Monday, January 19, 2015

Run Distribution & W%, 2014

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%:

Positive: TEX
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

Crude Team Ratings, 2014

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

Hitting by Position, 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

Hitting by Lineup Position, 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

Leadoff Hitters, 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

Hypothetical Ballot: MVP

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

Sunday, November 09, 2014

Hypothetical Ballot: Cy Young

The National League Cy Young voting will not entail much intrigue. Clayton Kershaw will win in a romp, and will probably win the MVP as well. And while I agree that Kershaw deserves the Cy Young, I believe that the margin in the voting will greatly overstate the value difference between Kershaw and his closest competitors, Johnny Cueto and Adam Wainwright.

Kershaw and Cueto each were worth about 70 RAR through their pitching efforts based on actual runs allowed adjusted for bullpen support. Kershaw had a much better RRA at 1.98 while Cueto’s was 2.55, but Cueto pitched an additional 45.1 IP. The difference between the two in runs allowed amounts to 45.1 innings of 5.07 RRA pitching. I estimate that the replacement level for starting pitchers is 128% of the league average runs allowed, which for the 2014 NL works out to 5.13. Thus there is essentially no difference between Cueto and Kershaw from a replacement level perspective. Cueto essentially tacked 45 innings of replacement level performance onto what Kershaw did.

Of course actual runs allowed are just one way to evaluate a pitcher. Cueto actually closes the gap when using eRA, which estimates runs allowed based on inputs, including actual hits allowed. Kershaw’s eRA was 2.30 to Cueto’s 2.73, a more narrow gap than the difference in actual runs allowed. Figuring RAR based on eRA, Cueto edges Kershaw 65 to 63. Kershaw has a significant advantage in DIPS measures, though, 2.47 to Cueto’s 3.60 in dRA, as Cueto’s BABIP allowed was just .246. And even considering just actual runs allowed, I am slightly biased towards the better performer on a rate basis rather than compiler. I concur that Kershaw is more deserving of the Cy than Cueto, but the gap just isn’t that large given Kershaw’s missed starts and 198 innings.

I’ve focused on Cueto v. Kershaw, but Wainwright is right on Cueto’s heels with 67 RAR. Wainwright also has an edge on Cueto in dRA (3.38 to 3.60), and could easily place ahead if one values the DIPS metrics.

For the rest of the ballot, Cole Hamels is a comfortable pick for fourth, and I have fifth as a close battle between Washington teammates, Tanner Roark and Jordan Zimmermann. Roark ranks ahead in RAR 50 to 46, but Zimmermann’s .78 dRA edge and superior peripherals are enough to slip ahead in my book:

1. Clayton Kershaw, LA
2. Johnny Cueto, CIN
3. Adam Wainwright, STL
4. Cole Hamels, PHI
5. Jordan Zimmermann, WAS

There will be more controversy associated with the AL award as Felix Hernandez and Corey Kluber jockey for the top spot. Kluber has become something of a darling among the DIPS-first portion of the sabermetric crowd, as his dRA is better than Hernandez’ (2.88 to 3.07) and he had around 38 additional opponent plate appearances. Cleveland’s fielders, in general, were bad--they ranked second-to-last in the AL in DER while Seattle led the AL.

However, I don’t believe in throwing out all elements of pitching results outside of the three true outcomes, nor do I believe that it’s a trivial matter to parcel out adjustments for fielding support amongst pitchers. For me, Hernandez’ large edge in runs-based metrics (Hernandez has a 12 RAR lead; the two differed in innings pitched by a single out, but Hernandez’ RRA of 2.54 was better than Kluber’s 2.98; using eRA, Hernandez has an even larger advantage of 20 RAR) is too large to ignore.

One point to note when using runs allowed metrics--Hernandez got less help from his bullpen then did Kluber. Hernandez bequeathed 13 runners, and 7 of them came around to score. Kluber bequeathed 20 runners and only 2 were allowed to score. That’s a seven run swing in the King’s favor that is not apparent from the traditional stat line.

Using RRA (which considers bequeathed runners), Hernandez’ advantage is 12 runs. Using eRA, which is just a component estimate, Hernandez leads by 20 runs. Using dRA, Kluber leads by 8. Quality of opposing hitters doesn’t change the picture much; according to Baseball Prospectus, Hernandez’ opponents combined for a .264 True Average, Kluber’s for .263. To vault Kluber ahead, one must put a lot more stock in DIPS or in the quality of adjustments for fielding support than I am willing to grant. I’m not saying it’s wrong to do so, but if you read any columns ripping the choice of Hernandez (and I don’t know there will be any), it’s likely you are dealing with a zealot.

And while I did not consciously consider it in my choice, another plus of choosing Hernandez is that I can dodge charges of pro-Indians bias.

For the rest of the ballot, I have stuck with the RAR order, as I see no particular reason to make any changes. Chris Sale is held back by just 174 innings, but he led the AL in RRA and dRA and was second in eRA to Garret Richards, who pitched five fewer innings:

1. Felix Hernandez, SEA
2. Corey Kluber, CLE
3. Chris Sale, CHA
4. Jon Lester, BOS/OAK
5. Max Scherzer, DET

Sunday, November 02, 2014

Hypothetical Ballot: Rookie of the Year

Just off the top of my head, the National League rookie class is one of the least inspiring that I can remember. Not only were there no real standout performances, there’s not a lot of competition for the top of the ballot, and there aren’t a lot of big-time prospects who simply didn’t produce in their first major league season (tragically, the closest to meeting this description is the late Oscar Taveras).

Jacob deGrom is the relatively clear choice with 33 RAR over just 22 starts. Over those 140 frames, though, he was excellent by any measure--his eRA and dRA were commensurate with his RRA, and it’s hard to argue with 9.5/2.8 strikeout/walks per game. Another pitcher is a name that I don’t recall seeing in much chatter about the award, Colorado lefty Tyler Matzek. Matzek pitched just 117 innings, which may help explain why he didn’t draw much attention, and of course his statistics don’t look very good without a park adjustment. After park adjustment, a 3.35 RRA was good for 23 RAR.

The other ballot spots go to batters; Ken Giles has drawn some attention, and he had an excellent season, ranking eighth among NL relievers with 17 RAR in just 45.2 innings thanks to sub-2 figures in all of the run average categories. Giles’ strikeout rate of 14.1 trailed only the usual suspects among NL relievers (Chapman, Kimbrel, Jansen). However, 45.2 innings is the rub--it's hard for a reliever facing less than 200 batters to stand up against even average everyday rookies.

The NL had three such players worthy of recognition. Travis d’Arnaud led NL rookies with 22 RAR and also led with 4.5 RG. While his defensive reputation is not great, it would take a fair amount of credit for fielding and baserunning to move Kolten Wong (15 RAR) or Billy Hamilton (11 RAR) ahead. Both appear to be good fielders and baserunners, Hamilton’s puzzling 23 caught stealings notwithstanding. Hamilton had 160 more PA thanks to leading off and not being subject to odd management by Michigan Mike, and he rates very highly in the various fielding metrics. After deGrom, it’s splitting hairs to fill out the rest of the ballot:

1. SP Jacob deGrom, NYN
2. C Travis d’Arnaud, NYN
3. SP Tyler Matzek, COL
4. CF Billy Hamilton, CIN
5. 2B Kolten Wong, STL

Were they in the AL, only deGrom would crack the ballot, as the AL crop put the NL’s to shame. Part of that is due to experienced international players, who are subject to bizarre treatment by the BBWAA. The BBWAA has rarely voted for Japanese rookies in recent years, but Jose Abreu will win the award despite having high-level experience in Cuba. Personally, I draw no distinction between international free agents and minor league graduates for award purposes.

Abreu is an easy choice for the top of the ballot, as his 55 RAR ranked sixth among all AL hitters and led first basemen, and his 7.2 RG ranked fourth in the league. The rest of the ballot spots go to pitchers, although Minnesota’s Danny Santana could certainly be considered with 30 RAR, and George Springer might have been a contender even with his late recall had he not been injured.

The three starters who made my ballot were Collin McHugh, Masahiro Tanaka, and Yordano Ventura. Tanaka looked like a Cy Young contender until his injury, but McHugh ended up edging him in RRA (3.01 to 3.05) in addition to pitching eighteen more innings. And while I wouldn’t have guessed it (largely due to McHugh toiling in obscurity with Houston), McHugh’s peripherals were every bit a match for Tanaka’s. It is worth noting that Tanaka, despite his experience pitching in NPB, is a year younger than McHugh.

Ventura pitched many more innings than either (183), but wasn’t as good on a rate basis and despite his ridiculous velocity struck out two batters fewer per game than either. In the end, 40 RAR for McHugh, 35 for Ventura, and 34 for Tanaka make it easy to justify any order depending on what factors one values. I’ve slid Tanaka ahead of Ventura thanks to better peripherals.
Apologies to Matt Shoemaker and Marcus Stroman, but the last spot on my ballot goes to Dellin Betances. Betances, unlike Giles in the NL, was a workhorse out of the pen, throwing 90 innings which helped him lead all AL relievers with 33 RAR. Only Andrew Miller and Brad Boxberger topped his 15.0 strikeout rate, and Betances was outstanding in the peripheral run averages as well. Were there an award for best reliever, Betances would get my vote, but on the rookie ballot he’s just fifth in a strong season for the AL:

1. 1B Jose Abreu, CHA
2. SP Collin McHugh, HOU
3. SP Masahiro Tanaka, NYA
4. SP Yordano Ventura, KC
5. RP Dellin Betances, NYA