OSU baseball enters 2015 coming off of one its worst seasons in decades. The Buckeyes went 10-14 in the Big Ten, their worst record since 1987, a record fueled by a seven-game Big Ten losing streak (longest since 1987). Their 5-12 road record was the worst since 1972. In 1988, Bob Todd took over as Buckeye head coach and wasted little time in turning the program around, turning 1987’s 19-27 overall, 4-12 B10 record into a 32-28, 16-12 team. Todd would go on to reign over the program for 22 more seasons which served as the second golden age of OSU hardball (13 NCAA appearances, 13 seasons with either a Big Ten regular season or tournament title).
Unlike 1988, 2015 will not follow a dismal showing with a new regime. Todd’s replacement, Greg Beals, enters his fifth season at the helm and needs to turn things around in order to secure his long-term status as OSU coach. He will attempt to do so with a team that has elicited a wide range of preseason prognostications, one from which a sheer performance and player development track record does not appear to be impressive but which some observers insist has a surfeit of potential.
Beals has been fond of catcher platoons and has never given senior Aaron Gretz the job on a full-time basis despite him appearing to be the best option. Gretz will once again share time behind the plate with fellow senior Conor Sabanosh, a JUCO transfer in his second season as a Buck. Both hit fairly well last season and may get at bats at DH as well. Sophomore Jalen Washington and freshman Jordan McDonough will serve as depth.
First base is an open position and may see three juniors rotate through the spot: Zach Ratcliff, Mark Leffel, and Jacob Bosiokovic. Ratcliff is limited to first defensively, but Leffel also is capable of playing third and Bosiokovic will be an option in all four corners. Each has shown flashes of being productive hitters (Leffel more as a hitter for average, the other two for power potential), but none has clearly emerged to grab the spot.
Second base will go to junior Nick Sergakis. Sergakis transferred from Coastal Carolina prior to 2014 and started the season on the bench before an injury to shortstop Craig Nennig pushed him into the lineup. Sergakis was a revelation as one of the team’s most productive hitters (and lead off despite the team’s lowest walk rate). Nennig, a junior, should be back and will play short, but while his fielding draws rave reviews he has yet to demonstrate any ability to hit (.201/.295/.225 in about 190 career PA). Nennig’s offense will make sliding Sergakis back to short a tempting option for Beals.
At third base, junior Troy Kuhn will start. He spent most of 2014 as the second baseman before being displaced for Sergakis upon Nennig’s return. Kuhn was among the team’s most productive hitters and paced OSU with six longballs, so he will be a key part of the lineup again and could move back to second if Nennig struggles. In that case, Bosiokovic and Leffel could play third. The infield backups will include the aforementioned Washington (that rare catcher/second baseman) as well as sophomore L Grant Davis (a transfer from Arizona State) and freshman Nate Romans.
The outfield should be one of Ohio’s strengths. Sophomore left fielder Ronnie Dawson was as fun of a hitter to watch as OSU has had in years and was the team’s best hitter in 2014 (.337/.385/.454). Sophomore center fielder Troy Montgomery was highly touted but did not impress in his debut (.235/.297/.353). Senior right fielder Pat Porter (obligatory mention that he hails from my hometown) had a very disappointing season, but rebounded to have a strong summer campaign and will likely be penciled in as the #3 hitter. Bosiokovic can play either corner and junior Jake Brobst has served mostly as a pinch-runner/defensive replacement. A pair of freshman, Tre’ Gantt (a speedster from Indiana in the mold of Montgomery) and Ridge Winand will complete the depth chart. The DH spot will most likely be filled by the odd men out at catcher and first base.
OSU’s #1 starter, at least to open the season, will be sophomore Tanner Tully, the Big Ten freshman of the year in 2014. Tully’s smoke and mirrors act featured a vanishingly low walk rate (.7 W/9) and low K rate (5.3 K/9) which scream regression even in northern college baseball. Senior lefty Ryan Riga will look to bounce back from an injury-riddled campaign--he and Tully are fairly similar stylistically so it would not surprise to see them split up with Travis Lakins taking the #2 rotation spot. Lakins is a sophomore who should be the easy favorite to be the ace at the end of the season; his talents were wasted somewhat in the bullpen in 2014, fanning 9.0 per nine and leading the pitchers with +12 RAA. Lakins is draft-eligible and barring injury this should be his last season in Columbus.
Junior Jake Post is a 6-2 righty with decent stuff who has yet to find consistent effectiveness but would my bet would be that he will displace Riga or Tully by mid-season. Other starting options are lefty John Havrid, a JUCO transfer from Mesa Community College and freshman Jacob Niggemeyer, a 28th-round pick of the Cubs.
The bullpen will be anchored by senior slinger Trace Dempsey, who may well become OSU’s all-time saves leader but had a rough 2014 (-7 RAA) after a brilliant 2013 (+13). Dempsey’s control abandoned him last year, drawing comparisons to another erstwhile Buckeye closer, Rory Meister. Past Dempsey the bullpen work is largely up for grabs--Lakins was the star last year and will be starting. It is possible that a pitcher like Post could be used as the setup man, foregoing some mid-week wins for conference bullpen depth.
Otherwise, redshirt freshman Adam Niemeyer looks like the key setup man--his true freshman campaign was limited to just three appearances due to injury. Otherwise, I won’t even hazard to guess who will emerge out of the following possibilities other than to note that Beals allows tries to cultivate at least one lefty specialist in his pen:
RHP: Curtiss Irving (SM), Seth Kinker (FM), Brennan Milby (R-FM), Shea Murray (SM), Kyle Michalik (R-FM), Yianni Pavlopoulos (SM)
LHP: Michael Horejsei (JR), Matt Panek (JR), Joe Stoll (SM)
Beals appears to have instituted a shift in scheduling philosophy, opting for more weekend series over multi-team “classics”/pseudo-tournaments. The Buckeyes’ only of the latter will be this weekend as they face George Mason, St. Louis, and Pitt in the Snowbird Classic in Port Charlotte. Subsequent weekends will include three game series at Florida Atlantic, UAB, and Western Kentucky before the home opener March 10 against Indiana-Purdue Fort Wayne.
The following weekend the Buckeyes will host Evansville for a three game series, Rider for a two-game mid-week series, and open Big Ten play March 20 hosting Michigan State. Subsequent weekends will see OSU at Rutgers, home to Penn State and UNLV (the latter non-conference of course), at Nebraska and Northwestern, home to Illinois and Maryland, and at Indiana. The mid-week slate will include home games against Toledo, Akron, Ohio University, Dayton, Kent State, Louisville, and Morehead State and trips to Miami, Cincinnati, and Youngstown State.
There is wide variety of opinion regarding OSU’s 2015 outlook. Perfect Game tabbed them as the #35 team in the country while Collegiate Baseball picks them tenth out of thirteen (with the addition of Maryland and Rutgers) in the Big Ten, which would see OSU miss the eight-team field for the Big Ten Tournament, to be held at Target Field May 21-24.
I tend to side much more closely to Collegiate Baseball’s view than Perfect Game’s. Aside from a second-place Big Ten finish in 2013, Beals’ teams have yet to live up to the hype that his recruiting has generated. Beals’ players do not seem to have developed according to expectations--in early years many of the key players were transfers rather than high school recruits, and there have yet to been many high producers among his high school crops, especially at the plate. And I have written many times about the horrific baserunning and other tactics employs by Beals. There are teams of fifth-graders that consistently make better decisions than Beals’ crew.
What has been particularly disturbing to watch as a fan of the program is that while the rest of the Big Ten has improved (Baseball America predicts that Illinois, Maryland, M*ch*g*n and Nebraska will all qualify for the NCAA Tournament, which would be a record for the conference), OSU has slid into irrelevancy--even with in the northern baseball picture. While Todd’s program was slipping from its heights near the end, he still managed to qualify for the NCAAs every other year. Beals has yet to make a NCAA Tournament appearance, and a sixth straight season (fifth under Beals) on the outside looking in would only extend OSU’s longest drought since 1983-1990 (once Todd led his team to a first tournament in 1991, he never again fell short in consecutive seasons). If OSU does not play up to the level of the optimists, then the program change that I would have liked to see after 2014 may be a fait accompli.
Monday, February 09, 2015
OSU baseball enters 2015 coming off of one its worst seasons in decades. The Buckeyes went 10-14 in the Big Ten, their worst record since 1987, a record fueled by a seven-game Big Ten losing streak (longest since 1987). Their 5-12 road record was the worst since 1972. In 1988, Bob Todd took over as Buckeye head coach and wasted little time in turning the program around, turning 1987’s 19-27 overall, 4-12 B10 record into a 32-28, 16-12 team. Todd would go on to reign over the program for 22 more seasons which served as the second golden age of OSU hardball (13 NCAA appearances, 13 seasons with either a Big Ten regular season or tournament title).
Monday, February 02, 2015
This is an abridged and belated version of one of my standard annual posts, in which I poke around the statistical reports I put together here and identify items of curiosity. Curiosity is the key, as opposed to those that encompass analytic insight--any insight to be found is an accident.
* Since 1961, the ten teams with the largest differential between home and road W%:
And the ten largest ratios of HW% to RW%:
* One chart I always run in this piece is a table of runs above average on offense and defense for each playoff team. These are calculated very simply as park-adjusted runs per game less the league average:
It has not been at all uncommon for the average playoff team to be better offensively than defensively and such was the case in 2014. Two playoff teams had below-average offenses while four had below-average defenses, and the world champions had the worst defensive showing of the ten.
* You can’t turn around without reading about the continual rise in strikeouts. Unlike so many, I don’t consider the current strikeout rate to be aesthetically troublesome. But you can get a sense of how crazy strikeout rates have gotten by looking at the list of relievers who strike out ten or more batters per game (I define “game” in this case as a league average number of plate appearances, not innings pitched; eligible relievers are those with forty or more appearances and less than fifteen starts):
Al Alburquerque, Cody Allen, Aaron Barrett, Antonio Bastardo, Joaquin Benoit, Dellin Betances, Jerry Blevins, Brad Boxberger, Carlos Carrasco, Brett Cecil, Aroldis Chapman, Steve Cishek, Tyler Clippard, Wade Davis, Jake Diekman, Sean Doolittle, Zach Duke, Mike Dunn, Josh Edgin, Danny Farquhar, Josh Fields, Charlie Furbush, Ken Giles, Greg Holland, JJ Hoover, Kenley Jansen, Kevin Jepsen, Sean Kelley, Craig Kimbrel, Jack McGee, Andrew Miller, Pat Neshek, Darren O’Day, Joel Peralta, Oliver Perez, Yusmeiro Petit, Neil Ramirez, AJ Ramos, Addison Reed, David Robertson, Fernando Rodney, Francisco Rodriguez, Trevor Rosenthal, Tony Sipp, Will Smith, Joakin Soria, Pedro Strop, Koji Uehara, Nick Vincent, Jordan Walden, Tony Watson.
That’s 51 of the 189 eligible relievers (27%); lower the bar to nine strikeouts per game and it would be 82 (43%); at eight or more there are 110 for 58%.
The lowest-ranking NL reliever by RAR was Rex Brothers (-8), whose strikeout rate was 7.4. The second worst was JJ Hoover (-7), who struck out 10.4 per game. I am not a huge user of WPA metrics, but Hoover’s season was noteworthy for just how bad it was from that value perspective as he was involved in a few huge meltdowns. Per Fangraphs’ WPA figures, Hoover was second-to-last in the majors with -3.56 WPA; only Edwin Jackson at -4.11 was worse, and Jackson pitched 78 more innings. Even among position players, only Jackie Bradley (-4.00) and Matt Dominguez (-3.76 ranked lower). Brothers was the closest reliever to Hoover, but his WPA was -2.31, 1.35 wins better than Hoover.
The anti-Hoover was his teammate Aroldis Chapman, whose numbers over 54 innings are simply ridiculous, with a 19.3 strikeout rate. It’s difficult to fathom that a pitcher with a walk rate of 4.4 could have a RRA of 1.02, an eRA of 1.15, and a dRA of 1.32, but Chapman did and led narrowly missed leading major league relievers in eRA and dRA (Wade Davis had him by a more-than-insignificant 1.1466 to 1.1471 in the former).
* In 2010, the Giants won the World Series with Tim Lincecum and Matt Cain combining to pitch 435 innings and compile 101 RAR. Over the last five years:
While the potential for starting pitcher ruin is well understood, if you’d told me in 2010 that the Giants would win the World Series in four years getting no contribution out of Lincecum and Cain, I would have thought that black magic was at work. It probably is.
* Speaking of bad starting pitchers, only two teams had multiple starters (who made fifteen or more starts) with negative RAR. The Cubs had two--Travis Wood and Edwin Jackson combined to start 58 games, pitch 314 innings, and compile -27 RAR. The Indians had three--Zach Allister, Josh Tomlin, and Justin Masterson combined to start 56 games, pitch 319 innings, and compile -25 RAR (figures do include Masterson’s time in St. Louis). Both of these teams may well be trendy picks to compete in the Central divisions, and this is a one reason that may make sense. The Cubs and Indians are taking different approaches to shore up the back end of their rotation, Chicago by bringing in an ace and a mid-rotation free agent and the Indians by counting on continued strong performances from young starters who stood out in the second half. Either approach figures to work out better than -25 RAR.
* Despite the poor CHN and CLE individual starters, there’ still nothing quite like Minnesota’s utter and complete starting pitcher futility. In 2012, they were last in starters’ eRA and second-to last in innings/start and QS%. In 2013, they completed the triple crown--last in IP/S (5.38), QS% (38%), and eRA (5.76). In 2014, they “improved” to their 2012 standings--second last in IP/S (5.64, COL starters weren’t far behind at 5.59), second last in QS% (41% to the Rangers’ 38%), and last in starter’s eRA (5.08, with Texas second at 4.95).
* Clayton Kershaw had a great season, and was a reasonable choice as NL MVP. I’m not trying to run him down--but there is some notion out there that he had a transcendent season. I think this notion can be tempered by simply comparing his rate stats to those of Jake Arrieta:
Kershaw was better overall than Arrieta, and pitched 42 more innings. But no one should confuse Kershaw 2014 with Pedro 1999 or anything of the sort.
* One of these starting pitchers is now forever known as a clutch pitcher, a modern marvel who harkens back to the days of Gibson and Morris and whoever else has been chosen for lionization. The other is an underachieving
prima donna who Ron Darling thinks is "struggling" as a major league starter. Their regular season performances were hardly distinguishable:
Madison Bumgarner and Stephen Strasburg.
* Cole Hamels was fourth among NL starting pitchers with 55 RAR, but won just nine games. This has to be one of the better pitcher seasons in recent years with single digit wins. Through the last decade of my RAR figures, here is the highest-ranking starter in each league with single digit wins:
This is an interesting collection of names--a number of outstanding pitchers and some who I hadn’t thought about in years (John Patterson, the late Joe Kennedy and Geremi Gonzalez). Since this comparison is across league-seasons, in order to rank these seasons it is necessary to convert RAR to WAR. Using RPW = RPG, Hamels’ 2014 actually ranks highest with 7.0 WAR (Harvey 6.9, Schilling 6.7, Jennings 5.9) since the 2014 NL had the lowest RPG (7.9) of any league during the period. Given that the likelihood of a starter having an outstanding season with fewer than ten wins is greater now than at any point in major league history, it’s quite possible that Hamels’ 2014 is the best such season. Sounds like a good Play Index query if you’re looking for an article idea.
* The worst hitter in baseball with more than 400 plate appearances was Jackie Bradley (2.2 RG). The Red Sox have collected a large collection of outfielders and Bradley is unlikely to be in their plans. The second-worst hitter with more than 400 PA was Zack Cozart (2.5 RG). His team traded for a young shortstop who had 3.4 RG in 266 PA (granted, Eugenio Suarez does not appear to be the fielder that Cozart is), yet Walt Jocketty was quoted as saying "Cozart is our opening day shortstop and he’s one of the best in the league."
In addition to Cozart, the Reds featured three other hitters with 250+ who were essentially replacement-level: Chris Heisey (3.4 RG for a corner outfielder), Bryan Pena (3.3 for a first baseman), and Skip Schumaker (2.9 for a corner outfielder).
* San Diego liked Justin Upton (or Matt Kemp?) so much that they traded for two clones of the same player (in 2014 performance, at least):
* Many hands have been wrung regarding the apparent shift in Mike Trout’s game to old player skills rather than young player skills, particularly with the dropoff in his base stealing exploits (54 attempts in 2012 to 40 in 2013 to 18 in 2014). Yet it should still be noted that Trout ranked fifth in the AL with a 7.2 Speed Score (I use Bill James’ original formula but only consider stolen base frequency, stolen base percentage, triples rate, and runs scored per time on base). In fact, his Speed Score was up from 2013 (7.0) although down from 2012 (8.7). Here are Trout’s three-year figures in each of the four components of Speed Score:
Just to make clear what these numbers represent, Trout attempted a steal in 29.3% of his times on first base (singles plus walks) in 2012, had a 85.2% SB% when adding three steals and four caught stealings to his actual figures, hit a triple on 2.1% of his balls in play, and scored 45.2% of the time he reached base. (These are all estimated based on his basic stat line as opposed to counting actual times on first base or attempted steals of second, etc.)
While these categories certainly don’t capture the full picture of how speed manifests itself in on-field results, it is clear that Trout has been dialing back the most visible such part of his game, basestealing. And his 2014 SS rebound is due to two categories that are subject to more flukes (triples) and teammate influence (runs scored per time on base). Still, it may be a little early to sound the alarm bells on Trout as a one-dimensional slugger. Eventually, the sabermetric writers who have developed a cottage industry of Trout alarmism will be right about something, but there’s no need to prematurely indulge them.
Meanwhile, Bryce Harper’s Speed Scores for 2012-14 are 7.5, 4.9, 2.7.
Monday, January 19, 2015
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 2014 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 2014 only. (I have a methodology I could use to do estimate win probabilities at each level of scoring that take context into account, but I’ve not been able to finish the full write-up it needs on this blog before I am comfortable using it without explanation).
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 2013, 74.5% of major league games were non-blowouts while the complement, 25.5%, were. Team record in non-blowouts:
It must have been a banner year for MASN, as both the Nationals and the Orioles won a large number of competitive games, just the kind of fan-friendly programming any RSN would love to have. Arizona was second last in non-blowouts in addition to dead last in blowouts:
For each team, the difference between blowout and non-blowout W%, as well as the percentage of each type of game:
Typically the teams that exhibit positive blowout differentials are good teams in general, and this year that is mostly the case, but Colorado is a notable exception with the highest difference. Not surprisingly, they also played the highest percentage of blowout games in the majors as the run environment in which they play is a major factor. The Rockies’ blowout difference is also correlated to some degree with their home field advantage--more of their blowouts are at home, where all teams have a better record, but they have exhibited particularly large home field advantages. This year the home/road split was extreme as Colorado’s home record was similar to the overall record of a wildcard team (.556) and their road record that of a ’62 Mets or ’03 Tigers type disaster (.259).
I did not look at the home/road blowout differentials for all teams, but of the 52 blowouts Colorado participated in, 38 (73%) came at home and 14 on the road. The Rockies were 22-16 (.579) in home blowouts but just 4-10 (.286) in road blowouts.
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. In 2014, the fourth run was both the run with the greatest marginal impact on the chance of winning and the level of scoring for which a team was more likely to win than lose.
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.
A theoretical distribution would be much preferable to the empirical distribution for this exercise, but as I mentioned earlier I haven’t yet gotten around to writing up the requisite methodological explanation, so I’ve defaulted to the 2014 empirical data. Some of the drawbacks of this approach are:
1. The empirical distribution is subject to sample size fluctuations. In 2014, teams that scored 9 runs won 94.2% of the time while teams that scored 10 runs won 92.5% of the time. Does that mean that scoring 9 runs is preferable to scoring 10 runs? Of course not--it's a quirk in the data. Additionally, the marginal values don’t necessary make sense even when W% increases from one runs scored level to another (In figuring the gEW% family of measures below, I lumped all games with between 10 and 14 runs scored/allowed into one bucket, which smoothes any illogical jumps in the win function, but leaves the inconsistent marginal values unaddressed and fails to make any differentiation between scoring in that range. The values actually used are displayed in the “use” column, and the “invuse” column is the complements of these figures--i.e. those used to credit wins to the defense. I've used 1.0 for 15+ runs, which is a horrible idea theoretically. In 2014, teams were 20-0 when scoring 15 or more runs).
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 its 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 more quirks into the data.
I keep promising that I will use my theoretical distribution (Enby, which you can read about here) to replace the empirical approach, but that would require me to finish writing my full explanation of the method and associated applications and I keep putting that off. I will use Enby for a couple graphs here but not beyond that.
First, a comparison of the actual distribution of runs per game in the majors to that predicted by the Enby distribution for the 2014 major league average of 4.066 runs per game (Enby distribution parameters are B = 1.059, r = 3.870, z = .0687):
Enby fares pretty well at estimating the actual frequencies, most notably overstating the probability of two or three runs and understating the probability of four runs.
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 in this post, but full details were provided here (***). 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: STL, NYA
Negative: OAK, TEX, COL
The Rockies’ -3.6 win difference between gOW% and OW% was the largest absolute offensive or defensive difference in the majors, so looking at their runs scored distribution may help in visualizing how a team can vary from expectation. Colorado scored 4.660 R/G, which results in an Enby distribution with parameters B = 1.125, r = 4.168, z = .0493:
The purple line is Colorado’s actual distribution, the red line is the major league average, and the blue line is their Enby expectation. The Rockies were held to three runs or less more than Enby would expect. Major league teams had a combined .231 W% when scoring three or fewer runs, and that doesn’t even account for the park effect which would make their expected W% even lower (of course, the park effect is also a potential contributing factor to Colorado’s inefficient run distribution itself).The spike at 10 runs stands out--the Rockies scored exactly ten runs in twelve games, twice as many as second-place Oakland. Colorado’s 20 games with 10+ runs also led the majors (the A’s again were second with seventeen such games, while the average team had just 8.3 double digit tallies).
Teams with differences of +/- 2 wins between gDW% and standard DW%:
Negative: NYN, OAK, MIA
Texas’ efficient distribution of runs allowed offset their inefficient distribution of runs scored, while Oakland was poor in both categories which will be further illustrated by comparing EW% to gEW%:
Positive: STL, CHN, NYA, HOU
Negative: SEA, COL, MIA, OAK
The A’s EW% was 4.9 wins better than their gEW%, which in turn was 5.8 wins better than their actual W%.
Last year, EW% was actually a better predictor of actual W% than was gEW%. This is unusual since gEW% knows the distribution of runs scored and runs allowed, while EW% just knows the average runs scored and allowed. gEW% doesn’t know the joint distribution of runs scored and allowed, so oddities in how they are paired in individual games can nullify the advantage that should come from knowing the distribution of each. A simplified example of how this could happen is a team that over 162 games has an apparent tendency to “waste” outstanding offensive and defensive performances by pairing them (e.g. winning a game 12-0) or get clunkers out of the way at the same time (that same game, but from the perspective of the losing team).
In 2014, gEW% outperformed EW% as is normally the case, with a 2.85 to 3.80 advantage in RMSE when predicting actual W%. Still, gEW% was a better predictor than EW% for only seventeen of the thirty teams, but it had only six errors of +/- two wins compared to sixteen for EW%.
Below are the various W% measures for each team, sorted by gEW%:
Wednesday, January 07, 2015
For the last several years I have published a set of team ratings that I call "Crude Team Ratings". The name was chosen to reflect the nature of the ratings--they have a number of limitations, of which I documented several when I introduced the methodology.
I explain how CTR is figured in the linked post, but in short:
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.
First, CTR based on actual wins and losses. In the table, “aW%” is the winning percentage equivalent implied by the CTR and “SOS” is the measure of strength of schedule--the average CTR of a team’s opponents. The rank columns provide each team’s rank in CTR and SOS:
I lost a non-negligible number of Twitter followers by complaining about the playoff results this year. As you can see, the eventual world champs had just the fourteenth most impressive win-loss record when taking quality of opposition into account. The #7 Mariners, #10 Indians, #11 Yankees, and #12 Blue Jays all were at least two games better than the Giants over the course of the season (at least based on this crude method of adjusting win-loss records). Note that this is not an argument about “luck”, such as when a team plays better or worse than one would it expect from their component statistics, this is about the actual win-loss record considering opponents’ records.
San Francisco played the second-worst schedule in the majors (90 SOS); of the teams that ranked ahead of them in CTR but failed to make the playoffs, Toronto had the strongest SOS (107, ranking seventh). Based on the Log5 interpretation of CTR described in the methodology post, this suggests that Toronto’s average opponent would play .543 baseball against San Francisco’s average opponent. The magnitude of this difference can be put into (potentially misleading) context by noting that the long-term home-field W% of major league teams is around .543. Thus the Giants could be seen as having played an entire 162 game schedule at home relative to the Blue Jays playing an even mix of home and road games. Another way to look at it is that Toronto’s average opponent was roughly equivalent to St. Louis or Pittsburgh while San Francisco’s average opponent was roughly equivalent to Milwaukee or Atlanta.
On the other hand, the disparity between the best teams as judged by CTR and those that actually made the playoffs is solely a function of the AL/NL disparity--the five playoff teams in each league were the top five teams by CTR. The AL/NL disparity is alive and well, though, as seen by the average rating by league/division (actually calculated as the geometric average of the CTR of the respective clubs):
While this is not the AL’s largest advantage within the five seasons I’ve published these ratings, it is the first time that every AL division is ranked ahead of every NL division. Typically there has been a weak AL division or strong NL division that prevented this, but not in 2014. Matchup the AL’s worst division and the NL’s best division (both the Central) and you can see why:
The two teams that battled to the end for the AL Central crown stood out, with the NL Central’s two combatants unable to distinguish themselves from Cleveland, who hung around the periphery of the AL Central race throughout September but was never able to make a charge. In all cases the Xth place team from the ALC ranks ahead of the Xth place team from the NLC. In fact, the same holds true for the other two geographic division pairings:
This would also hold for any AL/NL division comparison rather than just the arbitrary geographic comparisons, except for the NL East v. AL Central, where the NL-best Nationals rank ahead of the Tigers 129 to 123.
The AL’s overall CTR edge of 106-89 implies that the average AL team would have a .544 record against the average NL team, similar to the gap between SF and TOR opponents described above. This is very close to the AL’s actual interleague record (140-117, .545).
All the results discussed so far are based on actual wins and losses. I also use various estimated W%s to calculated CTRs, and will present those results with little comment. First, CTR based on gEW%, which considers independently each team’s distribution of runs scored and allowed per game:
Well, I will point out that by gCTR, the world champions are the epitome of average. Next is CTR based on EW% (Pythagenpat):
And based on PW% (Pythagenpat using Runs Created/Runs Created Allowed):
Last year I started including actual W-L CTR including the results of the playoffs. There are a number of reasons why one may want to exclude the playoffs (the different nature of the game in terms of roster construction and strategy, particularly as it relates to pitcher workloads; the uneven nature of the opportunity to play in postseason and pad a team’s rating; etc.), but in general the playoffs provide us with additional data regarding team quality, and it would be prudent to heed this information in evaluating teams. The chart presents each team’s CTR including the playoffs (pCTR), their rank in that category, their regular season-only CTR (rsCTR), and is sorted by pCTR - rsCTR:
Last year there was not a lot of movement between the two sets of ratings, since the top regular season teams also won their league’s pennants. It should be no surprise that both wildcard pennant winners in 2014 were able to significantly improve their standings in the ratings when postseason is taken into account. Still, San Francisco ranks just ninth, still trailing Seattle who didn’t even make the playoffs, and Kansas City is a distant third from the two teams they beat in the AL playoffs, Los Angeles and Baltimore.
Thursday, December 11, 2014
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 2014, 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:
The most notable deviations from historical norms (which, when limited to one year, are strictly trivia rather than trends) were present in the outfield, where all three spots provided essentially equal production. The shape was not equal--centerfielders had a higher batting average and lower secondary average (.213 to .236) than did corner outfielders. Catchers also outhit their usual levels, bringing the three rightmost positions on the defensive spectrum together around 95% of league average production. DHs rebounded from a poor 2013 showing (102 PADJ) to get back to their historical level.
Of course, a DH-supporter like myself can’t avoid commenting on pitchers, who are wont to set a new low every couple years but went so far as to fall below the negative absolute RC threshold in 2014, with a -4 PADJ eclipsing 2012’s 1 as the worst in history. Pitchers struck out in 41% of their plate appearances, double the rate of position players (20%). Still, I’ll take a moment and provide the list of NL pitching staffs by runs above average. 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 for pitchers, the formula for RAA was fun to write this year, since it involved adding the league average performance (well, subtracting the negative):
RAA = (RG + .15)*(AB - H + CS)/25.5
This marked the second straight triumph for Dodger pitchers as most productive, with Zack Greinke again the standout, although his numbers were much less gaudy than in 2013 (this year he hit .200/.262/.350). Pittsburgh’s hurlers extended a complete power outage to a third season; while they topped Miami and Milwaukee in isolated power thanks to a Gerrit Cole home run, their two extra base hits (Vance Worley doubled) were the fewest. This comes on the heels of 2012 (one double) and 2013 (zero extra base hits), giving them a three year stretch of 894 at bats with two doubles and a home run (.006 ISO).
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--MIL, 1B--DET, 2B--HOU, 3B--WAS, SS--COL, LF--CLE, CF--PIT, RF--LA, DH--DET
More interesting are the worst performing positions; the player listed is the one who started the most games at that position for the team:
I will take the poor performance of the Jeter-led Yankee shortstops as an opportunity to share some wholly unoriginal thoughts about the Didi Gregorius trade. I have no particularly strong feelings on Gregorius’ long-term outlook; I’ll leave that to the projection mavens and the scouts. However, some remarkably silly columns have been written about his assuming Saint Derek’s mantle. One sneered that he wasn’t likely to be a long-term solution. This is probably true, but most major league lineup spots are filled by guys who are long-term solutions. A minority of teams have a long-term answer at shortstop, let alone a ten-year answer.
But more importantly is that even a static Gregorius could be an immediate boost to the Yankees. No team in baseball got less out of the position offensively, and fielding? (Rhetorical question). Last year, Gregorius hit .222/.279/.356 in 292 PA (3.4 RG); Jeter hit .253/.294/.309 in 616 PA (3.1 RG).
I like to attempt to measure each team’s offensive profile by position relative to a typical profile. I’ve found it frustrating as a fan when my team’s offensive production has come disproportionately from “defensive” positions rather than offensive positions (“Why can’t we just find a corner outfielder who can hit?”) The best way I’ve yet been able to come up with to measure this is to look at the correlation between RG at each position and the long-term positional adjustment. A positive correlation indicates a “traditional” distribution of offense by position--more production from the positions on the right side of the defensive spectrum. (To calculate this, I use the long-term positional adjustments that pool 1B/DH as well as LF/RF, and because of the DH I split it out by league):
My comments on frustration are based on the Indians, who have often had a negative correlation but this year exhibited a more normal profile. The Tigers +.88 is about as high as you’ll see. Of course, offensive positions were their biggest producers with Cabrera and the two Martinezes, and their right fielders were their fourth most productive position. They did get more out of second than third or right, and more out of catcher than shortstop, but otherwise they were fell right in place.
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:
Washington led the majors in RAA from both their corner infield spots and the entire infield. Giancarlo Stanton almost single-handedly led Miami to the majors top outfield RAA total. Atlanta had the majors worst RAA from middle infielders. Philadelphia’s corner infielders had the lowest RAA in the NL.
I was bullish on Milwaukee this year, which looked smart for four and a half months before they wound up with an overall season record close to where most people picked them to finish. One factor I cited was how bad their production from first base had been in 2013. While the Brewers did not replicate their dreadful -37 RAA first base performance from ’13, they only gained a win or so by improving to -26, still the worst first base production in the NL. 84% of their first base PA went to Lyle Overbay (628 OPS as a first baseman) and Mark Reynolds (632), who had basically the same overall production with different shapes. And so it was no surprise that for the second straight year, Milwaukee had the NL’s most oddly distributed offense by position (based on the correlation approach described above). Cincinnati had the majors worst outfield production, and consistently so from left to right (-24, -20, -18).
The Dodgers were the only NL team to be above average at seven of the eight positions, but their catchers went all-in as the NL’s least productive unit. Los Angeles tied Pittsburgh with 123 total RAA, but they did it with opposite production from the backstops (the Pirates’ +24 was a perfect offset for LA’s -24). Dodger middle infielders led the NL in RAA. San Diego was on the other end of the spectrum, the NL’s only team with just one above-average position, with catcher once again serving as the exception. The Padres infield was the least productive in MLB.
Boston went from having one below-average position in 2013 to having just two above-average in 2014, which is how a team can go from leading the majors in total RAA to ranking second-last in the AL. Their infielders were the worst in the AL with a total of -46 RAA; their outfielders only tied for second-last, but matched the total of -46. Yet much of the winter discussion regarding the Red Sox has involved how they will parcel out their outfield surplus.
Detroit led the AL in corner infield RAA thanks to first base. Just eyeballing the charts, the Indians may have been the most average in terms of combining roughly average overall RAA with close to average production at many positions. Minus the outfield corners, there weren’t many extremes in Cleveland.
The Angels led the AL in outfield RAA; the corner outfielders washed each other out for a total of -4 RAA, but the Trout-led centerfielders could not be washed out. Houston had the majors best middle infield production, but the worst corner infield production, and the latter exceeded the former by 24 absolute runs. The Astros had three -20 positions (the corner infielders and left field, so get your JD Martinez victim of their own success jokes in); so did the Reds, but just barely (two of theirs were -20 and the other -24). Seattle had the AL’s worst outfield, largely due to trotting James Jones out there for 72 starts, then seeing Austin Jackson crater when they acquired him to address the problem. Texas had eight below-average positions, but made their one bright spot count with the AL’s best third base RAA.
The data for each team-position is available in this spreadsheet.
Wednesday, December 03, 2014
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 analytic 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. When I started I didn’t have easy access to HB, so they are not included in any of the stats, including OBA. 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 is the sixth consecutive season in which NL #3 hitters were the top producing lineup spot, while AL teams demonstrated more balance between #3 and #4. This is a fairly consistent pattern and the most interesting thing I’ve found from doing this every year. I have no explanation for this phenomenon and suspect that there really is none--the NL has had a run of outstanding hitters who happened to bat third in the lineup (e.g. Pujols, Votto, Braun, Gonzalez). NL hitters were more productive at spots 2-3 and 5-7, while the AL got more production at leadoff, cleanup, #8, and #9 (the latter is a given, of course).
The position that sticks out the most to me is AL #6; even with hit batters included, they managed an OBA of just .300 and outhit only AL #9, NL #8, and NL #9. I’d assume this is a one-year oddity and nothing more; in 2013 they created 4.55 runs, trailing only 3-5 among AL slots.
Next are the team leaders and trailers 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 when there is no fixed regular as in the case of the Astros #5 spot). Or poor Matt Dominguez, who was perhaps the worst regular hitter in MLB (.215/.253/.330 for 2.6 RG, only Zack Cozart was worse among those with 500 PA), but doesn’t deserve to be blamed for sinking two Houston lineup spots as he had plenty of help in both.
Some random thoughts:
* You can see why Seattle felt they needed Nelson Cruz, with the worst production out of the cleanup spot in the AL.
* Texas #3 hitters were a complete disaster. At 2.54 RG, they managed to outhit only five non-pitcher lineup spots. No #1, #2, #3 (obviously), #4, #5, or #6 spots in the majors were worse.
* Kansas City’s production was oddly distributed. Their #1-3 hitters combined for 3.59 RG (no park adjustment applied in this bullet), their #4-6 for 4.86, and their #7-9 for 3.76. I’ll call that a 3-1-2 pattern of hitting by batting order third (1-3 least productive, 4-6 most productive, 7-9 in the middle). 22 teams exhibited a 1-2-3 pattern; 4 teams a 2-1-3; and 2 teams each with 1-3-2 and 3-1-2. Texas was the other team with a 3-1-2 pattern.
* And then there are the Padres, who take on the role that was filled so well by the Mariners for many years of being the source of ridiculous offensive futility factoids. As you can see, San Diego got the NL’s worst production at four lineup spots, all at the top or middle of the order, but also had the most productive #7 and #8 hitters. In fact, Padre #7 hitters were the most productive of any of their lineup spots. Two teams got their top production from leadoff hitters, six from #2, seventeen from #3, two from #4, two from #5, but only one from #7:
The next list is the ten best positions in terms of runs above average relative to average for their particular league spot (so AL leadoff spots are compared to the AL average leadoff performance, etc.):
And the worst:
The -54 figure for Texas #3 hitters is a big number; I’ve been running this report since 2009 and that is the worst performance by a team batting spot, topping the -53 runs turned in by KC’s Mike Jacobs-led cleanup hitters in 2009. Considering that the AL average RPG in 2014 was 13% lower than in 2009, that one run difference is approximately a full win difference.
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:
If you are interested in digging in yourself, see the spreadsheet here.
Monday, November 24, 2014
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.
First, 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 Oakland 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. MIL (Gomez/Gennett), 5.9
2. MIN (Santana/Dozier), 5.8
3. STL (Carpenter), 5.6
Leadoff average, 4.8
28. CHN (Bonifacio/Coghlan), 4.1
ML average, 4.0
29. SD (Cabrera/Solarte/Venable/Denorfia), 3.5
30. SEA (Jackson/Chavez/Jones/Almonte), 3.5
The Twins leading the AL in run scoring rate for leadoff hitters is a surprise--usually leading teams in this category are good offenses or high OBA guys, neither category describes Minnesota. They combined for a .324 OBA,
just a tick above the major league average in the other obvious measure to look at. 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. STL (Carpenter), .366
2. HOU (Altuve/Grossman/Fowler), .352
3. WAS (Span), .346
Leadoff average, .322
ML average, .310
28. CIN (Hamilton), .295
29. SD (Cabrera/Solarte/Venable/Denorfia), .293
30. SEA (Jackson/Chavez/Jones/Almonte), .287
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), .349
2. HOU (Altuve/Grossman/Fowler), .326
3. WAS (Span), .325
Leadoff average, .294
ML average, .281
27. SEA (Jackson/Chavez/Jones/Almonte), .272
28. MIL (Gomez/Gennett), .270
29. SD (Cabrera/Solarte/Venable/Denorfia), .266
30. CIN (Hamilton), .253
Milwaukee’s leadoff hitters are a good example of why ROBA is not a quality metric. Their .325 OBA was slightly above average, but they also led leadoff hitters with 26 home runs. They also were caught stealing 13 times, which tied for the fourth-most among leadoff hitters, which brought it down some more. It’s CS that really brings down the Reds, as the Hamilton-led leadoff hitters led all teams by getting caught 20 times.
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. STL (Carpenter), .353
2. HOU (Altuve/Grossman/Fowler), .331
3. WAS (Span), .328
Leadoff average, .298
ML average, .287
28. SEA (Jackson/Chavez/Jones/Almonte), .274
29. SD (Cabrera/Solarte/Venable/Denorfia), .269
30. CIN (Hamilton), .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. LA (Gordon), 2.5
2. PHI (Revere), 2.4
3. BOS (Holt/Pedroia/Betts), 2.3
Leadoff average, 1.7
28. NYA (Gardner/Ellsbury), 1.3
29. DET (Kinsler/Davis/Jackson), 1.3
30. COL (Blackmon), 1.3
ML average, 1.1
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. STL (Carpenter), 1.6
2. LA (Gordon), 1.5
3. PHI (Revere), 1.5
4. KC (Aoki/Cain), 1.5
Leadoff average, 1.0
ML average, .7
28. PIT (Harrison/Polanco/Marte), .6
29. LAA (Calhoun/Cowgill), .5
30. DET (Kinsler/Davis/Jackson), .5
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. PHI (Revere), 34
2. TOR (Reyes), 29
3. LA (Gordon), 23
Leadoff average, 8
ML average, 3
28. LAA (Calhoun/Cowgill), -3
29. CHA (Eaton), -4
30. SD (Cabrera/Solarte/Venable/Denorfia), -7
Last year I noted that since 2007, the percentage of major league stolen base attempts from leadoff hitters has declined. It was up to 28.8% in 2014, so the 2007-14 figures are (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%, 28.8%
Shifting 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. HOU (Altuve/Grossman/Fowler), 789
2. MIL (Gomez/Gennett), 781
3. WAS (Span), 776
Leadoff average, 724
ML average, 704
28. NYN (Young/Granderson/Lagares), 654
29. SD (Cabrera/Solarte/Venable/Denorfia), 637
30. SEA (Jackson/Chavez/Jones/Almonte), 625
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. HOU (Altuve/Grossman/Fowler), 5.4
2. MIL (Gomez/Gennett), 5.2
3. NYA (Gardner/Ellsbury), 5.2
Leadoff average, 4.4
ML average, 4.1
28. NYN (Young/Granderson/Lagares), 3.6
29. SEA (Jackson/Chavez/Jones/Almonte), 3.1
30. SD (Cabrera/Solarte/Venable/Denorfia), 3.1
You may note that the spread in RG between team leadoff spots is not that great, ranging from just 3.1 to 5.4. This seemed very unusual to me, so I checked the last five years and it was in fact an unusual year (chart shows standard deviation and coefficient of variation of leadoff RG by team):
Originally I just included the most recent five seasons, but I’m glad I dug up the 2009 data, because the COV was similar to that in 2014. However, the history does indicate that this is an unusually small spread in production from the leadoff spot. It seems far more likely to a blip than anything of note, though.
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 -.215, the CS coefficient was -.582, and for other details refer you to that post. I then restate it per the number of PA for an average leadoff spot (736 in 2014):
1. HOU (Altuve/Grossman/Fowler), 16
2. STL (Carpenter), 12
3. NYA (Gardner/Ellsbury), 12
Leadoff average, 0
ML average, -5
28. CHN (Bonifacio/Coghlan), -12
29. SEA (Jackson/Chavez/Jones/Almonte), -21
30. SD (Cabrera/Solarte/Venable/Denorfia), -23
I doubt I would have guessed Houston in ten guesses at the most productive leadoff spot in MLB, but Altuve and Fowler both were very productive when leading off (Robbie Grossman started 43 games as a leadoff hitter but hit .262/.340/.337, and thus was not a major contributor to the Astros’ #1 rank). Seattle managed to contend for a playoff spot despite woeful leadoff production, and attempted to address the issue (and the related center field woes) at the trade deadline by acquiring Austin Jackson. But Jackson hit just .229/.267/.260 in 236 PA in those roles after the trade. A center fielder led off for Seattle in 117 of 162 games, an outfielder in 148 of 162 games.
For the full lists and data, see the spreadsheet here.
Tuesday, November 11, 2014
Last year, I thought that Clayton Kershaw was the most valuable player in the National League. The BBWAA voters did not concur, placing Kershaw seventh in the voting; the IBA voters were more generous at third. This season, though, it appears Kershaw is going to win the MVP award.
When you compare Kershaw 2013 to Kershaw 2014, it’s difficult to find good reasons for this (one obvious reason which I’ll discuss in a minute is anything but good). Granted, MVP voting does not occur in a vacuum--the 2013 field had much more to offer from a position player perspective, with Yadier Molina have an outstanding full season, his teammate Matt Carpenter, a pair of big-mashing first basemen in Joey Votto and Paul Goldschmidt, and the one holdover, Andrew McCutchen. Thus it makes sense that more voters would turn to Kershaw in a season in which there are fewer alternatives. Still, Kershaw pitched 38 fewer innings over six fewer starts in 2014. His ERA dropped slightly (1.83 to 1.77), but that hardly makes up for 38 innings. A big factor for the BBWAA will be his win-loss record, 21-3 in 2014 rather than a more pedestrian 16-9 in 2013, but it goes without saying on this blog that W-L is a silly basis to vote for MVP.
It might be useful to take a look at Kershaw’s performance in the categories that I feel are useful, with adjustments for league average since we are comparing across seasons, seasons in which the NL average RA dropped very slightly from 4.04 to 4.01 (the three RA figures have been divided by the league average RA; RAA and RAR have been very simply converted to WAA and WAR by dividing by the league average runs scored per game by both teams):
While Kershaw was slightly better in the pitching metrics that focus on actual results (RRA and eRA) and noticeably better in DIPS (dRA), the 36 inning difference looms large. I would take Kershaw’s 2013 season over his 2014 season. Obviously both were outstanding, but the fact that he will be an MVP afterthought in one and a strong winner in another speaks to the arbitrary and narrative-driven voting that still reigns supreme in the BBWAA even as more sabermetric approaches gain some traction.
For my ballot, last year I chose Kershaw narrowly over McCutchen. This year I’ve done the opposite. McCutchen starts with a 77 to 70 lead over Kershaw in RAR, but he does give some of that back. Per Fangraphs McCutchen was an average baserunner, while Kershaw created three runs at the plate with a .178/.228/.211 line. Since pitchers essentially average zero runs created per out, I credit the three absolute RC with no baseline. McCutchen also doesn’t fare particularly well in defensive metrics, -11 DRS, -11 UZR, -8 FRAA (Baseball Prospectus). Regressing these a little as I am wont to do, it’s very close between Kershaw and McCutchen.
However, Kershaw’s RAR is based on his actual runs allowed; were one to use eRA or dRA as the basis, he’d start from just 63 or 58 RAR respectively, and that would be too large of a gap to McCutchen to close with fielding, even with no regression. I have no issue with the notion of a pitcher being MVP, but I think it’s a pretty high bar, and when the alternate ways of valuing pitchers don’t support placing the pitcher ahead, I can’t do it either.
Giancarlo Stanton would have made things very interesting had he not been injured, although in the end there was very little difference between the amount of time missed by McCutchen and Stanton. McCutchen played 146 games with 632 PA; Stanton played 145 games with 633 PA. Stanton came in at 67 RAR, ten fewer than McCutchen, partially due to the position adjustment difference between right field and center field, but not exclusively. Based on my estimates McCutchen created five more runs (121 to 116) in six fewer outs, making him the superior (albeit well within the margin of error) hitter (62 to 56 runs above average, hitting-only). While Stanton fares better in the fielding metrics, Fangraphs has him as a -2 baserunner, and so the ten run gap holds up for McCutchen. Kershaw/Stanton for second is a tossup, but I went with Stanton on the same reasoning discussed in the prior paragraph.
After them I have the top Cy Young challengers, who each pitched significantly more than Kershaw despite less impressive rates (Johnny Cueto and Adam Wainwright). The rest of my ballot is pretty self-explanatory from my RAR leaders, except Anthony Rendon is placed ahead of Jonathan Lucroy and Cole Hamels thanks to strong showing in baserunning (+6) and fielding metrics (16 DRS, 7 UZR, -1 FRAA):
1. CF Andrew McCutchen, PIT
2. RF Giancarlo Stanton, MIA
3. SP Clayton Kershaw, LA
4. SP Johnny Cueto, CIN
5. SP Adam Wainwright, STL
6. C Buster Posey, SF
7. 3B Anthony Rendon, WAS
8. C Jonathan Lucroy, MIL
9. SP Cole Hamels, PHI
10. RF Yasiel Puig, LA
Many words were used to discuss the 2012 and 2013 AL MVP votes; many fewer will be used in 2014, but the basic story is the same for me--Mike Trout was pretty clearly the most valuable player in the AL. This year, the Angels’ record, Trout leading the league in RBI, and the lack of a triple crown stat standout other than Trout have combined to make the mainstream media agree. While comprehensive WAR metrics that include fielding with no regression may suggest this was the least valuable of Trout’s three full seasons, I would point out that offensively, there’s no pattern that could not be due to sheer random fluctuation. Trout’s RG relative to the league average for the past three seasons is 196, 209, 186. Trout is such a towering figure among intelligent followers of the game that he has become subject to intense scrutiny--Trout death watch has become a bizarrely popular topic at sites that should know better. This is not to say that Trout will continue to dominate baseball for the next decade with no risk, or that Trout will ever match his 2012-2014 performances. But if you think you've found a clear decline trend in a 23 year old who was the best player in baseball for a third straight season, you are likely overanalyzing. You may want to take a gander at Alex Rodriguez 1997-1999 as well. It’s more than a little uncouth if you ask me.
Rant aside, the rest of the ballot is not particular interesting, and I’ve mostly stuck with the RAR list. Some exceptions to note:
* Victor Martinez, a distant second among hitters with 64 RAR, just squeaks on to my ballot. Martinez was a -5 baserunner per Fangraphs, his RAR doesn’t penalize him for being a DH at all, and when he did play the field, he was poor in just 280 innings (-4 DRS, -6 UZR, -4 FRAA). It was not an easy choice to keep Martinez on the ballot ahead of Josh Donaldson or Adrian Beltre, who spotted him around twenty RAR but did everything else better.
* Similarly, Jose Abreu drops off entirely--it's the same story except he starts from 55 RAR.
* Jose Altuve is knocked down a few pegs thanks to fielding metrics; I kept him ahead of Cano among second basemen on the basis of his forty plate appearance edge, but with no strong conviction:
* Corey Kluber beats out Michael Brantley as Most Valuable Indian; the two are tied at 61 RAR prior to considering Brantley’s baserunning (good) and fielding (meh). Kluber would do worse using eRA, better using dRA, and the latter tipped the scales in his favor for me. After watching the Tribe all season, it would feel wrong to decide a tossup in favor of the pitcher rather than a fielder who played behind him. Cleveland’s fielding was dreadful and Brantley, while not a main culprit, did not really help either. I remain unimpressed by Brantley as an outfielder, even in left; his arm is solid, but he’s a left fielder, so...
1. CF Mike Trout, LAA
2. SP Felix Hernandez, SEA
3. SP Corey Kluber, CLE
4. LF Michael Brantley, CLE
5. SP Chris Sale, CHA
6. RF Jose Bautista, TOR
7. SP Jon Lester, BOS/OAK
8. 2B Jose Altuve, HOU
9. 2B Robinson Cano, SEA
10. DH Victor Martinez, DET