Year Two

2011 was the first season for Greg Beals as OSU coach, replacing the retired Bob Todd who served in that role from 1988-2010. It is obviously unfair to blame a new coach for everything that happened in his first year, but the facts must still be presented--the Buckeyes went 26-27, their first losing campaign since 1987. A couple of facts balance that--the team managed to go 13-11 in conference play and earn the #4 seed in the Big Ten Tournament, while the 2010 team failed to qualify for the tournament. And Beals played a more adventurous pre-conference schedule than had been the norm in the last decade or so of Todd’s career, including a predictably poor 1-5 trip through California. Still, Boyd Nation’s ISR ranking pegged OSU as #160 nationally, the program’s worst showing for the years (1997 on) for which the ISRs have been published (the previous low was #130 in 1998).

The 2012 team will not look radically different than 2011. Junior Greg Solomon will serve as catcher and figures to get the overwhelming majority of playing time once again. Solomon hit will early in the season, but his horrific strike zone judgment (42/4 K/W) caught up with him and by the end of the season he contributed little to Ohio’s offense. Nonetheless, Beals rode his hot hand and had Solomon hitting in the middle of the lineup as his slump began. Apparently, Solomon may also see significant time at third base, where his bat will be a major liability, with true freshman Aaron Gretz taking over some of the catching chores.

At first base, sophomore Josh Dezse will start. Dezse looks like a power hitter, but his main offensive contribution as a freshman came from a .332 BA (his SEC was only a respectable .280). The hope here is that Dezse will build on his Big Ten Freshman of the Year campaign and emerge as the true all-around batting star that OSU has lacked since Ronnie Bourquin. At second base, junior Ryan Cypret is a captain and a solid defender. His offensive game is BA-centric, but nonetheless figures to hit in the middle of the lineup.

It is not clear who will play third as incumbent Matt Streng graduated. Junior Brad Hallberg showed no power (.042 ISO) and didn’t hit for average (.254), but led the team with 28 walks and showed more promise as a freshman in 2010. However, it appears as if Beals is more comfortable using Hallberg as the DH rather than at third base; in fact, one of Beals first major personnel decisions was to flip Streng and Hallberg from one corner to the other. With Hallberg as DH, true freshman Ryan Leffel or Solomon could man third.

Four-year starter Tyler Engle is gone, leaving a gaping hole at shortstop which the Bucks plan to fill with junior college transfer (junior eligible) Kirby Pellant. Pellant played his freshman season at Marshall, then transferred to Chandler-Gilbert Community College, and is expected to bat second in the lineup.

In the outfield, only one starter returns in the same position--sophomore Tim Wetzel will leadoff and play center field. Wetzel was another Buckeye who flashed no power (three doubles and two triples in 176 at bats), but a little improvement in his OBA would allow him to contribute in his role. Sophomore Mike Carroll, who sat out 2011 after transferring from Duke, will take over left field and will be expected to provide a jolt to the offense. That will allow the Buckeyes to shift senior David Corna from left to right. Corna was only an average offensive performer last year, with his team-leading 16 doubles the most impressive part of his game.

Key reserves include Gretz (if he doesn’t break into the lineup as the regular catcher), Ryan Leffel at third, senior Brad Hutton as a 1B/DH/PH, redshirt freshman Phil Jaskot on the middle infield, and junior Joe Ciamocco and sophomore Blake Hutton in the outfield (corners for the latter). Freshman who don’t look to be in the mix include catcher Ryan Wonders and outfielders Austin Achter and Pat Porter (who incidentally hails from my hometown).

On the mound, junior Brett McKinney should assume the role of Friday starter. McKinney was only average last season, but seems to have good stuff and is easily the most experienced starter on the roster. The Saturday assignment will go to Brian King, a junior-eligible transfer from Paradise Valley Community College. Baseball America rates King as the best newcomer to the Big Ten, so the lefty figures to have a big impact. Righty Greg Greve, who struggled mightily at the beginning of his freshman season but started to pitch better as the year went on, is slotted in as the Sunday starter.

A pair of sophomore right-handers, John Kuchno and Jaron Long (a JUCO teammate of Pellant), figure to serve as the mid-week starters and could certainly push Greve for conference assignments. Junior Brad Goldberg, who transferred from Coastal Carolina a year ago, could get starting nods if he is able to get an eligibility situation resolved.

Three key relievers return: junior righty sidearmer David Fathalikani, senior lefty Andrew Armstrong, and Dezse. Dezse will serve as the closer, but hopefully Beals will only utilize him in that role if he is effective. Dezse throws hard, but had several epic meltdowns as a freshman, including one in the Big Ten Tournament that essentially was the death knell for the season. Fathalikani and Armstrong posted much better statistics with much less flash in situational roles that they figure to reprise. Another freshman sidearmer, Trace Dempsey, is expected to get significant relief innings. Other pitchers on the roster include senior Paul Guey (who got some mopup work in 2011), junior Tito Nava (a transfer from Duke), and freshmen Matt Panek, Robert Sakowsky, and Sam Shafer (all except Panek are right-handed). Tyler Giannonatti, another junior eligible transfer from Gilbert-Chandler CC who may have figured in the relief mix, was lost for the season to injury.

My primary interest in watching this season is continuing to learn more about Greg Beals and his philosophy. Beals did not impress me with his tactics in his first season, but while ideally the coach would be someone whose approach to the game I can embrace, the ultimate test is whether he can build a winning program. Even two seasons is far too soon to begin to form a judgment about that, so my focus will be on his game-level decisions rather than his program-building decisions.

The Buckeyes open the season Friday-Sunday with the Big Ten/Big East Challenge in the Tampa Bay area. The following weekend they travel to Atlanta for a three game series at Georgia Tech. The first weekend in March will see OSU in Port Charlotte, Florida for the Snowbird Classic, which includes a non-conference tussle with the forces of evil, who are one of two Big Ten opponents not on the conference schedule (the other is Iowa). Then OSU goes to Myrtle Beach for the Coastal Carolina tournament.

In a departure from past seasons, the Coastal Carolina tournament (which concludes March 11) will be an early conclusion to the typical Southern swing, as the Buckeyes play their home opening series March 16-18 against Austin Peay. The optimistic scheduling of March baseball in Ohio is made possible by the installation of FieldTurf in Bill Davis Stadium, which itself was made possible by a generous donation from OSU’s most notable recent big leaguer, Nick Swisher. The new Nick Swisher Field may not be aesthetically pleasing to purists, but given the harsh conditions of spring baseball in the North, it makes sound business and baseball sense to play on the fake stuff.

Midweek opponents include Ohio University, Akron, Xavier, Cincinnati, Bowling Green, and Youngstown State at home, with trips to Louisville, Dayton, Miami, and Oklahoma State (two games in a return of a 2011 series in Columbus).

Big Ten opponents are, in order: Purdue, @Michigan State, Minnesota, Nebraska, @Illinois, @Penn State, Northwester, and @Indiana. Thanks to the new eleven team Big Ten alignment (plus Nebraska, still without baseball at the joke of an institution in Madison), OSU will be off from Big Ten play on the weekend of May 11-13. That creates an unusual five game stretch of non-conference games (the trip to Oklahoma State and a three game weekend home series against Seattle) in the heart of the Big Ten schedule.

If OSU qualifies for the Big Ten Tournament, it will once again be played in friendly territory, at the Clippers’ Huntington Park from May 23-26.

I always find it difficult to make a prognostication for the season, as my knowledge is limited to just the Buckeyes and does not extend to the conference opponents. I can only go based on the general standard of the Big Ten and how a given year’s OSU team compares to previous ones. On that basis, I think OSU is probably about as good or better as they were in 2011. But when you go 13-11 in a bunched conference and have a below .500 record, and the season is so short, what does that really tell you? I think OSU should qualify for the Big Ten Tournament, but contending for the regular season title is probably not in the cards. The coaches’ preseason poll ranks Ohio 5th which seems reasonable.

Projected lineup:

1. 8 Tim Wetzel (SM)
2. 6 Kirby Pellant (JR)
3. 7 Mike Carroll (SM)
4. 3 Josh Dezse (SM)
5. 4 Ryan Cypret (JR)
6. D Brad Hallberg (JR)
7. 9 David Corna (SR)
8. 2 Aaron Gretz (FM)
9. 5 Greg Solomon (JR)

SP #1: Brett McKinney (JR)
SP #2: Brian King (JR)
SP #3: Greg Greve (SM)
SP #4 (midweek): John Kuchno (SM)
SP #5 (midweek): Jaron Long (SM)

RP: R Trace Dempsey (FM)
RP: L Andrew Armstrong (SR)
RP: R David Fathalikhani (JR)
Closer: R Josh Dezse (SM)

Note: I do not have any inside information--details that are not obvious have either come from my supposition as a long-time follower of the program, the information published on the official athletics website, or the B1G Baseball blog.

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 2011 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 2011 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.