Crude Team Ratings, 2011

Anyone can throw together a spreadsheet and declare that they have a ranking system for teams. It’s not particularly hard to construct a reasonable method by which to take an initial estimate of team strength, adjust for strength of schedule, recalculate each team’s ranting, adjust for SOS again, rinse, repeat. I have done just that, and will present the 2011 ratings here.

If you want the full details, please refer to the linked post. The gist of the system is:

1) Start with a win ratio figure for each team. It could be actual win ratio, or an estimated win ratio.

2) Figure the average win ratio of the team’s opponents.

3) Adjust for strength of schedule, resulting in a new set of ratings.

4) Begin the process again. Repeat until the ratings stabilize.

The resulting figure is in the form of an adjusted win ratio; I force the average team to a rating of 100. The ratings can be plugged directly into an odds ratio--a team with a rating of 120 should win about 60% of the time against a team with a rating of 80 (120/(120 + 80)).

I’ll present four different sets of ratings here, each using a different win ratio as the input. It’s overkill to run this many, but if for some reason you prefer a certain estimate of win ratio, it may be represented.

Since 2011 is in the past, there’s no particular value in predictive ratings, so I’ll focus on the CTR based on actual wins and losses:



aW% is the adjusted W% based on CTR; SOS is the weighted average CTR of the team’s opponents; rk is the team’s ranking among the thirty teams; and s rk is the SOS rank.

The results aren’t particularly surprising; the teams are ranked pretty close to how they would be in W%. In some recent years, the results would favor AL teams much more than just looking at pure W%, but the National League held its own with the AL in 2011 as seen from the league/division ratings (simply the average rating for each member team):



That makes for a nice rank order of divisions, with East > West > Central, and AL > NL in each case. Still, the overall AL/NL rating difference of 103/97 is a lot smaller than previous seasons, including 108/93 in 2010. While the NL Central remained the weakest division, 89 was an improvement over the 82 rating in 2010. If Houston was in the AL rather than the NL (and assuming all the ratings stayed constant), the leagues would have each had a CTR of 100.

The next set of CTRs is based on Game Expected W% as described in this post. Basically, gEW% assumes independence between runs scored and runs allowed in a given game, and uses the 2011 empirical W% for teams scoring or allowing X runs in conjunction with each team’s actual game-by-game distribution of runs scored and runs allowed to estimate their W%. The resulting CTRs:



Using classic Pythagenpat as the input:



Finally, using Pythagenpat estimated win ratios based on runs created and runs created allowed:



Obviously there exist any number of possible combinations of win ratio estimates one could use, regression can be mixed in, etc. What I’ve presented here is just the most straightforward ratings based on obvious single inputs.

Run Distribution and W%, 2011

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 cut out a lot of what I listed last year simply because I don’t have that much free time right now. The data was not particularly useful in any event—knowing how many runs teams scored and allowed in their wins and losses, or what percentage of their games fell into arbitrarily defined classes might offer some trivia but is not exactly essential material.

The first breakout is record in blowouts versus non-blowouts. I define a blowout as a margin of five or more runs. This is not really a satisfactory definition of a blowout, as many five-run games are quite competitive--"blowout” is just a convenient label to use, and expresses the point succinctly. I use these two categories with wide ranges rather than more narrow groupings like one-run games because the frequency and results of one-run games are highly biased by the home field advantage. Drawing the focus back a little allows us to identify close games and not so close games with a margin built in to allow a greater chance of capturing the true nature of the game in question rather than a disguised situational effect.

In 2011, 75.8% of games were non-blowouts and 24.2% were blowouts. The teams sorted by non-blowout record:



The standard deviation of W% in non-blowouts was .064, which as expected is less than the standard deviation for blowouts (.114) and all games (.070).

Records in blowouts:



Obviously the sample size on these games is pretty small, but Kansas City and Oakland at .500 in blowouts caught my eye.

This chart shows blowout W% less non-blowout W%, along with the percentage of games that were blowouts and non-blowouts for each team:



This is the second year in a row in which San Diego has ranked high in terms of difference between blowout and non-blowout record. Usually teams with large differences are the better teams; that description may have fit the Padres in 2010 but not in 2011. Cleveland was the most extreme team in either direction in the majors. Florida played in the smallest proportion of blowouts while Texas played in the most.

A more interesting way to consider game-level results is to look at how teams perform when scoring or allowing a given number of runs. For the majors as a whole, here are the counts of games in which teams scored X runs:



The “marg” column shows the marginal W% for each additional run scored. The second and third run were both worth about .15 wins on average in 2011, while scoring four runs was the cutoff point between winning and losing (on average, of course).

I use these figures to calculate a measure I call game Offensive W% (or Defensive W% as the case may be), which was suggested by Bill James in an old Abstract. It is a crude way to use each team’s actual runs per game distribution to estimate what their W% should have been by using the overall empirical W% by runs scored for the majors in the particular season.

Using the empirical distribution rather than a theoretical distribution has the upside of being simple (modeling the runs per game distribution is fairly messy), but the benefits are outnumbered by the drawbacks. A non-comprehensive list of said drawbacks:

1. The empirical distribution is subject to sample size fluctuations. In 2011, at least, each additional run increased W%. This is often not the case given the low frequency of high scoring games. Even so, the marginal values don’t necessary make sense--for instance, the marginal value of a tenth run is implied to be .006 wins while the marginal value of an eleventh run is implied to be .040.

2. Using the empirical distribution forces one to use integer values for runs scored per game. Obviously the number of runs a team scores in a game is restricted to integer values, but not allowing theoretical fractional runs makes it very difficult to apply any sort of park adjustment to the team frequency of runs scored.

3. Related to #2 (really it’s root cause, although the park issue is important enough from the standpoint of using the results to evaluate teams that I wanted to single it out), when using the empirical data there is always a tradeoff that must be made between increasing the sample size and losing context. One could use multiple years of data to generate a smoother curve of marginal win probabilities, but in doing so one would lose centering at the season’s actual run scoring rate. On the other hand, one could split the data into AL and NL and more closely match context, but you would lose sample size and introduce quirks into the data.

I will not go into the full details of how gOW%, gDW%, and gEW% (which combines both into one measure of team quality) are calculated here, but full details were disclosed in this post. The “use” column here is the coefficient applied to each game to calculate gOW% while the “invuse” is the coefficient used for gDW%. For comparison, I have looked at OW%, DW%, and EW% (Pythagenpat record) for each team; none of these have been adjusted for park to maintain consistency with the g-family of measures which are not park-adjusted.

For most teams, gOW% and OW% are very similar. Teams whose gOW% is higher than OW% distributed their runs more efficiently (at least to the extent that the methodology captures reality); the reverse is true for teams with gOW% lower than OW%. The teams that had differences of +/- 2 wins between the two metrics were (all of these are the g-type less the regular estimate):

Positive: BAL, PIT, ATL, FLA, HOU, SEA
Negative: BOS, NYA, TEX, 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. In the four years that I’ve been looking at these figures, I can’t recall a difference as large as the Red Sox’ deviation in 2011--a standard OW% of .610 and a gOW% of .572, a 6.2 win difference. Boston led the majors in OW%; their gOW% was still excellent and good enough for third in the majors, but they did not spread their runs across games in an efficient fashion. The Sox scored ten or more runs 25 times; Toronto was second with 19 and the major league average was 9. Boston scored 36% of their runs in that 15% subset of games; the major league average was 15%, and next on the list was Texas at 28%.

Differences in for gDW%:

Positive: DET, BAL
Negative: PHI, SD, TB

I combine gOW% and gDW% through some Pythagorean math to produce gEW%, which can then be compared to a team’s standard Pythagorean record (EW%). Of course, it could also be compared to actual W%, but I think the comparison to a method that also uses runs is more interesting than a comparison to the actual win totals:

Positive: BAL, PIT, CHA, DET, MIN, HOU, OAK, FLA
Negative: BOS, PHI, COL, NYA, SD, TB, LA, KC

There are so many large differences that I’m a little worried that I may have made a spreadsheet error somewhere along the way, although I have double-checked and can’t find anything. Below is a table with all of the metrics discussed in this post for each team, sorted by gEW%:

Crude NFL Ratings, 2011

The NFL is a distant third on my list of pro sports interests (baseball is #1, of course, and horse racing ranks #2), but I’m interested enough to run the teams through my crude rating system (see explanation here) and figure I might post the ratings here. They are based on points/points allowed, adjusted for strength of schedule. 100 represents a win/loss ratio of 1, and so the resulting ratings are adjusted win ratios and can very easily be used to estimate the probability of a team winning a particular game. A team with a rating of 100 should beat a team with a rating of 50 2/3 of the time (100/(100 + 50)).

Actually, let me first run a list based on actual wins and losses. I’ve actually calculated W/L ratio as (W + .5)/(L + .5) here just to avoid the (real in the NFL) possibility of a 16-0 team crashing the system:



In the chart, aW% is an adjusted W%; it averages to .500 for the NFL and will produce the same list in rank order as the CTR; I prefer the latter because of its Log5 readiness, but aW% is a more meaningful unit. SOS is the weighted average of opponent’s strength of schedule. “rk” is the team’s rank in CTR, while “s rk” is the team’s rank in the SOS estimate.

I really do not care for the actual W% presentation for the NFL due to the short season magnifying differences in the teams. The Packers tower over the league here, which is appropriate given a 15-1 record against a decent schedule, but it doesn’t have any predictive value. You will notice in the table above that the NFC does quite well, which will be carry through to the points-based ratings:



Green Bay does not even rank #1 in the league; both New Orleans and San Francisco rank ahead of them. The top nine and eleven of the top fourteen teams made the playoffs, which is pretty good I think.

The aggregate ratings for the divisions (simply the average rating of the four teams) illustrates the superiority of the NFC and why I don’t care for micro-divisions:



Last year, the NFC West in turned in a ghastly 29 rating. Led by San Francisco, they were from the worst in the league, a distinction that went to their AFC brethren.

This whole exercise would be devoid of a great deal of entertainment value if I did not use the results to estimate Super Bowl probabilities. The disclaimer list here is lengthy enough that I will skip it less I leave anything out. A credibility adjustment would be pretty simple to implement (adding 12 games of a 100 rating would do the trick), but this is just NFL stats, not something important. The playoff odds do consider home field advantage; the home team’s rating is multiplied by 57/43 to reflect a fairly average NFL home field advantage. I feel bad about listing the probabilities to the thousandth place, but there are so many possible combinations for the championship games and Super Bowl that those tables would look silly without it:



Two road favorites on the first weekend is probably pretty typical given the quality of teams that often win micro-divisions (particularly those like the AFC West). The Denver Broncos simply aren’t a very good football team (it is tough for me to leave it at that, but piling on more snark re: you-know-who is beyond excessive at this point).



I like reseeding in theory, but when your initial seeding insists that Denver ranks #4 in the AFC because they are the sharpest scissors in the kindergarten classroom, it loses some of its luster.



Life is tough enough as a Browns fan without having to worry about horrors like a Denver/Cincinnati AFC title game, but thankfully there’s a 99.8% chance that will not come to pass. Pittsburgh/Baltimore, on the other hand, is the most likely championship game scenario that doesn’t involve either conference’s #1 seed.



Combining all of these, here are the playoff probabilities for each team:



The system still considers Green Bay the Super Bowl favorites even though they rank below New Orleans and San Francisco, thanks to favorable second round matchups and home field advantage, which is much more significant in the NFL playoffs than in MLB. Ratings and home field aside, if the NFC title game turns out to be Packers/Saints, I’m picking the latter to win it all. These probabilities add up to a 57% chance of the NFC representative winning the Super Bowl.

Hitting by Position, 2011

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.

The first obvious thing to look at is the positional totals for 2011, 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:



The 2011 results were most notable for the poor performance by third basemen and the pathetic effort by left fielders, who were slightly less productive than the average non-pitcher. After a down 2010, DHs rebounded to a respectable 110. The other positions were fairly close to their historical norms, and pitchers avoided setting a new all-time low, although the difference between 7 and 5 is negligible.

Speaking of pitchers, here are the aggregate park-adjusted totals for NL pitching teams. This analysis is based on simple ERP, and thus ignores sacrifices and the other situational goodness that makes pitcher hitting such an exciting and integral part of our national pastime:



Milwaukee ranked second and Arizona first last year, but on the other hand the Mets were third in 2010 and dead last in 2011. AL pitchers don’t get enough opportunities to bother with a chart, but for trivia’s sake, Baltimore’s pitchers raked .405/.405/.630, while Kansas City’s failed to reach base in eighteen plate appearances.

Moving on to positions that are actually expected to hit, I figured park-adjusted RAA for each position. The baseline for average is the overall 2011 MLB average RG for each position, with left and right field pooled. The leading team at each position was as follows (these are generally unsurprising so I’ll spare you a big chart):

C--DET, 1B--DET, 2B--BOS, 3B--CHN, SS--NYN, LF--MIL, CF--LA, RF--TOR, DH--BOS

The only one of these that was a bit surprising to me even after looking at the final stats for individuals was the Cubs’ third basemen (led of course by Aramis Ramirez). But a lot of the usual suspects at third base had injuries and other issues this year (Longoria, Zimmerman, Wright, Youkilis).

Now the worst performance at each position, along with a column displaying the team leader in games played at that spot:



It’s mostly a coincidence that all of the worst-hitting positions were from AL teams, although they do generally get more PA in which to drive down their RAA. I wrote about the Twins and Angels catchers a little in the previous post, but note here that Houston’s catchers were second last with -31 RAA and the Angels managed -29. The continuing inability of Seattle to generate offense is a marvel, and Juan Pierre is an appropriate banner carrier for 2011’s crop of poor hitting left fielders.

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:



Third base and shortstop led the Mets to the highest infield RAA in the NL. Atlanta tied for the lowest outfield RAA in the NL. There must be something wrong with my spreadsheet as surely the Phillies first basemen combined for more than 8 RAA, led by their perennial MVP candidate.



St. Louis was the only team in the game to be above average at every position, and really stood at out at the three biggest offensive positions. Their outfield combined to lead MLB in RAA. Milwaukee’s offense was structured similarly, although right field did not stand out and they gave a lot of it back with a black hole at third base. The Cubs’ outfield production was evenly distributed and combined to tie Atlanta for the lowest mark in the NL. Pittsburgh’s infield tied for the NL’s trailer spot. Houston got decent production in the outfield but nowhere else.



The fact that the Los Angeles infield tied for the fewest RAA in the NL and yet the offense combined to lead the division should give you a quick idea on the offensive character of the NL West. While the World Series title makes it easy for some to overlook, San Francisco’s offensive struggles are persistent and pitching can only take you so far.



Boston’s offense was terrific despite right field, leading the majors in infield RAA. Toronto pulled a neat trick by combining for -17 RAA from the outfield despite having Jose Bautista.



Kansas City led the AL in outfield RAA, which not many would have predicted from Alex Gordon, Melky Cabrera, and Jeff Francoeur. Cleveland’s outfield was second-worst in the majors, and under normal circumstances -62 from the outfield would stick out more. The best thing that can be said about Chicago’s -98 RAA is that it was balanced -49/-49 between infield and outfield, with catcher and DH nearly average (+2/-2).



Texas kept the AL West from looking like it’s NL counterparts. Chris Iannetta and some guy whose name I can’t remember should do wonders for LAA. Oakland’s -50 runs from the infield was the worst in the majors, almost all driven by dreadful production at first base. And then there’s Seattle. What can one say about Seattle? Every outfield position was at least -20 (only five other outfield spots across the other 29 teams were at -20). Catcher, third base, and DH also stood out for the hapless Mariners.

Earlier I displayed some long-term positional adjustments that I’ve used over the years. It dawned on me in September that those were based on the ten-year period from 1992-2001, and that at this point, none of the most recent ten years are included in the sample. So I figured it would be an opportune time to recalibrate my position adjustments, using the ten years from 2002-2011 as the basis.

I figured two sets of PADJs; one which compared each position to the overall league average (including pitchers), and one that compared it to the league average less pitchers. There is very little difference, of course--the ones compared to the average including pitchers tend to be one or two points higher. This table compares the 1992-2001 and the 2002-2011 adjustments:



The big movers relative to 1992-2001 were the middle infield positions, improving offensively as first base/DH declined a little. In the end, though, the defensive spectrum one would draw based on offense doesn’t change at all, except for third base switching places with center field (and the differences were miniscule in both decades) to match Bill James’ spectrum.

A longer digression about the application of position adjustments, and some reasons why one might want to consider using offensive adjustments, will have to wait for another time, but would be appropriate here.

This spreadsheet includes the 2011 data by position.

Hitting by Lineup Slot, 2011

I devoted a whole post to leadoff hitters, whether justified or not, so it's only fair to have a post about hitting by batting order position in general. I certainly consider this piece to be more trivia than sabermetrics, since there’s no analytical content.

The data in this post was taken from Baseball-Reference. The figures for each team's runs are not park-adjusted--I intended to do so, but unfortunately I had already written the body of the post before I realized that they’d been omitted. The Padres having the worst 2, 3, and 4 production in the NL should have alerted me to this sooner. Then I had to go back and remove some comments that make no sense when ignoring park effects, so now the post is just a skeleton. Oh well. RC is ERP, including SB and CS, as used in my end of season stat posts. The weights used are constant across lineup positions; there was no attempt to apply specific weights to each position, although they are out there and would certainly make this a little bit more interesting.



This marks a third straight season that the most productive lineup slot in the majors was the NL’s #3 hitters…Pujols, Votto, Braun and company. Despite all of the seemingly silly things managers do with their batting orders, it is comforting to know that, from the cleanup spot down, each subsequent spot is less productive. Of course, that doesn’t excuse the feeble performance of NL #2 hitters, who just edged out the #8 hitters as the least productive NL spot filled by real hitters.

Next, here are the team leaders in RG at each lineup position. The player listed is the one who appeared in the most games in that spot (which can be misleading as the presence of Mitch Moreland demonstrates):



Houston actually had the NL’s most productive hitters at two spots; of course, they were two bottom of the batting order spots in which nobody contributes anyway. The least productive lineup spots:



As you can see, Minnesota had the worst production out of both the #8 and #9 spots. What makes this truly impressive, though, is that Drew Butera was the leader in games played in both spots. One thing I had meant to include in my meanderings post but forgot was a comparison of Mathis and Butera’s basic batting lines as I present them in my end of season stats. Neither had enough PA to qualify for those lists, but their seasons were too bad to just ignore:



Mathis was intentionally walked twice; both came in a June 17 game at the Mets. No word on whether or not Ron Washington temporarily replaced Terry Collins.

Note that Houston’s #9 hitters (the best in the NL at 2.3 RG) almost managed to outhit their #8 hitters (worst in the NL at 2.5 RG).

The next chart displays the top ten positions in terms of RAA, compared to their league’s average for each spot. A lot of the same suspects pop up, of course:



And the ten worst positions:



Finally, this table has each team’s RG rank among the lineup slots in their league. The top and bottom three in each league have been noted, which make Boston and Seattle stand out (for opposite reasons, of course).





Here is a link to a Google spreadsheet with the underlying data. The RG and RAA figures in this one are park-adjusted as should have been done throughout this post.

2011 Leadoff Hitters

This post kicks off a series of posts that I write every year, and therefore struggle to infuse with any sort of new perspective. However, they're a tradition on this blog and hold some general interest, so away we go.

This post looks at the offensive performance of teams' leadoff batters. I will try to make this as clear as possible: the statistics are based on the players that hit in the #1 slot in the batting order, whether they were actually leading off an inning or not. It includes the performance of all players who batted in that spot, including substitutes like pinch-hitters.

Listed in parentheses after a team are all players that appeared in twenty or more games in the leadoff slot--while you may see a listing like "BOS (Ellsbury)” this does not mean that the statistic is only based solely on Ellsbury's performance; it is the total of all Boston batters in the #1 spot, of which Ellsbury was the only one to appear in that spot in twenty or more games. I will list the top and bottom three teams in each category (plus the top/bottom team from each league if they don't make the ML top/bottom three); complete data is available in a spreadsheet linked at the end of the article. There are also no park factors applied anywhere in this article.

That's as clear as I can make it, and I hope it will suffice. I always feel obligated to point out that as a sabermetrician, I think that the importance of the batting order is often overstated, and that the best leadoff hitters would generally be the best cleanup hitters, the best #9 hitters, etc. However, since the leadoff spot gets a lot of attention, and teams pay particular attention to the spot, it is instructive to look at how each team fared there.

The conventional wisdom is that the primary job of the leadoff hitter is to get on base, and most simply, score runs. So let's start by looking at runs scored per 25.5 outs (AB - H + CS):

1. TEX (Kinsler), 6.8
2. MIL (Weeks/Hart), 6.5
3. BOS (Ellsbury), 6.4
Leadoff average, 5.0
ML average, 4.3
28. LAA (Izturis/Aybar), 4.0
29. STL (Theriot/Furcal), 3.9
30. WAS (Bernadina/Desmond/Espinosa), 3.9

Obviously you all know the biases inherent in looking at actual runs scored. It is odd to see St. Louis near the bottom as they had a good offense overall. Usually the leadoff hitters will manage to score some runs when they have Pujols, Holliday and Berkman coming up behind them whether they get on base that much or not.

Speaking of getting on base, the other obvious measure to look at is On Base Average. The figures here exclude HB and SF to be directly comparable to earlier versions of this article, but those categories are available in the spreadsheet if you'd like to include them:

1. CHN (Castro/Fukudome), .364
2. NYN (Reyes/Pagan), .364
3. BOS (Ellsbury), .362
Leadoff average, .324
ML average, .317
28. BAL (Hardy/Roberts/Andino), .287
29. SF (Torres/Rowand), .282
30. WAS (Bernadina/Desmond/Espinosa), .277

I would not have correctly identified the Cubs as having the highest OBA out of the leadoff spot in my first fifteen guesses, I don’t think. The seven point difference between the overall major league OBA and the OBA of leadoff men is a little smaller than it usually is, but last year the gap was just two points.

The next statistic is what I call Runners On Base Average. The genesis of it is from the A factor of Base Runs. It measures the number of times a batter reaches base per PA--excluding homers, since a batter that hits a home run never actually runs the bases. It also subtracts caught stealing here because the BsR version I often use does as well, but BsR versions based on initial baserunners rather than final baserunners do not.

My 2009 leadoff post was linked to a Cardinals message board, and this metric was the cause of a lot of confusion (this was mostly because the poster in question was thick-headed as could be, but it's still worth addressing). ROBA, like several other methods that follow, is not really a quality metric, it is a descriptive metric. A high ROBA is a good thing, but it's not necessarily better than a slightly lower ROBA plus a higher home run rate (which would produce a higher OBA and more runs). Listing ROBA is not in any way, shape or form a statement that hitting home runs is bad for a leadoff hitter. It is simply a recognition of the fact that a batter that hits a home run is not a baserunner. Base Runs is an excellent model of offense and ROBA is one of its components, and thus it holds some interest in describing how a team scored its runs, rather than how many it scored:

1. CHN (Castro/Fukudome), .339
2. NYN (Reyes/Pagan), .336
3. PIT (Tabata/McCutchen/Presley), .315
Leadoff average, .291
ML average, .285
28. SF (Torres/Rowand), .253
29. BAL (Hardy/Roberts/Andino), .253
30. WAS (Bernadina/Desmond/Espinosa), .247

You are probably starting to notice a lot of repetition in the leaders and trailers. Obviously a lot of these metrics measure the same thing in slightly different ways or measure similar things, so it’s to be expected.

I will also include what I've called Literal OBA here--this is just ROBA with HR subtracted from the denominator so that a homer does not lower LOBA, it simply has no effect. You don't really need ROBA and LOBA (or either, for that matter), but this might save some poor message board out there twenty posts, so here goes. LOBA = (H + W - HR - CS)/(AB + W - HR):

1. CHN (Castro/Fukudome), .344
2. NYN (Reyes/Pagan), .341
3. PIT (Tabata/McCutchen/Presley), .321
Leadoff average, .297
ML average, .292
28. BAL (Hardy/Roberts/Andino), .261
29. SF (Torres/Rowand), .257
30. WAS (Bernadina/Desmond/Espinosa), .252

In this presentation, the rank difference between ROBA and LOBA is barely noticeable.

The next two categories are most definitely categories of shape, not value. The first is the ratio of runs scored to RBI. Leadoff hitters as a group score many more runs than they drive in, partly due to their skills and partly due to lineup dynamics. Those with low ratios don’t fit the traditional leadoff profile as closely as those with high ratios (at least in the way their seasons played out):

1. LA (Gordon/Gwynn/Carroll/Furcal), 2.5
2. HOU (Bourn/Bourgeois/Schafer), 2.1
3. DET (Jackson), 2.0
Leadoff average, 1.6
26. WAS (Bernadina/Desmond/Espinosa), 1.2
28. KC (Gordon/Getz), 1.2
29. BOS (Ellsbury), 1.2
30. BAL (Hardy/Roberts/Andino), 1.2
ML average, 1.1

The presence of the Red Sox in the bottom three on this list should drive home the point about this not being a quality metric. The leadoff hitters that rank the lowest in R/BI are those that drive in almost as many runs as they score. If you had a leadoff hitter that was driving in many more runs than he scored, that might be cause for some reconsideration of your batting order, but having some scored/batted in parity is not inherently a bad thing.

A similar gauge, but one that doesn't rely on the teammate-dependent R and RBI totals, is Bill James' Run Element Ratio. RER was described by James as the ratio between those things that were especially helpful at the beginning of an inning (walks and stolen bases) to those that were especially helpful at the end of an inning (extra bases). It is a ratio of "setup" events to "cleanup" events. Singles aren't included because they often function in both roles.

Of course, there are RBI walks and doubles are a great way to start an inning, but RER classifies events based on when they have the highest relative value, at least from a simple analysis:

1. CHA (Pierre), 2.4
2. MIN (Revere/Span), 1.9
3. LA (Gordon/Gwynn/Carroll/Furcal), 1.8
Leadoff average, 1.0
ML average, .8
28. BAL (Hardy/Roberts/Andino), .6
29. BOS (Ellsbury), .6
30. MIL (Weeks/Hart), .6

Last year, the White Sox led handily in RER, due in large part to Pierre’s steals. This year, Pierre didn’t steal as many bases but still managed to slap his team to the top.

Speaking of stolen bases, last year I started including a measure that considered only base stealing. Obviously there's a lot more that goes into being a leadoff hitter than simply stealing bases, but it is one of the areas that is often cited as important. So I've included the ranking for what some analysts call net steals, SB - 2*CS. I'm not going to worry about the precise breakeven rate, which is probably closer to 75% than 67%, but is also variable based on situation. The ML and leadoff averages in this case are per team lineup slot:

1. HOU (Bourn/Bourgeois/Schafer), 29
1. NYN (Reyes/Pagan), 29
3. SEA (Suzuki), 26
Leadoff average, 11
ML average, 3
28. CHA (Pierre), -3
29. STL (Theriot/Furcal), -6
29. CLE (Brantley/Sizemore/Carrera), -6

The Indians have been just missed the trailer spots on a number of these lists. At least Cleveland and St. Louis are at the bottom largely because their leadoff hitters didn’t attempt that many steals. Only Milwaukee and Baltimore leadoff hitters (16 and 21 respectively) attempted fewer steals than Cleveland (24) and St. Louis (18). Neither the Tribe (58%) nor the Redbirds (56%) had success when they did steal, but they weren’t trying it all that much. The White Sox, on the other hand, were 31-48 (65%), a poor percentage and the eleventh-most attempts.

Let's shift gears back to quality measures, beginning with one that David Smyth proposed when I first wrote this annual leadoff review. Since the optimal weight for OBA in a x*OBA + SLG metric is generally something like 1.7, David suggested figuring 2*OBA + SLG for leadoff hitters, as a way to give a little extra boost to OBA while not distorting things too much, or even suffering an accuracy decline from standard OPS. Since this is a unitless measure anyway, I multiply it by .7 to approximate the standard OPS scale and call it 2OPS:

1. BOS (Ellsbury), 882
2. NYN (Reyes/Pagan), 835
3. MIL (Weeks/Hart), 834
Leadoff average, 733
ML average, 723
28. CHA (Pierre), 669
29. SF (Torres/Rowand), 645
30. WAS (Bernadina/Desmond/Espinosa), 630

Along the same lines, one can also evaluate leadoff hitters in the same way I'd go about evaluating any hitter, and just use Runs Created per Game with standard weights (this will include SB and CS, which are ignored by 2OPS):

1. BOS (Ellsbury), 6.7
2. NYN (Reyes/Pagan), 6.2
3. TEX (Kinsler), 6.1
Leadoff average, 4.6
ML average, 4.4
28. CHA (Pierre), 3.4
29. SF (Torres/Rowand), 3.4
30. WAS (Bernadina/Desmond/Espinosa), 3.4

Finally, allow me to close with a crude theoretical measure of linear weights supposing that the player always led off an inning (that is, batted in the bases empty, no outs state). There are weights out there (see The Book) for the leadoff slot in its average situation, but this variation is much easier to calculate (although also based on a silly and impossible premise).

The weights I used were based on the 2010 run expectancy table from Baseball Prospectus. Ideally I would have used multiple seasons but this is a seat-of-the-pants metric. Last year’s post went into the detail of how I figured it; this year, I’ll just tell you that the out coefficient was -.22, the CS coefficient was -.587, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (741 in 2011):

1. BOS (Ellsbury), 29
2. TEX (Kinsler), 26
3. NYN (Reyes/Pagan), 25
Leadoff average, 0
ML average, -3
28. CHA (Pierre), -20
29. WAS (Bernadina/Desmond/Espinosa), -20
30. SF (Torres/Rowand), -21

From an overview of all of these metrics, I think it’s safe to say that Red Sox and Mets leadoff hitters were pretty effective while White Sox, Nationals and Giants were not. I was a little disappointed that the Braves and Astros didn’t make any lists together here as each team used both Michael Bourn and Jordan Schafer in twenty or more games out of the #1 spot. Obviously that’s a possibility when players are traded for each other, but it would have been particularly amusing had one team been on the leader list and the other on the trailer list.

A spreadsheet with all of the data and the full lists is available.

Statistical Meanderings 2011

I have to apologize in advance for this--it sort of resembles a bad Jayson Stark piece with better metrics but less interesting tidbits.

* The discrepancy in R/G between the AL and NL (for the offenses) expanded to .33 (4.46 to 4.13) after a one-year blip that saw the two circuits only .12 runs apart. The leagues were equal in walk rate (.090 and .091 per at bat), but the AL hit for a higher BA (.258 to .253) and with more power (.150 to .139 ISO).

* I certainly do not intend to dispute the notion that Houston was the worst team in baseball, but Minnesota actually had a lower EW% and PW%. Based on runs and runs allowed, Houston “should have” won 61.8 games to Minnesota’s 61.5, and runs created expected a wider gap, 63.3 to 59.8. Obviously this does not consider strength of schedule, but it does put into perspective just how disastrous the Twins’ season was.

* Tampa Bay led the majors in converting balls in plays into outs by a wide margin; their DER of .712 was as far ahead of second place LAA as the Angeles were ahead of twentieth place STL. The Rays also led the majors in modified fielding average, albeit not by a runaway margin.

As a brief aside, “modified” fielding average is no more complex or accurate than regular old fielding average, except I remove strikeouts and assists from the formula. It would actually be easier to work with if I looked at the complement (errors/(putouts less strikeouts + errors)), but fielding average has been expressed that way for ever and it’s not a particularly telling metric in any event.

* In 2010, major league teams had an unusually high W% at home (.559) and 28 teams had a higher W% at home than on the road. This led to some speculation about whether there was something afoot.

2011 did not provide any such conspiracy fodder. Home teams had an abnormally low W% (.526), and only 23/30 teams (77%) won with a greater frequency at home. It was the lowest HW% for MLB since 2001 (.524), and 2005 was the last time that only 23 teams were better at home (only 20 were in 2001).

* The Giants scored 2.91 runs per game at home, the lowest output since 1972. They had to do the near impossible to achieve this by scoring less than the legendary 2010 Mariners (2.95). Offensive ineptitude combined with their good defense resulted in San Francisco playing in the lowest overall scoring context (7.09 RPG) in the majors since the 2003 Dodgers (6.98).

* Don’t tell anyone, but the two teams that struck out the fewest times were the Rangers (930) and the Cardinals (978). Both did unsurprisingly ground into a lot of double plays--Texas was sixth in MLB with 135 and St. Louis’ 169 was sixteen more than second place Baltimore.

* I always like to run a chart showing each playoff team’s RAA broken down by offense and defense:



As you can see, the average playoff team was fairly balanced. The only subpar unit in the group was the defense of the World Champion St. Louis Cardinals.

* At first glance, there was nothing remarkable about the Kansas City bullpen:



Their 4.26 relief eRA was equal to the American League average. But the interesting thing is that all of them were rookies except for Joakim Soria. I’ve already said nice things about Greg Holland in my Rookie of the Year post, so I won’t repeat that here.

* Someone beat me to it, but it is worth pointing out how low Trever Miller’s innings to appearance ratio was, particularly during his time in St. Louis. Miller recorded 47 outs in 39 appearances (1.21 O/G) with the Cards. I cannot state this absolutely, but I believe that is the lowest ratio in ML history for a pitcher with 20 or more appearances. The previous low I can find is Randy Flores with the 2009 Rockies (36 outs/27 games, 1.33). Miller’s complete season line was a yeoman 64 outs in 48 games, tying Flores’ record. A fitting achievement for Tony LaRussa’s final season if I may say so myself.

* One of the stats I track for relievers is inherited runners/game. In an era where leverage index is readily available, it doesn’t yield much marginal value, but I always like looking at closer usage through IR/G. Closers usually dominate the bottom of the IR/G list (I believe Mariano Rivera led full-time AL closers at .31, which was 71st out of 85 relievers), but it’s always fun to see which closers were never brought in with runners on base. If a manager never calls on his closer with runners on, he’s either really locked into bullpen roles, or he really doesn’t trust him. I’d assume the latter was the case with Kevin Gregg, who inherited zero runners in 2011. The former was the case for John Axford (1 in 74 appearances).

* Brian Wilson has taught us that a quirky personality, a ridiculous beard, and a World Series ring can get you a lot of commercials with 7 RAR. Who was the last closer so marginal that got so much publicity?

* Which Yankee reliever is which?



The point here is not to compare the two, but to point out that David Robertson had a really great season.

* You wouldn’t know it from watching the playoffs (and Ron Washington and the Rangers reluctance to use him that eventually turned into an outright dropping off of the roster), but Koji Uehara ranked fifth in RAR among AL relievers and was seventeenth last year. Of course, if all you went by was Washington’s managing, you would be shocked to learn where Nick Punto tends to rank on RAR lists.

* Five major league starters averaged 110 or more pitches per start this year, which has to be the most in some time. I’m pretty sure that hasn’t happened since I’ve been including P/S in my year end stat reports, although I didn’t go back and check to make sure. The five were: Verlander (117), Weaver (113), Halladay (111), Shields (111) and Sabathia (110).

* At the risk of cherry picking (as I’m sure I’m leaving out some pitchers that were talked about similarly but have had continued success, plus one season is obviously insufficient to draw conclusions in any event), I always find it a little satisfying when pitchers that were said to be DIPS beaters have either terrible or high BABIP seasons. Trevor Cahill is in the latter category--he wasn’t horrible by any means, and a .306 BABIP is not that high, but it still is not the kind of season a good DIPS beater should have. JA Happ, on the other hand, was atrocious and gave up an identical .306 BABIP. Even Charlie Morton sort of fits--even looking at his entire season, he wound up at -3 RAA with a .323 BABIP. Along those lines, what are the odds that Josh Tomlin is in the major leagues in five years? They can’t be that good.

* JoJo Reyes seemed to get a lot of attention for his lengthy (by time, especially) losing streak early in the year. Or perhaps my impression of that is off, magnified by the fact that I watched him get his first win pitching against Cleveland. In any event, Reyes may have had some bad luck along the way, but a lot of it evened out in 2011. A pitcher with a 6.45 RRA, 6.24 eRA and 5.21 dRA should consider himself darn lucky to wind up 7-11.

* PSA: David Freese is 28 and ranked 6th in RG among NL third basemen. I overlooked it, but Chase Headley actually had a .393 OBA and created 6.1 runs per game, second to Pablo Sandoval among NL third baseman. So postseaon hardware aside, Padres fans shouldn’t feel too terribly about which of their possible third basemen they actually have.

* AL players with negative RAR who at one time were actually good included Vernon Wells, Magglio Ordonez, JD Drew, Justin Morenau, Alex Rios, Chone Figgins and Adam Dunn. Morneau went from first among AL first baseman in RG in his concussion-shortened 2010 to last in 2011.

* AL players who had an OBA greater than their SLG were: Ryan Sweeney, Chris Getz, JD Drew and Adam Dunn. But for as bad as Dunn’s season was, Chone Figgins’ was actually worse on a rate basis. Figgins only played in 81 games to Dunn’s 122, but still held just a -15 to -17 RAR lead. Figgins created 1.75 runs per game, lowest among all major league players with 300 PA, lower even than Paul Janish (1.90).

* Which of these teammates would you assume was more valuable, based on the statistics presented here?



Of course, any opinion you’d form would be woefully incomplete, because I’ve only given you offensive statistics, without telling you anything about position or defense. Offensively, though, they are nearly indistinguishable. So what if I tell you that one of these players is a slow first baseman and the other one is a center fielder? Surely, the center fielder must have been more valuable, right?

How about these two teammates?



They both play the same position, but one of them was signed as a free agent and took the other’s spot at their common position (third base)--so the one who was pushed off played 105 games at 1B/DH and 55 games at the other infield positions. The one who got the fielding job was more likely the more valuable player, right?

One would think. But the first baseman finished 10th in the MVP voting and the center fielder finished 13th. The third baseman finished fifteenth while the 1B/DH finished 8th and got a first place vote.

IBA Ballot: MVP

My position on pitchers as MVP candidates is pretty simple: I think they absolutely should be considered. However, that doesn’t mean it’s a common occurrence for me to conclude that a pitcher was the MVP of his league. In general, I think that given modern workloads, it is much more likely for a batter to be the MVP than a pitcher. Additionally, when I conclude that a pitcher and a position player are indistinguishable in terms of value, I will usually hedge my bets and go with the batter. A corollary to this is that I’d like the pitcher’s peripheral statistics to indicate that he is equally or more valuable than his batting rivals, not just his actual runs allowed. This is a higher hurdle to clear, since the best pitchers in terms of runs allowed are more likely than not to have outpitched their peripherals.

The end result of this thinking is that somewhere around 2-4 pitchers are sprinkled through my MVP ballot, but rarely is one listed at #1. I’ve been formally writing up my ballots for this blog since 2006, which gives me ten league-seasons with which to quantify my thought process:



As you can see, on average I list three pitchers on my ballot, with the leading pitcher placed fourth. Obviously I’m biased, but I think this is a very fair treatment of pitchers.

All of this bloviating and laughably in-depth analysis of my own previous ballots is necessary because, for the first time since I’ve been doing this, there is a popular movement to vote a starting pitcher as MVP. I want to make it clear that, and I think I have, that if I don’t feel that Justin Verlander was the AL MVP, it’s not because of some bias against pitchers, but simply that I felt other player(s) were more valuable in 2011.

Verlander has gained traction as a candidate for two reasons. One, he pitched for a playoff team, and heavens knows that mainstream types will bend over backwards to try to give the MVP to a player whose contributions were “actually valuable”, or whatever argument they’d like to use to dismiss players whose teammates just weren’t that good. It also helps that of the AL playoff teams, Detroit was something of a surprise (they were certainly the most surprising to me, although the voters would probably give that nod to Tampa Bay), and they made a strong surge in August and September to run away with their division. That’s a good narrative.

Secondly, Verlander’s W-L record is very impressive (24-5), and we all know that the mainstream still is easily distracted by a shiny W-L record. And oh yeah, third, he pitched very well by any measure.

That last point, though, is where I’m not as enthusiastic about Verlander. The mainstream view is that Verlander was obviously the AL’s best pitcher in 2011--my view is that he was a solid #1, but Jered Weaver can’t just be laughed off. Verlander’s season is not historic by any means when viewed through the lens of RAR--for last five seasons, the AL pitching RAR leaders totals have been:

72, 84, 95, 76, 84

Verlander’s 84 is very good, but the average of the previous four AL leaders was 82. It’s a fairly typical league-leading type of performance, a very solid Cy Young-type season, but not one for the ages either.

However, I have Jose Bautista at 82 RAR/63 RAA, I don’t see any compelling reason to penalize him for his defense or baserunning (UZR doesn’t think much of him, but Dewan’s DRS and Wyers’ FRAA don’t share that evaluation), and I don’t care that his team finished in fourth place. Verlander does not look nearly as good when evaluated by dRA, and so when there’s reasonable doubt that the pitcher was more valuable than the position player, I side with the position player.

I also have placed Verlander’s teammate Miguel Cabrera ahead of him, albeit with much less conviction. Cabrera’s offensive value is essentially indistinguishable from Bautista’s--I estimate that Cabrera created 137 runs in 376 outs while Bautista created 134 runs in 363 outs (9.3 to 9.4 RG, 71 to 70 HRAA). However, Cabrera played first base and there’s reason to believe he’s a below-average fielder, putting Bautista ahead. Compared to Verlander, though, I think the case can be made that he was a little more valuable.

Among the other position player candidates to fill out the ballots, Jacoby Ellsbury ranks first in RAR, plus fielding and baserunning would seem to work in his favor. Adrian Gonzalez was right behind his teammate in RAR, and has a good fielding reputation and a decent showing in fielding metrics.

The other three spots all go to second baseman. I suppose one can argue that the positional adjustments I use are too kind to second basemen, but I just happen to think there is a collection of very talented second basemen in the AL at this time. Dustin Pedroia was just behind Ellsbury and Gonzalez in RAR. Curtis Granderson (56 RAR) and Mike Napoli (56) rank ahead of the trio of Robinson Cano (53), Ben Zobrist (52), and Ian Kinsler (50), but Granderson’s fielding raises at least a little concern. Napoli’s RAR gives him a full catcher position adjustment, but he actually played nearly as many games between first base and DH (53) as he did as a catcher (61). While his 8.5 RG ranked third in the AL behind Bautista and Cabrera, he also logged just 427 PA.

Among the three remaining second basemen, the offensive differences are small enough to throw a bone to Kinsler’s well-regarded fielding (at least by the various metrics)and baserunning, while keeping in mind that Zobrist like Napoli also played a fair amount at less demanding positions. Evan Longoria will probably get a lot more love from others, but he ranks 16th on my RAR list and would require more fielding credit than I am comfortable with (or a repudiation of the position adjustment for 3B relative to 2B) to make the ballot:

1) RF Jose Bautista, TOR
2) 1B Miguel Cabrera, DET
3) SP Justin Verlander, DET
4) SP Jered Weaver, LAA
5) CF Jacoby Ellsbury, BOS
6) 1B Adrian Gonzalez, BOS
7) 2B Dustin Pedroia, BOS
8) SP James Shields, TB
9) SP CC Sabathia, NYA
10) 2B Ian Kinsler, TEX

In the National League, there is no need for philosophical reflection about the value of a pitcher versus a position player, or any need for intricate comparisons of multiple players. There is only one question that needs to be answered: Can you make a case against Matt Kemp?

Kemp led NL hitters in RAR by 12, and was in a tied Ryan Bruan for the league lead with a 8.5 RG. His fielding is probably not great, but since no one else was particular close in RAR, you’d have to think he was pretty bad and that Ryan Braun or Prince Fielder or Jose Reyes was really good in the field to close the gap. I don’t see any reason to believe that, so Kemp is my runaway choice as NL MVP.

Filling out the rest of the ballot, Ryan Braun is a very strong candidate for #2. The three pitchers (Halladay, Kershaw, and Lee) that were very close for the Cy Young are all strong mid-ballot choices. Prince Fielder was very good, but inferior to his teammate at the plate and he’s not a strong candidate for fielding and baserunning credit. Jose Reyes and Joey Votto are also in the mix.

As you can see, I’m having trouble finding much to say about the NL ballot. My RAR list actually makes it pretty straightforward; obviously small differences are not meaningful, but I don’t see a lot of compelling reasons to step in and make changes. The only player who drops far below his RAR is Lance Berkman, who obviously is not much of a fielder at this point and who I would be loathe to argue was more valuable than teammate Pujols. And that leaves him without a spot:

1) CF Matt Kemp, LA
2) LF Ryan Braun, MIL
3) SP Roy Halladay, PHI
4) 1B Prince Fielder, MIL
5) SS Jose Reyes, NYN
6) 1B Joey Votto, CIN
7) SP Clayton Kershaw, LA
8) SP Cliff Lee, PHI
9) 1B Albert Pujols, STL
10) SS Troy Tulowitzki, COL