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 The Future of NBA Statistics: Part ThreeBy Kevin Peltonfor HOOPSWORLD.com Jun 18, 2004, 17:00 Email this article  Printer friendly page

Editor’s Note: This is the last column of a three-part series exploring where NBA statistical analysis is going in the future. In Part One, we examined the "old way", so to speak, linear-weights formulas. Part Two looked at better methods for evaluating defense. We wrap things up by considering alternatives to linear weights and exploring how different methods can be used in conjunction with each other.

In Part One of this series, I laid out what I think was a pretty solid case against using linear-weights formulas.

I also, to some extent, shot myself in the foot.

You see, while linear-weights formulas aren't that accurate and miss many nuances of a player's performance, they are valuable. In Basketball on Paper, Dean Oliver paraphrases Bill James to say, "reducing quality to one number has a tendency to end a discussion, rather than open up a world of insight."

But James also wrote an entire book about his "single number", Win Shares.

There is value in having that kind of single number. It can help find similarity or dissimilarity between players in terms of value much more easily than a set of numbers can. If one player has a PER of 20, and another has a PER of 10, you can bet the first player is likely the more productive one. In a setting like this column, single numbers are particularly useful to back up the claims I'm making, and the inability to use my linear-weights work in that role was the only reason I hesitated to write this series of columns. (Single numbers are also useful for studies, and it's unlikely I'll stop using VORP in that role.)

With that in mind, I'm going to introduce the rating system I'll be favoring in this column from now on, which doesn't really have a name other than its summary number, WARP -- Wins Above Replacement Player. Along with Oliver's Individual Win-Loss Records, I think it looks more like what summary statistics we'll see in the future will look like.

For more than two years, I've been working on the idea of rating players in a team context. That is, I'd like to consider what would happen when you added a given player to a team of four completely average players (with, assumedly, a completely average bench). Baseball Prospectus uses a similar theory for rating hitters, though it's much easier to do in baseball because there is relatively little interaction between teammates on offense.

It took me nearly that entire time to produce workable results. It wasn't until the middle of this season that I finally got results I was comfortable with. I'm not going to explain in complete detail how this rating system works. If you'd like to see that detail, here is a longer explanation.

Offense isn't too tremendously complicated. I start with points produced per 100 possessions, where possessions are FGA + (.44*FTA) + TO. To this, I add credit for assists and take away credit for an estimated proportion of assisted baskets, using a regression with actual assisted field goal data from 2002-03 from 82games.com.

Through this point, my offensive ratings are virtually identical to what John Hollinger calculates as Offensive Percentage in his Pro Basketball Prospectus series. The leaders in this, however, are annually players like Reggie Miller (who did lead the league last season). Well, these players are very efficient offensively, but they're not the best offensive players in the league, because they don't have that big of an impact on their team.

The first step I take to rectify this situation is put the player in the team context by creating a team rating that is the player's percentage of his team's possessions while he's in the game multiplied by his offensive rating plus the rest of the possessions multiplied by league average offensive efficiency.

Still, this is not enough to account for the fact that opposing defenses don't have to expend as much energy against non-scorers as they do go-to players. So I alter the league average by .25 points for each percent of possessions used above or below 20% (the inherent league average). In practice, this means that Tracy McGrady's teammates are rated at 92.6 points per 100 possessions, Ben Wallace's at 88.0.

Is this arbitrary? Absolutely. But it works well in practice, and I think it's the only fair way to appropriately evaluate a player's role in his team's offense.

Rebounding is primarily evaluated by the percentage of available rebounds a player grabs on offense and defense, with a pair of caveats. One is an adjustment that means I don't literally add four average rebounders to the player. The reason for this is that when a team adds a good rebounder, some of his extra rebounds will come away from his teammates.

Is this appropriate? Let's compare the on-court and off-court statistics of Ben Wallace this season. Despite Wallace being a great rebounder, he only makes the Pistons 1.5% better on the offensive glass and a paltry 0.3% on the defensive boards. If you take out Wallace's own rebounds and pro-rate the other four players to five, the Pistons go from 32.1% to 26.5% on the offensive glass, 68.3% to just 53.9% on the defensive boards.

I also make a positional adjustment for offensive rebounds. Ideally, I'd do this in all categories, because the additional player is theoretically added to average players at each other position, not four completely average players. However, it's very difficult, and doing it for offensive boards seems to be sufficient to overcome the bias towards big men I mentioned earlier.

Defense is and has always been the shortcoming of this system. To create a defensive rating, one has to use some team defensive statistics, which can be dicey.

"I have seen statistical ratings that work around this by assigning a 'team defense' rating to each player," Hollinger wrote in the first Pro Basketball Prospectus. "That approach is incredibly crude; giving as much credit to Keith Van Horn as to Jason Kidd for the Nets' defensive strength just doesn't make any sense. . . . The reason the PER does not consider position defense is because the alternative would be to assign a made-up rating for defense that has no basis in reality."

Harsh, but probably far. At the same time, I would respond that to give defense less importance, as linear weights formulas do, is equally escaping reality, even if not made-up. At the same time, giving players complete credit for their team's performance definitely is silly. At most, a player is only responsible for 20% of his team's defensive effort, less if he isn't a true iron man who plays all his team's minutes.

What I do, then, is create what I call the "Team Defense Factor", which is the player's minutes divided by his team's minutes. The player's team's rating in "team" areas of defensive -- forcing missed shots and forcing non-steal turnovers -- is found by the player's Team Defense Factor multiplied by his team's rating plus (1 - Team Defense Factor) times the league average.

The other three elements of defense -- fouling and sending players to the free-throw line, blocking shots, and stealing the ball -- are rated using solely the player's own performance and his four league-average defense.

The way I put this together is to estimate how many of the opposing team's possessions would end in steals, how many in blocked shots, how many in non-steal turnovers, how many in free throws, and how many in field goal attempts. Then I estimate how many points each free-throw attempt and field-goal attempt would result in, the latter based on the combination of the player's team's defense and league average as described above.

Putting it all together means plugging the "team" ratings for offense, defense, offensive rebounding, and defensive rebounding into the regression I produced using team data from 1990 through 2003, which I discussed briefly in this November column.

Beyond this, the other two summary statistics I calculate to take playing time into account are Net Wins -- (Win% - .5)*(Min/48) -- which is essentially how many games "above .500" the theoretical team is; and WARP -- (Win% - .35)*(Min/48) -- which describes by how many games the team improves adding the given player as compared to a replacement-level player.

Naturally, I have to put this new system to the same test -- measuring team performance -- that linear weights fail. This system is designed to succeed, in that the team's performance is captured within player statistics. And succeed it does. If we rate each team (by weighting each player's winning percentage by minutes), the correlation with team winning percentage is .927. Change it to point differential, and the correlation is an even superior .950. This is despite the fact that trades mean many teams have changed personnel mid-season.

If we are to calculate each team's offensive and defensive rating in the same manner as its projected winning percentages, the correlations with actual offensive and defensive ratings are very strong -- .976 and .960, respectively. I may still be making mistakes by giving credit to the wrong players, but not the wrong teams. This is also valuable because a team that only wins 30 games can't have players whose individual ratings add up to 40 wins, for example, which is a good reality check for any system.

On the other hand, I should point out that while I like what the Team Defense Factor does, it also means that the deviation in offensive ratings (3.17 points per 100 possessions standard deviation) is much larger than that for defensive ratings (1.37). That means that good offensive players are rated better than good defensive players and vice versa, and that offensive ratings tend to dominate the overall winning percentage more than they should.

That being said, I think my system also passes the "laugh test".

```Player      Ps   Tm  Win%  WARP
-------------------------------
Garnett     PF  MIN  .782  29.1
Kirilenko   SF  UTA  .736  23.3
Duncan      PF  SAS  .745  20.8
Stojakovic  SF  SAC  .635  19.4
Cassell     PG  MIN  .660  18.3
McGrady     SG  ORL  .674  18.0
Marbury     PG  NYK  .608  17.5
Marion      PG  PHO  .609  17.4
Wallace     PF  DET  .620  17.1
Bryant      SG  LAL  .684  17.0```

Marbury and Marion are the only two who weren't All-Stars this season, and a) their ratings are largely based on their high minutes totals and b) they were both All-Stars the year before.

You might also notice an interesting similarity with Dan Rosenbaum's ratings, which I recently discussed. I find it fairly remarkable that we both rate Kirilenko as an MVP-caliber player. Eight of our top ten players (on a per-minute basis -- different than above) were the same last season, and we only differed on Cassell (my list) and Baron Davis (Dan's).

As I mentioned in Part Two of this series, I think using multiple ratings and various different ways of looking at players will definitely be a key part of the future of NBA statistics. That means considering possession-based summary statistics (like mine and Oliver's) as well as team-performance based ones (plus-minus, WINVAL, DanVAL) and metrics that merely evaluate a single skill, like rebound rate and true shooting percentage.

On defense, I already detailed some of the many different ways when we can evaluate a player's defense, but to recap, on-court/off-court comparisons, opponent offensive statistics, and individual defensive statistics (both the meager ones currently tracked and potential new ones) all have their place.

There's a certain danger in looking at several ratings, in that an individual can cherry-pick the one that best confirms their bias, and in so doing, learn nothing from the statistics whatsoever. At the same time, the opportunity to add value and gain a more nuanced perspective is immense.

I think there is also an opportunity for these rating systems to work together. Rosenbaum did a great job of showing how this can work by using his adjusted plus-minus ratings to determine what weights to use in a pseudo-linear weight system. I'd love to see what Rosenbaum could do using the same method and a possession-based rating system. Eventually, that method could be expanded to give weights for things we begin tracking, like deflections or secondary assists (the pass that leads to the pass).

Another topic I believe will eventually draw some attention from NBA performance analysts is that of "playing a role". By accounting for offensive load, I think my system does that to some extent, but what I still can't account for is how adding a player will actually affect a specific team. In other words, my system won't tell you that a great defensive player would be more valuable to the Dallas Mavericks than a great scorer. That context is simply lost. A three-point shooter with a great post player … a true point guard on a team that lacks one … I'd love to see a way to evaluate the importance of these roles.

I can definitely say with great confidence that I have faith in the people in whose hands the future of NBA statistics lies. There are a great many very intelligent people who are putting their mental capacity towards trying to answer these questions, trying to build upon the work of and do better than those who came before them. I don't want to make the clichéd comparison to where sabermetrics were two decades ago, but, as in baseball, a large group of intelligent people will eventually produce great results. Enjoy the ride.

### "The Page 23 Club"

One of the unfortunate things about this column is that, because of my schedule, I can't commit myself to a specific day or time for publishing columns. To help my readers, I've started an e-mail list. If you want, you'll receive an e-mail whenever a new column is up with an introduction to the column and a link. If you're interested, e-mail me at kpelton@hoopsworld.com and let me know. I will, of course, make every effort to protect the privacy of your e-mail address.

Kevin Pelton is an intern for the Seattle SuperSonics and is responsible for original content on Supersonics.com. He writes "Page 23" for Hoopsworld.com on a semi-regular basis.

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