## Archive for the ‘**Player Value**’ Category

## Dollars Per Win

Today at FanGraphs, Dave Cameron wrote up a good explanation of the dollars per win system that MLB teams choose to operate under. Part of the analysis in effect shows why young players are so cost-effective, and in turn shows why teams like the Rays can compete with such a low payroll. Something that is often missed when looking at big free agent contracts is that just because a player is paid a billion dollars doesn’t mean that he’s overpaid, relative to the rest of baseball of course.

Now, I know there’s some sentiment that teams don’t pay for wins linearly, because a six win player is worth more than three two win players. While I agree with this in theory, major league teams just don’t operate this way. If you just look at the dollar per win costs for the multi-year contracts handed out to hitters last year, the cost per win was $4.3 million for guys with an average win value of 4.4 wins per player. Alex Rodriguez signed for about $3.8 million per win last year. Teams just don’t pay exponentially more for higher win value players than they do for average and below players. You could argue that they should (and I would probably agree), but they don’t. The dollar per win scale is linear.

So just because Sabathia and Santana are being paid boat loads of money doesn’t mean that they’re being paid more than their expected production because of their “marquee status.” In some cases, that may happen (Derek Jeter would be an example, maybe), but that’s more the exception than the rule.

## Replacement Level Pitching Part II

In part one of this, I talked about why we compare players versus the baseline of a replacement player instead of a bench player. Then, I showed what a sub-replacement level pitcher looks like in Kei Igawa, and gave an example of a replacement level pitcher in Sidney Ponson, who fits the definition perfectly. Now we’ll get on with the rest of it, talking about how to value pitchers in differing roles and situations, like Joba Chamberlain, Chien Ming Wang, and Andy Pettitte.

Andy Pettitte threw 204 innings with a 4.54 ERA this past season. Chien Ming Wang threw only 95 innings in an injury-shortened season, putting up a 4.07 ERA. Wang was better, but pitched in fewer innings, so who was more valuable to the Yankees? The same question can be asked about Joba Chamberlain and Mike Mussina. If you remember from part one, I said that replacement level for relievers is lower than it is for starters. So we also need to look at the time Joba spent in the bullpen, and also account for the fact that the 8th inning is more important than the first inning before saying how valuable he was. It might sound a little complicated, but I promise you it’s not, once it’s all spelled out in plain English. More after the jump…

## Replacement Level Pitching, in English

There’s been a lot of talk around the internet recently about replacement level players. This seems to have been sparked by FanGraphs adding a whole bunch of new features, exposing fans with a passing interest in stats to some of the more complex sabermetric ideas and concepts. I say FanGraphs is responsible because most of the conversation has been focused on hitting, which is expected because most of the new stats there have been hitting stats (and their accompanying articles/explanations).

So here today, I’m going to be talking about replacement level, and how it can be applied to pitching. To illustrate the concepts, I’ll be using some guys like Kei Igawa, Sidney Ponson, Joba Chamberlain, Chien Ming Wang, and Andy Pettitte. Each one of these pitchers represent a portion of the concept that may raise a question. So while Igawa might not serve a purpose on the field, he will in this article. *[Edit: This got kind of long, so I’m breaking it up into two parts. I’ll have something else up tomorrow, and then part two should be up on Friday]*

## Settling the Score

Today, in the comments section of this post at RAB, I got into a little bit of an argument with Ben over a potential Manny Ramirez acquisition. The comments by both of us are kind of scattered all over that thread, making it hard to follow. But the gist of the conversation was that Ben wants Manny Ramirez as the DH next season, which would put Hideki Matsui on the bench or on the trade block. I don’t want that to happen. Matsui can’t play the outfield–his knees are just too bad to either handle the position or to simply stay healthy over the long haul.

Ben’s plan is summed up in this comment, when he says, “The Yanks should go after Manny and deal with Matsui after the fact.” I responded by saying that doing that is incredibly shortsighted, considering that Matsui and his $13 million salary can’t just be cast off like it means nothing. Signing Manny to be the DH would mean that Matsui is on the bench used only as a pinch-hitter or occasional fill-in. He’s certainly not a defensive replacement, and can only play two positions–left and right field. So let’s see how this goes….

## CC Sabathia and his $161M deal

I’m lazy. Instead of trying to figure out all the dollars per win figures over the next 7 years, and figuring out how many innings CC will pitch, and how effective he’ll be… I’m just going to show the work of Tom Tango, and explain it all in simpler terms. Then at the end, I’ll discuss the opt-out.

Here’s his comment on the subject: Read the rest of this entry »

## What’s Nate McLouth Worth?

Why do I care what Nate McLouth is worth? I have no idea. Usually my ideas come from an interesting post or comment somebody made somewhere, but this one is completely out of left field. It’s almost 2 a.m. and I have a growing headache (which begs the question of why I’m writing this anyway), so that’s all the introduction you get today.

**Offense**

McLouth’s .276/.356/.497 line comes out to a .372 wOBA, which is worth 23 runs above average over his 687 plate appearances. wOBA measures overall offensive output in rate form. To get the equivalent runs above average, you take the difference between the individual and league wOBA and divide by 1.15. What is Nate projected to do next season? Both Bill James and Marcel have similar projections, both pegging him around 9 runs above average. Read the rest of this entry »

## Johan Santana, Part Deux

I’m not 100% sure that I feel comfortable writing this post, for two reasons. One, I’ve never used odds ratios before, so there could be some rule I’m violating without knowing. And two, I slept approximately zero hours last night doing a ~45 page group paper for my business management class. With that in mind, let’s see where this takes us…

First, I should introduce what an odds ratio actually is. It is defined by Wikipedia as “the ratio of the odds of an event occurring in one group to the odds of it occurring in another group, or to a sample-based estimate of that ratio.” In baseball terms, it means that we take the odds of an event happening for a pitcher, and compare that to the odds of the same event happening to the batter, and the formula spits out the expected outcome. Numbers must be converted into “odds ratios” before plugged in…don’t ask me why, but it seems to make sense. Here’s the formula, using on-base percentage:

- Translate OBP (or your rate of choice) into odds ratio form: (OBP/1-OBP) to get the odds ratio (OR)
(batter OR / lg OR) * (pitcher OR / lg OR) = (expected OR / lg OR)- Then reverse step 1 to get the expected outcome of the matchup.

Thanks to Pizza Cutter for the explanation on that one. So here’s how this relates to Johan Santana (3 paragraphs in). Peter Bendix, of Beyond the Box Score and FanGraphs fame, penned a piece for the latter about a week ago on the subject of Johan Santana. In it, he shared some of the same concerns that I did about the Mets’ ace. Peter said this about Johan’s LOB%: “His LOB% in 2008 was the highest of his career [at 82.6%]. Over the last three years, his LOB% has been 76.3%, 77.7% and 78.3%, respectively.” Generally, a sabermetrician would say that his LOB% is bound to regress towards the mean next season, and I would agree. But I decided to check out the veracity of that claim, using odds ratios in certain situations to see where he over- or under-performed the expected outcome. Read the rest of this entry »