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.

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.

Johan in the A.L.

Everybody remembers the Santana to the Yankees/Red Sox saga last year, and how the Mets finally swooped in out of nowhere to get him. I, for one, was against the trade (from the Yankees perspective), and still am. I’ve always been against it partially because of my man-crush on Phil Hughes, and I’m still against it because of three numbers: 7.91, 2.42 and 91.2.
Those are Johan’s K/9, BB/9, and average fastball speed, respectively, from last season. To illustrate the point, here are those numbers from the last three seasons:

• 2006: 9.44 / 1.81 / 93.1
• 2007: 9.66 / 2.14 / 91.7
• 2008: 7.91 / 2.42 / 91.2

In case you didn’t notice, that’s not a good trend. His walks are up, and his strikeouts fell right along with his velocity. So it’s not like he’s taking something off his pitches in order to improve command. Couple that with a move to the weaker NL and it’s even worse. Why is it worse? Because when pitching in the NL, he gets the added bonus of facing the pitcher (or a weak pinch hitter), instead of a potential threat, every time through the lineup. Against non-pitchers, Santana struck out 176 batters in 215.2 innings, good for a 7.34 K/9. And that’s not even representative of what he’d do in the AL, either. With the twins, he was facing the DH ever time through (and generally stronger lineups), in the NL, some of the non-pitchers include generally weak pinch hitters and middling 8-spot hitters. For reference, that 7.34 K/9 is on par with guys like Scott Baker, John Danks, and Justin Verlander–all good pitchers, but none of them received any Cy Young votes this past season.