Table of Contents

Thursday, October 18, 2007

Player Value, Part 2a: Offense - Run Estimation

To view the complete player value series, click on the player value label on any of these posts.

Player Value Series Contents

Runs Estimation

The first thing we have to decide upon when trying to quantify a player's offensive value is how to estimate the number of runs a player contributes to a team in a given season. And there are a lot of options available to us. Fortunately, there are some clear recommendations that we can make.

Brandon Heipp (aka U.S. Patriot) has a great set of essays on his site that give the low-down on a variety of run estimation techniques. I find his work very convincing and readable, so I'm not going to re-hash it. But I do want to go over the methods that I have used in light of his (and others', especially Tom Tango's) work. Readers will undoubtedly note that what follows is heavily influenced by those two individuals, though I've tried to reconstruct what follows myself as much as possible. If anything is directly lifted, I've linked to the source.

There are two primary approaches to estimating run production of teams or players: models and linear weights. Let's profile them one at a time.

Models

Models try to actually describe how runs are created mathematically, and therefore have the advantage of being constructed in a manner that reflects something about the nonlinear fashion in which teams actually score runs. They have a disadvantage in that a) they rely on the ability of their creators to actually model how teams score runs mathematically, which is no easy feat, and b) they are (usually) only applicable to estimating team runs, rather than runs provided by individual hitters, because they assume interactions between on base and advancement skill. Unless a player hits a home run, a player has to interact with his teammates in order to generate runs by either getting on base and then getting knocked in, or by advancing runners already on bases.

There have been a number of models that have been proposed over the years. Perhaps the best known is Runs Created, which was developed by Bill James. While revolutionary for its time, Runs Created, even in its most recent iterations, suffers from a number of flaws that have caused it to fall out of favor of many analysts (perhaps the most significant of these is how it values home runs). If you are interested in the details, Tom Tango has an excellent article series that breaks down some of the problems with runs created. Furthermore, Brandon Heipp has a more historical look at runs created, along with detailed discussion of some of its problems. On the basis of their compelling work, I have decided to discontinue the use of Runs Created on this site. I don't think it's a terrible stat, but I think it is critically flawed under certain situations...and there's a much better alternative out there that is just as easy--if not easier--to use (if nothing else, it's far more intuitive):

Base Runs

Among the statisticians that I read frequently, the most well regarded runs estimation model these days is David Smyth's BaseRuns. BaseRuns has strong theoretical underpinnings, is more accurate and flexible than Runs Created across a variety of run environments, is arguably much more intuitive and approachable than RC. In short, there's little reason to use anything else--at least for the time being.

BaseRuns is based on this simple equation, which could arguably be called a truism (this part is essentially lifted from Tango's article--sorry!):

runs scored = [batters reaching base] * [scoring rate] + home runs

This makes sense, right? Pretty straightforward. No other way of producing runs that I can think of...you either get on base and have a chance of scoring based on the performance of your teammates, you advance your teammates in such a way to increase their chances of scoring, or you smack a homer. Anyway, that equation can be rewritten as this

runs scored = [batters reaching base] * [(successes)/(opportunities)] + home runs
or
runs scored = [batters reaching base] * [(successes)/(successes + failures)] + home runs
or
runs scored = A * (B/(B+C)) + D

Now, we can directly quantify the number of batters that get on base (A): hits - hr + walks + hbp, right? And we can certainly count the number of home runs (D). And it turns out that "failures" (C) above is essentially just the number of team outs. So the only thing that we need to come up with is the "successes" (B) in the scoring rate term.

Smyth proposed a B term that works remarkably well, but additional work on this issue over the years has generated greater accuracy. Probably the best work on this front (at least that I've seen) is by Tom Tango, who empirically generated coefficients for the B term using 1974-1990 Retrosheet data, the results of which can be found in this table.

Nevertheless, either because these data are based on a lower runs environment, or (more likely) because the stats I have access to don't have some of the terms that Tango had in his retrosheet study, forcing me to leave some things out (most notably reached base on errors), my experience is that these data underestimate total runs slightly in modern baseball. Therefore, I have used a spreadsheet (found here) by Brandon Heipp to apply slight multipliers (~10% increase) to Tango's B coefficients to have the output match the runs scored based on 2003-2007 National League totals. The assumption here is that there will be sufficient correlations among the events I have and the events I don't have that this will result in a pretty close estimate of runs. Anyway, here's "my" final BaseRuns equation:

(Updated 12/1/07--corrects an issue with SH's, adds GDP's)

BsR = A * (B/(B+C)) + D
where:
A = H - HR + BB + HBP + 0.08 SH
B = 0.883 1B + 2.370 2B + 3.812 3B + 1.995 HR + 0.063 (BB - IBB) - 0.588 IBB + 0.198 HBP + 0.884 SH + 0.989 SB - 1.445 CS -1.445 GDP - 0.005 (AB - H - K + SF) - 0.069 K
C = AB - H + SF + 0.92 SH
D = HR
(note: the 0.08 SH in the "A" term, and the 0.92 SH in the "C" term, is due to those times when runners reach base on a sacrifice due to a fielder's choice)

I know it looks complicated, but it's not! Remember, A is just "men on base," C is just "outs", and D is just "home runs." The only voodooish part of the equation is B, which tries to estimate the degree to which runners will advance based on each of the different offensive events.

How well does base runs work? Well, here's a table showing 2007 NL teams and how well base runs, as defined above, predicts their runs scored:

Team ActualRS BsR
+-Diff %Diff
Arizona 712 705.7 6.3 0.9%
Atlanta 810 796.6 13.4 1.7%
Chicago_Cubs 752 753.8 -1.8 0.2%
Cincinnati 783 793.1 -10.1 1.3%
Colorado 860 851.8 8.2 1.0%
Florida 790 814.6 -24.6 3.1%
Houston 723 737.4 -14.4 2.0%
LA_Dodgers 735 741.7 -6.7 0.9%
Milwaukee 801 800.2 0.8 0.1%
NY_Mets 804 814.2 -10.2 1.3%
Philadelphia 892 899.4 -7.4 0.8%
Pittsburgh 724 713.3 10.7 1.5%
San_Diego 741 718.7 22.3 3.0%
San_Francisco 683 675.7 7.3 1.1%
St._Louis 725 722.1 2.9 0.4%
Washington 673 679.5 -6.5 1.0%

Overall, base runs explained the 2007 NL totals to within 4 runs! This was an exceptionally good result, but looking back from through the '03 season, it was never off by more than 1.2% across an entire season. Granted, this work slightly circular, given that I forced the B term to match the '03-'07 totals, but the accuracy I'm citing here has been repeated elsewhere (including a recent application with the Israeli Baseball League!). Furthermore, other studies (here's one by Tango) have demonstrated that Base Runs is accurate across a wide variety of conditions. Runs Created and Linear Weights fail the further you move away from league averages.

In sum, when estimating team or league offense, the best available approach is Base Runs. No runs estimation approach is simple, per se, but base runs is reasonably approachable, accurate, and flexible to both run environments and datasets (with a bit of caution you can add or subtract terms from the model depending on your needs and your dataset). For more on base runs, I highly recommend reading the articles by Patriot and Tango.
Update 12/1/07: Here's a version of the above equation that I've fudged to match '03-'07 MLB totals (not just NL totals):
A = H - HR + BB + HBP + 0.08 SH
B = 0.905 1B + 2.429 2B + 3.908 3B + 2.045 HR + 0.065 (BB - IBB) - 0.602 IBB + 0.203 HBP + 0.907 SH + 1.014 SB - 1.481 CS - 1.481 GDP - 0.005 (AB - H - K + SF) - 0.071 K
C = AB - H + SF + 0.92 SH
D = HR
Update: 12/27/08: Updated for '04-'08 MLB totals:
A = H - HR + BB + HBP + 0.08 SH
B = 0.903 1B + 2.422 2B + 3.897 3B + 2.039 HR + 0.065 (BB - IBB) - 0.600 IBB + 0.202 HBP + 0.905 SH + 1.011 SB - 1.477 CS - 1.477 GDP - 0.0050 (AB - H - K + SF) - 0.0708 K
C = AB - H + SF + 0.92 SH
D = HR

Linear Weights

While base runs works great for estimating team or league offense, it doesn't work well when applied individual hitters. The reason is that the on-base term, A, is designed to interact with the advancement term, B (this is the case with most models, including many versions of runs created). When you're dealing with an individual player, his on-base ability can't actually interact with his advancement ability to produce a run unless he hits a home run. Therefore, in order to assess individual hitter contributions, it's best to use a run estimator that doesn't try to describe interactions--otherwise you will tend to overestimate production among high OBP and high SLG individuals. That's where linear weights comes in.

Linear weights refers to a set of coefficients that you can apply to offensive events in order to estimate the runs scored by a player or a team. They can be generated any number of ways, including empirically (by measuring the average effect of each offensive event on runs scored in innings), or via models.
Given that we have such a nice run estimation model in BaseRuns, and that linear weights can change drastically in different hitting environments (i.e. good vs. bad offensive team or league), I find that model-derived linear weights are the most useful approach, as it allows us to customize our linear weights to fit a particular context.

One process of extracting linear weights from a model goes like this: we can use base runs to model a particular offensive environment, like I did with the '03-'07 National League. To see how many runs each single (or double, or triple, or walk, etc) typically adds in this context, we can take our '03-'07 National League data, and then add one single to those totals. The difference between the initial number of estimated runs and the number after we've added this single is the typical value of a single in that run environment. Repeating this process for each event (single, doubt, walk, hbp, etc), we can generate custom linear weights for our chosen runs environment.

Using Heipp's Base Runs spreadsheet to automate this process, I extracted this linear weights equation for the '03-'07 National League:

LWTS = 0.498 1B + 0.821 2B + 1.134 3B + 1.433 HR + 0.320 NIBB + 0.215 SB - 0.314 CS + 0.349 HBP + 0.179 IBB + 0.128 SH - 0.314 GDP - 0.097 (AB - H - K + SF) - 0.111 K

Notes: The coefficient in front of each event is the (estimated) average number of runs that each event produced in the NL from '03 to '07. [AB - H - K + SF] is a measure of non-strikeout outs. These values may differ slightly from those you may see around the 'net, but they are pretty close to those reported here by Tom Tango. The reason they differ is because they were generated using a different dataset--the '03 to '07 National League.

Because there are no interactions like in the actual BaseRuns equation, one can apply these numbers to individual hitters. Simply multiply each of the coefficients by the hitter's counting stats, and presto, you have an estimate of that hitter's total offensive contributions to the team! We could also apply these numbers to teams, and get an estimate that is probably pretty close to what we had with the BaseRuns equation. However, because linear weights essentially only tell us the average value of each event, we will often over- or underestimate runs scored on teams depending on each team's unique runs environment (offense quality, park effects, etc). I'll talk more about using custom linear weights for teams in a future article.

It's worth nothing that some versions of linear weights report values of outs that are roughly 0.16 higher than those I've used here. In those equations, linear weights give a value that is +/- average. I find the above versions, which give you an absolute measure of offense, to be more useful as a starting point. However, these have a slight disadvantage in that they only give you an estimate of the number of runs created by getting runners on base and moving them over, but not in avoiding outs. They do account for the fact that an out advances runners to the tune of about 0.10 runs less than other plays. Yet they don't account for the fact that they also make innings end sooner, reducing the offensive potential of subsequent batters. To "get around" this, it's most appropriate to use outs, rather than plate appearances, when converting these numbers to a rate. More on that in the next piece.
Update (12/1/07): Here's a set of linear weights that are extracted based on MLB '03-'07 totals, as opposed to just those of the National league. They're not much different, really...:

LWTS = 0.508 1B + 0.835 2B + 1.152 3B + 1.439 HR + 0.328 NIBB + 0.217 SB - 0.318 CS + 0.357 HBP + 0.185 IBB + 0.129 SH - 0.318 GDP - 0.099 (AB - H - K + SF) - 0.113 K

Update (12/27/08): New equation updated for '04-'08 totals:

LWTS = 0.507 1B + .834 2B + 1.152 3B + 1.439 HR + 0.327 NIBB + .218 SB - 0.318 CS + 0.357 HBP + 0.184 IBB + 0.130 SH - 0.318 GDP - 0.099 (AB - H - K + SF) - 0.113 K
Coming up next... what baseline, if any, should we use when evaluating hitters? And how do positional adjustments factor into player evaluation? Case studies include '07 Reds hitters.