I've had this WAR spreadsheet sitting around for a couple weeks now, just waiting for the perfect time to present it along with detailed instructions, zero kinks, and some community collaboration.  Well, that plan has been scrapped.  We're going instead with a "here's the spreadsheet, we'll work everything else out later" plan (which actually might not be a bad idea.)  So...

Feel free to download this thing and tell me what you think.  Some brief instructions:

• Change the information in the green cells, but don't touch the rest.
• Everything is measured in wins.  If you prefer to do your scratch work in runs, just divide by ten when entering the info in the spreadsheet.
• If you're not up with using wOBA, try using (OBP*1.75 + SLG)/3, which puts a properly adjusted OPS number on the wOBA scale.  If you're really lazy, you can go with OPS/2.25
• BR is baserunning, which probably will include both SB/CS info and non-SB/CS info, unless you're looking at Fangraphs for wOBA (which already includes SB and CS in wOBA).  I'd suggest assigning most players 0 for non-SB/CS BR, with some at +/- .25 wins and the rare player at +/- .5 wins.  Baseball Prospectus has some solid baserunning numbers, just make sure to ignore the EQSBR piece if appropriate.
  
• Fld is fielding relative to position.  You could even add in OF throwing arms from THT, although 2008 data isn't available yet.
• Once you have IP set for the relievers, change their leverages so that the team averages out to 1.0.  Closers should stay at about 1.8 with one setup guy at 1.3 and a bunch of guys below 1.0 to average it out.
  
• Please, please, please don't just look at 2008 data.  This is a projection.  All past performance matters to varying degrees, not just last year.  Even better, use projections done by people smarter than both you and me (CHONE and PECOTA are my favorites).  Or get community input and take a wisdom-of-the-crowds approach.  Or see how much over their heads the players on your favorite team have to perform in order to reach 90 wins.
  
• WAR stands for wins above replacement.  An average player over a full season is worth 2.0 to 2.5 WAR.  FA$ is the per-season value of each player's projected production priced at $4.84MM per win above replacement.

Feel free to ask questions or make suggestions in the comments.  I'll fix things as we go.  Be warned, depending on what changes need to be made, you maybe need to enter all the information again in the new version.  Remember, this is a BETA.

Our humble blog has been nominated as one of the best sports blogs of 2008 by The 2008 Weblog Awards folks.  Click on the badge below to go vote, if that's your sort of thing.  (Update: you can vote once every 24 hours through January 13th.)

Of all the finalists, I've heard of less than half and read only a couple with any sort of regularity.  I should probably check more of them out.  Any faves from you readers?

• Awful Announcing
• BLOGF1
• Deadspin
• F1 Fanatic
• Kissing Suzy Kolber
• Hugging Harold Reynolds
• With Leather
• The LoHud Yankee Blog
• The Big Lead
• Soccer By Ives
• Rumors and Rants
• Metsgrrl
  
• Cozybeehive

If we limit the discussion to just baseball, I honestly don't believe we belong on the list.  My nominations would include The Hardball Times, Fangraphs, Baseball Musings, Baseball Prospectus, The Book Blog, and USSMariner.

The Hardball Times, especially, is taken for granted more than it should be.  There is some amazing stuff over there on a high-frequency basis, they led the charge on public defensive metrics (via RZR), they have some great stats (xFIP, OF arm ratings), and it's completely free (thanks, studes, et al).  In fact, let's turn this BtB-pimpage into a plea for our readers to buy the Hardball Times Annual.  Do it!

Position adjustments are a hot topic lately.  Everyone agrees that first base is easier to fill than shortstop, but opinions on positions closer on the defensive spectrum aren't as unanimous.  And the relative rankings aren't as important as the magnitude of the differences.

Of the folks who attempt to measure positional adjustments, there are two main schools of thought:

1. Base the adjustments on the difficulty of switching from one position to another defensively.
2. Base the adjustments on offensive averages by position

I don't want to re-hash the arguments for either approach, so I'll just mention that I favor defense-based adjustments.  Right now I just want to show how the methods compare in practice. 

Here are Tango's positional adjustments, based on relative defensive abilities.  A positive number represents how many runs more valuable a player at that position is per 700 PAs compared to the average position.

These numbers are based on a study of UZR ratings of players who played multiple positions and account for the fact that left-handed throwers are at a severe disadvantage at 2B, SS, and 3B.  A weighted average (counting DH as half a position) puts the average position adjustment at -1 run.  One would think the average should be zero, but more on that later.

Here are 2008 positional adjustments based on offense at each position per 700 PAs across MLB.  Thanks to Devil_Fingers for doing the work:

The weighted average of these adjustments is -3.8 runs.  That's right, they appear to be even worse than the defense-based numbers.  But the reason that they don't average up to zero is that not all positions receive the same number of at-bats.  First basemen and corner outfielders hit more often than short stops and catchers, because managers know they're better hitters.  (And pitching is included, which it probably shouldn't be.)

So, before comparing the defensive-adjustments with the offensive-adjustments, we need to put them on the same scale.  The correct scale is open for debate, but because I like symmetry, I'm going to average each set out to zero while keeping the absolute differences between positions the same:

Now that's pretty interesting.  The systems are within two runs of each other for every position except two.  In other words, while the theoretical discussion is intriguing, for practical purposes, both methods produce almost exactly the same results.  However, among similar positions, there is some wider variation:

• The difference between the two systems in LF and RF has the opposite sign, meaning they disagree that the two positions are equally valuable by about three runs.
• The offensive adjustments put a much larger gap between shortstops and second basemen than the defensive adjustments do.
• The defensive adjustments have the infielders as four runs more valuable total than the offensive adjustments.

The differences at third base and designated hitter between the two systems shouldn't be ignored, either.  Since they've been discussed thoroughly elsewhere, I'll simply summarize the arguments.

Designated hitters as a group don't hit nearly as well as you'd think.  While David Ortiz, Aubrey Huff, and Jim Thome carried the position in 2008, you've also got Jose Vidro and AAAA-level platoons masquerading as DHs.  Should we compare David Ortiz to those crappy hitters, who probably shouldn't be DHing?  I don't think so, because the players who actually do DH are only part of the pool of players who could DH.  Most players at first base and the corner outfield spots, positions that outhit DHs, would be upgrades at DH.  So we should compare DHs to the best hitters without much defensive skill, not just players who fit that description and who also happen to be chosen by their teams to DH.

Third baseman are great hitters in today's game.  And, via Tom Tango's research, their fielding abilities are on-par with second baseman.  Given both of those statements, one is led to conclude that today's group of third basemen is more productive than the group of second basemen.  But with an offensive-adjustment approach, third basemen are compared only to each other in both hitting and fielding, making them exactly as valuable as second basemen, by definition.

Finally, let me note that these offensive adjustments are based on 2008 numbers only.  I'm sure they would be moderately different using even 2007 data, let alone 1998 data or 1958 data.  It would be an interesting study to see how they change on a yearly basis, all normalized based on runs-per-win and to the same mean.

This post is required of all sabermetric web sites, comparing the marginal cost of each team win above replacement level.  I think you've all seen this before (thanks, Doug Pappas) so I'll skip right to the table.

Payroll data from the USA Today salary database.

Note that I've added two wins to all AL teams and subtracted two from all NL teams to account for the difference in league talent.  Although, depending on how you interpret the numbers, that's not necessarily a good choice.  Some thoughts:

• Surely sinking $30MM more (mostly payments to arbitration-eligible players) into an 84-win Marlins team would pay for itself after a 10-win increase and playoff birth, no?
• The average payroll was $89.5MM in 2008.  Without the Yankees and marlins, it was $87.7MM.
  
• The average cost of a marginal win was $2.4MM.  Keep that in mind when we congratulate teams who sign free agents for just under the average of $4.8MM per win.  Free agents are really only good deals relative to other free agents.
  
• Make fun of the Yankees' $200MM payroll all you like, but they were still more efficient than three other teams last year.

A further analysis that might be interesting would be to use Pythagorean record or third-order wins instead of actual wins.  Both would better hold teams accountable for acquiring talent, instead of simply winning games.  I'd be happy to share my spreadsheet with payroll information and win totals with anyone who wants to tackle those questions.

Lastly, this analysis isn't actually a great way to judge the effectiveness of general managers.  Spending the first $25MM over the leaugue-minimum (mostly money to first- and second-year arbitration players) is MUCH more efficient than the $100MMth dollar (mostly on free agents by that point).  What we really want to do is ask, "Given a certain payroll level, how many wins would we expect a team to have, and which teams outperformed that number?"  That requires a non-linear estimate of dollars-per-win, and another post.  Stay tuned.

(Short version: download the 2008 RAR data for starting pitchers.)

Saber-Friendly Blogging 101 is my attempt to give team-specific bloggers article ideas and the data necessary to write their own saber-friendly articles -- the articles I want to read, but can't find enough of.  In the first installment, I took a look at BABIP, and what it can tell you about which pitchers were possibly lucky or unlucky in 2008.  Michael Taylor of Tribe Report did a nice job running with the concept.  But we can go a step further than just looking at BABIP -- actually a few steps further.  By taking all the things we know are under a pitcher's control (and only those things), we can estimate what a pitcher's ERA should have been, all else being equal*.

One basic statistic that estimates true-skill ERA is FIP (Fielding Independent Pitching).  It was created by Tom Tango and uses a basic arithmetic formula using K's, BB's, and HRs: (HR*13+(BB-IBB+HBP)*3-K*2) / IP + 3.20.  It works quite well and is available at both The Hardball Times and Fangraphs.  The Hardball Times has another similar statistic called xFIP, which uses a modified home run total instead of actual home runs.  As the semi-accurate cliche goes, pitchers allow fly balls, but hitters turn those fly balls into home runs.  Therefore xFIP uses the league-average home run-per-flyball rate combined with each pitcher's fly ball rate to estimate how many home runs a pitcher "deserved" to give up.

But the most advanced pitching statistic available just popped up this summer over at StatCorner, although there has yet to be a study to show that it's actually better than FIP or xFIP or even ERA.  (Many people assume it is, though.)  It's called tRA and uses eight categories of outcomes that are strongly under pitcher control: Ks, BBs, HBPs, HRs, GB%, LD%, OF FB%, and IF FB%.  In one sentence, tRA credits pitchers for their ability to induce those eight events, without caring about the actual outcomes of the balls hit into play.  And everything's park-adjusted.  For a longer explanation, read this.  For a no-numbers explanation, try this.

Ok, so let's assume we have this special number, tRA, that best represents a pitcher's true demonstrated skill.  (I actually add two adjustments -- one to account for NL pitchers not facing a DH and another to put it on the ERA scale -- and call it tERA.)  What can we do with it?  Well, we can value the production of pitchers, of course.  If tERA is our measure of quality, we next need to measure quantity.  Inning pitched is the obviously solution, although I prefer Statcorner's expected innings pitched (xIP).  Why?  Because if a pitcher is unlucky and extra balls are falling in for hits, he's getting docked outs and being credited with fewer innings than he deserves.

To measure a pitcher's total production over replacement-level, we compare his tERA to the replacement-level tERA of 5.75, divide by nine to put the savings on a per-inning basis, and multiply by the number of expected innings he pitched.  For example, Cliff Lee had a 2.64 tERA and 222 xIP in 2008.  His RAR is (5.75 - 2.64) / 9 * 222 = 77.  That production compares favorably to every position player except Albert Pujols, by the way.

What's that?  You want all the relevant tERA, xIP, and RAR information for your favorite team's starters?  Well, here you go. The data tab separates out contributions to different teams (thus, CC Sabathia will be listed twice) and the player pivot table allows you to select just the pitchers on any one team.  The team pivot table shows the total value provided by each team's rotations.

Ideas for a team-specific article:

• Explain why tERA is a better measure of pitcher value than ERA (it removes fielding, ballpark effects, luck, etc.)  Also explain it's limitations (see below).
• Present the xIP, tERA and RAR info for all starters on the team.
• Present the same data for the projected 2009 rotation.  You can pro-rate the RAR numbers to different innings totals based on 2009 projections.  Or compute them yourself given whatever ERA and IP projections you want and the RAR formula.
• Take a look at how your team's rotation stacked up against the other teams in MLB or in their own league in 2008.
• Discuss any potential free agent signings or trade targets in terms of their 2008 value.  Compare their 2008 tERAs to their actual ERAs to see if they're coming off seasons that were underrated or overrated.

For fun, here's the majors' best rotation in 2008, the Arizona Diamondbacks'.  Remember, their park is one of the more hitter-friendly parks in the majors, and their fielders were below average by thirty runs according to UZR. 

* "All else being equal" is a decent, but imperfect, assumption.  For example, some pitchers allow groundballs that are easier to find than other pitchers.  And some pitchers better adapt to situations and can change their approach when needed.  The effect of these other things are generally small, but they can become significant at the etremes.  The next stage of research will probably be aimed at picking apart these issues.