Tag: Some Plays Count

Why The 2008 Knicks Can’t Win (Some Plays Count)

Mike Kurylo 115 Comments

The other day I was on the train and overheard two Knick fans talking about the state of the team. The first man asked the other what was wrong with the team to which the second replied: “Isiah has to go. They have a good team on paper.” It seems that there’s the idea floating around Knick-nation that with a coaching change and a few tweaks the Knicks could have a good team. However, watching last Wednesday’s loss to the depleted Kings gave me a clear picture of why the Knicks just can’t win with this current roster. In reality it was just two Kings that helped sort things out: Brad Miller and John Salmons.

One one possession (4:28 1Q) Miller is on the left blocks being fronted by David Lee. Salmons has the ball, lofts it over Lee to Miller, and Brad has an unobstructed path to the hoop for an easy two points. After Lee fronts Miller, someone is supposed to give backside help. On this play Eddy Curry is on the weak side, but he’s oblivious to what’s happening with the ball. Curry is engrossed in covering the ever dangerous Mikki Moore on the weak side. Miller’s layup exposed two weaknesses – Lee’s inability to play better man to man defense and Eddy Curry’s lack of awareness on defense.

In the second quarter at the 5:51 mark, the Kings bring the ball up on offense. Brad Miller is on the far side behind the three point line while Garcia and Moore play the high pick & roll. Lee is defending Moore and helps double on the pick & roll. Garcia passes the ball to Miller who is standing behind the three point line. Even though Miller is able to hit from downtown, Curry gives him space is and is about 2 feet from the paint. Despite Curry playing Miller deep, Miller is able to dribble right past him. Lee, recovering from the high screen, comes over to help, but can only offer token resistance by putting up his arms. Miller scores an easy two points over David Lee. Again Curry and Lee have revealed their weaknesses on defense. This time Curry shows his inability to stay with his man on the perimeter (something I’ve mentioned often here) and Lee is unable to provide assistance in the form of shot blocking.

In this game, John Salmons scored a lifetime high of 32 points. Reading over the play-by-play Salmon had 6 baskets recorded as “Driving Layup”. Watching the game it felt like it was 30 baskets. I could have analyzed any of his layups, but I chose to review his first – 40 seconds into the game. At the top of the key, Miller passes the ball to Salmons who is at the free throw line extended. Miller sets a pick on Salmons’ defender (Jeffries). Miller’s man, Eddy Curry is supposed to help, but again he’s unaware of what’s happening and fails to react to the pick & roll. Salmons goes right past Curry unhindered. Zach Randolph watches the play unfold and moves in front of the restricted area in preparation for Salmons’ approach. Yet Salmons drives right past Randolph for the easy layup. A series of mistakes on this possession lead to an easy bucket: Curry’s inability to read the screen, his failure to slow down Salmons’ drive so that Jeffries can recover, and Randolph’s futile help under the basket.

These plays expose a fundamental flaw with the current Knicks team: the lack of interior defense. It’s no secret that nearly every player on New York is a bad defender, but good defense usually begins from the inside. There’s a reason that bigmen who are offensively limited but can prevent scoring can have long careers. Players like Eddy Curry, Zach Randolph, and David Lee aren’t strong defenders so they need a defensive minded compliment in the frontcourt. In Curry’s only winning season, he was flanked by a few strong defenders: Tyson Chandler, Antonio Davis, and Andres Nocioni. In Randolph’s only winning season, he was coupled with Rasheed Wallace, Arvadys Sabonis, and Dale Davis.

Instead of a frontcourt pairing of an offensive player with a defensive player, the Knicks have two poor defensive big men on the court at nearly all times. And this has been a recipe for disaster. New York is dead last in the league in defensive efficiency, and there isn’t a coach in the world that could make the current rotation average defensively. Without the addition of a defensive frontcourt player to the rotation, New York will remain a bad defensive team. The Knicks aren’t a good team on paper, they’re just plain bad on defense.

Some Plays Count: Stephon Marbury & David Lee 11/11/07 (Part II)

Mike Kurylo 62 Comments

In the last installment, I looked at a recorded version of the Knicks’ game against Miami on Sunday in order to get a better understanding of the team. Today I’m going to look at David Lee’s play in the first quarter. Due to Zach Randolph’s absence, Lee started but was removed only a few minutes into the game. From a layman’s perspective this might have seemed justified because his man Udonis Haslem scored 10 points on a perfect 5-5 shooting. Isiah Thomas sent Malik Rose, whose strength lies on the defensive end, to the scorer’s table just 7 minutes into the game. Since I was curious what Lee did that earned him a quick hook, I’m only going to look at the plays that are significant to this event.

10:40 [NYK 6-0]
Lee Slam Dunk Shot: Made (2 PTS)
Assist: Marbury (1 AST)

The Heat get distracted as Quentin Richarson fumbles the ball, but recovers it. Lee slips past his defender under the hoop and raises his hands. Marbury hits Lee with a pass, and David dunks the ball for an easy 2 points.

10:12
Davis Layup Shot: Missed Block: Lee (1 BLK)

Miami has the ball and attempts a pick & roll with Davis & Haslem. The pick & roll (especially with Haslem) will be a staple of the Heat offense all night long. Lee switches on the play, follows Davis to the hoop and blocks his shot attempt.

10:05
Crawford Turnover:Lost Ball (1 TO) Steal:Hardaway (1 ST)
9:59 [MIA 2-6]
Haslem Driving Dunk Shot: Made (2 PTS)
Assist: Hardaway (1 AST)

Crawford losses the ball as the Knicks bring it up, and Lee picks up Jason Williams in transition to slow the Miami fast break. Unfortunately nobody picks up Lee’s man Haslem, and Hardaway finds him for an easy score.

9:34
Lee Turnover:Lane Violation (1 TO)

This one speaks for itself. Lee steps into the lane too early on a Curry foul shot attempt, and the Knicks lose a point. This is a foolish mental lapse on Lee’s part.

9:20
Davis 3pt Shot: Missed
9:18
Lee Rebound (Off:0 Def:1)

David Lee is one of three Knicks that has to guard against Jason Williams’ incursion into the lane. Williams kicks the ball out to Ricky Davis who misses an open three. Lee grabs the miss.

8:48 [MIA 5-8]
Williams 3pt Shot: Made (3 PTS)

Another pick & roll by Miami. This time Crawford is on Jason Williams, and the pick is set by Haslem. Crawford is so far behind on the pick, that Williams is at the free throw line while Jamal is still behind the three point line. Lee does a good job picking up Williams and forces him to the baseline, preventing him from getting in the lane. Crawford recovers, and Lee leaves to cover Haslem. However with Crawford on him, Williams creates some space for himself and sinks a three pointer.

8:25
Lee Layup Shot: Missed

On the next series, Lee goes baseline against Haslem, but he misses the reverse layup. The Knick announcers state that it was a “nice move” despite the negative outcome.

8:17
Marbury Foul:Shooting (1 PF)
Williams Free Throw 1 of 2 missed
8:17 [MIA 6-8]
Williams Free Throw 2 of 2 (4 PTS)

Miami runs the pick & roll again, this time Marbury is on Jason Williams. Lee gives Stephon room to go under the pick. Despite being in a position where he should be able to defend the inside, Marbury is unable to prevent Williams from getting to the hoop. Marbury fouls Williams, who converts one of two.

7:45 [MIA 8-8]
Haslem Jump Shot: Made (4 PTS)
Assist: Williams (1 AST)

The Knicks miss a shot, and Miami is in transition. Quentin Richardson is playing center field, making sure no one gets an easy bucket. Marbury takes Richardson’s man, Ricky Davis. Suddenly Davis drives to the hoop towards Richardson with Marbury trailing. With two defenders on him, he kicks it out to Marbury’s man, Jason Williams. Lee rotates over, and Williams hits Lee’s man Haslem for an open jumper.

7:27
Lee Turnover:Bad Pass (2 TO) Steal:Hardaway (2 ST)

Richardson is posting Hardaway, and Lee tries to get Quentin the ball. Penny jumps in front and intercepts the pass. Miami tries to take advantage of the opportunity…

7:21
Davis Layup Shot: Missed
7:20
Lee Rebound (Off:0 Def:2)

…but Davis misses the shot and Lee grabs the miss.

7:01
[MIA 10-10]
Haslem Jump Shot: Made (6 PTS)
Assist: Williams (2 AST)

Jason Williams blows past Marbury on a Shaq pick & roll. Lee helps out on this play, and Haslem is wide open. Williams hits Haslem, who nails the open 12 footer.

6:24
O’Neal Layup Shot: Missed
Haslem Rebound (Off:1 Def:1)
6:21 [MIA 12-12]
Haslem Hook Shot: Made (8 PTS)

Shaq has the ball in the post and Lee double teams to assist Curry. Lee flails his arms as Shaq comes towards him, but O’Neal misses the shot. Looking at the replay, two things occur here. First is that Lee is shocked for a moment that he isn’t called for a foul on the play. It looks like he intended to foul Shaq to force him to convert from the charity stripe. This moment of hesitation may have cost him the rebound. Haslem beat Lee to the ball and puts it back for another score. The second thing is that Eddy Curry could have had the rebound. After Shaq misses the shot, Curry who is less than 6 feet from the hoop runs towards the offensive end, instead of trying to rebound the ball.

5:22
Lee Jump Shot: Missed

David Lee misses an open jumper. Lee had the ball by himself on the baseline, but Shaq was under the hoop conceding the shot, not allowing Lee to get closer.

4:58
Malik Rose seen sitting at the scorer’s table waiting to check in.

4:25
O’Neal Jump Shot: Missed
Lee Rebound (Off:0 Def:3)

Shaq misses, and Lee grabs the rebound.

3:41 [MIA 18-14]
Haslem Jump Shot: Made (10 PTS)
Assist: Williams (3 AST)

Williams and Haslem again run the pick & roll. Williams goes through it to his left, then back to his right. Marbury is unable to stay with Williams, and Lee helps out picking him up at the foul line. Williams passes the ball behind his back to Haslem, and Haslem buries his 5th shot. In this play, Lee was hampered by Marbury who ran into him trying to get Haslem, preventing him from getting to Udonis.

3:22
Lee Layup Shot: Missed
3:21 [NYK 16-18]
Curry Putback Layup Shot: Made (8 PTS)

Marbury & Curry run their own pick & roll. Marbury passes to Curry, who is quickly double teamed. Curry then hits Lee who is picked up by Shaq. Lee can’t make the layup, but Curry is there to clean up the mess.

3:11
Lee Substitution replaced by Rose

Looking back at Haslem’s perfect 5-5 stretch against Lee, 2 were in transition, 2 were on pick & roll plays, and one was due to an offensive rebound. However it’s hard to single out Lee as the culprit for these plays. For the transition baskets, Lee made sure he was back on defense, but had to cover someone else’s man. Similarly with the pick & roll, Lee had to defend the guard on the Haslem buckets.

While Lee didn’t have a good offensive start, his defense was at least adequate. Looking at these plays it’s clear that the Knicks’ defensive problems stem from more than just one player. It’s easy to point to the guards as the root of the cause, but New York’s defensive woes may go further than that. Take the pick & roll. I don’t recall the last time the Knicks “hedged” (where the forward steps out to slow down the guard) under Isiah Thomas. It’s funny because the hedge was a staple of the past Knick teams. In fact I can’t even think about Kurt Thomas without thinking how good he was at slowing down guards on the pick & roll. In fact they seem to do one of two things. Either the guard goes under, or the guard goes over and tries to catch up with his man. Unfortunately neither tactic seems particularly effective. The Knicks inability to come up with any way to slow down the pick & roll might be the fault of their players. But it might also be the fault of the coaching staff, who has been unable to put together an adequate defense.

Some Plays Count: Stephon Marbury & David Lee 11/11/07 (Part I)

Mike Kurylo 18 Comments

For better analysis nothing beats cranking up the old projector and going through game film. In the spirit of the “Every Play Counts” series made popular by FootballOutsiders.com, I’ve decided to analyze parts of the Knicks loss to the Heat from Sunday’s game. Instead of following one player during the game, I chose two players at two different times of the game.

The person I’ve chosen to review is Stephon Marbury in the final seconds of the fourth quarter. Fast forward to 47 seconds left in the game. The Knicks, who led for most of the game, has seen their lead dwindle to a single point in the final minute. New York needs to score in order to keep the game in their hands, and they use a timeout in order to draw their play. After the inbound pass, Marbury receives the ball on the top of the key. Curry sets a pick for Stephon who goes to his left and drives towards the hoop. The Heat counter with a double team him by Alonzo Mourning. Marbury leaves his feet and panics in mid-air throwing the ball cross court. The pass was intended for Crawford in the right corner, but Crawford left that spot and the pass sails out of bounds. Rewinding the tape reveals an unguarded Eddy Curry heading towards the lane, because the Heat left Curry alone to double Marbury. It was a standard high pick & roll play, and the Knicks’ point guard missed the most obvious recipient – the man that set the pick.

Now Miami has the ball with 37 seconds left, trailing by only 1 point. Jason Williams tries to get free from Marbury, and runs through two picks in order to shed his defender. To Stephon’s credit, he stays on Williams. The Miami guard drives towards the blocks, near Mourning, but gives it up to Ricky Davis on the outside. At this point in time, there are three Miami players on the left side of the court. Rickey Davis is behind the arc with the ball. Alonzo Mourning is trying to setup in the low post, with Jason Williams next to him due to the aborted drive. Curry has good position on Mourning, and doesn’t allow him to get his feet set. Marbury is next to Williams, in the vicinity of Mourning. As the play continues, Williams drifts out to the corner. Instead of following his man, Marbury inexplicably decides to stay with an off-balance Mourning to form a double team and prevent the ball from going inside. Davis passes the ball to a wide open Williams who hits the jumper to give Miami the lead for the last time.

The Knicks would have two more opportunities to tie or win. On one possession they would cough up the ball, and on the last New York failed to get anyone open for a game tying three pointer. While most games aren’t lost on a handful of possessions, the two plays above were at the most critical moments of the game. New York had the lead and failed to either extend it or protect it. The last time I ran a query like this, Eddy Curry was the goat of the game. This time Stephon Marbury made two mental mistakes which cost New York the game.

Stay tuned for part II, where I look at David Lee’s play against the Heat.

A Layman’s Guide to Advanced NBA Statistics

Mike Kurylo 16 Comments

This guide is intended for those that are interested in modern basketball statistics. In order to make it more accessible, I’ve decided to forgo the formulas and numbers. At times both fans and journalists alike struggle to use stats when it comes to basketball. Often enough, their interpretation is inadequate because they don’t have the right stats to explain what is happening on the court. Even worse is when stats are used improperly to arrive at the wrong conclusion.

Over the past few years basketball statisticians have learned a lot about the game. While most of it is based on the same stats you would see in boxscores, the findings go far beyond traditional stats. Evaluation on the team level is the most reliable aspect of basketball statistical analysis. In other words, we’re very sure what factors lead a team to victory. Although statisticians aren’t exactly sure how player stats equates to wins, there are many ways to better evaluate individuals than the classical stats.

Team Stats

What You Need to Know
When looking at team stats it’s important to understand that some teams play faster than others which skews their per game stats. Faster paced teams will get more chances to score per game, solely because they have more opportunities. It’s similar to two NFL RBs, both with 1000 yards rushing, but one had 300 attempts and the other only 200 attempts. In this case it’s not enough to know the totals, instead you have to account for the difference in the number of opportunities. The same applies for team stats.

So in lieu of viewing how a team performs per game, we calculate how a team does per possession. What’s a possession? A possession ends when a team gives the ball to the other team, usually through a score, a turnover, or a missed shot recovered by the defense. By using points per possession, we’re looking at how many points a team scores when they have the ball on offense. This is called offensive efficiency or offensive rating, and is measured in points per 100 possessions. Basically offensive efficiency answers the question “if this team had the ball 100 times, how many points would it score?” Similarly we can rate defenses by calculating how many points a team allows per possession, called defensive efficiency or defensive rating.

But it doesn’t stop there. We can break down what aspects of the game contributes to those rankings. Offense (or defense) is broken down into 4 crucial factors: shooting, turnovers, rebounding, and free throws. Shooting is by far the most important factor and is best measured by eFG% which is a better version of FG% (see “Shooting” below). Next come turnovers and rebounding which are about equal to each other, but less valuable than shooting percentage. Like points, turnovers are measured per possession (how many times you cough the ball up when you have it). Rebounding is measured by percentage of missed shots recovered. This is so teams that shoot poorly (have lots of misses to recover) are judged on an even platform with teams that can shoot. Last and least is free throw shooting. This is measured by free throw shots made per shot attempt.

In 50 Words or Less
Throw away points per game for team stats. Instead use offensive efficiency (or defensive efficiency), which is basically how many points a team would score in 100 possessions. Team stats are broken in four factors: shooting, rebounding, turnovers, and free throws. You can find these stats on basketball-reference (search for “Points Per 100 Possessions” and “Four Factors” on the team pages) and my stat site.

Examples Why
In 2006, Portland ranked 18th in points allowed per game, which means they should have been slightly worse than average. However they finished a paltry 21-61 that year. Their defense wasn’t adequately measured by points allowed per game, because they played at the league’s third slowest pace. Ranked by defensive efficiency they were 29th, which would make their 21 win season more understandable. Of course there’s the 1991 Denver Nuggets.

More Please
Dean Oliver (Points Per Possessions): http://www.rawbw.com/~deano/helpscrn/rtgs.html
Dean Oliver (Four Factors): http://www.rawbw.com/~deano/articles/20040601_roboscout.htm
Kevin Pelton: http://www.nba.com/sonics/news/factors050127.html
Basketball-Reference: http://www.basketball-reference.com/about/factors.html

Player Stats

What You Need to Know
Without a doubt per minute stats are more important that per game stats. This is because per minute stats makes valid comparisons between players of varying minutes. Using per game stats in the NBA is like using hits/game in MLB. In 2007 Michael Young averaged 1.29 hits/game to David Ortiz’ 1.22, but Young’s batting average was only .315 to Ortiz’ .332. Young had more hits because he had more at bats (639 to 549), not because he was a better contact hitter. Similarly you might find that one basketball player has better per game stats, but if he had more minutes then the comparison is invalid. Only per minute stats will clarify which player is truly better in a category.

The common notation for per-minute stats is using per 40 minute stats. This is because it’s easier to visualize 2.3 blk/40 min instead of 0.0575 blk/min. Measuring basketball stats per 40 minutes is similar to measuring earned runs per 9 IP in baseball (ERA). One thing to note, unlike ERA in baseball, basketball players’ per-minute stats stay the same despite their playing time. So while baseball relievers have lower ERAs than starters, the same is not true in basketball. Additionally this doesn’t mean a player should play 40 minutes, just as using ERA doesn’t mean that a pitcher should pitch a full 9 innings. It’s just a fair way to compare players.

In 50 Words or Less
Throw out a player’s per game stats, and look at per-minute stats instead. Per minute stats are usually measured per 40 minutes. Study, after study, after study shows a player’s per minute production to stay the same despite how many minutes they play. You can find them at basketball-reference for historical data, or my stat page for the current season.

Examples Why
Some examples of players that had good per minute numbers, but poor per game numbers due to a lack of playing time: Ben Wallace, Jermaine O’Neal, Gerald Wallace, and Michael Redd. Throw in a point guard, and that’s a pretty good team.

More Please
Kevin Pelton’s Stat Primer: http://www.nba.com/sonics/news/stats101.html
The Basketball Notebook’s Primer: http://basketballnotebook.blogspot.com/2005/12/basketball-notebook-stats-primer.html

Shooting

Another stat that should be replaced is FG%. Why? Field goal percentage doesn’t account for the scoring bonus in a three point shot, which is a lower percentage shot. Sharp shooter Kyle Korver’s career FG% (as of 2007) is a lowly 41.3%. If FG% rates a good shooter like Korver so poorly, then it’s obviously not a good stat to use. So replace FG% with eFG% (effective field goal percentage), which compensates for the extra point in a three point shot. Korver’s eFG% is a more robust 53.6%.

But eFG% isn’t the only statistic used to measure a shooter. True Shooting Percentage (TS%) accounts not only for three pointers, but free throws made as well. For instance a player that hits a layup, gets fouled, and hits the extra point is more valuable than the guy that just sinks a jumper. To compare players with respect to their total scoring contribution, this is the stat to use.

In 50 Words or Less
Field goal percentage (FG%) should be replaced by eFG% or TS%. Effective field goal percentage (eFG%) compensates properly for three pointers, while true shooting percentage (TS%) compensates for three pointers and free throws.

Examples Why
Well I used Kyle Korver above, but otherwise you can look at any player that takes a large amount of three pointers or gets (and converts) a lot of free throws. Players like Kevin Martin, Jason Kapono, Manu Ginobili, and Shawn Marion come to mind as players who are misrepresented by FG%.

More please
Kevin Pelton’s Stat Primer: http://www.nba.com/sonics/news/stats101.html
The Basketball Notebook’s Primer: http://basketballnotebook.blogspot.com/2005/12/basketball-notebook-stats-primer.html

Overall Player Value

As I mentioned earlier, it’s not exactly clear exactly how to calculate a player’s worth. However there are 3 main stats that have attempted to give a single number to represent a player’s total contribution. The first and most prevalent is Player Efficiency Rating (PER). Created by John Hollinger, it attempts to take add up the good things, subtract the bad things, and account for team pace and minutes played. It’s normalized to 15, which means the average player in the league scores a 15 PER. The league’s best players are around 30, while the worst are in the single digits. Following Hollinger is economist Dave Berri (and friends) who came up with Wins Produced and it’s cousin Win Score. Unlike Hollinger who chose his equation, Berri and co. statistically derived what factors went into Wins Produced.

But both stats have their weaknesses. According to Wins Produces, PER tends to overrate players that score a lot of points, but do so inefficiently (poor shooting numbers). Meanwhile PER says that Wins Produced overrate strong rebounders that score infrequently. Additionally since they both rely on box score stats, neither captures actions that occur outside of the stat sheet. For instance Bruce Bowen plays tough defense and forces Kobe Bryant to take a bad shot that Tim Duncan rebounds. The stat sheet will record Duncan’s rebound and Kobe’s missed shot, but Bowen doesn’t get any credit for his defense.

One stat that does capture Bowen’s effort is plus/minus stats. Currently kept by Roland Beech, +/- comes in a few different flavors. Among the most popular are offensive and defensive +/-, which measure how a team does with the player on the court. Also Roland Rating and net +/- attempt to evaluate a player’s value. However plus/minus doesn’t just capture than the individual effort, it captures the value of his teammates as well. When Bowen and Duncan prevent the Lakers from scoring not only do they get credit for the effort, everyone else on the court gets the credit as well.

In 50 Words or Less
Trying to create a player’s total worth using a single number isn’t highly reliable. But if you need to use one, you can try PER, Wins Produced, or +/-. Each has their strengths & weaknesses and are only good to begin a discussion, not end one.

Examples Why
The biggest hole in statstical analysis is defensive stats. Blocks, rebounds, and steals aren’t enough to tell the whole story on what happens on defense. Players who excel in this area of the court usually have strong defensive +/-, like Bruce Bowen (-9.6). However these numbers tend to fluctuate based on the strength of the team. A player that spends a lot of time on the court with strong defensive players will have their defensive +/- inflated.

More please
Kevin Pelton’s Stat Primer: http://www.nba.com/sonics/news/stats101.html
What is PER?: http://sports.espn.go.com/nba/columns/story?id=2850240
Dave Berri’s Site: http://dberri.wordpress.com/2006/05/21/simple-models-of-player-performance/
Roland Rating: http://www.82games.com/rolandratings0405.htm
Adjusted +/-: http://www.82games.com/ilardi1.htm
Online & Downloadable +/- stats: http://basketballvalue.com/index.php

One More Nail In the Anti-Per Minute Argument’s Coffin?

Mike Kurylo 45 Comments

One of the core tenets of basketball statistical analysis is the usage of per minute stats. When compared to per game stats, per minute stats are highly valuable in the evaluation of individuals. This is because per minute stats puts players of varying playing time on the same level. Using per game stats, starters will always dwarf bench players due to the extended time they get to accumulate various stats. Meanwhile per-minute stats allows to compare players independent of minutes, allowing for a more even approach in player evaluation.

Recently a debate has come up on the validity and usefulness of per minute stats. I’ve quoted the main parts below, but even abbreviated it’s a long read. If you have the time, I suggest reading it now so the rest of this article will make more sense. For those on a limited time constraint, a quicker summary is here:

Hollinger & Kubatko: “Hey per minute stats are a great way to evaluate players! In fact we’ve done a few studies and it seems that a player’s per minute stats increase slightly when they get more minutes. At the worst we can conclude that they should stay relatively the same.”

FreeDarko: “Per minute stats won’t stay the same if a player gets more minutes, because there is a division between greater and lesser players. A player that only gets 10-25 minutes per game is playing against lesser caliber players. Hence when that player sees an increase in playing time, he’s playing against steeper competition, so his stats should decrease.”

Tom Ziller: “That’s not true. Here is every 10-25 minute player in the last 10 years that saw an increase in minutes. Most of them (70%) saw an increase in per-minute production. To discount any of this data being from young players getting better as they age, I looked at 8+ year vets, and saw that about the same ratio of players increased (69%).

Brian M.: “Tom, the problem with all this data is a causality vs. correlation issue. It’s possible that these players saw more minutes first then improved. But it’s also possible that these players improved first which allowed their coach to play them more minutes.”

Brian’s case is a good one. To use an analogy, imagine I come across a person who calls himself Merlin Appleseed. He claims that just by touching apples he can magically make them taste better. He opens up a box of apples saying that he never touched any of them. He picks out 10, and imbues them with his magic. He asks me to taste each of them. I find all of them to be delicious. He says “here’s the same box I got my apples from. Now I want you to take 10 at random while blindfolded. You can compare them to my magic apples. I bet mine taste better.” I do just as he asks, and indeed my random set of apples are less tasty than his. So does Merlin Appleseed have magical power?

Maybe. Unfortunately this test wouldn’t be able to confirm or deny his magical power. Since Merlin gets to choose his apples, he might be selecting the best ones! To test Merlin’s abilities I would need something to gauge how good his apples are expected to taste. One way to do this would be to find comparable apples that have the same color, size, blemishes, etc. Then I can compare the taste of his apples to my apples. If Merlin’s has the magical powers he claims, then his apples will taste better than my apples.

Similarly with Tom’s study, Brian is saying that by selecting players who have seen an increase in minutes we might be choosing the best apples. This is because players who improve on a per minute basis could be given more playing time by their coaches. Therefore to show whether or not these players have improved, I need to find how good they’re expected to be. Then I can compare their actual performance to their expected performance. If FreeDarko’s theory is true, that role players should decrease their per minute production with more minutes, then they should perform worse than their expected values.

To separate the control group from the test group, I’ll only use players with an even numbered age for the control, and odd numbered ages for the test group. Since this study is intended for role players, which was defined by Ziller, I limited my control group to player seasons where:
* The player age was an even number.
* The player appeared in 41 games or more.
* The season was 1981 or greater.
* The player averaged 10-25 mpg.

Now I can calculate the expected production of the players in my group, by looking at per minute production (PER) over playing time (mpg).

Control Group

Just as expected, the graph tends to go from the bottom left (low production = low minutes) to the top right (high production = high minutes). That is players who receive more minutes are more productive. From the 1840 player-seasons in my data, I’m able to calculate the expected PER based on mpg (PER = .2158*mpg + 8.2941). So if a player averaged 10 mpg, you would expect his PER to be 10.45. This equation is represented by the red line on the graph.

Now that our control group is defined, I need to create the test group. Again this group was defined by Ziller as role players who saw an increase in minutes. I selected player seasons where:
* The player’s age was an odd number.
* The player appeared in 41 games or more.
* The season was 1981 or greater.
* The player averaged 10-25 mpg the year before.
* The player increased his mpg by 5+ from the year before.

Since I have the expected values based on mpg, all that is left is to compare their actual production to the control group. In our test group 185 players did better than their expected PER, while 177 did worse. On average each player gained 0.17 PER. This is a tiny gain, not enough to show that players increase production with more minutes. However it clearly shows that they didn’t decline and at least matched the predicted PER.

Another way to see how our prediction did is to calculate the regression (trendline) of this group, and compare it to the expected equation. The red line in the graph below shows the regression of PER/MPG for our control group.

Test Group

* Control: PER = .2158*mpg + 8.2941
* Test: PER = .2185*mpg + 8.3917

The test group, which has both the higher slope and y-intercept, will slightly outperform the control group. But not by much. The average player who saw 40 mpg, will see a .20 increase in PER, which is negligible. In other words, the test group has neither exceeded nor fallen short of our expectations, but rather has met them.

In the end what does this prove? Specifically this study removes the correlation between the role player group and players that saw extra minutes due to improvement. It debunks the thought that there is some kind of division between per minute stats, where the per minute stats of high minute players are more a representation of actual talent than those who play few minutes per game. But combined with the past works of Hollinger, Kubakto, and Ziller, among others, it makes an overall stronger statement. Players who receive 10 or more minutes per game are likely to keep the same per minute stats no matter what the increase in playing time is. Therefore per minute stats remains far superior to per game stats in terms of comparing and evaluating players.

EXTRAS:

  • “It’s a pretty simple concept, but one that has largely escaped most NBA front offices: The idea that what a player does on a per-minute basis is far more important than his per-game stats. The latter tend to be influenced more by playing time than by quality of play, yet remain the most common metric of player performance.” — John Hollinger
  • The great thing about this study is that I can perform it again, this time using the “odd” aged players as the control and the “even” aged players as the test group. This time the prediction equation was PER = .2039*mpg + 8.4439. And again our test players slightly outperformed the average. This time 192 did better than their expected PER, while only 161 did worse. On average each player gained 0.23 PER.
  • This article doesn’t mean that every player that has good per minute stats should see more playing time. It’s very clear that basketball stats don’t capture a player’s total ability. A player that does well on a per minute basis may have other flaws, such as poor defense, which prevent him from contributing more. This also isn’t an endorsement for any single per minute ranking system, like PER, WOW, etc. There are flaws in each of these in addition to being unable to account for attributes not captured in box scores.
  • Summary of the events that led to this article.

Back in 2005, I wrote an article outlining some of the pioneers in per minute research.

In the 2002 Pro Basketball Prospectus John Hollinger asked and answered the question ?Do players do better with more minutes?? For every Washington player, Hollinger looked at each game and separated the stats on whether or not he played more than 15 minutes. He found that when players played more than 15 minutes, they performed significantly better than when they played less. To check his work, he used a control group of 10 random players, and each one of those improved significantly as well.

The knock on Hollinger?s study is the small sample size, containing less than 25 guys from only one season. Enter Justin Kubatko, the site administrator of the NBA?s best historical stat page www.basketball-reference.com. Earlier this week Justin decided to re-examine the theory using a bigger sample size. Taking players from 1978-2004, he identified 465 that played at least a half season and saw a 50% increase in minutes the year after. Three out of four players saw an increase in their numbers as they gained more minutes, although the average increase was small (+1.5 PER).

Two independent studies have shown that NBA players get better when they get more minutes. A conservative interpretation is that per-minute numbers are universal regardless of playing time. So if a player averages 18 points per 40 minutes, he?ll do about that regardless of how many minutes he plays. A more liberal summary would say that underused players will see an improvement in their per-minute numbers if given more court time. A player that only averages 20 minutes a game is likely to be a little bit better if given 35. So the straight dope is per minute stats are a fantastic way to evaluate NBA players.

Recently, this research was questioned by the writers of freedarko.

The problem with this line of reasoning is that it assumes the homogeneity of court time. It assumes that if a player scored 20 points in 20 minutes, he would also score 40 points in 40 minutes. That there will by systematic differences between these two situations is almost too obvious to point out. It’s the difference between sharing the ball with Jordan Farmar while being guarded by Kenny Thomas, and sharing the ball with Kobe Bryant while being guarded by Ron Artest.

Insofar as the problem here is one of rotation, small-scale adjustments in minutes played shouldn’t create major distortions (it isn’t unrealistic to think that if Tim Duncan played 5 extra minutes per game, his per-minute production, as influenced by the level defense he’d face, would basically be the same). But when PER catapults bench players into the starting five (or vice-versa), be on the look-out for inflation. Call this the Silverbird-Shoals Hypothesis, or the THEOREM OF INTERTEMPORAL HETEROGENEITY (TOIH).

Enter Sactown Royalty’s Tom Ziller, to refute Free Darko’s theory.

Shoals and Silverbird are arguing that because low-minutes high-PER guys typically play against fellow bench players, their PER is higher than it would be if they played starter minutes. They aren’t arguing (as some surmised) that PER is useless, just that it is prone to inflation. The argument, from seemingly everyone on the ‘anti per-minute statistics’ side, is that if you increase a player’s minutes, his efficiency will suffer.

There’s a problem with this oft-repeated claim: It’s not true.

Thanks to the data-collection efforts of Ballhype’s own Jason Gurney, I’m going to try to ensure this claim never gets stated as fact ever again. Using seasons from 1997-98 to the present, we identified all players whom played at least 45 games in two consecutive seasons and whom saw their minutes per game increase by at least five minutes from the first season to the second. The players must have played between 10 and 25 minutes per game in the first season, to ensure we were not dealing with either folks who went from none-to-some playing time or superstar candidates who took over an offense and thus got a minutes boost. This is aimed at roleplayers whose role becomes more prominent — exactly the candidate FD’s Theorem of Intertemporal Heterogeneity implies will suffer from increased minutes.

Since I seem to express myself more clearly via Photoshop, here is the result of our mini-study.

No, increased minutes do not seem to lead to decreased efficiency. In fact, the data indicates increased minutes lead to… increased efficiency. More than 70% of the players in the study (there were 251 in total) saw their PER (which is, by definition, a per-minute summary statistic) increase with the increase in minutes. Players whose minutes per game increased by five saw an average change of +1.38 in their PER. The correlation between increased minutes and change in PER in this data set was +0.20.

One step further: Players who had at least five years of experience including their first-season in this study and got the requisite 5-minute increase (106 such players) saw an average change of +1.26 in their PER. It’s not just young kids who happen to improving and getting more minutes all at the same time — vets who get more minutes typically see their per-minute production rise. A full 67% of these players so positive changes in PER with the increased minutes. (And this answers one of Carter’s concerns with existing studies.) Let’s bump this up to players who had at least eight years of experience going into their minutes increase; we had 52 such cases. The average change in PER: +1.31. Of these players, 69% saw their PER increase with more minutes.

Case closed right? Well not if Brian M. has something to say about it.

Imagine we wanted to test the relationship between duration of exercise and reports of fatigue. We have two experimental conditions, one group jogs for 10 minutes and the other for 30 minutes. We predict that the group that jogs 30 minutes will report more fatigue.

But we must assign people to the two groups randomly in order for the data to have any bearing on the hypothesis. If we systematically assign people who are in better shape to the 30 minute jogging condition, we may find that in fact, if anything, people report less fatigue with longer durations of exercise. But the study is flawed in a fundamental way and so the data don?t tell us much of anything. At most what the results of this poor experiment tell us is that the effect of exercise duration on reported fatigue is not so strong that it overrides the differences in health between the two groups. But that is a really limited conclusion, especially if we don?t even have means to quantify how much the two groups differed in health to begin with.

Knicks 2007 Report Card (A to Z): Nate Robinson

Mike Kurylo 194 Comments

KnickerBlogger: New Yorkers absolutely loved Nate Robinson when he first came to the Knicks. Coming out of the University of Washington, Robinson was a lilliputian guard with colossal physical abilities. Last year Robinson did what you’d expect from an undersized shooting guard. He led all Knick guards in eFG% (51.3%) and 3P% (39.0%) and showed despite his short stature he could get to the line (TS% 55.2%, second among Knick guards). Due to his efficient scoring ability, Robinson was second on the team in points per 40 minutes (19.0 pts/40) only behind Eddy Curry. Not just a one dimensional scorer, among Knick guards Robinson was the best in respect to offensive rebounds (1.6 OREB/40) and turnovers (2.1 TO/40), and second best in respect to steals (1.5 STL/40). Yet despite all that, Robinson is no longer a fan favorite. So what happened?

Simply put, Nate Robinson is his own worst enemy. Along with his diminutive stature and his youthful enthusiasm, Robinson comes with a childlike temperament. There’s a fine line between having a zest for the game and acting like a grade schooler. Robinson not only crosses that line, he lives on it. Less than one month into the season, Nate attempted an in game alley-oop dunk on a fast break, only to be called for traveling on the play. Throwing away points on a losing team for the sake of showboating is among the game’s cardinal sins.

Robinson exacerbated his image problem by perpetually arguing with officials. It’s annoying when a marquee player like Tim Duncan disputes every call, but it’s downright unbearable when a bench guy like Robinson does it. Unfortunately, Nate gave himself plenty of opportunities to argue with officials as his foul rate (4.7 PF/40) was equal to Marbury (2.7 PF/40) and Crawford’s (2.1 PF/40) combined.

Robinson’s immaturity causes his actions to be viewed by the public through tinted glasses. Take for instance Nate’s role in the Denver melee. In the past plenty of Knicks have improved their public image through fisticuffs. Fighting improved Starks, Childs, and L.J.’s popularity among Knick fans. Although Nate was an instigator in the event, it’s hard to believe that a player with a calmer outward demeanor like Eddy Curry would have been seen in the same light. Had Curry been involved, the local airwaves would be talking about his moxie and willingness to defend his teammate. But Robinson was vilified for his role. It’s ironic considering a few years ago, Knicknation was up in arms when no one came to the rescue of Tim Thomas after Jason Collins slammed him to the floor.

To be fair, Nate’s negatives aren’t all in his head. His defense is suspect, and his assist rate is minuscule for a guard. While 82games.com says the Knicks are 2.4 points per 100 possessions better defensively with Robinson on the floor, opposing PGs are better than average (16.3 oPER) when Nate guards them. To the eye Robinson struggles mightily against the pick & roll, and other than the steals he doesn’t do anything particularly well on defense. I would rate him a mediocre to average defender.

Most people expect Robinson to be a point guard due to his height, but he’s really more of a shooting guard. Even accounting for that, his assist rate is subpar. As I said earlier, the Knick offense allows all the guards to play the point interchangeably. But it seems that Robinson isn’t sharing enough with his teammates. To put things in perspective, his 2.7 AST/40 is about the same as David Lee’s 2.4 AST/40 who rarely touches the ball. Nate does have the ability to make the spectacular play, and can pass the ball on his drives. It just that he desires to take the shot instead of making the pass. Normally you wouldn’t mind that from a guard that shoots as efficiently as Robinson. But then again Robinson suffers from his poor image, one that being a greedy guard certainly fits in with. In a way, for Nate Robinson hell is other people.

KnickerBlogger’s Grade: C, due to bad behavior.

2008 Outlook: With Nate Robinson entering his third season, it’s time to evaluate whether his poor decision making in the past was just youthful exuberance, or if it will continue to be a Rasheed Wallace like permanent petulance. I don’t expect Nate Robinson to turn into John Stockton, because he’s such an excitable person. What I would like to see is for Nate to take his job a little more seriously.

Robinson played 21.4 min/g under Larry Brown, and 21.2 min/g under Isiah Thomas. It seems that two coaches, who had very different views & philosophies, saw Robinson in the same light. If Nate wants to shed his role as spark off the bench, he’ll need to shed his image as a circus act crammed into a basketball uniform. It’ll be interesting to see how Nate plays in the preseason. I can envision Isiah giving Robinson more minutes due to his strong summer showing. If Nate can continue his productive ways, it could mean more playing time when the season starts. That would be a good thing, since the Knicks are paper thin at shooting guard, and they could use Robinson’s production.

Dave Crockett

In many ways KB’s take on Robinson has been by far the most “fair and balanced” (pardon the regrettable and unintended pun) I’ve read. I agree with his take on Robinson in total, but I also wish to offer a complementary perspective that’s less about Robinson’s performance than Robinson as a character in the theater that is professional sports. It’s easy to forget that sports is more than the simple pursuit of competitive dominance since that is precisely what the regular visitors to this blog come to read about and discuss. But, pro sports is also improv theater and all good theater (or “good copy,” to use the parlance of journalists) needs “heroes,” “bad boys,” and “villains.” As the great fat sage, Charles Barkley, is purported to have once said, “They can love you or they can hate you. Both sell tickets.”

Robinson, through a combination of his own immaturity as well as the fickle nature of media and fans, has gone from being a precocious but impish bad boy to something of a villain in just two full seasons. Though Robinson has clearly been the catalyst for his own fall from the good graces of many Knicks fans I also think he’s suffered from a demand for a steady of supply of villains that is becoming insatiable. Most of the time in professional sports players move seamlessly between the basic “villain,” “bad boy,” and “hero” roles for any number of reasons through a process that is reasonably organic and not always totally predictable. (I suspect many readers aren’t old enough to remember when Muhammad Ali was a villain to much of the American sporting public. He was hated in no uncertain terms. He had perhaps the most amazing role transformation ever.) But increasingly, the theater of pro sports has come to resemble the theater of pro ‘rasslin’ in its predictability, its cardboard cutouts of who gets assigned to which roles and for how long.

In Robinson’s case, since the Denver fight I see him being typecast as a particularly crappy villain archetype, and I really hope he’s allowed to work his way out of it. I call it the “Jeff George” villain archetype. Sometimes a player opens himself up to fan/media disdain by doing something over-the-top or exposing himself as a jerk and for whatever reason isn’t allowed much of a shot at redemption. Soon, the guy just can’t do anything right. The media fits him with a black top hat and a curly-Q mustache and it becomes obvious to the audience that he’s the guy to hate. (Note: I’m talking about sports-related stuff here NOT criminal or near-criminal behavior.) If you remember former NFL QB Jeff George, he was by most accounts a pompous jerk; universally reviled by fans, media, opposing players, even teammates and coaches. You would think by the way people couldn’t wait to denounce him that the NFL was not littered with similarly unbearable jerks. But of course it was, and is. As much as I truly loath Kansas City Star (and former ESPN.com) columnist Jason Whitlock, I must agree with his sentiment that no one can point to anything George ever said or did that was uniquely awful.

Robinson, though not having “achieved” anything approaching the pariah status of George, seems to be quickly approaching the “can’t do anything right” status that is the hallmark of the Jeff George villain archetype. Hell, watch any Knick’s telecast with Mike Breen (even before the fight) and you’ll see what I mean. Regardless of what Robinson actually did on the court Breen would raise questions about his immaturity and decision-making, typically citing his ball-handling, shot selection, and his role in the Denver fight as prima facie evidence. So a poor shooting night or any turnover became proof of Robinson’s immaturity and poor decision-making. Yet somehow a good shooting/low turnover night did not indicate maturity or improved decision-making. The “Nate Robinson cautionary tale” always spins such a night as proof of how much talent Robinson is potentially squandering by his immaturity and poor decision-making.

My outlook for Robinson in 2008 completely mirrors KB’s in most respects. I believe Robinson is quite important to the Knicks playoffs chances. Not only are the Knicks thin at the SG, my entirely intuitive suspicion is that Crawford’s injury last season may be the first in a string of small-but-ongoing leg-related ailments that may keep him shuttling in and out of the lineup. So I believe the Knicks need Robinson to improve; it’s not a luxury. To do so he will have to start with the man in the mirror. Whether he is the new Jeff George or the new Bozo the Clown he simply must learn to focus on things that help the team win and leave the nonsense alone–period. But, I also urge the fans not to give up on this kid. He’s already a useful player and has the chance to get even better.

Brian Cronin – Man, Dave just reminded me of how annoying Mike Breen can be sometimes. The man is a GREAT announcer, but I think he works better on national telecasts, where he is not close to the situation, because man, he certainly seems to have soured upon the Knicks.

Breen reminds me of the stereotypical middle age guy complaining about how the NBA is “all thugs” nowadays. Those guys annoy me so much.

Anyhow, as to Robinson, the guy definitely exhibits some weird behavior, but since the fight, I thought he was actually a lot calmer than before the fight, and he seemed like a real nice asset to the team as an outside shooter. I hated when he tried to control the offense at times (that is not his specialty), but as a guy there to hit the outside shot, I like him there more than most other Knicks, and I think he will be a useful player this season.

Trading David Lee for Kobe Bryant Straight-Up: Shrewd Sabermetrics or Laugh Test Flunkie?

Michael Zannettis 51 Comments

In Basketball on Paper, Dean Oliver devoted an entire chapter to comparing the individual rating systems of several NBA analysts. He argued something that I, and most people who do informed analysis, subscribe to: Any system of statistical analysis cannot only be internally consistent, but must also pass the “laugh test.” A statistical model can be built elegantly and beautifully and pass many confidence intervals within its own logical parameters, but if it’s results are absurd, then there’s obviously a need to return to the proverbial drawing board. Oliver thought of the “laugh test” as a litmus. It’s a very broad, absolutely basic determinant of whether a statistic is logical or not. If your rating system projects the best players with the best numbers, then it’s probably onto something. On the other hand, if your rating system argues that Jerome James is a better center than vintage Shaquille O’Neal, then you better recheck your assumptions.

While no single computation can perfectly encompass the entire contribution of a basketball player, John Hollinger developed a system to sum up a player’s boxscore contribution and express them in one number. Player Efficiency Rating (PER) is a sophisticated equation that goes so far as to adjust for the yearly value of possession and the pace a team plays. In Hollinger’s analogy, PER serves as a way of considering players from different positions, allowing an “apples to oranges” comparison. But while PER is a handy little number, what it doesn’t do is convert statistical efficiency into actual wins. That’s where Dave Berri’s Wages of Win (WoW) steps in. WoW takes the same boxscore statistics that PER uses and converts it to a formula that measures how many wins a player produces. This metric can evaluate a player’s total contribution over the course of a season and break it down per minute. Like PER, WoW serves as a way to summarize a player’s contribution in one number.

Now, let’s ask PER who were the most productive basketball players on the planet this past season. PER picks these as its starting five:

1. Dwyane Wade SG 29.2
2. Dirk Nowitzki PF 27.9
3. Yao Ming C 26.7
4. Tim Duncan C 26.4
5. Kobe Bryant SG 26.3

Nothing to laugh at here. In fact, it’s a pretty amazing team. Wade is the best player, slightly ahead of Dirk, who is just a bit ahead of Ming, Duncan, and Bryant, who are in a dead heat for third best. If you were starting a basketball team and were given first pick at any player in the NBA you couldn’t go wrong by picking any of these five players. They’re the best of the best. Granted, PER isn’t intended to be the final word on basketball performance, but it is a good starting point for figuring out relative worth. Would you trade your 15 PER performer for a 29 PER man? Almost certainly. Of course you’d take into account team composition, need, age, defense, contract terms, but all else being equal, you’d be doing your team a service by having the greater PER over the lesser. And if the PER was almost twice greater, like say Dwyane Wade over Jamal Crawford, well, then there’s really no thinking involved. Of course you’d rather have Wade. It’s a no-brainer. In fact, by this measure, you’d rather have Wade than any single player on the Knicks current roster.

Now, WoW gets to pick its own top five. Note that in order to compare WoW to PER we’re using Wins Produced per 48 Minutes (WP/48), since these are both rate stats:

1. David Lee PF .403
2. Jason Kidd PG .403
3. Marcus Camby C .371
4. Shawn Marion F .370
5. Carlos Boozer PF .351

Look at that again. David Lee led the NBA in wins produced rate. Um…really. So according to this sophisticated, statistical model, the most productive professional basketball player on the planet is David Lee. The best. On. The. Planet. Let me say that being a die-hard Knicks fan, I will be the first to argue that Lee is an All-Star caliber forward. He’s cool, he’s great. He’s an out-of-the-box rebounding, ambidextrous-finishing, no-look passing, efficiency machine. He’s awesome! It’s just that, you know, he really doesn’t create much offense. He’s more of a great glue guy than a centerpiece. And that’s why he’s not exactly a superstar.

Now, I really love the guy. Don’t get me wrong. I wouldn’t trade our man for the world. Oh, wait. Yes. Yes, I would. I’d trade David Lee in a heartbeat. For Tim Duncan. Or Yao Ming. Or Dwyane Wade. Or Kobe Bryant. Or Dirk Nowitzki. Or Lebron James. Or Amare Stoudemire. Or…OK, you get the point. I’d trade him for at least a dozen players who aren’t just All-Stars, they’re legitimate championship-level franchise cornerstones. Yet, right there in plain black and white, Wages of Win’s assumptions fail Oliver’s “laugh test.” WoW argues that Lee is the best player in the entire league, and that’s ridiculous.

WoW makes a very big deal about bucking conventional wisdom. And sure enough, statistical analysts are the ones who’re supposed to be bucking said conventional wisdom. At the Wages of Wins Journal, Berri argues that “perceptions of performance in basketball do not match the player’s actual impact on wins” because “less than 15% of wins in the NBA are explained by payroll.” However payroll isn’t a good measuring stick of perception due to the complexities of a closed system like NBA free agency. There are a host of factors on why a player may be overpaid from the talent available to the desperation of the team involved. In other words conventional wisdom thinks Rashard Lewis is overpaid at $126M, too.

So although conventional wisdom has a tendency to be wrong in some areas, figuring out sport superstars is not one of its weaknesses. There usually is a consensus on the league’s best players from both statistical analysis and conventional wisdom. The cream of the crop in the NFL are Peyton Manning, LaDanian Tomlinson, and Larry Johnson whether you go by the numbers or eyes. In MLB it would be Albert Pujols, Ryan Howard, Manny Ramirez, David Ortiz, Alex Rodriguez, and Johan Santana. At the top of the ladder of player evaluation, conventional wisdom is pretty much dead on.

According to WoW, David Lee (.403) is a far more productive player than Kobe Bryant (.242). Since teams with more productive players win more games than other teams, then Lee is better for your basketball team than Bryant. But why stop there? The Knicks could trade Renaldo Balkman (.272) straight up for Dwyane Wade (.255) and lose productivity. That’s right. WoW is arguing that if a Lee for Kobe, and a Balkman for Wade trade went through, then the Knicks would be a worse team for it. They’re arguing that Bryant and Wade, at the cost of our two young, talented forwards will hurt the Knicks’ productivity. You’ve got to be kidding me.

As the Knicks GM, would I pull the trigger on a Lee for Bryant deal? Is there even a debate? Who wouldn’t? Oh, right, WoW wouldn’t. WoW doesn’t even think it’s close. We can all disagree on which player is the very best (or the most productive), but WoW’s results are “laughable.” Dave Berri has criticized PER in the past, but before people can begin to take WoW as seriously as a tool for evaluating player performance as PER, it’s obviously going to have to address what caused this terrible absurdity in its rating process.