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Tag Archive - NBA HD

NBA HD: How To Get Your Free Agents Half-Off

August 26, 2010 in by with 6 Comments

Lost in the whole Free Agentpalooza of 2010 was the fact that the party could have been bigger. Outrageously bigger.

With the cap-slashing climate over the past few years, the writing was on the wall well before the calendar reached July 1, 2010: this class of free agents were due for an enormous payday.  Seeing the formation of the storm on the horizon, organizations wisely arranged meetings with their imminent 2010 free agents and their representation in effort to prevent their prized players from hitting the market at all.  The plan? Sign them to a contract extension.

Contract extensions can be mutually beneficial; the player receives job security  from the team and the team gets the player at a discount.  The latter part of the deal isn’t guaranteed by any means but the team doesn’t have to compete with other bidders to sign their player long-term.  And that exclusivity is a huge advantage for teams.  But how can we quantify that advantage?

Let’s compare some contracts.  Of course, every free agent’s situation is different but to responsibly compare apples to apples, let’s examine the 2006 draft class whose rookie scale contracts were generally due to expire after the 2009-10 season, allowing them to become free agents this past summer.

First, the guys who cashed in early.  Can you imagine if Brandon Roy, Rajon Rondo, and LaMarcus Aldridge joined the free agent sweepstakes? Believe it or not, each of these players could have waited to test the free agent waters but elected to sign long-term with their respective clubs in the fall of 2009.  But they weren’t alone; Andrea Bargnani and Thabo Sefolosha also agreed to contract extensions before hitting free agency.  How much did they sign for? Let’s take a look.

For each player, the first two columns after their name tell us the contract length and dollar amount, with the third column calculating the average salary over that contract. For example Rajon Rondo inked a contract extension with the Boston Celtics in early September 2009 for 5-years, $55 million for an AAV (average annual value) of $11 million.

Then, for each player, I included their 2008-09 Wins Above Replacement Player (WARP09) with the “09″ signifying the year.  I chose 2008-09 to reflect their output before they signed their contract extension.  The WARP numbers are courtesy of BasketballProspectus.com and the brilliant work of BBP author and Indiana Pacers consultant Kevin Pelton.  To be clear, this version of WARP is not his newest version, WARP2, which incorporates an added bonus for players who space the floor with 3-point shooting.  Why? The Basketball Prospectus site has not updated their databases with WARP2 yet so for continuity purposes I opted for the older version.

So, this chart tells us that Rajon Rondo received a $11M AAV contract extension after a 13.2-win season in Boston, meaning he was being paid $0.8 million for each win that he accrued that season.  To be sure, teams pay for future projected performance not past performance, but this provides a quick dollar-value conversion that I’ve outlined in previous articles.

Through some research, I found that teams roughly paid $2.25 million for each win in this past free agency period.  Using that standard, the contracts handed to Rondo, Roy, and Aldridge were incredible bargains for their respective organizations.  Sefolosha received a contract fairly in-line with the going rate and Bargnani’s salary hasn’t quite reflected his production in the eyes of the WARP model (although WARP’s opinion is not unique in the statistical nor the scouting world).

All in all, the players who received contract extensions were paid about $1.4 million per win which is far below the free agent price observed this season.  Rondo undoubtedly would have received a max contract had he tested free agency and a case can be made that Aldridge would have pulled one down as well, given his age and productivity.  They left money on the table for job security, ensuring that they’d be set long-term should a career-altering injury occur in 2009-10 (which happened to Roy to some extent).

But how much money did they leave on the table? To find out, I looked at the going rate for their fellow 2006 draftees who received at least three-year deals in free agency: Rudy Gay, Tyrus Thomas, J.J. Redick, Jordan Farmar, Ronnie Brewer and Kyle Lowry. (The three-year qualifier captures players in the same stratosphere as those worthy of an extension and excludes players like Shannon Brown.)

While these free agent deals aren’t all completely egregious, the free agent premium bears out in this small group with the average price for a win costing $3 million compared to the previous group’s $1.4.  In fact, according to this method, inking an extension gave the parent organization about a 50% discount on the commodity of wins.

The biggest difference? In free agency, it’s nearly impossible to sign a talent like Rondo at a clearance markdown price.  Rondo has nearly four times as much impact on the standings as Rudy Gay but the latter will earn about $25 million more over the next half-decade.

So how are teams able to convince players to sign extensions that are probably below their market value?  Well, it’s not easy.  It’s paramount for an organization to produce a winning attitude from the top on down.  That means not just winning in practice but also in style (right, Dan Gilbert?).  It’s the responsibility of the owner, front office staff, and the coaching staff to make the players feel like there’s no sense to risk losing the professionalism, commitment, and comforts they can enjoy at home.  In other words, make your lawn as green as green gets.

NBA HD: Market Update II

July 30, 2010 in by with 2 Comments

Richard Jefferson signed a few days ago which mean’s it’s time for another market update on the free agency price.  To recap, I’m comparing every newly-signed player’s salary to their WARP2 from last season. It’s a quick and handy measure of how much teams are willing to pay for talent this summer.  Last year, the going rate for free agents was $1.49M for each win.  This year? It’s risen to $2.2M.

Here’s the full run down:

To reiterate from last week, the last row on this table subtracts players who likely signed for lower than the free market rate (LeBron James, Dwyane Wade, Chris Bosh, and Dirk Nowitzki).  This takes a more accurate view of what teams pay free of cap spending restrictions.

$2.2 million per win is an increase since the last count because the newest additions have been sold at the rate of $3.3 million.  WARP2 didn’t think much of last year’s production of Ronnie Brewer, Joel Anthony, Richard Jefferson, and Marquis Daniels but teams were willing to pay more than the minimum and in some cases, much more than the minimum for these near replacement-level efforts.

Newly minted Matt Barnes looks like one of the best deals of the summer and should help bolster the Lakers’ chances of bringing home another championship.  It’s hard to imagine Ridnour posting another 5.3-win season but this objective method makes David Kahn look like a genius.

Next week, look for a team by team and position by position break down.  We’ll see if the summer’s $700,000 premium sticks.

Much thanks to Kevin Pelton of Basketball Prospectus.

NBA HD: Market Rate Update

July 15, 2010 in by with 8 Comments

In last week’s post, I calculated the going rate for free agents this summer by applying the dollars-per-win method that others have used in the past.  If wins were a known commodity (which they aren’t), then this would represent the price.  To be sure, not all teams value wins at the same rate.  Some teams have an easier time swallowing risk or ignoring risks altogether.  But aggregating the talent and dollars gives us a good window into the pricing climate.

I found that teams were paying about $2.23 million per win according to Kevin Pelton’s WARP system.  Since then, Kevin has kindly pointed out that he made an updated version of WARP (referred to as WARP2) and they haven’t been published yet on the Basketball Prospectus player cards just yet.  Being the helpful gent that he is, I was sent the WARP2 numbers for last season (denoted 10WARP2) and those numbers are reflected below.

You’ll notice I have cited two prices here.  The $1.69 million pricetag reflects the entire free agency lot with the max guys included.  However, several high-end players had their salaries artificially capped due to the CBA rules that restrict the max salary to about $20million annually if resigned by their former club.  Moreover, we witnessed a rare circumstance where players took paycuts below their max cap (LeBron James, Dwyane Wade, and Chris Bosh) so that the team could have more money to fill out the roster. Consider that Joe Johnson will get paid more than James despite only being half the player James is.

There isn’t a perfect way to handle this designed deflation but I chose to ignore the players fulfilled the following qualifiers a) received the max allowable contract AND b) produced at least 10 WARP2.  This takes out Dirk Nowitzki, Bosh, James, and Wade —  all of which would almost undoubtedly receive bigger contracts if the CBA gave them that right.  It’s arguable whether someone would have paid higher than the max for Johnson, Stoudemire, and Rudy Gay, but their included in the Total w/o “max” price.

So, with that in mind, it seems as though the market rate has slid from $2.23 million per win to $2.01 million per win (or just $2 million) over the past week.  As I mentioned in my previous post, the 2009 summer going rate was approximately $1.49 million which means NBA teams a whole are paying about half a million dollars more for each win.

You could interpret this finding in a number of ways.  An argument can be made that agents are simply getting better at selling the product but that’s probably not something that happens collectively overnight.  It could also be a sign that front offices are desperate to pacify their anxious fans and thus, will use their enormous cap space to land someone of name-value.  Remember, fans have been promised the moon in the Summer of 2010 TM and that demand has pushed up the price.  But it’s also very possible that the impending lockout has caused more players to enter the free agency market to secure a long term contract.  In turn, the supply has been raised as well, but maybe not as much.

It’s also worth noting that the $1.49m price from 2009 was derived with the original version of WARP and thus, the price could change slightly.  However, I don’t have any reason to believe it would change the price significantly as the system adjustment was merely a minor tweak to allocate more credit to 3-point shooters for floor-spacing.

So the $750,000 premium we saw last week has been reduced down to a not-too-shabby $500,000.  The players will take it — while they can.

NBA HD: This summer’s $740K premium

July 8, 2010 in by with 8 Comments

With Joe Johnson receiving a max contract and Darko Milicic taking in $20 million from David Kahn, it seems as though teams are recklessly showering free agents with money this summer. It’s a sellers market; teams are flush with cash and promising the world to their fans.

But what do we mean when we say that a team overpaid for a free agent?  Whether you know it or not, our minds gather bundles of basketball information (How good is this player?), transfers that to a dollar amount (What is that product worth?), and compares it to the price tag (Was it a good deal?).  The wondrous mind is able to perform this function in a matter of seconds.  But let’s try to slow it down and put it on paper.

One approach is to quantify player value on the court and then observe how much that product costs on the market.  The market prices stabilize only after several deals have been made and they change from year to year as player as new money enters the market (say, a Prokhorov arrives or salary cap threshold rises) and/or the product line changes.   The product line has never been stronger and the suit pockets have never been deeper.

There are several player metrics out there that attempt to quantify player value on a scale of wins: John Hollinger’s Estimated Wins Added (EWA), Dave Berri’s Wins Produced (WP) and Justin Kubatko’s Win Shares (WS).

The player metric I’ll use for these purposes is Kevin Pelton’s Wins Above Replacement (WARP) which applies the same framework in Baseball Prospectus’ WARP to the NBA.  To account for player value, I will use the player’s WARP for the 2010 season.

So far, the going rate this summer for one WARP is $2.23 million.  This means that in this climate, a 4 WARP player would generally command about a $9 million per annum contract.  Of course, this isn’t ironclad and as shown by Chris Duhon and Steve Blake, who both received four-year contracts after contributing sub-replacement level performance last year, this model will bend going forward.

Remember the Drew Gooden contract that people drew all sorts of insta-snark?  That measures out to be the best bang for the buck deal of the summer up to this point, along with Boozer’s deal.  The years may be long on Gooden but the Bucks got the veteran big man at a steep discount most likely because of his questionable motor and perception that a oft-traded player equals a flawed player.   If he continues to produce on the court, Gooden could be a steal at this climate.

Surprise, Surprise: Darko Milicic was one of the worst deals so far this off season.  The Timberwolves overpaid about $13 million ($2.23 x 0.8 x 4) on the fringe contributor and the signing did little to change David Kahn’s rep as a showrunner.

One shortcoming of this model, as you can see with the cases of Blake and Duhon, is that a straight $/WARP calculation can produce some wonky results off of a poor season.  I looked at adjusting the WARP input to reflect an average of the past two seasons but the going rate remained nearly unchanged ($2.23 per win vs. $2.1 per win).  With that adjustment, Duhon and Rudy Gay became the summer’s worst deals.

Another assumption that this model makes is that production is constant.  Joe Johnson’s contract doesn’t look nearly as bad as it would if we considered his career arc and likely depreciation.  It’s the length that’s egregious; a two-year, $20 million is a much better deal than six-years , $120 million.

So is $2.23m/win an inflated price?  Compared to last year’s free agency, yes.  In fact, teams are paying about a $740,000 premium per win this offseason compared to last summer.  Using the same system for last year’s free agency, teams paid $1.49 million for each WARP unit in 2009.

But there’s still plenty of time for the Grand Opening excitement to calm and the price will likely slide a bit.  The other capped max contracts have yet to be handed out (Wade, LeBron, and Bosh) and their contracts will actually drive the going rate downward since they’re not paid on the free market.  The near $1 million premium may drop down to $500K or $250K by the end of summer.

But right now, players are seeing green.

NBA HD: Winging it with Length

June 17, 2010 in by with 3 Comments

The NBA draft is approaching.  Prepare for the cliches and buzzwords.

Freakish.

Tremendous.  Upside.  Potential.

Ceiling.

Length.

You’ll hear that last one probably the most. Length. Why do we care about length?  Well, extra length gives players advantages within a variety of basketball actions; deflections, live rebounds, and getting shots off requires extension above the opponent, to name a few.

Today’s post looks into the length of this year’s crop and who has notably long or short arms for their height.  It would be amusing to think of it the other way around but we don’t. This guy has the arms of a 6’2″ guy but he’s 6’8″!  He’s a giant!

Before we dig in, let me first say that Jonathan Givony’s Draft Express is our best friend and our valuable resource around draft time.  You can get all sorts of historical player measurements there.  I should also mention that they just started publishing advanced stats for college basketball.  Amazing stuff happening at Draft Express.

Anyway, the first thing I did was calculate the proportional wingspan of a player relative to his height using the prospect measurements dating back to 2000.  I used a regression to predict wingspan from player height (WITHOUT shoes on) with a sample of 916 NBA prospects.

Here we see a pretty strong linear relationship between player height and wingspan.  Want to know what your NBA prospect wingspan should be? Take 98.5% of your height in inches and add about 5.5.  My wingspan should be about 76 inches long.

Who has the longest go-go gadget arms of players that actually got drafted? Let’s take a look.

Oh my, the stars!  Not a very inspiring list, eh? Jason Maxiell had the most “freakish” arms as he’s one of those guys who can touch his knees without bending over.  Fingertip to fingertip, he is 10 inches longer than head to toe.  All told, his wingspan is about six inches longer than we’d expect and proportional for a 6-9 guy.  His extra wingspan is partly responsible for his rebounding rate we’d typically see from a center.

The rest of the list has some big time busts who may or may not have been overrated by their length.  Saer Sene got drafted 10th overall, Fred Jones 14th overall, and Shelden Williams 5th.  As we saw in Game 6, freakishly long arms cannot guarantee a successfully converted dunk in the biggest game of your life (right, Shelden?).  Marvin Williams, the second overall pick in 2005, sits just outside this list as his wingspan was 4 inches longer than we’d expect.

Some other notable extra wingspans: LeBron +0.5″. Thabeet +0.6″.  Durant +3.3″.  Beaubois +4.0″.

Let’s head over to the short arm prospects or as I like to call them, the capital T’s (just look at a capital T).  These guys have shorter arms than we’d expect given their height.

Martynas Andriuskevicius played only 9 minutes in his NBA career, way back in 2005 but he deserves a shot out here.  His arms were over six inches shorter than we’d expect given his 7’1.25″ height.  In reality, he has the wingspan of a 6’6″ guy.  Yes, observant reader, Fred Jones has a longer wingspan than Marty Andy.  For your information, he grabbed four rebounds in 9 minutes which translates to a crisp 17.8 rebounds per 40 mins.  Sample size be damned.

We have some pure shooters (Redick, Kapono) as well as some truly undersized point guards (Maynor, Jordan).  Redick relies on his hops more than most shooters since he wasn’t gifted with long arms. He got away with it in college but he’s taking threes at a much slower rate in the NBA, granted, for several different reasons.  Perhaps one of them is that he has the arms of a 6-footer.

Some notable curbed wingspans: Jon Brockman -3.7″, Chris Kaman -4.1″, Stephen Curry -3.0″, Jared Dudley -3.2″

While we’re at it, let’s go ahead and pull up the go-go gadgets arms and T’s of this crop of 2010 prospects.  First, the long arms:

And the T’s:

Looks like I’ve snooped out the market inefficiency that Coach K has been exploiting for years.  Short armed scorers.

In the future, expect some application of these measurements to NBA production to test their significance in predicting professional success.

NBA HD: Blocks, Steals, and the Scoreboard

June 11, 2010 in by with 1 Comment

One of the great mysteries in basketball is how to grade individual defense.  For years, the basketball fan glanced at a player’s steals and blocks to derive their opinions on whether a player was a good defender or not.  Blocks and steals were the extent of objective  information at our fingertips and conceptually, it made sense to use blocks and steals as a proxy for quality of defense.   While blocks and steals are both good contributions as a defender, they alone offer just a small window into the challenge of stopping the opposition from scoring– the ultimate goal of the defense.

Thankfully, we have more data these days.  The great resource that is Basketballvalue.com publishes the on court/off court lineup data for every player in the league.  What do on/off court defensive numbers tell us? They answer the following question:  How many points does the opposing team score when the player is on the court relative to when he’s on the bench?

In many basketball analytics circles, this is the most useful measurement available.  It ignores box score statistics all together and strictly looks at how a defender affects the bottom line: the scoreboard.

What happens when we compare the on/off court defensive stat to the box score stats?  Maybe you are interested in who tallies loads of blocks and steals, but fails to impact the team’s efforts on the scoreboard.   Perhaps you can’t sleep because J.J. Redick totaled just 32 steals and blocks in 1,808 minutes and you believe those numbers do a poor job of measuring his defensive ability.  Well, I’m gonna touch on all that in today’s post.

First, I gathered every player’s steal percentage (estimate of steals per possession on the floor) and block percentage (estimate of blocks per possession on the floor) from Basketball-Reference.com.  They are estimated because B-R’s calculation doesn’t have actual possession data but the estimate is very accurate. I used these instead of other stats measures because they remove the pace and playing time bias from overall stats numbers on the back of your old Fleer Ultras.  Then, I only looked at player’s who logged more than 500 minutes this season to remove the wild variances due to little playing time.

Next, steal percentage:

The negative trendline tells us that the players who steal the ball a lot tend to have a better (more negative) defensive on/off court differential and steal percentage is statistically significant in predicting defensive on/off differential (p=.0493).   While stealing is significant, you’ll notice that only a small relationship exists.  If they were perfectly correlated, you’d see a straight line of points but the distribution is much more scattered.

There are several reasons steals have a low correlation (-0.108) with on/off differential. For one, a player can steal the ball without playing good defense.  Most steals come from the stripping variety but players also can “steal” the ball by being in the right place at the right time and picking up a loose ball that falls to their feet.  Just like you can’t assume a double play in baseball, you can’t assume the possession would change until a player physically picks up the ball.  Additionally, players who go for steals all the time are playing risky basketball.  What we’d really like to look at is net steals, or the ratio of successful steal attempts to failed steal attempts.  Many players get tons of steals without actually improving their teams defense. Who are they? One way to find these sly cats is to compare their percentile ranks in steal percentage and defensive on/off court differential.  Don’t look at Andrei Kirilenko. He racks up a ton of steals (2.5 stl%, 95 percentile) and also helps the teams bottom line on defense (-5.1 points, 94 percentile).  Direct your eyes to these folk.

This should be a good time to mention that defensive on/off court stats have their faults as well.  It is a fact statistic but it isn’t adjusted to take into account the quality of their substitute players and the lineup they tend to play in.  Additionally, it’s still subject to random variation since there’s a lot of different lineups to tease out one player’s contribution.

Nonetheless, this is still an eye-opening exercise.  All these players saw their teams play better defense (fewer points allowed) when they were off the court.  Monta Ellis has one of the highest steals rates in the league but opposing teams scored more 4.1 points when he was playing defense than when he was on the bench.  Many of these guys are speedy guards but you also have Rasheed Wallace and Jeff Green in there too.  Jeff Green was this year’s worst defensive player if you use the on/off court number as the measuring stick.

I wouldn’t say all of these players are categorically overrated as defenders but we also shouldn’t let their steal numbers color our evaluations.  Steals are good but these players probably exhibit more risky and opportunistic defensive strategies rather than staying home and forcing bad shots.

Let’s move on to blocks.  Here’s what it looks like when we plot block percentage vs. defensive on/off court differential:

There’s  a stronger relationship between blocks and defensive on/off court differential than there was for steals.  Shot blockers clog the paint and prevent high percentage shots whereas guys who accumulate steals only get a couple per game and have less of an impact on preventing scoring.

Still, only 2.7 percent of the variation in a player’s defensive on/off court differential can be explained by their block percentage.  That might seem miniscule but also consider that there’s a fairly large percentage of variation that is simply random/luck.  But there are players who are empty shot blockers.  Typically, you see these guys swat every ball in sight, thereby leaving themselves vulnerable to pump fakes and weak side cuts.  Additionally, they tend to foul a lot and fouling a player going up for a shot is just about the worst thing a defender can do aside from laying down on the floor.

Let’s take a look at the shot blockers who actually don’t improve their team defense (as measured by defensive on/off court).

Theo Ratliff saw a huge boost in minutes after arriving to Charlotte from San Antonio this season.  Always a big-time shot blocker, he’s no longer the defensive asset of old.  He doesn’t take advantage of his height on the boards and he’s certainly slowed down at age 36.  The Bobcats were about 4 points better defensively with him on the bench, despite his shot blocking talents.

With Kurt Thomas, Nazr Mohammed, and Ratliff, we have a nice group of older veterans who may not have the quickness to stay in front of their younger opponents.  But we also have some youngsters (DeAndre Jordan, Brook Lopez, and JaVale McGee) who may have some more work to do.

As a reminder, the on/off court data aren’t adjusted for substitute effects and they are still vulnerable to random variance.  For non-rookies a good follow-up would be to do a 3-year average on/off court to get more accurate data but that would ignore yearly improvement and development.

In the end, it’s best to look at several measures to get an objective defensive evaluation. Clearly, there’s more to defense than just steals and blocks but you wouldn’t know that by looking strictly at the box score.  Steals percentage has little effect on the aggregate scoreboard and it’s best to not let them paint your overall evaluation of the player.

View the data here and you can see that J.J. Redick’s defensive on/off court differential sits in the 9th percentile.  Not so good.

Big thanks to Aaron Barzilai of basketballvalue.com for publishing on his site.

NBA HD: At the Rim, In Living Color

June 2, 2010 in by with 0 Comments

This post updates a chart I published a while back in this here space.  It takes a look at how each team shot at the basket against good, average, and poor at rim defenses.  Today’s post does two things: 1) updates the data for the full regular regular season 2) adds standard error.  It’s colorful.

We find that teams do shoot better against poor at rim defenses (63.8 FG%) compared to good at rim defenses (58.3 FG%).  Some teams reflect this trend uniformly and others buck the trend all together. Let’s take a look at some of those teams.

Washington: Somehow the Wizards managed to shoot better against strong basket defenders than they did against the rest of the league and the only team to do so.  In fact, their 62.9 FG% ranks as the second best mark next to the Cavs.  The Wizards experienced some serious roster turnover this year, using 25 different starting lineups and giving 24 different players run on the court, so if I were to bet on a team to post an odd progression like this, the Wiz would be a pretty good choice.

Boston: As I pointed out in an ESPN Insider preview for the Celtics-Cavs series, the Celtics had struggled mightily against good basket defenders during the regular season and I predicted their performance against the Cavs and Magic would be more of the same.  And they did struggle against Dwight Howard but less so against the Cavs.  The C’s shot just 55.6 FG% against the Orlando (game log here) and 60 FG% against their LeBron and Co.  However, I thought that this would be Boston’s fatal flaw. In the end, it was just a minor flaw and nothing more.  In case you haven’t hear, they made it to the Finals.

New Orleans: The Hornets put up the biggest variability in their finishing abilities at the basket with a standard error of 14.2.  Most of that variability can be blamed against their 20-for-60 shooting against the Chicago Bulls frontline in their two games (and that’s WITH Chris Paul).  The 33.3 FG% shooting at the basket ranked as the worst match-up performance in the league.

Want to learn more about the team vs. team matchups? Behold:

The color fill at the intersection represents a team’s at rim FG% against the  corresponding opponent in their head-to-head matchups this season.  Green’s good for the vertical axis team and red’s bad.  You can go ahead and ignore the white dots.

See the dark red square about four squares in from the left? That’s the NOR vs CHI matchup I just mentioned.  It’s convenient having Charlotte and Chicago as next door neighbors as you can see how the Hornets did well against the Bobcats but not the Bulls (two very good basket defending teams).  Hence the big standard error in the first chart.

You can see Toronto owned the Clippers while Philly destroyed Minnesota at the basket.  Look up your favorite team and how they performed against your nemeses.

NBA HD: Leveling the Draft

May 27, 2010 in by with 3 Comments

It helps to follow multiple sports.  I download a ton of analytical ideas from baseball’s sabermetric community, which is admittedly light-years ahead of basketball’s analytical field.

One of the concepts that I’ve applied to basketball comes from Beyond the Box Score, a must-read site for basketball analysis that I’ve been digging for a couple years now.  It’s their WAR graphs (seen here) that have me and the rest of the sabermetric community going buck-wild.  Today, FanGraphs, the infotastic site for advanced baseball stats, debuted their own adaptation of the BtB’s WAR graphs, allowing the reader to pick and choose their own players to compare.

What are WAR graphs? They compare player careers by charting their best seasons, as measured by Wins Above Replacement (WAR), in descending order to create a career arc.  It tightly consolidates lots of information about a player’s career.

I’d like to present my own version of the WAR graphs that looks at the NBA.  But instead of player careers, I’m looking at NBA Draft talent.  You often hear about a draft class being particularly deep or top-heavy but do we ever follow up on that prognosis? Let’s do that now.

Here’s a BtB-type graph that looks at the talent level of each draft, as measured by EWA, John Hollinger’s WAR equivalent for the NBA.

That’s a colorful bowl of spaghetti, no?  Each line represents a draft class distribution of talent from their best player (as measured by yearly EWA) down to the 30th best player.  It’s probably information overload for some but we’ll shorten the invitation list later in the post.  But let’s go through this one.

If you were to look up “top-heavy” in the dictionary, you’d either find a picture of Stewie Griffin or the 2003 draft class.  LeBron James, Dwyane Wade, Carmelo Anthony, and Chris Bosh were all drafted in that year, not to mention players like David West, Kirk Hinrich, Josh Howard and Chris Kaman who were selected in 2003 as well.  But after Bosh, the talent level drops off and flattens out around the 8th best player.

Looking for the deepest draft? That would be 1999′s draft class, represented by the hollow blue line.  Elton Brand was the top overall pick in that year and also owns the highest yearly EWA among 1999 draftees, but his 13 yEWA doesn’t stand out among the other classes.  You can see the blue hollow line nestled underneath the several classes on the far left.  But listen to this roll call of talent: Shawn Marion, Manu Ginobili, Andre Miller, Jason Terry, Baron Davis, Andrei Kirilenko, Steve Francis, Lamar Odom, Rip Hamilton, Corey Maggette, and Ron Artest.  That’s why you see the hollow blue line’s elbow out in the open at the 12th best player.

All that depth in 1999 drained the talent pool of the following class of 2000 in the hollow orange.  Only one player (Michael Redd) averaged more than 5 yEWA in the NBA while the 1999 class featured 12 such players.  2000 not only had incredibly shallow depth at the top but it remained shallow throughout the draft.  The twelfth best player of the 1999 draft by this measure is starting for the championship favorites this year (Ron Artest) while the twelfth best player in the 2000 draft is starting the NBA unemployment line (Speedy Claxton).

So these are the different shapes of the NBA draft.  Want to ease the eyes and look at just the past 10 years of drafts?

Once again, the 2000 class does it’s best impression of the Jolly Green Giant.  No difference in this trimmed graph.  But now, we get a clearer look at the talent distribution of last year’s draft class.  Blake Griffin’s return from injury and Ricky Rubio’s Western migration will probably pick this class up a bit down the road so it at least has an excuse for its shallow depth.  As is, it’s probably too early to assess the class as a whole.  We saw what a year’s grind did to Goran Dragic, Russell Westbrook, Robin Lopez, and George Hill.  We’ll check back in next year.

What will 2010′s class look like?  The experts suggest this year’s draft is filled with talent top-to-bottom.  If that’s the case, you’ll probably see a talent distribution much like 2005 with John Wall taking the spot of Chris Paul.  Notice the blue dotted line on the first graph and how it sits on top of the others.  That’s what it looks like to have a widely dispersed talent pool.

For more in-depth draft stuff, check out the D.R.A.F.T. Initiative series I ran at ESPN Insider last year.  You can find it on the ESPN NBA Draft frontpage at the bottom.

NBA HD: Inching For A Rebound

May 21, 2010 in by with 4 Comments

In last week’s post, I looked at the relationship between team height and rebounding.  It’s a good jumping off point to investigate the value of height in today’s game.  Building from that, I’d like to tighten my focus and hone in on the play-by-play data.  Aaron Barzilai graciously publishes play-by-play lineup data on his website basketballvalue.com and I’ll be playing around in that sandbox today.

Looking at the lineups, how much did a height advantage affect rebounding?

This approach improves upon last week’s analysis in a couple ways.  To start, we’re looking at play-by-play lineup data as opposed to overall rebounding numbers on a season-level.  This allows us to look into detailed matchups and focus on units rather than full rosters.  Secondly, I’m solely looking at the 3, 4, and 5 positions to better reflect those who affect rebounding the most.  In last week’s study, a few people pointed out that Derek Fisher’s smallness skewed the Lakers height numbers even though he doesn’t really matter on the boards.  (Perhaps the best solution is to weight the effective height by position).

The way the data is presented in the basketballvalue format is as follows:

Which ten players were on the floor? How many possessions were they on the court for?  How many offensive and defensive rebounds did each unit have?

Before I dove headfirst, I made some qualifications.  The units would have to have at least 30 possessions on the floor together.  Thirty possessions isn’t a whole lot (a little more than a quarter’s worth of a basketball game) but with the players held constant, it seemed like a fair line in the sand.  This qualifier eliminates about 63,000 lineup units and leaves a remaining sample of 326.

From there, I calculated the average height of the 3,4, and 5 players on each team and compared them to their opponent.  The widest margin of average height between two lineups was 10 inches or 3.3 inches per player (Grizzlies vs. Rockets). We’ll take a closer look at that matchup later.

For each lineup, I gathered their offensive, defensive, and total rebounding percentages during that time period. So, for example, a dataset may read as follows:

Westbrook, Sefolosha, Durant, Green, Krstic
Frontcourt avg height (Durant, Green, Krstic): 82.0 inches
ORR,DRR,TRR: 30.4%, 87.5%, 53.8%
vs.
D.Williams, Miles, Matthews, Millsap, Boozer
Frontcourt avg height: 79.3 inches
Margin: 2.7 inches
Possessions: 48

So, here we see that this particular Thunder unit made the most of their height against this specific lineup of the Jazz, grabbing 53.8 percent of all rebounds and locking up the defensive boards in partifular (87.5 pct).    Boozer and Millsap are known as talented board cleaners but against the Thunder height,they couldn’t make up the difference.  But not all matchups work in the taller team’s favor.  Digest:

Conley, Mayo, Gay, Randolph, Thabeet
Frontcourt avg height (Gay, Randolph, Thabeet): 83.0 inches
ORR, DRR, TRR: 10.5%, 72.2%, 40.5%
vs.
Brooks, Martin, Ariza, Scola, Hayes
Frontcourt avg height: 79.7 inches
Margin: 3.3 inches
Possessions: 36

Here, we see that the far superior team (in terms of height) got worked on the boards by their smaller foes.  In this matchup, the Grizzlies missed 19 shots but only recovered 2 of those missed shots.  That’s quite an accomplishment for a team giving up 10 inches underneath.  Once again, we bow down to Chuck Hayes’ rebounding prowess.

But these are just two of the 326 matchup pairings that lasted 30 possessions.  What does it look like when we look at all observations in the sample?

Here, we have all 362 lineups and their total rebounding percentages.  As you can see by the positive trendline, the taller the frontcourt, the more rebounds collected (p<.0001).  Earth-shattering stff, I know.  But how much does an inch help? We can use that handy regression equation:

Total Rebounding Percentage = 0.0165*(Height Advantage) + 0.4983

So, for every inch gained in Height Advantage, meaning each frontcourt player has an inch on average on their opponent, we would predict the total rebounding percentage to increase by 1.65 percent.  What’s 1.65 percent? For reference, the Thunder are the sixth best rebounding team in the NBA according to TRR and they are 1.69 percent better than average.   All else equal, having an inch on your opponent matters, but it won’t guarantee a rebound.  In case you were wondering, the lone blue dot on the bottom right represents the Memphis Grizzlies lineup mentioned above.

How about strictly looking at offensive rebounding? Does height more critical on the offensive boards?

It doesn’t appear that height margin matters more on the offensive boards than overall, given that they have nearly identical coefficients (0.01645222 vs. 0.01645211 to be exact).  Like overall rebounding, an inch in average height advantage tends to lead to 1.65 percent more offensive boards.  Any interesting matchups? As you can see on the graph, some units failed to get a single offensive rebound (0.0% ORR).  Who were they? For fun, let’s take a gander:

Curry, Ellis, Maggette, Hunter, Tolliver (-1.7")
vs.
Nash, Richardson, Hill, Stoudemire, Lopez
11 rebounds
31 possessions
-
Rondo, Allen, Pierce, Garnett, Perkins (-1.3")
vs.
B.Davis, Butler, Thornton, Camby, Kaman
13 rebounds
35 possessions
-
Udrih, Greene, Casspi, Thompson, Hawes (+0.3")
vs.
Fisher, Bryant, Odom, Gasol, Bynum
12 rebounds
31 possessions
-
Fisher, Bryant, Artest, Gasol, Bynum (+1.0")
vs.
Foye, Miller, Butler, Jamison, Haywood
9 rebounds
32 possessions

You got the obvious Warriors Nellieball lineup but you also have the Lakers on both ends of the deal.   I’m not surprised that the Odom, Gasol, Bynum lineup grabbed all 12 available rebounds even with Donte Green at the 2.  That’s a ridiculously tall lineup.  However, I didn’t expect the Lakers to be on the losing end of the deal against the pre-trade Wizards.  Interesting stuff, I say.

On the offensive end, it still holds that the taller your frontcourt, the more offensive boards you will tend to collect.  Let’s finish up with the defensive side of the boards.

Again we see a positive relationship with height advantage and rebounding rates but the coefficient here is greater (0.0175).  This means that an inch advantage on the defensive end tends to increase defensive rebounding percentage by 1.75 percent.  This contradicts my finding in last week’s exercised that showed that team minutes-adjusted height had almost a completely random correlation with defensive rebounding rate.

In the end, conventional theory holds true that it helps to be taller in order to grab rebounds. However, it’s not as strong of a correlation as I expected.  Of course, height doesn’t explain all the variation in rebounding rates because there are other factors that contribute to rebounding: wingspan, vertical jump, horizontal jump, reaction time, positioning, etc.  I like that this study holds the lineup personnel constant but I would always like to get a larger sample size. But that has it’s drawbacks too.  When we increase the requirement threshold, we lose reliability.

How do we reconcile this study with last week’s that said height mattered more to offensive rebounding?  Last week’s looked at the forest rather than the trees.  I only had 30 teams in the sample size and the height effects could have been clouded in the aggregate numbers.  Additionally,  in this post, I tried to improve the validity of the measure by only looking at the frontcourt height rather than the whole five.  Perhaps a weighted height would be the best option, giving more weight to the bigger position and less weight to the point guard position.  For another time.

Again, have to thank Aaron for providing such a helpful resource.  Assist point to you, sir.

NBA HD: Does Effective Height Have an Effect?

May 12, 2010 in by with 13 Comments

Height is one of the most sought after commodities in the game.  Being taller than your opponents helps grab a live ball, get a shot off cleanly, and block shots.  But there’s more to length than being tall head to toe.  A player’s wingspan, vertical leap, and reaction time can make a “shorter” athlete play several inches taller than his height.

More and more teams are employing “small ball” lineups that try to gain an edge by emphasizing speed and quickness over height.  On the other hand, you have teams like the Lakers who can throw three 7-footers at the opponent without missing a step.  So how important is height for a team?

In today’s post, I wanted to look at the relationship between team height and rebounding.  To do this, I gathered the heights of every player that played in the NBA this season and grouped them by their respective team.  Instead of calculating the average height for each team, I weighted the team heights by minutes played.  This adjustment eliminates the bench bias for teams that employed guys like 7-2 Primoz Brezec who rode the pine all year (Philly and Milwaukee).  I’m not calling you a tall team if the players on the court aren’t tall.

After calculating the minutes-weighted height (or effective height), I compared the team length to their rebounding rates.  So, who’s the tallest team in the NBA?

The Sacramento Kings ran the tallest team in the NBA this year with an effective height of about six-foot eight.  Much of their lofty ranking comes from Donte Green who stands 6-11 at the small forward position. In fact, the most common lineup in the Kings system this year was Beno Udrih( 6-3) – Tyreke Evans (6-6) – Donte Green (6-11) – Carl Landry (6-9) – Spencer Hawes (7-0) [have to give an assist finger point to Aaron Barzilai of basketballvalue.com fame].  The third most common lineup could touch the sky with Donte Green playing at the two and Tyreke Evans running the show.  To round out their rotation, the Kings also have 6-9 Omri Casspi to play the three and 6-11 Jason Thompson to play the four.  That’s a tall team.

Despite being really long, the Kings didn’t rebound any better overall than the average team (50.4 TRB%).  That’s largely because Donte Green has arguably been the weakest 6-11 rebounder to get big minutes in the NBA of all time.  A premature superlative?  Possibly. But just remember that his 7.8 percent career total rebound rate is the lowest among any 6-11 or taller player to play at least 100 games in the NBA.   Moreover, Spencer Hawes fits the mold of a shooter who just happens to be extremely tall and doesn’t rebound nearly as well as his center contemporaries.  His teammate Jon Brockman grabs twice as many offensive boards as he does despite boasting the height of a typical shooting guard.  Actually, the team’s above-average offensive rebounding rate could be attributed solely to Brockman’s knack for collecting his team’s missed shots.

You’ll notice that three of the five tallest teams in effective height have pedestrian rebounding rates.  Not coincidentally, each of them (Kings, Suns, and Raptors) like to have a stretch 5 on the floor at the expense of collecting boards.

And the shortest teams? Well, the Warriors employ Nellie-ball which takes small-ball to the extreme.  Several of their most used lineups included Corey Maggette at the four spot which would get any normal coach fired.  But Don Nelson isn’t just any coach– he has over 1,000 losses on his resume.  Contrast the rebounding rates of Golden State to Houston who lost their resident redwood Yao Ming in the offseason.  Despite having the second smallest team in the NBA, they rank very nicely amongst their NBA competitors and about the same as the Toronto Raptors.

To draw a better picture, I’ve included three graphs that chart effective height against their rebounding rates.  Each chart includes a trend-line in red.

As you can see, I broke up the plot area into quadrants to help interpret the orientation a bit better. You can see how well the Spurs rebounded this year despite having one of the shortest effective heights in the league.  That, my friends, is the power of DeJuan Blair.  Blair has vertically-challenged rebounding abilities unseen since Danny Fortson.

If you haven’t figured out already, you don’t want to be in the top-left quadrant.  That area’s reserved for the teams who try to stretch the floor with their height but often lose the battle for live balls.  It’s no surprise that each of these teams (NJN, IND, NYK, WAS, MIN, and TOR) lost more games than they won.

This year, the correlation between eHt and TRB% was .33 which means that there’s a decent relationship between the two entities.  30 teams isn’t a big sample size, to be sure.  Aside from the numbers, there’s much  more to rebounding than height.  As I mentioned earlier, physical attributes like wingspan and vertical have an effect in addition to more mental qualities like positioning and reaction time.

But let’s go further and separate rebounding into two parts: offense and defense.

Here’s offensive rebounding rate and how it relates to effective height.

Offensive rebounding and effective height have a much stronger relationship than overall rebounding– the correlation in this (small) sample was .42.  A one inch increase in effective height translates to about a two percent uptick in offensive rebound percentage (say 24 percent to 26 percent).  Perhaps with more defenders in the lane to rebound the ball, height gives you that extra edge needed to steal a board.

Of course, as the Pacers can attest, sometimes height matters nothing.  I mean, 7-2 Roy Hibbert grabs fewer offensive boards than Chuck Hayes who is eight inches shorter.  To reiterate, a big vertical can close the gap underneath and Hibbert’s ineptitude demonstrates this quite nicely.  Moreover, their power forward Troy Murphy slings it from the perimeter and therefore, rarely gets in position to grab offensive boards.  The Pacers can trot out a tall team but it doesn’t mean they’ll play tall.

However, this seems to be the exception more than the rule.  It’s very hard to get offensive rebounds with a short team.  As much as it is a height issue, it’s probably also a product of strategy.  If I’m coaching a short team, I’m more often than not sending my players back to defense on the shot release since it’s a longshot that they’d collect an offensive board anyway.  Houston and Philly do this better than anyone but they still aren’t quite elite.

Here’s where it gets interesting. On the defensive end, it doesn’t seem to make a difference whether you’re a tall team or not.  The relationship is essentially random with a correlation of -0.03.  Check it out.

I wouldn’t pay too much attention to the negative trend-line as the relationship is about nil and the sample size isn’t enormous.  Phoenix and Cleveland have about the same effective height this season but 27 teams separate them in defensive rebounding rate.   Sacramento has the tallest team but they rebound no better than average and Houston, as small as they are, actual rebounds better on the defensive end.  Golden State still rebounds worse than a lightweight suffering from a Franzia hangover.

Why is it random? An extra rebounder matters more than an extra inch.  On defense, it’s common to have all five defenders waiting for the ball so the individual height advantage tends to vanish.  Of course, there will always be matchups where this isn’t the case but on the whole, defensive rebounding isn’t sensitive to height differences with a full five eyeing the rebound.

With lineup data available, a logical next step might be to see how much height matters in the play-by-plays.  Not just for one team but on a matchup level too.  How much does a couple inches of height matter against a tiny squad like the Warriors? How about versus the Kings?  Also, going back further years would give this study a huge boost (if someone hasn’t done that already).   For now, the main takeaway is that tall teams benefited from their height advantage most on the offensive boards and not on the defensive end.

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