Jeremy Lin Advanced Statistics: Is Lin the next Nash or Stockton?

The answer to the question above, you will find, is a definite maybe.  To evaluate this query – can Jeremy Lin be the next Steve Nash or John Stockton? – the Editorial Board of Basketball I.Q. decided to compare the age-23 seasons of all three players (which happens to be the first season in which all three players averaged more than 20 minutes per game).  The age-23 season serves as a nice basis of comparison for each of these three players: they are each the same age, of course; it was the second professional season for each player; it marks a strikingly similar arc in their professional careers, as each player also completed four full years of college ball at a Division I mid-major; and they each averaged a very similar number of minutes per game in these respective seasons.  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

Despite all the surface similarities between the current, age-23 season for Jeremy Lin and the same respective seasons for John Stockton and Steve Nash, Lin has accumulated his playing time in stark contrast to the other two.  Stockton, for instance, was a back-up to able veteran Rickey Green of the Jazz, and achieved his playing time steadily, with very few starts.  Nash, similar to Stockton, was not at the top of the depth chart, but third behind two All-Star veterans in Phoenix – the young Jason Kidd, and the old Kevin Johnson – and also accumulated his minutes in a steady fashion, rarely starting a game for the Suns.

But Lin has been buried behind no one on the Knicks: he has simply waited his turn behind an ineffective Toney Douglas and a worn-out Mike Bibby, and barely played while doing so.  When he finally got his turn, his minutes surged, and he has averaged his 24.5 minutes per game (Stockton had 23.6, Nash had 21.9) by becoming an entrenched starter after having barely played at all.

The first question we will address, using the alternative metrics of Shot-to-Assist Ratio (SAR), Shot Selection Index (SSI), Three-point Rate (3PR), and Three-Point Skew (3PS), is whether or not Lin, Stockton and Nash are comparable players at all.

                        SAR     SSI       3PR      3PS

Stockton 23     0.93     .440     .032     .009

Nash 23           2.39     .147     .334     .302

Lin 23              2.27     .484     .176     .125

Indeed, based on the SAR of each player, Stockton, Nash and Lin all fall into the first quintile of player classification, the Primary Distributor (referred to in the vernacular as “point guard”), as each player has an SAR below 2.77.  As you can see, Lin and Nash have extremely similar SARs, and at age 23 made their own scoring a mildly prominent part of their game.  Stockton, however, accumulated more assists than shots taken in his age-23 season, which is a remarkable achievement, even for a Primary Distributor.  Clearly, passing was an even more important part of Stockton’s game than Lin’s or Nash’s – but they are still similar players, stylistically, with a knack for playmaking.

Based on the SSI, you can see that the two most similar players were Stockton and Lin, who each had almost half as many free throw attempts as field goal attempts.  This suggests that each player, when he chose to score, attempted to do so by driving to the hoop (and getting fouled a fair amount of the time).  This is further corroborated by their 3PR and 3PS: a small proportion of their field goals attempted (about 18% for Lin, and only 3% for Stockton), and even smaller fraction of their field goals made (about 12% for Lin, and only 1% for Stockton) were from the perimeter.  The difference between Lin and Stockton, in regards to three-point attempts, can probably be explained by their different eras: in 1985-86 (Stockton’s second year), the three-point shot was reserved mostly for the latter seconds of a possession, whereas the current game sees treys going up relatively early on in possessions.

Nash, on the other hand, played his individual offensive game out on the perimeter: a third of his shots were treys, and nearly a third of his makes (he was an astonishingly good three-point shooter).  Not surprisingly, given that he was so far away from the basket when he shot, Nash did not get to the line all that much (he took only 15% as many free throws as field goal attempts).

In sum, all three players were Primary Distributors, with slightly different flavors: Lin was a willing passer who drove to the basket; Nash was a willing passer who shot from the perimeter; and Stockton was an absolute pass-first player who, like Lin, drove toward the basket when it was time to score.

Having established that Lin, at age 23, is sufficiently similar in style to both Nash and Stockton, let’s turn our attention to their performance statistics.  The first statistics that we will evaluate are the standard ones:

                        PPG     RPG     APG     FG%     FT%     MPG

Stockton 23     7.7       2.2       5.1       .489     .839     23.6

Nash 23           9.1       2.1       3.4       .459     .860     21.9

Lin 23              14.4     2.8       5.8       .471     .766     24.5

The standard statistics suggest that, of the three, the age-23 Lin is clearly the best player: he scored many more points per game, collected more rebounds, and distributed the most assists.  Even if you reduce these numbers by 10% for Lin, acknowledging that he played about 10% more minutes per game, he still comes out with the better numbers.  If we try to explain away this apparent superiority by his higher field goal attempts, this argument is weakened by his field goal percentage – all three players shoot at virtually the same rate, with the slight differences in percentage explained by their varying rates of three-point attempts and makes.  It is only in free throw percentage that Lin demonstrates a weakness relative to the other two.

Can this be true?  Is the 23-year-old Jeremy Lin really better than John Stockton and Steve Nash at the same age?  Though the standard statistics suggest that this is the case, the alternative metrics suggest a slightly different conclusion.  Let’s take a look at those comparisons, using the Successful Possession Rate (SPR), Turnover-Adjusted Points per Shot (TAPPS), and Turnovers per Touch (TOT):

                        SPR      TAPPS TOT

Stockton 23     .687     .855     .100

Nash 23           .616     .954     .081

Lin 23              .588     .875     .129

The SPR reflects a player’s ability to create a successful scoring opportunity for his team as a whole, and the 23-year-old Stockton is clearly superior to Nash and Lin at the same age.  Some of this is due to statistical skew, since Stockton’s low SAR would emphasize the value of an assist, and thus inflate this statistic.  Still, even taking this statistical artifact into consideration, Stockton’s SPR is much better than that of the other two, and he accomplished this on a mediocre team.  Nash’s SPR is slightly better than Lin’s, and this is mostly accounted for by his superior turnover rate.

The TAPPS reflects a player’s ability to create a successful scoring opportunity for himself, and in this arena the 23-year-old Nash is clearly superior to Stockton and Lin at the same age (in reality, Stockton is 12 years older than Nash, and 27 years older than Lin).  Nash’s superiority in this stat is mostly explained by his extremely low turnover rate, but also by the contribution of his excellent three-point shooting (and playing in an era that, strategically, placed great value on the three-point shot).  Lin’s TAPPS is slightly better than Stockton’s, but this is mostly accounted for by the different eras in which they played, and the relative emphasis on the three-point shot in each era.

The TOT reflects the rate at which a player turns the ball over relative to his meaningful touches.  The watermark TOT for a Primary Distributor is 10%, which is exactly the number that the 23-year-old Stockton achieved.  Lin’s TOT of nearly 13% reflects the most glaring weakness in his game – and the reason why, no matter what the standard statistics say, Lin is a notch below Stockton, Nash and the like at the same points in their careers.  Nash’s turnover rate of about 8% is absolutely phenomenal for a player who takes so much responsibility for the ball, and getting it to his teammates.

Taken all together, the SPR, TAPPS and TOT suggest that, even at age 23, Stockton and Nash were showing glimpses of greatness, while Lin is a very good player who lags a degree or so behind them.  A couple different ways to appreciate the aggregate of these statistics are the statistics of Earnings (E) and weighted Cumulative Earnings (wCE).  The E combines the three stats analyzed above, whereas the wCE does so while taking into account playing time.  In this manner, the E tells you what a player does when he is on the court, whereas the wCE tells you his relative contribution for a full 48 minutes, including the time in which he sits on the bench.

When looking at E and wCE in a young player, it is helpful to evaluate their growth – what is their improvement (or decay) from one season to the next.  To evaluate Earnings Growth (EG) or weighted Earnings Growth (wEG), one can calculate the E and wCE from a preceding year and compare it to the year in question.  To that end, the E and wCE of Lin’s, Stockton’s and Nash’s age-22 and age-23 seasons have been calculated, and their growth from their rookie year to their sophomore campaign has been estimated:

                        E          wCE     EG        wEG

Stockton 22     2.31     0.88     --          --

Stockton 23     2.44     1.20     5.6%    36.3%

Nash 22           2.29     0.50     --          --

Nash 23           2.49     1.17     8.7%    134%

Lin 22              2.21     0.45     --          --

Lin 23              2.33     1.19     5.4%    164%

Taking first things first, the age-23 Earnings for Stockton and Nash are indeed excellent – and Lin is, in fact, a step behind them.  When playing time is taken into consideration (wCE), the age-23 Lin, Stockton and Nash all brought virtually identical value to their respective teams – but that is because Lin played more than the other two.

The interesting thing to consider here is growth: is there something about the change in performance from one year to the next that suggests that Lin is not a flash in the pan, but on a career arc that would justify comparison to some of the game’s better players?  From the rookie season to their second year, each player’s EG (what they did when they were on the court) improved significantly, with Nash’s jump almost off the charts.  But Stockton and Lin grew quite nicely, and at similar rates – which suggests that Lin is on a segment of the learning curve comparable to that of the game’s better players.  Lin may never be as good as the best, but early signals suggest that he will, in the least, continue to improve.

The stat of wEG, for these players at this stage of their careers, is, admittedly, meaningless.  The tremendous jumps in wEG for all three (especially Nash and Lin), merely reflect the fact that they were getting into the game for meaningful minutes in their second years, while in their first years they were used sporadically.

So back to the original question: Is Jeremy Lin the next John Stockton or Steve Nash?  The early returns suggest that he is not, although he might come pretty close.  But let’s close our eyes and dream a little.  Let’s say that, over the next year or two, Lin demonstrates a growth in on-court Earnings of 5%, and does so while averaging 36 minutes per game.  That would project, for Lin, an E of 2.44, and a wCE of 1.83.

Put in perspective, in 2010-11, league MVP Derrick Rose posted an E of 2.45 and a wCE of 1.91.  So the current Lin phenomenon may represent equal parts reality and wishful thinking, but there is enough substance there to suggest that happy dreams should not be extinguished.

More Special Rookie: Irving or Rubio?

In today's ESPN TrueHoops, the question is asked whether Kyrie Irving or Ricky Rubio is the more special rookie in this year's NBA.  The question is subsequently addressed with traditional statistics, though no definitive conclusion is reached.  This is understandable, given that an analysis utilizing alternative statistics also reaches an equivocal conclusion.  Please refer to the Basketball I.Q. archives for the derivations of the statistics used here: Successful Possession Rate (SPR); Turnover-Adjusted Points Per Shot (TAPPS); Turnovers per Touch (TOT); and Shot Selection Index (SSI).

Rubio: SPR .598; TAPPS .754; TOT .113; SSI .402
Irving: SPR .584; TAPPS .958; TOT .110; SSI .286

Keep in mind, when analyzing these stats, Rubio has a Shot-to-Assist Ratio (SAR) of 1.40, putting him in the Primary Distributor ("point guard") category, whereas Irving has an SAR of 3.30, making him a Combination Distributor ("combi-guard" or "off-guard").  Rubio and Irving, therefore, fulfill different roles on the court.

The SPR assesses the ability of a player to create a successful possession for his team, given the opportunity.  The slight edge here (.598 vs. .584) goes to Rubio -- though the margin is quite small.  When one considers that the statistical skew created by assists favors a Primary Distributor over a slightly less willing passer (or, depending on how you  look at it, a more intent finisher), the SPR evaluation is either a dead heat between Rubio and Irving, or slightly in favor of Irving.

The TAPPS assesses the ability of a player to finish an offensive possesion successfully on his own, and favors the more intent scorer (in this case, Irving) because of their generally lower turnover rates.  In this case, however, there is a wide chasm between Irving and Rubio, despite the fact that Irving turns the ball over at a similar rate to Rubio: Irving's TAPPS of .958 is excellent, and an ocean away from Rubio's .754 (this is due to Rubio's much poorer shooting).  The advantage here is clearly in favor of Irving.

The TOT is a turnover rate in relation to a player's meaningful touches.  This appears to be a dead heat between the players (Rubio .113 vs Irving .110), but this actually favors Rubio: it is expected that players with very high assist totals should turn the ball over more, since passes toward the basket are inherently risky.  Combination Distributors should have TOTs that are below 10%, and Irving does not.  The slight advantage here is to Rubio.

The SSI assesses the likelihood that a player will get fouled when he shoots, suggesting that a high SSI means a player has good shot selection.  This STAT is more qualitative, as opposed to quantitative, than the other stats mentioned here.  With an SSI of .402, Rubio has an excellent shot selection, whereas Irving, at .286, is merely average.  However, the traditional stats bear out that Irving is the much better shooter, and so he may be allowed wider leverage in his shot selection.

In conclusion, Irving can take a game over all by himself, whereas Rubio is more likely to help his teammates do so successfully.  Both players need to take better care of the ball.  Rubio needs to work on his shooting, Irving should work on his shot selection and get to the free throw line more (and hit his free throws more accurately).  Though it is close, and any team would be happy to have either player, the edge here goes to:

Kyrie Irving.

Kyrie Irving vs. Derrick Rose: A Comparative Analysis of Rookie Seasons

Though the last Basketball I.Q. post promised that today’s entry would compare Jeremy Lin with Minnesota’s Ricky Rubio, our Editorial Board had a last-minute change of heart and over-ruled our Managing Editor.  Instead, the Board decreed, it was time to turn our attention to another up-and-comer who does not benefit from the bright lights of New York City: Kyrie Irving.  Specifically, the Board wished to challenge the assertions of one of our own staff writers, who, prior to this year’s draft, ranked Irving as the number one prospect coming out of college, and boldly compared Irving to the NBA’s reigning MVP, Derrick Rose.  As such, today’s post will compare Irving’s current rookie campaign to that of Rose, in his first year with Chicago in 2008-09.  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

Let’s begin today’s comparative analysis with a disclaimer: a comparison between the current Kyrie Irving and the rookie Derrick Rose, though not exactly apples and oranges, is nevertheless comparing two apples of different species.  Think McIntosh versus Golden Delicious, and you can decide who is who.  Though Rose as a rookie was clearly a Primary Distributor (with a Shot-to-Assist Ratio, or SAR, of 2.60), the rookie version of Irving is more of a Combination Distributor.  His SAR of 3.30 indicates that he is a willing passer, but nevertheless is looking for his shot a little bit more than the typical court facilitator.  This is not a value judgment on either player’s game – it’s just an acknowledgment that Rose and Irving have distinct approaches to the way they play a team game, and Basketball I.Q. tends to shun comparisons of players from different statistical quintiles (see our archives).

But there is not a continental divide between the two players, and the passing and scoring tendencies in their respective rookie years may reflect the extreme differences in the supporting cast around them more than anything else.  Let’s begin with a comparison of the alternative statistics, Successful Possession Rate (SPR), Turnover-Adjusted Points per Shot (TAPPS), Turnovers per Touch (TOT), and the Shot Selection Index (SSI), of the current Kyrie Irving and the rookie Derrick Rose:

                                    SPR      TAPPS   TOT   SSI

Irving (2011-12)          .584     0.958     .110   .286

Rose (2008-09)            .583     0.887     .083   .207

What stands out immediately is that the rookie Irving and the rookie Rose have virtually identical SPR’s, which means that each player could facilitate a successful team offensive possession with similar efficiency (the .001 difference could be considered a rounding error).  The difference in the two players’ SAR quintiles, however, suggests that the advantage in this category actually goes to the rookie Irving:  the lower one’s SAR, the higher you would expect that player’s SPR to be, since the relatively high assist totals of Primary Distributors tends to skew this statistic in their favor.  As such, the rookie Irving has been able to post a virtually identical SPR to the rookie Rose, despite his higher SAR and lower assist total – this is a significant accomplishment.

Of course, the reasons why Irving has been able to make up for lower assist totals than Rose (at the same stage in their careers) is easy to discern from their traditional statistics: Irving scores more than the rookie Rose did, and he does so more accurately.  Rookie Irving beats rookie Rose in points per game (18.6 vs. 16.8), field goal percentage (.487 vs. .475), and free throw percentage (.855 vs. .788).  Three-point shooting weighs heavily in the favor of rookie Irving: he has shot over 40% from beyond the arc, while Rose barely got above 20% in his rookie year.  These significant differences were diminished – evened out, as it were – by Rose’s superior assists per game stats (6.3 vs. 4.9) and lower turnover rate (discussed later).

The second alternative stat, TAPPS, which demonstrates how efficiently a player can facilitate a scoring opportunity for himself, also favors Irving, though by a significant margin.  Again, much of the difference in this statistic, from player to player, can be explained by SAR quintile.  All things being equal, you would expect the player with the higher SAR (Irving) to have the higher TAPPS.  But the reason for this skew in the statistic is that, on average, higher SAR players have lower turnover rates, and thus a decreased adjustment in their points-per-shot value.  But Irving actually has a significantly higher turnover rate than rookie Rose (again, discussed later), and he still manages a much higher TAPPS.

The explanation for this is shooting.  Rookie Irving is a much better shooter than rookie Rose was.  As mentioned above, Irving shoots field goals, free throws and three-pointers with more accuracy (the latter with an accuracy that is twice as good).  Not only that, Irving takes many more three-pointers than Rose – Irving has a Three-Point Rate (3PR) of .203, while rookie Rose’s 3PR was less than one-third of that (.060).  And, of course, Irving makes about 44% of his three’s, while rookie Rose made about 22%.  Even after adjustment for SAR quintile, Irving is the clear victor in the comparison of TAPPS.

The third alternative statistic, TOT, lies in clear favor of Rose, who has a much lower turnover rate (.083 vs. .110).  Rookie Rose’s TOT is very low for a Primary Distributor, and this has actually improved as his career has progressed (as has Rose’s shooting, which is why he is the reigning MVP).  Irving’s TOT is quite average for a Primary Distributor – however, Irving is NOT a Primary Distributor.  Irving is a Combination Distributor, and thus you would expect a lower TOT than he actually posts.  Irving, like many new NBA players, has to learn to take care of the ball better.  The advantage in this alternative statistic lies clearly with Rose and, because of the different quintiles inhabited by the two players, the gap between Rose and Irving in this category is actually quite large.

The last statistic discussed here, SSI, assesses the quality of a player’s shot selection by creating a ratio of free throw attempts to field goal attempts – the logic being that the higher percentage your shot selection, the more likely you are to be fouled.  Rookie Irving, at .286, hovers right around the NBA average (though, for a backcourt player whose offensive game is relatively distant from the basket, this is pretty good).  Rookie Rose, meanwhile, had a slightly below average SSI (.207), though it was right about the average for backcourt players.  In subsequent years, Rose has improved his SSI significantly (perhaps the by-product of becoming a better shooter in general), and now posts an above-average SSI of .335).

In conclusion, it is fair to say that the rookie Kyrie Irving compares quite favorably to the rookie Derrick Rose.  What makes Rose so special is that he never ceded his strengths (taking care of the ball, passing creatively), while he turned his relative weaknesses (shooting and shot selection) into formidable strengths themselves.  If Irving can do the same – keep up the excellent shooting and shot selection, but turn the ball over less – we might be seeing the emergence of another dominant backcourt player in the NBA’s Midwest.

It appears that the Basketball I.Q. analysts who evaluated Irving as a college player were spot on.  However, it should be noted that Irving and Rose, in their rookie years, project to be different kinds of players: Rose would have projected to be a Deron Williams-type player (and he has certainly achieved that), whereas Irving appears to be headed more toward a Dwyane Wade or Manu Ginobili style (which any GM would lock in in a heartbeat).

Is Jeremy Lin a Legitimate Impact Player?

After an extended hiatus, the researchers at Basketball I.Q. have returned with a topical analysis of New York Knick Jeremy Lin.  Specifically, today’s post represents a comparative analysis of Lin vis a vis the current professional game’s best point guards.  Today’s analysis looks to establish whether it is justified to consider Lin – at this point an international, cultural sensation – among the NBA’s elite point guards (or more accurately, in the parlance of Basketball I.Q., among the league’s elite Primary Distributors).  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

Jeremy Lin appears to have saved the New York Knicks’ season.  Struggling at 8-15, the team appeared to be heading toward one of its most disappointing seasons in the face of high expectations, and in so doing created an unusual agreement in perception shared by the team’s coach, Mike D’Antoni, and the New York sports media: that is, neither side could envision the team’s success without a capable point guard running the offensive end of the floor, especially given the nature of the coach’s ball screen-heavy offense – and the woeful start appeared to prove the point of both sides.

And then Lin arrived.  Quickly, the 8-15 team found itself at 16-16, suddenly challenging Philadelphia and Boston for their division lead.  And, everyone agreed, the missing ingredient all along had been Lin, the hidden point guard that had been buried on the bench and nearly cut by three NBA teams in the span of two months.  Beyond that, there has been cautious speculation (and, at times, not so cautious speculation) that New York is observing the ascension of one of the game’s premier players.

Today’s post will challenge that claim – at least in terms of Lin’s comparison to the game’s premier Primary Distributors (Basketball I.Q. prefers to not use the term “point guard,” which most commonly refers to the shortest player in a team’s starting line-up; rather, we use the term “Primary Distributor,” which refers to those players in the lowest quintile of Shot-to-Assist Ratio, or SAR, and represents those players most likely to pass the ball to his teammates.).  The formula for SAR is summarized in the glossary above, with a detailed explanation of this alternative statistic found in the Basketball I.Q. archives.

The best Primary Distributors in basketball, defined as those players with an SAR less than 2.77, would include Chris Paul (1.86), Deron Williams (2.52), Rajon Rondo (1.51), Steve Nash (1.04) and Derrick Rose (2.51).  With an SAR of 2.27, Lin easily falls within the Primary Distributor quintile, with a passing tendency that is not quite as generous as Nash, Rondo and Paul’s, but more generous than the generally unselfish play of Rose and Williams.

Today’s post will compare Lin to these five excellent players in four alternative statistical categories, all defined in the glossary above, as well as in previously archived Basketball I.Q. posts: Successful Possession Rate (SPR); Turnover-Adjusted Points per Shot (TAPPS); Turnovers per Touch (TOT); and the Shot Selection Index (SSI).

                        SPR      TAPPS TOT    SSI

Lin                   .564     0.894   .134     .503

Paul                 .601     1.001   .065     .266

Williams          .577     0.873   .110     .318

Rondo              .611     0.816   .109     .416

Nash                .656     0.943   .125     .207

Rose                .613     0.982   .082     .335

Each of the above statistics sheds a slightly different light on the game of each player, but for Lin, the statistics that seem to corroborate what everyone can tell just by watching him are the last two, TOT and SSI.  For all of Lin’s heroics, his box scores and late-night highlight reels are all tempered by his turnover rate, and his TOT substantiates this: at a rate of approximately 13.4% of all of his meaningful touches, Lin has the highest TOT of all the players recruited for this comparison.  Lin’s TOT is not that much higher than Nash’s (.134 vs. .125), and only moderately higher than that of Williams (.110) and Rondo (.109).  But it is considerably higher than Paul (.065) and Rose (.082), who take care of the ball with uncommon diligence for players who touch it so much and send it flying in so many different directions.  Perhaps it is unfair to compare Lin to those two players, who might have the best handle on the ball in the history of the game, since you would expect a Primary Distributor to turn the ball over approximately 10% of the time – but this is the only statistical category in which Lin is an outlier.  The subsequent alternative statistics will demonstrate that Lin is in fact “hanging” with the greats of the game – but if he wants to elevate his game to the level of the elite, he will need to take far better care of the ball and improve in the TOT category.

What redeems Lin of his recklessness with the ball is his uncanny shot selection – another point which even a casual observer could note with simple observation.  Lin takes the ball right at the basket, and he does so aggressively and without apology – which is why greater than 50% of his shots are free throws (.503).  This remarkable SSI demonstrates that Lin consistently gets himself open looks within a few feet of the basket, in which it behooves the defense to play him aggressively and perhaps even foul him, since the shots he takes are so high-percentage if not fiercely contested.  Lin’s knack for finding the lay-up lanes does not only cause the defense to collapse on him and open up the floor for his teammates – it results in several trips to the foul line, with lots of completely uncontested shots awarded to him for his effort.

No other player on this list comes close to creating so many high quality shots, where the defense would essentially prefer to foul you rather than just let the ball go up, with the possible exception of Rondo (.416).  But Rondo is a terrible free throw shooter – about 20% worse than the average player on this list – and it is possible that Rondo is fouled so often because his free throw shooting is actually lower percentage than much of his field goal shooting.  Williams, Paul and Rose all have SSI’s that hover around the NBA average, whereas only Nash is well below such an average.

The first two statistics, SPR and TAPPS, looks at a player’s ability to execute a play that results in his team’s ability to score in relation to his own missed shots and turnovers (SPR), and a player’s ability to score the basket himself in relation to his own missed shots and turnovers (TAPPS).  Lin has the lowest SPR on this list, though it is close to that of Deron Williams, and clearly this is the result of his low assist-to-turnover ratio.  This stat demonstrates that Lin would not only help his own game, but that of his entire team, if he can learn to make wiser decisions with the ball (and perhaps dribble it a little more effectively).

For TAPPS, Lin is right in the middle of the pack, ahead of Williams and Rondo, nipping at the heels of Nash, but well behind Paul and Rose (who, again, take remarkable care of the ball).  In this regard, if Lin were looking to improve his own ability to score with efficiency, he would not only reduce his turnovers, but he would also improve his free throw shooting: with the exception of Rondo, Lin is the poorest free throw shooter on this list (though, at 74%, it is not terrible).  If he could improve his free throw shooting by about 6% (not unusual for a player early in his career) and reduce his turnover rate by about 2 or 3%, he would be a legitimate back-up to Rose on the Eastern Conference All-Star team.

A Basketball I.Q. composite statistic that takes into account SPR, TAPPS and TOT is Earnings (E), whose derivation is listed in the glossary above, as well as in archived posts.  Here is a list of the aforementioned players’ earnings, in descending order of value:

                        E

Paul                 2.537

Rose                2.513

Nash                2.474

Williams          2.340

Lin                   2.324

Rondo              2.318

Looking at this list, there appears to be a top tier of Primary Distributors, consisting of Paul, Rose and Nash (two of these players have been MVPs, and Paul is in the perennial discussion).  Then, there is a slight drop-off in performance, and the emergence of a second tier – with Lin solidly ensconced in the middle of that tier.

The initial assessment of Lin, only two weeks into the essential birth of his professional career, appears to be favorable – he is, indeed, an impact player at the NBA level, performing at a level just below that of an All-Star.  If he indeed turns out to still be on the steep part of the learning curve, then we may be witnessing the ascension of a backcourt player that will one day be mentioned alongside Frazier, Monroe and Barnett – but first he’ll have to hold on to the ball more, and practice his free throws.

Next post: Jeremy Lin vs. Ricky Rubio.

2011 NBA Draft Card: Enes Kanter

In today’s post, I will continue the series of comparative “draft cards” on players who have declared their eligibility for the 2011 NBA Draft.  Today’s analysis takes a slight turn, as for the first time we analyze the professional prospects of an international player who never competed in college – Enes Kanter from Turkey.  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

When it comes to composing the Basketball I.Q. alternative statistics of an international player who has never played the American game – particularly when those statistics will be juxtaposed against those of a player who played NCAA Division I basketball – the analysis requires innovation.  Enes Kanter of Turkey, for example, has only played European basketball under international rules, and so the composition of his 2011 Draft Card began with a lot of questions:  Which statistics should be used?  What do you do about certain statistics – turnovers, specifically – that are not available?  And if the player to whom Kanter is being compared is not an international one – and in this case it is Al Horford, who played his college ball at the University of Florida – which of that player’s statistics should be used as the basis for comparison?

The rectification of this problem was a three-step solution I’ll now refer to as “Euro-triangulation”:  The highest level of pre-professional international competition at which the European player competed was chosen; missing statistics were re-created based on historical norms; and then they were compared to the college statistics of a player that played not only at a similar level of competition, but at a similar age.  The method, admittedly, is a little creaky, but it is a little bit better than comparing apples to oranges – it is more like comparing McIntosh apples to Fujis.

To start, I chose Kanter’s performances at the European U18 Championships in 2008 and 2009.  I chose those two competitions because, taken together, it is still only 17 games, which barely qualifies as an adequate sample size.  The European U18 Championships were chosen because they were the highest level of competition Kanter faced in which he received substantial playing time.  Though Kanter was only 16 and 17 at the time of these games (as were most of his opponents), I felt that this was a legitimate comparison to Division I college basketball, since the international rosters were comprised of the 12 best players from each country (without the dilution seen on the benches of the NCAA’s 300 Division I programs); since almost all of the players at the European U18 Championships go on to play professionally (indeed, some already are playing professionally), and they do so side by side with former NCAA players who could not gain their footing in the NBA; and they do so at an age which is only one or two years younger than the very best players in American college basketball (who tend to leave college early).  If anything, I thought that the European U18 Championships might represent stiffer competition than a season in Division I NCAA.

For the sake of comparison, Al Horford was chosen because his name is frequently mentioned in media outlets as a player to whom Kanter might be comparable.  I chose Horford’s sophomore year at Florida as the basis of comparison, since he received substantial playing time that year; he was, like Kanter, still only a teenager; and, in helping lead his team to an NCAA Championship, Horford played against a level of competition similar to that of U18 international basketball.  Let’s begin by looking at some of the traditional statistics that define Kanter and Horford:

                                    FG%     FT%      PPG      RPG     APG

Kanter, U18                 .593     .685     18.8     15.6     0.9

Horford, SO                 .608     .611     11.3     7.6       2.0

I prefer to look at the statistics that are reflected as percentages – in this case FG% and FT% -- as opposed to total compiled statistics (such as points in a game) because they tend to minimize the differences between the pace of different styles of play, and the different roles a player may have on each team.  By those numbers, it would indeed appear that Kanter and Horford are similar players – both have almost identical shooting percentages, and were both accomplished field goal shooters, yet terrible free throw shooters.  The compiled statistics, such as points, rebounds and assists, heavily favor Kanter – but since the percentages on each player are so similar, we know that these differences in points scored, etc., are more of a reflection on the different pace of the international game, and the different relative roles that each player assumed on his team.

A more accurate determinant of the Kanter and Horford’s roles would be the qualitative analytics:  Shot-to-Assist Ratio (SAR); Shot Selection Index (SSI); 3-Point Rate (3PR) and 3-Point Skew (3PS):

                                    SAR      SSI        3PR      3PS

Kanter, U18                 15.3     .412     .023     .023

Horford, SO                 4.71     .495     .007     .015

At first glimpse, it seems like Kanter and Horford indeed have similar games:  Their shot selections are very good, and virtually identical – taken in combination with their high field goal percentages, and the fact that both players rarely even attempt a three-point shot, you can see that both Kanter and Horford execute a very economical offensive game, played very close to the basket.

But the glaring difference here is the SAR:  Kanter shoots 15 times for every assist he makes, whereas Horford shoots less than five times per assist.  This is unanticipated, because it is usually the European big man who you think of as being the nifty passer in the paint (think Pau Gasol or Arvidas Sabonis) and the U.S.-trained post player as receiving the ball and taking it immediately to the hoop (think Shaq or Amar’e).  But through their teenage years, Horford is the player who can get to the basket with commendable accuracy and still move the ball around, whereas Kanter is the one who is taking the ball right to the hoop (which might explain his higher scoring average).

To be fair, Kanter’s statistics at the 2008 and 2009 European U18 Championships were different:  though the scoring statistics were about the same, by 2009 Kanter was taking only about 12 shots per assist made, which still qualifies him as being within the Finisher quintile (Horford would be considered a Balanced Scorer), though with a passing tendency that is comparable to many NBA big men.  Still, the most notable difference in the qualitative statistics between Kanter and Horford is their passing tendencies, and it seems like Horford (who remains a good passer in the NBA) had a more complete game at the same level of development, and one that would predict success in an offense that stresses ball movement.

Let’s conclude with a comparison of Kanter’s and Horford’s quantitative alternative statistics: the Successful Possession Rate (SPR); Turnover-Adjusted Points per Shot; Turnover per Touch (TOT); and Earnings (E):

                                    SPR      TAPPS    TOT   E

Kanter, U18                 .531     1.014     .090   2.46

Horford, SO                 .580     1.011     .087   2.50

For starters, I should note that Kanter did not have turnover statistics from the U18 Championships readily available, and so I had to synthesize the statistic based on an assumed turnover per touch rate of 9% -- a number that is neither great nor terrible for a player in the Finisher quintile.  As it turns out, this rate is virtually identical to what Horford actually achieved during his sophomore season of college basketball.  It should be noted, however, that players who pass a lot tend to make more turnovers, too, and so, taken in context, Horford’s TOT of .087 is actually far better than Kanter’s assumed .090.  The rub, of course, is that it is entirely possible that Kanter took care of the ball much better than I am assuming – but he might have done far worse, as well.  In any event, this is the greatest flaw in any comparative argument I make between Kanter and Horford.

Going by SPR, Horford comes out on top, largely because of his assists – it is not entirely fair to compare the SPR between players with low SARs (Balanced Scorers) versus those with high ones (Finishers), because the increased assist tally tend to skew the statistic.  That said, it is not as if these assists fell out of the sky – Horford earned them, and he did so playing inside in the paint, where it isn’t that easy to find an open man.

The TAPPS and TOT for each player is virtually identical, and taken together, the Earnings column leans in the favor of Horford.  Again, the differences in the two players really comes down to one thing: the ability to pass creatively, a skill which Horford has in uncommon abundance for a big man.

And so, to conclude this post, I would say that Enes Kanter projects to be a pretty good player – but not quite as good as Al Horford.  That’s no insult, of course: Horford has been selected by the coaches to be a reserve on the last two All-Star teams (probably because he is such a creative team player), and falling short of that is hardly an embarrassment.  I would expect Kanter to be a player who scores and rebounds at similar rates to Horford, but, as of yet, has not figured out how to distribute the ball as meaningfully.

Having completed the draft cards on four players, the current rankings heading into the 2011 NBA draft would be:

1.      Kyrie Irving

2.      Derrick Williams

3.      Enes Kanter

4.      Brandon Knight

Next post:  I will rank the top 30 players in the draft in order, and then resume comparative draft cards.

2011 NBA Draft Card: Brandon Knight

In today’s post, I will continue the series of comparative “draft cards” on college players who have entered the 2011 NBA Draft.  Today we will analyze the relative professional prospects of Brandon Knight, from the University of Kentucky.  To make such an analysis more accessible, I will start with a glossary of statistical terms, which can be referred to by the reader:

SPR = [2PFGM + 1.5(3PFGM) + (FTM/2) + AST]/[FGA + (FTA/2) + AST + TOV]

TAPPS = PTS/[FGA + (FTA/2) + TOV]

TOT = TOV/[TOV + FGA + (FTA/2) + TRB + STL + AST]

SSI = FTA/FGA

SAR = [FGA + (FTA/2)]/AST

3PR = 3PFGA/FGA

3PS = 3PFGM/FGM

E = SPR + TAPPS + (1 – TOT)

wCE = (MPG/48) x [SPR + TAPPS + (1 – TOT)]

P/E = Salary/[SPR + TAPPS + (1 – TOT)]

wP/E = Salary/(MPG/48) x [SPR + TAPPS + (1 – TOT)]

EG = (Present Year’s E – Previous Year’s E)/(Previous Year’s E)

wEG = (Present Year’s wCE – Previous Year’s wCE)/(Previous Year’s wCE)

As was the case with the subject of our previous draft card, Kyrie Irving, Brandon Knight only has one year of collegiate statistics on which to base his professional projections.  Unlike Irving, however, Knight played a full season – and, in fact, played a lot of minutes in just about every game – and so there appears to be a more valid sample size for Knight, at least in comparison to Irving.

The player to which we will compare Knight is Jason Terry, the veteran guard for the Dallas Mavericks and a former NBA Sixth Man of the Year, for the reason that several draft publications have drawn similar comparisons between these two players.  Terry had a very different college career than Knight, however:  Terry was a reserve on a good team in his freshman year, though only played about a quarter of every game; he became a starter at the University of Arizona as a sophomore, and helped lead his team to a national championship, but then saw his minutes reduced drastically as a junior; and in his senior year, having wisely used all of his college eligibility, Terry played so well that he was named a First Team All-American.  As such, the year that will be used to compare Terry to Knight will be Terry’s sophomore season, his first as a starter and one in which he played similar (though reduced) minutes to Knight.

The first group of statistics that will be compared between the two players will be the qualitative ones:  Shot-to-Assist Ratio (SAR); Shot Selection Index (SSI); 3-Point Rate (3PR); and 3-Point Skew (3PS):

                                    SAR      SSI        3PR      3PS

Knight, FR                    3.76     .333     .450     .401

Terry, SO                     2.20     .361     .432     .323

A first glance of the comparative qualitative statistics reveals that Knight, as a freshman, and Terry, as a sophomore, indeed had similar games.  The most glaring difference, of course, is their respective SARs:  Terry’s 2.20 puts him in the lowest quintile of basketball players, the Primary Distributors (sometimes called “point guards”), while Knight’s 3.76 is in the second quintile, called Combination Distributors (sometimes called “combination guards”).  Beyond Terry’s tendency to create a little bit more for his teammates and a little bit less for himself, the two players had very similar games:  Both had only average shot selection, though Terry got to the free throw line with slightly more frequency.  Both took nearly half of their field goal attempts from behind the three-point line (which is probably why they were not fouled very often), but Knight’s three-point shots accounted for a higher percentage of his makes (.401 vs. .323) because he was a much better long-range shooter.  What these statistics say, comparatively, is that Knight will probably have an easier transition to the further NBA three-point line than Terry did, since Knight appears to have been the better long-distance shooter at the earlier age.  As a result of this propensity to bomb, however, do not expect Knight to get to the free throw line very often, unless he changes his game.  Also, given the higher SAR, it would be expected that, at least in the beginning of his career, Knight may need to be slightly more of a scoring focus than Terry did.

Now let’s move on to a comparison of the quantitative statistics: the Successful Possession Rate (SPR), Turnover-Adjusted Points per Shot (TAPPS), Turnover per Touch (TOT), and Earnings (E):

                                    SPR      TAPPS    TOT   E

Knight, FR                    .556     .914     .151     2.32

Terry, SO                     .596     .892     .100     2.39

What stands out the most, at least to me, is the turnover rate: Knight turns the ball over about 15% of the time, which is an exceedingly high number, even more so when considered that he is not a Primary Distributor – a quintile that is expected to make a lot of turnovers because of their increased ballhandling and passing loads.  Terry, in his first year as a collegiate starter, turned the ball over exactly 10% of the time, which is average for a player in the lower quintile, and much better than what Knight accomplished as a freshman.

The other statistics, really, are similar:  though Terry’s SPR is much higher, some of that difference can be attributed to his assist total, as players who concentrate on scoring (such as, comparatively, Knight) are expected to have slightly lower SPRs on average.  The TAPPS of each player is virtually identical, too – with Terry’s inferior three-point shooting balanced out by Knight’s terrible ballhandling.

In the end, Knight’s fast and loose nature with the ball costs him in a head-to-head analysis with Terry:  the Earnings statistic has Terry as being about 3% more valuable than Knight, with 5% of that advantage coming from Terry’s ability to take better care of the ball as a collegiate player.  If Knight can learn to take the ball better – much better, unfortunately – than it is reasonable to expect that he can become as productive a player as Terry has become.  One of the things working in Knight’s favor, of course, is that in this comparison, he is a full year younger than Terry, and still only a teenager.

In sum, Brandon Knight’s brief college career pales slightly to a similarly representative sample from Jason Terry’s.  This is hardly a knock:  Terry was a 10^th overall pick in a pretty decent draft (in 1999 he was picked behind Elton Brand, Baron Davis, Lamar Odom, Rip Hamilton, Andre Miller and Shawn Marion, and ahead of Ron Artest – all players still enjoying productive careers), he is a former Sixth Man of the Year, and he may very well be on his second trip to the NBA Finals.  With some work, I think it is reasonable to expect Knight to set his sights on a Terry-like achievement as a pro – which is to say a valuable complementary piece on a team that is already good without him.

To bring the draft analyses up to date so far, the current rankings would be:

1.      Kyrie Irving

2.      Derrick Williams

3.      Brandon Knight

The subject of the next draft card has not been decided.  If I can figure out a way to model out European players, it will be Enes Kanter of Turkey.  If not, it will be Tristan Thompson.