A new framework for offensive evaluation: Total Production

If you’ll apologize with me being brief with you, I have some (you guessed it) linear weights for your edification and entertainment.

This is an update to the “RE from zero” method I introduced a while back. There are two key differences:

1. Instead of doing the entire Retroera, I simply used what I am calling the modern offensive era, 1994-2007. (I don’t have 2008 Retrosheet data yet, because nobody does.)
2. I have made some corrections to the way I was figuring out the negative run value of the out.

Here is the run expectancy table, 1994-2007:

OUTS	RUN1	RUN2	RUN3
0	0.395	0.613	0.842
1	0.265	0.412	0.658
2	0.131	0.229	0.287
3	0.000	0.000	0.000

And now, the linear weights, broken down by component:

SHORTNAME	LONGNAME	NUM	RUN	RBI	OUT	LWTS
Out	Generic Out	1223130	0.005	0.028	-0.132	-0.099
K	Strikeout	419719	0.001	0.003	-0.117	-0.114
SB	Stolen Base	35402	0.170	0.000	0.000	0.170
CS	Caught Stealing	19585	0.036	0.000	-0.271	-0.236
NIBB	Walk	203512	0.256	0.065	0.000	0.320
IBB	Intentional Walk	17489	0.196	0.005	0.000	0.201
HBP	Hit By Pitch	22391	0.265	0.082	0.000	0.347
1B	Single	395520	0.272	0.206	-0.004	0.474
2B	Double	117508	0.426	0.337	-0.002	0.761
3B	Triple	12505	0.606	0.429	0.000	1.036
HR	Home Run	69809	1.000	1.401	0.000	1.401

And now I have some ‘splaining to do. These are “absolute” linear weights, as compared to linear weights above average (the usual presentation). These are not derived from a standard run expectancy table, and so no additional effort was made to reconcile them with absolute runs scored and as such I have no method employed. (Since converting LWTS from above-average to absolute runs is a topic of some confusion and no little controversy – or at least that’s how it feels to me – I really need to do a longer write-up of the issue at some point.)

I’ve arranged the values so that they match up with the three basic elements of scoring runs: providing a baserunner, advancing a baserunner and making outs. You’ll note that I am double-counting the home run; this is because doing so provides us with linear weights values that match up well with a player’s runs scored and runs batted in, at least for the population as a whole.

The benefit of this approach – at least to my thinking – is that you can present linear weights as context-neutral runs scored and runs batted in, with a third component – a player’s negative contribution by making outs.

I’m calling it Total Production, because to be quite frank I can’t come up with anything better and I should be in bed already. I am throwing myself on the mercy of you, the reader, to give me a better name. Please give me a better name.

And… here is your Total Production leaderboard from 1994-2007, minimum 300 PAs. (For no other reason than the size limit of a table I can publish via EditGrid.)

From here, you could do a lot of things with the numbers – add in park adjustments, provide versions for different baselines like above average or replacement level, combine them with some defensive metrics so that the “total” part of the name isn’t an absolute lie.

I should note that I’m not claiming any benefit from using these weights over any similarly capable set of linear weights appropriate to the time period involved. The only potential benefit I’m claiming here is in the presentation of the data – it occurred to me that some people might have an easier time approaching linear weights if they were presented in the guise of the traditional counting stats. Let me know if you think so, too.