Last week I examined ESPN’s mid-May fantasy baseball rankings in the context of hitting. Now I want to write a few things about pitching. As with hitting, the rules of the game make all the difference. Here, again, are three examples.

First, head-to-head is not rotisserie. ERA and WHIP are incredibly volatile on a week-to-week basis. Far more predictable are strikeouts, saves, and wins (see my Wins by Category analysis). Saves are a special category of their own (see below). But any fantasy manager can prioritize strikeouts. And any manager can examine the depth chart of run-scoring teams to find decent, if not great, pitchers that get some strikeouts and compete for wins. By this standard, Tim Hudson is wildly over-rated for head-to-head play. His prospective run support is good, but not great, and despite his sterling ERA and WHIP, he has only 27 Ks in 64.1 innings pitched. I’d rather gamble on Ian Kennedy.

Second, most leagues, rotisserie and head-to-head, use some kind of start or inning limits. In leagues with deeper rosters this often leaves room for quality non-closing relievers such as Daniel Bard or Luke Gregerson, neither of whom makes ESPN’s top 300. Both of these pitchers have more strikeouts than Tim Hudson in half the innings pitched. They help push down ERA and WHIP. Most importantly, these guys are generally available to fill out a 23- or 24-man roster. They cost little to nothing and in leagues that use a start limit, they add strikeouts, quality innings, and vulture wins and saves while subtracting no starts.

Finally, let’s consider closers. I believe that most analysts undervalue closers. In head-to-head, this problem is magnified.

The disdain for closers arises from the all-or-nothing nature of the save stat. If a high-priced closer gets injured or replaced you lose almost all of your investment. Even coupling a closer with his backup doesn’t necessarily protect you. The backup is simply not likely to have the talent or the job security of the original closer. (I know this well. I owned Eric Gagne and Yhency Brazoban in 2005.)

However, the flip side of this risk is high reward. The limited number of closers makes saves the single most predictable stat category in head-to-head. While a team of middling hitters can often take a few hitting categories from a superior hitting team, and a team of middling starting pitchers can easily make a run at the ERA and WHIP points,  the team with three or four closers will almost always take the saves point. If you go into a single elimination playoff game against an opponent with more closers, you are likely down a point before the match even starts.

High-risk goes with high-reward. The former fact should not obscure the latter.

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ESPN has published its mid-May fantasy baseball rankings. It is an average of the individual rankings of their various experts and so represents a kind of generic zeitgeist on who’s good and who’s not. What is missing is the context of the game being played.

After the top 50 players or so — the ones desired in any format in any matchup — the game being played makes all the difference. Here are three examples that focus on hitting. I’ll write about pitching in another post.

First, player strengths in head-to-head formats are different than in rotisserie formats. When stats zero out every week against a new opponent, batting average or OBP are far less important than runs, home runs, RBIs and steals (see my Wins by Category analysis). Hitters must have power or speed or both to make a significant difference in head-to-head.

Second, roster depth matters. If a league offers deep enough rosters for a manager to platoon, David Ortiz, for one, is much more attractive. Even as his terrible April has turned into a very good May, he is most potent and most likely to get at bats against right handed pitchers. A platoon of Ortiz and any comparable hitter will give you just as much value as a single slugger ranked twice as high — one, it is important to remember, that you would not be platooning.

Finally, the rankings are based on projected player performance for the rest of the season. This is the nature of the exercise. The results tell you who to own, not who to play. But most leagues allow a substantial number of transactions, making who to play right now a decisive question. If you can play a great player for a month, then get an adequate substitute off the waiver wire, you need to do it.

This is the case among catchers. There are a handful of great hitting catchers and all the rest are interchangeable. So it is bizarre to see Mike Napoli ranked so low. Since starting catcher Jeff Mathis went on the DL, Napoli has not only hit extremely well, with a 1.106 OPS in May, but has played almost every day. Angels Manager Mike Sciosa went from hating the guy to running him into the ground, and Napoli has done nothing but hit. The rub against Napoli is that he will get bumped into a backup roll again with Mathis’ return. This may be true. But it shouldn’t obfuscate the fact that at this given moment Napoli is one of the top fantasy catchers in the game. He’s up there with Joe Mauer. In head-to-head leagues where batting average or OBP is less important (see above) he is, quite simply, the best.

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In the game of making predictions, fantasy gurus are increasingly calling upon peripheral statistics to support their claims.

Peripheral statistics generally come from analysis of how a ball is put in play (or not) in relation to the outcome of the play. Perhaps the best known is batting average on balls in play, or BABIP, which measures “the number of batted balls that safely fall in for a hit (not including home runs).” BABIP averages are compiled for ground balls, line drives, and any number of other splits.

BABIP is most often invoked to estimate luck. A batter hitting lots of line drives and not getting hits is supposedly due for a positive correction. Likewise a pitcher getting ground balls and not getting outs.

Thus, Tristan Crowcoft recently used two BABIP averages to tell owners not to give up on Kevin Slowey:

Kevin Slowey’s BABIP numbers very much support his case as a big-time buy-low candidate. Both his .357 BABIP on ground balls and .833 BABIP on line drives were noticeably higher than the league averages…

Going many times better, A. J. Mass processes so many peripheral stats in his recent Hit Parade column that he can’t even reveal what they are:

Next, we take the velocity [read the column] and tweak it according to a complex formula based on each hitter’s skill set, determined by a combination of stats that measure patience at the plate, ability to put the ball in play, power potential and speed.

If he told you the formula he’d have to kill you.

How much do I care about BABIP? Not much.

First, these analyses often reveal the obvious. I’m keeping Kevin Slowey because he pitched well last year and because he has sterling minor league numbers, not because he has good peripherals.

Second, these analyses rely upon the premise that the player will continue to produce (or give up) the same types of hits. Your slumping star’s line-drive BABIP may return to the league average over time, but that means nothing if your slumping star hits fewer line drives. The correction you expect won’t have any effect. The market can stay irrational longer than you can stay solvent.

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Baseball Crank has begun posting his established win share levels reports for 2009.

In brief, the win share levels use statistical algorithms to analyze each MLB player’s share of his team’s wins:

Win Shares seek to measure a player’s total contribution to a team’s bottom line win/loss record, in the case of non-pitchers through combining batting and fielding contributions. The system makes the assumptions that a team’s total wins can be rationally connected to its runs scored and allowed. Thus, each player is assigned a share of the team’s total wins based on his contribution to scoring and preventing runs.

Baseball Crank produces two numbers. The raw EWSL number for each player identifies a specific rating for data from the the last three years. Raw EWSL reflects past results. The adjusted EWSL number attempts to project the future. This means quantifying an appropriate number for rookies with a starting job, and adjusting for age.

The age adjustments provides one of the most basic parameters for predicting player performance. Before the age of 28 or 29, players tend to improve each year. After 30, they tend to decline. Sometimes dramatically. Individual projections, here as with any expert evaluation, are not that meaningful. But you still have to deal with the odds.

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Every year, Baseball Crank produces his established win shares. This evaluation of player capability as a factor of team win/loss record is not fantasy-focused. Great players on bad teams don’t evaluate well.

But it is still a good resource for strategic thinking. First, good teams make good players better. Hitters are more likely to hit with runners on base — and score once they make it on base. Pitchers are more likely to get wins if supported by good hitters and a good bullpen. So thinking of players in the context of the team provides a way to assess off-season moves and rookie call-ups. Second, Baseball Crank is a smart guy who has plenty to say about individual players.

Consider some of the comments from this year’s ESWL (just scroll down):

• Jeff Francis may be harder to replace than most teams’ aces, but he still will never contribute as much positively to the Rockies as a guy in another park who can throw 20 more innings and exert more influence on the game.
• Given that [Yadier] Molina is only 25, it may turn out that he will hit some after all; his brother Bengie didn’t hit until he was 28.
• [Derek] Jeter … seems on the path of slow, gradual decline, with age starting to eat away around the corners of several of his assets, breaking down his weak defense and stripping some of his speed and power. I expect Jeter to continue to be productive into his late 30s, like similar hitters like Paul Molitor and Pete Rose; just a little less like the Jeter of old.
• I can’t add much to the Joba [Chamberlain] saga except to note the obvious that his future path will probably be determined less by his own performance than by Mussina’s and by Mariano’s health.
• Dontrelle Willis has escaped the Marlins’ woeful defense (well, except for Cabrera) that contributed to a terrible .682 DER last year, but defense alone didn’t drive up Willis’ rates of homers, walks and line drives allowed (his HR rate nearly tripled since 2005), nor the decline in his K rate. This season will tell us a lot about whether Willis is healthy or not — if he is, he seems a good turnaround candidate, but the markers pointing to latent arm injuries have been flashing red for a while.

This is a resource most useful for draft preparation, but it is also a good filter for making mid-season adjustments.

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A good fantasy resource you never knew about is Yahoo’s Sabermetrics:

For batters (2007).

For pitchers (2007).

The tables aren’t sortable (A.J. Pierzynski is not the best of anything) but they are easy enough to copy off the screen and paste into Excel.

If you don’t have Excel, get it. Or an equivalent. It’s what Bill James uses.

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The premise of Bill Simmons most recent column is this: Bill Simmons sends an email 25 years into the past to the eighth grade Bill Simmons. This reeks of deadline floundering. I almost didn’t click through. But Simmons actually has a thesis. For sports fans, life is good, as evidenced by the things we complain about. For example:

ESPN’s Web site doesn’t put all the baseball and basketball box scores on one page like a newspaper does, so every night, you have to click on each individual game to see the scores. You will complain about this….

Sometimes on Sundays, when you’re checking your up-to-the-minute fantasy scores on whatever Web site you’re using, so many people are checking at the same time that the site will load slowly. You will complain about this….

I once participated in a fantasy baseball league in which the commissioner heroically typed stats into a spreadsheet from the pages of USA Today every week. We almost had to throw out the first three weeks of the season because our guy used an AVERAGE function to calculate staff ERA instead of entering individual innings pitched and earned runs.

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