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.

2011 NBA Draft Card: Kyrie Irving

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 Kyrie Irving, from Duke University.  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)

There is not a lot of data through which to parse on Kyrie Irving, the Duke University guard (Combination Distributor, to use Basketball I.Q. parlance) who declared for the NBA draft after his freshman year.  Not only do we have but a single season to provide us with data on Irving, he missed all but 11 games during his single collegiate campaign due to an unusual toe injury.  Because Irving is leaving three years of eligibility on the table, I have decided to compare his limited collegiate statistics to two players who also left for college with at least half of their eligibility remaining:  Chris Paul of the New Orleans Hornets, who left college after his sophomore season, and Derrick Rose of the Chicago Bulls, who departed after his freshman year.

As such, we will not compare the growth of these respective players, since only Paul played for more than one season, and was thus the only player for which we could derive year-to-year development (we will use Paul’s freshman season for comparative purposes).  Likewise, because Irving’s minutes were somewhat limited this year (he averaged less than 30 minutes per game – in the beginning of the season, this was likely because he was brand new to college basketball; by the end of the season, this was probably due to a graded return from injury), we will not compare weighted earnings or weighted growth, either.

To be sure, an interpretation of Irving’s statistical achievements in college should be viewed cautiously, because of the small sample size.  Nevertheless, we have no choice but to work with what we have.  We will begin with a comparison of the qualitative statistics between Irving, Paul and Rose – the Shot-to-Assist Ratio (SAR); the Shot Selection Index (SSI); the Three-Point Rate (3PR); and the Three-Point Skew (3PS):

                                    SAR      SSI        3PR      3PS

Irving, FR                     2.97     .683     .375     .327

Paul, FR                       1.97     .654     .316     .296

Rose, FR                      2.85     .470     .239     .168

Upon review of the qualitative statistics, Irving has a very similar game to Paul.  Both players had extraordinarily high SSIs during their freshman seasons, taking about two-thirds as many free throws as field goal attempts (the NBA median is about .290, whereas both Irving and Paul were above .650), indicating that both players exhibited excellent shot selection.  This is supported by the fact that each player shot over 50% from the field as freshmen.  But what is so amazing about each player’s SSI is that both Irving and Paul were astounding free throw shooters – Paul shot above 80%, and Irving shot above 90%.  This means that, despite the fact that each player was a virtual lock from the free throw line, their opponents felt that their looks at the basket were so good that they still fouled them on a high proportion of their shot attempts.  For this strategy to make sense for the defense on a 90% free throw shooter, it would mean that the shots on which Irving was fouled had a 90% or better chance of going in.  When your SSI is .683 – and remember, this is a perimeter player who is playing away from the basket – and you are shooting 90% from the free throw line, you are making a ton of smart decisions on the court.  It should be noted that during his freshman year, Paul’s statistics in this regard were outstanding – not surprising for a player who has gone on to be a legitimate MVP candidate in the NBA – but Irving’s are a tad bit better.

The omission of Rose from the discussion is not meant as a disparagement – an SSI of .470 is very good, well above the average, and considerably so for a backcourt player.  It just was not quite as good as Irving’s and Paul’s.

The rate at which Irving and Paul both attempted and made three-pointers is also very similar, although Irving took (and made) more as a percentage of overall field goals.  As such, with the three-point arc moving back at the next level, you would expect this to have a mildly deleterious effect on Irving’s performance – but it would be expected to be similar to that experienced by Paul, and he does not seem to have suffered as a professional.  Rose, somewhat to my surprise, did not attempt a lot of three-pointers, in comparison to the rest of his shot attempts.

Looking at the SAR, Irving is actually more similar to Rose than to Paul:  Irving and Rose both qualify as Combination Distributors, whereas Paul is a Primary Distributor.  All three players appear comfortable with distributing the ball, and creating opportunity, for their teammates (although Paul is the more likely distributor).  Upon comparison of the qualitative statistics, one might conclude that Irving has a style and balance to his game that is on a par with aspects of both Paul’s and Rose’s games (he shoots like Paul, he passes like Rose).

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

                                    SPR      TAPPS    TOT   E

Irving, FR                     .670     1.15       .101   2.72

Paul, FR                       .660     1.04       .101   2.60

Rose, FR                      .584     .925       .101   2.41

Again, Irving and Paul have very similar, excellent games.  Rose, too, was an excellent performer during his freshman year of college, but not quite as good as the other two – though there is an excellent reason for this:  Free throw shooting.  As freshmen, both Irving and Paul went to the free throw line much more than Rose did, and when they got there, they rarely missed, while Rose shot just 71% from the line in his freshman year.  Had Rose gotten to the line just a little bit more, and shot his free throws with similar accuracy, his numbers would have been every bit as good as Irving’s and Paul’s.

In fact, as a pro, that is exactly what Rose has done, especially this past year, when he won the MVP.  He now shoots better than 85% from the free throw line, which is a large part of why he is so dangerous, and he has an SSI this past year of .781 – which puts him on a par with the likes of Shaq during his prime (but imagine if Shaq hit 85% of his free throws).

But we are not talking about Derrick Rose as a third-year pro – we are talking about Kyrie Irving as a college freshman, and how he compares to Rose and Chris Paul at similar stages in their development.  And I would say that Irving is already doing as a college freshman what it took Rose two full years of professional ball to learn.  Now, this does not guarantee that this trend will continue – Paul, for example, still has an excellent SSI (.414), but it is not as good as where he was in college, whereas Rose has improved tremendously – but it is an excellent starting point.

All three players have identical turnover rates as freshmen.  Irving has the highest TAPPS and SPR because of his free throw shooting and successful three point shooting, with Paul right behind him.

In sum, Kyrie Irving’s college statistics compare quite favorably with those of Chris Paul and Derrick Rose.  Of course, due to injury Irving’s freshman season was only 11 games long, and so the reproducibility of these excellent statistics may be called into question.  Likewise, it is unlikely that any player would demonstrate the remarkable growth at the professional level that Rose has, though one would hope that he could keep it together as well as Paul.

To conclude, it is fair to compare the collegiate Kyrie Irving to both Chris Paul and Derrick Rose – he appears to have a similar potential to these MVP-caliber players.  With only two collegiate players having been analyzed, the current rankings would be:

1.      Kyrie Irving

2.      Derrick Williams

The next post will analyze the draft potential of Brandon Knight from the University of Kentucky.

2011 NBA Draft Card: Derrick Williams

In this week’s post, I will begin a series of comparative “draft cards” on college players who have entered the 2011 NBA Draft.  In the first of this series, I will analyze the relative professional prospect of Derrick Williams from the University of Arizona.  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)

College players who have entered the upcoming draft are often compared to a current (or past) NBA player, to whose achievement they might realistically aspire if they continue to develop at the professional level.  For Derrick Williams, an oft-cited comparison is David West of the New Orleans Hornets, who played his college basketball at Xavier University in the early 2000s.  In this comparative analysis, Williams’s two years of college basketball (his freshman and sophomore seasons, as he is departing college with two years of eligibility remaining) will be compared to the last two years of West’s college career (which represent his junior and senior seasons, as West exercised all four years of his college eligibility).

The first group of statistics that will be analyzed are the qualitative statistics of the two players: their Shot-to-Assist Ratio (SAR), Shot Selection Index (SSI), 3-Point Rate (3PR), and 3-Point Skew (3PS):

                                    SAR        SSI      3PR      3PS

Williams, FR                18.09   .823    .057     .025

Williams, SO               12.69   .871    .195     .186

West, JR                      9.73     .658    .076     .046

West, SR                      5.32     .617    .063     .042

Reviewing the SAR values for each player, you will see that Williams is a Finisher, who has a heavy skew toward shooting the ball when he has it (as opposed to attempting to create an assist), although from his freshman to sophomore seasons he began to share the ball a little bit more.  As a junior, West was also a Finisher (though he had a far less “black hole effect” than Williams’s freshman year) – but by his senior year, it appears that West had changed his offensive game enough to qualify as a Balanced Scorer, as he clearly looked to create quite a bit more for his teammates.  As a reminder to readers, these values are qualitative, not quantitative, and they are not meant to judge players as selfish or unselfish, etc.  These numbers merely state that, through his sophomore season, Williams was primarily looking to put the ball into the hoop whenever he had it, whereas West had begun to incorporate more ball distribution by his final year.

It is hard to predict what this means at the professional level, but it should be noted that Williams began to distribute the ball much more in his sophomore year than his freshman year, which suggests that his game is still developing and his mind is open to change.  It should also be noted that whereas West had absolutely astounding SSI values in his last two years of college, Williams was even better (taking 82% as many free throw attempts as field goal attempts in your first year of college is off the charts, and then Williams got even better).  What these qualitative statistics suggest is that each player had an excellent shot selection at the college level – so good that their opponents foul them almost as much as they let them shoot – but Williams was somehow even better than West.  The fact that Williams was greater than a 50% shooter tells you even more about how good his shot selection really was.  The fact that Williams has gotten his free throw shooting up to about the 75% level tells you that, with a relatively high rate of getting to the free throw line, in addition to a low-post game with high percentage shots, Williams is a dangerous offensive player – perhaps more dangerous than West was as a college player, and West is a legitimate NBA All-Star.  If Williams continues to work on his free throw shooting, and improve it by about another 5%, he could become a terrible headache for opponents at the NBA level, if West is used as a standard of comparison.

The most surprising of the qualitative statistics, I think, is the 3PR and 3PS that Williams demonstrated during his last college season.  In West’s last two college seasons, he, not surprisingly, shot three-pointers infrequently, and made them with a reasonable accuracy – they were not a big part of his college game, and the perimeter game is not a cardinal feature of his professional prowess. 

In contrast, in his sophomore season, almost 20% of Williams’s field goal attempts were three-pointers, as were nearly 20% of his makes – in fact, he shot over 50% from beyond the arc, which is astounding for a big man.  Now, as an NBA player it is unlikely that, with an arc that is three feet further back, Williams will either attempt or make so many threes – but this statistic, in combination with his three-point shooting percentage, tells you that Williams is already prepared to be the type of low-post player who can step outside the post and drill the 19-footer, a la Amare Stoudemire and Kevin Garnett.

So, based on these qualitative alternative statistics, it appears that Williams has an even better shot selection than West did as a college player (and West’s was pretty darn good), and Williams complements his game with a justified perimeter presence that West just did not have.  West appears to have been the more willing passer, but the accuracy of Williams’ shooting seems to justify the Finisher role that he assumed.  On first analysis, I believe that Derrick Williams compares favorably to David West.

Now we will move on to some quantitative alternative statistics: the Successful Possession Rate (SPR), Turnover-Adjusted Points per Shot (TAPPS), and Turnovers per Touch (TOT):

                                    SPR      TAPPS    TOT

Williams, FR                .552     1.06       .084

Williams, SO               .601     1.15       .096

West, JR                      .540     1.00       .101

West, SR                      .581     1.03       .074

Comparing these alternative statistics, Williams gets the edge over West at the college level in everything except turnover rate.  Once again, these statistics illustrate examples in which West demonstrates remarkable achievement, but Williams just manages to be even better.  West, for example, has very high rates of successful possessions – but Williams is significantly better, despite playing in an SAR quintile (Finisher) in which lower SPRs are anticipated due to lower assist rates.  Once again, West demonstrates a phenomenal TAPPS (at least 1.00 for each of his last two years), but Williams – who is, season for season, two years younger than West – manages to eclipse him.  The only comparative statistic in which Williams falls short here is TOT – he must take better care of the ball at the professional level, as a turnover rate of almost 10% is far too high for a player who does not pass very much, or bring the ball up the court.  It should be noted that West, in his final year of college, took very good care of the ball.  Overall, however, the alternative statistics suggest that, at similar levels of competition, Williams was the better player.

The final statistical analysis would include the cumulative accomplishments of each player – Earnings (E) and weighted Cumulative Earnings (wCE) – as well as their year-to-year growth:

                                    E          wCE     EG        wEG

Williams, FR                2.53     1.78     --          --

Williams, SO               2.66     2.00     5.13%  12.36%

West, JR                      2.44     2.09     --          --

West, SR                      2.54     2.32     4.10%  11.00%

Put simply, when Williams was on the floor in college, he was a better player than West (despite the fact that, as stated, West was quite good, and West was, season for season, two years older).  The Earnings column illustrates this clearly.  However, West played many more minutes per game than Williams, and thus his weighted earnings (which account for playing time) were better than Williams.  There are probably a few reasons for this: One, the seasons being compared here are the freshman/sophomore for Williams, versus the junior/senior for West; it is likely that, for as good as Williams was, he was still earning his playing time as an underclassman, whereas West had already put in the time that had earned him minutes.  Another reason for the differences in playing time may be the teams that each player was on: As a mid-major competitor, West had fewer “blue chip” teammates with whom to split the minutes, and thus he received the lion’s share.  Williams, however, attended one of the country’s higher profile teams, and, as a team player, likely had to surrender minutes to other blue-chippers who were nevertheless less-deserving than he.  Foul trouble would NOT be a reasonable explanation for the minutes discrepancy, as West committed 2.66 and 2.96 fouls per game, respectively, in his last two college seasons, whereas Williams committed 2.52 and 2.79.

That said, it appears that the cumulative statistics show that Williams was the better player when he was on the court, and were he to receive minutes that are commensurate with his skill, his weighted statistics would far surpass West’s.  They also demonstrate that, as much as West grew from his junior year to his senior year, Williams as a sophomore was growing even more so (not surprising).  What this means is that, as college players, Williams was better, younger, and on the steeper part of the learning curve (all good things when trying to extrapolate future achievements).

So, the first of the 2011 draft cards concludes with the determination that Derrick Williams compares quite favorably to David West, and I would project Williams to mature into an All-Star level NBA player.  Those who counter with the point that Williams nevertheless is not deserving of “first overall” distinction, I have a few replies:

First, West came out of the 2003 NBA draft.  Ahead of West were players that included LeBron James, Carmelo Anthony, Dwyane Wade, and Chris Bosh – an uncommonly deep draft, and one that stands in stark contrast to this year’s relatively shallow one.  Also, though West was picked at #18 in 2003, several less-deserving players (including Mike Sweetney, Darko Milicic and Chris Kaman) were chosen ahead of him, and in retrospect it could be argued that West should have been the fourth or fifth pick in that draft.  And finally, though one could argue that it would have been unwise to have drafted West ahead of the likes of James or Wade, you could no more make the argument that West was a bad pick than if you argued that Hakeem Olajuwon was a bad pick because he was taken ahead of Michael Jordan.

So, is the 2011 draft a little skinny on talent?  Yes.  Is Derrick Williams the best of the lot?  I’m not sure yet.

But does Derrick Williams’ college statistics project that he will be at least as good a pro as a legitimate NBA All-Star?

Almost certainly.