Shake Charts: Measuring and Visualizing (In)Consistency
It's amazing (kinda creepy sometimes) how Dave and I approach the same topic at the same time without even mentioning it to each other. This morning, Dave took up the topic of roster management and concluded at the end of his piece that a key problem as currently constructed is the inconsistency of the role players. Dave wrote...
Having Jerryd Bayless dominate a couple games, Rudy Fernandez set a rookie record for threes, Martell Webster churn out awe-inspiring quarters, and Travis Outlaw hit buzzer-beaters certainly makes the team more exciting. In the long run having it all happen on the same team, a team which lacks consistent performances from any of said players, probably doesn't make the team better. Eventually we need fewer guys to get excited about (that kind of excited, anyway) and more guys to depend on. Clockwork predictable .700 ball beats randomly exciting .550 ball every time.
That elusive "clockwork predictable" basketball is valued here in Portland as much as it is anywhere else in the league. With a slow-down, ball-control, super-efficient offense led by two uber-reliable players in Brandon Roy and LaMarcus Aldridge, consistency is perhaps the number one attribute the coaching staff and management is looking for from the rest of the roster.
"Consistent" (or "Inconsistent") is one of the most used adjectives to describe Blazers players. I'm too embarrassed to go back through my media row reports to see how often I use it. If almost any player at any time follows up a 20 point game with an o-fer the first word that comes out will be "inconsistent." Often, other contributions such as defense and rebounding will find themselves washed away in the inconsistency talk as well. That's one reason Nate McMillan will regularly go out of his way to praise those efforts from players that perhaps weren't hitting from the field.
While labeling a player "inconsistent" can be a crutch, there is no doubt that some players bring it every night, some simply don't and everyone else falls on a continuum in between. My goal in this post is to explore scoring (in)consistency for the Trail Blazers by quantifying it and then visualizing it. From there, we'll take a brief look at the relationship between scoring consistency and playing time consistency.
If you're bored already, stay with me. I'm about to put some pretty charts on your face. And take a look at the Camby trade from a different perspective. And rank the Blazers in terms of scoring consistency this year (and last year). And show you the correlation between consistency of minutes and consistency of scoring for the Blazers over the last two years. And and and and just click through the jump and read this please.
-- Ben Golliver | benjamin.golliver@gmail.com | Twitter
What Is "Shake?"
"Shake" is a term I made up to refer to a player's scoring (in)consistency. It is essentially a measure of a player's variance in scoring. For those interested, here's how I calculate it.
- Calculate the absolute value of the difference between a player's one game point total and his season average. If a player averages 10 points per game and he scores either 8 or 12 points, the number is 2.
- Do this for every game of a season or, in this case, through the 2009-2010 All Star break. Add them all up to get "Total Shake." Brandon Roy is the Blazers' most consistent scorer. His total shake through the All Star break was 254 points.
- Divide the "Total Shake" by the number of games played. For Roy, this would be 254/40 or 6.35. This becomes Roy's "Average Shake Per Game."
- Take the player's average shake per game and divide it by his average points per game. In Roy's case, that's 6.35 / 23.1. This step yields a percentage. In Roy's case: 27.49%. This is what I call his "Shake."
Shake therefore represents the relationship between a player's scoring variance and his scoring average. If a player scored 10 points (or 20 points, or 30 points) every single game his Shake would be 0%, perfectly consistent. If he alternated scoring 0 points 41 times and 20 points 41 times over the course of a season, his shake would be 100%, perfectly inconsistent.
What's a Good Shake?
Generally speaking, if you're an NBA coach you want a relatively low "Shake" from your players. Nate McMillan probably has dreams during which guys like Rudy Fernandez, Martell Webster and Jerryd Bayless turn into robots that produce exactly the same amount of points every single night.
As you might expect, a Shake of 27.49% for Brandon Roy is very good (although not as good as his Shake last year). Here's a little table to give you some perspective.
Some notes on this:
- Kevin Durant is currently going through an historically consistent stretch of scoring. He's basically an outlier in and of himself.
- From the numbers I've run so far, a shake between 20% and 30% for a go-to scorer is a pretty good sweet spot. It's enough room to account for the occasional blast-off game but also includes a baseline of stability that reflects an inability to avoid too many debacles.
Shake and This Year's Blazers
Now that we've defined Shake and seen how some of the league's best scorers stack up, let's take a look at this year's Blazers. Here are the Shake ratings for the top 8 Blazers in terms of minutes played.
Some notes on this:
- If you like to praise Roy's steady play, it's difficult to dog LaMarcus Aldridge for being inconsistent. The two players are surprisingly close when it comes to Shake and Aldridge is right in that sweet spot zone that I mentioned earlier. For a guy that's had to change positions, adapt to new roles and play heavy minutes, it's downright remarkable. If I had to grade Aldridge's season to date I would give him no lower than an A.
- Andre Miller has been called inconsistent by me a number of times and his Shake supports that but only to a degree. Amazingly, he's a clear cut #3 on this team in terms of consistency. I wasn't necessarily expecting that going into this exercise.
- Finally, you can see the bane of Nate McMillan's existence this year. 5 of his top 8 players are all over the map when it comes to consistency of points production. You might be thinking: Well, he should stop yanking their minutes around!!!! We'll look at the relationship between playing time and scoring later. But for now the takeaway is that McMillan has been dealing with a lot of scoring shakiness this season.
Numerically we can see the impact of all the injuries this season. Look how many games guys played last year and look how they responded. Five Blazers scored more consistently last year than Andre Miller (3rd this year) is scoring this year. The top 7 last year were more consistent than Blake, who was 4th this year. Even Brandon Roy and LaMarcus Aldridge have been slightly less consistent this year than last year.
A note: as the number of games increases, it's reasonable to expect Shake to decrease to some degree. As the last third of the season plays out, we'll see whether some of these guys find an equilibrium and increase their consistency.
Shake and Outlaw and Blake
I found it extremely interesting that Travis Outlaw and Steve Blake were last year's 3rd and 4th most consistent scorers respectively and that both were traded this week.
Obviously, Outlaw was lost for most of the season this year due to injury. After looking at his consistency of production last year and the team's vacuum of consistency this year, I believe he would have lasted the full season here in Portland had he not been injured and performed close to the standard he set last year. He would have likely been no worse than the 4th most consistent scoring option and he would have had plenty of minutes to shine. Keeping this in mind while watching his Clippers press conference is even more painful. This, more than anything, helps put into perspective how cruel the "Breaks of the Game" can be.
As for Blake, "Steady Steve" simply was no more. While he was the Blazers' fourth most consistent option last year and remained so this year, his shake increased from 39.55% to 55.92%, a drop of more than 16%. That's pretty big, especially when you consider his points per game average dropped from 11 to 7.6 as well.
On Tuesday night, I asked Kevin Pritchard for his explanation of Blake's drop in production this year. "I think it is a change of roles," Pritchard told me. "We felt like he played well for us and I give a lot of credit to him. He's a terrific human being first and he's a great basketball player. We're going to miss him. There's going to be times where we're going to miss him. He's been everything we've asked of a player in this rebuilding process. He's been in the core of it. He'll be missed."
With all due respect to both Pritchard and Blake, the Blazers already missed the 2008-2009 version of Blake. Will they miss this year's Blake too? Yes, but not nearly as much. I believe had Blake performed with the same consistency and volume as last year, he would still be a Blazer today.
Shake Charts: Eye Candy
OK, enough number crunching, let's get to the fun stuff. I've designed what I call Shake Charts. The idea of the Shake Chart is to visualize a player's scoring (in)consistency. This is a shake chart.
Not only is that a Shake Cart but it's Brandon Roy's Shake Chart for this season through the All Star break.
How to Read Shake Charts
I've set up the Shake Charts like seismographs. The X axis represents a player's point production in a game. The y axis represents the games played during the season: the top is opening night and you work down through the course of the season.
The blue line connects the player's scoring as it goes through the season. It's the main graphical element to focus on. As I mentioned earlier, Roy has been the most consistent scorer for the Blazers this year. As a result, the blue line doesn't vacillate all that much on his chart. Especially when compared to someone like Jerryd Bayless.
The more blue, the more Shake. It's that easy.
The red line running up the middle of the each Shake Chart represents a player's scoring average. It's included as a visual baseline. The green and purple lines represent one degree of "Average Shake" from the player's per-game average. This is included to mimic standard deviation but isn't really standard deviation. Roy (6.35) and Bayless (5.50) actually have similar average shakes even though Roy averages more than twice as many points as Bayless.
It's fun to note as you look at these charts how often players score within their average shake range (between the green and purple lines) compared to how often they don't. Just compare Roy and Bayless's charts again. Roy mostly rolls in that comfort zone down the middle while Bayless is much more often outside the lines.
A final, important note on the charts themselves: for maximum visual impact, the X axis on each chart is individually calibrated based on each player's minimum and maximum point totals. Using the same X scale for each player made it difficult to see the scoring inconsistencies of the role players who haven't produced the same high-end individual game scoring totals. So be sure to take note of the X axis domain when you look at each chart.
With that said, here are some more Blazers Shake Charts. First, here's Aldridge. A great visualization of his overall consistency, his slow start and then the increased production as of late.
Here's Andre Miller. The 52 point game never looked so hilarious.
Martell Webster remains all over the freaking map despite some blast-off nights in 2010.
Rudy Fernandez hasn't played a ton this season due to injury and has struggled to find consistency when he has played.
I think Juwan Howard just broke the seismograph.
Earlier I mentioned Steve Blake's increased shake from last year. Here are two charts for Blake: first last year's and then this year's. I think they do a nice job of illustrating the increased inconsistency point and also his downward shift in scoring.
Do you want to see shake charts for Kevin Durant, LeBron James, Dwyane Wade and Kobe Bryant? Click here.
Shake and Minutes
Again, comparing the numbers from this year to last year, you can see the turmoil caused by all the injuries. You can also be reminded that only 4 of the Blazers top 8 minutes played guys from last year are in the top 8 again this year (Roy, Aldridge, Fernandez and Blake, who just got traded). That's a ton of turnover. In turn, Nate McMillan deserves a ton of credit for managing this.
Now that we know the players' minutes Shake and their scoring Shakes, let's compare them on a graph.
Click on the image below to enlarge it or click here.
On the X axis you have the players' Minutes Shake. On the Y axis, their scoring Shake. So as you move left to right you find players whose minutes are more prone to go up and down. As you move up the chart you have players who are less consistent in their scoring. No surprise: Brandon Roy and LaMarcus Aldridge lead the pack by quite a distance.
What's most striking about these charts is how nearly linear the relationship between (in)consistency of minutes and (in)consistency of scoring is for the Blazers, regardless of personnel. This makes sense. If someone is scoring off the charts well, their minutes will increase. If they go cold and stay cold, their minutes decrease.
But man the two are tied together tightly. The correlation between playing time consistency and scoring consistency for the Blazers' top 8 players last year was .6671. This year it's .6482. Nearly identical. Given everything that's happened, the development of some players and the loss of others, that's pretty incredible. It makes total logical sense but it's still interesting to quantify and visualize.
As to my original question: is there anyone who should be playing a lot less or a lot more minutes based on their scoring consistency? Not really. The biggest discrepancy is Rudy Fernandez, whose scoring Shake rates him 7th on the Blazers while he plays the 5th most consistent amount of minutes. The trade of Blake and Outlaw should further increase his consistency of minutes; hopefully his scoring output follows suit. Otherwise, McMillan is pretty much on point.
Conclusions
- LaMarcus Aldridge deserves much more credit than he has received for his consistency in scoring this season.
- If Outlaw hadn't been injured and Blake had played to his standard from last year, I think both would still be Blazers.
- The Blazers just traded away last year's 3rd and 4th most consistent scoring options and their #1 scoring option both this year and last year is injured. Their role players have been all over the map. That's a major issue (if not the single biggest issue) to keep in mind down the stretch.
- The relationship between playing time and point production over the last two years for the Blazers is incredibly similar despite all the changes in personnel and injuries.
- Seismographs don't get enough love, generally speaking.
-- Ben Golliver | benjamin.golliver@gmail.com | Twitter
(Note: The first table was updated on 2/23 to fix a previous typographical error that overstated Kobe Bryant's inconsistency. His shake chart was formatted properly.)
119 comments
|
31 recs |
Do you like this story?
Comments
Lets see LMA’s shake chart for rebounding.
Official Adrian Wojnarowski Hater.
The Ardent Optimist.
by fajunga on Feb 18, 2010 11:54 AM PST up reply actions 4 recs
i dont mean to be harsh but i think you went through a lot of work for not. that or im missing something.
this is what standard deviations tell us….you could even plot them the opposite way with point totals (or range of point totals) on the x axis and n (number of games with that point total) on the y axis and draw a curve. you can then ‘visualize’ all the same which players have broad appearing curves and which ones are “pointy”. broad = high variability.
just as easy mathematically stdev does the same. 1 stdev i believe contains 68% of values randomly selected. 2stdev is 95% and 3 is 99.7%
Please, for the love of all that is holy, please stop using the following: "Book it.", "FTW", "Epic" & "Fail".
...no seriously--stop.
by nima on Feb 18, 2010 7:07 PM PST up reply actions 1 recs
he's using the absolute deviation
divided by the average.
If he had used the std. dev. his “shake” measure would be the variation coefficient.
Da Da Duh Da Duh (Mickey D's Jingle)
I’m lovin it.
Follow me on Twitter @invisininjapdx
by InvisibleNinja on Feb 18, 2010 11:44 AM PST reply actions
Good chart, but it can be a bit misleading
try standardizing the scale so that its easier to compare from one player to the next. Andre and Brandons high scoring night kinda made you shift the scale…so their normal production tends to yield less blue. When you make the scale larger…it seems as if the production isnt as chaotic when looking at the scale IMO.
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 11:46 AM PST reply actions 1 recs
I agree. I think it is wise to take out the low and high outliars for the avg. Esp if they are WAY
higher or lower than usual.
Just put the blue and purple in the same spot on every chart
and let outliers go outside the chart boundaries.
" It was as if the Suns hatched a fiendish plot to ruin John Wayne's movie career by casting him as a cowboy." - Dave 2/10/2010
you cant do that
the purple and green are dependent on the scoring average of every player…its not fair to have juwans boundary be the same as roys. i’m not sure how ben derived these boundaries but i’d imagine he took some kinda standard deviation into consideration. standard deviation would be cool to do on star players who get touches…but for someone like our rarely utilized bigs like joel….it wouldnt be too useful. 20% +/- of joels average doesnt provide much of a margin.
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 2:03 PM PST up reply actions
removing outliers is good
but i am a big fan of leaving them in this sort of data, just forcing graph formats to keep the same scale. I know most software makes it difficult to maintain scale between different data sets.
The real tricky comparison is the time vs games year on year comparisons. the two different scales of that chart makes it hard to judge the difference visually. But the data is grat, again, big, big fan of the seismograph…
"Oh Yeah!" ~ Kool Aid Man
Don't really see why Miller being third most consistent is a surprise..
Except for the big 52 points game outlier, he’s been pretty consistent in giving us 10-14 pts, 7-10 assts, and 4-7 boards per game, when he gets starter minutes. There are certain nights he’ll score more or rack up more assists, but my guess is that his PER would likely fluctuate even less than his points. (with my disdain for PER notwithstanding)
Good pure PGs don’t try to score unless his team gets cold and needs the scoring. Dre has been doing a good job of trying to step up scoring when other players get cold, IMHO.
"I think he’s been doing some good things. I think he’s been doing some good things. He’s had to play a lot of minutes lately with Blake being out. I think he’s been doing some good things." -Nate McMillan
Plus, Rudy, Martell, and Jerryd has been wildly inconsistent..
much more so than Miller. The chart just proves what we knew all along, really.
"I think he’s been doing some good things. I think he’s been doing some good things. He’s had to play a lot of minutes lately with Blake being out. I think he’s been doing some good things." -Nate McMillan
Their shake will go down now as roles will be more clearly defined.
Official Adrian Wojnarowski Hater.
The Ardent Optimist.
at the same time
rudy and bayless havent had consistent roles throughout the course of the season. no one really has on our team…but andre has had a more stable role than anyone off our bench.
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 11:57 AM PST up reply actions
True...and I think injury issues also played a part with Rudy and Jerryd.
Still, end result is that they have been erratic and hopefully the thinning of roster/getting healthy will help them.
"I think he’s been doing some good things. I think he’s been doing some good things. He’s had to play a lot of minutes lately with Blake being out. I think he’s been doing some good things." -Nate McMillan
Great work Ben. I’m going to have to read that again.
by botanyjames on Feb 18, 2010 11:54 AM PST via mobile reply actions
cool
Heartbroken..... Our goats have escaped.
by Starvin' Marvin on Feb 18, 2010 11:55 AM PST reply actions
I couldn't read the entire thing
Was there mention that shake isn’t necessarily bad, if a player steps up by scoring more when the usual scorers need help (down game, out injured, etc.)?
I get the paper, so I don't care!
Here is something that I said in the JD about this
The better question is “Does it even matter?”
Out of curiosity I ran some shake numbers for Jordan. His shake% was consistently in that 17-22% range, the same neighborhood that LeBron and Wade are in this season. There didn’t seem to be any difference in his shake% when the team was medicore and when his team was winning championships.
i think there is less shake with #1 players
bc their minutes will be pretty consistent over the course of the season barring injury…their roles on offense will likely be the same as well. regardless of how well or poorly brandon is playing…we know that he’ll get the ball most of the time. even if kobe is having a poor shooting night…chances are he will be the most likely to take the critical shots at the end. these guys will always get their touches.
supplemental players aren’t so lucky. their touches can vary from game to game. if rudy, bayless, or martell arent hitting…it’d be rare for them to receive 10+ shots a night.
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 1:59 PM PST up reply actions
yeah, roster turmoil this season probably has led to some of the higher shake ratings
less turmoil around aldridge and roy so their numbers have not fluctuated much from last year. Rudy’s role was furry early, now his injury and subsequent rust are impacting this…
"Oh Yeah!" ~ Kool Aid Man
I've noticed that players with a lower PPG have a higher shake.
That seems normal to me. Because a variance of plus or minus two points is going to register a giant percentage on a player averaging 5 pts./game as compared to a player averaging 25 pts./game. Did you take this into account? I admit to skimming parts that I plan to look at later.
Wearing the black band for Jarrett Jack, Ime Udoka, Fred Jones, Sergio Rodriguez, Channing Frye, Luke Schenscher, Shavlik Randolph, James Jones, Josh McRoberts, Steven Hill, Jarron Collins, Michael Ruffin, Steve Blake and Travis Outlaw. Sacrificed to the unmerciful god of progress.
by T Darkstar on Feb 18, 2010 11:57 AM PST reply actions 1 recs
i think the scale also plays into effect
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 12:00 PM PST up reply actions
Agree completely.
It’s the law of averages at work. Guys who get to take more shots should be more consistent.
Also, a guy like Brandon has the ability to demand more shots when they aren’t falling, thereby artificially inflating his consistency. On the flip side, he’s also the guy most likely to be asked to score more to carry the team and one of the guys most likely to forgo shots on nights the game is in the bag to let other guys score. Then again, guys like Howard are completely at the mercy of outside factors. We rarely run plays for them, so even if they are perfectly consistent and predictable, their output will vary with their opportunity.
At the end of the day, I think these stats and charts are interesting, but not very illuminating. Measuring the players’ ability to provide consistency, rather than consistency derived from a large number of shot attempts or a consistent role on the team, is a very complicated endeavor.
"...it was like he brought his own personal cross-wind to the arena." - Dave
No Oden?
Perhaps his sample size this season was too low…
Ben, much like Shaun White “pushes” and “changes” the sport of snowboarding, you are blazing new trails in basketball statistical analysis.
Portland > Tacoma
by CaptainSexyJacob on Feb 18, 2010 12:00 PM PST reply actions
hmm
Your second to last conclusion seems to invalidate the exercise. If players aren’t consistent at being consistent, this is a major flaw, although I loved what you did visually and analytically here.
Howard Shake
What does his minute shake look like after joel went down?
Roles
you also have to consider roles. if you’re a scorer and you have bad shake, that’s worse than a defensive player having a bad shake. if both martell & batum had the same shake, i would conclude that batum is more consistent, because he get his points whenever he can where as martell is a more prominent figure on offense. same goes with juwan howard and the soon to play marcus camby.
Upsides of shake?
Does this mean our coaches use players more or less depending on match-ups? That our players are willing and unselfish enough to let hot hands shoot? That we take advantages of weak defenders in an egoless, multipronged attack?
Not trying to definitively say any of those things, but they’re all possible interpretations.
Very interesting read
Ben,
Am I correct in reading your Minute Shake VS. Point Shake Graph for 2009 that the four players who are under the diagonal line: Roy, Aldridge, Bayless, Howard have been more consistent relative to the team as a whole, while Fernandez and Blake were the least consistent?
Howard and Bayless have had the most inconsistent minutes. The fact that Howard the seasoned vet has handled that relatively well has rightly won him a lot of praise. On the other hand, Bayless who is 21, and only received 600 minutes of PT as a rookie, has not gotten much love around here or from Nate.
As I pointed out this morning in a Fanpost entitled “Avoiding a Rush to Judgement.”
Prior to his thigh injury, Bayless had a stretch were he scored in double figures for nine out of ten games. Nate responded to this burst of consistency by pulling Bayless from the starting line-up and reducing his minutes. If we are going to expect consistency from the young players, doesn’t it make sense to expect some consistency from the coaching staff?
Let me further note that Bayless shot over 50% for this stretch of games. It is possible that Bayless is still suffering lingering effects from the sore wrist and the thigh bruise but to the best of my knowledge we haven’t heard anything official.
Separate and apart from my Bayless fixation, this is interesting data. What I would like to see discussed is the question of causation vs. correlation. Are guys playing inconsistent minutes because they are scoring inconsistently, or are they scoring inconsistently because Nate is giving them inconsistent minutes. In the case of Bayless, my guess is that Nate is very much a part of the problem.
by upper left corner on Feb 18, 2010 12:17 PM PST reply actions
No suprise about Miller
I and others have been stating for a long time he is a consistent player or has been ever since him and Nate got into it. I really wish a lot of the other Blazers could do this however Miller is the vet and the others not so much, Juwan excluded. I agree I am sad to see both Blake( Yes I know) and Travis go. However its the best trade we could do right now due to injuries. Good article.
Nice work, Ben.
I like fun with numbers. Of course, shake is much more important when considering guys with a higher scoring average, who you are really counting on for points. The Travis value is noteworthy, and supports us Travis lovers – it isn’t all just personality folks, spare me the BBIQ lecture, if you are so smart, you recognize that the offensive production Travis provides is golden. And I still believe he is not through improving on defense. I am personally seriously bummed if we are not just “lending” him to Clippers till summer.
Our boy Durant is awesome, once again. No I am not over it. Still counting on Oden to do damage this year though. We will make due. :-)
"Travis went all wang-dang diddly wubba SPROING wow-wow on everybody " Dave's recap, season opener
Redo the statistic
Calculate the statistic in losses and in wins, subtract the two and that is your shake. I suspect you’ll see Roy’s statistic is near zero and the role players having a wider range of performance. Averages don’t mean much in sports because it’s the individual performances that win or lose.
Do the statistic again for games with Roy and without. Roy gives a psychological edge to role players who are more confidant they are doing the right thing when Roy continues to count on them during a game: i think this might be measurable in the “shake” statistic.
Is that an order, sir?
How long do you think it takes to do this stuff.
"Travis went all wang-dang diddly wubba SPROING wow-wow on everybody " Dave's recap, season opener
that is an excellent suggestion
A nice (well designed) MySQL DB, once painstakingly loaded with all the relevant player data, would allow running through a wide variety of interesting formulas for quick observation at the drop of a hat…..
Perhaps you should be giving orders … :-)
"Travis went all wang-dang diddly wubba SPROING wow-wow on everybody " Dave's recap, season opener
Call me a numbers geek
because I loved every word of this, Ben. Well done. It would be interesting to run this same sort of analysis for other stat categories, or run it for the 2010 free-agent class…possiblilities would be endless, but it would be useful in many situations.
Minutes shake vs. Points shake
Isn’t the obvious explanation here that when someone’s minutes get changed around, their points naturally follow? Most people will score twice as many points in 20 minutes as in 10.
If you want to compare scoring consistency vs minutes consistency, I would look at points per minute instead of points per game.
Per minutes shake for a game = |ppm – avg ppm|
minutes shake = |minutes – avg minutes|
comparison = pms / ms
Disclaimer: everything I know about basketball I learned on Blazersedge.
by pualo on Feb 18, 2010 1:04 PM PST reply actions 3 recs
Yeah, I was thinking this as well.
You have to look at ppm each game by itself, because otherwise you’re just comparing overall inconsistency – it doesn’t tell whether inconsistency in one leads to or is connected to the other.
You can measure skill and talent with your eyes, but productivity is shown through statistics.
And as someone said above
obviously low scorers are going to have high “shake”. If you average 4 ppg, and happen to make a couple extra buckets, you have 100% shake, but it means absolutely nothing.
Give Blake the MLE in 2010!
Farewell to #2 and #25, good luck to you!
#7 #10 #52 -- #5 & #88 are back!
Yeah I feel like the second half of the post was really not anything new to me
The only interesting information I gleaned from it is how different players might react differently to variation in minutes played.
Things happen for a reason they say, but I say there's a reason things happen.
I took Miller's Shake Chart and scratched it into a record. It said, "Natas si Rellim." I haven't tried playing it backwards yet.
by tominhawaii on Feb 18, 2010 1:07 PM PST reply actions 4 recs
that's interesting
since you’re also a broken record
dinasour type of guys choir boys
by mittsabishy on Feb 18, 2010 6:12 PM PST up reply actions 1 recs
Good Idea, Bad Analysis
So, I really like the idea of measuring variance in player’s statistics, but I’m a little disappointed in how you did it.
I find it strange that you set out to look at scoring variance, called it variance, and then proceeded to calculate a term that isn’t officially variance at all. (The difference is squaring the residuals before adding them up.)
There’s a reason there are standard terms for variance and standard deviation. Probabilistic events such as scoring averages follow a normal distribution. In this setting the standard deviation (square root of the variance) is the correct way to measure variance because it gives you predictable ranges (ie 68% of the time you’ll score within 1 standard deviation of the median, 95% of the time you’ll be within two, etc.)
Shake is a nice term, but it’s like reinventing the wheel by creating an elliptical tire instead of a circular one. Why not use the established, proven metric?
Second, I think it’s not a clear thing for you to say that low shake is necessarily good. Lower variance helps keep things stable, but over the course of a full season it’s the median scoring value that matters. I do think variance does matter to some extent if you look at the decreasing marginal returns of each extra point a player individually scores. Unfortunately, there was no discussion of this point, it was simply stated as fact.
I love the idea of numerical analysis, but the caveat is that you have to make sure you do it rigorously and correctly. If not, you lose much of the significance from your results.
Finally, when people look at analyses like this one, it is important to do it with a critical eye. Try not to get too caught up in the numbers and charts. That said, the difference between Shake and Standard Deviation is somewhat minor, so it doesn’t have to be a big deal. Just be careful when reading posts with a lot of numbers. Just because there’s a lot of math doesn’t make it automatically correct.
by Moozh on Feb 18, 2010 1:14 PM PST reply actions 9 recs
I was thinking nearly the same thing.
Standard deviation would do the same thing, probably a bit better.
You can measure skill and talent with your eyes, but productivity is shown through statistics.
at first i thought the same
but later…not so much. its hard to come up with a statistic that satisfies every player. for instance….a player like joel who averages 4 pts a game…his standard deviation would yield practically no spread. since his average is low to begin with…his point totals could drastically be different than his average.
joel scoring 10 pts would be like the equivalent of brandon scoring like 50 or so due to the nature of joels average being so low to begin with.
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 2:08 PM PST up reply actions
Sure, but that still doesn't address Shake vs STD
The point is that standard deviation is a better measure of variance when considering a number that is the sum of several independent random events.
If you want to look at the relative variance, why not just do Relative Standard Deviation (STD / Median), which uses the standard deviation.
by Moozh on Feb 18, 2010 2:21 PM PST up reply actions 1 recs
sorry i misunderstood
i thought u were referring to the purple and green lines that ben was using
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 2:26 PM PST up reply actions 1 recs
good points about variance... though Mean Absolute Deviation (MAD) is an established metric in statistics
For reference: http://en.wikipedia.org/wiki/Median_absolute_deviation
In addition, scoring is not technically normally distributed (it is censored) so the rationale for using variance or standard deviation could be questioned.
The decision that is most questionable in my eyes is dividing the Mean Average Deviation by scoring average. Higher scorers might have higher scoring MADs or scoring variances, but not shakes. What does that mean? I don’t know.
Just finished my stats midterm ...
And couldn’t agree more. I now know just enough about stats to be dangerous.
Agreeing here
At first this looked like a lot of pretty numbers and charts, but really it seems like an attempt at inventing statistics by someone who does not know a lot about the subject.
Things happen for a reason they say, but I say there's a reason things happen.
Don't mean this as a slight to Ben, it's good work and real inferences can be made from the data
But the execution is lacking, and a full grasp of what’s going on mathematically seems to be missing.
Things happen for a reason they say, but I say there's a reason things happen.
by sixth on Feb 18, 2010 5:43 PM PST up reply actions 1 recs
Probabilistic events such as scoring averages follow a normal distribution.?
Statistical models are typically based on the handy, user friendly normal, random event distribution curve. Subsequently, the elaborate derived methods provide interesting, potentially useful models of what is really going on. But the basic assumption of “probabilistic events” (a accumulation of random coin tosses) is just an assumption , of tenuous validity when you get out to something as complex as human behavior. So, all the more you are quite correct when you say
Just because there’s a lot of math doesn’t make it automatically correct.
But, yeah, if the standard deviation is close, might as well use it to impress those who care.
Re-sign Travis Outlaw !
Statistics gone crazy
Ben,
I agree with the people who are saying you reinvented the wheel a bit with this analysis, but there’s more to it than standard deviation. This is going to get a little complicated but the idea is simple: statistics has tools for exactly what you are trying to measure.
Standard deviation is one part of what statistics focuses on with ‘moments about the mean’. The second moment about the mean is variance (standard deviation squared); the third is skewness; the fourth is kurtosis.
What that means for what you are trying to do is that you can measure the ‘shake’ around the scoring average by looking at twice the standard deviation (square root of the variance) to determine what a normal (95% probability) range of something is. This measures the ‘shake’ as you calculated.
The third moment about the mean is skewness, which measures how far away from a normal distribution any other distribution is (skewed left or right as you look at it on a probabalistic distribution). This roughly measures how far above or below the average of NBA players scoring is. Skewed to the left of normal would be an underperforming player compared to the average NBA player, to the right would be outperforming.
Then there is the fourth moment of the mean, kurtosis. This measures how tall the probability around the mean is and is also a good measure of ‘shake’. A high kurtosis corresponds to a higher ‘shake’, and a lower kurtosis corresponds to lower ‘shake’. However, the measure of kurtosis will not be biased by the points-per-game average that your measure is. Players who score more points per game will, other things equal, probably have a lower ‘shake’ because there is more room for fluctuations in points per game. If Brandon Roy’s scoring fluctuates by 5 points per game, he will have a lower ‘shake’ than if Steve Blake had the same 5 point fluctuation. This could be one bias in your measure that helps LaMarcus. As a leading scorer, he gets more margin for error in terms of fluctuation in points scored in each game.
"travis just took the worst shot attempt i have ever seen [...] let me pause to explain this [...] travis pump fakes and dribbles to his left, gets in traffic left hand finger rolls the ball backwards over his own head, from like 14 feet out"--Ben in pre-season game 1 vs. SacKings
by bgblazer on Feb 18, 2010 11:11 PM PST up reply actions 1 recs
I don't know a lot about statistics, but I do have a math minor and this sounds an awful lot like calculus to me . . .
Things happen for a reason they say, but I say there's a reason things happen.
Absolutely - and...
It would be much easier to “see” these moments if the individual points wer compiled into a histogram instead of a line chart. The “shake” chart shown in this post is fun, because it looks like something is being shaken, and it shows slumps and streaks, but beyond that it isn’t a good way to visualize the type of analysis that Ben is trying to make.
Database
This is why tabular info on the web is of little value because without the ability to do queries it makes good statistical analysis for amateurs (those that don’t have an organization to collect the data) very time consuming.
Clearly examining the entire league is a better way to develop meaningful statistics and a database would be extremely valuable. Consider, it would be easier to question statistical conclusions of commentators, team management, NBA officialdom and would make conversations like this capable of developing good statistical information.
Anybody know of an accessible database?
Wouldn't using points per minute make more sense
Then role players aren’t penalized for covering when starters go out and their minutes go up and vice versa.
"What we have here, is a failure to communicate."
Then...points per possession?
You can measure skill and talent with your eyes, but productivity is shown through statistics.
This is much more meaningful
Since low scoring by itself means little to the success of the team.
"The one thing we said about this team right away is they mirror what their coach's personality is and that's to be hard-nosed and play extremely hard and play with intensity." - Alvin Gentry
It would also be interesting to have at variance in scoring efficiency
For instance, a role player like Howard is really only expected to score efficiently when he’s left open. Some games that might mean he gets two shots. In others eight. The number of points he scores isn’t as fully in his control as a guy like Brandon. But Howard’s ability to consistently convert the opportunities he does get, as opposed to a more streaky guy like Rudy, would be telling.
"...it was like he brought his own personal cross-wind to the arena." - Dave
there are not "have to's" in stats
it would just tell you something different… and I doubt there is much variance in pace when comparing players on the blazers.
I disagree with your premise
This notion that an ideal player would have exactly the same game every night is questionable. Or that coaches want that.
I think that players, coaches and fans want to see improvement which by its very nature implies inconsistency.
In other words, we don’t want to see LMA get 7.5 boards per night. We want to see 12.5. I’d much rather he get 18 then 10 then 19 then 9 then get 7.5 every night. In fact, I never want to see him get his average until his average is significantly higher.
To put it another way, I can’t imagine a coach ever thinking "I’m really happy role player X got 2 points, a rebound and a turnover while he was out there. That’s exactly what I expected him to get when I looked at the season stats today, and boy would I have been disappointed had we gotten more production out of him. “Old steady.” That’s what we call him. So long as he never changes those numbers, he’s got a job with me!"
Ben, I'm sorry, but I have to call you out:
“There are three kinds of lies: lies, damned lies and statistics.” – Mark Twain
Overall Ben I think you have done an excellent job in using statistical data to present the progress of this team and it’s players over the season. That being said, even professional statisticians walk a dangerous path and can wander off into the mists and get lost. I think your notion of “Shake Charts” are cool and deserve further work, but I think your execution this time around has a critical flaw. I’m keying in on this closing observation of yours:
What’s most striking about these charts is how nearly linear the relationship between (in)consistency of minutes and (in)consistency of scoring is for the Blazers, regardless of personnel. This makes sense. If someone is scoring off the charts well, their minutes will increase. If they go cold and stay cold, their minutes decrease.
The problem here is that you are looking at this data and inferring causation that doesn’t necessarily exist. You say, “If someone is scoring off the charts well, their minutes will increase.” What about the inverse: “If your minutes are cut, your scoring will decrease.” That’s a pretty inarguable statement, right? In other words, I could look at the same data you are looking at, and conclude that the inconsistency of our players is caused entirely by Coach McMillian’s wacky rotations. Fire Nate!
I think in order to make this data more valuable and relevant, you need to either PPP, PPM, or per-48 averages rather than raw points as your input. That will eliminate minutes played from your initial results. Then you work the minutes played back into your calculations and see how this skews your results from the original values. At that point, I think you can then evaluate which players are consistent regardless of minutes played, and which ones are suffering from being yanked around.
I can see above that I’m not the only person dinging your methods here, but don’t let that discourage you. Sometimes “interesting failures” are a lot more fun and provoke more learning on all sides. FWIW, I really enjoyed Vanilla Sky.
by conspirator5 on Feb 18, 2010 2:12 PM PST reply actions 2 recs
part of the fun in sports is inconsistencies
its seeing the 99% ft shooter miss a pair, the underdog that takes down the goliath, its martell webster scoring 20+ in a quarter, etc. if everyone hit their averages every game…well then it’d take all the mystery out of the game itself right?
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 2:20 PM PST up reply actions
oops
didnt mean to reply to you, but make a general post
I'm going on a Dave boycott until AK1984 is brought back.
"Did they really expect me to bow down to Jesus?!?" ~Sophia
"At first glance, I saw a fairly unremarkable penis." ~Sophia on Greg Oden
by Philthyanimal on Feb 18, 2010 2:20 PM PST up reply actions
I'm 100% with you
and i don’t want to be made to look ignorant because I’d like to see Brandon explode for 50 some nights.
Love the idea, but question the execution.
Others have already made the case for PPP since a nominal pts/game doesn’t account for minutes played, but I’d just like to add that using PPP would also account for efficiency. The only problem I foresee is that lower usage players are going to see wild swings in their PPP/game since the sample sizes are so tiny.
interesting
Wow this really focuses on the central issue for the Blazers right now, player consistency.
Love the concept of trying to quantify player consistency, and lots of excellent criticisms of the particular methodology have been pointed out.
Only main problem, it probably would have to be refined to about 10x or 100x the sophistication of the initial concept to really be a valuable tool.
And, even then it still wouldn’t account for the things that don’t show up the stats, e.g. a lot of the contributions on the defensive end.
outliers
“Don’t really see why Miller being third most consistent is a surprise.. Except for the big 52 points game outlier, he’s been pretty consistent in giving us 10-14 pts, 7-10 assts, and 4-7 boards per game, when he gets starter minutes”
Sometimes when looking at statistics you allow for a few outliers to be pulled out of the graph so that they don’t unduly influence the results. If that were done here then Miller doesn’t get nailed for that damn 52 point performance.
Not to pick nits, but statisiticans have better tools for this.
Rather then inventing a new metric called “shake”, why not use the tried-and-true statistical measure of “variance”, or its square root, “standard deviation”?
To compute the variance, rather than computing the average of the absolute deltas from the mean, compute the average of the SQUARES of the deltas from the mean. The squaring function gives you an absolute value built in, and contains a larger effective “penalty” for wild swings. To compute the standard deviation (or “sigma”), take the square root of the variance.
I am Spartacus and I approved this message
Some Better Statistics to Analyze
Earlier I criticized the math behind this thread. I really don’t want to come off as a negative nit-picker. I really do like the idea of more numerical analysis applied to sports. Thus, since I didn’t agree with the given analysis, I felt I should provide some ideas of my own.
The idea behind variance being a bad thing needs to be looked at with a very critical eye. Sagcat put it exactly right: when weighing how much things matter, it’s much much more significant to increase your average value than decrease your variance.
Let’s take a quick look at the Central Limit Theorem. If you want, you can Google it. As a quick summary, the idea is that as you add multiple independent random events, the resulting distribution approaches a normal distribution (or a bell curve around the expected mean). The normal distribution is weighted heavily in the center. Thus, the more you play, the more likely you are to be close to your average, regardless of variance.
It’s not perfect, but you can look at how much a specific person scores on each individual possession as an independent random variable (this is certainly not exactly true, the events will not be perfectly independent, but it’s probably close enough for our purposes). This is why we would use a standard deviation metric over an absolute deviation (such as shake).
The catch is that this also applies when combine separate normally distributed values. While each individual value will have some inherent variance, the combination of all of them will usually reduce the overall variance. This is because some values will range too high while other values will range too low. This is case when we combine individual scoring to get the meta-metric of team scoring. While we may have high variance in individuals from game to game, the variance in the whole team’s scoring will be much more consistent. This is one reason why individual variance isn’t such a huge deal.
In more simple terms, having a bunch of streaky players isn’t necessarily bad, as usually some will be off, but some will be on fire, thus balancing each other out. Conversely, a bunch of consistent players will reach the same endpoint, just in a more regular way each night. Remember though, in basketball, it’s the TEAM score that counts in the end.
If we want to go into things even more, we can observe that variance of individual scoring over a given night isn’t independent. It’s actually extremely dependent!
Let’s look at our Shaketastic friend Kobe. He’s all over the place. Does this mean that the Laker’s final scores are crazy too? No! Kobe’s large scoring days will affect his teammates. On those days, the rest of the Lakers are likely to have quieter nights. Conversely, when Kobe is having a low scoring night, his teammates are very likely pitching in more than their share. In the end, it still balances out. In the end, the only meaningful statistic is the TEAM score at the end of the game.
Let’s go even further. Sometimes variance can be a GOOD thing. Let’s say that the Lakers are very consistent. They score exactly 105 points every night, no variance at all. Now let’s say the Blazers are not as good. Let’s say they average 100 points a night. Now, if the Blazers are consistent, they lose to the Lakers every single night. In a season, their record would be 0-82! But let’s look at a violently swingly Blazers team. They still average 100 points a night, but that’s because they score 90 half the time and 110 half the time. Wow, all of a sudden our record improves to 41-41! We can go even further. What if over the course of an 82 game season the Blazers had 4 games where they got shut out. Of course, the other 78 games they scored 105.1282 points (in a crazy parallel universe where scores don’t have to be integers). Well, the Blazers just went 78-4 and set the single season record for wins. All while averaging 5 points per game LESS than their opponents.
The lesson here is that for a disadvantaged team, it may be in their best interest to adopt a higher variance style of play. A real world analogy would be a team of smaller players who shoot only 3 point shots.
Also, points per game depends on the opponent too. You need to take the opponent into account. Also, points per minute can be affected by a team’s pace. Thus, the best metric for scoring variance is to look at points per possession divided by the opponent’s average points per possession allowed.
Even more interesting would be to chart these values and look for correlations between that and the opponent’s points per possession allowed. I would be very curious to see the results. For example, against teams with low points per possession allowed (the best teams in terms of defensive efficiency), it would be reasonable to expect the top scorers points to increase over their average values. This is because the best defensive teams play team defense and force opponents to score in one on one situations. Conversely, against poor defensive teams, it would be reasonable to expect star player’s points to drop below their average and role player’s points to increase (on a points per possession metric). This is because the star does not have to do all the heavy lifting.
I think that would be a very interesting analysis. My guess is that it would potentially explain some of Kobe’s huge variance. Because Kobe has a better supporting cast, he’s more able to let his teammates help him out against weaker teams. Conversely, Lebron, Wade, and Durant don’t have that luxury. They have to bring the scoring every night! You would naturally expect less variance from them.
by Moozh on Feb 18, 2010 2:53 PM PST reply actions 7 recs
I think you can look at an even lower level
to assert that variance in scoring is a good thing. Obviously you want two or three guys who are scoring relatively consistently, but beyond that, is it really preferable to have your 5th option scoring 8 points a game, or 16 points every other game? Having that player score 0 points doesn’t kill your chances of winning a game, but getting 16 points out of him will go a long way towards getting a win.
Sure, we rail about Martell being Mr. Inconsistent, but isn’t it better that he has the ability to score 28 points and carry us in a few games than being a guy who chips a little every game. We don’t always need Martell to score 11 points to win games, but sometimes we do need him to score 20+ to pull out a game. While I’d like Martell to be able to score 20 points a night, using a metric that penalizes him because he sometimes does strikes me as off.
#52
by Royster on Feb 18, 2010 5:33 PM PST up reply actions 1 recs
I agree
Having several guys who are all capable of exploding and winning a game for you can be a good thing. You need the steady guys who will keep you in every game, but those explosive role players can be very valuable. Rudy comes off the bench, or Martell nails a 3, and the other team immediately says, “Oh, no, here we go.”
It puts a lot of pressure on the defense, and that creates opportunities for everyone. Ideally, you want your top two-three scorers to be pretty dependable, and you want dependable defenders and rebounders. But scoring? Explosive scorers can win games for you even when they aren’t hot, just because of what they force the defense to do.
Give Blake the MLE in 2010!
Farewell to #2 and #25, good luck to you!
#7 #10 #52 -- #5 & #88 are back!
"A real world analogy would be a team of smaller players who shoot only 3 point shots."
E.G. the NCAA men’s basketball tournament.
Things happen for a reason they say, but I say there's a reason things happen.
These are valid points that improve the study
Your point about winning the game is so often lost in the rush to the microscopes.
Your point about defensive abilities (opponent’s PPP) is also completely lacking in an evaluation of minutes played. The Blazers are scoring fairly well this year but their defense is what is lacking so players who defend better may play more minutes than a better scorer.
And I applaud your point about high scoring games by one player may drop the scoring of his teammates. Add to this the difficulty of role players scoring when the stars are not commanding a double team. The variables seem way too numerous to quantify into a single determinative of who deserves the minutes.
"The one thing we said about this team right away is they mirror what their coach's personality is and that's to be hard-nosed and play extremely hard and play with intensity." - Alvin Gentry
Great post
I think you’re spot on.
I wonder if variance of performance might be a more meaningful analysis when comparing players who fill similar roles? For instance, comparing star caliber players like Kobe, Roy, Labron, and Durant to one another. And comparing solid starters, like LA, David West, Josh Smith, and the like to each other. And comparing back-up scoring guards, etc. I say this because there are many variables that impact a player’s output beyond the consistency of their skills, and those variables would appear to vary according to role.
Star players will inevitably score more in big games that are close. Look at yesterday’s Cavs/Nuggets game. It went down to the wire, so Melo and James took nearly every shot down the stretch, as well they should have. That’s a positive deviation each team needed from their star. On the flip side, in a blowout, those guys might shut it down early. Moreover, as you mentioned, supporting cast and team strength have a bearing on variance. When we look only at star players, that impact is predictable. Looking at bench warmers, it’s predictable in the opposite direction (e.g., blow-outs will yield higher usage and hopefully higher production).
At the end of the day, however, I suspect variance in production, when properly controlled for, would begin to have a very high correlation with scoring efficiency. Efficient scorers know how to produce high percentage opportunities. Inefficient ones don’t. All of the other variables impacting shake are probably explainable by factors outside of the players’ control (e.g., minutes player, shot opportunities, etc.).
"...it was like he brought his own personal cross-wind to the arena." - Dave
Ben's posts have been consistently
high quality through the season, but this appears to be an outlier because of the reasons already discussed before me. What is Ben’s good post/bad post Shake % now?
And why do I have a sudden craving for a chocolate/peanut butter shake?
chocolate caramel rules
but Ben’s post is a lot better than some of the myopic types in here give it credit for. Just because it can be done better doesn’t mean there isn’t a connection between scoring variance and minutes played.
That is an achievement worth noting. Now it can be refined.
by blacknoiseNW on Feb 18, 2010 3:58 PM PST up reply actions
I was just trying to shake things
up with some humor. Sorry for the deviation…
I chuckled at the first
But your “Deviation” pun here is solid gold
Things happen for a reason they say, but I say there's a reason things happen.
The crucial question nobody has answered:
What does Jarrett Jack’s shake look like?
I am Spartacus and I approved this message
Ben just needs to hire a statistician assitant
Then he will be a superman.
Disclaimer: everything I know about basketball I learned on Blazersedge.
Concept appreciated - results validated
As other statisticians here have noted, multiple tools are available for statistical analysis. However, any time you can show correlation – you have identified a valid relationship.
Establishing correlation trumps all – it is more important than considering how many other statistics may prove better at quantifying that correlation.
Like others have said, this is a great start. We now have some insight to McMillan’s decision-making that we didn’t have before. No one else has connected the dots between variance and mpg, and that is an achievement (at least that was made available on my favorite website).
Unfortunately, Pandora’s Box has been opened. I too want to see if there are other metrics that affect McMillan’s decisions. If we use some advanced metrics like PPP, aPPP, PER or APER, will we see if McMillan really does account for contributions outside of scoring. What about rebounds, steals, etc. Does Nate sense the intangibles? Or would we see that +/- is how Nate allocates precious minutes at the expense of quantifiable intangibles???
What about in-game variance? How much first quarter vs. any other quarter variance affects a players mpg?
I want more!!!!! (and I’m hoping someone else gives it to me :)
Variation can be very a good thing, a quality that championship teams often possess.
Having players that are willing to play a role most of the time and yet have the ability to go off from time to time is a very good quality for a team to have if they want to be able to adjust to, “what the defense gives them.” Role players on championship teams who have had the ability to go off include Robert Horry, Vinnie Johnson, Bruce Bowen, Lamar Odem, John Paxon, Tony Kukoc, Eddie House, Rajon Rondo, Steven Jackson, Tayshaun Prince, Rasheed Wallace, etc…
#52+1.5
Shake is shaky at best
the more you play, the more points you score, the less shake. It’s pretty simple.
Blazers need to
work on the shake, rattle, pick, and roll.
Great post Ben
It would be fun if Ben would run a thread called “Know Your Blazers”. In this thread, he’d show us a statistical chart (without names) and a throw out a question, such as “Which Blazer player is the most consistent scoring across all Blazer wins”. Obviously the questions could get more interesting than that. You get my drift.
We would all get our chance to see if we are reading the players and their games correctly. How much do we really know about the players we criticize and cheer?
Witty Unpredictable Talent and Natural Game
I find LaMarcus consistent
consistently disappointing. I wish he was averaging 5 more points per game and 3 more rebounds. i just want him to be that #2 all-star.
Juwan Howard, drafted June 29th, 1994. Netscape first released November 14th, 1994
I don't agree with most of the post
but I’ll comment on a Dave’s quote posted here.
I’f I’m playing a clockwork 0.700 team, I’d rather be a wild 0.550 team than a clockwork 0.550 team. At least I have a chance to get hot.
Some of you folks are really smart
I just like to watch good basketball although I do love the chart with far more W’s than L’s.
Given that Greg's per minute shake was WAAAY to the right...
Does that mean Greg wasn’t given a…
Fair Shake?
hi ben.
The Faith don't panic, the faith freaks out, burns out farms, and torchs small villages in the name of The Faith.
Head Czar of Amerika <--- Mortimer said so so there!!!
This is a courageous start which has engendered some really good suggestions to incorporate into your next effort
(which I hope comes soon!)
The graphics are really interesting. The PPP and opponent PPP charted together (or simply the difference) would be a great second step.
"The one thing we said about this team right away is they mirror what their coach's personality is and that's to be hard-nosed and play extremely hard and play with intensity." - Alvin Gentry
I'd like a Camby point shake, please
with whipped cream and a cherry on top!
Do you have any peanuts?
Is Camby a consistent scorer? [shake, or shake-squared would tell me what I don’t know; good on Ben],
Or does his FG% matter more, [or FG%-by-range, even morer]?
or does Nate need to just tell him “No Jumpers, attack the boards.” even morest?
Ever the optimist {homer} I think Camby will have a big impact, not necessarily by scoring.
Nothing new under the sun.
I like the shake…even if you want to quibble about it being an accurate statistical measurement.
There’s nothing unexpected here. Your main scorers that you count on night in and night out are going to be more consistent. The other players are going to be called upon to score in different situations so their point totals will vary more.
Theres not a lot more to it than that.
"Bart, with $10,000, we'd be millionaires! We could buy all kinds of useful things like...love!" Homer Simpson
Shaken up the statistics
Thank you Ben! That was a very informative post. It covered one of those areas that I complained about in some players this season but didn’t know there was a way to quantify it…
"He's just so big and strong and he overpowers everybody on our team," ~ Kurt Rambis
everyone knows that if you shake more than twice
your just playing with it
#7, #10, #52
Pie Doesn't Win Championships, Cake Wins Championships
'shakes'
I don’t really get this analysis. Most of the Blazers, other than Roy and Aldridge, have had very varied roles, varied things asked of them – last year even, let alone this year! So it is no surprise that their ‘average output’ would vary quite radically. If a player was getting steady minutes night in, night out, and asked to do the same things night in, night out, this ‘shake analysis’ would make sense. But that’s not the case for most of the Blazers. Particularly this year for the guards, I don’t think even the coach knows who he is going to ask to do what on a given night, even if he knows what players are available! That being said, it does seem to be true that Rudy and Martell are inconsistent, and as a result, get their minutes jerked around. Nicolas, on the other hand, I think would be consistent if he had an established role and established minutes.
Very interesting, although i'm not quite sure what to do with this info
is this something college scouts should consider? does someone with “less shake” = better NBA player?

by 





































