A Layman’s Guide to Advanced NBA Statistics

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


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

Do Stats Lie?

Lately I’ve been thinking about the greatest offensive team of the last 20 years. Led by Michael Adams and Orlando Woolridge the mighty 1991 Denver Nuggets punished opponents by scoring 119.9 points a night. That Nuggets offense just beats out the the 1992 Mullin-Hardaway Warriors (118.7 pts/g) and the 1989 Chambers-K.J. Suns (118.6 pts/g). Certainly since the 1991 Denver Nuggets scored more points per game than any team since 1987, they were the NBA’s best offense in that timespan.

Or are they? This seems to be a dubious claim. Looking at the 1991 Nuggets, none of the players were voted to the All Star team that year. There aren’t any Hall of Famers on that team. Denver went a rancid 20-62 that year. Of the three teams above, there are no champions. No Michael Jordan. No Magic Johnson. No Larry Bird. No Shaq. No Steve Nash.

How can a 20-win team be one of the great offensive teams of all time? You might say that the stats are “lying” because they’re misrepresenting what we believe to be true. But that’s not the case. The numbers are 100% accurate. If you watched every game of the last 20 years, you would not have found a team that scored more points in a season than the 1991 Nuggets. Saying the 1991 Nuggets scored the most points per game in the last 20 years is true. Saying the 1991 Nuggets are the best offensive team in the last 20 years is false. The deception is in the interpretation of the statistics, not in the stats themselves. The problem is in equating “most points per game” with “best offensive team”. The correct interpretation for “most points per game” is “most bountiful offense”, which is quite different from “best offensive team”.

Take this example: Going into the 2007 season, the Chicago Bears have a good chance to win the Super Bowl. One vegas line has their odds at 8-1 to win it all. One of their best players is Rex Grossman who has a fantastic 17-5 record as a starting QB.

Once you pick yourself off the floor laughing, it’s easy to see where the fallacy is. The Bears do have a good chance to win the Super Bowl this year. Their odds to win, at least from one vegas site, is 8-1. Rex Grossman has a 17-5 record as a starter. All these things are true. However they’re not one of the best teams in the NFL due to their QB. Rex Grossman is by all accounts a bad quarterback. Carson Palmer, an All Pro, has a winning percentage of only 55.6%. The deception is in saying that QB win percentage indicates the quality of the QB. There are better ways to judge the ability of a QB like completion percentage, TD-INT ratio, yards per attempt, etc.

Getting back to our original example, those 1991 Nuggets scored so many points per game because they ran a very fast offense (and also a very fast defense). Denver led the league in pace averaging 113.7 possessions per game. To show how much an aberration this was, the league average was only 97.8 and the second fastest team was the Golden State Warriors at 103.6 possessions per game. A team can increase its points per game by simply increasing its pace. This reveals a flaw in the relationship between “points per game” and “best offense.” It’s obvious that points per game isn’t the best measure of a team’s offensive capability.

To more accurately judge which team had the best offense, you need to account for this disparity in possessions per game. Offensive efficiency, sometimes known as offensive rating, calculates how many points a team scores per possession (or more accurately 100 possessions). The importance of offensive efficiency is that it evens the playing field between the fast and slow paced teams. The 1991 Nuggets had an offensive efficiency of 105.2, which placed them 21st out of 27 teams that year. The best offensive team in 1991? The Chicago Bulls, who scored 114.9 points per 100 possessions. This was Jordan’s first championship team, and clearly they were better than the Nuggets on offense that year.

In the end, stats don’t lie. They are numerical records of history. The 1991 Denver Nuggets did score 119.9 points per game. Rex Grossman had a record of 17-5 as a starter going into 2007. The problem is not in the numbers, but rather the people that use these statistics to make claims that they don’t support.


  • For more information on points per possession, check out Dean Oliver’s excellent book: Basketball On Paper. Or read this and that.
  • During the season I keep track of offensive efficiency on the stats page. Historical offensive efficiency can be found at basketball-reference.com
  • The team with the highest offensive efficiency over the last 20 years? The 1996 Bulls at 115.8. Does this make them the best offensive team of the last 20 years? Well you might want to account for league average, but that’s a discussion for another day.
  • For more information on the 1991 Nuggets, see this link.
  • For a really good way to rate QBs, I would use DVOA.

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

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.

Four Factors and Five-Man Units

Knickerblogger.net will be in full playoff mode until the 2007 champion is crowned. But before we let go of the Knicks’ forgettable season I wanted to take a brief look back through the window of five-man units. Because Thomas? played a lot of people a fairly high number of minutes I thought it would be worthwhile to look at how they fared on Dean Oliver?s four factors with help from our good friends at 82games.com. I?ll look at offense today and defense later.

I thought looking at five-man units might be particularly interesting for NY because so many different units played significant minutes. We generally expect starters to play the lion?s share and other units to play comparatively fewer minutes. That is, minutes tend to have a skewed distribution. Of course some teams play their reserves more than others but the top minute-getting units play a lot of minutes together. For example Chicago?s top unit played 618 minutes, Detroit?607, Cleveland?467, New Jersey?460, and Toronto?321. Miami?s opening night starting five (Williams-Wade-Kapono-Haslem-Mourning) played fewer than 160 minutes because of injuries, but its top unit (Williams-Jones-Posey-Haslem-O’Neal) still played 301 minutes. By contrast, New York?s top unit played only 192 minutes together.

Unfortunately, the raw data needed to calculate all four factors directly are not available by five-man unit. Shooting is available directly. I can also use net turnover percentage and net free-throw attempts to create a ?quick and dirty? picture of turnovers and free throws. I must leave rebounding out of this analysis however, because the available data doesn?t break it out into its offensive and defensive components.

Before getting to the factors, let?s take a quick look at the best and worst five-man units based solely on plus-minus (+/-).

The Best and Worst Five-Man Units

The 3 best:

# Minutes rank Unit Minutes +/-  
1 13 Robinson Francis Collins Rose Curry 58 33
2 5 Marbury Crawford Lee Frye Curry 137 26
3 20 Robinson Crawford Q-Rich Lee Frye 34 14

The 3 worst:

# Minutes rank Unit Minutes +/-
18 12 Marbury Crawford Q-Rich Curry James 62 -30
19 4 Marbury Crawford Jeffries Frye Curry 142 -38
20 3 Marbury Crawford Q-Rich Frye Curry 162 -68

Some of the heaviest-minute units were just awful. The Marbury Crawford Q-Rich Frye Curry unit was undeniably terrible, outscored by 68 points in 162 minutes, roughly -20 points per 48. Swapping in Jeffries for Q-Rich ?improves? this unit to just plain bad. But note, swapping David Lee into this unit for Q-Rich or Jeffries makes a substantial improvement. In comparable minutes Lee’s presence is the difference between the unit ranking 19th or 20th in +/- or fifth. He appears to really click with Marbury, Crawford, Frye, and Curry. His impact is not nearly as striking with any other mix of players. (Lee also plays on NY’s top minute-getting unit?Marbury Crawford Q-Rich Lee Curry?which ended up only -2 on the season.) Another interesting feature of these two tables is the surprisingly good +/- of the unit that ended the season: Robinson Francis Collins Rose and Curry.

The Four err… Three Factors

Top 5 shooting units:

# Minutes rank (minutes played) Unit Eff FG (eFG) Close%
1 10 (67) Marbury Crawford Jeffries Lee Curry 0.614 52
2 13 (58) Robinson Francis Collins Rose Curry 0.604 59
3 9 (78) Robinson Crawford Lee Q-Rich Curry 0.59 46
4 14 (54) Marbury Crawford Q-Rich Rose Curry 0.58 48
5 15 (47) Robinson Crawford Balkman Lee Curry 0.561 55

It is probably no surprise that the five best shooting units all include Eddy Curry. However it may surprise that only two of the best shooting units include Stephon Marbury while three include Nate Robinson. The little man quietly had himself a nice sophomore season?well maybe ?quietly? isn?t the right word, but you get where I?m going. For all the people trying to run him out of town for his immaturity, consider that Robinson was fairly efficient (39% 3-pt shooter, 55.3% TS) and one of the least turnover prone players on the roster despite some of his shenanigans.

Perhaps the most unpleasant surprise here is that none of the team?s top five units in minutes was among its five best shooting. Channing Frye?s sophomore-season-to-forget is certainly one culprit, though not the only one.

Marbury-Crawford-Q-Rich-Lee-Curry (.503 eFG/192 min)
Marbury-Francis-Q-Rich-Frye-Curry (.506 eFG /162 min)
Marbury-Crawford-Q-Rich-Frye-Curry (.460 eFG /162 min)
Marbury-Crawford-Jeffries-Frye-Curry (.519 eFG /142 min)
Marbury-Crawford-Lee-Frye-Curry (.509 eFG /137 min)

Top 5 turnover units:

# Minutes rank (minutes played) Unit Net TO% +/-
1 20 Robinson Crawford Q-Rich Lee Frye 5 14
2 11 Marbury Crawford Q-Rich Lee Frye 4 -7
3 18 Marbury Crawford Q-Rich Jeffries Curry 3 2
4 15 Robinson Crawford Balkman Lee Curry 2 8
5 6 Marbury Francis Jeffries Frye Curry 1 -17

Turnovers have plagued this team like it is stuck inside some sort of biblical curse plague dome. The table shows the only five units that managed to create more turnovers than they lost. I feel reasonably confident that none of these units created many turnovers but rather were better than others at taking care of the ball. Note again that only one of these units was in the top 10 in minutes played.

I also added in the +/- numbers for those units. Two of them still managed to be badly outscored despite holding onto the ball better than opponents. The worst unit on net turnover percentage was Robinson Collins Jeffries Frye Curry unit (-13 TO%). There appears to be no truth to the rumor that these players will petition David Stern to bring back the composite ball.

Top 5 free-throw units:

# Minutes rank (minutes played) Unit Net FT Attempts
1 5 (137) Marbury Crawford Lee Frye Curry 53
2 2 (162) Marbury Francis Q-Rich Frye Curry 35
3 7 (93) Marbury Q-Rich Jeffries Frye Curry 29
4 1 (192) Marbury Crawford Q-Rich Lee Curry 28
5 8 (88) Marbury Francis Q-Rich Lee Curry 27

Overall, we know the Knicks are good at getting to the free throw line. They rank seventh in FTAs per FG. For five-man units only net free-throw attempts is available. The Marbury Crawford Lee Frye Curry unit has a whopping 53-attempt advantage. That?s more than a 2.5 more attempts per minute. New York?s top 15 units all take more free-throws than their opponents. Last season only their top 11 units took more free throws.

Knicks 35 Bulls 117

Random Notes & Thoughts:

* I’ve come up with a new game to play during Knick games, I call it the Jared Jeffries game. Anytime an announcer says Jeffries stats, add the words “a career high” after it. It works well when they say things like “Jeffries has 5 points on the night” or “That’s Jeffries 3rd rebound.” It helps take the pain away when Jeffries misses from inside the paint.

* The Knicks normally have been good in third quarters, which had us guessing what Isiah does at halftimes. So far the most prevalent notion is “live cock fights.”

* My roommate asked me what Jeffries cost the Knicks. My response: “giving minutes to any small forward that might be useful.”

* I understand the Knicks are a walking M*A*S*H unit, but can’t Isiah sign someone from the D-League to help? This guy is the NBDL’s second highest scorer, is coming off knee surgery, and was drafted by the Knicks a few years back. A 10 day contract couldn’t hurt and I’d love to hear what the New York announcers could do with his name. That alone could keep me going until the end of the season.

* A Mike Sweetney sighting! If Frank Williams and Cezary Trybanski had been signed to 10-day contracts, I could have just dusted off a 2004 KnickerBlogger post and I don’t think anyone would have noticed.

* There was some discussion on what wins games (shooting, turnovers, rebounding, etc.), and those serious about the endeavor should Google “Four Factors Dean Oliver”. This link written by Dean himself, gives a pretty good plain English explanation.

* This cracks me up.

Henry Abbott & The Art of Automobile Maintenance

I love sport lists. Get 10 NBA fans together and ask them who the 10 best NBA players of all time are, and you’re likely to get 10 different lists. Even getting a consensus on the best NBA player of all time proves to be difficult. Many will point to Russell’s rings, and just as many will claim Wilt was a man among boys. Some might say Jordan was clutch, while others might argue the “Big O” was the most versatile.

Lists tend to reveal a lot about the person making the list. You may have Russell at the top of your list if you think winning a team championship is the best measure of an individual. On the other hand if you think that winning a championship is more a team effort and doesn’t adequately reflect a single person’s accomplishments, then Wilt might be your guy. If you feel that today’s athletes are far superior and face tougher competition than those of yesteryear, then Jordan would be #1. While lists are subjective, it’s not as open as choosing your favorite ice cream flavor. While “pistachio” would be an acceptable answer at your local ice cream shop, saying that Bill Cartwright was the greatest NBA player of all time is just wrong.

Recently ESPN asked their writers to rate the top 10 centers of all time. Henry Abbott of truehoop.com, and ESPN newbie, filled out his form and included Bill Walton & Dave Cowens, but omitted Moses Malone. Bill Simmons made a short blurb about Malone’s exclusion in one of his columns, and Abbott felt the need to explain his reasoning at truehoop. While there might be valid reasons for ranking Moses 11th or greater, I think Abbott’s position is a bit odd.

If he had been 6-9 he probably would have made it. I’m into overachievers. But Malone’s awfully big. And strong. Not fair, but true, I’m afraid.

Three different sites list Malone at 6-10, but I don’t want to split hairs over an inch. Abbott’s point is that he would have viewed Malone’s accomplishments more favorably if he were a smaller player. But I have to ask: given all other things being equal, how would being shorter made Moses Malone a better player? I just don’t get the argument there, because it leads down a slippery but not very steep slope where smaller players get more credit for their achievement.

If Henry’s discussion ended there, you wouldn’t be reading about it here. But he continues onward.

I’ll tell you this much, though: you’re not going to convince me just with stats. I play basketball, or something close to it. I never thought being a winner was necessarily about getting the most points and rebounds. It’s about building a team.

I’d like to state the obvious and say there’s a strong correlation between getting the most points and winning. But seriously, what is the purpose of bringing out the “I play basketball” card? Is Abbott suggesting that there is a division between those that can ball and those that can multiply fractions? Or rather that only those who play basketball are qualified to understand what makes a winning team? I play basketball too. So does Dean Oliver, and I’m sure there are tons of people in the statistical community that can lace them up. Nonetheless you don’t need to be a great basketball player to understand what being a winner is about. Isiah Thomas and Kevin McHale were much better players than Jerry Colangelo, but who would you rather have building your team?

Abbott continues his thoughts on statistics:

But for now, I’m convinced that points and rebounds, as freestanding indicators of a player’s quality, are a total crapshoot. If Eddy Curry were a total ballhog, he’d shoot every time he touched it, and no doubt score more. But of course he’d really hurt his team in the process. No way to factor that in when we put points and rebounds on the altar as sacred stats.

I can see four major problems in Henry’s example. The first is that Curry is a notoriously poor rebounder. So by using points and rebounds as an indicator of Curry’s quality you’d probably get a good understanding of his value. In other words if you were in a coma for the last 8 years & I gave you a newspaper with Curry’s stats, you’d probably have a fair idea of what Eddy brings to the court.

Second is Abbott’s assumption that Curry could score much more if he were a ballhog. There is no doubt that Curry can score one on one against just about any center in the league. However, like any NBA player, Curry can’t score with two defenders draped on him. And unfortunately Curry is unable to find the open man when double teamed. Hence opposing teams find it a low risk move to double team Curry, and by using this tactic they can limit how many points he can score. If Eddy Curry were able to find his teammates for an easy bucket, then opponents wouldn’t be able to double team him as much, and therefore Curry would be able to increase his scoring averages. So Curry’s scoring average isn’t predicated on some kind of statistical altering greed, but rather it’s limited by his poor passing ability.

Third Henry assumes that if Curry scored more it would hurt his team. But Curry’s primary function is scoring. Other than grabbing offensive rebounds, Curry doesn’t do much else well. If Eddy Curry were able to drop 29 points a night instead of 19, it would benefit the Knicks. The Knicks are trying their hardest to get the ball to Eddy more, not less, in an effort to increase his usefulness to the team.

Finally Henry asserts that should Curry hurt his team by going for personal glory, that there is “no way to factor that in” with stats. O RLY? Should Curry become a “total ballhog” you’d see a steep rise in his turnovers per minute, and his PER would plummet. To see how this affects the team you could look at the team’s offensive efficiency, or you could go to 82games.com and check the offense’s +/- with Eddy on the court. So in fact, there are many ways to statistically “factor in” a player that is in over his head.

It seems to me that Henry Abbott’s main gripe is that statistics isn’t the panacea of NBA analysis. That is you can’t take a single formula & use it to find precise answers as to the net value of a player. In some cases statistics do a poor job of capturing a player’s worth. Guys like Bruce Bowen, Quinton Ross, and Raja Bell aren’t adequately represented by their statistics. But should we just discard all statistics because it gives a few players the short end of the stick? That would be like eliminating capitalism because of the poor. Statistics bring such a surfeit of unbiased data that we can live with their deficiencies.

Abbott frequently uses the term “crude” when describing statistics (“using one players’ individual’s points and rebounds as a major tool in that debate is like using a shovel as a major tool for brain surgery: so crude it hurts.”). However, I find statistics to be an accurate and elegant way to communicate information. Take for example my assessment of Eddy Curry. Curry is a highly efficient scorer (19.3 ppg, 58% FG%) who doesn’t rebound well (7.1 reb/g) especially on the defensive end (4.6 dreb/g). He isn’t among the league leaders in scoring because he doesn’t pass well (0.9 ast/g, 3.4 to/g). The Knicks aren’t doing well mainly because of their defense (26th defensive efficiency) and some of the blame points to Curry (0.6 blk/g, defense 5.7 points worse with Curry on the court). Without the numbers to back it up, my view of Curry might be skewed by my allegiances.

Finally, Henry uses this analogy:

Points and rebounds are only ubiquitous because they are so simple to measure. Any idiot with a clipboard can chart that.

Similarly, it’s really easy to tell if your car’s headlights are working. But that’s a bad bit of investigation if you want to figure out if you’re going to make it safely cross-country. For that you gotta pop the hood and get your hands dirty.

I have to agree with him on this, but I feel as if Abbott missed his own advice. Moses Malone has more points, rebounds, free throws, MVP awards, and All Star appearances than Walton & Cowens combined. The only advantage I can see that Walton & Cowens have over Malone is that they’ve won more titles. I can’t say for sure what Abbott’s exact criteria was (rings?, desire?, height?) but by ignoring the wealth of statistical information available, he certainly didn’t pop that hood open and get his hands dirty.

Four Questions About the Knicks’ Four Factors

Sorry this is up so late today gang. Things got busy at work. You know the drill.

While we are still in something of a Knicks news black hole I thought it might be interesting to pose four questions to the readership about the upcoming season that call for rampant speculation. We’re all good for that, right?

But, to provide this post with at least the thin veneer of being at the analytical forefront of the sports blogosphere I’ve organized the questions around Dean Oliver’s “Four Factors”. Let’s restrict this round to offense mostly–just to see how this goes.

Question 1 (Shooting): In 2006 the Knick effective FG% was 48.1%, 22nd in the league. Denver was 15th last season at 48.8%. Will the Knicks increase their eFG% to 48.8% or better in 2007? Why?

Question 2 (Turnovers): New York was dead last in the league in 2006 at 19.5 turnovers per 100 possessions, more than a full turnover behind next-to-last Boston. The Clippers were 15th at 15.9 per 100 possessions. Can the Knicks keep their TO’s to 15.9 per 100 or fewer?
(Okay, almost certainly not but do you expect to improve in this area? How much?)

Question 3 (Rebounding): New York was 4th in the league in offensive rebounding percentage (31.2%) in 2006. At least three reserves who contributed double-digit rebound rates (Qyntel Woods, Mo Taylor, and Jackie Butler) are gone. Replacing them are Jared Jeffries–who was the basic equivalent of Taylor on the boards last year–along with uber-rebounder David Lee, and possibly rookie Ronaldo Balkman. Will the Knicks be able to remain a top 5 team on the offensive glass?

Okay, so I lied. I will ask one defense-oriented question because getting to the FT line, the fourth factor, is kinda boring.

Question 4 (Defensive Rebounding): Unfortunately the Knick prowess on the offensive glass did not translate to defense. The Knicks lacked the knack for keeping other teams off the boards. [Read that last sentence in Clyde’s voice. It’s almost like watching MSG.] They allowed a respectable 27.2% of opponent misses to be rebounded, good for 13th. The Heat lead the league at 23.6%.

The team’s unwillingness to rebound on the defensive end may be the singularly most inexcusable aspect of their play last year. They already were a high turnover team that didn’t shoot especially well or play good defense. However, there doesn’t seem to be much reason why a team can pound the offensive glass with the best of them but remain mediocre on defensive glass–other than “want to”. It was the widest disparity between offense and defense among the four factors for the Knicks in 2006. So, can Isiah inspire this bunch to become a top 5 defensive rebounding team? Why or why not?

Alright, have at it…