I used my PA100 datasets from 2009/10, 2010/11, and 2011/12 to study how player performance changes with age. This certainly isn't the first study to investigate aging and performance in the NBA, but because the PA metric looks at offense and defense separately, I think it does paint a unique picture of the NBA aging process.

To find rates of decline, I combined my datasets and looked at the average change in performance for players going from their 19 year old season to their 20 year old season, their 20 year old season to their 21 year old season.... and so on up until age 35 where the sample gets very slim.

The graphic below shows the rate of change in PA100 offensive and defensive scores with age. The solid line at 0 represents the point where players reach their peak performance. The values above the 0 line indicate the expected improvement in points generated per 100 possessions in the next season. The values below the 0 line indicate the expected decline in points generated per 100 possessions in the next season:

Discussion below...

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KEY

PA100.off:

Points added per 100 offensive possessions above the average player at his position. Gives credit for creating better offensive situations (i.e. rim or three for himself or others). Also gives credit for outperforming the situation of his shots by shooting with accuracy above expectation. For example, when Ricky passes to an open Kevin Love three-pointer, Ricky gets credit for the expected value of an assisted three above and beyond the lowest value shot (an unassisted midrange jumper). Love then earns points for making that shot above average, and loses points for making it below average.

PA100.def:

Mostly the PA100.off score of player's positional counterparts. However, credit for blocks and steals is given to the blocker/stealer. For example, when Nikola Pekovic blocks Steve Nash, Pekovic gets credit for a missed rim shot, while Rubio is debited for an allowed rim shot.

PA100.dif:

PA100.off - PA100.def

HLP and component numbers:

These numbers show the difference in how non-counterpart opposing players perform when ego is in the game. Do opposing shooting guards perform worse with Ricky is in the game, or on the bench? What about opposing centers? This measure gives players a quarter share of responsibility for the performance of each non-counterpart opposing player. The HLP measure simply combines the player's impact on the four other positions over an average 100 possession period.

The biggest concern to keep in mind when interpreting these numbers is the cohort effect. Some players are almost always on the court together, and this makes the HLP measure for that position largely useless.

KEY

PA100.off:

Points added per 100 offensive possessions above the average player at his position. Gives credit for creating better offensive situations (i.e. rim or three for himself or others). Also gives credit for outperforming the situation of his shots by shooting with accuracy above expectation. For example, when Ricky passes to an open Kevin Love three-pointer, Ricky gets credit for the expected value of an assisted three above and beyond the lowest value shot (an unassisted midrange jumper). Love then earns points for making that shot above average, and loses points for making it below average.

PA100.def:

Mostly the PA100.off score of player's positional counterparts. However, credit for blocks and steals is given to the blocker/stealer. For example, when Nikola Pekovic blocks Steve Nash, Pekovic gets credit for a missed rim shot, while Rubio is debited for an allowed rim shot.

PA100.dif:

PA100.off - PA100.def

HLP and component numbers:

These numbers show the difference in how non-counterpart opposing players perform when ego is in the game. Do opposing shooting guards perform worse with Ricky is in the game, or on the bench? What about opposing centers? This measure gives players a quarter share of responsibility for the performance of each non-counterpart opposing player. The HLP measure simply combines the player's impact on the four other positions over an average 100 possession period.

The biggest concern to keep in mind when interpreting these numbers is the cohort effect. Some players are almost always on the court together, and this makes the HLP measure for that position largely useless.

!!!! WARNING !!!! The numbers for the Raptors and especially the Nets are based on a smaller sample than other teams. For some reason, the files I use to build my dataset did not capture every game this season, and the Nets were by far the biggest victim of this problem.

NOTES:

BOS

- Avery Bradley is an amazing defender, and an amazingly bad offensive player.

- The steamer would look great as our backup defensive big. Might take at least 9/3 to pry him from Boston though

NYK

- What do folks think about Jeffries as a vet minimum backup to Love?

- Amare isn't one of the worst players in the league, but with his contract, he may be one of the worst assets

- Stats guys are supposed to hate Carmello right? Not this one.

PHI

- It is hard to overstate how awesome a Williams for Iggy swap would be for us… Maybe it is a pipe dream, maybe not... I really don't know how to read the Iggy/Philly situation.

- Note Sweet Lou's HLP measure. Helps the argument that his defensive is more the product of his surroundings.

RAPS

- James Johnson, former apple of Kahn's eye is a fantastic defensive player

- Jose Calderon on the other hand is towards the bottom of the barrel defensively every season.

- This was a breakout year for Bargnani who was a -5.16 in 2010/11 and a -3.67 in 2009/10. Proof that busts can become good players... but don't count on it.

NETS

- At first blush the Nets look terrible. However, Lopez has been about a +2.5 to +3 player in the past and should be continuing at the level next season. Williams was 5.7 and 4.5 in 2009/10 and 2010/11 and looking at his HLP measure, his defensive value may be adversely impacted by his teammates this season. Wallace was an excellent +6.4 with a -3.39 defense in 2009/10 and then posted around a +2.5 with good defense in 2010/11. A core of Williams, Brooks, Wallace, Humphreys, and Lopez is actually a collection of really good players, whose skills should complement each other really nicely... the problem is that Brooks is the only guy on that list currently under contract for next season. So.... good luck Brooklyn!

You can't truly evaluate the draft until several years down the line. However, that shouldn't stop us from taking a look back at this past season and evaluating the early returns on the 2011 draft. Here is a list of this year's rookies in order of the draft position:

For a breakdown and analysis, continue below the fold.

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KEY

PA100.off:

Points added per 100 offensive possessions above the average player at his position. Gives credit for creating better offensive situations (i.e. rim or three for himself or others). Also gives credit for outperforming the situation of his shots by shooting with accuracy above expectation. For example, when Ricky passes to an open Kevin Love three-pointer, Ricky gets credit for the expected value of an assisted three above and beyond the lowest value shot (an unassisted midrange jumper). Love then earns points for making that shot above average, and loses points for making it below average.

PA100.def:

Mostly the PA100.off score of player's positional counterparts. However, credit for blocks and steals is given to the blocker/stealer. For example, when Nikola Pekovic blocks Steve Nash, Pekovic gets credit for a missed rim shot, while Rubio is debited for an allowed rim shot.

PA100.dif:

PA100.off - PA100.def

HLP and component numbers:

These numbers show the difference in how non-counterpart opposing players perform when ego is in the game. Do opposing shooting guards perform worse with Ricky is in the game, or on the bench? What about opposing centers? This measure gives players a quarter share of responsibility for the performance of each non-counterpart opposing player. The HLP measure simply combines the player's impact on the four other positions over an average 100 possession period.

The biggest concern to keep in mind when interpreting these numbers is the cohort effect. Some players are almost always on the court together, and this makes the HLP measure for that position largely useless.

.. but still really important to the Timberwolves.

Ricky Rubio's rookie season seemed to get everyone at the Target Center and on Canis Hoopus pretty damn excited. Ricky is an fun player, and when he was on the court, things just seemed to be working for the first time since the Garnett years. Throughout the season I have repeatedly seen Ricky identified as "the centerpiece of the Wolves' future", "untradeable", "max contract player", "better than Kevin Love", "better the Nikola Pekovic"... Many Minnesota fans don't just see Ricky as the starting point guard we have needed since Cassell. They see him as the golden-boy savior of the franchise.

I was there watching the games this season, so I know where this sentiment comes from. I had the same experience watching the Wolves finally compete on a regular basis, only to watch the season crumble after Rubio tore his ACL making a hustle play that may have beaten the Lakers. However... I think if we let the hype-train get going too fast, many folks are going to experience a serious let-down, and I also worry the back-swing will make it difficult to appreciate Ricky for the good value he legitimately does bring to the Wolves. This is why I view it as a public service to unmask your hero and disabuse you of your hope.

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KEY

PA100.off:

Points added per 100 offensive possessions above the average player at his position. Gives credit for creating better offensive situations (i.e. rim or three for himself or others). Also gives credit for outperforming the situation of his shots by shooting with accuracy above expectation. For example, when Ricky passes to an open Kevin Love three-pointer, Ricky gets credit for the expected value of an assisted three above and beyond the lowest value shot (an unassisted midrange jumper). Love then earns points for making that shot above average, and loses points for making it below average.

PA100.def:

Mostly the PA100.off score of player's positional counterparts. However, credit for blocks and steals is given to the blocker/stealer. For example, when Nikola Pekovic blocks Steve Nash, Pekovic gets credit for a missed rim shot, while Rubio is debited for an allowed rim shot.

PA100.dif:

PA100.off - PA100.def

HLP and component numbers:

These numbers show the difference in how non-counterpart opposing players perform when ego is in the game. Do opposing shooting guards perform worse with Ricky is in the game, or on the bench? What about opposing centers? This measure gives players a quarter share of responsibility for the performance of each non-counterpart opposing player. The HLP measure simply combines the player's impact on the four other positions over an average 100 possession period.

The biggest concern to keep in mind when interpreting these numbers is the cohort effect. Some players are almost always on the court together, and this makes the HLP measure for that position largely useless.

SOUTHEASTERN:

I finally have the PA100 metric to a place where I am pretty happy with it. It isn't perfect. I think I need to account for diminishing returns in a few areas, namely shot creation and offensive rebounding, but I trust the results. Individual scores explain about 97-98% of team efficiency differential. This is higher than I thought I would get given that I am not including any "team" statistics, even unassigned rebounds and turnovers (like shot clock violations.) Furthermore, season to season PA100 scores are correlated at just around 80% for players logging at least 2000 possessions (.8 for differential and offensive and .75 for defensive.) Honestly, I don't know what I should be shooting for here, but not even controlling for age, I can't imagine reality is much better than an 80-90% correlation.

In addition to the basic PA100 numbers I am also including another new gizmo I have been working on. The "Help" defensive statistics are not factored into PA100 scores, but offer a handy diagnostic tool for identifying players whose defensive scores owe something to cross-matching or who cover the whole floor rather than focusing on locking their man down.

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The most annoying thing about building this metric, has been constantly changing my opinion on players as I fix bugs in the code and/or add more information into the model. It is always embarrassing to present a new reality every few weeks, but I am pretty happy with where I am not. There were a number of issues with my play-by-play data parser which means any of my past PA100 numbers came from a really flawed data-set. I managed to fix that , so now my numbers match up perfectly with websites like Hoop Data.

Since Timberwolves fans are constantly asking the question, "which wings should we target in free agency," I figured I would through up a list of how PA 100 describes the current landscape on NBA wings:

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Michael Beasley was an impossible Williams comparison to avoid before last year's draft. Both were scoring-centric star collegiate bigs with tweener SF/PF bodies and games. Those of us looking at the number noticed a significant discrepancy in the two players' "usage" in college, but everyone seemed pretty comfortable at least dubbing Derrick Williams a "lower-usage" version of Michael Beasley.

Now that Derrick Williams' rookie campaign is nearly finished, that comparison is looking to be eerily appropriate.

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Really, really, good

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Here are links to the Google Spreadsheets for all PA100 data for the 2009/10 (by PA100.s, by Team), 2010/11 (by PA100.s, by Team) and 2011/12 (by PA100.s, by Team) seasons. No commentary, just data. Feel free to tell your own stories with it.

BTW; discount any PA defense and differential data I posted a few days ago. There were a few glaring errors in the code that skewed the numbers.

Here is a glossary to help you interpret the numbers:

SCoff =

[The expected value a player creates (based on accuracy and foul rates from three, mid, and rim assisted and unassisted) each time he tries to create (captured by: unassisted shoot, pass to shot, or turnover) above or below the mean attempt by an NBA player.]

X

[The number of creation attempts a player has each team possession]

X

100

SEoff =

{[The value created by a player each time he takes a shot by hitting more accurately than expected (based on situation)]

X

[The number of attempts from each situation each team possession]

+

[Value created by getting to the line and making your (non-technical) free throws above expectation based on shooting attempts each team possession]

+

[Value created by taking the team's technical fouls and making them above expectation]}

X

100

PAoff =

SCoff + SEoff + (ORB - NBA mean ORB)

SCdef =

[The SCoff of a player's matchups]

-

[Value created by stealing the ball from opposing players other than your matchup]

X

100

|| Negative is good ||

SEdef =

[The SEoff of a player's matchups]

-

{[The value created by blocking shots by opposing players other than your matchup (value adjusted for the type of shot blocked) per team possession]

+

[the value created for the opposition by committing technical fouls and 3 second violations per team possession]}

X

100

|| Negative is good ||

PAdef =

SCdef + SEdeff + (matchup ORB - NBA mean ORB)

|| Negative is good ||

PA100 =

PAoff - PAdef

.s =

Points added over the entire season.

r more information on how PA 100 is calculated go here. Also see the All-Star Break report here. Remember that this is an offense only metric, so players like Gerald Wallace are criminally undervalued.

Let's see how teams helped or hurt their offense with deadline moves.:

Warriors before:

Warriors after:

___________________________________________________________________________

Bucks before:

Bucks after:

______________________________________________________________________________

Lakers before:

Lakers after:

_______________________________________________________________________________

Cavs before:

Cavs after:

_________________________________________________________________________

Blazers before:

Blazers after:

___________________________________________________________________________________

Nets before:

Nets after:

_________________________________________________________________________

Wolves before...... and after:

Some extra players of note who moved at the deadline:

I will keep posting more as I get around to it. Going to the game tonight, so hopefully put them all up by then. Let me know if you see any errors.

ere is how the current NBA landscape looks according to PA100.

Rapid-fire stats reference:

Solo: value (in points) added above the mean player by creating better shot opportunities for himself over 100 possessions.

Pass: value (in points) added above the mean player by creating better shot opportunities for teammates over 100 possessions.

SC100: Solo + Pass + Turnovers

rim/mid/tre.exe: value (in points) added above the mean player by executing in different situations with superior efficiency over 100 possessions.

ft.exe: value (in points) added above the mean player by getting to the line and/or making free-throws with greater efficiency than expected based on shot distribution over 100 possessions.

SE100: Sum of rim, mid, tre, and ft.exe

PA100: SC100 + SE100 + value created by offensive rebounding

You can find a more thorough discussion of how to interpret these numbers here.

I'll start with everyone's favorite team:

The best players in different skill areas described by PA 100:

Mid-season MVP:

Mid-season All-PA100 team:

PA100 mid-season ROY:

Remember, this is an offense only metric. Guys who make a difference on the defensive end are not fully captured by PA 100..

Edit -- There was an error in my original ROY list, I shamefully omitted Kenneth Faried, who if he keeps up his current rate of production should be second behind Irving by the end of the season. Other Rookies who are doing well, Alec Burks, Tobias Harris, Kemba Walker, and Marshon Brooks (probably more, but I don't list age in my data so I need to find rookies by eyeballing.)

If you need help interpreting these numbers, refer to this post. If you are still confused, ask. Remember this is an offense only metric, so it isn't capturing the full value of guys like Kevin Garnett.

Quick rundown of the columns: SC 100 is the expected value (in points) each player created over the average 100 possession span. SE 100 is the value they created through better execution in different situations. Poss+ is basically the value created by offensive rebounds every 100 possessions. PA100 is the number of points the player is likely to add to the teams offensive above and beyond the average player every 100 possessions. PA/g is PA100 by the number of possessions per game, so the real points added (or subtracted) by that player in every game relative to the mean player. I report the total points added rather than per game, for the 2012 season.

If you value your sanity, don't look at Wes Johnson's 2012 numbers. Just skip past that part.

Points Added 100" is a metric I have been working on the past couple weeks. PA 100 is an offense only metric that measures how many points a player adds (or subtracts) from a team's production across 100 offensive possessions. I will be outsourcing many of my opinions to this model in the future, so I figure I should present it on Canis Hoopus.

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Here are Ricky Rubio's Euro numbers during the first 5 years of his professional basketball career:

Back in June, I used those numbers to try and predict what kind of player Ricky Rubio will be with the Wolves. Here is what I wrote:

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This list includes 9 of the best NBA forwards ordered by points per 40 minutes. In addition to scoring totals, I have also included scoring efficiency (TS%), the percentage of team possessions that player uses when on the court (USG), and the percentage of their shot attempts that are assisted. See if you can match names to numbers before jumping below the fold: (Aldridge, Anthony, Bosh, Durant, Griffin, James, Love, Nowitzki, Stoudemire)

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The Timberwolves swapped Kurt Rambis for Rick Adelman this offseason. After 10 games, I'm looking at the numbers to see if this seemingly important change really made a difference.

via i.imgur.com

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I made an earlier attempt to project Derrick Williams (here), but rereading that post I am not entirely happy with it. I don't mind the Luis Scola and David West comparisons that I arrived at, but I think I underrated Williams' athleticism, and spent too much time comparing him to high-usage players and strong rebounders. These aren't good comps because Williams is unlikely to fit into either mold. Since Williams ultimately became a Timberwolf, I felt the need to rework my assessment and try to construct as good of a projection as I can. This post tries to find a better set of comparisons than I used in the last, and then reiterates the problem of projecting scoring-centric players like Derrick Williams.

via basketballonmymind.files.wordpress.com

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Ricky Rubio

PG

6'4" 

180lbs

20 years old

via t1.gstatic.com

Ricky Rubio.  The Spanish phenom who began playing professional ball in Spain at 14 years old, was an international celebrity, a youtube favorite, an olympic hero, and a household name among NBA fans long before his 18th birthday.  The hype machine is on overload for this prospect.  No man can completely clear his mind of Rubio's flashy passes, catchy name, and floppy hair when trying to project him as an NBA player.  Rubio's uniquely overblown reputation makes careful, sober, objective analysis all the more important.  The numbers don't see any of the flair or hear any of the praise, and thus moreso than for any other prospect, we need to stay grounded in the numbers in assessing Ricky Rubio.  So let's see what those numbers say.     

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It's that time of year again.  Everyone has their pet idea for how to turn our horrid franchise around.  Swap this mediocre asset for that mediocre asset, go all in on this overpaid veteran, open up papa Taylor's wallet and spend in free agency, swap the #2 pick for X, rights to Rubio for Y, Beasley and Pekovic for Z...  There are a lot of different moves that we could hypothetically make this offseason, many of which I believe are superior to the status quo.  However, this proposal is different.  This proposal leads to increased wins without bringing in any additional salary commitments, or using any of our limited tradable assets.  This means that it can work together with many of the myriad offseason plans outlined on this blog.  Given that the Timberwolves needs are currently greater than their available cap space and tradable assets can satisfy, this kind of costless improvement is a necessary step towards success.  I think there is a way to bring this team close to 30 wins next season simply by reevaluating the talent already under contract and adjusting how our key players are utilized.

After all of this fanfare, I should probably have a deep and complicated strategy for turning the Timberwolves around, but really my solution is quite simple.  Slide each of Wes, Beasley, and Love down one position on the starting roster.  Play Kevin Love exclusively at center, play Michael Beasley exclusively at power forward, and play Wesley Johnson exclusively at small forward.  At first this seems like too simple of a solution to actually work.  However, after looking over the numbers, this strategy doesn't seem to simple to work, it seems too glaringly obvious to ignore.     

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