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.

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.

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.

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.

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.

It seems as though I stumble upon a new basketball advanced stats site every week.  I found statsbynumbers.com after it was linked to in the APBRmetrics forum last Tuesday and I’ve been very impressed with what I’ve seen.  So far, the founder of the site has uploaded the post season data and will be publishing regular season data soon.  Each box score on the site is packed with data found in the play-by-plays, some of which you can’t find anywhere else.

In today’s post, I’m particularly interested in the Time Of Possession box.   Haven’t seen this in an NBA box score before.  NFL, yes, but this is new territory for basketball analysis as far as I can tell.  The number tells us how long each team controlled the possession for the game.  For example, in the box score from Suns-Spurs Game 1, we find that the Spurs had possession of the ball almost 2 minutes longer than the Suns did (24 mins 51 seconds vs. 23 mins 09 seconds).  Simple stuff but has some interesting implications for our understanding of pace.

Typically, when we talk about a team’s pace, we usually refer to the number of possessions in a game and then we aggregate those possessions over the course of a season to get a team’s average pace.  So the Golden State Warriors play at ludicrous speed and their average possessions per game of about 103 reflects their lead foot in the automotive proverbial sense of the phrase.  Contrast that with the Blazers who play at a slow crawl (90 possessions per game).

But on an individual game level, the possession total has minimal interpretation value.  If a regulation game lasted 100 possessions, we know it was up-tempo but we wouldn’t know who stepped on the gas.  We can make judgments from our prior knowledge of each team’s typical pace but often times that misses the mark.

That’s where Time of Possession comes in. Turns out, time of possession can vary wildly in the same game.  In Game 3 of the Lakers-Thunder series, the Lakers had the ball 5 minutes and 38 seconds longer than the Thunder.  That’s a half a quarter’s worth of time!   Like I said, statsbynumbers.com only has this year’s playoff data up on the site but that didn’t stop me from digging deep into the numbers.  For each game, I gathered each team’s time per possession and their total time of possession listed.

Who had the quickest one-game pace of the playoffs so far?  That would be Denver when they lost by 3  to Utah in Game 2 of their series, averaging just 13.7 seconds per possession in that contest.  Here are the top ten fastest of the postseason:

That Utah – Denver series was fast wasn’t it? They both like to play up-tempo basketball so it’s not a surprise to see track meets when they went head-to-head.  Also interesting was that Chicago’s time per possession in their Game 4 was slower than Cleveland’s Game 2 dud against Boston even though Chicago saw seven more possessions.  92 possessions may seem like a methodical pace but actually Cleveland did their best to speed up that game.

I expected the top ten to be filled with teams on the wrong end of a blowout since, anecdotally, teams down big like to get quick shots off to extend their breathing room.  But we have some close wins here as well as some routs.

How about the slowest performances of the playoffs?

Lakers opened up that Oklahoma City series by running through their sets and not letting their youthful opponents dictate their style of play.  Eventually, the Lakers were going to find their holes in the defense through patience and crisp passing.  In this one, the Lakers had the ball for over four minutes longer than the Thunder.

Portland was the slowest team in the NBA and they certainly didn’t give in to the Suns up-tempo style of play in Game 5.  On average, the Blazers drained the shot clock to 6 seconds remaining on every play in that game.  Didn’t expect to see two teams lose by double digits near the top of this list but the teams themselves (Charlotte and Portland) didn’t shock me.

Watching Game 7 of the Milwaukee – Atlanta game was like taking an Ambien.  Also, it’s worth noting that Game 3 of the Lakers – Thunder series featured some of the slowest and fastest tempos of the playoffs with the Thunder winning the battle by 5 points.

Comparing the two lists, we find that about five seconds separated the fastest time per possession to the slowest.  We don’t have regular season data on hand to look at the Denver vs. Portland games but Phoenix sufficed as a tortoise vs. the hare matchup– Phoenix had a faster average pace than Denver during the regular season.

Let’s take a step back and look at the fastest average time per possession of the playoffs.  Remember, this is a small sample, especially in the case of Charlotte who played just four games in the playoffs.

Phoenix finished the regular season with a faster tempo than Denver, but only by a nose.  In the postseason, the Nuggets actually paced the field before their first round exit.  Some other notables on this list include Boston matching Phoenix and Lakers not letting the fast pace of the Jazz and Thunder rush their triangle offense.

I’m not sure time of possession will stick in our basketball vernacular anytime soon but it undoubtedly adds some valuable information.  As we’ve seen, the number of possessions can mislead us into thinking that each team followed the same speed, which simply isn’t true– just look at Game 3 of the Lakers vs. Thunder series.  With more data in the regular season, we could analyze how teams respond to dissimilar styles of pace more accurately.  In general, do teams mold to their opponent’s pace or do they proudly go in the opposite direction? How does a coaching change affect their team’s tempo?  Which teams keep their tempo most consistently? All these questions are fair game, thanks to the assembly of data from statsbynumbers.com.

In last week’s post, using regression analysis I looked some of the players who get blocked more than they should given how they often they get to the rim for their shots.  I also flipped it around and looked at who avoids the block while also taking into account how often they get to the rim.

We can look at another part of the game that often gets overlooked: the And-1.  Most NBA websites don’t track how often a player gets an And-1 but Hoopdata tracks every single And-1 that has occurred since 2006-07.   To clarify, the And-1 that Hoopdata presents does not require the bonus free throw to be made for it to be classified as an And-1.  Dwight Howard and Shaquille O’Neal should be very thankful.

So why should we care about And-1s? With And-1 numbers, we can find the strongest finishers in the game.  It takes a guy who can withstand hard contact and use his strength to convert the basket.  Additionally, it takes not only strength but a strong vertical to buy time to redirect an altered shot.  With this in mind, let’s take a look at the And-1 leaders as measured by And-1 percentage (And1%) which is simply percentage of total FGA that end in an And-1 (20 minutes per game, 20 games required).

I’ll note that the Hoopdata dataset has kept each stat line for traded players so Brendan Haywood’s And1% above covers only his time in Washington.  Given Dwight Howard’s superhuman athleticism and strength, it should come as no surprise that he gets And-1s more often than any other player.  This list features mostly large centers (Haywood, Oden, and Shaquille O’Neal) as well as some more athletic bigs (Smith, Stoudemire, and Varejao).  I’m surprised by Varejao’s ranking on this list but he has a way with selling contact to the referees.

How about the players who get the least And-1s?

Yup, Jarvis Hayes has yet to get an And-1 this season in over a thousand minutes of playing time.  Steve Blake has accumulated just two this entire season.  This ranking answers the question “Which regular gets And-1s the least often?”  but I’m not satisfied with this Q&A.  Why?  These guys don’t take the ball to the rack.  Ever.  This might provide some good fodder for bar conversations (you’re welcome) but I’m more interested in taking this further.

Perhaps this is a more revealing question: Given how often a player gets to the rim, how often should he be getting And-1s?

It’s nearly impossible to consistently get And-1s on the perimeter so a player who strictly plays at the basket will automatically tally a few And-1s just by habitat.  For example, does Brendan Haywood have a And-1 skill or does he simply shoot nothing but gimmes around the basket?  In order to get closer to our quest, I drew up a scatter plot that charts And 1% and At Rim percentage (the percentage of a player’s shots taken at the basket).

Just like last week, we’re looking for players who separate themselves from the norm (as illustrated by the trendlines.)  Steve Blake and Jarvis Hayes’ lack of And-1s can be directly attributed to the fact that they never shoot near the rim where they can draw contact.  Likewise, they find themselves near the trendline.

The trendlines offer us the ability to derive an expected And-1 percentage (eAnd1%) through regression analysis.   Given the position and appetite for at rim shots, how often should they get And-1s? Using this expected And-1 percentage, we can really find the strongest finishers and not just the ones who play near the hoop.

So I have gone ahead and sorted each player by their eAnd1% differential. First, the ones who beat the model’s predictions the most.

Well, it seems as though Dwight Howard doesn’t care for my adjustment; he still tops the list.  As a center with 58.2 percent of his shots coming at the rim, we would expect that his And-1 percentage would be 4.3 percent but he’s nearly double the expectation.  Actually a couple players double their expectations, one of which is unsurprisingly LeBron James. Perhaps the most surprising member of this ultra-exclusive club is Kevin Martin who in Sacramento posted an And-1% that one might expect for a big man.  His And-1 rate is superb for a player who only took one fifth of his shots at the rim.  In fact, 16 of Martin’s 37 makes at the rim in Sacramento earned him a shot from the charity stripe.  Now in Houston, Martin is an extremely underrated finisher at the rim especially after considering his thin frame.  Rockets fans must love having two of the toughest finishing guards in the game with Martin and Kyle Lowry in the backcourt.  It’s also worth noting that Lowry was also acquired in a midseason trade executed by GM Daryl Morey last year.

Now that we have identified the strongest finishers, what about the softest ones?  These guys get fewer And-1s than we would expect given their position and shot taste.

With the regression, the basket allergic guards who dominated the previous list have all disappeared.  Instead, we have bigs who get fewer And-1s than we would expect.  Is Shawn Marion the softest big in the game? Well, not exactly.  Maybe it’s more appropriate to say he’s the softest big who still shoots at the rim.  Plenty of bigs would rather sit on the perimeter than take it to the rim (I’m looking at you Channing Frye!).  Nonetheless, Marion has the fifth highest blocked percentage among regular small forwards (7.8) and only takes 1.8 free throws per game.  Not exactly a thunderous presence down low.

Elsewhere, it’s amazing to me that 6-6 Chuck Hayes has more And-1s this year than Samuel Dalembert despite getting guarded by the same personnel.  If I were Hayes, I would remind Dalembert every time they play eachother, which unfortunately isn’t often.

As I mentioned in the last post, it might be a worth a look to add predictor variables to At Rim Pct.  I could see height, free throw percentage, and assist percentage all being significant factors in predicting And-1 percentage.  I’ll save that for a later time.  For now, feel free to brag to your friends at the bar that not only do you know who draws the most And-1s, you  also know the hardest and softest finishers in the game.