Crude NFL Ratings, 2013

Since I have a crude rating system set up to evaluate MLB teams that relies on win ratio and identity of opponents and thus can be adapted to any number of sports, I see no reason not to apply it to the lesser NFL once a year. Since I am only a casual follower of the NFL, I will endeavor to avoid excessive comment on the results.

As a brief overview, the ratings are based on win ratio for the season, adjusted over the course of several iterations for opponent’s win ratio. They know nothing about injuries, about where games were played, about the distribution of points from game to game; nothing beyond the win ratio of all of the teams in the league and each team’s opponents. The final result is presented in a format that can be directly plugged into Log5. I call them “Crude Team Ratings” to avoid overselling them, but they tend to match the results from systems that are not undersold fairly decently.

First are ratings based on actual wins and losses. 12.2 games of regression are included when figuring the win ratios (this will apply to the point-based ratings as well). CTR is the bottom line rating, aW% converts it to an adjusted W%, and SOS is the average CTR of the team’s opponents:



I prefer to focus on the ratings based on points and points allowed, which are coupled with a Pythagorean approach published at Pro-Football Reference to generate the win ratios:



As you can see, the top five teams all hail from the NFC South and West, which unfortunately had a maximum of four playoff spots available, leaving Arizona as the odd team out. Note that despite going 10-6, a raw record that was bettered by nine NFL teams, the Cardinals ranked sixth in win-based rating, so this is not a Pythagorean fluke. Arizona was a legitimately outstanding team based on the actual on-field results in 2013, but will sit home as far lesser teams battle it out thanks to the vagaries of their micro-division.

The Browns are second-to-last either way you figure it; by W-L record the Redskins are worse, but rank 30th by points, and by points the Jaguars are worse, but rank 27th by W-L.

I use the geometric mean of the CTR of each team to calculate division and conference ratings:



The NFC West would rank fourth if it was a team--it was an absurdly strong division, with all of its teams among the top ten. The ratings imply that the composite NFC team would be expected to win about 55.2% of the time against its AFC counterpart.

The ratings can be used to feed playoff odds, naturally; here home field is assumed to be a 32.6% boost to CTR (equivalent to a .570 home W%). I’m not going to bother with the round-by-round breakout of potential matchups as I do for MLB, but here are the overall crude odds:



It’s worth acknowledging that each of the last two Super Bowl champs were longshots by this or any other estimate--last year’s Ravens were given only a 3% chance. Of course, I’d also point out that the probability of any longshot winning (let’s define that as 5% rounded probability or lower) is 20% and was 14% in 2012.

These odds imply a 60% chance that the NFC champ will win the Super Bowl, but also a 95% chance that the NFC champ will be favored by the odds to win the Super Bowl. The AFC’s best team, Denver, would be favored in only two potential Super Bowl matchups, as would...all five other AFC teams. The top four playoff teams in CTR hail from the NFC, the next six from the AFC, and then the winners of the micro-division lottery, Philadelphia and Green Bay. The NFL frequently provides examples of why I dislike tiny divisions, but never as clearly or as destructively as in 2013.

Hitting by Position, 2013

Of all the annual repeat posts I write, this is the one which most interests me--I have always been fascinated by patterns of offensive production by fielding position, particularly trends over baseball history and cases in which teams have unusual distributions of offense by position. I also contend that offensive positional adjustments, when carefully crafted and appropriately applied, remain a viable and somewhat more objective competitor to the defensive positional adjustments often in use, although this post does not really address those broad philosophical questions.

The first obvious thing to look at is the positional totals for 2013, 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 overall major league average (this is a departure from past posts; I’ll discuss this a little at the end). “LPADJ” is the long-term positional adjustment that I use, based on 2002-2011 data. The rows “79” and “3D” are the combined corner outfield and 1B/DH totals, respectively:



In 2012, there was an unusual convergence of overall positional RG for third base, DH, and all three outfield spots. This did not carry over to 2013 as a more typical spread returned to the defensive spectrum. Still, when compared to the long-term averages, there were quirks as usual. Catchers continued their strong performance with a PADJ of 94 after a 97 in 2012. Right fielders went back to their recent trend of solidly outhitting their left field cousins (one of the quirks that one must be cognizant of when attempting to use offensive data to craft positional adjustments). DHs were about as low as they’ve ever been (a 102 in 1985 is the only lower showing), and pitchers rebounded from a historical low of 1 to post a PADJ of 3, which obviously vindicates any continuing resistance to the DH.

That provides a useful segue from which to take a quick look at the performance by team of NL pitchers. I need to stress that the runs created method I’m using here does not take into account sacrifices, which usually is not a big deal but can be significant for pitchers. Note that all team figures from this point forward in the post are park-adjusted. The RAA figures for each position are baselined against the overall major league average RG for the position, except for left field and right field which are pooled. So pitchers as you can see from the chart above are compared to their robust average output of .11 runs per 25.5 outs:



Dodger pitchers led in BA, OBA, and SLG and ran away with the RG lead. Zack Greinke was the standout, hitting a raw .328/.409/.379 over 72 PA thanks to a .396 BABIP. Greinke drew seven walks, as many or more than the pitching collectives of the Padres, Marlins, Cubs, Reds, and Brewers. However, the most remarkable performance is that of Pittsburgh’s pitchers, who trudged through 318 plate appearances without a single extra base hit. In 2012 the Pirates only mustered one double in 304 PA. I assumed last year that the Pirate performance was without precedent, and clearly a .000 ISO has never been topped. San Francisco gave Pittsburgh a run for their money at the bottom of the list with a .099 BA and just one double and one triple.

I don’t run a full chart of the leading positions since you will very easily be able to go down the list and identify the individual primarily responsible for the team’s performance and you won’t be shocked by any of them, but the teams with the highest RAA at each spot were:

C--MIN, 1B--CIN, 2B--NYA, 3B--DET, SS--LA, LF--STL, CF--LAA, RF--WAS, DH--BOS

More interesting are the worst performing positions; the player listed is the one who appeared in the most games at that position for the team:



The Marlins, Blue Jays, and Yankees all land multiple names on the list, but Houston’s centerfielders were the very worst outfit, a hole that has been plugged elegantly by trading for Dexter Fowler. Jeff Mathis was also replaced in Miami by Jarrod Saltalamacchia, and Carlos Beltran should improve the Yankees production at right field and/or DH. Yankee DHs .186 BA was the worst of any non-NL pitcher spot, with Chicago, Toronto, and Miami catchers all posting a .193 mark. Or, to express their futility in another manner, it seems kind of shocking that only twelve team positions were less productive in terms of RG than Yankee DHs.

Teams with unusual profiles of offense by position has been of interest to me in recent years because of the way the Indians have been constructed--often they have gotten good production from positions on the right side of the defensive spectrum while struggling at the more offensively-inclined positions. The easiest way I’ve come up with to express this numerically is the correlation between a team’s RG by position and the long-term positional adjustment (I’ve pooled left and right field but not 1B and DH in this case; pitchers are excluded for all teams and DHs excluded for NL teams, and I’ve broken the lists out by league because of this):



As usual, the Indians had a negative correlation between PADJ and RG, but they were only the seventh-most extreme team in the majors. Seattle is the team which had the highest correlation, as they got little production from catcher and middle infield (2.6 RG from backstops, 3.2 from the keystone positions) while the four corners and DH all created at least 4.5 RG. On the flip side was Minnesota, largely due to the fact that catcher was easily their most productive position with 6.4 RG and their left fielders and DH created 3.3 RG, only better than their shortstops.

Boston and St. Louis won their pennants largely thanks to respectively having the best offense in their leagues, and in a neat coincidence here, they were near the middle of the pack in correlation for their leagues with identical marks of +.44.

The following charts, broken out by division, display RAA for each position, with teams sorted by the sum of positional RAA. Positions with negative RAA are in red, and positions that are +/-20 RAA are bolded:



Atlanta led the NL in corner infield RAA. New York was last in the NL in outfield RAA. Miami had the worst offense in the majors with a remarkable six positions at -20 runs or worse, and the left fielders just missed at -18. Only the Giancarlo Stanton-led right fielders were above average, and their +18 only managed to offset the opposite outfield corner. The whole division struggled with production from centerfield; the division total of -104 RAA from one position was easily the worst in the majors as the next worst division total was -49 from NL Central shortstops.



St. Louis led all of the majors in outfield RAA as they were the only team with two +20 positions in the outfield. Pittsburgh’s McCutchen-led centerfielders had the highest RAA of any position in the NL. Cincinnati’s offense continues to look wobbly post-Choo as only the star led first base, center, and right units were above average. As seen above, Milwaukee had the most unusual distribution of offense by position in the NL, and it’s actually somewhat impressive that they managed to field an average offense despite -37 runs from first base. Chicago had the worst middle infield RAA in the majors and their infield as a whole was awful at -70, with only the disaster in Miami sparing them from finishing last.



Los Angeles middle infielders led the NL in RAA; San Francisco and Arizona tied for the NL lead for total infield RAA. Colorado had the worst corner infield RAA in the NL, which may explain the desire (albeit not the decision) to give Justin Morneau a multi-year deal. This division had the highest total RAA for a position with 59 RAA from their shortstops.



Boston led the majors in total RAA as only their third basemen were below average. Red Sox middle infielders led the majors in RAA. The Yankees finishing with just two above average positions is still jarring; another way to look at their troubles is that they spent $50.5 million on their intended corner infield starters and wound up with the worst corner infield RAA in the majors.



Detroit led the majors in corner infield and overall infield RAA thanks almost solely their third basemen compiling a whopping 71 RAA (all Cabrera has other Tiger third basemen combined for 85 PA with a .222/.341/.306 line). The rest of their offense was far from impressive, though, although it wouldn’t be fair for me to snark too much about it since the 1,000 run talk was non-existent in the spring. The Indians were close to average around the diamond except for catcher and second base (excellent) and third base (bad). Kansas City’s middle infielders were last in the AL in RAA and as the corner infielders were bad as well, the infield’s total RAA was also last in the league. Minnesota had only one above average position and the worst outfield production in the majors. Chicago had just two above average positions, but just barely with a total of 3 RAA between, leading to the lowest team total RAA in the AL.



Angel outfielders led the AL in RAA, which of course is due to the great Mike Trout. Seattle’s offense is still bad, but the last two seasons have moved them past the laughingstock phase and into consistent organization deficiency status. Houston had only one above average position, but at least they have the excuse that they weren’t really trying; what can the Yankees say?

The full spreadsheet is available here.

Hitting by Lineup Position, 2013

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 are park-adjusted. 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.



NL #3 hitters have now topped all positions in RG for five years running, and again the AL demonstrated balance between #3 and #4 while NL teams got superior performance out of #3 hitters. The other curiosity that stands out to me is that #3 and #4 were the only lineup slots in which the NL had a higher RG. Throw in the fact that the other most celebrated “key” lineup spot (leadoff) was essentially even between the two leagues, and there’s enough fuel to construct some sort of theory (for which there wouldn’t be enough evidence to proceed logically, as if that’s ever stopped anyone before).

During the playoffs I remarked that it seemed like 2013 had been a year in which the notion of batting one’s best hitter #2 had gained traction; when presented with the actual numbers here, I’d be hard pressed to defend that statement. In addition to the overall RG, if this was the case I’d expect to see an uptick in isolated power for #2 hitters. However, AL #2 hitters collective .137 ISO was better only than that of AL #1, #8, and #9 hitters, and the same was true of the NL’s .130.

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, particularly for the bottom the batting order where there is no fixed regular as in the case of the Dodgers #8 spot, or guys who move around the batting order like Jason Castro who takes the blame for Houston’s #3s):



And the worst:



The domination of bad AL lineup spots by just four teams is something I’ve not seen since I’ve been running this report. It’s not that unusual to have one team with several dead spots (Seattle’s hapless offenses pulled this off), but the White Sox, Astros, and Yankees all had multiple such holes. Chicago boasting four such disasters is an impressive feat. Meanwhile, while Ryan Howard hit better than the Phillies collective cleanup hitters, it’s still amusing to see they were the worst unit in the NL.

The next list is the ten best positions in terms of runs above average relative to average for their particular league spot (so leadoff spots are compared to the league average leadoff performance, etc.):



Baltimore’s #5s were significantly more productive than their #3s or #4s (4.4 and 5.4 RG respectively) thanks to Buck Showalter keeping Chris Davis in that spot for much of the season. The only other #5 spot to outhit both the #3s and #4s was Philadelphia (4.5, 4.1, 5.5 RG respectively) on the backs of the Dominic Brown-led performance which paced NL #5s.

The worst positions:



Chicago’s #9 hitters had a lower RG than three groups of NL #9s (LA, COL, and PHI). They were last among AL lineup slots in BA and OBA and just narrowly missed completing the rate stat sweep as NYA #9s slugged .265 (the only other AL lineup slot with a sub-.300 SLG was SEA #9 at .275). While some passage of time in baseball is sad, like Travis Hafner and Adam Dunn-fronted spots landing on this list, it’s comforting to still have Juan Pierre to kick around.

The last set of charts show each team’s RG rank within their league at each lineup spot. The top three are bolded and the bottom three displayed in red to provide quick visual identification of excellent and poor production:





It so happens that each pennant winner sticks out as having fielded a well-balanced, productive lineup--they ranked #1 and #2 in the majors in R/G, so it’s not a surprise, but other than the very bottom of the St. Louis lineup, there were no weak links in either team’s batting order.

The spreadsheet used to generate these figures is here.

Leadoff Hitters, 2013

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 started in twenty or more games in the leadoff slot--while you may see a listing like "OAK (Crisp)” this does not mean that the statistic is only based solely on Crisp's performance; it is the total of all Atlanta batters in the #1 spot, of which Crisp was the only one to start 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. It should go without saying on this blog that runs scored are heavily dependent on the performance of one’s teammates, but when writing on the internet it’s usually best to assume nothing. So let's start by looking at runs scored per 25.5 outs (AB - H + CS):

1. STL (Carpenter/Jay), 7.2
2. CIN (Choo), 6.3
3. BOS (Ellsbury), 5.9
Leadoff average, 4.8
ML average, 4.1
28. PHI (Rollins/Revere/Young/Hernandez), 3.7
29. HOU (Grossman/Villar/Altuve/Barnes), 3.4
30. MIA (Pierre/Yelich/Hechavarria), 3.0

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. CIN (Choo), .397
2. STL (Carpenter/Jay), .371
3. MIL (Aoki), .347
4. OAK (Crisp), .346
Leadoff average, .324
ML average, .314
28. NYN (Young), .289
29. MIN (Dozier/Presley/Carroll), .283
30. MIA (Pierre/Yelich/Hechavarria), .278

The next statistic is what I call Runners On Base Average. The genesis for ROBA is 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. STL (Carpenter/Jay), .352
2. CIN (Choo), .348
3. BOS (Ellsbury), .322
Leadoff average, .294
ML average, .283
28. SEA (Miller/Chavez/Saunders), .260
29. MIA (Pierre/Yelich/Hechavarria), .254
30. MIN (Dozier/Presley/Carroll), .252

The Cardinals move ahead of the Reds here, making up the 26 point gap in standard OBA. Part of this is the obvious – home runs, as Cincinnati leadoff hitters hit 21 to St. Louis’ 11. But another factor is caught stealing, as we’ll see a little later--Reds leadoff hitters were just fifteen for thirty on stolen base attempts, tied for the second most caught stealing. St. Louis leadoff hitters were just three for six on steal attempts--no other team had fewer than ten stolen bases and only Kansas City had as few caught stealing (albeit with 15 SB), so the Cardinals easily had the fewest attempts (Detroit was next with fourteen).

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, by not implying that I think home runs are bad, so here goes. LOBA = (H + W - HR - CS)/(AB + W - HR):

1. CIN (Choo), .358
2. STL (Carpenter/Jay), .358
3. BOS (Ellsbury), .327
Leadoff average, .300
ML average, .290
28. SEA (Miller/Chavez/Saunders), .268
29. MIN (Dozier/Presley/Carroll), .257
30. MIA (Pierre/Yelich/Hechavarria), .257

There is a high degree of repetition for the various OBA lists, which shouldn’t come as a surprise since they are just minor variations on each other.

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. MIA (Pierre/Yelich/Hechavarria), 2.2
2. MIL (Aoki), 2.1
3. PIT (Marte/Tabata), 2.1
7. TB (Jennings/Joyce/DeJesus), 1.9
Leadoff average, 1.6
27. CHN (DeJesus/Castro/Valbeuna), 1.3
28. MIN (Dozier/Presley/Carroll), 1.2
29. KC (Gordon), 1.2
30. TEX (Kinsler/Andrus/Martin), 1.1
ML average, 1.1

Again, this is not a quality list, as indicated by the mix of good and bad OBAs among the leaders and trailers. This is also a good interlude at which to remind you that the players listed are those who started twenty or more games in the leadoff spot for their teams and they are not solely responsible for the overall performance of the team’s leadoff hitters. David DeJesus lead off 66 games for the Cubs and 20 for the Rays and thus finds himself as part of both the leaders and trailers list here.

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. NYN (Young), 1.7
2. HOU (Grossman/Villar/Altuve/Barnes), 1.7
3. MIL (Aoki), 1.4
Leadoff average, 1.0
ML average, .7
27. PIT (Marte/Tabata), .7
28. DET (Jackson/Dirks), .7
29. LAA (Shuck/Aybar/Bourjos), .7
30. SEA (Miller/Chavez/Saunders), .6

Since stealing bases is part of the traditional skill set for a leadoff hitter, 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. BOS (Ellsbury), 47
2. NYN (Young), 27
3. BAL (McLouth/Markakis), 18
Leadoff average, 5
ML average, 3
28. CIN (Choo), -10
29. ARI (Prado/Pollock/Eaton), -11
29. HOU (Grossman/Villar/Altuve/Barnes), -11

Since 2007, the percentage of major league stolen base attempts from leadoff hitters has declined (2007 is an arbitrary endpoint due to it being the first year I have the data at my finger tips):

30.2%, 29.6%, 27.8%, 25.9%, 27.9%, 25.1%, 25.9%

Leadoff hitters should have a disproportionate share of stolen base attempts for three obvious reasons:

1. they by definition get the most plate appearances of any lineup slot, creating more opportunities to get on base
2. as a group, they usually have above-average OBAs more heavily tied up in singles and walks, creating more good opportunities to steal bases
3. managers still tend to strongly consider speed when choosing a leadoff hitter

While #1 is an unalterable truth and #2 is generally supported by sabermetric orthodoxy, #3 is a factor which may decline in importance in a more sabermetrically-minded game. The percentage of steal attempts from leadoff hitters is something I’ll be keeping an eye on in future seasons as an imperfect indicator of shifting reasoning.

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. CIN (Choo), 881
2. STL (Carpenter/Jay), 832
3. OAK (Crisp), 795
Leadoff average, 727
ML average, 717
28. MIN (Dozier/Presley/Carroll), 639
29. NYN (Young), 625
30. MIA (Pierre/Yelich/Hechavarria), 607

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. CIN (Choo), 6.4
2. STL (Carpenter/Jay), 5.8
3. BOS (Ellsbury), 5.4
Leadoff average, 4.4
ML average, 4.3
28. HOU (Grossman/Villar/Altuve/Barnes), 3.2
29. MIN (Dozier/Presley/Carroll), 3.2
30. MIA (Pierre/Yelich/Hechavarria), 2.9

It’s kind of sad not having the Mariners offense ranking last in just about everything anymore, but the Marlins leadoff hitters were just part of a valiant effort by Miami to take up the mantle.

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. The 2010 post goes into the detail of how this measure is figured; this year, I’ll just tell you that the out coefficient was -.216, the CS coefficient was -.583, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (739 in 2013):

1. CIN (Choo), 32
2. STL (Carpenter/Jay), 22
3. BOS (Ellsbury), 19
Leadoff average, 0
ML average, -2
28. HOU (Grossman/Villar/Altuve/Barnes), -20
29. MIN (Dozier/Presley/Carroll), -21
30. MIA (Pierre/Yelich/Hechavarria), -25

A common theme in these rankings has been the turnaround for Cincinnati leadoff hitters, who last year were historically awful. Truly, unbelievably (especially for a playoff team) awful. In 2012, Reds leadoff hitters led by Zack Cozart and Brandon Phillips were last in the majors in R/G (3.8), OBA (.247), ROBA (.224), LOBA (.229), R/BI (2.2), RER (.6), 2OPS (575), and LE (-32). To be fair R/BI and RER are not good/bad categories, but they indicate that the Reds did not fit the traditional leadoff hitter mold.

This year, the Shin-Soo Choo led Reds were tops in R/G, OBA, LOBA, 2OPS, RG, and LE. The bad news is that it was just a one year fix; the good news is that Bryan Price may have a more modern take on leadoff decisions than Dusty Baker. Still, the Reds better have sent Manny Acta a fruit basket for making Choo a “proven” leadoff hitter.

For the full lists and data, see the spreadsheet here.