Archive for October, 2015

How to Get a Swinging Strike by Pitch Type and Location

Red = High swinging-strike rate (swing and a miss / total pitches)
Yellow = Medium ; Green = Low

The size of the circle also represents how high the whiff rate is

Numbers are in Feet, with -X being inside (handedness neutral) and Z being height in feet above the center of the strike zone (as per PITCHf/x strike zone top and bottom)

Some observations (and probably repetition of prior research):

1) Four-seam fastballs are great between 0.8 to 1.4 feet above the middle of the zone and between -.5 and .5 across the plate (i.e., if you want to get a swing and a miss on a four-seamer, throw it high and right down the middle). Will have similar views for GB% and HR% soon.

2) Sliders, changeups and curveballs all need to be thrown low in the zone; doesn’t appear to matter inside or outside, though changeups need to be around the plate (or they don’t get swings).

3) There is almost nowhere you can throw a two-seamer to get swings and misses, though down and in and basically high appear to be the best places to get strikes.


More to come if you think this is interesting!

The Risk and Reward of Attempting to Pick Runners Off

Recently, Dave Cameron examined a planned back-pick by Russell Martin and the Blue Jays in Game 1 of the ALDS.  The play didn’t have a chance to happen because Delino DeShields put a 2-1 change up in play.  Not just in play, but on the ground to directly where the second baseman Ryan Goins would have been had he not been breaking for second in anticipation of the pick.  Dave wrote a great article that covered the play in depth, so feel free to go read it here.  In this article, I analyze the strategy of calling for a set pickoff attempt. What I found not only vindicates Martin and the Jays, but also questions one of my longest-held beliefs about pickoffs.

My strategy for evaluating the set pickoff was to calculate the break-even point (BEP) for a pickoff attempt using Run Expectancy (RE), similar to previous analyses on bunting and stealing. To calculate the BEP for a given pickoff attempt, I calculated the RE benefit (to the defense) of an out and the weighted RE cost of a safe call or an error.  This sounds simple enough, but calculating the RE after an error involved some guesswork.

Although errors can result in multiple outcomes, I chose to pick one outcome for each base to simplify the analysis. Thus, I assumed 2 bases for all runners on an errant throw to first, 1 base for all runners on an error to second, and, after much thought, 2 bases for runners on second and 1 base for runners on the corners on an error to third. If you have data that can replace these assumptions, please let me know.  Otherwise, be cognizant of my assumptions when you attempt to make use of the findings.  For example, if there is a slow runner on second, the BEP for a pickoff attempt to a corner will be overly conservative (inflated).  Additionally, I didn’t differentiate between pickoff attempts from the pitcher and the catcher.  The pitcher has a shorter, unobstructed throw, and favorable balk rules when picking to second or third, but still has to deal with the risk of a balk, especially to first, along with the added difficulty of throwing off the mound.  Finally, while calling for a back-pick from the catcher can put a defender out of position, I chose to ignore this factor because a) I assume it is rare for a hitter to find the vacated hole, and b) the defense can choose to avoid contact.

In order to weight the cost of a failed pickoff attempt appropriately, I had to estimate what the error rate would be on attempts.  While we do have data on pitcher error rates on pickoff attempts (around 0.95%), the data are only from throws to first.  Set pickoff plays are more challenging for the defense, so the error rate should be higher than on typical attempts to first.  My solution, in lieu of empirical data from actual set pickoff attempts, was to estimate catchers’ throwing error rates from the 2015 season.  I chose this strategy for two reasons: First, catchers are one of the primary players who can attempt a set pickoff, so it made sense to sample from their performance.  And second, catchers accumulate a large portion of their assists under similar conditions to the pickoff attempt (for example, in 2015 nearly 40% of all catcher assists came from caught stealing).  Thus, I expected catcher throwing error rates to approximate the error rates we would observe on set pickoff plays.

While not a perfect method, I estimated catcher throwing error rate as Throwing Errors / Assists + Throwing Errors + Stolen Bases.  The mean throwing error rate in a sample of catchers (n = 38) who played at least 500 innings in 2015 was 3.6%.  Do you accept that set pickoff plays will result in 3.8 times more errors than typical pickoff throws to first? If not, adjust your own estimates accordingly.

Using the estimated throwing error rate for catchers, the formula for estimating the BEP on a set pickoff attempt is RE cost / (RE cost – RE benefit). In this equation, RE benefit = RE after a pickoff – RE before a pickoff; RE cost = RE before a failed attempt – RE with a failed attempt, and RE with a failed attempt = (RE of a safe call *.964) + (RE of an error *.036).  Using the RE tables found here, I generated Table 1 below.


Runners Outs First Second Third
1 _ _ 0 3.51%
1 3.32%
2 3.24%
1 2 _ 0 3.32% 2.18%
1 4.21% 1.93%
2 9.17% 2.33%
1 _ 3 0 2.37% 0.74%
1 3.47% 1.92%
2 6.72% 5.99%
_ 2 3 0 1.70% 1.41%
1 1.93% 1.73%
2 5.06% 5.06%
1 2 3 0 10.21% 1.97% 1.64%
1 4.85% 2.78% 2.48%
2 7.58% 3.92% 3.92%
_ 2 _ 0 1.54%
1 1.43%
2 1.26%
_ _ 3 0 0.11%
1 1.74%
2 5.61%

Table 1.  Success rate required to attempt a pick at each base.

Table 1 presents the BEP for the defense of (successful pickoffs / attempts) X 100.  In other words, Table 1 provides the minimum expectation of success required for the defense to attempt a set pickoff and it be a break-even strategy. Unfortunately, it is difficult to guess how successful set pickoff attempts typically are.  In Dan Malkiel’s study of pickoffs to first, he found that righties and lefties were successful about 2% and 4% of the time, respectively.  However, Malkiel’s study sampled situations with base-stealers on first, so the stolen-base rate was between 17% and 21%.  It’s impossible to know what percentage of successful pickoffs occurred when the runner intended to steal, but it’s safe to say 2% and 4% success rates are a little high if the runner on first isn’t planning on going. Set pickoffs usually work differently than throws to first, since neither the pickoff nor the steal are always expected. Therefore, the data on picks to first can only serve as a point of reference, helping to calibrate expectations rather than serving as predictions themselves.

One way to assess if teams are over- or under-utilizing set pickoffs is to compare their pickoff to error ratios with the BEPs for that metric. Unfortunately, I could only find data for one special case of the set pickoff: a catcher back-pick to first.  In the Malkiel study, successful back-picks were 96% of back-picks plus errors.  If we assume an error puts the runner on third, the BEP for pickoffs/pickoffs + errors is 50%, suggesting that catchers have room to get much more aggressive in attempting to pick runners off first.  Without more data, it’s difficult to comment further on current MLB behaviour regarding set pickoff plays. Nevertheless, the estimates in Table 1 provide interesting insights into the risks and rewards of pickoff plays. Below, I list six lessons that can be gleaned from Table 1.  At least two of these lessons fly directly in the face of my own long-held beliefs, and maybe yours too!

Lesson 1

If, at any time, the defense notices that it has better than a 15% chance of picking off a runner, they should attempt the pickoff.

Lesson 2

Pickoff attempts require greater confidence with two outs, with three exceptions.  Often, the required success rate is over 5%, requiring a fairly egregious mistake by the runner to warrant a throw. The exceptions to this rule are with a runner on first, a runner on second, or a pick to second with runners on first and second.

Lesson 3

A runner on second with no runner ahead of him should probably be targeted frequently.  The BEPs are consistently low for attempting the pickoff to second, while the runner is motivated to be aggressive by the chance to score a run or steal third. Even failed attempts have the favorable by-product of keeping the runner close, a factor not considered in Table 1.

Lesson 4

Throwing behind the runner on first with runners on first and second or the bases loaded is dangerous.  This doesn’t mean it’s a bad play if the runner on first opens the door, but the defense should be really confident to make the throw.

Now for the lessons that go against everything I thought I knew…

Lesson 5

Pitchers should throw over to third with runners on 1st and 3rd in a steal situation.  Ever since the MLB outlawed the fake-to-third move, pitchers haven’t been allowed to bluff the throw in hopes of catching the runner breaking from first.  Based on Table 1, it seems strange that pitchers ever faked the throw to begin with.  With no one out, the defense would only need to pick the runner off third 8 times per 1000 attempts, or nail the runner stealing second 3 times per 100 attempts, or a combination of the two to break even.  Additionally, if the runner on first breaks for second it’s an easier throw from third than from first, which was often the result with the fake-to-third move.  While many old-school baseball people will object to throwing over to third, the common refrain “he’s not going anywhere!” doesn’t necessarily apply to the 1st and 3rd steal situation.  The runner could be trying to get closer to home so he can steal on the catcher’s throw to second, making it the perfect time to throw over.  Although the third baseman’s positioning will sometimes make a true pickoff attempt at third difficult, the rules do not require the pitcher to throw directly to third.  Thus, teams can make legitimate efforts to get the runner on third when the situation allows it, while other times making throws away from the base solely to catch the runner on first breaking for second.

Lesson 6

The situation that requires the lowest probability of success to attempt a pickoff is when there is a runner on third with no one out.  The defence needs to nab merely 2 runners out of every 1000 attempts to break even. And get this, the BEP on pickoff attempts to third with 0 out is lower than the BEP for typical throws to first, even with the much lower error rate on throws to first (0.95%), and even after adjusting the assumed cost of an error to one base.  Holding probability of success constant, the pickoff attempt to get a runner on third with 0 out is the least risky pickoff attempt possible. The LEAST risky.

Of course, a runner who is on third with no one out should be taking no chances.  But that doesn’t mean a pickoff will never work…


An Introduction to Determining Arbitration Salaries: Starting Pitchers

My name is Rich Rieders and I am a 2015 graduate of Rutgers Law School. Over the winter, I participated in Tulane University’s 9th Annual Baseball Arbitration Competition and we finished in 2nd place overall out of 40 teams.

In order to prepare for the competition, I created a database (going back to 2008) consisting of all arbitration awards and players who signed 1-year contracts avoiding arbitration along with their respective statistics. Using regression analysis, I was able to determine which statistics correlate most with salary. In turn, I have created a projection system that can accurately predict arbitration salaries. My projections are more accurate than the ones featured on MLBTradeRumors.

I will be releasing my 2016 projections once the season is over and all awards are announced.

The goal of this article is to properly explain how arbitration salaries are determined and how to choose the best comparative baseball salaries (comps) as outlined in Article VI, Section E, Part 10(a) of the CBA. You can think of the comps as legal precedent. The closer the comps are to the player’s stats, the more comps you have and the more recent those comps are, the stronger your argument.

First and foremost, the purpose of the arbitration process is to compensate the player for his actual results on the field, not to give him a salary based on what we expect he will produce in the upcoming season. We concern ourselves with only the traditional stats. I know this is a complete departure from the way we normally think here on FanGraphs, but salary arbitration is a completely different animal. In essence, arbitration salaries are determined by the accumulation of traditional counting stats.

For our purposes, there are six types of players who are up for arbitration in a given offseason and each type has its own separate valuation. The six types of players are:

(1) First-year-eligible SP

(2) SP who have previously been through the arbitration process

(3) First-year-eligible RP

(4) RP who have previously been through the arbitration process

(5) First-year-eligible position player

(6) Position players who have previously been through the arbitration process.

I will explain, in detail, how to properly choose player comps for each of the six group of players. In this segment, we will focus just on the starting pitchers.

For a SP who is arbitration eligible for the first time, here are the statistics that correlate most with eventual salary:

Platform IP: 60.83%

Platform GS: 57.59%

Platform SO: 54.41%

Platform W: 53.12%

Career IP: 50.56%

Career SO: 47.45%

Career W: 42.76%

Career GS: 37.10%

When initially looking for player comps, these are statistics we are going to focus on. Keep in mind that although ERA is not listed, it is nonetheless important as ERA is still one of the default statistics during a hearing and the first basis for comparison. Note that rate stats almost always have a very low correlation since rate stats do not take into account playing time.

Let’s use Atlanta Braves starter, Shelby Miller, as an example of a first-year-eligible SP.

Shelby Miller is arbitration-eligible for the first time going into 2016 with 3 years and 30 days of service time (3.030). In his platform season (2015), Miller made 33 starts recording 6 wins, 171 SO with a 3.02 ERA in 205.1 IP. Over his career, Miller has compiled 575 IP, 32 W, 483 SO with a 3.22 ERA in 96 GS. The objective here is to find the players who avoided arbitration by signing a 1 year contract with statistics that are most similar to Miller’s. The more recent, the better. The best way to do that is to set a floor and a ceiling and then work your way towards the middle.

From Miller’s perspective, let’s look at Miguel Gonzalez’s 2014 platform season. Like Miller, Gonzalez posted a low win total despite a very strong ERA. Gonzalez made 26 starts, recorded 10 wins, 111 SO with a 3.23 ERA in 159 IP. Over his career, Gonzalez compiled 69 starts, 30 wins, 308 SO with a 3.45 ERA in 435.2 IP. Although their ERA and win totals are extremely close, Miller bests Gonzalez in all the most important categories and has significantly more playing time and strikeouts. Therefore, we can definitively state Miller should receive more than Gonzalez did. As such, Gonzalez’s 2015 salary of 3.45 million should be the floor.

From Atlanta’s perspective, let’s look at Chris Tillman’s 2014 platform season. Like Miller, Tillman pitched a similar amount of innings and games with a pretty low ERA. In his platform season, Tillman made 34 starts recording 13 wins, 150 SO and a 3.34 ERA in 207.1 IP. Over his career, Tillman compiled 45 W, 680.1 IP, 511 SO with a 4.00 ERA in 118 GS. Although Miller has the better ERA, Tillman is superior in all the other major categories. Hence, we can conclude that Miller will receive less than Tillman. We can use Tillman’s 2015 salary of $4.315 million as the ceiling.

Given the above, Shelby Miller is likely to receive somewhere between $3.45 million and $4.315 million. Now that we have a range, let’s find someone towards the middle.

In 2011, Justin Masterson made 33 starts with 12 W, 158 SO, 3.21 ERA in 216 IP. Over his career he made 87 starts, with 28 W, 485 SO, 3.92 ERA in 613.2 IP. Those numbers are quite similar across the board with Miller having a better ERA, but fewer IP. Masterson’s 2012 salary was $3.825 million. Alex Cobb ($4.0 million in 2015),  Travis Wood ($3.9 million in 2014) and Steven Strasburg ($3.975 million in 2014) are all good comps as well.

As for my model, Miller projects to receive $3,859,816 +/- $145,351 which is perfectly in line with the comps above. MLBTradeRumors projects him at $4.9 million, which is not only significantly higher than the above comps, but would beat the record for a first-year player by nearly 600K.

For a player who has already been through the arbitration process before, the valuation is completely different as career statistics are no longer used the 2nd, 3rd, 4th, etc. time around (except in a few rare cases). This group of players are the most difficult to project since we use fewer variables due to the exclusion of career stats and how there are fewer SP across the league than relievers or position players. Nonetheless, we can still get a pretty good idea what their eventual salary will be.

For an SP who has previously been through the arbitration process, the stats that correlate most with eventual salary are:

(1) Platform W: 69.12%

(2) Platform RA9-WAR: 64.04%

(3) Platform SO: 60.97%

(4) Platform fWAR: 58.93%

(5) Platform IP: 58.34%

(6) Platform GS: 49.75%

For example, let’s look at Angels SP Garrett Richards who is arbitration eligible for the second time going into 2016. As a Super-2 going into 2015, Richards received a $3.2 million salary. That figure includes everything he had done in his career up to that point. Thus, when determining his 2016 salary, we don’t need to focus on previous seasons. We need only determine what his 2015 season was worth and give him a raise. In his platform season (2015), Richards made 32 starts recording 15 wins, 176 SO, 3.65 ERA, 2.5 fWAR and 2.8 RA9-WAR in 207.1 IP. We want to find the players whose stats are most similar to Richards.

First let’s discuss Matt Garza’s 2010 platform season (a bit old, but still useful) where he made 32 starts recording 15 wins, 150 SO, 3.91 ERA, 1.9 fWAR and 2.8 RA9-WAR in 204.2 IP. Other than the strikeout numbers, we have a virtually identical season. As such, Richards is likely to receive a raise higher than Garza’s $2.6 million raise going into 2011. We can consider a raise of $2.6 million to be his floor.

Next let’s look at C.J. Wilson’s 2010 platform season (again old, but useful still) where he made 33 starts recording 15 wins, 170 SO, 3.35 ERA, 4.1 fWAR and 5.1 RA9-WAR in 204 IP. Wilson has the same amount of wins and virtually the same number of SO although Wilson has a clear advantage in fWAR and RA9-WAR with a slightly better ERA so it’s pretty safe to say that Richards is likely to get a raise lower than Wilson’s $3.9 million raise. The $3.9 million should be the ceiling.

Homer Bailey’s 2012 platform season is a great final comparison. Bailey made 33 starts recording 13 wins, 168 SO, 3.68 ERA, 2.7 fWAR and 2.8 RA9-WAR in 208 IP. Both players are virtually identical statistically. Bailey received a raise of $2.925 million so Richards is likely to receive a very similar raise himself. Shaun Marcum ($3.1 million in 2011), Jordan Zimmerman ($3.050 million in 2011) and Max Scherzer ($2.975 million in 2013) are all good comps as well.

Therefore, we can be certain that Richards will receive a raise somewhere between $2.6 million and $3.9 million. As for my model, Richards projects to receive a raise of $2,923,484 for a total salary of $6,123,484+/- $336,500 and, unsurprisingly, that is perfectly in line with the comps above. MlbTradeRumors is projecting a raise of $3.6 million for a total salary of $6.8 million which I think is a bit generous given the comps we have at our disposal, but not unreasonable.

Next up: Relief Pitchers.

What If Prior Playoff Success Were the Only Thing that Mattered?

Ed. note: this was probably intended for a few days ago, but it just showed up, so, enjoy!

Determining playoff success ain’t like predicting outcomes during the regular season. Smaller sample sizes, emotions, momentum, and magical realism have been blamed for seemingly unexplainable outcomes in baseball’s postseason. Common knowledge about predicting success doesn’t add up, “shouldn’t the best teams win the World Series every year?” It might depend on what we call, “best”.

It’s not always the best regular-season teams that win the World Series; in the last 20 years only four teams that had the best record in baseball have gone on to win the World Series (20%). Even momentum heading into the playoffs doesn’t seem to amount to much World Series success either; the 10 best September records heading into the playoffs haven’t amounted to a World Series victory during the Wild Card Era[1].

Despite these results we still can’t get away from favoring the best teams every single year — after all, “nothing succeeds like success.” There is something to be said about previous playoff success within the wild-card era, and whether it is maintaining the rosters of successful teams or a cultural revitalization within these teams, previous playoff success has paid off. In fact, 13 of the last 20 years of World Series championships (65%) belong to only 4 teams: Yankees, Red Sox, Cardinals and Giants.

A new study on previous success by Rosenqvist and Skans (2015)[2] may have shed some light onto this phenomenon. Their experiment compared golfers of seemingly equal skill and ability: golfers who marginally made the cut for a golf tournament vs. golfers who marginally missed the cut for the same tournament. They found that golfers who made the cut showed an increase in performance in subsequent tournaments compared to those golfers who missed the cut. Early luck leads to increased confidence, which later leads to more success.

Success, either accidental or otherwise, seems to be contagious. Baseball, however, isn’t golf (unless you’re Brandon Belt). Baseball is comprised of teams of individuals, each with their own history of success or failure, confidence or doubt. However, using this theory, could it be the case that teams that are comprised of players with more playoff success have the confidence to do it again?

I totaled all of the playoff experience for every player on the 8 playoff teams in the ALDS and NLDS. To have contributed to previous playoff success, a player had to have played at least once during a previous playoff run (pitched at least one pitch, come in to pinch-run, come in to play defense, or taken at least one at-bat). Below, each team has an “average player profile” that defines each team’s average postseason player. The profile is comprised of the average experience and success across five variables: years that a player has contributed to a playoff team, total playoff games won with each contributed team, playoff series won with each contributed team, World Series appearances with each contributed team, and World Series victories with each contributed team.

Kansas City Royals

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


8.16 2.08 0.72 0.08


Though the 2015 Royals have carried their 2014 playoff experience with them, it’s not last year’s remaining players that are most intriguing. In some savvy acquisitions the Royals have padded their already experienced squad with some playoff warhorses. The 2015 acquisitions of Joba Chamberlain, Ryan Madson, Franklin Morales, and Jonny Gomes all come with some serious playoff success – each have a World Series ring.

Yet, despite the playoff experience added by this year’s Royals, their 2015 playoff roster doesn’t include Joba Chamberlain or clubhouse glue-guy Jonny Gomes, each with a ring. Omar Infante was also left off, who had the second-most World Series appearances across all 8 teams, tied with Matt Holliday with 3. Despite the youth-driven movement from last year’s team, the 2015 Royals are surprisingly the second-oldest team in this year’s postseason. Their age comes with some success – the average 2015 Royal has been to the playoffs, won an average of 8 games, won at least 2 series, and been to a World Series.

Houston Astros

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


1.56 0.28 0.04 0.00


Young teams with no playoff experience can play like they have nothing to lose; they’re young, they’re talented, and there’s a belief that if they’re this good now, they’ll be able to make the playoffs again in the future. It runs counter to the success-confidence-success theory, but this could be the story for the 2015 Astros who could propel themselves to an accidental World Series appearance.

The only player on the 2015 Astros to have been to a World Series is Scott Kazmir with the 2008 Rays. Overall, the pitching staff is older (m = 30.1 years old) and more successful (m = 2.27 games won) compared to their position players (m = 26.9 years old, m = 1.00 games won). This composition is counter to the 2015 Mets, who have pitching youth paired with position-player experience. The Astros are a young team; they’ll be looking to pull a 2014 Royals on the 2015 Royals.

Royals in 5

Toronto Blue Jays

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


3.72 0.80 0.24 0.00


When the majority of a team’s playoff experiences comes from a duo of former Rockies, you know two things: 1) It’s been a long time since the Blue Jays have reached the playoffs and 2) the current team lacks playoff experience. The only player who knows what it takes to win a World Series is Mark Buehrle, and apparently his late-season implosion was enough to leave him off the postseason roster. The Blue Jays sport the second-oldest average player (m = 29.3) including the oldest player in this year’s postseason to be in the playoffs for the first time – the 40-year-old R.A. Dickey. The Blue Jays join the Mets and the Astros to field a team without any players who have won a World Series.

This is the opposite of playing with accidental confidence, where a young or inexperienced team suddenly finds themselves in the playoffs and plays the game like they’ve got nothing to lose, “there’s always next year”. Well for these Blue Jays, next year isn’t a guarantee. They may not be playing with a blithe spirit of reckless abandon but the fleeting dreams of older players who may never reach the playoffs again. But who knows, maybe the exuberance of being in the playoffs for the first time is enough to spark a youthful movement? The theory disagrees.

Texas Rangers

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


7.28 1.56 0.64 0.08


The average Texas Ranger profile is a bit deceptive – heavily weighted by those that have previously been to the playoffs. The average Texas Ranger who has previously been to the playoffs has won 15.2 games, has won 3.9 playoff series, and has been to almost 2 World Series (m = 1.78). In fact 36% of the 2015 Rangers’ postseason roster have been to a World Series. The Rangers have very quietly maintained many of the players who got them to the back-to-back World Series’ in 2010-2011 (Lewis, Holland, Moreland, Andrus, Napoli, Hamilton).

This team will be overlooked for their lack of pitching, but their postseason success cannot be ignored. With the average Texas Ranger having nearly double the success of winning playoff series than the average Blue Jay, we might expect this series to be a cakewalk for the Rangers. Then again, it’s a five-game series, and the Blue Jays have some serious star power.

Rangers in 4.

Chicago Cubs

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
0.92 3.76 0.92 0.20 0.12


In “the year of the rookie” it only makes sense to have two young teams representing each league in the postseason. If you removed Austin Jackson, the 2015 Cubs start to look a lot like the 2015 Astros. The pitching staff is older (m= 29.9 years old) and more successful (m = 4.73 games won) compared to the Jacksonless position players (m = 27.1 years old) and (m = 1.92 games won). The Cubs are lucky to have both a position player (David “Dad bod” Ross) and a pitcher (Jon Lester) with World Series rings, along with 16% of postseason players with World Series exposure. So, maybe the Cubs are a slightly more seasoned version of the 2015 Astros.

The Cubs are also deceptively successful in the playoffs. Despite an average of only 1 year in the playoffs, the average Cub has won almost 4 games and 1 series. Compare this to the New York Mets who have relatively the same amount of experience, but with far less success.

Between the minds of Theo Epstein and Joe Maddon are some great ideas about utilizing leadership, team chemistry, and plenty of other intangibles. Count on the Cubs to take advantage of the balance between youth and experience during this year’s playoffs.

St. Louis Cardinals

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
2.64 14.6 3.56 0.84 0.32


The average Cardinal has some serious playoff success. The average Cardinal has been to the playoffs at least 2 years, won at least 14 games, and won at least 3 series. The 2015 postseason Cardinal has not only been to the World Series, but 25% of the 2015 postseason Cardinals have won a World Series; all of this playoff success and still a relatively young team. The Cardinals have the 2015 player with the most postseason experience in Yadier Molina, who has appeared in 4 different World Series and won 2 of them. The saddest part about the 2015 Cardinals is the absence of Randy Choate, who won a World Series with the 2000 Yankees (2 World Series rings in 16 years would have been a cool story).

The Cubs might give the Cardinals some fits, but the theory says that the Cardinals shouldn’t have a problem disposing of the Cubs. Count on the Cardinals making it back to the World Series.

Cardinals in 4

New York Mets

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age

0.96 2.32 0.40 0.04 0.00


If there are counters to the success-confidence-success theory it’s the 2015 Mets, who are basically the 2010 San Francisco Giants: loaded with young talented pitching and complemented with older, experienced position players. The Mets are in fact the youngest of the 8 playoff teams, though their youth comes with a price. In fact, the Mets’ pitching staff is so young and inexperienced, if you removed Bartolo Colon, you’d only have 1 pitcher with playoff experience (Tyler Clippard with the hapless 2012 and 2014 Nationals).

Quite similar to the 2010 Giants, the only player on the 2015 Mets to have won a World Series is Juan Uribe (2005, 2010) who did so with the Giants in 2010, yet due to injuries isn’t on the playoff roster. The Mets will have some decent playoff success with Curtis Granderson, David Wright, and Michael Cuddyer who can describe to young players what it’s like to lose in the playoffs. The only player to have even been to a World Series is Granderson when he was on the Tigers who lost to the Cardinals in 2006.

Los Angeles Dodgers

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
2.04 6.64 1.28 0.24 0.08


Chase Utley + Jimmy Rollins + Good Starting Pitching = 2007-2011 Philadelphia Phillies. The Dodgers are hoping they get the 2008 version, though by the looks of things, it may resemble more of the 2010 version. The average Dodger has been to the playoffs, won a few games, and won at least 1 series. It’s really a smattering of success and experience despite being the oldest team in 2015 postseason (m = 29.6 years old).

Their playoff success says that the Dodgers should be able to handle the Mets, though the real test will be whether this older group of players will take to the leadership and previous success of Utley and Rollins. If the Dodger players are smart, they’ll humble themselves as much as possible, hone in, and play as a team.

Dodgers in 4


Conclusion and Caveats

  1. Yes, skill and ability is obviously something to take into consideration. Though, players who are highly skilled tend to find themselves on more successful teams, so the two may be related. The same can be said for age: the older you are, the more playoff experience you’re likely to have. However, if you look at this year’s teams, average age and average playoff success don’t seem to be related at all.
  1. Yes, in recent memory, the 2010 Giants and the 2014 Royals have been successful in the postseason despite a lack of playoff experience and success. However, in the playoff era, how many have actually won the World Series? Few come to mind.
  1. Yes, skill seems to be valued more that experience. Most managers tend to stick with their highest-performing players, and you can’t blame them. However, if this theory holds true, maybe you can blame them. The second season might benefit from previous playoff success. The counter to this is to picture a 2015 postseason team with 67-year-old Johnny Bench, 84-year-old Willie Mays, and “Mr. October” 69-year-old Reggie Jackson (all have some serious playoff success, right?). Recall from #1 that skill and ability obviously count, but the theory states that previous success might count too.



[2] Rosenqvist, O. & Skans O.N. (2015). Confidence enhanced performance? – The causal effects of success on future performance in professional golf tournaments. Journal of Economic Behavior & Organization, 117, 281-295.

Hardball Retrospective – The “Original” 1977 Pittsburgh Pirates

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. Therefore, Dan Quisenberry is listed on the Royals roster for the duration of his career while the Tigers declare Charlie Gehringer and the Senators claim Goose Goslin. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the finest single-season rosters for every Major League organization based on overall rankings in OWAR and OWS along with the general managers and scouting directors that constructed the teams. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony LaRussa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.


OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams


The 1977 Pittsburgh Pirates          OWAR: 53.6     OWS: 347     OPW%: .524

GM Joe Brown acquired all of the ballplayers on the 1977 Pirates roster. Based on the revised standings the “Original” 1977 Pirates tied for second place with the Phillies, one game behind the Cardinals. Pittsburgh topped the Senior Circuit in OWS during consecutive campaigns (1977-78).

Dave Parker (.338/21/88) collected his first batting title and paced the League with 215 base knocks and 44 two-baggers. “Cobra” merited his first All-Star nomination and Gold Glove Award while placing third in the NL MVP balloting. Mitchell Page (.307/21/85) pilfered 42 bags and finished runner-up in the Rookie of the Year vote. Don Money cracked a career-best 25 circuit clouts and received his third All-Star nod. Al “Scoop” Oliver contributed a .308 BA with 19 round-trippers. Richie Zisk clubbed 30 long balls and knocked in 101 baserunners. Willie Randolph laced 11 triples and tallied 91 runs.

Willie Stargell swatted 13 big-flies despite missing almost two-thirds of the 1977 campaign. “Pops” ranks ninth among left fielders according to Bill James in “The New Bill James Historical Baseball Abstract.” Teammates enumerated in the “NBJHBA” top 100 rankings include Parker (14th-RF), Randolph (17th-2B), Oliver (31st-CF), Manny Sanguillen (42nd-C), Dave Cash (50th-2B), Money (55th-3B), Richie Hebner (56th-3B), Zisk (69th-RF), Freddie Patek (73rd – SS), Bob Bailey (79th – 3B), Tony Armas (89th-RF) and Rennie Stennett (90th-2B).

Willie Randolph 2B 4.69 20.08
Mitchell Page LF 6.07 29.41
Dave Parker RF 5.02 32.6
Don Money 3B/2B 4.11 21.87
Al Oliver CF/LF 2.04 20.19
Richie Hebner 1B 2.7 16.01
Milt May C 1.66 9.63
Freddie Patek SS 1.55 14.76
Rennie Stennett 2B 3.57 17.98
Art Howe 2B 1.83 13.91
Richie Zisk RF 1.82 20.15
Tony Armas CF 1.49 7.76
Dave Cash 2B 1.11 17.12
Frank Taveras SS 1.01 13.78
Ed Ott C 0.67 10.69
Willie Stargell 1B 0.63 8.47
Omar Moreno CF 0.47 12.62
Craig Reynolds SS 0.1 6.74
Gene Clines LF 0.04 5.48
Bob Bailey 0.02 1.61
Jimmy Sexton SS -0.05 0.58
Mike Edwards 2B -0.12 0.16
Miguel Dilone LF -0.33 0.32
Dale Berra 3B -0.41 0.42
Manny Sanguillen C -0.44 10.27
Ken Macha 3B -0.55 0.66
Mario Mendoza SS -0.58 1.22
Bobby Tolan 1B -0.68 0.26

John “Candy Man” Candelaria earned his lone All-Star appearance with a 20-5 record along with a League-best 2.34 ERA. Dock Ellis supplied 12 victories and an ERA of 3.63. Rick Langford surpassed the 200-innings mark while losing 19 of 27 decisions. The bullpen subdued late-inning rallies by the opposition, co-anchored by Gene Garber (2.35, 19 saves) and Kent Tekulve (10-1, 3.06).

John Candelaria SP 8.07 24.69
Dock Ellis SP 2.37 16.07
Rick Langford SP 1.02 8.44
Bruce Kison SP 0.39 5.71
Timothy Jones SP 0.64 1.73
Gene Garber RP 2.19 15.16
Kent Tekulve RP 0.95 11.48
Bruce Dal Canton RP 0.25 1.6
Doug Bair RP 0.21 5.46
Al Holland RP -0.09 0
Ed Whitson SP 0.27 1.21
Woodie Fryman SP 0.08 1.95
Rick Honeycutt SP 0.02 1.18
Silvio Martinez RP -0.22 0.07
Odell Jones SP -0.28 2.24
Bill Laxton RP -0.64 3.32
Ramon Hernandez RP -0.67 0.1
Larry Demery SW -1.04 2.06

The “Original” 1977 Pittsburgh Pirates roster

NAME POS WAR WS General Manager Scouting Director
John Candelaria SP 8.07 24.69 Joe Brown Harding Peterson
Mitchell Page LF 6.07 29.41 Joe Brown Harding Peterson
Dave Parker RF 5.02 32.6 Joe Brown Harding Peterson
Willie Randolph 2B 4.69 20.08 Joe Brown Harding Peterson
Don Money 2B 4.11 21.87 Joe Brown
Rennie Stennett 2B 3.57 17.98 Joe Brown Harding Peterson
Richie Hebner 1B 2.7 16.01 Joe Brown
Dock Ellis SP 2.37 16.07 Joe Brown
Gene Garber RP 2.19 15.16 Joe Brown
Al Oliver LF 2.04 20.19 Joe Brown
Art Howe 2B 1.83 13.91 Joe Brown Harding Peterson
Richie Zisk RF 1.82 20.15 Joe Brown
Milt May C 1.66 9.63 Joe Brown
Freddie Patek SS 1.55 14.76 Joe Brown
Tony Armas CF 1.49 7.76 Joe Brown Harding Peterson
Dave Cash 2B 1.11 17.12 Joe Brown
Rick Langford SP 1.02 8.44 Joe Brown Harding Peterson
Frank Taveras SS 1.01 13.78 Joe Brown
Kent Tekulve RP 0.95 11.48 Joe Brown Harding Peterson
Ed Ott C 0.67 10.69 Joe Brown Harding Peterson
Timothy Jones SP 0.64 1.73 Joe Brown Harding Peterson
Willie Stargell 1B 0.63 8.47 Joe Brown
Omar Moreno CF 0.47 12.62 Joe Brown Harding Peterson
Bruce Kison SP 0.39 5.71 Joe Brown
Ed Whitson SP 0.27 1.21 Joe Brown Harding Peterson
Bruce Dal Canton RP 0.25 1.6 Joe Brown
Doug Bair RP 0.21 5.46 Joe Brown Harding Peterson
Craig Reynolds SS 0.1 6.74 Joe Brown Harding Peterson
Woodie Fryman SP 0.08 1.95 Joe Brown
Gene Clines LF 0.04 5.48 Joe Brown
Bob Bailey 0.02 1.61 Joe Brown Rex Bowen
Rick Honeycutt SP 0.02 1.18 Joe Brown Harding Peterson
Jimmy Sexton SS -0.05 0.58 Joe Brown Harding Peterson
Al Holland RP -0.09 0 Joe Brown Harding Peterson
Mike Edwards 2B -0.12 0.16 Joe Brown Harding Peterson
Silvio Martinez RP -0.22 0.07 Joe Brown Harding Peterson
Odell Jones SP -0.28 2.24 Joe Brown Harding Peterson
Miguel Dilone LF -0.33 0.32 Joe Brown Harding Peterson
Dale Berra 3B -0.41 0.42 Joe Brown Harding Peterson
Manny Sanguillen C -0.44 10.27 Joe Brown
Ken Macha 3B -0.55 0.66 Joe Brown Harding Peterson
Mario Mendoza SS -0.58 1.22 Joe Brown Harding Peterson
Bill Laxton RP -0.64 3.32 Joe Brown
Ramon Hernandez RP -0.67 0.1 Joe Brown
Bobby Tolan 1B -0.68 0.26 Joe Brown Rex Bowen
Larry Demery SW -1.04 2.06 Joe Brown Harding Peterson


Honorable Mention

The “Original” 2012 Pirates    OWAR: 46.1     OWS: 303     OPW%: .597

The Bucs seized the National League pennant with 97 victories and topped the circuit in OWS. Andrew McCutchen (.327/31/96) led the League with 194 base hits, earned a Gold Glove Award and finished third in the 2012 National League MVP balloting. Aramis Ramirez (.300/27/105) drilled a League-leading 50 doubles. Pedro Alvarez went yard 30 times while Jose A. Bautista launched 27 bombs in an injury-shortened campaign. Jeff Keppinger delivered a .325 BA in a utility role. Paul Maholm posted a record of 13-11 with a 3.67 ERA and fellow hurler Bronson Arroyo accrued 12 wins with a 3.74 ERA in 202 innings.

On Deck

The “Original” 1931 Athletics

References and Resources

Baseball America – Executive Database


James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

Young Guns: 2015’s Top Eleven Rookie Pitchers

The 2015 season featured the emergence of a whole passel of top-flight young arms. And these pitchers weren’t just appearing on rubble-clearing franchises like the Phillies. Five of the top 11 rookie pitchers (by bWAR) are on playoff teams. Let’s go to the list (thanks to Baseball Reference’s Play Index):

1. Lance McCullers   2.5 WAR     121 IP     80 ERA-     Age: 22

McCullers has been a key engine in the Astros relaunch, turning in 11 quality starts in 21 attempts since his arrival in the majors on May 18. He’s been the third most valuable pitcher on the ‘Stros, behind Cy Young contender Dallas Keuchel and Colin McHugh. While his innings load has been a concern, McCullers has thrown over 100 pitches in just 8 of his starts, and went over 110 just once. He throws the hardest curve in the charted universe, which probably accounts for his astronomical strikeout and walk rates in the minors.

_________     K/9         BB/9

Minor Lance     10.7           4.5

Major Lance       9.2           3.1

McCullers shaved 1.4 walks per 9 after his promotion, at the cost of 1.5 strikeouts, a trade probably worth making given the success he’s had so far.  Major-league starting pitchers with a walk rate of at least 4.5/9 are rare, and mercifully so. By FanGraphs’ count, there have been 58 such pitchers since the beginning of divisional play in 1969. As you can see, these are generally the guys you’ll find in your grocery’s frozen-rope section. McCullers may yet revert to his minor-league form, in which case he can still be a bullpen force (where his many doubters thought he would end up), but right now he looks like a top-of-the-rotation starter.

2. Eduardo Rodriguez     2.5 WAR     122 IP     91 ERA-     Age: 22

Acquired by the Sawx from Baltimore in July 2014 in exchange for reliever Andrew Miller … well, let’s stop there. How good would E-Rod look in the Fighting Showalters rotation? Hey, that’s a question that can be answered with research!

Orioles Starters            WAR          ERA-

Wei-Yin Chen               3.5             81

Ubaldo Jiminez           2.5             94

Kevin Gausman           1.3             97

Miguel Gonzalez         0.6           119

Chris Tillman               0.6           123

Orioles fans are unlikely to curse Andrew Miller in the same way Cubs’ old-timers curse Ernie Broglio, but this trade left a bruise. Chen is likely to depart in free agency this winter, and putative rotation saviors Dylan Bundy and Hunter Harvey will need maps to find their way back to the mound after spending years exploring the further reaches of America’s medical-industrial complex. It’s unclear whether even he can save the Birds from dropping in 2016.

3. Cody Anderson     2.5 WAR     91 IP     76 ERA-     Age: 24

It’s been a forgettable year in Cleveland, but the Indians have quietly assembled a decent pitching staff. Anderson is their 4th best pitcher by bWAR, and something of a surprise. Drafted in the 17th round by the Rays in 2010, Anderson did not sign, instead returning to Feather River College in Quincy, California. The move paid off, as the Spiders drafted him the next year all the way up … in the 14th round.

It’s hard to believe the kind of run suppression Anderson displayed this year can last. The only qualifying starter this season with fewer strikeouts per 9 than Anderson’s 4.3 is Mark Buehrle (4.1). But if Anderson can find a way to edge his strikeouts up to the 6.8/9 he displayed in the minors, he could carve out a solid career as a back-end starter. He’s already accumulated more WAR than anyone else from Feather River College.

4. Carson Smith     2.1 WAR     69 IP     61 ERA-      Age: 25

Carson Smith was 12th in the majors this year in K/9 (11.83). Carson Smith was 109th in the majors this year in average fastball velocity. There is only one possible conclusion: the velo thing is hype. You heard it here first.

5. Nate Karns     2.1 WAR     147 IP     95 ERA-     Age: 27

In another questionable trade of a young starter, the supposedly pitching-rich Nationals sent Karns to the Warehouse by the Bay in exchange for Felipe Rivero, Jose Lobaton, and former first-round RF Drew Vettleson, whose on-base skills were last seen floating down the Schuylkill. Karns has trouble keeping the ball in the yard but his other rate stats are solid. At age 27, there’s probably not a lot of upside here, but Karns will remain a useful rotation piece as long he’s still cost-controlled. At just 5.65 IP/start, he puts pressure on his bullpen; more efficiency would help.

6. Noah Syndergaard     2.0 WAR     143 IP     90 ERA-     Age: 22

Syndergaard’s 5.2 K/BB would put him 8th in the majors if he qualified. Yet another traded prospect, Thor came to the Mets from the Blue Jays in exchange for R.A. Dickey and a crate of Jerry Grote bobbleheads. The Jays are steamrolling toward the World Series, and Alex Anthopolous’ hyperkinetic roster manipulations have a lot to do with that, but you have to believe this is one he’d like to have back.

(And no, I don’t really believe Karns is better than Syndergaard – for purposes of this post I’m just taking the bWAR list as it stands.)

7. Aaron Nola     1.9 WAR     78 IP     91 ERA-     Age: 22

Doug Melvin and Ruben Amaro, Jr. sailed away from GM Middle Earth this year, but they each left their respective teams at least 2/5 of a good young starting rotation. The Phillies moved Nola to the majors quickly, but he was an advanced prospect when drafted and faced no serious resistance at any minor league level.

There are some signs of danger: lurking menacingly behind Nola’s 3.59 ERA is a 4.04 FIP, mainly the product of a high HR/9 rate of 1.3. Nola kept the ball in the minor-league yards, so there’s reason to believe he’ll figure it out in the majors, but Citizen’s Bandbox is notoriously unforgiving of hanging curves. The one down side of Nola’s quick ascent to the majors is that he didn’t have time to develop a changeup. The good news is that, given the Phillies dilapidated state, his next 150 innings will be low leverage.

8. Jerad Eickhoff    1.9 WAR     51 IP     67 ERA-     Age: 24

Not nearly as prospect-y as Nola, Eickhoff is former 15th rounder acquired by the Phillies in the Cole Hamels trade. (So that makes 4 guys on this list who were obtained by trade. Perhaps reports of the death of the prospect trade have been somewhat exaggerated.) Like McCullers and Anderson, Eickhoff is beating his minor league rate stats in the majors, but, as with Anderson, some of this may simply be fruits of the dreaded small sample size.

It may be reasonable to expect strikeout regression, but at least Eickhoff gives some hope to Phillies fans who wake up with night sweats after witnessing serial arsonists like Jerome Williams, David Buchanan, and Sean O’Sullivan. Nola and Eickhoff are the only two current Phillies starters with a bWAR over 1.0.

9. Roberto Osuna     1.9 WAR     69 IP     57 ERA-     Age: 20

Selected K/9 rates from pitchers in Toronto’s minor-league system by the end of 2011:

Noah Syndergaard          10.37

Drew Hutchison          10.31

Nestor Molina              10.22

Aaron Sanchez               9.28

Deck McGuire                8.90

Roberto Osuna                   5.49

Okay, Osuna was only 16, so maybe this isn’t entirely fair – he threw just 19 2/3 innings in the Mexican League in 2011 before being acquired by the Blue Jays in August. But it’s highly unlikely that you would have predicted in 2011 that of the pitchers on this list, Roberto Osuna would make the most significant contribution to the Blue Jays in 2015 unless you are a close relative of Roberto Osuna.  No Carson Smith he, Osuna cooks with 95.5 mph gas, and has never struck out fewer than 9 per 9 at any level since 2011. And he’s only 20.

10. Luis Severino     1.8 WAR     55 IP     68 ERA-     Age: 21

A tough case of an obviously talented pitcher badly needed on a contending team, but who also probably could have used a bit more work in the minors. His ERA (2.89) is shiny, but his FIP (4.37) is less impressive. This mainly stems from the relatively high walk rate (3.2 – the AL average is 2.6), and a slightly high homer rate (1.3 – the AL average is 1.1). On the bright side, eight of his eleven starts were quality, with only one being of the faux (6 IP, 3 R) variety. That start came against the deadly Jays lineup, who incinerated him the next time he faced them, but did little against him the third and final time. In short, he fought the best lineup since vitamin B-12 to a draw; a mighty impressive accomplishment for someone who has yet to log 100 innings at any one level.

Still only 21, Severino has a better chance than anyone on this list of developing into a #1 starter (with the possible exception of McCullers) but Yankees fans should probably temper their expectations slightly for the immediate future. Girardi deserves credit for careful usage (just two starts over 100 pitches, none over 107), and this plan should probably continue until Severino can more consistently minimize the Two Bad Outcomes.

11. Andrew Heaney     1.8 WAR     106 IP     92 ERA-     Age: 24

Acquired in a trade … what, that’s like 5 now, right? … from the Dodgers in exchange for Howie Kendrick, Heaney righted the ship this year after an ugly 2013 in Loria Land, largely the product of bad home-run luck. His 8.9 K/9 in the minors has shriveled to just 6.5 in the majors, and he’s been a fly-ball pitcher this year, so there could be some risk here that the homer bug will return. Heaney has the amazing Mike Trout in center, so as long as the flies stay in the yard, a lot of them will be outs.

What Can We Expect From Kris Bryant Next Year?

We’ve come to the end of the 2015 regular season and it’s time to start looking towards the playoffs. As with every year there have been surprises and disappointments. One of the most anticipated events of each season is the debut of rookies and how they will perform throughout the year. Big things were expected from Kris Bryant this year and he definitely did not disappoint. Originally drafted by the Blue Jays in 2010 in the 18th round (546th overall), he was committed to the University of San Diego and the Jays didn’t offer enough to sway him. In 2013, the Cubs drafted him 2nd overall and he did nothing but climb the ranks until he made his MLB debut on April 17, 2015. His first game didn’t go as well as he hoped, going 0-4 with 3 K’s, but debuts mean nothing except for a little extra media hoopla. He cruised the rest of the way through the season on his way to one of the most impressive rookie seasons in recent memory, posting the 3rd highest WAR of any rookie since 2001. Only Mike Trout (10.3 WAR in 2012) and Albert Pujols (7.2 WAR in 2001) posted higher better WARs in their rookie campaigns.

I was looking over Bryant’s stats and his K% really jumped out at me. Although Bryant hit 26 home runs on the year, I began to wonder if there were any comparable seasons. Now the only criteria I used for comparison was: (1) as many or more home runs (26) and (2) equal or greater K%. Only 13 other players met this criteria since 2001 and they are listed in the table below.

Kris Bryant 2015 151 650 26 99 0.275 0.369 30.6 11.8 6.5
Chris Davis 2015 157 656 45 112 0.258 0.355 31.4 12.3 4.9
Chris Carter 2014 145 572 37 88 0.227 0.308 31.8 9.8 1.8
Chris Davis 2014 127 525 26 72 0.196 0.300 33.0 11.4 0.8
Chris Carter 2013 148 585 29 82 0.223 0.320 36.2 12.0 0.5
Adam Dunn 2013 149 607 34 86 0.219 0.320 31.1 12.5 0.3
Pedro Alvarez 2012 149 586 30 85 0.244 0.317 30.7 9.7 2.2
Adam Dunn 2012 151 649 41 96 0.204 0.333 34.2 16.2 2.0
Mark Reynolds 2011 155 620 37 86 0.221 0.323 31.6 12.1 0.1
Adam Dunn 2010 158 648 38 103 0.260 0.356 30.7 11.9 3.0
Mark Reynolds 2010 145 596 32 85 0.198 0.320 35.4 13.9 1.7
Mark Reynolds 2009 155 662 44 102 0.260 0.349 33.7 11.5 3.3
Mark Reynolds 2008 152 613 28 97 0.239 0.320 33.3 10.4 1.3
Ryan Howard 2007 144 648 47 136 0.268 0.392 30.7 16.5 3.1

Besides an awfully high K%, for which he ranks 23rd overall since 2001, out of all the players on this list, he posted the most impressive WAR. He’s also in some pretty elite company with respect to power hitters. There are four 40+ home run seasons on that list and many 30+ homer seasons. In addition to providing value with his bat, he also provided a positive UZR rating at a highly demanding defensive position. This combination is what made Kris Bryant so attractive to teams since the 2010 draft.

Using the same player list as above, I looked at their seasonal BABIPs, and I found one particular season of interest. Bryant’s 2015 season. Bryant posted a 0.381 BABIP this year, and the next-closest player on the list was Mark Reynold’s 2009 season at 0.338 which is still quite a difference. Looking at Mark Reynold’s seasonal stats from 2008 to 2011, his batting average follows the same pattern as his BABIP.

Name Year BABIP
Kris Bryant 2015 0.381
Chris Davis 2015 0.315
Chris Carter 2014 0.267
Chris Davis 2014 0.242
Chris Carter 2013 0.311
Adam Dunn 2013 0.266
Pedro Alvarez 2012 0.308
Adam Dunn 2012 0.246
Mark Reynolds 2011 0.266
Adam Dunn 2010 0.329
Mark Reynolds 2010 0.257
Mark Reynolds 2009 0.338
Mark Reynolds 2008 0.323
Ryan Howard 2007 0.328

And a plot showing the relationship between AVG and BABIP (data from 2001 to 2015). There is an increasing relationship between the two, but there is some pretty wide variation. Nonetheless, I’ve highlighted Bryant’s data point from the 2015 season in red and it’s pretty clear that it represents an outlier for his batting average.

If we consider that the players listed in the tables above are from the same pedigree, their career BABIPs average out to around 0.298. Now I’m not saying Kris Bryant is going to follow the same trend, but based on the strikeout rate he posted this year he’s very aggressive at the plate and I know we are going to expect that inflated BABIP to come back down to Earth so I think we can expect some regression next year. As a reference Danny Santana posted a BABIP of 0.405 in 2014 only to drop down to 0.290 this year which saw his WAR plummet from 3.3 to -1.4. I looked at the relationship between HR, SB and a few other stats and batting average showed the highest correlation with BABIP from the stats I looked at. Based on this I expect his batting average will be the most likely to be affected with a downfall of BABIP. I really don’t think the home runs are going to go anywhere, but I think we can likely expect to watch that batting average fall. It remains to be seen how this will affect his peripheral stats, but as long as he continues providing solid defense at the hot corner he is going to provide lots of value on a major-league roster. I’m sorry to say Cubs fans I think you should expect some offensive woes next year.

Kershaw vs. Arrieta: Battle for the NL Cy Young

Now that the regular season is over, it’s time to talk about awards. I mean people were already talking about awards, but now it’s time to really start talking about awards. Perhaps the most hotly contested award this year is the NL Cy Young. Clayton Kershaw, Zack Greinke, and Jake Arrieta are in three-headed race for the award, and they’re all incredibly close in terms of quality of performance, making it nigh impossible to pick a single winner. So I thought I’d give picking one the old college try. For simplicity reasons I decided to only compare Kershaw and Arrieta, who seem to be the two most often pegged as deserving in the sabermetric community. So, let’s dive in.

First things first, Kershaw has a pretty significant advantage when it comes to FIP metrics. His 29 K-BB% is seven percentage points higher than Arrieta’s 22%. Kershaw’s huge lead in strike-zone control more than makes up for the fact that he’s let up home runs a bit more often than Arrieta (10% and 8% HR/FB rate respectively). Boiling it down, Arrieta’s 61 FIP- trails behind Kershaw’s (52) by almost ten points.

Where things start to get murky is when one looks into their contact management ability. On the surface it appears Arrieta has a leg up here. Their IFFB% is basically identical. But as I mentioned before Arrieta has given up a few less home runs, and has also induced more groundballs.

Arrieta’s production on groundballs is also much better: he’s allowed a .377 OPS on grounders to Kershaw’s .468. Although it’s not that simple, because all defenses are not created equally, and the quality of the fielders behind you can have a big effect on the production of the groundballs you induce. So in that respect it’s worth noting that the Cubs ranked 6th in UZR/150 among all teams, while the Dodgers ranked 13th.

But there’s other ways of determining groundball production. Grounders that are pulled, generally, are more likely to turn into outs than grounders hit to the opposite field. Thus, it would make sense that a pitcher who gets batters to pull their grounders more often would have better production on their grounders, regardless of the quality of his team’s defense. So who’s induced pulled grounders more often? It turns out Arrieta – although only by the slightest margin. He’s induced a pulled groundball on 0.052% of his pitches, while Kershaw’s done the same on 0.047% of his.

So the difference between their pull rates is essentially negligible. But there’s more ways yet to evaluate groundball production. For instance, the velocity on those groundballs. Logic dictates that it’s easier to field a slow-moving groundball than a fast-moving one. After all, slow things are generally easier to pick up than fast things. Thus a pitcher who is more disposed to generate grounders of modest velocity is more likely to have better production on those groundballs, once again regardless of defense.

To figure out who had been better at coaxing soft grounders, I employed Baseball Savant’s PITCHf/x search tool. I set the batted-ball type to groundballs for obvious reasons. I set the maximum batted-ball velocity at 80 MPH because I couldn’t find the league average and 80 seems like a reasonable number. As it turns out, Arrieta has produced soft grounders on a greater number of his pitches than Kershaw (4.4% to 3.6%). Again the difference isn’t huge (the separation between the best and the worst in this particular metric is only about 4.5%) , but further implicates that Arrieta has been the better manager of contact. To sum it up, It does appear as though Arrieta has an advantage in the contact-management department, but not as large as it looks at first glance.

At the end of the day, these are two similarly great pitchers having two great similarly great seasons, and both should be celebrated as such. But if I had to pick one for award purposes, I think I’d go with Kershaw. If only because I believe more in his strike-zone-control numbers than Arrieta’s contact-management ones.

OK, the American League Really IS Sweden

Last month, I wrote about the two leagues, noting that

  1. The American League, perceived as being bad this year, was actually a good deal better than the National League overall, and
  2. The perception of the American League’s weakness was due to a near-record level of parity, with neither great nor bad teams.

Let’s start with the second point. At the time of the post, through games of September 5, the standard deviation of winning percentages among American League clubs was the lowest it has been in the 30-team era. Projected onto a 162-game season, the standard deviation of wins for American League teams was 7.8, barely eking out 2007’s 7.9 as the most egalitarian distribution of wins since 1998.

Since September 5, a .500 record has become a black hole, exerting irresistible gravity throughout the American League galaxy:

  • Of the teams with the six best records in the league on that date–the Royals, Blue Jays, Yankees, Astros, Rangers, and Twins–only Toronto and Texas had a winning record the rest of the season.
  • Baltimore, the sixth-worst team in the league as of the morning of September 6, tied the Jays for the best record in the East thereafter. Boston, then the third-worst team, went 15-12 the rest of the way.
  • Cleveland, four games below .500 at the time, scrambled to finish 81-80.

Overall, parity in the already-equality-loving Junior Circuit increased, by so much that I looked beyond the post-1998 30-team era. I calculated the standard deviation of winning percentages for every league-season since 1901. I then multiplied the standard deviations by 162 to arrive at the standard deviation of wins over a 162-game season. Yes, I know, most of those seasons were shorter than 162 games, but that’s OK; I’m just looking to turn the standard deviation of winning percentages, which is not an intuitive figure (e.g., American League, 1930, 0.1107), into something that is recognizable (17.9 wins). Here are the ten seasons in baseball history with the highest parity, that is, the lowest standard deviation of wins:

The 2015 American League is the most egalitarian, populist, tax the rich/feed the poor, Kumbaya-singing league in baseball history. As I suggested in September, it’s the Sweden of leagues.

(The National League finished 2015 with a standard deviation of 13.1 wins, ranking it 102 out of 230 league-seasons in terms of parity. It was the ninth-most unequal among 36 league-seasons since the expansion to 30 teams in 1998. For Gini coefficient detractors, the most unequal league ever was the 1909 National League, which featured the 110-42 Pirates, 104-49 Cubs, and 92-61 Giants, along with the 55-98 Dodgers, 54-98 Cardinals (Yadi was hurt), and 45-108 Braves.)

Now, as to the other point, the American League’s superiority over the National League despite its group hug ethic, here’s a chart.

Twelve years and running.

The Pittsburgh Pirates and Two Missed Opportunities

1. The Pirates finished the year with a 98-64 record, the second best in all of baseball. That ties them with the 1979 and 1908 clubs for the third most wins in franchise history. (The 1909 Pirates won 110 and the 1902 club won 103.) The Pirates’ record, however, included a losing record against two of the worst teams in the game, the Cincinnati Reds (8-11) and the Milwaukee Brewers (9-10).

Let’s break that down. In games in which the Reds didn’t play the Pirates, they were 53-90. In games in which the Brewers didn’t play the Pirates, they were 58-85. So in their non-Pirates games, the two clubs combined for a 111-175 record, a .388 winning percentage. Had they played at that pace in their 38 games against the Pirates, they would have won .388 x 38 games = 15 games, losing 23. Turned around, the Pirates would have gone 23-15 against the Reds and Brewers.

The Pirates were 81-43 in their games that weren’t against Cincinnati or Milwaukee. Had they gone 23-15 against the two clubs–that, is had they been as successful as the rest of the teams in the majors were–their record would have been 104-58. That would have given the Pirates the best record in baseball. They would be enjoying four off days, looking forward to Wednesday’s wild-card game between the Cardinals and Cubs to see whom they’d face at home to kick off the Division Series on Friday.

2. The Pirates had four relief pitchers who pitched at least 60 innings: Mark Melancon, Tony Watson, Jared Hughes, and Arquimedes Caminero. Of the four, the pitcher with the lowest average leverage index when entering a game was Caminero, wasting his namesake’s leverage expertise.