Send ‘Em Home, Part 2

141, 141, 135.

Those were the top three RBI totals from 2009, courtesy of Prince Fielder, Ryan Howard, and Albert Pujols. The top three for the ’09 Minnesota Twins is much less impressive (103, 100, 96, from Jason Kubel, Justin Morneau, and Joe Mauer), but that doesn’t mean that we don’t care about them. Morneau compiled his 100 from a strong first half and a rather weak second half, complete with missing the last three weeks of the season. Joe Mauer missed all of April, yet he nearly broke the 100 mark. Kubel played the whole season, but just barely had more RBI than the other two. Surely Morneau and Mauer were more efficient in driving in runners…right?

We can certainly argue over what defines a player as being “efficient” when advancing or driving in runners. For my research, I only focused on the act of advancing them, regardless of the means necessary to accomplish this. My friend Steven, as he mentioned a few posts back, prefers to think of efficiency as being able to move runners up without sacrificing outs. Honestly, he’s correct. A more valuable tool for measuring the worth of a player to his team would have been by looking at how a player put his team in a better position to win (look at the scoring expectancy matrix from my previous post for more information).

However, I deliberately ignored this. Why? Because I figured that if I didn’t, I’d know that someone like Nick Punto or Matt Tolbert would score poorly. They’re known for “doing the little things,” like sacrificing themselves to move a runner up a base. Was this the best thing for me to do? No. Yet, it is mistakes like this that causes a person to make adjustments for the future if he or she wants to continue doing research for a particular topic. Don’t worry Steven, the next time I do this, it’ll be much better.

Similarly, this was why I looked at the percentage of runners driven in by a hitter. Going back to what I said above, Jason Kubel appears to have had the most plate appearances out of the top three RBI hitters for the Twins last year. Upon a closer look, this turns out to be untrue, for Kubel actually finished with a fewer number of plate appearances as Mauer and Morneau. Mauer had 606, Morneau 590, and Kubel 578. Perhaps this means that Kubel was actually the most efficient in scoring runners last year.

I feel like I’ve teased you enough. Finally, I present to you the percentage of runners in scoring position driven in, along with the percentage of all runners driven in.

Surprise, surprise. As I mentioned before, your leader in this category is not Mauer, Morneau, or Kubel…it’s Delmon Young. I’m sure you’d prefer if I sorted these in order, so here you go, with each value reduced to 3 decimal places.

1 Delmon Young 0.356 1 Joe Mauer 0.192
2 Joe Mauer 0.339 2 Delmon Young 0.183
3 Jason Kubel 0.335 3 Jason Kubel 0.183
4 Justin Morneau 0.304 4 Justin Morneau 0.171
5 Denard Span 0.276 5 Orlando Cabrera 0.169
6 Carlos Gomez 0.272 6 Denard Span 0.152
7 Matt Tolbert 0.254 7 Michael Cuddyer 0.137
8 Brendan Harris 0.252 8 Joe Crede 0.135
9 Orlando Cabrera 0.250 9 Carlos Gomez 0.129
10 Michael Cuddyer 0.249 10 Brendan Harris 0.125
11 Joe Crede 0.240 11 Nick Punto 0.125
12 Nick Punto 0.232 12 Matt Tolbert 0.116
13 Jose Morales 0.184 13 Brian Buscher 0.099
14 Alexi Casilla 0.183 14 Alexi Casilla 0.091
15 Brian Buscher 0.174 15 Jose Morales 0.088
16 Mike Redmond 0.111 16 Mike Redmond 0.071

Delmon Young is a very odd hitter. He hits well with runners in scoring position, but not when runners are on base. He hits best in high leverage situations, but not late in games. I don’t quite understand it… As for other players, I’m a bit surprised to see Michael Cuddyer in the middle of the pack. It seems like the players are ranked in order of “ability” (batting average, OPS, whatever you like), excluding Cuddyer. Therefore, I ran a bunch of correlation tests to see what correlated best to these numbers.

In case you were curious, WPA stands for win probability added. WAR is wins over replacement (this is explained in two 7-part series, so you may want to save those for when you have plenty of time to spend). I’m sure you know that anything that has “w/ RISP” is “with runners in scoring position,” while anything that has “w/ RO” is “with runners on.” The bolded numbers are the best correlation between two statistics, while the boxed numbers are the best three correlations for my two statistics. I feel that it’s understandable that RBI and some variation of slugging percentage correlate best with these two statistics, since obviously you must have driven in runners in order to have a good RBI total, and a high SLG means you had more total bases, leading to more runners being moved around the bases and driven in (unless all players’ SLG were terrible with RISP or runners on base). However, since the correlations are only fairly strong (a value of 1 would be a perfect positive correlation and it’s preferred to be above .9), either there are statistics that I didn’t look at that would have yielded better correlations, or there isn’t anything available that would correlate well with this data. I’m leaning more towards the latter here.

I’m thinking of taking Steven’s advice to look at how these players would have done in situations that did not lessen the team’s chance of scoring a run. I’m sure that if I did that, then the correlation with my new data and a player’s WPA would correlate much better. Maybe some other stuff will have better correlations also when I adjust my data. Within the next day or two, look for my post(s) on the hitters’ ability to move runners up a base and how many bases they were able to move then over.


10 Responses to “Send ‘Em Home, Part 2”

  1. TwinsFoghorn Says:

    I commented back on your first post before reading this one, so going to post here as well.

    First, I have to disagree with Steven on sacrificing always being a bad play. The point is not that getting a hit is always better than sacrificing, of course that’s true. The point is that the sacrifice is always better then just getting an out.

    Two examples (using Steven’s table) using simplest outcomes:

    1) Runner on first with 1 out (.573)
    – Hit that advances the runner to 2nd (.971)
    – Sacrifice (.344)
    – Out (.251)

    2) Runners on first and third with one out (1.243)
    – Hit that advances both runners one base (.971, run scores)
    – Sacrifice, both runners advance one base (.344, run scores)
    – Sacrifice, man on third can’t advance (.634, no run scores)
    – Out (.538, no run scores)

    In fact, I could argue that the in the second outcome of the second example you’ve actually improved your situation. This is because you’ve turned run *potential* into an actual run! Because you scored a run I think the before/after looks more like this:

    1.243 < (1 + .344)

    This could be turned into:

    Situational Run Expectancy Delta = (Runs Scored + Ending Run Expectancy) – Starting Run Expectancy

    This could then be used to make three derivative stats:
    – sum the deltas
    – average the deltas
    – tabulate the deltas to get the percentage of the time the batter improves the situation (roughly what I think you did)

    Second, I agree on the Cuddyer thing. It appears that his home runs are being swamped by his high SO and AB numbers.

  2. TwinsFoghorn Says:

    After thinking about this some more, it occurs to me that I’m doing what Steven did, not what I was saying.

    To fix this you’d need to do something like “Situational Run Expectancy Over Out” where you use the worst case scenario (batter is called out without advancing any runners):

    Situational Run Expectancy Over Out = (Runs Scored + Ending Run Expectancy) – Starting Run Expectancy) – (Run Expectancy After Out – Starting Run Expectancy)

    Which could then be reduced to:

    Situational Run Expectancy Over Out = (Runs Scored + Ending Run Expectancy) – Run Expectancy After Out

    Using my examples above, would get you the following numbers using the two situations (SRED/SREOO):

    1) Runner on first with 1 out
    – Hit that advances the runner to 2nd (.398/.720)
    – Sacrifice (-.229/.093)
    – Out (-.322/.000)

    2) Runners on first and third with one out
    – Hit that advances both runners one base (.728/1.433)
    – Sacrifice, both runners advance one base (.101/.806)
    – Sacrifice, man on third can’t advance (-.609/.096)
    – Out (-.705/.000)

  3. TwinsFoghorn Says:

    Also, it should be noted that in both these methods the batter might get punished if a runner gets out as well:

    1) Runner on first with 1 out
    – Fielder’s Choice (-.322/.000)
    – Double play (-.573/-.251)

    2) Runners on first and third with one out
    – Fielder’s Choice, thrown out at home, runner to second (-.777/.072)
    – Double play (-1.243/-.538)

  4. Andrew Says:

    I’m a math major and even all of this is making my head spin!

    Maybe that’s just because I’m sitting in a computer lab getting ready for a class. I’m sure when I have some free time, I could look over that. It does sound like it would be interesting and useful to compute, and it might appease Steven, lol.

  5. Steven Says:

    I’m a little confused by all the math stuff, but are you saying that sacrificing an out to advance a runner is a good thing? Because it most certainly is not.

  6. TwinsFoghorn Says:

    No Steven, a sacrifice will *usually* be better than getting an out with nothing to show for it. I’m not saying that batters should be trying for sacrifices over hits. Everyone agrees that getting a hit is always better than sacrificing.

    But saying that sacrificing is always bad just doesn’t hold up using the table you linked to.

  7. TwinsFoghorn Says:

    Steven, just to be a little more clear, I’ll walk through an example where a sacrifice is potentially a “good thing”. Since you linked the table, I have to assume that by “good thing” you would accept the definition of the Run Expectancy to be higher after the play than before it.

    So, back to one of my original examples:

    It’s the bottom of the ninth, with one out. Casilla on third, Tolbert on first, Nick Punto at the plate. Gardy calls a suicide squeeze. As of this moment the Run Expectancy, from your table, is 1.243.

    Now a couple things can happen, but since the squeeze is assumed to be a sacrifice play, lets stick to that first. Punto being Punto, he lays a bunt down the first base line runs his little heart out and of course make a flying head first slide into first base. The pitcher is on top of it, but its far enough up the line his only play is a flip to first base, just catching Nicky. Because both runners were going, they both advance and Casilla scores.

    So what is our Run Expectancy now? Well it’s now 2 outs with a man on second, so according to your table it’s 0.344. Now, it sounds to me like you’re saying “See, the Run Expectancy just dropped!” But I’d disagree and say, “Yep, but a run just scored. We just turned an *expected* run into a real one. Give Nicky some credit!” Since the “expected” run turned into a “real” run, I’d suggest adding the run back in. Adding the run back in would give this situation a value of 1.344. Since 1.344 is more than 1.243, I’d argue that the Twins situation “improved”.

    So then you’d respond, “Yeah but Punto should have tried for a hit!” OK, fine. Same situation, but this time Nicky hits a bloop single. Both runners advance, Casilla scores. After the play, we have runners and first and second and still only one out, so the Run Expectancy is 0.971.

    Wait, what? Using the rules from above, 0.971 is less than 1.243 so the situation got *worse* by Nicky getting a hit? That doesn’t make sense! Let’s add that run back in to show that we turned an “expected” run into a “real” one again. Now, we get 1.971 which better represents what actually happened.

    Further, since 1.971 is more than 1.344, I’d agree it *is* better to get a hit than sacrifice. But here’s the rub, Nick has .228 batting average. So, 77% of the time he’s going to get an out!

    So now let’s see what happens if Nick grounds out in a way that the runners can’t advance. We still have runners on first and third, but now we have two outs, so the Run Expectancy plummets to 0.538!

    Sure, 1.971 is more than 1.344, but 1.344 is also quite a bit higher than 0.538.

    Gardy’s job is to increase the chance of runs scoring. Unless something goes catastrophically bad, running the squeeze isn’t going to be any worse than if Nicky swings away and can’t advance the runners. But the squeeze also has a chance of catastrophic success (Nicky beats the throw, which would look just like a hit).

    The fact is most of the time managers *do* swing away. About the only time they use the squeeze is in really close situations. When they swing away they have a chance of bigger rewards, but also bigger risks. I think we can argue that a sac bunt is a more controlled situation than a swing away. We can look it up, but I bet you get more double plays swinging away than on a sac bunt.

    So in the end, my answer is still “it depends”, but that’s a lot different than “never”.

    • Andrew Says:

      Ok, this is making sense to me now. However, (I’m sure Steven could say the same) the table clearly says that it’s the “expected” number of runs to score. Once that run scores, it’s no longer “expected,” so it can be ignored. I do see what you’re doing though, it’s basically a runs scored + runs expected sort of thing.

  8. TwinsFoghorn Says:

    And for Andrew’s sake, note that the runs aren’t carried on to later batters. In the example above, Punto “after” number is 1.344, but Span (the next batter) has a “before” number of 0.344.

  9. Research Revisions, Pt. 1 « Off The Mark Says:

    […] Home, Part 1: An introduction to my research, with an explanation of assumptions that I made. 2. Send ‘Em Home, Part 2: The results for Twins hitters and their success rates of driving in runners in scoring position […]

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