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July 8, 2014

Baseball Therapy

What is a Fast Runner Worth?

by Russell A. Carleton


“He brings us that athletic dimension that we’ve been missing.”

“He’s can beat you in a bunch of different ways. That’s what we like about him.”

“He won’t clog up the bases.”

“He’s really fast.”

A confession: I reflexively recoil when I hear someone try to justify why someone is a good player by citing the fact that he’s fast. It’s not that I don’t get that speed on the basepaths has value to a team. In that weird part of the universe where everything else really is equal, I’d rather have a fast player than a slow one. The problem is that when I start hearing these arguments, it’s generally in the form of “Well, he’s not a very good hitter, but he’s fast. And his speed will cover for that fact that he’s not a good hitter.”

How much is speed worth anyway? Here at BP, we do have a baserunning metric (Base Running Runs, or BRR) that attempts to measure the additional runs that a player adds to a team by virtue of his performance on the basepaths above what we might expect from a league-average baserunner. For example, if a hitter goes from first to third on a single, he has “stolen” an extra base. Last year’s best baserunner by that metric was Matt Carpenter (seriously) checking in at 8.4 runs. (If you’re wondering how Carpenter, who’s no speed demon—seven career steals—won that title, it’s that he tended to be on base a lot when members of the Cardinals got hits, and he was adept at advancing.) If 8.4 runs is close to the upper limit, then why do people make such a big deal about speed? Sure, that’s nothing to sneeze at, but it’s also nothing to cough at.

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Related Content:  Batting Order,  Speed

7 comments have been left for this article. (Click to hide comments)

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Paul Clarke

It's weird that super-slow runners lead to more runs scored than average runners when batting 5th, 8th or 9th. Possible causes I can think of:

1) The average runners try to steal too much and lose value by getting caught, whereas the super-slow guys never try to steal.

2) It's just random noise and would disappear if you ran the simulation more times.

3) There's a bug in the simulation.

Any others?

Jul 08, 2014 08:32 AM
rating: 2
 
MGL

Yes, that last chart is completely implausible.

Jul 08, 2014 08:45 AM
rating: 0
 
MGL

Russell, I think that you are suffering from severe sample size problems. 10,000 games is not nearly enough, I don't think, to smooth those out the random fluctuations.

For example, if you put 9 league average players in a lineup, why would speed affect any spot more than another?

And slow players adding runs? Come on! You may even have a bug or bugs in your model.

Jul 08, 2014 08:39 AM
rating: 2
 
cmaczkow

Agree completely. Going by the final two (cloned player) charts, a super-slow runner in the 8 spot is worth 1.3 runs more than a super-fast runner!

I wonder if this hints at something about our assumptions given a player's speed. I mean, we can all agree that it's better to be fast than slow, right? (Right? I hope?) But I'm wondering if the simulation is somehow overstating the risks that faster players take on the basepaths. Perhaps the super-slow group is assumed to never, ever try for the extra base or take ANY other risks, while the super-fast group is assumed to take so many risks that the payoffs are totally negated?

Jul 08, 2014 13:20 PM
rating: 0
 
MGL

It entirely depends what Russell put in the MC simulation. Obviously one can program a fast runner being too risky but that is not what happens in reality (fast runners have +5 to +10 baserunning linear weights), so that would be a programming mistake.

So remember, we are not finding out anything about actual fast and slow base runners with this simulations, only what Russell's simulation does with them.

Now, he should be making sure that his fast and slow runners do what they do in reality, which is easy enough to find out. Simply look at all players with low and high speed scores and see how often they take the extra base, get thrown out, etc.

I can tell you from the work I have done with base running linear weights that fast runners do NOT get thrown out on the bases very often. In fact, no one gets thrown out very often. The fast runners get their edge from taking the extra base and don't give back much if anything from getting thrown out.

Jul 08, 2014 14:12 PM
rating: 0
 
TangoTiger

10,000 games is not enough. You need one million games to get the rounding error to under 1 run per 162 games.

Overall though, you can run a model here, add +.15 or subtract .15 runs to each of the values in the chart, and you will find a change of 0.4 runs per game as the impact of speed in terms of taking the extra base.

http://tangotiger.net/markov.html

Of course, speed comes into play in hitting and fielding and basestealing. When we talk about speed for hitting, it turns say a guy with a .300 wOBA into a .330 wOBA because of his speed, etc.

So, you have to be careful how you frame the discussion.

You may find this old article by Tom Tippett interesting:

http://207.56.97.150/articles/ichiro.htm

Jul 08, 2014 18:39 PM
rating: 1
 
MGL

The assumption here is that the faster the player, the more extra bases he takes, the more he advances on outs, and the fewer double plays he runs into on the bases, including getting thrown out on the bases.

So the model has to reflect that. It is fair to use that model to see how speed (fast or slow) is leveraged in the various slots in the order. It is not possible for a slow player to gain runs regardless of the slot in the order, as in the last chart, since the model is supposed to assume that faster = more value. So there must be something wrong with the model.

As well, as I already noted, if all the players in the order have the same batting profile, there should be little to no difference in the value of speed regardless of the batting slot since the order is a loop and not a line with endpoints. So, again, something is wrong with the model.

Jul 08, 2014 19:33 PM
rating: 0
 
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