The hot topic in basketball these days is the measurement of a basketball player’s productivity. For years we have had the traditional box score which can be viewed through the lens of NBA Efficiency, TENDEX, Points Created, PERs, Game Score, Wins Shares, Win Score, and Wins Produced. And then we have the non-box score approaches of plus-minus and adjusted plus-minus. With all these measures out there, it seems unlikely that we need to call attention to anything else. Nevertheless, I thought I would devote a post to a measure that’s in my morning paper each day.
The Miller Metric
Each morning the Salt Lake Tribune is delivered to my house in Cedar City. Not surprisingly, the Utah Jazz gets quite a bit of coverage from this paper. And part of this coverage is a measure of performance that I don’t think is seen much outside of Utah. Larry Miller – the long-time owner of the Jazz who recently passed away – devised a measure called the Miller Metric. The measure is calculated as follows:
Miller Metric = Points + Rebounds + Steals + Blocked Shots + Assists – Turnovers – Shot Attempts – Personal Fouls
Each time the Jazz play the Miller Metric is reported and it’s also part of the season statistics reported for the team.
Looking at the Miller Metric I am reminded of Win Score, or the simple measure of performance we introduced in the Wages of Wins.
Win Score = PTS + REB + STL + ½*BLK + ½*AST
– FGA – ½*FTA – TO – ½*PF
These two measures are not exactly the same. The obvious differences included the weighting of blocked shots, assists, and personal fouls, as well as the inclusion of free attempts in Win Score. But the measures are similar in how shooting efficiency is treated. Unlike NBA Efficiency, TENDEX, Points Created, PERS, and Game Score; Win Score and the Miller Metric require players to shoot efficiently from the field. Specifically, instead of subtracting missed shots (the approach taken by NBA Efficiency), bothe the Miller Metric and Win Score subtract field goal attempts. Consequently, despite the differences cited, the Miller Metric (per 48 minute) and Win Score (per 48 minute) have a 0.92 correlation (from 1977-78 to 2007-08).
Evaluating the Jazz
To see the similarities, Table One presents evaluations of the Utah Jazz – after 60 games – by both metrics.
Table One presents position adjusted figures for both measures. For the Miller Metric the position adjusted measure – what I call PAMM [Position Adjusted Miller Metric] – is not as necessary. This is because free throw attempts are ignored by the Miller Metric (at least, I get the numbers the Tribune gets when I ignore free throw attempts). As a consequence, although shooting efficiency from the field is required by the Miller Metric, from the line efficiency is not required (i.e. the more you shoot from the line the better you will look regardless of free throw percentage).
If you add free throw attempts to the Miller Metric – by subtracting ½*FTA from this measure – the correlation with per 48 minute Win Score rises to 0.94. And then a position adjustment will clearly be required. In other words, as you move from a measure that focuses more on scoring totals to one that focuses more on scoring efficiency, you have to consider where a player is playing on the court.
Regardless of the free throw attempt issue, there are clear similarities between both PAMM and PAWS (Position Adjusted Win Score). The top six players on the Jazz – Carlos Boozer, Deron Williams, Andrei Kirilenko, Paul Millsap, Ronnie Brewer, and Mehmet Okur — are the same by both measures. And both measures regard these six as the only above average players on the team. In sum, both PAMM and PAWS are telling similar stories.
If we turn to WP48 [Wins Produced per 48 minutes] – as reported in Table Two – we again see the same story. The Utah Jazz again have six above average players and three players – Ronie Price, Morris Almond, and Jarron Collins — who rank as the least productive players on the team.
In sum, it appears that Larry Miller was on to something. By creating a measure that emphasized shooting efficiency from the field he created a metric that comes fairly close to a player’s contribution to wins.
Finding Talent in Utah
Of course it’s not clear how much this measure informs decision-making in Utah. After all, most owners are not picking players on their teams. Nevertheless, I think one can make an argument that Utah does have some ability to find productive players.
To see this, consider the trajectories of the two teams that met in the NBA Finals in 1997-98. After the 1998 Finals the Chicago Bulls saw a number of players responsible for their title depart. The duo of Stockton and Malone – the two players who led the Jazz — stayed together for five more seasons. And across these years the Jazz managed to keep making the playoffs.
Given these scenarios, one would think Chicago would have re-built faster. While Utah kept appearing in the post-season, Chicago kept adding top-10 picks in the draft (see Elton Brand, Marcus Fizer, Jamal Crawford, Eddy Curry, Jay Williams, Kirk Hinrich, Ben Gordon, Tyrus Thomas, Joakim Noah, and Derrick Rose). But despite these additions, Chicago – across the past 10 years – has yet to win 50 games in the regular season. Meanwhile Utah – if it can win 13 of its last 22 games this year – will have reached the 50 win threshold for the third consecutive season.
Again, we don’t know the Mille Metric has been used to select players in Utah. But of the six above average players on the roster, only one (Deron Williams), was a top ten choice in the draft. So apparently the Jazz – despite lacking the advantages in the draft bestowed on the Bulls — have some ability to find productive players. And it’s just possible the Miller Metric – with its emphasis on shooting efficiency (at least from the field) – has helped.
Then again, maybe this is just something that makes my morning paper in Utah a bit more interesting.
The WoW Journal Comments Policy
Our research on the NBA was summarized HERE.
Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:
Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.