in a made basket to be counted.
The fifth column is titled “Lost Man.” This was recorded every time a player failed to stay on his man, resulting in score. This occurred most frequently in one of two ways: either a player simply wasn’t paying attention and allowed off-the-ball movement (such as a backdoor cut) for a score, or a player failed to chase his man quickly enough through screens.
The sixth and final column containing raw data is “Silly Fouls.” While obviously the most subjective of the six categories, it was generally pretty easy to determine. Fouls that occurred off the ball and away from the play were the biggest culprits.
The rest of the data is computed based on those six categories. I included each player’s minutes played to serve as a reference point. “FG% Allowed” was calculated as follows: Baskets Allowed / (Baskets Allowed + Forced Misses). This statistic does not mean the field goal percentage of the man-to-man assignment. Because Forced Misses and Baskets Allowed are not always credited on a man-to-man basis, FG% Allowed is a bit more complicated. Obviously, a lower percentage is a better percentage.
The final six columns are the six raw categories calculated on a per-minute basis. Like last time, I calculated them in the form of “minutes per stat” as opposed to “stat per minute.” This is to avoid presenting very small numbers. For positive stats such as Forced Misses and Good Help D, a lower number is better (in other words, a player achieves these stats more frequently and therefore in less minutes on average). For negative stats such as Baskets Allowed and BB/MD, a higher number (or no number at all) is better.
Finally, some observations:
I enjoyed tracking the individual defensive performances of the Magic, and I think this type of information sheds a great deal of light on what’s going on beyond the box score. Although I don’t know what Synergy Sports or the Magic track defensively about their players, I imagine data such as this is of interest to them simply because it’s so useful. I plan on doing this again soon.
Just some personal information that I thought should be out there.
A week ago I tracked the hustle plays in a game between the Los Angeles Clippers and the Memphis Grizzlies. Tracking hustle plays is presumably something most, if not every, NBA team does. After all, box scores are pretty limited. Even if we use the play-by-play data to do thorough analysis, it still doesn’t include things such as diving for loose balls, deflections, missed blockouts, etc. But teams would like to know these things, so they must track it themselves.
I decided to track the hustle plays during last Saturday’s game between the Magic and the Golden State Warriors. During the game, I kept track of five things. First, I tracked players going for loose balls. In my experience with a college team, we only record plays where a player dove for a loose ball. But since this is the NBA, and effort is often lacking, I include all plays in which a player ends up with the ball, regardless of whether or not he dove. A second thing I track is drawn charges. You can somewhat glean this from the play-by-play data, but it is much easier to just record it yourself.
Thirdly, I kept track of good sprints. I define these as plays in which a player creates a play for himself or others by sprinting the floor and forcing the defense to adjust. For this game featuring the fast-paced Warriors, I had to be more selective in my criteria or else we’d have a lot of good sprints. A fourth thing I tracked for this game was deflections. This is relatively easy to define and track. Basically it includes any deflection that is not recorded as a steal, rebound, etc. Finally, I kept track of missed blockouts. These were most noticeable when they led to an easy offensive rebound, and they were much more rare in this game than in my first one.
Of course, these aren’t all the hustle plays that players can make. Traditional box score stats such as offensive rebounds and steals often reflect hustle plays. Defense is also largely a product of effort, but that is something I will track another time.
Below is a link to a spreadsheet that contains the hustle stats for the Magic-Warriors game. On the left side of each tab is the raw numbers. On the right side is the per-minute numbers. Instead of presenting them as “statistic per minute,” they are presented as “minute per statistic.” I did this because the numbers are so low. As it turns out, this method is not too difficult to grasp conceptually. For positive statistics such as deflections, a lower number is better (a blank number means the player did not record any deflection at all, which obviously is bad). For negative statistics such as missed blockouts, blank numbers are the best and low numbers are the worst.
I have a few observations about the data:
The Magic did not win this game because they outhustled the Warriors. In terms of effort, both teams were solid and about even. The Magic won, obviously, because of a huge run late in the fourth quarter in which they hit their shots and the Warriors made silly plays.
Up next I’d like to track the defense of the Magic. With a few games in the data set, we may be able to rate the defense of Magic players in other ways besides Defensive Rating, plus-minus, etc.
Making major changes to your team when you are already very, very good appears to be the thing to do in today’s NBA. The Lakers essentially swapped Trevor Ariza for Ron Artest, the Magic swapped Hedo Turkoglu for Vince Carter, and the Cavaliers added Shaquille O’Neal. Each of these teams was among the best in the league last year, and we’ll see how messing with a good thing turns out.
Of course, since this is a Magic blog, I will look at Orlando’s decision to let Hedo Turkoglu walk and trade for Vince Carter. I will be using a number of advanced statistics that, thankfully, I didn’t have to calculate myself. There is a wealth of basketball statistics available on the Internet these days, and everything I will discuss today is publicly available. The numbers I will be using were gathered from BasketballValue.com, my own Composite Score statistics, Basketball-Reference.com, 82games.com, and a new site called Hoopdata.com.
Overall Player Rating Statistics
Let’s start with a cursory glance at overall player ratings for Carter and Turkoglu. With these numbers, Turk fares better in adjusted plus-minus and Composite Score, while Carter has the upper hand in PER and Win Shares. The difference in Composite Score is the most dramatic, and that is mainly due to differences in their Defensive Composite Scores (which I will get into later). There is also a pretty substantial difference in PER, and I think that is a reflection of Carter’s overall production per minute being more high volume than Turkoglu’s production. The fact that Carter had more Win Shares than Turkoglu despite playing on a pretty bad team is quite impressive.
According to the numbers, this is Carter’s biggest advantage. In just amount any offensive metric you use, Carter looks better. He is more efficient and produces more total offense than Turk. Similarly, he had a greater impact on his team’s offense in terms of plus-minus. Offensive Composite Score reflects all of these things.
Here, Turkoglu strikes back. Carter looks below average in just about every category, and this supports his reputation. Turk, on the other hand, recorded numbers well above average in every category. The trickiest part about these comparisons is team context. It is something I’ve mentioned constantly when talking about my Composite Score numbers. Because of the way stats are tracked (at least publicly), it’s very difficult to separate a player’s individual contribution to his defense. How much of this is Hedo’s own doing, and how much of it is due to the fact that Orlando featured a very strong all-around defense? It’s hard to say, but I do think Turkoglu was probably a better defender than Carter.
One of Turkoglu’s biggest benefits to the Magic, and something I thought they may miss, was his ability to create looks for others. This was magnified in the playoffs when the Magic dominated the Cavaliers behind the creativity of Turkoglu. Of course, Carter is no slouch in this area either, and the numbers above reflect this. His Assist Rate was actually higher than Turkoglu’s, and he was able to take better care of the ball in the process. Despite this, 82games.com gave Turk a better “Passing Rating,” although a worse “Hands Rating.” Regardless of the tiny differences on each side, I think it’s safe to say that Turkoglu’s playmaking abilities are no better than Carter’s.
These numbers, which are available at Hoopdata, show what types of shots that the two players assisted on. They are pretty similar across the board. I think it’s interesting that Carter assisted on slightly more shots that were converted at the rim than Turkoglu did, despite the latter playing with one of the best (if not the best) finishers in the game in Dwight Howard. It’ll be interesting to see how these numbers look after this season.
Partly because of his success in the playoffs, Hedo Turkoglu developed the reputation of being a clutch scorer and player. Carter has been a go-to guy late in the game for much of his career, so how do the two compare? Last year, Carter was actually more productive and more efficient shooting-wise than Turkoglu. Both were great from the free throw line and reasonably good playmakers, but the difference in effective field goal percentage was pretty dramatic. Carter’s was above average, while Turkoglu’s was well below average. Most players find it more difficult to hit their shots in crunch time when defenses tighten up, so the fact that Carter actually became more efficient with the game on the line is quite impressive.
Ignoring all of the other players involved (although we definitely should not understate them), did the Magic make the right move by switching from Turkoglu to Carter? VC is better offensively, and two of Turk’s most famous skills, playmaking and clutch play, are performed as well or better by Carter. The only concern is defense, especially since the Magic lost Courtney Lee. However, we don’t know for sure how great of a defender Turkoglu is when he isn’t playing in front of Dwight Howard, so that aspect remains to be seen. All in all, considering Carter’s potential to put them over the top, the other players they acquired, and the amount of money Hedo was demanding, it appears to have been the right move for Orlando.
In my last two pieces of research, I took a look at the impact of superstars on their teammates’ three-point shooting. Specifically, I looked at how often and how efficiently teammates shot three-pointers when a particular superstar is on the court versus when he is off of it. The theory has always been that superstars create open looks for their teammates, and my research for the most part confirmed that.
Unfortunately, I only took a look at ten players. What about the rest of the league? How good do you have to be to have a positive impact on three-point shooting? What kind of impacts do role players have? Do weak offensive players make three-point shooting harder for their teammates because defenders don’t pay them attention?
Today I will provide the data that should answer many of those questions. My methodology for generating this data was the same as before, only on a much larger scale. For each player, I calculated the three-point attempt percentage (three-point attempts divided by total field goal attempts) and three-point percentage for each of his teammates when the given player was on the court and off it. Each of those teammates was then weighted based on their total number of attempts. Finally, I calculated a weighted average for each player. A player’s own three-point shooting was excluded from teammate effects.
In case you didn’t care about any of the stuff in the last paragraph, all you need to know is the following: the table below contains two numbers for each player who saw action in the NBA last year. The first number is the average increase (or decrease) of all teammates’ three-point attempt percentage when the given player is in the game. The second number is the average increase (or decrease) of all teammates’ three-point shooting percentage when the given player is in the game. In both cases, a positive number is generally good.
Before you take a look at the numbers, beware of small sample sizes. Some players may appear to have very positive or very negative impacts on their teammates, but it is most likely due to that player receiving very little playing time or some other confounding factors.
The numbers, courtesy of Google Docs:
Below are some leaders and other interesting categories:
Top Impacts on Three-Point Attempt Percentage (Outliers Removed)
Nothing too shocking as there are a lot of great playmakers at the top of this list (except for Gray). We’ll see if the Magic miss Turkoglu’s playmaking ability, although he benefitted by often playing with Dwight Howard and Rashard Lewis.
Top Impacts on Three-Point Shooting Percentage (Outliers Removed)
Unless these ten seemingly random players have some special abilities we’re not all aware of, I think this list is strong evidence that perhaps a player’s “impact” on his teammates’ three-point shooting efficiency can often be a matter of luck or hidden factors such as common teammates. There are some offensive stars with high impacts in this area (such as Shaquille O’Neal and Chris Paul), but this measure appears to be rather inconsistent.
Top Big Man Impacts on Three-Point Attempt Percentage (Outliers Removed)
Some randomness, although I think Howard at the top is no fluke. Orlando’s offense often runs through him and he’s more than willing to kick out the ball for an open three.
Top Point Guard Impacts on Three-Point Attempt Percentage (Outliers Removed)
Top Swingman Impacts on Three-Point Attempt Percentage (Outliers Removed)
I plan on doing more studies like this in the future, as well as working more with the data I presented here today. There may be some randomness to the numbers but there also may be some relevance, so I’d like to dig deeper.
Today I’m going to unveil some more player ratings using the TPS system I developed recently, which produces customizable rating systems that are adjusted for position. To see last time’s ratings, click here.
The first rating today is called the Workhorse Rating. It consists of three components: PER, Usage Rate, and Assisted Rate (the percentage of a player’s shots that were assisted). It is a player rating system that focuses especially on a player’s ability to generate his own offense without the help of others. This isn’t always a good thing (see: ball hog), but it’s often useful to have a player who can create his own shot. PER is a great all-around player rating system that factors in Usage Rate. The reasons for including the other two components are pretty obvious. The top 10 players in this category are:
And the bottom 10 are:
The next rating is called Defender and Outside Shooter Rating (not very creative but very self-explanatory). It consists of three components: Defensive Composite Score, Three-Point Attempt Percentage, and Three-Point True Shot Percentage. The leaders in this category are (remember they’re adjusted for position):
There are some names you’d expect (Bowen, Battier, Artest, and even Wallace) with some others you wouldn’t expect (Ilgauskas, Lewis, Nelson). The bottom 10 in this category are:
The third and final rating I will discuss today is my Interior Defender Rating. This one is also self-explanatory. It consists of Defensive Composite Score and Rebound Rate. You can be a good rebounder without being a good defender (cough, Carlos Boozer, cough), but someone who is good in both usually is a good interior defender. Notice that I didn’t include blocks. It is not one of the 25 components in the TPS system, and I’ve done studies that question the importance of them. Keeping the position adjustments in mind, here are the top 10:
I don’t know about Chris Paul in there, but I suppose he is a good rebounder for his position. The bottom 10 in the league are:
To see the numbers for every player, go to:
Stay tuned to Basketball-Statistics.com for more TPS Reports.