“Surely some revelation is at hand;
Surely the Second Coming is at hand.
The Second Coming! Hardly are those words out
When a vast image out of Spiritus Mundi
Troubles my sight: somewhere in sands of the desert” -Yeats, The second Coming
Over the course of the last week I have been troubled by an idea that needed to get out. It wouldn’t let me be. It wouldn’t let me sleep. It demanded my attention. The genesis of this idea come from the idea that the scarcity of particular resource (for example the short supply of tall people) can drive up the value of an asset (i.e. an NBA player). There is compelling data recommending a difference in valuation for big men in particular, their weight against gross productivity and the increased risk versus a talent dropoff at that position.
The key driver for all these pieces is that the value of the average player is by position can be influenced based on the scarcity of those players. Now our standard approach assumes equal value at the baseline at each position which is perfectly valid. Our alternative approach (which I’m calling Wins over Replacement Player or WORP for short) goes from the assumption that the population of talent available at each position is not homogenous.
Calculating Wins over Replacement Player (WORP)
For understanding how to calculate Wins over Replacement Player (WORP) you need to have an understanding of Wins Produced (explained here, are you back? Good, let’s continue.)
Wins Produced is based on the marginal productivity for each team. If I tweak the equations for Wins Produced to look at the math in a slightly different way:
Wins Produced Team=
Sum_for_all_your_Players (Prod_by_min*Minutes_Played) –
Avg_for _all_teams (Prod_by_min*Minutes_Played) +
The 41 games you would win with an average team
Wins Produced Player= Players _Prod_by_min*Minutes_Played –
The .100 term means that if you break even on average you’ll win half your games.
For Wins over Replacement Player (WORP) we will be tweaking the terms for the equations. They will still add up to the same numbers overall and the correlation will remain the same (trust me I checked) but the individual value by player will change. So the equations will look like:
Wins over Replacement Player Team=
Sum_for_all_your_Players (Prod_by_min*Minutes_Played) –
Productivity_for_a_team_of_avg._replacement_players (Prod_by_min*Minutes_Played) +
The games you would win with a replacement team
Wins over Replacement PlayerPlayer=
Players _Prod_by_min*Minutes_Played –
Avg._Replacement_Player at Position_Prod_by_min*Minutes_Played +
There are two key changes in the second equation. First, I’m using the Replacement Level Player as the zero point for marginal value but the second change is equally as important. I am calculating the third term as the net win productivity by a replacement player who plays the same position. This means that rather than assuming an equal share across all positions I am calculation what percentage of the baseline production is on average allocates to C,PF,SF,SG & PG.
But enough buildup, I ‘d like to see what the numbers say.
I calculated WORP for every year since the merger. But I will be publishing the data from 2006 on. I am including Minute Allocations, Raw ADJP48, WP48 and Wins Produced from 2006 on as a nice bonus (you too can play along at home, we here believe in modeling by open source). The data set is here.
Let’s look at some charts.
The first is how wins are reshuffled by position (W1 to W5 are Wins Produced ,WR1 to WR5 are WORP):
Wins moved from SG and SF to C and slightly to PF. PG suffered because of low baseline productivity but made it up with the scarcity of their skill set
Here’s a view of Wins Produced vs WORP for those Years:
If I look at the Change vs Wins Produced:
So what does it mean?
Let’s clarify something first, I actually don’t think there’s one unique/optimum strategy to determine the value of a basketball player. I think using the average (as in the Wins Produced model) is a valid strategy for success. But I think adjusting the point of comparison from the average to the replacement (a la WORP) would just provide a different strategy to allocate resources that would also lead to success.
Our first value model recommends accruing assets that are exceptional when compared to their position averages (and we know this works see all the half baked theory stuff I’ve written for this blog). The alternative recommended approach would emphasize accruing assets that are exceptional in a limited pool. I think this could be succesful as well (and weirdly this seems to be what my beloved celtics are doing :-)).
Simply put if average Centers (or even Point Guards) are in general more scarce resources than Shooting Guards then skilled labor at that position should command a higher premium. Let’s put this in manufacturing terms. Shooting guards are base line operators, point guards are skilled operators/group leads and centers are something like technician/operators. The baseline value of the labor is different based on the role and is a function of the actual labor pool (which is were the replacement level comes in).
I think the logic behind the second approach to me goes back to the scarcity of a resource. What the analysis is telling me is that average Centers and PG are in general more scarce resources than SG and so skilled labor at those positions should command a higher premium.
In manufacturing terms shooting guards are base line operators, point guards are skilled operators/group leads and centers are something like technician/operators. The baseline value of the labor is different based on the role and is a function of the actual labor pool (which is were the replacement level comes in). It really becomes like looking at different medical specialties and figuring out their value( http://www.medfriends.org/specialty_salaries.htm) (and centers seem to be the orthopedic surgeons of the NBA).