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November 29, 2012, 12:29 PM ET
Mookie Blaylock Was (Probably) Quite Good

by Neil Paine

Randomly perusing the historical players in Daniel Myers’ newest Advanced Statistical Plus/Minus spreadsheet, I came upon Mookie Blaylock’s numbers and remembered just how much SPM-style stats have always adored him:

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Year	Age	Team	G	MP	OASPM	DASPM	ASPM
-------------------------------------------------------------
1990	22	NJN	50	1267	-2.4	+0.6	-1.8
1991	23	NJN	72	2585	-1.1	+1.2	+0.2
1992	24	NJN	72	2548	+0.4	+1.2	+1.6
1993	25	ATL	80	2820	+1.3	+1.0	+2.3
1994	26	ATL	81	2915	+1.7	+2.4	+4.1
1995	27	ATL	80	3069	+2.0	+2.0	+3.9
1996	28	ATL	81	2893	+2.1	+1.6	+3.7
1997	29	ATL	78	3056	+2.7	+2.5	+5.2
1998	30	ATL	70	2700	+0.7	+2.0	+2.7
1999	31	ATL	48	1763	+1.1	+2.4	+3.5
2000	32	GSW	73	2459	+0.2	+0.4	+0.6
2001	33	GSW	69	2352	+0.3	+0.9	+1.2
2002	34	GSW	35	 599	-2.3	-0.8	-3.0
-------------------------------------------------------------
Career		       889     31026	+0.9	+1.5	+2.4
-------------------------------------------------------------

The +4.1 in 1994 and +5.2 (!) in 1997 really jump off the page, as does that +2.4 career mark. But ASPM is only an estimate designed to emulate Regularized Adjusted Plus/Minus. How would he compare to modern players if we had real RAPM back in the 1990s?

One way to do this is to compute “Fake RAPM”, as Jerry Engelmann has done for the 90s at his site. (There, Blaylock’s fake RAPM is only +1.1.)

But another way to answer that question is to look at what kind of errors ASPM makes when predicting RAPM for current players.

Engelmann recently posted a 12-year, non-prior-informed RAPM dataset which can be used for that very purpose. I grabbed all players who played at least 11,900 minutes between 2001 and 2012, and looked at the distribution of the differences between their actual RAPM and the RAPM we’d predict from ASPM. It turns out that the distribution of the errors is approximately normal with a mean of 0.0 and a standard deviation of 1.7, meaning we can use a standard normal probability distribution to determine the odds that a given player’s career RAPM would have been a certain number based on his career ASPM (as long as he met the same playing-time requirements as the sample of modern players I started with, which Blaylock does).

With a +2.4 career mark, there’s a 91.7% probability that Blaylock’s career RAPM, if we were able to calculate it, would have been above average. His career PER of 16.8 roughly translates to a +0.7 ASPM, so there’s an 83.4% chance that PER is underselling Blaylock’s true contributions. In fact, there’s a 77.5% chance that Jerry’s “fake RAPM” is underrating Blaylock.

But here’s the real fun part. For players who started their careers since 2001, we know what their career RAPMs are (through last season). So let’s pick out the best point guards of that era, and compute the odds that Blaylock’s RAPM would have been better…

Chris Paul: +6.2 RAPM … 1.5% chance Blaylock was better
Russell Westbrook: +3.8 RAPM … 21.5% chance Blaylock was better
Mike Conley Jr.: +3.5 RAPM … 26.9% chance Blaylock was better
Kyle Lowry and Gilbert Arenas: +2.5 RAPM … 48.2% chance Blaylock was better
Devin Harris: +2.3 RAPM … 52.8% chance Blaylock was better
Deron Williams: +1.7 RAPM … 66.0% chance Blaylock was better
Raymond Felton: +1.5 RAPM … 70.1% chance Blaylock was better
Tony Parker: +1.4 RAPM … 72.1% chance Blaylock was better
Derrick Rose: +1.0 RAPM … 79.2% chance Blaylock was better

I’ll stop there. As you can see, Blaylock may not have been on Chris Paul’s level, but there’s a pretty good chance his RAPM would have been better than such luminaries as Deron Williams, Tony Parker, and Derrick Rose! (And that’s not even accounting for the fact that Blaylock’s +2.4 ASPM spans his entire career, including his twilight years in Golden State, while the current players are still in their primes.)

These days Blaylock is practically remembered as much for being Pearl Jam’s original namesake as for his playing career, but it bears mentioning just how good he (probably) would have been revealed to be in those days, if only we could have calculated the kinds of advanced plus/minus stats we keep now.

Email Neil at np@sports-reference.com. Follow him on Twitter at @Neil_Paine.

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