Comments on Bill James Gold Mine 2010, pt. 1

I quite enjoyed the third edition of the Bill James Gold Mine, even though I didn't get around to reading it until a few months after it was published. It jogged some thoughts, which lead to this post, which is not fully based on James' essays but on the semi-related paths they sent my mind down. To me, that is one of the tests of a really good sabermetric work--does it get you thinking, even if not about the exact topics covered? James' book passed that test for me.

However, I do think that the book would be stronger if it contained more of James' essays and less "statistical nuggets". The nuggets were of less interest to me, and seemed to be present in lesser quantity than they were in the first two editions of the book. The reverse was true for the essays, and those are what compel me to buy the book. Not being a subscriber to Bill James Online, I'm not positive about this, but I believe that James writes a number of additional essays in each year that are not included in the book.

If that is indeed the case, I believe that they'd be much better off to collect all of Bill's essays in the Gold Mine, and leave the nuggets for the individual to drudge up themselves online. Not only does the website lend itself more to the statistics (the data there is much more extensive than what can be printed in the book even if the book were the size of one of the old Great American Baseball Stat Books) and the essays to the printed page, but if there are any folks out there who still refuse to use the Internet and are interested in James, I'd think they'd be more enticed by the essays. A book of just the essays, with some other filler of some sort, would have a character not unlike that of the 1990-1992 Baseball Books, which I liked very much.

Of course, since it appears that the book is not even being published in 2011, those suggestions are for naught.

I have three subjects to touch on, two of which could be considered critiques and one of which is just a good old-fashioned tangent. This post went a lot longer than I originally intended so it's been broken up into two portions:

1. Starting pitcher rankings

The longest essay in the book deals with a system to rate starting pitchers based on where they place among other starters in each of their league seasons. James first ranks pitchers by Season Score (*), and then assigns points based on the pitcher's standing in the league. Each league season has 5.5 points per team available. In a fourteen team league, the top ranked pitcher gets 12 points, the #2 ranked pitcher gets 11 points, and so on down to the #11 pitcher who gets 2 points. There are also three-point bonuses, up to nine points per season, available for truly historic seasons. The resulting metric is called Strong Season points.

(*) James does not give the formula for Season Score in the article, but explains that is based on W, L, IP, ERA, K, W, and SV. "The point of the system is to evaluate a pitcher's record without context"..."This was a way of trying to say 'How good are the numbers themselves?', rather than 'How good was the pitcher who compiled these numbers?'".

Personally, I'm not sure that I have a whole lot of interest in rankings of pitchers based on a method that deliberately ignores context (and James certainly does not deny the importance of context). Setting my objection aside, though, it seems to me as if the Season Score is yet another result of a process that James has repeated over the course of his career: the re-invention of Approximate Value. Of all of his methods, my impression is that there is none that James personally likes more than AV. Even Win Shares is in some respects a return to AV--while it attempts to adjust for everything, it still expresses the result in an integer. The scale is higher than that of AV (a 20 AV would be an extraordinary season, while 20 WS is good but ordinary).

And so after attempting to adjust for everything, it seems James still had a void in his own toolkit, and so he filled it with the Season Score.

Digression aside, James found that a career total of 43 strong season points marks a fairly clear line for the Hall of Fame in retrospect. Only five pitchers retired for a significant length of time have more than 44 points and are not in the Hall--Vida Blue, Bert Blyleven, Ron Guidry, Carl Mays and Billy Pierce. James says that Blyleven and Guidry (60 points) are the only two pitchers that were far above 43 yet are excluded from Cooperstown. (Blyleven has been elected since James wrote the book and I wrote this post, obviously).

Since Guidry's is the most surprising result of James' survey, I'll take a closer look at him. I do not intend the discussion about Guidry to be a commentary on his Hall worthiness or even his value, but rather as a means of discussing the issue I have with the strong season method. It is important to note that James does not in any claim that the strong season method must be used in ranking pitchers, that it is better than any methods X, Y, and Z, or any such thing. James does not argue that Guidry should be in the Hall of Fame because of his showing in the system.

Guidry earned points for six seasons in James' analysis--1977-79, 1982-83, and 1985. Suppose we apply James' method, but use a different metric--a simple Runs Above Replacement, figured using total runs allowed and adjusted for park. How many points would Guidry earn under such a system?

* James ranked Guidry #6 in the AL in 1977, which is worth seven points. I have him #7, worth six points.

* James and I both have Guidry #1 in 1978 with an extraordinary season for 12 points (James awards the 9 point bonus, and I'll do so as well to keep things comparable). Guidry turned in 101 RAR, seventeen more than the next closest pitcher and nine more than any other AL pitcher in any of these six years.

* James had Guidry #3 in 1979 for ten points. I have him second, for eleven points.

* James ranks Guidry #11 in 1982 for two points. I have him all the way down at #26. His RA was 4.22 in a league in which 4.5 runs were scored per game, and he pitched in a moderate pitchers' park (.97 PF). At 34 RAR, he is eleven runs behind the eleventh-place pitcher (Geoff Zahn, 45). Presumably Season Score gives Guidry a boost because of his 14-8 record, one of the most impressive in the league (seventh in the league in Win Points).

* James ranks Guidry #4 in 1983 for nine points; I have him #6 for seven points.

* James ranks Guidry #2 in 1985 for eleven points; I have him #11 for two points. This is another season in which Guidry's W-L record seems to give him a huge season score boost (22-6).

Add it all up, and I have Guidry at 47 points--suddenly not that far above the Hall of Fame line James observed. I followed his scoring method exactly, but the results changed significantly simply by changing ranking methods.

More interesting, IMO, is how the use of in-season rank elevates the importance of very small performance differences. In 1979, Guidry ranked second in RAR at 71. However, Tommy John (71) and Jerry Koosman (70) were right behind him. Given that Guidry relied much less on his fielders, I strongly support the notion that he had a better season than the other lefties. Still, negligible differences in actual performance are given much greater impact when one uses a points system like James'.

Another example is 1985, in which Guidry ranks eleventh on my list at 61. Jimmy Key ranks sixth at 62--there are six pitchers within two RAR of each other. Guidry could very easily rank sixth in this season, which would be worth an additional five points. That would vault him from 47 points to 52 points, and give him a great deal more clearance over the HOF line.

This is not to say that James' ranking system is without its strong points with respect to its aims--it values peak performance and it sets an equal total value relative to the size of the league, which depending on one's perspective might be very good properties. My contention is that such a system is very sensitive to small changes in statistics, ones that would have no impact on a career-based evaluation. If Guidry had been evaluated at 62 RAR and thus sixth in 1985, the extra run saved would have zero impact on your evaluation of his career RAR total--and rightfully so. Allowing one run to exert a significant difference in a player's rank on an all-time list strikes me as utterly illogical and unsatisfactory.

You may object and say that I am using RAR rather than Season Score, and that Season Score is not subject to minute differences in performance having a large effect on rank order as is the case for RAR. While it is true that RAR and Season Score are very different methods, and that their application to Guidry might be very different as well, any metric is going to be subject to the same concerns when making a rank order over one season. There is always the potential that a very small margin could be the difference between a batting title and third place, between fifth in the league on a list and out of the top ten. That is true for any metric you want to pick, from BA to home runs to ERA to Season Score to RAR.

Into the Great Wide Open

For as long as I have been writing this blog, I have done an annual preview of the OSU baseball team. I have never before been at such a loss for words or offered so little insight about the team that will appear on the field in the upcoming season. While I have never possessed nor claimed any sort of insider status, any sort of knowledge about the thinking of the coaching staff and the player’s performance in off-season drills and the like, I’ve always felt that I had a pretty good handle on what to expect the lineup to look like. The tiny scraps of information available through the media about the team, coupled with my knowledge of the personnel from the previous season and years of observation of Bob Todd’s decision-making made it a relatively simple guessing game.

The 2011 Ohio State team is anything but a simple guessing game. There has been massive roster turnover, with the team’s two best players (ace Alex Wimmers and catcher Dan Burkhart) now playing professionally, and, worst of all from a prognostication standpoint, the fact that Coach Todd has retired, and Greg Beals is now in charge of the program. Beals had moderate success at Ball State, but his Ohio roots and purported recruiting skill landed him what is likely the most attractive coaching position in Midwest baseball. He has filled out his staff with Chris Holick, his pitching coach from Ball State (and former OSU pitcher) Mike Stafford, and volunteer coach Josh Newman (another former Buckeye hurler and briefly a major leaguer with the Rockies and Royals).

Holick returns to coaching after spending two years in private industry that came on the heels of a seven-year assistant coaching career (Kent State, Arizona State, and Florida International). Stafford served as pitching coach under Beals at Ball State since 2003; prior to that he had been a bullpen catcher for the Columbus Clippers and pitched four years in the minors after ending his OSU closing career in 1998. Newman is entering his first season as a collegiate coach.

The talent they have to work with does not inspire a great deal of confidence for 2011, as there is very little in the way of proven performers. My fears about this were enhanced when Beals, speaking on the radio halftime show of an OSU football game this fall, answered a question about expectations for the season with vapid generalities about “playing hard”, “playing the game the right way”, “learning how to win”…the sort of talk you hear from a coach that knows he’s building for the future. At a recent meet the team event, Beals said that due to new NCAA rules on bats “It's going to be a faster paced game, but a safer game with less runs scored," Beals said. "Our game is going to become a game of the finer skills - defense...throwing strikes...aggressive on the base paths - and we want to be ahead of this curve." Uh-oh.

Even if the entire roster from 2010 was back, it would be difficult to get a good read on the team’s prospects. 2009 was a great season for the Buckeyes, with a Big Ten regular season title and a second-place regional finish, but 2010 was a disaster despite high expectations. OSU was in first place (although by a very slim margin in a very tight conference race) in late April when Wimmers went down with a hamstring injury. The few starts he missed might have been enough to mark the difference between finishing near the top of the conference and failing to finish in the top six to qualify for the Big Ten Tournament--the first time that fate had befallen an OSU club since 1996. Ohio’s 11-13 conference record was the first and only sub-.500 record in Big Ten play in Coach Todd’s twenty-two year career.

Usually I write my preview and lineup expectation a month or so before the season starts; this time I’m doing it less than a week prior to Opening Day. As such, the ensuing discussion is based largely on the preview posted at the official athletic website; I’ve had to rely on such accounts to get any sort of feeling for how the lineup would shake out

Burkhart’s successor behind the plate will be Greg Solomon, a sophomore-eligible transfer from Paradise Valley Community College in Arizona. Solomon missed most of last season with a knee injury and did not hit particularly well in his 2009 freshman season, so this position is a huge question mark (you’ll note questions as a recurring theme). Beals’ comments on him praise his defensive skill with little to say about his bat. Burkhart was such an ironman for the past three seasons that none of the other catchers on the roster have any experience. Redshirt freshman Steel Russell, son of former Pittsburgh skipper John, would seem to be the logical choice as backup. True freshman Josh Bokor, junior-eligible JUCO transfer Brad Hutton, and his brother Blake, a true freshman, could also be options. The Hutton brothers are both listed as C/IF on the roster, so it is possible they could see time at the corners as well.

For the past two seasons, first base belonged to senior Matt Streng, but he has flipped corners with sophomore Brad Hallberg. I was quite surprised to learn of this move, as Streng has never struck me as particularly athletic, but he will man the hot corner in 2011 after a very disappointing 2010 campaign that saw him hit just one home run (coincidentally, it came in the only game I was able to attend) and turn in a -13 RAA performance. Hallberg was -4 runs in 112 PA in his debut season.

Hallberg will split first base and DH duties with true freshman Josh Dezse, the most impressive member of the OSU recruiting class and a 28th round pick of the Yankees. Given the fact that Dezse is also expected to be a key part of OSU’s bullpen, it would probably make more sense to have him at first base more often than not, but we’ll have to wait and see.

Second base is another position where the Bucks have to fill a big whole as one of my favorite players, Cory Kovanda, completed his eligibility. Kovanda was a consistent on base threat, slapping infield hits, drawing walks, and getting plunked. His replacement will be redshirt sophomore Ryan Cypret, whose father was a member of the previous coaching staff. Cypret served something of a utility infield role last season, but had just one extra base hit in 51 PA and thus has much to prove with the stick.

Shortstop is the field position with the most continuity, as Tyler Engle has spent most of his three years in Columbus playing good defense but producing little at the plate. Last year he posted an ugly .224/.342/.376 line; his 28 walks were second on the team and the only thing that made him playable. If Engle could regain something approaching his 2009 form (.285/.411/.423), it would be a huge help as the infield looks as if it will struggle to create runs. Two true freshmen Indiana natives, Derek Hannahs and Jacob Hayes, and local product Phil Jaskot figure to provide depth, with Hayes also being trained for left field.

The outfield features one returning starter, as senior Brian DeLucia will play right field. DeLucia is easily OSU’s top returning hitter, with a .320/.395/.503 line in 2011, and he’s a very good outfielder as well. The rest of the Buckeye outfield will have big shoes to fill, as left fielder Zach Hurley (.385/.438/.602) and center fielder Michael Stephens (.360/.395/.556) were the team’s two most potent hitters outside of Burkhart in 2010. Also gone to graduation is DH Ryan Dew (.348/.420/.498), leaving OSU short six of its top seven offensive performers--this from a team that averaged 6.6 runs to the Big Ten’s 6.8.

It was expected that sophomore Hunter Mayfield would get one of the open positions, but he transferred to Rollins College (a Division II school that managed to beat the Bucks last year, perhaps costing them more than just the game), so there is no returning experience of which to speak outside of DeLucia. Left field will be a platoon between junior David Corna and sophomore Joe Ciamocco. Despite having burned three years of eligibility combined, they have a total of one collegiate at bat between them, so it’s impossible to count on much offense out of them. Center field will belong to true freshman Tim Wetzel, described by the web account as having--you guessed it--"speed and defensive prowess”.

One can only hope that the unproven players will be better batters than the advance billing would suggest, because otherwise it seems as if Ohio will have significant trouble putting runs on the scoreboard. This is particularly unfortunate since the 2010 pitching staff was Wimmers and anyone who could stay healthy, and Wimmers, perhaps the best pitcher at OSU since Steve Arlin, is now a Twins farmhand.

The Friday starter should be senior Drew Rucinski, who had been constantly shuttled back and forth between the bullpen and the rotation by the previous staff. Rucinski was a good complement to Wimmers, but does not figure to match up well with the other top starters in the conference. Another senior, Dean Wolosiansky, has been a rotation stalwart throughout his career and should get the Saturday starts. Wolosiansky has always fit the profile of a league-average innings muncher, which is a good thing to have, but in a perfect world he would be the Sunday starter. That role apparently will go to true freshman Greg Greve, a 45th round pick by San Francisco in last year’s draft.

Greve’s spot in the rotation may not hold up if Brad Goldberg is granted a waiver to pitch in 2011. The Coastal Carolina transfer is a junior in eligibility, but may not be able to use it until 2012. He pitched just five innings for Coastal last year after pitching fourteen effective innings out of the pen as a freshman. If eligible, he figures to be a big boost to the Buckeye mound staff.

Josh Dezse, slated to start at 1B/DH, is also the favorite for the closer role. While two-way players are fairly common in the college game, especially as closer, the only prominent OSU two-way player of recent years was JB Shuck, now a solid prospect in the Astros organization. However, Shuck was used as a starting pitcher, not a reliever, and rarely was in the lineup when he pitched. It appears as if Beals is more comfortable with hybrid players than Todd was.

Senior Jared Strayer’s role and effectiveness increased as the season went on last year, and he figures to be the key middle reliever in 2011. Unfortunately, it seems as if the new staff has taught Strayer a more conventional delivery--last year he adopted a three-quarters/sidearm delivery that was a pleasure to watch and a rare sight on the Bill Davis Stadium mound.

Sophomore Brett McKinney showed some promise last year, but was way too wild and way too hittable (32 walks and 78 hits in 59 innings). He figures to be the Wednesday starter/weekend long man at this point, but he has a chance to be a quality contributor at some point during his career. Two left-handed pitchers--senior Theron Minium and junior Andrew Armstrong--figure to be the other candidates for Wednesday starts and weekend middle relief options. Armstrong showed much promise as a freshman in 2008, but was injured during the ’09 season and missed all of last season.

Junior Brian Bobinski and sophomore Cole Brown combined for eighteen ineffective innings last year and in a perfect world will be mopup pitchers this season, provided they have shown no improvement. Three other pitchers on the roster do not figure to see significant action: true freshman lefty Ben Bokor (twin brother of backup catcher Josh), junior walkon Paul Guey, and a freshman walkon who was unsuccessful in attempting to make Wake Forest’s roster last year, John Kuchno.

The OSU pre-conference schedule is significantly different than those of past years, showing a change in philosophy at the top. The schedule opens the weekend of February 18 with the Big Ten/Big East challenge in Florida, where the opponents will be Cincinnati, Louisville, and St. John’s--the latter two have both been problems for OSU in recent years, with Louisville burying the Bucks in five games over the past three seasons by a combined 62-32 score, and St. John’s having knocked Ohio out of the NCAA Tournament in 2005.

The weekend of February 25th sees the Bucks back in Florida four a four-game series with Western Michigan, a team with OSU has a rich tradition--the teams played ten times in the NCAA Tournament between 1951 and 1967. The weekend of March 3 will be a trip to North Carolina to face Army, Western Carolina, and Akron, and the weekend of March 10 is another Florida trip to play Illinois State, Bradley, and Army.

The annual spring break trip, which this year will run from March 18-26, is the one in which the philosophical differences emerge. Coach Todd used the spring break trip as a week of tuneup for conference play, going to Florida to face mostly northern teams that OSU should expect to go 6-2 or so against. He had not taken his team to the West Coast since a 2002 trip to Albuquerque that featured a crazy 38-15 game against Toledo, and the last trip to California came around twenty years ago. In his first season, Beals will take his charges to play a three game series in Berkeley (in what sadly appears to be the final campaign for Golden Bear baseball), two games at Fresno State, and three at Cal St.-Bakersfield.

The home opener will be Tuesday, March 29 against Xavier. Other standard one game mid-week home opponents will be Miami, Akron, Bowling Green, and Toledo. The Buckeyes will travel to play at Ohio University on one Wednesday, and have two out-of-region opponents coming in for two game series: North Florida and Oklahoma State.

Big Ten play begins April 1 against Northwestern; in subsequent weeks, OSU goes to Indiana, hosts Michigan State, travels to Penn State, hosts the forces of evil, goes to Illinois, hosts Iowa, and goes to Minnesota. If OSU is able to make the top six, they’ll be able to stay home and play downtown at Huntington Park; the Big Ten Tournament will be held there May 25 - 28.

Sadly, if I had to hazard a guess, I would say that for the second straight season six other Big Ten teams will be playing in Columbus that weekend. While I thought that Todd’s program was healthy enough, 2011 would have loomed as a possible rebuilding year in any case. Coaching changes often excite fans at the outset, but first seasons tend to be rough.

Crude Team Ratings 2010

In a previous post I explained the methodology behind these rankings, and acknowledged a fairly decent number of shortcomings they possess. I will not harp on either of those topics again here, but that is simply to avoid being repetitive--these rankings are far from perfect and should be taken in that spirit.

I have will present four different sets of rankings based on four different inputs. The manner of calculating the rankings is identical for all four; the only difference is which initial win ratio is used. I tend to believe the final set based on Runs Created/Runs Created Allowed is the most indicative of true talent, but any season aggregate metric is going to have obvious deficiencies when it comes to estimating true talent, and in reality some combination would probably be superior for that purpose.

The first ranking is what I call actual CTR, since it is based on the actual W/L ratio of each team. This is an attempt to evaluate teams based on their actual game outcomes, just adjusted for strength of schedule. Some might argue that even if actual record is used for the team, some component-estimated record should be used to gauge SOS, and they have a point, but this approach equates team strength to W/L ratio.

In the chart below, aW% is adjusted W%, and "s rk" is the rank of the team's SOS (#1 = toughest schedule):



You can see from the chart that a 100 CTR does not correspond to a .500 aW%. This is because the rankings are designed to give the average team a 100 CTR; since the properties of W/L ratio ensure that the average W/L will be > 1, an average W/L ratio is not the same time as the W/L ratio of an average team (which is 1, of course). If this scale distortion bothers you, use aW%--and stop using ERA+, because the distortion is similar. I am unconcerned about the issue because I want the average rating (but not necessarily the median) to be 100.

The three toughest schedules are the bottom three in the AL East (BAL, TOR, BOS, although it's hardly fair to refer the latter two teams as being at the bottom of anything). Of course, this is a consequence of playing in the stronger league and having to play a bunch of games against the two highest-rated teams. What I don't like about this definition of SOS is that one can make the case that Tampa Bay's schedule was equally as tough on paper as Toronto's (assuming an equal distribution of games against non-AL East opponents)--but Tampa Bay's success in winning games makes Toronto's harder in practice. It would be difficult to devise a SOS technique that took that point of view, however.

The more notable weakness of the schedule-adjustment (and thus the ratings themselves) is they make no correction for the influence that a team has upon the win-loss record of its opponents, and thus might be acting in a distortive manner at the extremes.

I also have some division/league ratings; these are simply the average CTR of all of the teams in the division:



Four of the six divisions were relatively equal, with an average CTR in the 96-102 range. However, one extremely good division and one extremely bad division result in the AL having a 108 ranking to the NL's 93. Those overall league rankings imply that the average AL team would be expected to have a .537 W% against the average NL team. The 2010 interleague record for the AL was .532, which of course was not generated through balanced schedule, neutral-field meetings and covers a sample of 252 games.

While I developed the rankings this year, I ran 2009 through the spreadsheet and the AL/NL disparity was estimated to be greater (113/89, .559). The AL West was the top-rated division (126), and the NL Central fared a tick worse than they would in 2010 (80).

The weak NL Central allowed the Reds to have the worst CTR of any playoff team (108) in 2010, although that figure was also better than the Cardinals' 2009 low of 103.

Switching gears, here are the gCTR figures. These are based on gEW% (described in this post), which takes into account the distribution of team runs scored and allowed per game (but does so independently of the other):



I'm not going to have a lot to say about the charts for each input, since they track the differences between their inputs and actual W%, which I have already written about in one form or another. Next is eCTR, which uses standard Pythagenpat W-L record as its starting point:




Finally, pCTR, based on Runs Created/Runs Created Allowed used to fuel Pythagenpat:




Here the AL East really looks good, with the top four teams in baseball. The potential for this type of clustering of strong teams (and, in the case of the NL Central, lousy teams) in one division is one of the reasons I oppose treating winners of small divisions playing with unbalanced schedules as sacrosanct.

pCTR and the associated aW% are the closest in construction to other popular ratings that account for strength of schedule and use component rather than actual W-L inputs, namely the third-order records published by Baseball Prospectus and the TPI rankings figured by Justin at Beyond the Box Score. Here are the most comparable winning percentages for each methodology--my aW% based on pCTR, the third-order winning percentage from BP, and the TPI from Justin:



You can see that there is general agreement between all of the methods, which is a good sign. The best correlation is between CTR and BP (+.983); the worst between CTR and Justin (+.942), with the BP/Justin correlation falling in the middle (+.965). The methods are all in general agreement about the proper spread of the teams--the standard deviation of the BP and Justin figures is .059 compared to .060 for the CTR-based figures, .060 for my estimate of PW% without any schedule adjustments, and .068 for actual W%.

The BP approach and my own to generating the underlying W% estimate are essentially the same, except for the use of different run estimators (BP uses EqR and I use Base Runs). Justin’s approach is a little different, and I’m personally not wild about it--it breaks defense down into pitching and fielding, making use of FIP and defensive metrics like UZR and Dewan’s Runs Saved. The approach used by BP and myself looks at the actual total component statistics surrendered by the defense. This does not allow one to split defense into pitching and fielding, but it also makes use of the actual observed interaction between the two on the field rather than using estimates that might make sense in isolation but leave something missing when the two are considered as one unit.

In any event, it is encouraging to see that CTR is able to produce similar results to systems developed by others that have been around a little or a lot longer as the case may be. If CTR returned very different results, I would probably conclude that it had a serious methodological error rather than the minor though not insignificant flaws that I am already aware of.

Great Moments in Hulu Sports Information

Crude Team Ratings

This methodology is really unnecessary, and very possibly has a flaw somewhere inside that I was unable to spot. However, one night in September the Rays/Yankees game I was watching went into a rain delay, and not wanting to do some real work I used this time to fiddle around with a rating system for teams that incorporated strength of schedule. Never one to simply let an idle experiment rest in peace without milking a couple of blogposts out of it, I am compelled to describe the Crude Team Rating (CTR) here.

There is nothing novel about the idea--similar ratings which are likely based on better theory are published by Baseball Prospectus, Beyond the Box Score, Andy Dolphin, Baseball-Reference, and others. The concept is simple; the execution in this case is likely muddled.

Let me offer an example of how this works with a hypothetical four-team league composed of the Alphas, Bravos, Charlies, and Ekos. The 90 game schedule is not balanced; the Alphas/Bravos and Charlies/Ekos are in "divisions" and play each other 40 times, playing their cross-divisional foes 25 times each. The Alphas go 25-15 against the Bravos and 17-8 against the Charlies and Ekos; the Bravos go 14-11 against the Charlies and Ekos; the Charlies go 14-6 against the Ekos. We wind up with these standings:



The Bravos and Charlies are equal in the standings, but we have every reason to believe that the Bravos are a better team--they won their season series against both teams from the other division, and had to play forty games against the Alphas, who are clearly the league's dominant team. Obviously strength of schedule worked against the Bravos.

Let's start by looking at each team's win ratio (W/L, which of course is also W%/(1 - W%)):



These do not average to 1, because of the nature of working with ratios rather than percentages (the average W% is .500 in this league--don't worry, I made sure my standings added up). The average win ratio for the league will always be greater than one, unless all of the teams are .500. What we can do now is calculate the average win ratio for each team's opponents. The Alphas' opponents had an average win ratio of (40*.915 + 25*.915 + 25*.636)/90 = .838. This is the initial strength of schedule, S1.

Next, we can adjust each team's win ratio for their strength of schedule. First, though, I need to point out a flaw that is inherent in the CTR approach--it does not recognize the degree to which a team's SOS is affected by their quality. The Alphas will take a hit on this count, because all of the teams they play against have poor records. Of course, based on what we know, the Alphas actually play the second-toughest schedule in the league because of the forty games with the Bravos. To the algorithm used here, they have a weak schedule because all the teams they play have losing records. That is true, but the reason the Bravos have a losing record is because they play the Alphas so often.

I could attempt to adjust for this in some way, but I've chosen to let it slide--that's one of many reasons why these are self-proclaimed crude ratings. Of course, the effect is much more pronounced in this hypothetical when teams are playing 44% of their games against the same opponent, and is much less pronounced in a 30 team league in which the most frequent opponents play only 12% of the time.

Once we have the initial strength of schedule S1, we need to find avg(S1)--the average S1 for all teams in the league. In this example it is 1.092. To adjust each team's W0 for SOS, we multiply by the ratio of S1 to avg(S1). This gives us the first-iteration adjusted win ration, which I'll call W1:

W1 = W0*(S1/Avg(S1))

For the Alphas: W1 = 1.903*.838/1.092 = 1.459

Now that we have an adjusted win ratio, we can re-estimate SOS in the same manner as before, producing S2. This is necessary because we now have a better estimate of the quality of each team, and that knowledge should be reflected in the SOS estimate. S2 for the Alphas is .916.

In order to find W2, we need to compare S2 to S1. We can't simply apply S2 to W1, because W1 already includes an adjustment for SOS. S2 supercedes S1; it doesn't act upon it as a multiplier or addition. We also can't apply S2 to W0, the initial win ratio, because the schedule adjustment of S2 is based on each team's W1. At each step in the process, the previous iteration has to be seen as the new starting point, and the adjustment has to be a comparison between the new iteration and the one that directly preceded it.

So W2 is figured in the same manner as W1, except the ratio of S2 to the average is compared to the same for S1:

W2 = W1*(S2/Avg(S2))/(S1/Avg(S1))

For the Alphas, W2 = 1.459*(.916/1.029)/(.838/1.092) = 1.692

Then we use W2 to figure S3, and use S3 to figure W3, and the process continues through as many iterations as you feel like setting up in Excel (at least for me). For the purpose of this example, I did nine; for the actual spreadsheet for ML teams, I went a little overboard and did thirty iterations. That results in a "final" estimate of win ratio, W9. It is not of course truly final as that would be W(infinity). The "final" estimate of SOS is S10, so that the last estimate of SOS is based on the last estimate of team quality.

One undesirable effect of the iterative process is that the average W9 is no longer equal to the actual average win ratio for the league, and the distribution is not the same. Thus, when the adjusted win ratios are converted into winning percentages (W% = WR/(1 + WR)), they are not guaranteed to average to .500, which of course is a logical must.

In order to convert W9 into an adjusted winning percentage, first figure an initial W% for each team:

iW% = W9/(1 + W9)

Dividing by the average of iW% will force the new adjusted W% to average to .500 for the league:

aW% = iW%/Avg(iW%)*.5

The results for the theoretical league are illustrative of the strength of schedule problem I touched on earlier--the Alphas' aW% is lower than their actual W%, as they did not have to play against themselves. It really doesn't make sense to consider what a team's record would be if they played themselves, but thankfully this distortion becomes more trivial as the number of teams in a league increases.



aW% might seem like a logical measure to use as the final outcome of the system, but I actually prefer a scale which remains grounded in win ratio. So I convert W9 into the final product, Crude Team Rating, by dividing W9 by the average W9. This is different from aW%, which forces the average to equal the initial average win ratio for the league; this adjustment forces the average to 1 (or 100 when the decimal point is dropped), which is a nice property to have for the overall rating:

CTR = W9/Avg(W9)

In the same manner, a final strength of schedule metric that is centered at 1 is:

SOS = S10/Avg(S10)

The CTR and SOS for the four hypothetical teams are:



This post is already a mess to read, so at this point I'm going to break it up into sections on particular aspects of the methodology that I wish to expound upon:

Why is win ratio used throughout the process rather than W%?

I use win ratio because it is much easier to work with; for instance, the ratios between the various SOS estimates can be multiplied by the win ratio to find an adjusted win ratio. The math would not be anywhere near as straightforward if W% were used. Using win ratio also allows for a larger spread in the final CTRs; I could use CTR = aW%/Avg(aW%), but the range would be narrower.

Most importantly, though, is that win ratios can be plugged directly into Odds Ratio (equivalent to Log5) to estimate W% for matchups between teams. If a team with a win ratio of 1.2 plays a team with a win ratio of .9, they can be expected to win 57.1% of the time--1.2/(1.2 + .9). There would be equivalent but messier math if working with W%.

Due to this property, we can use CTR to estimate head-to-head winning percentages. CTR is not a true win ratio, since it has been re-centered at 100, but the re-centering is done with a common scalar and so it has no effect on ratios of team CTRs. So a team with a CTR of 130 can be expected to win 52% of the time against a team with a 120 CTR--130/(130 + 120).

I estimated W% for the Alphas against each of their opponents using CTR. The fact that they are close to the actual results should not be taken as any type of indication that the head-to-head estimates are accurate, as I obviously came up with the numbers to follow logically. They are offered just to show how the ratings can be used to estimate W% in a head-to-head matchup:



Your ranking system is novel and unique, right?

Wrong. There's nothing new about it. It is really quite similar to the Simple Ranking System used by the Sports-Reference sites, except it operates on ratios rather than differentials. As mentioned above, there are a number of similar and likely more refined approaches utilized by other analysts.

It's really quite simple:

1. Assign each team an initial ranking
2. Use those initial rankings to estimate SOS for each team
3. Compare SOS to the average schedule and adjust initial ranking accordingly
4. Repeat until rankings stabilize

You haven't adjusted for home-field advantage, have you?

No, I haven't, either in the process of estimating team strength (I don't breakdown a team's schedule into home and road games) or by producing adjustments for CTR at home and on the road (i.e. having a formula that tells you that a 130 overall CTR team has an equivalent 110 CTR on the road and 150 at home). The former falls outside the scope of an admittedly crude rating system; the latter is something that is easy enough to account for on the fly.

While there is a lot that could be said about incorporating home field advantage (writing about some of it is on my to-do list), the simplest thing to do is to incorporate a home field edge into the odds ratio calculation. The long-term major league average is for the home team to win 54% of the time, which is a win ratio of 1.174. I call the square root of that win ratio "h" (1.083). If a team is away, divide their CTR by h; if they are home, multiply it by h.

Suppose that we have a 125 CTR team hosting a 110 CTR team. In lieu of home field advantage, we'd expect the home team to win 125/(125 + 110) = 53.2% of the time. But the 125 team is now an effective 135.4 team (125*1.083), and the 110 team is now an effective 101.6 team (110/1.083), and so the expected outcome is now a home win 57.1% of the time.

Equivalently, one can figure the odds ratio probability by first dividing the two CTRs (125/110 = 1.136), and dividing by that ratio plus one (1.136/2.136 = 53.2%). If you approach it in this manner, you need to multiply by the ratio by h^2 (1.174) rather than h (125/110*1.174 = 1.334 and 1.334/2.334 = 57.1%). I prefer the former method, because it produces a distinct new rating for each team based on whether they are home or away rather than accounting for it all in one step, but it is a matter of preference and has no computational impact.

CTR is based on actual win ratio. Why don't you use expected win ratio from runs and runs allowed or runs created and runs created allowed?

One can very easily substitute expected win ratio for actual win ratio. I have two variations, eCTR, gCTR, and pCTR. eCTR uses win ratio estimated from actual runs scored and allowed, while gCTR uses "Game EW%" (based on runs scored and allowed distribution taken independently in this post) and pCTR uses win ratio estimated from runs created and runs created allowed.

The discussion of what inputs to use helps to illustrate another flaw in the methodology--there is no regression built in to the system. For the purpose of ranking a team's actual W-L results, there is no need for regression, but if one is using the system to estimate a team's W% against an opponent, it is incorrect to assume that a team's sample W% is equal to the true probability of them winning a game. Even if one did not want to regress the W/L ratio of the team being rated, it would make sense to regress the records of their opponents in figuring strength of schedule. I've done neither.

Are there any other potential applications of CTR?

One can always think up different ways to use a method like this (and since methods similar in spirit but superior in execution to CTR already exist many of them have been implemented already); the question is whether the results are robust enough to provide value in a given function. I'll offer one possible use here, which is using a team's strength of schedule adjustment to estimate an adjustment factor for their players' performance.

There are some perfectly good reasons why one would not want to adjust individual performance for the strength of his opponents, but if that is something in which you are interested, CTR might be useful. If a team's opponents have an expected win ratio of .9, then based on the Pythagorean formula with an exponent of two, their equivalent run ratio should be sqrt(.9) = .949. Custom exponents could be used as well, of course, but two will suffice for this example.

So a pitcher on a team with a .9 SOS could have his ERA adjusted by dividing by .949 to account for the weaker quality of opposition. This approach assumes that the team's opponents are evenly balanced between offense and defense. One could put together a CTR-like system that broke down runs scored and allowed separately, but that would require the use of park factors or home/road breakdowns, and would greatly complicate matters.

Speaking of park factors, one could use an iterative approach to calculate park factors (Pete Palmer's PF method takes this path). Instead of simply comparing a team's home and road RPG as I do, you could look at the team's road games in each park, calculate an initial adjustment, and iterate until the final park factors stabilized. At some point I’ll cover this application in detail.

Wait, there's something messed up with the 100 scale. A .500 team will not get a 100 CTR.

You're right, they won't, but it's not a problem with the scale--it's a feature of it. (You are free to prefer a different scale, of course). This issue is hinted at in the section about why win ratio is used, but I didn't address it explicitly there. The scale is designed so that a team with an average win ratio gets a 100 rating, not so that an average team (which would by definition play .500) gets a 100 rating.

Others have pointed out the potential dangers in working with ratios rather than percentages--assuming that ratios work as percentages can result in mathematical blunders. Suppose we have two football teams that comprise a league, one which goes 1-15 and the other that goes 15-1. Obviously, the average record for a team in this league is 8-8 with a winning percentage of .500. Such a team would have a win ratio of 1.

But what is the average win ratio for a team in this league? Not the win ratio for a hypothetical average team--the arithmetic mean of the win ratios of the teams in this league. It is (15/1 + 1/15)/2 = 7.533. It's not even close to 1.

Obviously this is an extreme example, but the principle holds--teams that are an equal distance from .500 will not see their win ratios balance to 1. The average win ratio for a real league will always be >= 1. The effect is stronger in leagues in which win ratios deviate more from 1. In the 2009 majors, for instance, the average win ratio was 1.039. In the 2009 NFL, it was 1.386.

I could have set up the ratings so that a team with a .500 record (i.e. 1 win ratio) was assured of receiving a ranking of 100 by simply not dividing what is called W(f) below by Avg[W(f)], but it also would have ensured that the average of all of the team ratings would not be 100. In the first spreadsheet I put together, that's exactly what I did, but I decided that it was more annoying to have to remember what the league average of the ratings was (especially when looking at aggregate rankings for divisions and leagues) than it was to remember that a .500 team would have a ranking of 96 or so. It's purely a matter of aesthetics and personal preference.

Generic Formulas

W0 = W/L for aCTR; (gEW%/(1 - gEW%) for gCTR; (R/RA)^x for eCTR; (RC/RCA)^y for pCTR
where x = ((R + RA)/G)^z and y = ((RC + RCA)/G)^z, where z is the Pythagenpat exponent (I use .29 out of habit)

for a league of t teams, where G is total games played for a team and g(i) is the number of games against a particular opponent:

S1 = (1/G)*{SUM(i = 0 to t)[g(i)*W0(i)]}

W1 = W0*(S1/Avg(S1))

S(n) for n > 1 = (1/G)*{SUM(i = 0 to t)[g(i)*W(n-1)(i)]

W(n) for n > 1 = W(n-1)*(S(n)/Avg(S(n))/(S(n-1)/Avg(S(n-1)))

For final win iteration (f) in the specific implementation (f = 30 in my spreadsheet):

S(f + 1) = (1/G)*{SUM(i = 0 to t)[g(i)*W(f)(i)]

iW% = W(f)/[1 + W(f)]

aW% = iW%/Avg(iW%)*.5

CTR = W(f)/Avg(W(f))

SOS = S(f + 1)/Avg(S(f + 1))

Great Moments in CBS Sportsline Depth Charts

Run Distribution and W%, 2010

A couple of caveats apply to everything that follows in this post. The first is that there are no park adjustments anywhere. There's obviously a difference between scoring 5 runs at Petco and scoring 5 runs at Coors, but if you're using discrete data there's not much that can be done about it unless you want to use a different distribution for every possible context. Similarly, it's necessary to acknowledge that games do not always consist of nine innings; again, it's tough to do anything about this while maintaining your sanity.

All of the conversions of runs to wins are based only on 2010 data. Ideally, I would use an appropriate distribution for runs per game based on average R/G, but I've taken the lazy way out and used the empirical data for 2010 only.

This post also contains little in the way of "analysis" and a lot of tables. This is probably a good thing for you as the reader, but I felt obliged to warn you anyway.

I've run a post like this in each of the last two years, and I always started with an examination of team record in one-run, blowout (5+ margin), and other (2-4 run margin) games. While I was aware of the havoc that bottom of the ninth/extra innings can unleash on one-run records, I have to admit that I was understating the impact, and thus giving a lot more attention to one-run records than they deserved. So this year I've made it simple with two classes: blowouts (5+ margin) and non-blowout (margin of 4 or less).

72.6% of games are non-blowouts; 27.4% are blowouts. Of course, "blowout" is not an appropriate description for many five run games, but I've got to call it something. Team records in non-blowouts:



The standard deviation of W% in non-blowouts is .050, compared to .068 for all games and .138 for blowouts. Blowout records:



The much wider range is easy to see. The next chart shows each team's percentage of non-blowouts and blowouts, as well as the difference in W% between the two classes (figured as blowout W% minus non-blowout W%):



Colorado had the fewest blowouts, with only 20%, which is a bit of an oddity as a strong hitter's park increases the likelihood of a blowout (obviously team quality is a strong factor as well). Milwaukee led the way with 36% blowouts, and while their 25 blowout wins tied for tenth, their 33 blowout losses trailed only the Pirates.

Anyone who reads this blog doesn't need the reminder, but a quick perusal of this list should remind you that the notion that good teams make their hay by winning close games is bunk. Good teams have better record in blowout games than non-blowout games as a rule. Even a team like San Francisco which did not have many blowouts (only 22%) had a strong record (22-13) when they did find themselves in such a contest.

Blowouts/non-blowouts is not a very granular way of looking at margin of victory data; they simply divide all victory margins into two classes. This chart shows the percentage of games decided by X runs, and the cumulative percentage of games decided by less than or equal to X runs:



The most common margin was one run, and each successive margin was less frequent than its predecessor (except for sixteen, and by that point so what?) About half of games were decided by two runs or less, and about 10% were by seven runs or more.

Team runs scored and allowed in wins and losses can serve as interesting tidbits, and as a reminder that there is nothing unusual about having a large split between performance in wins and performance in losses. First, here are team runs scored for wins and losses, along with the ratio of each to the team's overall scoring average:



The average team scored 6.06 in their wins, which is 139% of the overall average; in losses the average is 2.69 runs, or 62% of average.

Runs allowed in wins and losses:



Another application for the data is the average margin of victory for each team. The chart that follows lists each team's average run differential in wins (W RD); losses (L RD); and the weighted average of the two (absolute value--otherwise we'd have plain old run differential). The average margin of victory was 3.41 runs:



The White Sox were the model team, with their winning and losing margins both scoring direct hits on the league average. The Yankees had the highest run differential in wins (3.93), while Baltimore had the lowest (2.51). High losing margins congregated in the NL Central, as Pittsburgh, Milwaukee, Houston, and Chicago were all at or just below four and lost by .22 runs more than fifth-ranking Kansas City. In what is just a coincidence, Texas and San Francisco had the smallest average losing margins; the Rockies and Rays joined them in keeping losses under three runs/game.

Texas' average game was the closest in the majors (3.02 differential). Milwaukee was on the other extreme, with their 4.00 margin easily the highest in the majors. I would figure the medians, but a column filled with 3s wouldn't be very interesting.

I will now shift gears from margins of victory to counts of runs scored and allowed in games. For a manageable look at the data, I've split games into three classes. Low scoring games are those in which a team scores 0-2 runs; 30.6% of 2010 games fell into this class, and teams had a .144 W%. Medium scoring games are 3-5 runs, representing 38% of games and a .503 W%. High scoring games feature 6+ runs, occurring 31.4% of the time with teams winning at a .844 clip.

These classifications are nice because they are somewhat symmetric. Each category contains roughly 1/3 of games (that might be too strong, but it's the best one can do), and medium games are very close to a .500 W%, with low and high scoring games having roughly complementary W%s. Of course, it doesn't work out so neatly when the run environment is more extreme. 2010's 4.38 R/G average was pretty standard, but if you look at a season like 1996 (5.04 R/G), things don't work out as nicely: 24.3% low-scoring (.123 W%), 37.5% medium-scoring (.441), and 38.2% high-scoring (.798).

Caveats aside, here are the game type frequencies by team:



I'll let you peruse these tables for whatever tidbits you might find, because at this point it's no longer sporting to point and laugh at the Mariners, and it largely corresponds to what you already know about team offense. The same holds for the flip side--runs allowed broken down into low, medium, and high:



Of course, teams had a .856 W% in games in which they hold their opponents to a low score, .497 in medium games, and .156 in high-scoring games.

We could also look at teams records in games by scoring class. This chart shows team records in games grouped by runs scored:



To the extent that this data tells you anything, it tells you more about the opposite unit (defense in this case). It has a lot more use as trivia than analytical fodder. Detroit was 1-37 (.026) in games in which they scored 0-2 runs, while Philadelphia was 13-38 (.255) in such contests. Even when Seattle had a high-scoring game, they didn't clean up to the extent that other teams did (19-6, .760).

I found a few of the factoids in the reverse (record in games grouped by runs allowed) a little more interesting:



Boston (42-1, .977) and Baltimore (28-1, .966) were heads and shoulders above the rest of the teams when holding opponents to a low-scoring game--the next fewest number of losses was four, with the Yankees having the most wins with four losses. Even allowing a moderate number of runs spelled doom for the Mariners, as they were just 16-40 (.286) when allowing 3-5 runs; Pittsburgh's 22-38 looked good in comparison.

The Mariners' performance when allowing six or more runs was something else entirely: 1-53 (.019). What's odd is that Cincinnati, despite having a good offense, was the next worst team when allowing a lot of runs (1-31, .031). Philadelphia and Boston each managed to slug their way to a .304 W% in those games, meaning that Boston was the top-performing team both when their defense sparkled (42-1 in low scoring games) and when it was poor. It was a pedestrian record in the middle RA games (30-33) that held the Red Sox back.

I'll close with the most useful analytical tools in the article--what I call game Offensive W% (gOW%), game Defensive W% (gDW%), and game expected W% (gEW%). These metrics are based on a Bill James construct from the 1986 Abstract to evaluate teams based on their actual runs distribution. Instead of using the Pythagorean formula to estimate a team's W% assuming the other unit was average, he proposed multiplying the frequency of each runs scored level by the overall W% in those games. The resulting unit is the same as classic OW%, but it is based on the run distribution rather than the run average.

In order to figure gOW% (and the other metrics), one first needs an estimate for W% at each runs scored level. The empirical data for 2010:



The "marg" column shows the marginal benefit of each additional run; going from one to two runs increases W% by .122. Each run is incrementally more valuable through four, and then the benefit begins to decline, although this year's results have a blip in which the sixth run had a higher marginal impact than the fifth run.

The column marked "use" is the value I used to figure gOW% for each runs scored level. To smooth things out (crudely so, but nevertheless assuring that each additional run either left unchanged or increased the win estimate), I averaged the results for 9-12 runs per game. The "invuse" column is the complement of those values, which are used to figure gDW%. A more comprehensive explanation about how all of the W%s are calculated is included in the 2008 edition of this post.

For most teams, gOW% and OW% (that is, estimates of offensive strength based on run distribution and average runs/game respectively) are very similar. Teams gOW% higher than OW% distributed their runs more efficiently (at least to the extent that the methodology captures reality; there are other factors including park effects, non-uniform inning games, covariance between runs scored and allowed, and failure of the empirical estimates of W% for each runs scored value); the reverse is true for teams with gOW% lower than OW%. The teams that had differences of +/- 2 wins between the two metrics were:

Positive: SEA, HOU, BAL, CLE, OAK
Negative: PHI, COL

You'll note that the positive differences tended to belong to bad offenses; this is a natural result of the nature of the game, and is reflected in the marginal value of each run as discussed above. Runs scored in a game are not normally distributed because of the floor at zero runs, and once you get to seven runs you'll win 80% of the time with average pitching, so additional runs have much less win impact.

The teams for which gDW% was out of sync with DW% by two or more games:

Positive: CHN
Negative: SF, BAL, TEX, NYA

Both pennant winners had inefficient distributions of runs allowed, but it obviously didn't hurt them too much. San Francisco's gDW% of .572 was still good for second in the majors behind San Diego, which had a similarly inefficient runs allowed distribution (although not quite extreme enough to make the above list).

gOW% and gDW% can be combined with a little Pythagorean manipulation to create gEW%, which is a measure of expected team W% based on runs scored and allowed distributions (treating the two as if they are fully independent). The teams which had two game deviations between the two estimates:

Positive: HOU, CHN, SEA, KC, NYN
Negative: SF, PHI, NYA, TEX, COL, SD

This happened to break down pretty clearly into a good team/bad team dichotomy, but that is not always the case. gEW% had a lower RMSE in predicting actual team W% than did EW% (2.63 to 2.83), which is not as impressive of a feat as it might sound. Given that gEW% gets the added benefit of utilizing run distribution data rather than aggregate runs scored, a tendency to under-perform EW% would be indicative of a serious construction flaw. In any event, both RMSEs for 2010 were well below the usual error for a R/RA W% estimated, which is generally in the neighborhood of f our games, which tells us nothing but as a long-time user and constructor of W% estimators, it certainly caught my eye.

Here is the full chart for the game and standard W%s discussed above, sorted by gEW%:

Hitting by Position, 2010

Offensive performance by position (and the closely related topic of positional adjustments) has always interested me, and so each year I like to examine the most recent season's totals. I believe that offensive positional averages can be an important tool for approximating the defensive value of each position, but they certainly are not a magic bullet and need to include more than one year of data if they are to be utilized in that capacity. So the discussion that follows is not rigorous and focuses on 2010 only.

The first obvious thing to look at is the positional totals for 2010, with the data coming from Baseball-Reference.com. "MLB” is the overall total for MLB, which is not the same as the sum of all the positions here, as pinch-hitters and runners are not included in those. “POS” is the MLB totals minus the pitcher totals, yielding the composite performance by non-pitchers. “PADJ” is the position adjustment, which is the position RG divided by the position (non-pitcher) average. “LPADJ” is the long-term positional adjustment that I use, based on 1992-2001 data. The rows “79” and “3D” are the combined corner outfield and 1B/DH totals, respectively:



Most of the positions matched the default PADJ fairly closely in 2010, with the big exception being DH (and thus, the 1B/DH combination as well). DHs only created 4.76 runs per game versus a position player average of 4.65. While first basemen were 18% better than average, 1B/DH were a combined 13% above average, about equal with RF. RF has outperformed LF since 2007, by similar margins, but I wouldn't read anything into that. As recently as 2006 LF out hit RF 5.6 to 5.4, and the players are fairly interchangeable. Perhaps there has been a slight trend to put weak-hitting leadoff types in left (like Scott Podsednik or Juan Pierre), but the samples for single seasons are small enough that a couple unconventional team choices can create the narrow margin between the two corner spots.

Pitchers hit at just 5% of the position player average, which is as low as they've ever been, although not significantly different than the performances over the past few seasons. For the NL teams, pitchers' offensive performance was as follows (for this and all the subsequent team data, park adjustments are applied. Runs above average are figured against the overall 2010 major league average for each position--no distinction between AL and NL. Sacrifices are not included in any way which is not a big deal for any position other than pitcher--this is their performance when allowed to swing away (or when failing to get a bunt down for a sacrifice)):



After two years of Cubs pitchers leading the way, there was a new leader as Yovani Gallardo (.254/.329/.508 in 72 PA with 4 longballs) powered Milwaukee to the top of the heap. LA's -11 was the worst in the last three seasons, as Dodger pitchers' extra base contributions consisted of just two doubles.

Among AL teams, pitchers' PA ranged from 15 to 25, but only Boston managed to create more than one run per game (5.2 off a .306/.327/.368 line). After two consecutive years of failing to put a single pitcher on base, Toronto pitchers mustered a single, a double, and a walk. Two AL teams failed to have a pitcher reach base this season--Detroit and, naturally, Seattle. The nearly unfathomable offensive outage in the Pacific Northwest extended to the already ill-supported hurlers as well.

Next, let's look at the leading and trailing positions in terms of RAA. As mentioned above, all figures are park-adjusted but utilize the 2010 major league positional average, not the averages for each league. Additionally, left field and right field (but not first base and DH) are based on a combined average for the two positions.

I will not go through the formality of a chart for the leading positions, as the individuals responsible should be fairly obvious, and will rank highly on individual leader boards. It's a little different when dealing with batting order position, since even outstanding hitters are sometimes shuffled around in the lineup. Most top players stick to one position, though. The teams that led at each position were:

C--ATL, 1B--STL, 2B--NYA, 3B--BOS, SS--COL, LF--TEX, CF--TOR, RF--TOR, DH--MIN

Toronto's center fielders were a bit of a surprise for me, with Vernon Wells rebounding to lead the way. Pittsburgh and Andrew McCutchen were second on the list. CF had the smallest standard deviation of RG (.72); RF was close behind at .75. The largest standard deviation was at first base (1.5).

For the worst RAA at each position, the player listed is the one who played in the most games in that fielding position. It doesn't necessarily indicate that the player in question is personally responsible for such a dreadful showing:



Baltimore and Seattle each had multiple positions among the worst in the league; of course, it had to be Seattle that led with three. As an Indians fan, it is a shame to see the center fielders here; what made it worse was the Grady Sizemore contributed to the ineptitude when he was healthy enough to be in the lineup.

The following charts give the RAA at each position for each team, split up by division. The charts are sorted by the sum of RAA for the listed positions. As mentioned earlier, the league totals will not sum to zero since the overall ML average is being used and not the specific league average. Positions with negative RAA are in red; positions with +/- 20 RAA are bolded:



Philadelphia tied for the NL lead with six above average positions, although I can't for the life of me figure out how a position manned by the $25 million man couldn't muster any more than three RAA. Atlanta was the only NL team to boast three +20 positions. Florida led all of the majors with 70 RAA from middle infielders, which was enough to lift the entire infield to the top RAA in the NL despite below-average hitting from both corners.



Cincinnati tied with the Phillies in the NL with six above-average players, led in corner infield RAA, and led in overall positional RAA. St. Louis had the NL's lowest middle infield RAA, but the highest outfield RAA and the #1 single position in baseball (first base, naturally). You can see that almost all of their plus performance came from their three positions manned by star-types (yes Tony, Colby Rasmus), but that outside of them only right field was above average. Pittsburgh and Houston both had just one above-average position, but the Astros only managed a +2 out of right field while Andrew McCutchen carried Pirate center fielders to a +23. Thus, Houston trailed the NL and 28 other clubs with -103 positional RAA.



It's hard to find a division with a smaller range of offensive performance from position players, 33 runs from top to bottom. San Francisco was the only team in the NL that didn't have a +/- 20 RAA position. San Diego had the worst offensive outfield in MLB, but that -37 was offset by first base alone, and the rest of the positions were just a bit below average.



New York was the only team above average at all positions, but only their second basemen were real standouts. It was still enough to lead in total positional RAA, although Cincinnati had a higher average given the DH. Toronto tied for the AL lead with three +20 positions. Baltimore was one of two teams to have four -20 positions, had the worst single position in MLB (first base), and had the lowest middle infield RAA in MLB.



Minnesota tied for the AL lead with three +20 positions. Kansas City was the only AL team without a +/- 20 RAA position. Without Shin-Soo Choo and right field, Cleveland would have been in big trouble. It's worth noting, though, that Indian DHs were pretty much average. While Travis Hafner's contract has inarguably been a huge impediment to the organization, Hafner the ballplayer is not killing the team on the field, and that's a distinction that fans are having trouble making.



Texas led the majors in outfield RAA despite a well-below average contribution from their centerfielders. The rest of the team offered little notable offense; the rest of the division even less. Seattle, as you can imagine, led in a lot of negatives: fewest above average positions in the AL, most -20 positions in the majors, lowest corner infield RAA, lowest middle infield RAA, lowest infield RAA. The five -20 positions all ranked in the bottom seventeen positions in all of MLB. Let me write that gain: of the seventeen worst offensive positions in the majors, five of them belonged to Seattle.

Here is a link to a spreadsheet with each team's performance by position.