Comments on Baseball Prospectus 2011

At some point it becomes bad sport to write the same thing about an annual book--if there’s a certain characteristic of the book that you find yourself dissatisfied with several years running, it might be a you problem. It’s one thing to decide that a certain book is not for you; it’s another to continue to believe that it will when it’s obvious that the writers have something else in mind.

Much of what I could say about the Baseball Prospectus annual for 2011 is the same as I said about in 2010, and 2009…and so I’ll try to avoid saying it again. By now, it’s clear that BP is what it is, and that can either be a great thing or a bad thing or a mostly good thing, depending on your perspective. My perspective is that it’s mostly a good thing--the redeeming qualities of the book outweigh its flaws fairly easily from my perspective.

I still felt compelled to jot down a few comments on the book this year because I might have been a little unfair in nitpicking a few things in the past. Now that there is a lot of new blood on board, it’s more apparent that some of the issues (like stats not matching up between the comments and the data directly above) are systematic, and probably endemic to producing a book of this kind. To put together a tome of that size in a few months is a massive undertaking, and there are thousands of moving parts, so expecting them all to be dialed in to the same setting is unrealistic.

The cover still has the infamous phrase that I will not repeat about PECOTA; this is obviously out of the hands of the writers. They do redeem the cover with a great caption under the little photo of Albert Pujols.

That being said, I do have one major bone to pick with the new, slimmed down statistical offerings. It’s great that they stopped doubling up on metrics that measure the same thing (in the past, there have been simultaneous displays of VORP and WARP, or EqA and MLVr), and with one glaring exception the new stat lines still manage to give you most of the key metrics. That glaring exception is the lack of any kind of component ERA (or RA, which I’d prefer anyway) figure for pitchers.

It’s not simply a matter of limiting your choice to vanilla, while having to leave chocolate, strawberry, and cookies and cream aside (after all, there are a lot of flavors of component ERA). There is none whatsoever. Instead, BP has listed Fair RA, which is a fine metric constructed by Colin Wyers and the primary input for pitcher WARP. But if the choice is between having Fair RA and a component ERA in a book that is largely aimed toward predicting performance in 2011, it’s not a choice at all. Sticking with metrics under the BP umbrella, peripheral ERA and SIERA would fit the bill.

Of course, if I could strike any category from the pitcher stat line to clear space, it wouldn’t be Fair RA--it would be W-L or saves or WHIP. But since a big target audience for the book is fantasy players, that is not an option. However, it leaves everyone (including fantasy players) without a backwards looking metric that gives us the best estimation of how the pitcher’s overall effectiveness in the past. I certainly hope that they will figure out a way to include Peripheral ERA or SIERA or something similar in the 2012 edition.

PECOTA is in good hands with Colin Wyers, and I’m sure there are still some bugs to be worked out, so please take this comment as more amusement than criticism: some of the PECOTA comps seem way off. I’m sure this happened in the past, and I didn’t bother to make note of it, but two players that really stood out to me were Gregor Blanco and Nick Franklin. Blanco’s top comps are Richie Ashburn, Kenny Lofton and Freddy Guzman. One of these things is not like the other, and two of them are nothing like Gregor Blanco (Lofton was still in the process of breaking out, but had already established himself as clearly better). The Franklin comps are more understandable since he’s a younger player with less of a track record, but it’s still an odd juxtaposition to see a player ranked as the #44 prospect in MLB while his top comps are identified as Adrian Beltre (ok), Hank Aaron and Willie Mays.

There are only a few team entries that have extensive sabermetric (as opposed to applied sabermetric) content. One of these is the Arizona entry, and sadly I have a bone to pick with it. The author accepts the mainstream view that Arizona’s copious strikeout totals in recent campaigns had doomed their offense. He (or she; I still maintain it would be more interesting to know which author is responsible for the team entry) asserts that “when the majority of the lineup falls prey to empty at-bats of this sort, highly volatile run-scoring can result.”

While there have been some studies done on the relationship between shape of offense and scoring distribution, I am personally unaware of any comprehensive or well-established enough to make a statement like that without the need for supporting evidence. The only statistic brought in to support that position is that Arizona scored three or more runs per inning as much as the NL average, but scored two or less more often.

That is a very odd and not particularly helpful way to break down innings, because it lumps scoreless innings in with one and two run innings. To be absurd for a moment, if an offense never scored three or more runs an inning, and scored 0-2 in 100% of their innings, but 40% of those were one run and 10% were two runs, they would average a healthy 5.4 runs per game. It is true that Arizona scored in a smaller proportion of their innings than did the average NL offense--25.9% of Arizona innings resulted in a run scored compared to 26.5% for the league as a whole. But Arizona was more likely to have a multi-run inning (12.4%) than the average NL team (12.2%).

Another odd thing about this perspective is that it makes the inning the unit by which scoring volatility is measured. It’s true that the best perspective from which to understand how runs are scored is the inning level, since the events that transpire in each inning is independent of those that occurred in previous innings in terms of scoring in runs (I hope it’s clear that I’m talking about baserunners and outs from one inning affecting each other, not lineups turning over and pitchers being removed and the like, but you never can tell) but from a win/loss perspective, it is the run distribution per game that is crucial. Admittedly, the two are very closely related, but any time you extend the time period over which such volatility is projected, its impact is reduced.

One crude but simple and reasonably sensible way to consider the win value of a team’s per game scoring distribution is a method that I call Game Offensive Winning Percentage (gOW%) and have published here for the last three years. It is based on a Bill James idea; instead of estimating an OW% from average runs scored per game, use the team’s actual distribution of runs scored. If in a given season teams that score one run win 11.8% of the time (as they did in 2010), then credit the offense with .118 wins for each game in which they score exactly one run. Repeat for all scoring levels and average and you have an alternative OW%.

There are of course flaws with this method--the unit of games doesn’t always represent the same things (i.e. there are not always 27 outs per game), the use of the actual W% by runs scored in any given season is subject to sample size fluctuations, there is no adjustment for park, etc.--yet it’s still reasonable to think that if a team’s run distribution was particularly unusual, it would manifest itself in a comparison of gOW% to standard OW% based on average runs per game (in this case, without a park adjustment so as to better match gOW%).

The Diamondbacks led the NL in strikeouts in 2009 and 2010 and were second in 2008. In 2007, they ranked eleventh (and made the playoffs, see!), so those three seasons are the relevant high strikeout seasons for the team. In 2008, Arizona’s gOW% was .485 while their OW% was .479--considering their run distribution rather than just their average suggests an additional win. In 2009, it was .484/.483--no difference. In 2010, the split was .492/.502, which is -1.6 wins. So for the three years considered together, the net total is -.5 wins.

Of course, this does not conclusively demonstrate that Arizona’s offense was as efficient as a typical offense with their scoring average, and it certainly doesn’t allow us to make any statements about the effect of high strikeout offenses generally. However, neither does anything offered or referenced in the BP essay, yet the author chose to make much stronger assertions than I would dare to here.

My comments on strikeouts should not be taken as a negative judgment of the book as a whole--my book “reviews”, such as they are, generally serve as an opportunity to discuss issues raised by the author rather than to offer a summary judgment on the book itself. By now, you already know whether BP is a book for you or not.