-Doctor Who “The 11th Hour”

Is this thing on?

A while back, I used to write regularly in this space. Then the Lockout happened and we moved to the new consolidated wages of wins site. Naturally my focus shifted, partly from necessity (lack of games and player news) and partly from the situation. I went from a daily chronicler to an analyst. My prose shifted from short and snappy to a more in-depth style. From quick takes to long term projects.

And I liked it. It fit the moment perfectly. The Lockout needed a more sober and serious take.

A little dark can be awesome

That time is over.

I’m back.

Now, things are going to be a little different. I’ll still write for the wages of wins site but my muse cannot be contained. There are things to be written and time waits for no man.

But new and different can be a very good thing.

Enough with the chitchat, let’s get to some silly little stats.

For today I give you the Free Agent List for 2011. (The full method behind this list can be found here)

Next up is team needs, winners and losers, then rookies followed by preseason WP numbers , then the picks challenge.

Etcetera.

You get the picture.

To quote the Doctor: “So… all of time and space, everything that ever happened or ever will – where do you want to start? “

-Arturo

Then a week of silence.

Did you miss me?
No worries. Regardless of the circumstances this space is not going away.

My good work got recognized and I was called up to the big show. At the Wages of Wins Journal I get a bigger forum and the ability (as editor) to pick and choose material. It’s a very nice gig.

But I’m going to keep this sandbox here so I can mess around. I’ll beta test content here. I’ll write and post without a filter and I’ll test material that isn’t quite ready for the big show.

And sometimes we need to keep it blue to truthtell.

Case in point. Here’s something I’ve been working on.
Click here and just press play.

That is every player’s Wins Produced numbers year by year  from 1978 onward in a Google motion chart ( I apologize to your supervisors in advance for lost productivity hours).

It’s so awesome I need to break out some theme music.

As for coming content?

The past is prologue.

30 to 16 to 1.

Build me a winner

The Free Agent Guide.

Enjoy your new toy in the interim.

Hey all. You may have noticed that I’ve stopped posting as often.

Hey now, Relax.

What you may or may not have noticed is that I’ve taken over as Editor over at the Wages of Wins.What this means is that I will be splitting my posting between the two sites.

In fact, this, will be a simulpost.

So if you were missing your daily fix just go over there and you will be sure to find the typical output from me.

Better now?

Let’s rate this Draft!

Projecting the 2011 NBA Draft

I built some models to project rookie performance based on the available stats for the prospective NBA players.

I built two models specifically (go here for the model build parts 1 & part 2 ) and I called them : Yogi and Booboo.

This is a totally legit picture

To give you an idea of the value of the models I decided to look at:

• The probability of landing a better than average player (>.090 WP48) for his first four seasons
• The probability of landing a good player (>.150 WP48) for his first four seasons

I also decided to show this for:

• Any qualifying pick (>400 MP in his rookie Year)
• Any Top 5 pick
• Any Top 10 pick
• Any 1st Round Pick
• And Both models.

And this was done for 1995 to 2009. The table is here:

If I rank the factors in order of predictive value for player success for this data from best to worst I get:

• Both models calling for you to draft the player
• Yogi picks
  
• Boo Boo picks
• A player being a Top five picks.

Yogi is more picky, Boo Boo casts a broader net and is more accurate.The takeaway though is that the models work.

Last year I did it for the 2010 Rookies and early returns were very good.

See here:

And in summary:

If we compare the top 10 picks versus model selection and set our benchmark for success at .080 WP48 for rookies, one model picks performed identically to top 10 picks and two model picks were can’t miss prospects in this draft. Only 30% of all picks were successful contributing rookies and the models clearly outperformed that mark.

The GM’s and scouting again get fairly trounced by the math.

The takeaway? Math always wins and it’s worth doing this again.

So here goes.

2011 NBA Draft Rankings

There are 6 good prospects in this draft according to the models and one slightly less attractive one (but he can be had cheap).  The best prospect is easily Kenneth Faried, who will in all probability wind up on a pretty darn good team.  Kyrie Irving and Derrick Williams the prospective 1 and 2 picks look to be good picks and not busts.

The other three good prospects are Kawhi Leonard, Alec Burks and Markieff Morris (all to be had outside the top 5).

The real steal may end up being Travis Leslie in the second round.

Enjoy the draft!

-Sherlock Holmes  “A Study in Scarlet”

You may have heard that a draft is coming.

No. A draft not The Draft.

And you already know I’m fascinated by the NBA draft and of course I’m going to say some words about it.

But before we get to this year’s draft, let’s talk last year and review some of the historical work.

We are going to woolgather a bit

Let’s talk origins and background people.

Specifically we are going to get you nice and caught up with all the pertinent draft work and do a nice early review of the the draft picks made in this space last year.

Before we start sling tables and numbers, if you’re a rookie here yourself go read the Basics. Let’s give some background:

Background(in green, fell free to skip)

With this series I will rank and review rookie performances during this NBA season. This will be one of the lynchpins of my blog for this season (and however long we continue to inhabit this space). As longtime readers of this blog know, nothing inspires me like the NBA draft and rookies. To illustrate and provide some background, here are a few of my pieces.

The Draft:

Part 1: Finding Elite Rookies in the NBA Draft or How the NBA Draft is a Lottery

Part1a: The Top 33 Rookies in the Past 33 Y ears

The WSJ Piece: Arturo Galletti Evaluates 30 Years of the NBA Draft for the Wall Street Journal

Part 2: Ranking 30 Years of Draft Picks

Part 3 :A Sunday Kind of Piece: Return of The Draft

Where I ask How good are GMs at finding talent?

(The answer: Not Very)

Part 3a: The Draft,The Draft,The Draft………

Where I concluded that:

• Talent is always available in late in the first round.
• The trick is finding it with some accuracy (which I postulate we can do).
• Given that the identification is risky a later,cheaper pick is better.

This lead to a lengthy draft strategy segment in my guide to running an NBA franchise (Build me a winner rev.2). Which you can go read for detail (it’s really good I swear )

Based on my findings I wanted to see if I could build a model to predict rookie performance. So I did.

The Rookie Models:
I built two models (go here for the model build parts 1 & part 2 ). I called them : Yogi and Booboo.

This is a totally legit picture

To give you an idea of the value of the models I decided to look at:

• The probability of landing a better than average player (>.090 WP48) for his first four seasons
• The probability of landing a good player (>.150 WP48) for his first four seasons

I also decided to show this for:

• Any qualifying pick (>400 MP in his rookie Year)
• Any Top 5 pick
• Any Top 10 pick
• Any 1st Round Pick
• And Both models.

And this was done for 1995 to 2009. The table is here:

The best performing scenario is both models calling for you to draft the player, followed by Yogi then Boo Boo then having the Top five picks. Yogi is more picky, Boo Boo casts a broader net and is more accurate.

So with a good model in hand, I got to work on evaluating the incoming rookie class.

The Rookie Projections for 2010-2011:

The full rookie projection is here

For the reader’s digest version, It took all the player data, fed it into the models and got this (ranked by projected wins):

Only Griffin projected out as a difference maker for his team based on the model and his college numbers.

At reader’s requests, I then added some of the undrafted players:

The final component in this recap is rookie performance for the preseason:

One final note (and it’s been previously noted here). I miss assigned the Talented Mr. Fields position to SF and not SG. The corrected model is the one that will be shown.

Still here? Good. Now that you’re all caught up on all my silly little stats about rookies and the draft from the preseason we can talk about how the rookies actually performed for their rookie season

2010 NBA Rookies

How I do? Much like the playoff model, we did pretty darn well. Let’s summarize:

If we compare the top 10 picks versus model selection and set our benchmark for success at .080 WP48 for rookies, one model picks performed identically to top 10 picks and two model picks were can’t miss prospects in this draft. Only 30% of all picks were successful contributing rookies and the models clearly outperformed that mark.

The GM’s and scouting thus get fairly trounced by the math.

The takeaway? Math always wins and it’s worth doing this again.

And I will for 2011.

In Part 2 of course.

Ain’t I a stinker?

Who’s  the best basketball team of all time?

This is a question that’s guaranteed to spark animated and passionate discussion among all fans of the NBA. The numbers tell us one thing but our heart argues another. But at the end of the day we are left with no clear and definitive answers. But with the tools at our disposal we might be able to change that.

In two earlier posts ( Measuring the Quality of Basketball in the NBA & Measuring the Quality of Basketball in the NBA Take 2: Adjusting for Pace), I took on the challenge of ranking the quality of basketball in the NBA. In this post my intent is to expand that analysis and take a first stab at ranking all the NBA teams since the merger.Remember satisfaction guaranteed or your money back.

First let’s review and update our previous analysis (I went back and rechecked all the numbers on this) and then we move on to the main event.

Calculating Pace in the NBA

To calculate pace  in the NBA we need to estimate the number of possesions per game in the NBA. In the world of Wins Produced (and really in the world of basketball) possession of the ball is currency and what you do with it is what determines whether you win or lose. To quote another sage: “Ball don’t Lie”. So for any truly comparative measure of true basketball productivity and quality (our value delivered) adjusting for the possessions (our spend) is critical.

How do we calculate the number of possessions?    Initially I used :

Possessions = .96 * (FGA − ORb + TO + (.44 * FTA)) (source)

But for this analysis I will use:

Possessions =FGA − ORb + TO + (.44 * FTA) which is the approximation Prof. Berri uses.

If we calculate possessions per game since 1978 (adjusting for overtimes) looks like this:

Or as a pretty chart:

So the pace of the NBA slowed significantly and at a consistent rate from 1978 (218.8 poss. a game) to a low of 181.8 poss a game in 1999 before picking back up to about 190 possession per game (95 per team) since. With this information we can now adjust  or quality measure accordingly.

Average Player Productivity &Pace Adjusted Quality in the NBA

The following Table contains (for every year since the merger):

• Team productivity per 48 minutes played based on simply adding the average production (ADJP48 click here for detail) for all 5 position (Center,Power Forward, Small Forward, Shooting Guard &Point Guard).
• Average Player Productivity which is Team Productivity per 48 divided by five.
• Average number of possessions per game
• Productivity per 200 possesions =  (Avg Player Productivity per Game (ADJP48)/Avg Number of Possessions per Game)*200

In table form this looks like:

As graph it looks like:

So once we adjust for pace, we see that the league had a golden era from 1991 thru 1997 but the last three Years have actually been better for quality than anything in the data set. Now let use this to rank all the NBA teams since the merger.

Ranking the Teams:

I did the ranking the following way:

• I got the Wins Produced numbers for each team based on the Automated build from Andres Alvarez, Prof. Berri’s numbers as well as my own WORP (Wins over replacement player).
• Equivalent WP48 is the average of the three Win Produced Calculations divided by total games played (adjusted for ovetime)
• Avg win production is Team productivity per 48 minutes played based on simply adding the average production (ADJP48 click here for detail) for all 5 position (Center,Power Forward, Small Forward, Shooting Guard &Point Guard).
• Raw wins per game is Equivalent WP48 + Avg.Win Production -.100. This is the Raw wins generated by that team per 48 minutes played without accounting for the opponent.
• Pace adjusted Raw Wins per game adjust everything based on 200 possesions versus the average number of possessions for that season. So the 96 Bulls for example see their numbers increase by a factor 0f 200/187.595.
• Finally Adjusted wins calculates the amount of wins this team would be expected to have against the league average opponent since the merger ( who posts a raw win total of 1.981 pace adjustedraw wins per game). To illustrate, the 2009 Celtics generated 2.344 pace adjusted raw wins. So, (2.344 -1.981 +.500) * 82 =70.8 wins against the league avg. opponent since the merger.

Here’s the Top 30 regular season teams since the merger:

The Jordan/Rodman/Pippen Bulls dominate this list taking the top two slots. The 2008 Celtics place third and the 2009 Cavaliers get the dubious distinction of best team ever not to win it all (or even reach the finals). Some small consolation for the Cavs in that there are 11 teams on this list from the last three years (Boston,Cleveland,LA,Orlando twice Detroit, Portland, Utah once) and only three champions. Overall the top 30 contains only ten champions and Utah and Seattle get four appearances without a title. In fact, the overwhelming conclusion is that you can build an all-time team but you still have about a one in three chance to win it all.

I missed a post yesterday and given my current job situation (stressful, challenging, like trying to juggle three angry blind mountain lions who are on fire by their tails but strangely incredibly fun) I knew it was coming.

Strangely these are the times where I step up and go: "I got this" (Image courtesy of xkcd.com)

Lucky for you, the ideas keep coming and building up a nice queue.

Throw in my strange compulsion to post something every day and you’re getting two ideas for the price of one today. Even if saturday’s are the lowest trafficked day of the week at this blog.

I forgive you for having personal lives .

Let’s start by debunking the ongoing narrative that Lebron somehow choked against the Mavs. The story going around our friends in the media is that something must have been the matter with Lebron, That he choked and that if had been himself the Heat would have won.

This is funny but totally misleading. Dirk is not trowing the ring into a Lake of Fire.

Hmmm.

I know this is wrong.

How do I know?

That’s Lebron James’ Wins Produced per 48 for every opponent in the regular season. Please note that Lebron had by far his worst performances of the season against the Dallas Mavericks.

This is not about what Lebron did or didn’t do but rather about what Dallas consistently did to fluster him.

Their defense, put a small fast guy in front of him and take away his lanes to the basket with zone or help defense should be studied and copied by each an every team in the league. It reduces his options to:

1. Shoot a bad percentage jumpshot
2. Post someone up ( and we all know how well he does that)
3. Pass to someone else for the shot (and that will also be a low percentage shot)

All in all bad times for the King.

Jordan looks at Barea/Kidd and thinks "And1"

The second thing I’m putting up is a nice,neat little poster followup to my last post (where all is explained). It’s a slightly different of presenting the data.

I knew I was going to miss it.

This allows me do some fun things.

I can take the top 6 players by minutes for each team and each series. Then I can work out:

1. The Classic Wins Produced stats (explained here)
2. My own opponent adjusted Wins Produced Stats (explained here) to adjust for opponent production.
3. My own do it Yourself Offensive Point Margin Stat Combined Margin Combined Point Margin (explained here) to adjust for opponent point differential.

I can work them out per 48 minutes then convert Combined Pint Margin per 48 to wins ( CPM48/15.5 +.100 if you’re curious) average all three numbers for a combined barometer statistic (calling it combined wins per 48).

For fun, I can convert the result to Point Margin per 48 ((CWP48 -.100)*15.5 again if you’re curious).

The result by series looks like so:

Dwade? Still the best player in the Finals. Chandler? Finals MVP.

Lebron & Kidd? below average

The Miami bigs really sunk the Heat though. It was the big two and a half indeed for Miami but the cast was Wade,Chalmers, Lebron not Lebron,Wade and Bosh.

For the Playoffs it looks like so:

Miami really did waste and epic Wade Finals (good job by Bosh,Anthony and Haslem). Terry (DAL-LAL), Ray(BOS-NYK) and Dirk(DAL-LAL) top the list. Russell Westbrook, Bill Walker, Marvin Williams and DeShawn Stevenson bottom it out.

Was it good for you?

• You can download it direct – Wages of Wins Podcast 6/15/2011
• You can find us in iTunes – iTunes Store
• You can subscribe to our RSS Feed – RSS Feed

 Super Stat Team Assemble!

• Devin of NBeh?
• Dre of Nerd Numbers

Dallas vs. Miami

You can check out each game thanks to Arturo

• Game 1
• Game 2
• Game 3
• Game 4
• Game 5
• Game 6

You can also check out regular season numbers thanks to Dave

• 2011 Regular Season

There are tons of fun stories about why each team won the finals

• People have an optimal hear rate for “stress”. Can lack of experience influence that? (See Blink)
• Jason Terry’s tattoo.
• Dirk’s Sickness
• Free Throw Shooting
• Refereeing – both directly or indirectly

Predicting How Things will Turn Out

• The True Hoop Network had their annual Stat Smackdown – Congrats to Ben Morris for his victory
• The Wages of Wins Network had our own Stat Smackdown – Devin won with our new rules, Arturo would have won using traditional rules.
• Arturo has things right when he looks at how likely each outcome is as opposed to claiming any individual scenario is a huge favorite.
• The Drunkard’s Walk points out a 2-1 favorite in the playoffs would need a 23 game series to win reliably enough.
• Ian got busy and stopped updating his picks. Dre was content to rest on his laurels in terms of predicting player awards.

Coaching

• We discuss Stan Van Gundy’s interesting take on coaching.
• Should you run a play to end the game or just take the shot? Also which is more memorable?
• Devin contends the 1998 “shove” by Jordan was not a foul

The Draft – A Sample

• Devin is looking at all teams draft patterns since the lottery
• The Lakers haven’t had a top 9 draft pick since the lottery has been in place.
• Toronto has had 40% of their picks in the top 9.
• I need to reply to Ben Morris’ point about number 1 picks helping you out.
• Devin suggests trading down in the draft and trying to get Faried with a lower pick.
• We didn’t mention it but Ty has a helpful draft guide for each team.
• Does taking a shot on an undrafted player work?

Enjoy!

-Dre (@nerdnumbers)

The 250 Best Wins Produced games of the 2011 playoffs.

Sized by wins produced and sorted by Series.

Enjoy!

This was an epic series and it was won by the superior team.

3-1 in Miami.

6-2 for the season against Miami.

+3.375 thru Eight Games.

Dallas was the better team. They were more talented. They played harder. When Miami had leads in the fourth they took their Milkshakes.

I told you so.

Let’s see, we ended the Playoffs at 12-3 for Picks or an 80% average. More on this in a future post.

Let’s dig deep for this series. First the totals:

Dwayne Wade was twice as good as anybody else in this series. Lebron was good but he was good early. Jason Kidd lead a team effort for Dallas in winning the title. Kidd,Chandler,Terry,Stevenson, Dirk and Marion all got a half a win or better.  Miami went three deep and that combined with an excruciatingly bad Chris Bosh performance took them out of contention.

For Miami, everyone but Dwayne Wade played that series like they were scared of the moment. Everyone in Dallas answered the call at one time or another.

Oh and before I forget:

Lebron on JJ. Actually happened. Have it on tape.

Puerto Rico for the win. Dale Boricua!

Let’s get all the numbers for the playoffs up as well.

Miami had the best two players in the playoffs in Lebron and Wade in Wins Produced. Too bad Dallas had 5 of the top 15.

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated 6/6/11)

The Box-score follows (I fixed TS% and added totals).

Heat-Mavs Game 6

The Dallas Guards continued their resurgence from Game 5 and took the Heat out. Kidd, Terry, Barea and Stevenson killed out there. Per minute they were better than every single Miami player. Dirk had a horrible night (particularly in the first half where he made only one bucket) and this was the only thing that kept this game from becoming a rout.

As for the Heat, a few points:

• You’re not winning the NBA title if Bosh is your best big man. Pat Riley knows this. Pat Riley will fix this. Pat Riley probably called Tyson Chandler’s agent after the final whistle blew (what’s the line on Miami in 2012 again?).
• Wade injured hip was probably a much bigger deal than anyone thought. Sadly, injuries are a feature with Wade.
• Lebron needs a post game. Every team will cover him as a PG with little men until he develops one. Pat Riley probably already booked some time with Hakeem for him.
• Final Lebron numbers vs the Mavs:

Regression to the mean was not Lebron’s friend.

Let’s end with a hearty congrats to the Mavs.

The Best player in the game.

We are all witnesses.

Lebron James does not do anything in half measures. He hasn’t really his entire adult life. He’s lived the largest possible life on the largest possible stage.

And the drama he provides is exquisite.

He gives us the highest of highs and the lowest of lows.

Case in point, Lebron now owns the Best and Worst triple playoff double since 1991. Yes, Game 5 was the worst playoff triple double I could find.

Let’s illustrate:

That’s 61 Playoff triple Doubles since 1991 and 989 Regular season ones since 1986. What’s the population Breakdown?

So for the most part  triple doubles are very good things.

Getting back to game 5, Here’s how it stacks up:

Lebron’s game 5 is only the second below average Triple Double in the Playoffs since 1991. It’s about 2.66 std deviations from the mean.

In short, it was a bad night.

One final point. For those arguing regression to the mean for Lebron coming back and having a monster game in this series?

The evidence does not back your beliefs.

Miami’s fate is not in the hand’s of the King.

He may not be the GOAT but he may be the most entertaining.

Just like before…

Everything you say to me
Takes me one step closer to the edge
And I’m about to break
I need a little room to breathe
Cause I’m one step closer to the edge
And I’m about to break “- Linkin Park One Step Closer

Lebron James can do anything apparently. Including laying up a below average triple double (.035 Wins Produced) . Show up for what he called the biggest game of his career in the 4th Quarter? Not so much.

The best player in basketball should not get outplayed in the most important game of his life by Jason Terry, JJ Barea and Mario Chalmers.

Before you get mad, just remember, he said so himself

The Mavs continue to roll like a team. Jet, Kidd and Barea all came back from the Hereafter to haunt Miami

As for the German dagger? Fun Stat time: 4th Quarter Scoring in the Finals Dirk 52, Lebron 11.

Feeling it in the 4th

For the series so far the numbers look like so:

A masterful Wade performance is in danger of being wasted here. Lebron can take solace in the fact that he’s not hurting his team like a Bosh (but really that’s not enough here). The Miami centers however are being taken to school by Tyson Chandler. Bosh is worth the money.

For Dallas.

Peas in a pod apparently

Oh and before I forget:

Did someone feel a breeze?

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated 6/6/11)

The Box-score follows (I fixed TS% and added totals).

Heat-Mavs Game 5

The Jet and Barea showed up as Dallas finally had a good shooting game. Jason Kidd finally disproved my wife’s point shaving allegations against him. Mario Chalmers was the best player for the Heat (seriously, he’s about to catch Lebron’s WP48 for the series this is not a good sign for Miami).

The big story for the game was the Wade injury. Would it have been enough for Miami to win if he’d been healthy? Given that Lebron played his best when he was out , I don’t think so. But the nature of things is that we can only speculate.

One Dallas victory from the title. Given that at this point I don’t trust any Miami lead, I’m sticking with the German and his crew.

Mavs in 6.

Everyone realizes we're a crazy Chalmers heave from Mavs in 5, right?

Paging Lebron James.

But for those who cruely tear out
my still beating heart
neither nettles nor thorns do I grow
I grow a white rose
-Jose Marti “Cultivo una Rosa Blanca” as translated by some blogger

Before we get started you need to go read:

• Jonah Lehrer’s post  Basketball and Jazz at wired.com.  A post which reviews research explaining the mental aspect of rebounding. In essence, Rebounding more than a learned skill is a rare skill. The implication is that it should sought and prized.
• Then you can follow up with Prof. Berri’s writeup of the piece:  Rebounding is a Mental Skill .

This is yet another brilliant piece highlighting the value of rebounding.

Don't post Angry

We’ve been here before and old arguments die hard.

I therefore decided in the interest of prevention to beta test a new feature we’ve been debating privately. The intent is to forestall some common arguments by putting together a nice compilation of  some of the rich & varied material that has already been done in a particular vein in one place.

Get ready for Argument Stoppers: Rebounds are Overrated rev.1.

We start simple by quoting Pat Riley: “No Rebounds, No Rings”.

I bet you haven't thought of these guys in a while

We move to one of mine:

The Rebounding Myth

“Insanity: doing the same thing over and over again and expecting different results.”-Albert Einstein

I wrote in a private e-mail today:

“Yesterday’s piece was one of those pieces that just happen. I’d been working all weekend on some consulting work I need to hand in and when I went to write the Around the Wow piece the quote “oh the places you’ll go” popped into my head. Before I knew it, I was looking up Dr. Seuss books.

For some reason, my spontaneous/unplanned stuff can be really good (the last 4 pieces were all like that), whereas some of the more thought out stuff leaves me unsatisfied. Weird.”

This is fortuitous because two days ago I wrote a post on the 100 worst seasons/players since 1978. This inspired some familiar responses.

Rebounding Again!!!!!!

Now the studies referred have some technical problems ( If you run a regression you must account for other variables that could affect your result). This is why you’ll see me control for small samples (minutes), age, position and teams frequently. Prof. Berri’s covered this before in the comments here http://dberri.wordpress.com/2010/05/01/ted-leonsis-endorses-stumbling-on-win or very thouroghly in Stumbling on Wins just buy the book). I’ve covered this before (see The Basics , and here ).

In the book (which I recommend you buy ), the finding is that there is diminishing returns with respect to defensive rebound but not for for offensive rebounds. The overall though the effect is quite small.

So Wins Produced consistent season to season? Check

Rebounds not varying radically? Check

Unnamed individuals pestered into writing additional material on this? Check and check (You’ll just have to wait to find out).

Scott Meyer needs to get out of my head!!!

We continue with another one of mine:

Re-examining myths and explaining how regression works

You know, sometimes it’s fun to poke the bear.

Did you know that when the bomb squad wants to defuse a bomb and they don’t know how to, they take it out to an empty field somewhere and blow it up? That’s kinda the opposite of what we do here. My defining trait is that I’m an engineer and a scientist. I don’t want to ignore questions and problems, I want to take a tool to it and try to solve or understand it.

Yesterday’s post was a result of this. I knew I was kicking an anthill. Today is no different.

Grab a drink, take a bathroom break, because this will take a while.

One of the hallmarks of society is intelligent polite discussion. If any argument,tool or theory is worth anything it can stand up to scrutiny and review (such as Wins Produced see The Basics) . I may not agree with you but this is why I take the time to respond to the questions. I typically arrive at surprising and unexpected conclusions.

The point of having a blog is to invite discourse. As long as everyone has a thick skin and is prepared to be wrong (and right ) , including me. We’ll keep advancing our understanding. Together.

And you know in this case, Guy is right.

What!!!!!

Not like you think (sorry, couldn’t help myself ). We do have the data to answer some of these questions and some of the answers are really surprising.

Before we get to that, lets talk a bit about linear regression.

Let's put that Ivy League education to good use

Linear regression is one of the most commonly used approaches to modeling the relationship between an variable y (say wins) and one or more variables X (say box score stats). Linear regression is simple, useful and well understood and in a lot of cases it works. It’s typically used for two things:

• Predicting or forecasting Y (say Wins) based on a know set of X’s. This is something we continually do here at ASLS.
• Given a variable Y (say wins again) and a numbers of variables (say, I don’t know, points, def. rebounds, offensive rebounds, assists, etc.) then linear regression analysis can be applied to quantify the strength of the relationship between them. This is what Prof. Berri did. He did it again in Stumbling on Wins. I did it here and at least five other times.

What does this mean for this discussion? I’m happy with the coefficients math gave me for rebounding and other variables, I’m not changing them.

You shut your mouth when math is talking! (Image courtesy of xkcd.com)

Ok, let’s get to the question and answer portion of the program. First let’s look at rebounding. Are there diminishing returns for rebounding? As I said, I have the data (yeah, every single player and season since 1979, I made excel tap out today), let’s take a look:

If I look at the best rebounder (by qty i.e the most rebounds for that team) rebound rate per 48 minutes for every team vs. the rebounding rate there appear to be a diminishing effect. A correlation of 3.4% does not much water. Did I just prove diminishing returns for rebounds? Not so fast there Kemosabe:

If I take out the best rebounder (by qty) for every team, I may not be getting the best rebounder by rate (per 48 minutes), however if I take out the best two rebounders on each team I get a surprising result. There is a positive relationship between the two best rebounders on each team. So having two good rebounders next to other increases returns (everyone else does see some mild dropoff at the extreme). Who knew?

Phil Jackson knew that's who!

Again fairly weaksauce correlation though.

The second question had to do with above average rebounders. Can I find one with >15 treb per 48 whose teamates where above average. Here’s a list of all the players whose rebound rate is >15 per 48 who led their team’s in total rebounds since 1979 :

So the average rebound rate per 48 for teams without their best rebounder is 7.65. By my count 40% of the people on this list qualify.This concludes the rebounding portion of our program.

Let’s talk Wins Produced. If I repeat the exercise I did for rebounding of comparing the best player on each team (in terms of wins produced) vs the rest of the team:

No diminishing returns here. The data is a little stratified so let’s repeat the trick of looking at the second best separate from the rest but this time let’s add the third best:

Now Color coded for your protection

Hmm. Fascinating. Playing with good players makes you a better player. Instead of diminishing returns for WP48, we see increasing returns, and it holds for your top three. And it doesn’t affect the rest of the team that much. Again who knew?

Anything is Possible

We end with

Some links:

1. First the Wages of Wins FAQ
2. My FAQ (the Basics)
3. If you still feel that rebounding is being misrepresented (and have a few days to spend being entertained) go read Ben Morris’ Rodman Opus and don’t miss the bit about rebounding being undervalued.

Drive safely and remember some things never change.

Although New Actors may play old roles

• Download Direct – Wages of Wins Podcast for 6/8/2011
• Find us in the iTunes Store – iTunes Store
• Subscribe to us via RSS – RSS Feed

Super Stats Team Assemble

Weirdly, they held a Finals game in Dallas tonight and only one of the Heat showed. This is very unexpected.

Prior to the Finals these were the Heat:

Lebron and D-Wade played like superstars (Wade was limited somewhat by some injuries). Throw in excellent performances from Bosh and Miller and Miami looked like world beaters.

A funny thing happened when we got to the Finals:

On the biggest possible stage, with the biggest possible stakes, Dwayne Wade has cemented his claim as the true heir to his Airness.

No Kobe. Not Yours. 6-24

Lebron seems to have gotten lost as of late. Bosh is on a Milk Carton.

Miami is a crazy heave three from Chalmers (that probably should have been called a backcourt violation) from being down 3-1 and facing elimination.

As for Dallas, who you may have heard won the game, even the German fever couldn’t keep him down. However, Rick Carlisle be warned, I watched the game with my wife, a passionate Barea fan, and it only took a quarter for her to start blaming his girlfriend for his trouble. The experiment was not a success.

Fun stat time: Dallas is + 14 in the last three fourth Quarters in this series.

For the series so far, the numbers look like so:

D-Wade is fantastic but he’s getting beat by a basketball team. His teammates have become hard men to find.

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated 6/6/11)

The Box-score follows (I fixed TS% and added totals).

Heat-Mavs Game 4

Tyson Chandler as the Dallas MVP for the game. Marion and DeShawn stepping up. My wife joking that Jason Kidd played like he had money on Miami with those crazy passes. The best basketball player alive looking anything but for yet another finals game (2 for 8 career by my count).

Dirk’s night wasn’t very different from Lebron’s. Except for the fact, that once again, fever and all it got better in the end.

Critical Game 5 coming up.

Paging Lebron James.

Like MacGyver, I'm a bit busy tonight (Image courtesy of xkcd.com)

But it’s a good one.

Enjoy.

One of the most commonly held truths in the NBA is the following: “You need the big man to win” . This illustrates to me that the perception of value by position in the NBA is fixed. The fixed nature of this notion to me is disconsonant with the way markets work in the real world.

Markets in the real world are fickle,variable things. Values and Prices change and move based on an almost limitless and not easily quantifiable set of variables. Predicting them as investors, the Fed & other world-wide institutions have sadly found out is a nontrivial challenge. However in sports, we have certain advantages .

Markets in sports have the advantage of being confined to a discrete set of parameters and results. Points, Wins, Losses and other easily measurable statistics are available for analysis and proper valuation of assets (independent of whether or not this analysis is done, the data exists). This is what allows us to make a reasonably accurate quantitative assessment of an NBA players’ value to his team.

One of the innovative features of Wins Produced is that it focuses on the Marginal Value of an NBA player vs others playing the same position for the particular set. According to Wikipedia, marginal value is:

1. a value that holds true given particular constraints,

2. the change in a value associated with a specific change in some independent variable, whether it be of that variable or of a dependent variable, or

3. [when underlying values are quantified] the ratio of the change of a dependent variable to that of the independent variable.

What this means for Wins Produced is that a player is always compared to his peers and his value is measured based on the competitive advantage in wins he provides to his team. So knowing a players individual statistics in a vacuum is not enough to evaluate him, you have to know who he will be playing against,the talent pool, to make a truly informed decision.

So now we can finally get to some pretty charts. When we look at the Average player value measured in ADJP48 (the oft mentioned position adjustment) over time we get the following:

Which looks like this in a Chart:

What this data shows is that the level of talent at a given position is not fixed over time but rather an ever evolving variable. The marginal Value of a player to a team is a function of the talent pool of peers competing against him. The value of an employee (the player) to the employer (NBA Teams) is heavily influenced by other available employees.

Let’s pick some examples to illustrate what this means. I decided to use the best seasons available for players who played solely in one position (except for SG where I adjusted up from 98% beacuse of scarcity).

Name Pos Year Team Sum WP Total WP48

Chris Paul PG 2009 NOH 28.81 0.461

David Robinson C 1991 SAS 29.25 0.454

Larry Bird SF 1987 BOS 25.79 0.412

Charles Barkley PF 1993 PHO 24.59 0.413

Dwyane Wade SG 2009 MIA 21.61 0.340

We will call these five seasons our Standards Seasons by Position. These were all great seasons when they happened but what would their value be if we moved them over time:

That Would be a hell of a Team

It’s clearly evident from these charts the talent floor in the NBA is not fixed. As impressive as Chris Paul season in 2009 was it would have been 4.4 wins better in 1978 when the quality of point guards was much less. There’s a long period (about 85-05 ) where big men were scarce but now a dominant big man is not as much of a competitive advantage (sorry Magic fans). Right now the biggest marginal values are to be had at Shooting Guard, Point Guard & Small forward (sorry everyone who isn’t a Heat fan).

Big men (C & PF) have won championships and probably will again but right now the little men look to be Kings.

Hunter S. Thompson “Hell’s Angels”

My first impression on seeing the Chalmers heave go in was that the game could very well come down to that one lucky break.

This game was played on the edge. It was a back and forth. Dallas was sloppy in the first half and Miami took advantage. Dallas turned up the pressure and cleaned it up and came back. Wade and Nowitzki dueled like masters all night. The refs made homecourt manifest.

Dallas came all the way back but the last shot was down two instead of up 1. Made all the difference.

For the series so far, the numbers look like so:

For Series MVP there is but one answer.

That would be Mr. Two Time Finals MVP Dwyane Wade

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated 6/6/11)

The Box-score follows (I fixed TS% and added totals).

Heat-Mavs Game 3

Seems fitting that a pivotal game in a Mavs- Heat finals is all about Wade and Dirk. Both were Amazing. In the end Flash won out over the Dagger. For this game though, who didn’t show up was the most important. Bosh was horrible but JJ and the Matrix out stunk him tonight.

Honey, I think you put in the wrong DVD

The developing story for this series seems to be that only two guys for the Heat are showing up but Dallas is playing such uneven games that it isn’t really mattering.

And of course, Dwyane Wade is the the greatest shooting guard since Michael Jordan.

Must Win for Dallas coming up.

Pick the 150 Best Performances.

Use it to make a kickass poster.

Or two.

Enjoy your sunday.

See you after Game 3 .

Or a simple fun post.

This is an oversight we are going to correct .

You can blame my partner in crime Andres Alvarez and Benjamin Morris for this one.

Andres Alvarez (@NerdNumbers) for asking the following on Twitter:

“Opinion question. Does getting the #1 Pick in the Draft Lottery really up your odds at a title?”

And Ben for writing a nice piece in response (see here ,seriously, go read it).

Ben’s finding is that yes it does matter.

Long-time readers know that this is old and fertile ground for me (see here for a good example).

Let’s just say inspiration hit like a sledgehammer.

Or like this

I dug out some data sets and confirmed  that I do agree with Ben. The devil though is in the details. Which  I’ve spent some time actually working out.   The key point is drafting right and knowing when to trade.

Coming up with an actual pick value chart helps. That looks like so:

Consider that a  preview of future draft content (any NBA teams that are interested in a table version of that in advance make all checks payable to Arturo )

Spoilers and Foreshadowing

But let’s get back on track.

I decided that it would be fun to look at the draft pedigree of all the NBA Finalists since 1978. I also decided that to actually count as the team getting something from you in the finals you had to crack the Top 6 in the Rotation ( see the Half Baked Notion section of the Basics for explanations).

So I did:

Turns out Miami-Dallas 1 and 2 have been the most loaded finals in terms of Draft Talent since 1991 Bulls-Lakers.

That’s not the poster though.

This is:

See you tomorrow.

At 7:14 left in the 4th Quarter, Dwayne Wade made an open three to put Miami up 88-73. The Miami Crowd went insane. Lebron and Wade celebrated in front of the corpse of the the Dallas Bench.

Mission Accomplished right?

Wrong.

It’s always darkest before the dawn.

Feel free to press play.

Because Dirk? He just did it again.

Mark Jackson was right for once.  There goes that man. He just had the wrong man.

Enter the Matrix

We tend to forget that Shawn Marion is a really good player.

22 to 5 in Dallas’ favor from that point on and a win for the ages. Cue a hell of a lot of sportwriters with  a rewrite on their hands.

Apparently Chuck talked to the Mavs during the commercial break after the Wade 3

I kept watching the game and thinking:

• Miami is getting outrebounded and taking bad shots .
• Dallas is playing sloppy ball and that combined with Miami’s athleticism is what’s costing them the game.

Regression to the mean is a hell of a mistress.

Game on.

Before we get to the boxscores: that Podcast I promised? It’s up (just not here, see I didn’t miss a post we just relocated to a new home).

Foreshadowing, you'll get this eventually. (image courtesy of xkcd.com)

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated soon)

The Box-score follows (I fixed TS% and added totals).

Heat-Mavs Game 2

Dwade was legendary for the Heat and Lebron and Bibby were fantastic. On a typical night that would have been enough. But the rest of the Heat didn’t show up to the party (or maybe left early to catch happy hour on south beach?).Bosh may have actually  had a few before gametime as he seemed to be playing for the wrong team at times.

The secret, Mavs fans is that Dirk didn’t put together a great game, Shawn Marion was the One true MVP for the Mavs in this one. Dallas showed up six deep (Marion,DeShawn,Kidd,Terry and Dirk) to steal homecourt.

I thought the game 2 winner would win the series. Still do.

Heat fans, meanwhile, learned a lesson familiar to any soccer fans reading this space:The Jerry will always break your heart.

Did I jinx us? Next question.

OK, maybe I have time for one cute kitty

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated Now)

The Box-score follows:

Heat-Mavs Game 1

Lebron and Wade were great for Miami. Mike Miller was good. Bosh pitched a .000 WP48 night. For Dallas, the Matrix was great but not so much for their other key players. Haywood, interestingly had a good night despite being stuffed by the rim.

Haslem continued to not play well but foul trouble for Chandler and an off night for Dirk kept it from being a major issue.

For me, the game 2 winner wins the series.

More tomorrow.

11-3 thru 14 series.

Let’s get the post-mortems out of the way and make that pick.

Because this model comes with references

Background first:

It’s about to get math intensive here so if it’s your first visit (or if you need to bone up) that’s what the Basics are for.

The NBA Playoff Preview and the Picks (Part 1: Just the Facts) Breaks down all the Maths and theories in detail.

with the visual summary here:

The Playoff Checklist for all the Factors:

And The Team Vitals for everyone:

The Team Vitals (Top 6 Approximation):

The NBA Playoff Preview and the Picks (Part 2: Skin in the Game) Breaks down all the 1st round picks.

First Round Recap and Second Round Playoff Preview by the Numbers: Has the post-mortem on the First round Picks (and why in a choice between math and instinct you should go with math) and breaks down all the 2nd round picks.

Pure Win: Round 2 Wrap-Up, Conference Finals picks by the numbers and Smackdown Update:

Has the post-mortem on Round 2 and the continuing inspirational story of math triumphing over incredible odds.

Numbers are your friends (Image courtesy of xkcd.com)

Time for the Round 3 Recap:

Eastern Conference Finals: Chicago vs Miami

I ran the numbers:

In ever possible iteration, the Bulls emerged as the superior team. This lead to this:

and:The Results

Series 1: Chicago vs Miami: Win % @ Neutral Site: 55% for Bulls

The Numbers said: Bulls in 7

Result: Miami in 5 (6th most likely predicted scenario at 6.8%)

And then the rotations all changed and the last piece of the Miami Superfriends I expected finally showed up (hello Mr. Miller) and it killed all our predictions.

Next Year in the East, Not looking good. Pray for some bad Heat luck. Pray.

The Heat rolled with the 5 players they wanted (Lebron, Wade, Bosh,Haslem and Miller). The Heat adjusted to the Bulls. The Bulls failed to adjust. The experienced Heat beat the less experience Bulls (but we covered that already).
It’s important to note that given all that the Bulls were still a hair’s breadth away from being up 3-2.

The Recap follows:

For the Bulls, Rose was very bad. He looked his age. The good news for Bulls fans is that I expect him to continue to look his age in the future and get better.

On the opposite side of the ball, Lebron led his second team to the Finals. Bosh and Miller played very well. Two important points for the finals pick are that Wade did not look right at the end of that series and that reports of Haslem’s comeback are vastly overrated. Case in Point:

Keep in mind that those are the 5 games Haslem has played in 2011.

Notes so Far:

• Lebron very,very good (so good that he merits his own theme music)
• Miller and Bosh good
• Wade good but possibly banged up
• Haslem playing but not really back

Let’s head west.

Western Conference Finals: Dallas vs OKC

I ran the numbers here as well:

And:

You’ll note that I added with and without Dirk into the equation and much like the Bulls and Heat in every possible iteration I came up with:

And a pick of Dallas in 7 (but Dallas in 5 was close behind)

The results

Series 2: Dallas vs OKC: Win % @ Neutral Site: 53% for Mavs

The Numbers said: Mavs in 7

Result: Mavs in 5 (2nd most likely predicted scenario at 17.9%)

OKC was done after their Game 4 Collapse.

Particularly when Dirk Nowitski was historically offensive.

The series wrap looked like so:

Five Mavs played above average ball to 4 for the Thunder. Russell Westbrook did kill OKC but he, like Rose simply looked his age. He’ll figure it out soon enough.

Couple of key learnings:

1. Nobody is stopping Dirk. No one. He will get his point even if you hit him with a nine iron. Haslem is Miami’s best option here (but he still looks off).
2. Kidd can run a heck of an offense and he’s tricky and more than a little dirty on D. Miami has nothing on the point to match him.
3. The Matrix looked like his old self. He’s going to need that Mojo because a bad man is coming for him
4. Tyson Chandler will be the best center in this series.
5. Barea and Terry are both bipolar. Dallas needs tham on the positive side of the equation.

Let’s talk Finals.

The Finals

That’s the plain wins produced numbers for the playoffs. The Mavericks have looked significantly better than Miami in the playoffs against what I would consider equal opposition (perhaps even slightly better opposition given the Boston series).

But keep in mind that we use the actual minute allocation each team has used for the playoffs combined that with the raw productivity (ADJP48 not Wins Produced because having a big as your sixth man? In general better than having a small).

Please note I said : “In General”

There are some key assumptions I had to make for this series as I did my model:

• Dallas has looked better than their full regular season numbers. In fact in general they’ve looked more like the second half team.
• No-Dirk Dallas games are not considered in the model.
• Haslem is not healthy and his 2010-11 regular season numbers are a small sample size (about 300 minutes) and not representative of the current reality. So straight playoff numbers for him.
• Wade looks banged up but I’m assuming he’s 80% and improving.
• Mike Miller is back.
• Miami will go with the minute assignment from The Bulls series.

With all those assumptions, the pertinent numbers for this series are:

Which is how each team projects based on playoff minute allocation and their ADJP48 numbers.

To summarize, Mavs better post-all star break and in playoffs. Miami better in regular season and overall (by a sliver).

This series really is a coin flip. Miami has the Homecourt though.

It’ll come down to one man.

Udonis Haslem is the key. I think he’s not there yet and that will make all the difference at the 4 and 5.

The final numbers are:

To be clear, the straight model likes Miami by the barest of margins (i.e. Miami in seven). I’m choosing to give Dallas full credit for what they’ve done since february. The math in both scenarios looks like:

In the end, I am going off book again. The full numbers like Miami in 7 (a 20% chance by the straight numbers or #1 option and a 17% chance by the second half favoring numbers i’m going to roll with or #2 option).

I’m putting my faith in what Dallas has done lately and trusting the post All Star Break and playoff numbers.

My final pick is Dallas in 5 ( a 14.2% chance straight and option #3 and a 21% percent shot by the second half favoring numbers).

Besides, it wouldn’t be any fun to root for the Heat against this Dallas team. I’m also hoping for a little big Z.

Instead I decided to illustrate the hell out of these playoffs.

Because information is beautiful.

And funny too

The Rundown

Let’s start with a quick roundown of the stats we are going to use:

• Basic information: Player , Team, Number of games, Minute per game, Position
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position. Thes will be in total and per 48 minutes mode.
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• The new additions are Game Impact stats. Given that series lengths are different and player minutes are different as well,  I wanted to work out the player value both on Wins and Point Margin. I worked out Wins Produced per game, the Wins Produced that could be expected from an average player and I also worked out the point margin generated by that player ( This is (WP per game for Player- WP per game for Average player)*15.5)

Rounds are sorted by game impact. Overall is sorted by total impact

Ready. Fight!

Round 1

First we sort by overall impact.

Then we sort by team.

Finally a Word Cloud by point margin produced per game (upside down text is bad i.e. negative contribution).

CP3 is your round 1 MVP followed by Lebron and then Dwight.

LVP goes to Hansbrough.

Round 2

First we sort by overall impact.

Then we sort by team.

Finally a Word Cloud by point margin produced per game (upside down text is bad i.e. negative contribution).

Round 2 MVP goes to Wade followed by Dirk and the JET.

LVP goes to Jamal Crawford.

Round 3

First we sort by overall impact.

Then we sort by team.

Finally a Word Cloud by point margin produced per game (upside down text is bad i.e. negative contribution).

Your round 3 MVP goes to JKidd followed by Lebron and Harden.

LVP goes to DeShawn Stevenson but Westbrook and Rose get dishonorable mention here.

Overall

First we sort by overall impact (remember this time it’s by totals).

Then we sort by team (again remember this time it’s by totals).

Finally a Word Cloud by point margin produced per game (upside down text is bad i.e. negative contribution).

Playoff MVP? The King.

Playoff LVP? Kendrick Perkins

Maybe Danny wasn’t completely insane.

This one needs some theme music.

I feel a debt was owed. Let’s pay this baby off, my word after all is my bond.

16 Teams, 30 Nights and a fantastic floor show.

After all that only two remain. Let’s talk about the surprising end of the last conference finals.

And on the final night before the Finals, Lebron joined Dirk in the pantheon of  the soul crushing playoff performances.

The Career leader and Chairman of the Board

Chicago again looked like they had control after 4o minutes and again failed to convert. Miami was fantastic in overcoming a 15 point lead in a three minute span.  Much like OKC they have to be thinking what the hell just happened.

We learned a few things in this series:

1. Bulls beat the crap out of Miami in Game 1. Coach Spo adjusted his game plan completely. Coach Thibbs never countered. Miami is well coached.
2. Lebron is an all world defender who can drive an opposing player to drink and if he can actually get a post game we will be talking of him as the Greatest of all time.We knew that already though ( I just  might need to revisit one of the classics then)
3. Udonis Haslem and Mike Miller were vital to the Heats chances of winning the title.
4. Inexperience kills in the Playoffs. Derrick Rose really showed that he lacks those playoff reps. When Miami put Lebron on Rose, Rose should have licked his lips, put his head down and drawn contact . I think of Jordan, Wade or Kobe in that situation and I see a smirk on their faces and a thought bubble with the words : “I’m gonna foul this M#$%F#%#er out”. He’ll get there (he’s 22, people) but he clearly is not the MVP. Thibbs again did not help.
5. A full health Miami team is unexpected and fantastic.

Let’s get to that recap:

Lebron is the clear Wins Produced MVP for this series. Bosh sits at number 2 and Boozer (yes Carlos Boozer) at three. I would argue that Boozer’s dramatically bad defense is underrepresented by the numbers. Wade was bad for him and so was Bosh. Rose however was catastrophic and looked every inch the 22 year old Point Guard (another recurring conference finals them right Russ?).

The return of Mike Miller was the true difference maker for Miami in a series where the scores were actually tighter than the 4-1 end indicates.

I could say that bodes ill for Dallas or I could say it’s not quite enough. But I won’t.

At least not yet.

Let’s get to it.

Links now:

• Dirk and his other very offensive night (Weirdly unsurprising that Barkley is the only player since 1991 to have a more offensive playoff night)
  
• Full Picks and the Recap for Round 2 and the Smackdown Update (I am now in lead currently 11-2 thru 13 series)
  
• Wages of Wins Podcast: Love and Basketball
• MVP post.
• 1st round wrap-up and second round picks are up (for some crazy reason I had the Mavs )
• Recaps for 1st Round series
• Andres Alvarez with: Bulls-Pacers
• Andres Alvarez with: Thunder-Nuggets
• Heat-76ers by Mosi Platt of the Miami Heat Index:Heat Produced: Breakfast by the Bay
• By Yours truly:Knicks-Celtics
• By Yours truly: Spurs-Grizzlies
• Recaps for 2nd Round series
• Andres Alvarez with: Mavericks-Lakers

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated Now)

The Box-score follows:

Bulls-Heat Game 5

The three best players for the Bulls were Noah, Brewer and Thomas.  They all played less than 28 minutes. Bad Job by the Bulls of optimizing their resources (I’m not even going to get with their tendency to shorten the playbook to Iso-Rose ). Contrast that with the Heat getting the absolute max from their stars and you see why it ended the way it did.

As for the end of that game, Lebron was so unreal that the next image comes to mind.

He's Lebron. Basically, Run.

The interesting bit is that their opponent brings this image to mind:

Daleks Dirk, Jason, Matrix, Jet, Chander and Boricua. Relentless and not running.

And in true sci fi tradition, this one is to be continued in the wrap-up and the preview.

Coming to you this fine Memorial day weekend.

Because I am definitely a madman with excel.

Here’s a nice little bonus:

and here’s another:

and one final one:

A while back I looked at  Championships Produced and it was cool.

The concept was that winning the championship is about winning in the playoffs. A team needs 16 wins in the playoff to win it all now (and 12 or 15 at different times in the past).

A player’s greatness should be a function of his contribution to winning a championship. His wins. His share of a championship. If I take the Wins Produced by each player in the playoffs and divide it by the wins required to win a title I come up with a nifty little metric : Championships Produced.

Which I then graphed like so:

Dre thought it wasn’t quite right, asked me for the files and did a quick remix and it was also cool.

But it wasn’t quite there yet.

In this one only seasons with >.1 Championships produced and only Players with >.2 Championships Produced are shown. Players with >1 Championships get Gold Fill. Players with >.75 Championships get Silver Fill. Players with >.50 Championships get Bronze Fill. NBA Finalists are in Bold for that Year. NBA Champs are in Blue,Bold and Underlined for that Year.

The simple findings?

• You need a gold or silver player to win. Full stop.
• No team wins without a gold or silver player who’s done it before. Magic, Horry, Duncan,Kobe, Manu,Billups, Prince, Rondo did it on the first go but were playing with Kareem, Hakeem,Robinson, Shaq, Duncan,Wallace & Garnett (all bigs). So Howard needs that plus one to get it done (Lebron had it right).

Oh and given those rules the Contenders I identified were:

It was all about Boston,Miami, LA, San Antonio and Dallas.

Orlando and Chicago had some pieces but I don’t think they’re there.

All there in black and white.

Sometimes, I can outsmart myself.

Not a Mark or Reggie fan I suppose (Image courtesy of xkcd.com)

Boxscore post will follow in the AM.

OKC was not coming back from the Game 5 Collapse.

Particularly when Dirk Nowitski has officially become the third player in my lifetime who I’m stunned when he misses in the last 5 minutes of a game (Bird, Jordan and now Dirk).

Let’s start with the series wrap first:

Kidd and Harden are the team MVP’s for the series by the numbers but the numbers don’t quite tell the tale for Dirk. We have to consider the fact that if Collison had popped Dirk with a hockey stick to the head as he was going for a Layup there’s a better than 50% chance he’d have gotten called for the charge.

Russell Westbrook did kill OKC but let’s get some perspective. He carried them against LA last Year. He’s a 22 year old point guard who looked inexperienced against Jason Kidd and made some dumb calls. He’ll figure it out soon enough

Let’s get to it.

Links now:

• Dirk and his other very offensive night (Weirdly unsurprising that Barkley is the only player since 1991 to have a more offensive playoff night)
  
• Full Picks and the Recap for Round 2 and the Smackdown Update (I am now in lead currently 11-2 thru 13 series)
  
• Wages of Wins Podcast: Love and Basketball
• MVP post.
• 1st round wrap-up and second round picks are up (for some crazy reason I had the Mavs )
• Recaps for 1st Round series
• Andres Alvarez with: Bulls-Pacers
• Andres Alvarez with: Thunder-Nuggets
• Heat-76ers by Mosi Platt of the Miami Heat Index:Heat Produced: Breakfast by the Bay
• By Yours truly:Knicks-Celtics
• By Yours truly: Spurs-Grizzlies
• Recaps for 2nd Round series
• Andres Alvarez with: Mavericks-Lakers

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated typically the day after)

The Box-score follows:

Mavs-Thunder Game 5

Five years ago, if you told me on of the the Seven Seconds or Less Suns would help lead the Mavs back to the Finals, Shawn Marion would not have been my first choice. It took a team effort for the Mavs to get it done as the Thunder played a complete game (except for the insanity with Maynor waving everybody off to go ISO on Dirk but at this point with a Scotty Brooks team that the rule not the exception).

Congrats to the Matrix for getting to his first Finals.

For some reason this came to mind.

Should have, Would have, Could have.....

• Super Stat Team Podcast – 05/25/2011 – Direct Download
• Our RSS Feed
• Find us in iTunes

Below are some extra notes to help you enjoy the podcast even more (as if that were possible!)

Super Stat Team Assemble

• Andres Alvarez of Nerd Numbers
• Arturo of Arturo’s Silly Little Stat’s
• Daniel Alvarez of Alvarez Films
• Devin Dignam of Nbeh?

Falling Stars

• Russell Westbrook has played well, what happened?
• Derrick Rose is now negative in this round of the playoffs.

All Time Greats?

• Jason Kidd is old and great. He may just have good timing.
• Is Dirk top 10. . . vs.
• Karl Malone? Malone’s Playoff numbers aren’t so good.
• Charles Barkley? Barkley was and still is offensive.
• Miami and the colluding stars is nothing new.
• The Best Center in the League Always Wins A Title? Really?

Management

• Arturo likes Mike Brown to the Lakers. His reasons may not be altruistic.

• John Canzano of The Oregonian writing about the “break-up” between the Blazers and ex-GM Rich Cho. There’s also a story about Brandon Roy from the same writer.
• Collangelo was re-signed in Toronto. Could he possibly help the franchise?

Bad Language and Consequences

• J. A. Adande on the difference between the Noah/Kobe fines
• Article with a transcript of a conversation between Noah and TrueHoop blogger Kevin Arnovitz (who is an out gay sportswriter)

Podcast “Extras”

• This may be the only podcast to ever confuse Reggie Evans with Reggie Miller. Spike Lee has probably heckled both.
• Have you seen Love and Basketball? If not you’re in for a treat (spoiler alert!)
• UStream is what we use to record our podcasts. If you try and listen to yourself you get a fun feedback effect!

Enjoy!

-Dre

Rotation,Rotation, Rotation.It’s all about rotation.

The last piece of the Miami Big Superfriends I expected finally showed up and it killed all our predictions (because even our resident Heat expert had the rotation wrong )

It's going to be a problem when he's healthy next year. For the rest of the NBA.

At the beginning of the season,I wrote quite a bit about the Miami Heat. It was only natural as it was the biggest off-season basketball story in three years.

Miami finally got to roll with the 5 players they wanted (Lebron, Wade, Bosh,Haslem and Miller) to roll with and it threw off all our predictions.

The Miami Heat are now finally the Bad Guys, the main event.

It's game time.

The fantastic part is that we now have a proper foe for them as well.

The German Assassin, the Puerto Rican Flash, the Defensive Powerhouse, The ex-Sun and the old Kidd

The Finals (if they’re Dallas and Miami and it certainly looks that way) will rock.

Let’s get to it.

Links now:

• Dirk and his other very offensive night (Weirdly unsurprising that Barkley is the only player since 1991 to have a more offensive playoff night)
  
• Full Picks and the Recap for Round 2 and the Smackdown Update (I am now in lead currently 10-2 thru 12 series)
  
• The Miami Heat Ruin a Prediction (and other notes from the Weekend Podcast) (new podcast is coming today)
• MVP post.
• 1st round wrap-up and second round picks are up (for some crazy reason I had the Mavs )
• Recaps for 1st Round series
• Andres Alvarez with: Bulls-Pacers
• Andres Alvarez with: Thunder-Nuggets
• Heat-76ers by Mosi Platt of the Miami Heat Index:Heat Produced: Breakfast by the Bay
• By Yours truly:Knicks-Celtics
• By Yours truly: Spurs-Grizzlies
• Recaps for 2nd Round series
• Andres Alvarez with: Mavericks-Lakers

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated typically the day after)

The Box-score follows:

Miami-Chicago Game 4

Miller and Chalmers at one and two for the Heat.  Deng and Boozer for the Bulls. Rose would be at number two, if he played for the Heat. I will defend  him somewhat in that as good a defense as Thibs runs, his offensive sets (hey, it’s an iso party!!!) leave a lot to be desired.

For the series so far:

Rose is pulling a mini-Westbrook on the Bulls. Mike Miller a mini-harden.

Podcast coming in a few and I just might have a bonus post up today as well.

We'll keep your basketball geek fed today

OKC was up 15 with five minutes left and then their best player fouled out.

Starting to Remind me of Manu (and I mean that in a good/bad way)

And then it became Dirk time.

German Assassin

Dirk, Kidd and Marion on D. The Thunder got pantsed by the Mavs.

I was mad at the refs after the last game that Dirk was getting mugged by Collison every time he had the ball. Dirk however adjusted and went completely medieval on the poor sod.

To channel Charles Barkley, this one is over. Dallas is going back to the Finals.

Let’s get to it.

Links now:

• Dirk and his other very offensive night (Weirdly unsurprising that Barkley is the only player since 1991 to have a more offensive playoff night)
  
• Full Picks and the Recap for Round 2 and the Smackdown Update (I am now in lead currently 10-2 thru 12 series)
  
• The Miami Heat Ruin a Prediction (and other notes from the Weekend Podcast)
• MVP post.
• 1st round wrap-up and second round picks are up (for some crazy reason I had the Mavs )
• Recaps for 1st Round series
• Andres Alvarez with: Bulls-Pacers
• Andres Alvarez with: Thunder-Nuggets
• Heat-76ers by Mosi Platt of the Miami Heat Index:Heat Produced: Breakfast by the Bay
• By Yours truly:Knicks-Celtics
• By Yours truly: Spurs-Grizzlies
• Recaps for 2nd Round series
• Andres Alvarez with: Mavericks-Lakers

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated typically the day after)

The Box-score follows:

Mavs-Thunder Game 4

Surprisingly, it was Dallas’ other first ballot hall of famer that was the MVP of the game. Kidd owned Russell.  Durant continued to show but losing Harden crippled the Thunder.  The Thunder were up 99-84 when he fouled out. They lost 112-105 (on a 28-6 run).

The story for the series so far:

Missing your best player is death in a close game as the Thunder found out.

As a final bonus here’s the total numbers for Round 3 so far (for every player sorted by Wins Produced and ranked by categories for all players with >15 MP per game):

Russell is Killing OKC.

The good news for Chicago is that Carlos Boozer finally showed up on offense.

The bad news is that means you have to live with Bosh feasting on Boozer’s bad interior D.

The refs were bad but well within the expectation for Homecourt Advantage.

As for game MVP? The same guy who probably deserved the most votes of the guys playing in the Eastern Conference Finals.

The Should be three time mvp (possibly)

Let’s get to it.

Links now:

• Dirk and his very offensive night (Weirdly unsurprising that Barkley is the only player since 1991 to have a more offensive playoff night)
  
• Full Picks and the Recap for Round 2 and the Smackdown Update (I am now in lead currently 10-2 thru 12 series)
  
• Latest Podcast 
• MVP post.
• 1st round wrap-up and second round picks are up (for some crazy reason I had the Mavs )
• Recaps for 1st Round series
• Andres Alvarez with: Bulls-Pacers
• Andres Alvarez with: Thunder-Nuggets
• Heat-76ers by Mosi Platt of the Miami Heat Index:Heat Produced: Breakfast by the Bay
• By Yours truly:Knicks-Celtics
• By Yours truly: Spurs-Grizzlies
• Recaps for 2nd Round series
• Andres Alvarez with: Mavericks-Lakers

Boxscore Explanations in gray:

I’m going to be posting an advanced Box Score during the playoffs after every game.

It contains:

• Basic information: Player , Team, Game ID (Who,what and when)
• Classic Stats:Points ,Shots, Offensive Rebounds, Defensive Rebounds,Steals,Blocks, Assists (Because the classics are classics for a reason).
• Simple spins on classics: % of Team Minutes (player minutes as % of total minutes available), Position (average player position)
• Possesion and Play stats:
• Offensive Plays : Field Goal attempts + .434 Free Throw Attempts + Turnovers
• Usage of Offensive Plays : % of Offensive plays used by player when in the game
• All the classic Offensive Efficiency stats (and some slightly modded ones):
• Effective Field Goal %=(FG + 0.5 * 3P) / FGA
• True Shooting %=Pts / (2 * (FGA + 0.44 * FTA)
• Points per Shot = Pts/FGA
• Points per Offensive Play= Pts/Offensive Plays
• Do it Yourself Offensive Point Margin Stats:
• Offensive Point Margin: this is the marginal value created by the player per offensive play spent. The calculation is:
• OPM = (Points per Play for Player- Avg Points per play for Player for League)*Offensive Plays for Player
• Defensive Point Margin: this is the marginal value surrendered by the player per offensive play spent. The calculation is:
• DPM = (Points per Play for Opponent- Avg Points per play for Player for League)*Offensive Plays for Opponent. I’m doing this one by position averages per game.
• Combined Margin: this is just OPM-DPM
• Rebounding Rates: % of Rebounds on Offense, % of Rebounds on Defense.
• The Classic Wins Produced stats (explained here) and my own opponent adjusted Wins Produced Stats (explained here). The difference? Classic assumes the opposing player is average. Opponent adjusted goes out and checks by position.

Parking Lot items :

1. Do DPM based on average points per play for opponent.
2. Add other advanced metricss (PER,Win Shares,EZPM, etc.)

Remember kids, my tastes are reflected in this (and they may not line up with yours).

I encourage you to comment and make suggestions as the eventual goal is to build an automatic version of this.

The raw data is here as a google doc (updated typically the day after)

The Box-score follows:

Bulls-Heat Game 3

Two guys showed up for the Heat (The King and Bosh), one guy did for Chicago (Boozer). It was as simple as that.

The story for the series so far:

Lebron is owning this series and so is Bosh. Brewer and Bosh are tops for Chicago but Brewer is not playing and Boozer is killing them on D.

The MVP so far is a total no show.

Rondo did better against the Heat on one arm.

How fitting that the series and the season come down to one game for the Bulls and its down to Rose.

What’s in a name indeed.

As a final bonus here’s the total numbers for the Playoffs so far (for every player sorted by Wins Produced and ranked by categories for all players with >15 MP per game):

Funny that Miami and OKC are both carrying 2 of the top 4 and 1 each of the bottom 3.