Archive for August, 2016

Constructing a Lineup for the Blue Jays

Jose Bautista recently came off the DL for a second time this year. John Gibbons has stated that he will predominantly DH and not occupy his usual spot in RF (this is good news if you’re a fan of the Blue Jays or a fan of outfield defense in general). But perhaps the bigger question is, where in the lineup is he going to hit? Last year he was their No. 3. He then moved to the leadoff spot, and he’s even hit second for 10 games this year.

The reason this is even a perceived issue is that Devon Travis has looked quite decent in the leadoff spot in Bautista’s absence. But let’s get something straight: Travis is no Bautista. Coming into the 2016 season, Bautista’s numbers compared to every other human in MLB since 2010 have him 1st in HR, 4th in wRC+, 3rd in wOBA, 3rd in runs scored, and 2nd in BB%. That’s spectacular. The issue has become do you keep both Travis and Bautista at the top of the lineup and simply shift Josh Donaldson and Edwin Encarnacion down one slot?

That’s the reason I started thinking about this; I was perplexed that both Bautista and Travis were going to be put ahead of Donaldson and Encarnacion. The idea that the Blue Jays’ two best hitters would be moved down the lineup for a player with just about a full season of MLB under his belt, and another who’s had a little more than 80 plate appearances since late June didn’t add up. This is a pennant race, supposedly the most critical time of the year.

Don’t get me wrong, this isn’t about dumping on Bautista, or even Travis. They’re great and good hitters, respectively. It’s about maximizing the production of your lineup. So before I moan and let everyone know my opinion is best, I thought I’d look at the data and let the numbers speak for themselves.

Hitting statistics from 2002 to 2015 were gathered and filtered by batting order. This produced 420 cases (each team the last 14 years) with six variables per place in the batting order (variables were: wOBA, BB%, ISO, wRC+, OBP, & HR). Data was then analyzed utilizing multiple regression analyses to identify what metrics at different spots in the order best predicted team runs. Results can be seen below.

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Figure 1. R2 values for total team runs with wOBA values for each spot in the batting order.

The most obvious component of Figure 1 is the drop in R2 for the 3rd place hitter. At first this may seem counter-intuitive, as it’s typically assumed that your 3rd place hitter is the team’s best.  And as that that player goes, so should the team. But the most likely rationale is that most teams, regardless of how awful or great they are, can typically muster at least one decent hitter. They place that hitter 3rd and away they go. Think about the Blue Jays and the Tigers in 2015 — they had Miguel Cabrera and Jose Bautista. So comparing Cabrera and Bautista’s 2015 stats shows an edge for Cabrera with a .413 wOBA compared to Bautista’s .389. Yet the Blue Jays vastly outscored the Tigers. This is because good to great teams have more than one “3rd place hitter.” And they apparently stack them 2nd and 4th in the order. In fact, these two spots in the batting order combined to account for slightly more than 50% of the variance explained when analyzing team runs (using the 2nd and 4th place hitter’s wOBA).

So if a team like the Blue Jays can afford to have Jose Bautista taking up the “menial” 3rd spot in the order, shouldn’t they do it? The mean wRC+ and wOBA for No. 3 hitters in the AL last year was 116 and .351. Bautista, who’s having a down year by his standards, has a wRC+ of 115 and wOBA of .346 as of August 29th. ZiPS has him closing out the year with a wRC+ of 132 and wOBA of .369, well above league average. So it could work.

But does Bautista hitting 3rd help them?

Well, since Donaldson has the best offensive numbers (and is the reigning MVP), the offense should be built around him. And the 2nd and 4th spots in the order are the most crucial in the presented team-runs-scored analysis, so we’re going to move forward with the idea that Donaldson hits 2nd. A runs-scored model that predicts the amount of runs a player will score when batting in the #2 spot can be seen in Figure 2.

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Figure 2. Predicted runs for the #2 hitter by actual runs scored. Using wOBA values for all 9 hitters [((315.5*wOBA2)+(132.15*wOBA3)+(65.6*wOBA4)+(71.63*wOBA5))-100].

This analysis produces an R2 value of .7 and incorporates, in order of model entry, the wOBA of: the 2nd hitter, 3rd hitter, 4th hitter, and 5th hitter respectively. The variables that enter are quite sensible; the greatest amount of variability of the 2nd hitters runs scored is the wOBA of that hitter. Followed by the next three spots in the lineup, all in sequential order. If this is done for each spot in the lineup the same pattern emerges. Where the hitter’s own wOBA is the greatest source of runs-scored variability and the 2-3 hitters following him account for an additional ~20%.  So essentially, if you want to score runs, bunch your hitters together. Don’t spread them out, and don’t try to place a poor hitter in the middle to get him more fastballs. Stick all of your threats in a row.

But is there a specific combination of clustering? Predicting how many runs Donaldson will score in a season, depending on the order of Bautista and Encarnacion around him, reveals the following results: 132 runs/season where the order is JB – JD – EE, 130 runs/season where the order is JD – EE – JB, and 129 runs/season where the combination is JD –JB – EE.  Again, this is the number of runs scored specifically by Donaldson when batting in the 2nd spot of the order. These results reveal error-term differences between combinations, something that wouldn’t be significant over the remaining ~30 games of the season. So as long as they’re clustered together it’s fine. This indicates that sensible lineup options have JB – JD – EE batting in succession, with Donaldson occupying the 2nd spot, and with some combination of Travis, Troy Tulowitzki, and Russell Martin surrounding them (pending on matchups/splits/who’s hot/etc.).

If clustering these three hitters together is the option, the question that follows will invariably be: who leads off? Answer — It really doesn’t seem to matter (as long as that person isn’t Kevin Pillar). Using a similar run-prediction model has Travis, Martin, Tulo, Saunders, Bautista, & Upton all averaging over 110 runs/season when batting leadoff having JD and EE behind them.

Dave Dombrowski Still Can’t Value Relievers

In 2015, the Boston Red Sox had one of the worst bullpens in Major League Baseball. Red Sox relievers were worth -1.3 WAR with  a FIP of 4.64, finishing 30th in the league in both measures. They allowed opposing hitters to hit .261 with a BABIP of .300. Unsurprisingly last offseason, newly-installed president of baseball operations Dave Dombrowski set out to remake Boston’s bullpen. Throughout his long and storied career as a general manager, Dombrowski has consistently turned lagging franchises into contenders. His one weakness, as Dave Cameron pointed out last year, has been constructing bullpens. After examining Dombrowski’s tenure with the Detroit Tigers, Cameron wrote, “There was not a single aspect to pitching that the Tigers bullpen excelled at during Dombrowski’s tenure.” In the 2015 offseason, Dombrowski made two significant trades to bolster the back end of the Red Sox pitching staff. He shipped four prospects to the San Diego Padres for closer Craig Kimbrel and sent left-handed starter Wade Miley to the Seattle Mariners in exchange for reliever Carson Smith. Both of these moves reveal that despite his years of experience, Dombrowski still has difficulty properly valuing relievers.


From 2011-2015, Craig Kimbrel led all relievers with 12.6 WAR. He struck out 40.9% of opposing hitters, allowing a .159 batting average with a 1.73 FIP. Only Aroldis Chapman struck out more hitters over the same time period. Kimbrel’s league-leading 224 saves were 58 more than the closest reliever, Huston Street. The difference between Kimbrel and Street is roughly equivalent to the difference between Street and Addison Reed, who had the 15th-most saves from 2011-2015.

A closer examination of Kimbrel’s peripheral stats, however, reveals that he’s been slipping from his career peak in 2011 and 2012. In 2015, Kimbrel’s FIP rose to 2.68. Opposing hitters hit more home runs against him and their batting average against his four-seam fastball rose from .180 from 2011-2014 to .212 in 2015. In 2016, this decline has continued. Kimbrel’s walk rate has ballooned to 12.2%. His ground-ball and fly-ball rates have reversed themselves and he’s allowing much more hard contact. Just take a look at the chart below.

2011-2015 1.33 20.2% 45.6% 34.2% 12.3% 20.1% 55.6% 24.3%
2016 0.64 21.0% 30.9% 48.1% 5.1% 14.8% 53.1% 32.1%

Opposing hitters are now hitting more of Kimbrel’s pitches as fly balls, they’re grounding out less often, and they’re making more hard and less soft contact than ever before. These factors have turned Kimbrel from an otherworldly reliever to merely an effective one. Looking at his yearly WAR figures, we can see that this transformation has been underway for a while now.

2011 2012 2013 2014 2015
WAR 3.2 3.3 2.3 2.3 1.5

In 2015, Kimbrel ranked 19th in reliever WAR, right between Justin Wilson of the Yankees and Keone Kela of the Rangers. That’s hardly inspiring, especially since Kimbrel earned $9 million in 2015 while Wilson and Kela made the league minimum.

Considering the price in prospects the Red Sox paid to acquire Kimbrel, they need him to perform at an elite level. In November 2015, Boston sent 3B Carlos Asauje, SS Javier Guerra, OF Manuel Margot, and LHP Logan Allen to the Padres for Kimbrel. Asuaje profiles as a utility infielder. According to Ben Badler of Baseball America, Logan Allen, whom the Red Sox drafted in the 8th round, had the talent of a 2nd or 3rd round pick. Margot and Guerra were both among the top 100 or even top 50 prospects in the minors depending on which prospect list you prefer. Using the prospect valuation system developed by Kevin Creagh and Steve DiMiceli (you can read about their methodology here), I’ve estimated the cost to the Red Sox in terms of the surplus value of Margot and Guerra. Due to the varying nature of prospect valuations I’ve included the players’ rankings in Keith Law’s Top 100 prospects and Baseball America’s Top 100 as of February 2016.

Prospect BA Ranking Surplus Value Keith Law Ranking Surplus Value
Manuel Margot 56 $22,400,000 25 $62,000,000
Javier Guerra 54 $22,400,000 34 $38,200,000
Total $44,800,000 $100,200,000

Even if Kimbrel were the pitcher of 2011-2012 that would still be an astronomically high price to pay for a reliever who throws 60-70 innings per year. Now that Kimbrel is a 2-WAR reliever, it’s even worse.


After acquiring Kimbrel, Dombrowski wasn’t finished remaking the Red Sox bullpen. On December 7, 2015 he traded left-handed starter Wade Miley and right-handed reliever Jonathan Aro to the Seattle Mariners for right-handed reliever Carson Smith and left-handed pitcher Roenis Elias. Aro is currently pitching at Triple-A Tacoma and Elias has a grand total of three appearances for the Red Sox this season, so the crux of the trade is Smith for Miley.

Based on their salaries and performances in 2015, Smith and Miley were both valuable pitchers and trade assets. Relying heavily on his slider, Smith held opposing hitters to a .194/.278/.262 batting line. He struck out 32.4% of opposing hitters with a 2.12 FIP and finished fifth among relievers with a 2.1 WAR. Additionally, Smith comes with five more years of team control. He isn’t arbitration-eligible until 2018 and won’t become a free agent until 2021. In 2015, Miley was a 2.6-WAR pitcher, best among any qualified starter on the Red Sox. From 2012-2015, Miley threw an average of 198 innings per season. Prior to the 2015 season, he signed a team-friendly three-year, $19.5-million contract from 2015-2017 with a $12-million club option in 2018.

After signing David Price to a seven-year contract in December 2015, the Red Sox believed they had an excess of starting pitching. With Price, Rick Porcello, Miley, Clay Buchholz, Joe Kelly, and Eduardo Rodriguez, they had six starters for five rotation spots. Additionally they had prospects Henry Owens, Brian Johnson, and knuckleballer Steven Wright waiting in the wings. In order to bolster the bullpen, Dombrowski decided to trade Miley, recognizing that he was the most valuable trade chip among the remaining starters. Porcello had just underperformed in 2015 and was entering the first year of a four-year, $82.5-million extension. Joe Kelly, while having an electrifying arm, had not really shown himself to be an effective starter. While Buchholz had pitched well in 2015, he managed only 18 starts. And Eduardo Rodriguez, the 23-year-old left-hander and potential top-of-the-rotation starter, was untouchable. This left Miley as the most logical trade chip.

By trading Miley, a serviceable innings eater, the Red Sox left themselves open to injuries and ineffectiveness. While Steven Wright effectively stepped into the rotation after Rodriguez dislocated his kneecap in spring training, Buchholz and Kelly were disasters. In 22.1 innings as a starter, Kelly allowed opposing hitters to hit .316/.437/.564 for a wOBA of .419 or the equivalent of Mike Trout this season. He sported a walk rate of 16% and a 5.88 FIP. In his 88 IP as a starter, Buchholz allowed opposing hitters to hit .268/.347/.470, good for a .349 wOBA and a 5.68 FIP. Since 2010, Buchholz has never been healthy and effective at the same time. For all of the talk about Kelly improving last season, a look at his peripheral numbers revealed a pitcher that was merely getting lucky with stranding runners as opposed to improving his underlying performance. By trading away Miley, the Red Sox cost themselves a cushion for the failures of Buchholz and Kelly. In order to fill the rotation void, Dombrowski traded highly-regarded pitching prospect Anderson Espinoza (the 19th-best prospect in baseball according to Baseball America) to San Diego for Drew Pomeranz. Carson Smith, meanwhile, underwent Tommy John surgery in May after straining a flexor muscle in spring training.

In trading for Craig Kimbrel and Carson Smith, Dave Dombrowski has revealed that his biggest weakness remains properly valuing bullpen talent. For a baseball executive with a generally sterling record, this may seem like a minor flaw, but it’s one that caused him to overpay for a declining closer, to trade Miley while relying on a pair of risky starters, and then to swap a prospect who garners comparisons to Pedro Martinez to fill the resulting void in the rotation. With Smith’s injury and the failings of Buchholz and Kelly, Dombrowski has little to show for all his bullpen efforts other than generously restocking the Padres’ farm system.

Yasmany Tomas Is Better Than You Think

When the Arizona Diamondbacks signed Yasmany Tomas to a $60-million deal, many thought the Cuban “third baseman” would be an instant star. Little is known about Cuban players when they come over; their skills are often exaggerated and their numbers in the Cuban National Series inflated. While some players, such as Yoenis Cespedes and Jose Abreu, do come over and become instant stars, others, such as Hector Olivera, simply don’t have what it takes to make it in the majors. For the better part of last year, Tomas seemed a lot closer to the bust category than major-league stardom. That assessment seems destined to change soon.

Too quick and binary is our collective assessment of players. They’re either good or bad and we know within the first weeks of April. We care little about their story, or struggles to adapt. It’s the Twitter era; context and nuance is dead.

That is the story of Yasmany Tomas. The Diamondbacks miscast Tomas as a third baseman and the metrics hated him there. They probably knew he was not a third baseman, but there he was. Unable to help the team defensively, and struggling a bit in his first offensive season at the major-league level, Tomas got a label. He was a bust, just another of the missteps in a reign of terror  for a Diamondbacks front office that doesn’t even know the rules.

But that label loses all context. Craig Edwards reminded us about context with regards to Byron Buxton’s struggles. To paraphrase Edwards: Buxton has been really bad, but he’s also young and has plenty of time to figure it out. With Tomas, the story is similar. Yes, Tomas was a -1.3 bWAR and -1.4 fWAR player in 2015, but reducing a player to a single number does him an injustice. He was actually a positive contributor on offense. As a starter (non-pinch-hitter) he had a 103 OPS+. That’s not bad. He was also just 24. Joc Pederson and Jorge Soler are just 24 this year and we think of them as young players. Why are we so unforgiving with Tomas?

Fast-forward to this year and Tomas is still not very well regarded in baseball circles. He’s at 0.2 bWAR, -1.1 wins above average, and 0.6 fWAR. He still grades out as a very average player, but is now around 10% better than league average offensively according to the advanced stats. He’s got 26 homers (24th in the league) and a .519 slugging (30th in the league), both better than Paul Goldschmidt, Carlos Beltran and Giancarlo Stanton.

Those raw numbers suggest that Tomas is already among the top-30 or so sluggers in the league.  He even gets on base at a non-Trumbonian clip. But my early season introduction to xSLGBB said that Tomas was due for some improvement in his slugging percentage based on his batted-ball profile. Andrew Perpetua’s set of stats based on batted-ball information (the Google doc at the bottom of the post, also available on appears to show that Tomas has leveled. Perpetua’s xSLG stat shows Tomas’ expected slugging percentage at .549. Such a mark would tie him with Josh Donaldson.

I appreciate Perpetua’s stats, but I made up my own (xSLGBB), just for these types of analysis. I ran the numbers and by my xSLGBB, based on the league-wide expected set of outcomes from when I ran this the first time in May, Tomas is expected to improve by a grand total of .005 points of slugging.

Still, even if he has already normalized to the stats that we would expect based on his batted-ball profile, a 25-year-old with 26 home runs and a top-30 slugging percentage is pretty darn good. Yes, he has deficiencies in his game, but Tomas still has room for improvement. He’s never going to be Kris Bryant, Nolan Arenado, or one of the other MVP-type of young stars in the game, but he’s quietly hitting himself out of the bad label that we too quickly stuck him with.

Ask Not for Whom the Bell Tolls; It Tolls for the NL DH

Depending on the news outlet, the designated hitter coming to the National League is either a foregone conclusion, or something that will never, ever happen.  Regardless of which outcome is true, it’s still a fine idea to think about, and it’s also fun to try to identify which current 2016 teams might most benefit from the inclusion of a DH spot on their roster.

To create a manageable list of names, I searched for player-seasons since 2013 that resulted in an offensive runs above average (Off) value of 25 or greater, a defensive runs above average (Def) value of -5 runs or less, and filtered for National League teams.  My thinking was that by FanGraphs’ own rule of thumb, 25 Off is a notch below great, and -5 Def is starting to make its way into the poor range.  The results are as follows:

Player Qualifying Seasons
Paul Goldschmidt 4
Freddie Freeman 3
Joey Votto 3
Jayson Werth 2
Andrew McCutchen 1

This is interesting for a couple reasons.  The first of which is that this once again proves that the answer to pretty much any question about baseball can be “Joey Votto.”  The second of which is that most of the names on this list are generally thought to be acceptable, sometimes even exceptional, fielders.

Andrew McCutchen’s lone appearance on this list is somewhat of an outlier: a -8.6 in 2014.  He’s been below average defensively in the past, but this is his low water mark for the past five years, going beyond the lower boundary of my arbitrary cutoff of 2013.  I think that we can ignore this for another reason: his bat doesn’t play at DH going forward at 2016 levels of production, rendering his inclusion moot for this purpose.  His contract extends to 2017 with an option for 2018, so I doubt we’ll see NL DH at bats for McCutchen on the Pirates before then.

Jayson Werth shows up twice, but outside of his defensively tremendous 2008 season, I don’t think that anyone would put him in the company of elite defenders.  Like McCutchen, Werth also is only under contract until the end of the 2017 season, which would qualify him for a maximum of one year of Nationals DH service.  I think that he can be dismissed from the list.

In his first full season with the Braves in 2011, Freddie Freeman was a disaster at first base (-23.6).  He was better in his second year with a -13 Def.  He’s gotten better over the intervening years, with his high water defensive mark coming in 2015 with -3.9 Def.  He has gone from an awful defender to a below-average defender, and playing for a Braves team that won’t be good for a couple years will probably stay at first even with the inclusion of a DH spot.  But, being that he is under contract until 2021, should the DH rule be put into effect, we may see him getting meaningful at bats as a DH before the end of the decade.

Signed through 2018 with a club option for 2019, Paul Goldschmidt’s defensive rating seems to be a victim of positional adjustment more than anything.  His worst defensive performance was -11.5 Def in 2012, his first full season in the MLB.  Since then his performance has been within the run adjustment for his position, outside of this current season.  He is on pace to have a truly bad defensive year in 2016.  But, he’s not even the most eligible DH candidate on his own team.  The Diamondbacks would likely be better served putting Yasmany Tomas at DH and let Goldschmidt continue to play most days in the field.

The third first baseman on the list is Joey Votto.  Like Goldschmidt, he is penalized heavily for being a first baseman.  But, dissimilar to Goldschmidt, he is on the wrong side of 30 and signed to an immensely long and expensive contract.  Of all the teams with players on this list, the Reds might be one of the best-positioned to take advantage of the DH immediately.  Outside of a magnificently terrible start to 2016, Votto has shown that he is still an offensive juggernaut, with a skillset that doesn’t seem to be deteriorating at all.  His defense, on the other hand, peaked in an injury-shortened 2012 season and has gotten progressively worse in each full season he’s played since.  For the 2016 season he’s performed at his worst in the field, with a -13.6 Def.  He accounts for 100% of the Reds’ currently committed payroll for 2021, and is still signed for two more years beyond that.

I’m sure there are more teams that would benefit from the addition of the DH, and I’m sure there are teams that would acquire other talent to man their DH positions.  Realistically, I think that most teams would end up using the DH much the same way as it’s been used in the AL for decades: as an extension of a hitting career, and a half day off for players while still keeping their bats in the lineup.  But, among the teams that met the criteria laid out above, I think that the Reds would be the team most suited to immediately improve through the designated hitter.

An Inquiry Into How Players are Ranked

How we rank players in our own minds can tell us a lot about what we value in a ballplayer. For decades the statistics that mattered to sportswriters and the public at large were those that were simple, easily understood, and still relevant to the game. Stats like batting average (AVG), runs batted in (RBI), and home runs (HR) were regularly quoted when writing articles or voting for MVP awards. Each of these numbers tells a piece of the story of what a ballplayer is. AVG shows a players ability to put a ball in play and reach base, RBI is a representation of run creation and hitting while men are on base in front of you, and HR show your power in hitting.

These numbers still hold great significance today. That said, they are not flawless expressions of player prowess with the bat. A player could have a high average and still struggle to get on base often due to strikeouts or weak contact. RBI is often a product of opportunity as much as hitting success. After all, you can still receive RBI when creating an out. HR meanwhile can be a very one-sided affair if your average is low, leading to an all-or-nothing scenario for a hitter.

I’m not trying to disparage anyone from using AVG, RBI, and HR in a debate of great players, but when you use them keep in mind that they make up only a fraction of what a ballplayer can be.

Modern statisticians have begun using much more advanced numbers like WAR or OPS+ to determine a players quality. These numbers take into account positional skill differences, park factors, and many other aspects of the game. Much like the traditional stats mentioned before, these stats have both positive and negative aspects to them. No one stat can give you a complete picture of a player’s skillset and value.

Whenever an article comes out discussing the quality of a player’s career or season we often get quotes like these:

“Since Trout debuted in 2011, he leads all players with 37.9 WAR. Further, that 37.9 WAR through Trout’s age-23 season are the most by a player in the modern era.” — ESPN Stats & Information


“Harper finally displayed his prodigious tools last season, as he led the National League in runs (118) and home runs (42) while leading MLB in OBP (.460) and slugging percentage (.649).” — ESPN Stats & Information

While all of the numbers in these quotes are valuable, and even more so impressive, they come with very little context with respect to the league as a whole. It’s great that Trout has 37.9 WAR since 2011, but who is second? And by how much is he second? So Harper led the league in OBP, but what was the league average? Or how many plate appearances did he have? Did he miss any time with injury?

Each of these questions would further add to our understanding of the value and quality of the players mentioned, but that information is never going to be answered in this context. Additionally, this practice of “cherry picking” the best stats to fit our argument negates the whole and presents the players out of context. For example, these numbers neglect the fact that Harper struck out about 25% of the time that season. Even by today’s standards that is a lot of strikeouts. I understand of course that a lawyer is never going to give out unnecessary information about a client’s failings, but in the context of ranking players it is paramount that we take into account as much of the information as we can. Ultimately, we find ourselves back where we started.

If all stats are flawed, then how are we to determine an adequate ranking for players? I propose that we use more stats. That’s right. More stats, not less.

When you fixate a ranking on a single stat, then that stat accounts for 100% of your result every time. It doesn’t matter if the stat is meant to incorporate a host of stats together. Your results are the result of a singular point of reference. If you use three stats, then each is equivalent to one-third of your conclusion.

What would happen if we used 20 different stats to determine a ranking? While each individual stat is devalued, the whole average together will give us a better understanding of the whole spectrum of a player’s ability in the game. Be warned…results may incite head-scratching.

There is a great axiom in the world of baseball stats that goes something like this: “Just because a stat has Babe Ruth at the top and Mario Mendoza at the bottom does not mean it is a good stat.” Like all statistical analysis, take this one with a grain of salt.

My process here is rather simple. Take a group of player data, a single year or all-time, across 20 stats. Rank each player individually against the others in the set from 1 to the total number of players across all the data. Finally, average each player’s rankings across the 20 stats. Our result…rAVG (Rank Average).

For ease in data gathering and processing, I’ve decided to use the 19 dashboard stats from FanGraphs plus hits to make 20 total stats. For all-time stats, the pool of players has been limited to players with a minimum of 5,000 plate appearances.

• Each position has t50/b50: how many times a player ranks in the
  top 50 or bottom 50 across all categories.
• * denotes active player.

All-Time • Position Players (895 total)

Name - Pos
Willie Mays - OF
Barry Bonds - OF
Tris Speaker - OF
Rogers Hornsby - 2B
Stan Musial - 1B/OF
Ty Cobb - OF
Alex Rodriguez* - SS/3B
Honus Wagner - SS
Mel Ott - OF
Eddie Collins - 2B
Babe Ruth - OF
Hank Aaron - OF
Mickey Mantle - OF
Ted Williams - OF
Lou Gehrig - 1B
Charlie Gehringer - 2B
Larry Walker - OF
Chipper Jones - 3B
Frank Robinson - OF
Jimmie Foxx - 1B
Mike Piazza - C


  1. Larry Walker. At first glance this list appears to contain all the requisite names for a best-of-all-time list… that is until you reach #17 Larry Walker. I can assure you that I have not fudged the data in anyway. I, like you, are equally as shocked to find Mr. Walker parading alongside greats like Ruth, Mays, and Gehrig. Maybe we all should re-evaluate our opinions on Larry Walker.
  2. Mike Piazza. I have included him at the bottom of the chart, because he is the highest-ranking catcher of the 73 that met the 5,000 plate appearance requirement. While ranking #102 would appear to be a slight to him, when viewed in the context of the total list of 895 players…Piazza ranks in the top 12% of all players in history.
  3. Babe Ruth. Many of you, me included, probably feel that there is no way that the Great Bambino could rank outside of the top 10 all-time. I will remind you that this list is a ranking of statistics. It cannot evaluate impact on the game, cultural relevance, or popularity. It simply counts each stat as 5% of the whole and spits out a result. A closer look at Babe’s numbers and you will find that he was a terrible baserunner (SB & BsR) and his defense left much to be desired as well. Out of 421 outfielders he ranks 229 in SB, 411 in BsR, and 110 in Def. All this serves to remind me that no player, however great they might be, is without deficiencies.

As part of my research into this topic I ran numbers for each of the nine positions all-time and the cumulative all-time list seen above. In order to keep this article from becoming a novel, I’ve chosen to only include the top 20 of all-time here. The rest of this information will be available for viewing some time in the near future either on here or on my website.

While I may not agree entirely with the outcomes of this exercise in rankings, I do feel that it has caused me to better consider the totality of a player’s stat line rather than a few simple metrics. No one stat can give you a well-rounded, complete view of a player’s value and skill.

I await your fevered comments below.

Using Statcast to Substitute the KC Outfield for Detroit’s

As I write this post the KC outfield defense is ranked No. 1 in Defensive Runs Saved (DRS) with 43, and is No. 2 in UZR at 28.6 (first is the Cubs with 29.0).  KC sports one of the best, if not the best defensive outfield in the majors this season.

Detroit on the other hand has a fairly poor one.  They rank last in DRS, with -44, and last in UZR at -31.8.  Though Baltimore gives them a good run for their money, Detroit is probably the worst defensive outfield in the majors so far this season.

So I wondered if we could do an analysis to show what would happen if we substituted them entirely for one another?  How would that work?  Well, one simple approach would be to just use the DRS metrics for each team and basically say that DET would go from -44 to +43, so that’s a swing of +77 runs. Using the 10 runs per win thumb-rule, that’d be a pretty big swing, nearly eight games. Detroit is a whole lot better.  But I’m not sure this method is really the best we can do.  After all, we have all this Statcast data now.  Could we use that?

I set out to try to do just that.  So my first step was to hypothesize that the likelihood of a ball hit to the outfield actually dropping for a base hit could be correlated to the launch angle provided by Statcast and then that this likelihood would change depending on the team.  So to test this theory out I went to Baseball Savant and grabbed all the Statcast data for balls hit to the outfield for KC and for Detroit.

The KC data consisted of 1722 balls hit to the OF (when removing the few points that had NULL data for launch angle).  I took these 1722 points and bucketed them by launch angle in buckets that were 2 degrees each.  I then calculated the percentage of hits to total (hits + outs) for each bucket.  This percentage was the likelihood that a ball hit to the outfield at a certain launch angle would end up being a base hit.  This led me to my first realization, which was that anything that was basically < 8 degrees on launch angle (so including all negative angles), and made it to the OF, was a guaranteed hit.

The results of this analysis for the 1722 KC points made a lot of sense intuitively.  As the launch angle increased, so did the likelihood that it was an out, so my hit percentage trend went down.  Using a simple linear regression projecting the likelihood of a hit by angle had a 92.5% R^2.  This equation was going to work nicely.

I then considered running the same drill but this time using exit velocity of the hit to see how that impacted the likelihood of a ball being a hit.  There have been at least a couple article written on this topic, and the results I got matched up with the projections I had seen in other articles on the topic.  That’s to say the trend isn’t linear, but more parabolic. Using a simple second-order polynomial trend, a very reasonable projection could again be made of a hit likelihood based on the exit velocity of a ball hit to the OF.
Using these two points of data for any ball put in play to the outfield (exit velocity and launch angle) it seems as though OF defense could be projected fairly reasonably.
I proceeded to re-run those same drills using Baseball Savant’s Detroit outfield data. Launch angle provided another great fit, 95% R^2 and a slightly higher overall trendline than KCs (notice the higher y-intercept or “b” value).  KC’s OF was almost 4% more likely to catch a ball just from the “b” value.
Using a simple second-order poly trend for Detroit’s exit velocity also resulted again in an 85% R^2, very similar to that of KC.  It also showed the expected parabolic action.
What I now had was a way to project the likelihood of the KC outfield or the DET outfield making a play on any ball hit to the outfield.  All I needed to know was what the angle and exit velocity was.  Lucky for us, Statcast gives us all that information.
My next step was to take all the OF plays made by Detroit and, using my newfound Detroit projection system, project the number of real hits based on the hit events to the OF.  My Detroit projection system projected 1089 hits, in reality there were 986 hits. Not perfect, and something that could undergo some more tweaking, but reasonable.  My projection system was overly simplistic — I took the likelihood from the angle * the likelihood from the exit velocity.  If the multiplication was > 25% (i.e. 50% for each as the minimum threshold) then I projected a hit; else, an out.
So my Detroit projecting Detroit resulted in 1089 hits.  When I substituted the KC projection equations in, the Detroit projected hit to the OF dropped to 903.  This was a reduction of 186 expected hits!  Wow.  That’s some serious work the KC outfielders would’ve done.
The last step here was then to attempt to convert this reduction in hits to a reduction in runs.  I grabbed FanGraphs’ year-to-date pitching stats by team and used that to do a simple regression on hits allowed to runs allowed.
This showed strong correlation with a ~77% R^2.  Using the slope of this equation it shows that each hit allowed correlates to 0.7298 runs.  This means that a reduction of 186 hits would correlate to a reduction of 136 runs! Again, using the 10-run thumb-rule, that’s a nearly 14-win move.  That’s amazing improvement.   Now of course we are expecting drastic improvement; we’re talking about replacing the worst OF defense in the league with the best!
Are there some bold assumptions made here? Yes.  However, I do think it’s a fairly reasonable approach.  It’s fun to see all the different ways this new Statcast data can be used.  This same drill could be run on all sorts of “swap” evaluations and could be a whole lot of fun for a variety of what-if scenarios.  I enjoyed attempting to answer this question using the new data and hopefully you found this entertaining as well!

Power and Strikeouts

Adam Dunn Photo.png

Adam Dunn is an all-time leader in both home runs and strikeouts, a connection that could be universal. (Photo by Danny Moloshok for the Associated Press.)


I’ve been a Washington Nationals fan since the team moved to D.C. in 2005. One of my favorite players to watch — though he was with the team for just two seasons — was Adam Dunn. The 6’6, 250-pound lefty masher was an incredible physical specimen who could hit home runs like nobody’s business. Unfortunately, the only thing he did better than hit homers was strike out. He’s 36th on the MLB all-time home run list with 462, and third on the all-time strikeout list with 2,379. Because of his high strikeout numbers and sub-par batting average on balls in play, he sported a lifetime batting average of just .237.

I bring up Adam Dunn because he’s a prime example of the baseball truism that I’ll be investigating today: Do power hitters tend to strike out more often?

This claim is deceptively tough to evaluate because there’s no one clear way to tell if, and to what degree, a player is a power hitter. I came up with as many rational ways to measure power as I could and compared each with strikeout rates. I’ll let you decide for yourself exactly how well each metric relates to power.

Traditional Stats

Let’s start with the most obvious measure of a power hitter: Home-run hitting.

Here’s the correlation between a player’s home-run rate (HR/AB) and strikeout rate (K/AB).

HR per AB v. K rate.png

r = 0.527

A correlation coefficient of 0.527 isn’t bad, and you can see a clear upward trend in the data, but let’s keep going.

Home runs obviously aren’t the only way to measure power. Let’s see what happens when we expand our study from home runs to all extra-base hits.

EBH per AB v. K rate.png

r = 0.427

So it turns out there’s actually even less of a correlation with extra-base-hit rate than with home-run rate.

There is a flaw to evaluating power using per at-bat rates. If a player has a high strikeout rate his rate of any type of hit will be lower. Here’s what happens when we redo the previous two graphs using home runs and extra-base hits per hit instead of per at-bat.

HRsperH vs. K rate

r = 0.609

EBHperH vs. K rate

r = 0.627

Much higher correlation. Correlation in the .600 range isn’t the goal — but it’s definitely an indication that something’s there. Since non-per-at-bat rates seem promising, let’s try per ball in play as opposed to per hit.

HRperBIP vs. K rate

r = 0.634

EBHperBIP vs. K rate

r = 0.669

Even stronger correlation. Let’s move on now to a classic measure of power: Isolated power (ISO).

ISO vs. K rate

r = 0.508

Good correlation, but not as strong as we just saw with HR and XBH per hit and per BIP. But when you look at what ISO actually is, it’s a per-at-bat rate statistic.

Screen Shot 2016-08-16 at 7.19.48 PM.png

Why don’t we redo ISO as per hit or and per ball in play instead of per at-bat?

ISOperH vs. K rate

r = 0.642

ISO per BIP v. K rate

r = 0.673

So it turns out reworking ISO as per ball in play actually gave us our strongest correlation yet at 0.673.

Side note: I tried adjusting the ISO coefficients a couple of different ways since valuing a triple twice as much as a double and a home run three times as much as a double but just 1.5 times as much a triple seemed odd to me. As it turned out, the correlation didn’t get any better. Touché sabermetrics community, touché.

Statcast Stats

One of the great things about doing this study in 2016 is that we aren’t limited to traditional outcome-based stats. That being said, one of the less great things about doing this study in 2016 is there’s only one full season of publicly available Statcast data. As a result, I’m lowering my minimum observations per player from 1000 plate appearances to 100 at-bats. For context Manny Machado led the league in plate appearances in 2015 with 713. So we’re clearly going to see decreased correlation because of poor sample size. To give you an idea of what that looks like, here’s a few of the correlations from the previous section compared with what they would have been had I used 2015 Statcast data instead:

Stat 1000 Plate Appearance Correlation 100 At-Bat Correlation
HR per BIP 0.634 0.457
EBH per AB 0.427 0.133
ISO per BIP 0.673 0.495
HR per AB 0.527 0.302

What you should take from this is that the strength of pretty much all of the correlations we’re going to look at will be diluted. Many stats that appear to have rather weak correlation could have a real relationship given more data, we just can’t know. It’s unlikely we’ll see some really indicting evidence that a specific measure of power implies a higher strikeout rate, but it could give us a good clue of where to look in the future. So with that out of the way, let’s crunch some numbers.

One obvious way to use Statcast to measure power is to look at exit velocity. If you tend to hit the ball hard, chances are you’re a power hitter. Here’s how average exit velocity correlates with strikeout rate.

Avg. EV vs. K rate

r = 0.338

There’s some correlation, albeit pretty weak. Perhaps power isn’t best represented by whose hits on average are the hardest but rather who has the highest rate of very hard-hit balls. Home runs tend to be hit at least 95 mph, so let’s check the correlation between rate of 95+ mph balls in play and strikeout rate.

HR.EV vs. K Rate

r = 0.393

There’s better correlation, but it’s still rather weak. Let’s move on.

Next up is launch angle. Power hitters hit more fly balls because that’s the only way to get a ball out of the park and a common way to hit a double.

Avg. LA vs. K rate

r = 0.260

There’s even less correlation than with exit velocity, and when I looked at the rate of “home-run launch angles” (25˚ – 30˚) the correlation went down even further to 0.093. While we’re on the subject, I checked the correlation for the rate of balls in play that both had an exit velocity of at least 95 mph and a launch angle between 25˚ and 30˚ and got 0.323 — lower than both exit velocity-only correlations.

Perhaps distance will yield better results. Below is the correlation between average ball in play distance and strikeout rate.

Avg. Dist. vs. K rate

r = 0.353

Still not much correlation, but as with exit velocity it would make sense for the true sign of power to be high rates of balls in the 300 feet range rather than the exact distribution of balls hit 100/200 feet.

300perBIP vs. K rate

r = 0.398

So we see improved correlation, but 300 feet was a rather arbitrary number. Let’s try 350 feet.

350perBIP vs. K rate

r = 0.481

There’s some decent correlation here, but maybe we’ve made a mistake in lumping together distances to all parts of the field. Here’s what happens when we redo the previous two graphs but only count balls hit to center field that went an extra 50 feet.

300:350perBIP v. K rate

r = 0.416

350:400perBIP vs. K rate

r = 0.463

The correlation went up from 300 to 300/350 and down from 350 to 350/400 (interestingly both by .018). This brings up an interesting question: Does power manifest itself more or less on balls in play in different parts of the field? In looking at this I organized players by their handedness — dividing balls in play by pull/center/opposite field not LF/CF/RF. (I omitted switch-hitters from this part and looked only at balls hit to the outfield.) Rather than show 21 graphs, I made a table below with the correlation coefficients.

Location Avg. Exit Velocity Avg. Launch Angle Avg. Distance HR Range Exit Velocities 300+ ft. 350+ ft. 400+ ft.
Pull .306 .433 .399 .327 .386 .442 .293
Center .410 .148 .270 .379 .267 .353 .388
Oppo .336 -.147 0.021 .293 .028 .054 .215

The last stat I’m going to look at is arc angle. Arc angle is a stat I created to evaluate a batted ball’s trajectory. You can find out more about it in my Hardball Times article. Just note that it’s only for balls hit in the air and lower angles are fly balls while higher angles are line drives.

Avg. AA vs. K Rate

r = -0.474

So none of the Statcast stats yielded a correlation coefficient of 0.5 or more. As I said at the top this is likely — at least in part — a sample-size issue. I’ll update these numbers after the season to see what difference that makes.


That was a lot, so here’s a table of all the correlation coefficients and increase in strikeout rate per unit of the stat for the comparisons we made.

Stat Correlation  Coefficient Increase in K Rate per 1 Unit of Stat
Home Runs per AB .527 2.16
Extra Base Hits per AB .427 1.40
Home Runs per Hit .609 0.63
Extra Base Hits per Hit .627 0.53
Home Runs per Ball in Play .634 1.85
Extra Base Hits per Ball in Play .669 1.44
Isolated Power .508 0.67
Isolated Power per Hit .642 0.21
Isolated Power per Ball in Play .673 0.61
Average Exit Velocity .338 0.01
Home Run Exit Velocity Rate .393 0.32
Average Launch Angle .260 0.01
Average Ball in Play Distance .353 0.002
300 + ft. Balls in Play Rate .398 0.49
350 + ft. Balls in Play Rate .481 0.77
300 + ft. LF/RF 350 + ft. CF Rate .416 0.72
350 + ft. LF/RF 400 + ft. CF Rate .463 1.12
Average Arc Angle -.474 -0.01
Location Avg. Exit Velocity Avg. Launch Angle Avg. Distance HR Range Exit Velocities 300+ ft. 350+ ft. 400+ ft.
Pull .306 .433 .399 .327 .386 .442 .293
Center .410 .148 .270 .379 .267 .353 .388
Oppo .336 -.147 0.021 .293 .028 .054 .215

As to our initial question: Does power correlate with strikeouts? I think it’s pretty clear that yes, power correlates with strikeouts in some capacity. As for how much it correlates and what exactly power is? That’s not clear. Hopefully additional seasons of Statcast data will help.

The Heyward Fault

It’s no secret that Jason Heyward is having an epic, epic bad season. Heyward is not just last in wRC+ for right fielders, but last by a wide margin. He has the seventh-worst ISO in all the land, worse than Billy Hamilton. Worse than Cesar Hernandez. Worse than Alexei Ramirez. Alexei Ramirez, for God’s sake. Finally acknowledging the soul-crushing reality, Cubs manager Joe Maddon benched Heyward last Friday.

This is historically bad power from a right fielder. In the wild-card era, Heyward’s ISO constitutes the 11th-worst power season for a right fielder. Of the ten other seasons, Ichiro! owns five of them, and Nick Markakis two more. So only four actual guys have managed a worse ISO in right than Heyward since 1994.

Heyward’s power has declined against all pitches, but not evenly. (I’m actually using ISO x 1000 to eliminate those pesky decimal points):


Pitch               2016 ISO                Career ISO                  Diff

4-seamer             143                           154                           -11

2-seamer             087                           177                          -90

changeup            029                          160                         -131

slider                   083                           157                           -74

curve                    000                          122                         -122

In battling the 4-seamer, 2016 Heyward looks pretty much like the factory model. Against the other pitches, 2016 Heyward looks like Enzo Hernandez. Back in May, Jeff Sullivan wondered why Heyward was swinging disproportionately often at high pitches when historically he had been a better low-ball hitter. The above chart may provide an answer. Four-seamers tend to live upstairs, while the other pitches like to drink Milwaukee’s Best down in the basement den. Heyward may have made a rational adjustment, swinging more often at the pitch he can hit (or rather, pitches that look like the pitch he can hit) and less often at the others.

Even if accurate, this simply answers one riddle with another. What could have made a historically good low-ball hitter suddenly lose the lower half of the strike zone? And the power disappearance was indeed sudden. In 2015, Heyward actually hit with more power in the second half, though his ISO did drop off in September.

Heyward may have begun hearing the spine-tingling incidental music back in 2014. That year his power against lefties, seldom menacing, completely winked out.

Year                   ISO vs. L

Career                   .119

2013                      .191

2014                      .056

2015                      .093

2016                      .096

This may have been foreshadowing, or not. There is no clear pitch-type pattern evident in Heyward’s disappearing power against lefties. He collapsed against all offerings, doing somewhat less badly only against the slider. Indeed, one of the main criticisms of his eight-year contract was that Heyward had become a platoon player.

In 2016 the platoon split has disappeared, but not in a good way. Heyward has actually hit lefties with more power than righties this year (.096 vs. .083). But let’s face it, for hitters, almost any number that begins with “.0” is a wrong number.

The most likely explanation is some form of injury. Heyward had wrist problems earlier this year, and wrist injuries notoriously sap power. But .088 is whole lotta sappage. When Derrek Lee hurt his wrist in 2006 his ISO plummeted to … .189. Certainly one can imagine any number of nagging injuries that slow bat speed or reduce plate coverage. But it seems peculiar that Heyward would struggle least against the pitch that is usually the most overpowering. Perhaps Heyward is selling out to get to the 4-seamer because the injury has slowed his bat enough that he simply has to get started early.

Another possibility, perhaps, is a vision problem, as very briefly suggested in the comments to Sullivan’s post in May. Perhaps Heyward is able to pick out the 4-seamer, but unable to differentiate reliably among the other pitches, thus approaching them all with punchless caution. A vision problem could also be causing Heyward to sell out as discussed above. In either case, selling out would seem to cut against Heyward’s grain as a (sometimes maddeningly) patient hitter.

There is nevertheless some evidence Heyward is trying to start the bat earlier, not because of ocular or muscular problems, but because of a complex, misaligned swing. There have been a number of stories concerning Heyward’s poor mechanics, but most of them were written this season, when the poor results became manifest. Outright criticism of his swing, at least in public, was relatively uncommon before this year.

But there were signs, perhaps (as signs are wont to be) obvious only in retrospect. In 2014, David Lee wrote an excellent piece scouting Heyward’s rapidly evolving stances — the pictures alone are worth a look. Two years earlier, Terence Moore wrote about Heyward’s swing coach praising Heyward for having Plans A, B, and C at the plate. Both of these pieces are hopeful, treating Heyward’s willingness to tinker as a sign of dedication — a player relentlessly seeking continuous improvement.

But relentlessness doesn’t solve every problem, and improvement is very rarely continuous. Hitters can be comically addicted to routine, fearing that the slightest change will plunge their careers into Oylerian Darkness. But there is some virtue to having a baseline from which to work. In music, it’s literally a bass line. In oral presentations, it’s a theme. In cooking, it’s a recipe. In none of these cases does the baseline translate directly into real results, but it provides critical direction so that the (or at least an) end result actually results.

It’s possible that Heyward has lost his anchor. He wouldn’t be the first player to do so. Roy Halladay famously had to reconstruct his pitching motion in the purgatory of Dunedin. But Halladay had become an arsonist, spraying the field with a 10.64 ERA. Until this year, Heyward hadn’t ever truly pancaked. It’s possible that Heyward is tinkering his career into oblivion. I’m not sure I buy this, but at this point there is even less evidence for the competing theories. A serious bone, muscle, or vision problem probably would have landed him on the DL.

Heyward may be treating his swing like jazz, but baseball is the blues. At least he plays in the right city to learn that lesson.

DJ Lema-Who?

The Rockies tend to have hitters with eye-popping stats as a byproduct of playing in Coors Field. This year alone, they have four players with an ISO of .219 or higher (five if you include recent call-up David Dahl). But, on the other side of the offensive equation, there’s only one Rockies player this year who has an OBP over .400. That man is their starting second baseman, DJ LeMahieu.

I noticed that LeMahieu had an above-average .358 OBP last year, but I was ready to dismiss him because he ran an insane .362 BABIP and his walk rate was merely average at 8.1%. Was he really hitting the ball well enough to sustain such a figure? To find out, I looked over a data set of the 253 hitters with at least 200 batted ball events in 2015. LeMahieu had an average exit velocity of 90.6 MPH that year. The average of the group was about 88.9. To put that number in context, LeMahieu had the 60th-hardest exit velocity of the 253. Yes, that number is well above average, but the second-hardest hitter on the list (David Ortiz) only ran a BABIP of .280. To control for the Coors effect, consider this: LeMahieu’s teammate, Nolan Arenado, had an average exit velocity of 91.7, but only ran a .287 BABIP last year. So, LeMahieu’s numbers didn’t appear to be so special, and if his BABIP fell off, there wouldn’t be much left: as I mentioned, his plate discipline was ordinary. In addition, he certainly did not hit for power. He only had a .087 ISO last year, well below the league average of .150. Things didn’t look good for his power behind the scenes either, as his average launch angle was a mere 4.1 degrees; the average for my sample was 10.8. That figure was bad enough for the 244th-highest launch angle out of the 253 hitters in the sample.

So, I wrote off LeMahieu. Boy, was I wrong to do so. This year, his ISO is hovering around league average at .154. His BABIP is an even more insane .383. He’s improved his walk rate to 10.8%, well above league average. He’s also lowered his strikeout rate from 17.3% to 12.9%. This is a huge step for a non-prospect in his age-28 season. What the heck happened?!

For starters, his exit velocity and his launch angle have both improved tremendously. I took a new data set for this year, expanded to include all hitters with at least 150 batted ball events (since the year isn’t quite over yet). There were 255 hitters in the sample. This year, LeMahieu has an improved average exit velocity, at 93.1, which is good enough for 16th out of the 255 hitters in the sample. The average is around 89.4. The average for this group is up 0.5 MPH from my other group, but LeMahieu went up 2.5!

In regards to launch angles, LeMahieu is now at a much improved 5.7 degrees. The new average for this group is around 11.7. So, the group average is 0.9 higher than the other group, but LeMahieu is 1.6 higher! It’s also worth mentioning that in his excellent article published earlier, Andrew Perpetua listed LeMahieu as having the second-lowest percentage (for hitters with at least 300 PAs) of poorly-hit balls. He also had LeMahieu’s xOBA at .388, which is right near his actual wOBA of .391. Lastly, his soft/medium/hard-contact percentages last year (if you prefer this data to exit velocity) were 12.1/61.3/26.6. This year, the soft rate has remained relatively the same, but the medium rate has lost some to the hard. Here’s his contact-quality triple slash: 12.5/51.9/35.6. So, this year LeMahieu’s performance is really not looking fluky. However, one question remains unanswered: what changes did he undergo in order to make this transformation happen?

I think the answer lies in his improving plate discipline. Take a look at the changes across the board from last year to this year:

Season O-Swing% Z-Swing% Swing% O-Contact% Z-Contact% Contact% Zone% F-Strike% SwStr%
2015 24.8% 63.5% 43.4% 72.5% 90.6% 85.2% 48.0% 58.7% 6.4%
2016 23.9% 61.3% 42.0% 78.2% 95.6% 90.5% 48.4% 59.8% 4.0%
Career 28.9% 65.7% 46.6% 73.7% 92.3% 86.3% 48.2% 62.0% 6.3%

His O-Swing percentage has improved tremendously the last two years, and is down another 0.9% this year. His Z-Swing% has dropped as well, and though that isn’t a good thing per se, it does illustrate that LeMahieu is implementing a more selective approach at the plate, maybe not swinging at borderline strikes as much. He’s also making a lot more contact – probably because he’s swinging at better pitches, swinging at fewer borderline strikes and fewer balls. The improved contact rate and improved eye has led to a career-best 12.9% strikeout rate and 10.8% walk rate.

In conclusion, I would like to congratulate our friend DJ on proving me wrong. He has earned a lofty BABIP (though his true talent is probably not quite .383, because almost no one’s is). He has made tremendous strides with his batting eye, and that has helped lead to much-improved contact quality and in turn, more power and base hits.

Data is from Baseball Savant and FanGraphs.

Thanks for reading!

The Twins Gave Up on Pitching to Contact Before We Did

For many Minnesota Twins fans, the recently vintage dominance of the AL Central that spanned seemingly the entirety of the first decade of the 2000s had been taken for granted. I, for one, am guilty of this, and like many fans, am starting realize that winning is not easy, although the Twins made it seem as easy as Torii Hunter made robbing home runs look effortless. Nostalgia aside, the Twins, and their fall toward mediocrity, are an interesting topic to look into. To some, they seemed a similar team to the Oakland Athletics (perhaps aiding in the creation of a post-season rivalry). The Twins, who were not quite as much of a small-market team as Oakland, seemed to develop from within. They had a deep minor system, so deep that when Johan Santana or Torii Hunter deemed it time to cash in, the Twins were able to find a quick replacement and continue their success. Santana, and Hunter, as well as Joe Mauer and Justin Morneau (who have both had their careers altered due to more recent concussions) and many other corner pieces, all made their debut in a Twins uniform and became cornerstones, yet they could never win the big playoff series.

They did not have the ability to flex the financial muscle that the Red Sox, Yankees, and even division rivals Detroit Tigers were capable of; however, they still managed to win the AL Central six out of the 10 years in the previous decade, including a loss in a playoff game to decide the division winner in 2008. The success carried into the Target Field era, represented by a beautiful ballpark that fans spent what seems like an eternity waiting for. After another disappointing playoff loss to the hated Yankees, the Twins entered 2011 looking to improve, with a similar roster and the intrigue of Japanese second baseman, Tsuyoshi Nishioka. That year was filled with injuries, and despite a post-All-Star Game push, the Twins ended the year with the worst record in the American League. Since then, the Twins have failed to reach the playoffs, and are currently battling with the Atlanta Braves for the worst record in baseball. Not to mention, long-time general manager Terry Ryan, the one credited with building the farm system leading to the team’s prior success, was fired on July 18th. Time to find out where the Twins went wrong.

Those successful Twins teams were always credited for their small-ball and defensive skills. With Joe Mauer behind the plate, Torii Hunter (replaced by Carlos Gomez, who could also flash some leather) and many other solid defenders manning the diamond, a lot of the Twins’ success was credited to this defense.

Yet the Twins were far from a one-dimensional team. The Twins had a solid pitching staff, including, most famously, Johan Santana, who was a two-time Cy Young winner with the club, before being sent off to New York. The Twins also produced one of the most exciting pitching prospects at the time in Francisco Liriano. Liriano’s career was marred by injuries, which led to his inconsistency. Despite Johan’s departure and Liriano’s ineffectiveness, the Twins’ pitching was still an effective unit. The Twins raised their pitchers not on the attractive strikeouts, but on “pitching to contact.” The premise behind this was that pitchers would attack the lower half of the strike zone, induce weak contact, and show excellent control to give up few walks. It seemed to work, as pitchers with low to average strikeout rates were able to be effective pitchers, such as Scott Baker, Nick Blackburn, Kevin Slowey, and Brian Duensing.

Before I delve into my research, I should point to Voros McCracken’s ideas about Defense Independent Pitching for those less sabermetrically inclined (if you are sabermetrically inclined, feel free to skip the next few paragraphs). If I were to give a brief summary of his work, I would say McCracken’s main point is that if a pitcher does not give up a home run or strike out or walk a batter, then he has little control of what happens to the batted ball in play. A lot of what happens can be credited to luck, sequencing, and how good his defense is. For those unaware of sequencing, it is the idea that if a pitcher gave up three singles and a home run in an inning, there are many different possibilities of what could happen. The three singles could come in a row, followed by the dinger, for a total of four runs, or, two singles could come early, the pitcher gets a double play or some other way to get out of the jam, then gives up a home run with the bases empty, followed by another single and an out. In that scenario, only one run was surrendered, despite an equal amount of hits. McCracken suggests there is randomness in this effect, which combined with the quality of defense behind the pitcher and a good deal of luck, can make ERA a poor indicator of a pitchers true skill.

McCracken looked at defense-independent pitching stats (HR, BB, K) and defense-dependent stats (ERA), and noticed that the defense-independent stats correlate much better from year to year, and are a better indicator of how a pitcher will perform, since a pitcher does not have control of what happens to balls in play.

While McCracken did not actually create FIP, his work was a building block for modern pitching analysis. FIP (Fielding Independent Pitching) tracks what a pitcher’s stats would look like if he played behind a league-average defense and experienced league-average luck. It is a much better indicator of future performance than ERA. All the data I used was from 2007-2014. Over that span, for pitchers who pitched more than 100 innings in at least a two-year span, a pitcher’s ERA from one year to the next (tracking how consistent the stat is in tracking performance) had a correlation coefficient of 0.338. FIP, conversely, had a correlation coefficient of 0.476. Clearly, FIP performs better when predicting future performance, as McCracken suggested.

To end my digression on McCracken’s importance, if I had to sum up its importance to this article, it is that pitchers have little or no control over what happens to a ball in play.

When I was talking Twins recently with some recent, justifiably uneasy Twins fans, they attributed the Twins’ recent troubles to injuries and inconsistent pitching. This was when I was reminded of the “pitch to contact” philosophy heralded by the Twins. Since the days of recently past successes, the Twins have changed management, and hopefully have let go of this ideology. Anyways, I thought to myself that McCracken’s work and subsequent furthering of the topic do not go along with the pitch-to-contact philosophy. Sure, if a pitcher can prevent walks and home runs, then it does go along with part of McCracken’s ideas. But, if the goal is to induce weak contact, yet the pitcher does not have control of what happens to a ball when it is contacted, then there is a bit of a discrepancy.

So, like any other statistically-oriented college mind looking for how to spend the rainy days of my summer break, I decided to run some regressions to test if “pitch to contact” actually succeeded and the Twins were able to induce weak contact, or if the relative success of the pitching staff is related to luck and a good defense.

To reiterate, the data I looked at came from the seasons of 2007-2014. To sum up the Twins’ pitching through the period, the period starts with solid pitching from guys who lack the ability to post high strikeout rates, excluding the one season Santana pitched in the study. Guys like Scott Baker and Nick Blackburn had solid seasons early on, but Blackburn and many others faded once things went downhill for the team. From the outside looking in, it may seem like a chicken-or-the-egg scenario, whether it was pitching that caused the downfall or some other factor that caused the pitching to fail.

I gathered data for Twins pitching over this span, and compared it to the rest of the league. The pitch-to-contact philosophy was easily visible, as over this eight-year span, only five Twins pitchers had higher strikeouts per nine innings than league average (Johan Santana, Phil Hughes, Scott Baker, Francsico Liriano, Kevin Slowey). At the same time, only four pitchers had a walks per nine innings above league average (Nick Blackburn, Boof Bonser, Sam Deduno, and Liriano), and most of those seasons came in that pitcher’s last season with the team. The data shows that despite few strikeouts, Twins pitchers found some success in limiting numbers of walks. However, for those pitchers who struggled with control, their combined ERA in those seasons was 4.82, with a FIP of 4.60. Clearly, if a pitcher struggled with control, their success was hindered by the high walk rate.

Much of the Twins’ pitching was inconsistent over this time as well, as pitchers such like Blackburn or Brian Duensing seemingly went from quality starters to below-average pitchers. For the most part, I found this to be a team-wide theme. For pitchers with multiple years with the club, I correlated year-by-year ERA and FIP, to see if any consistent trends arose. Amazingly, there was no correlation from ERA from one year to the next, as the R-squared value was 0.002, stressing no relationship at all (graph). FIP, on the other hand, showed an R-squared value of 0.15; so while not a concrete relationship, a weak relationship exists (graph).

Why this lack of consistent ERA and FIP? This is where I think BABIP comes into play. Since FIP does not take into account BABIP, it did produce more reliable data. A few outliers threw off the data, and since it is not a large sample size, those outliers did affect correlation. By the nature of the relationship, this probably did more to affect the FIP correlation than the ERA, but nonetheless, the small sample size of pitchers from this period did affect the relationship. Interestingly, but perhaps not surprisingly, I performed a regression graphing FIP to ERA, and a solid relationship exists, with an R-squared of 0.36 (graph). This would be even better of a correlation if I took out seasons by Phil Hughes and Liriano, as in those two seasons their FIP was almost a full point lower than their ERA, respectively. This shows the validity of FIP as a metric, as it accurately predicts how a pitcher likely will perform based on independent factors.

Nonetheless, there is a clear difference here in the two pitching metrics. FIP implies a relationship, while ERA does not. How can this be? My theory is that it has to do with the pitch-to-contact philosophy. If pitchers are constantly relying on luck and defense to produce outs, rather than getting batters out themselves, then random variation will play much greater of a role in a pitcher’s effectiveness. Additionally, a team’s defense will play much greater of a role in pitching.

How much can a defense affect pitching? Well, I graphed the total WAR produced by the various Twins defenses against the team ERA from the 2007-2014 seasons. I additionally graphed BABIP against team defense. Amazingly, an ERA to defense regression produces an R-squared of 0.47 (graph), while a Defense to BABIP regression produces a 0.37 R-squared value (graph). Team defense clearly has a relationship with team ERA and team BABIP, as when the Twins defense was in its prime (2007, 2010), pitching performed well. Similarly, in the defense’s worst two seasons, the team also had its highest BABIP (2013, 2014). For those wondering, FIP to team defense produces no correlation (as we expect, since it does not account for a team’s defense) with an R-squared of 0.003.

What does this all mean?

Putting it all together, we notice a few trends. After 2010, the defense took significant steps back, along with pitching (ERA). As we expect, the team’s BABIP was affected by the defense’s regression. FIP, on the other hand, remained fairly constant through the span, showing how the defense must play a role in team ERA. For example, we will look at 2014. This was the defense’s worst year in the span, with a defensive WAR of -46.5. Team ERA was second-worst in this year, at 4.58. FIP, conversely, showed the team had its second-best year in pitching, with a value of 3.97. This shows that if the Twins would have had an average defense, their ERA would have been much lower.

As team ERA ballooned, the quality of the Twins’ defense fell. Since Twins pitchers were taught to rely on their defense through the pitch-to-contact ideology, this relationship was amplified. Pitching to contact, although relying on luck and defense, may have had some merit when the Twins’ defense was in its prime. If the team could get to more balls, produce a few more outs, then as long as the pitchers kept batters from getting on for free via the walk, the team would succeed. The pitcher would not need to strike out as many batters since the defense would make more outs than the normal team. This sounds nice on paper, but as the team defense decayed, the pitching regressed. This is most evident in 2014, as a solid pitching staff was marred by the defense behind them.

If the Twins were to truly focus on pitching to contact, then they should have looked at the defense, not the pitcher. At the same time, pitching to contact is flawed in a way. Why should a pitcher rely on a defense if he can just get the batter out himself? Teaching a pitcher not to use his natural talent to strike out a batter is counter-productive. I am not saying the Twins’ coaching staff directly did this, but when only four pitchers in an eight-year span have above-average strikeout rates, it raises the question. Perhaps the Twins looked for pitchers who were undervalued because of their low strikeout rates, and used these undervalued pitchers in their pitch-to-contact system. Yet, this does not seem to be the case, as the Twins pitchers with the lowest ERAs and FIPs were the pitchers with the highest strikeout rate, excluding Brian Duensing, whose downfall could have been predicted by his 3.82 FIP (to a degree), as it showed is 2.62 ERA would be much closer to 4.00 with an average defense. Even in a pitch-to-contact system, the pitchers with the best ability to get the batter out without putting the ball in play were the best pitchers.

If pitching to contact were to have a textbook year, it would be 2007, where a team with a 4.37 FIP had an ERA of 4.18. Yet, soon after, the defense plummeted, bringing the team pitching down with it. Clearly, through the team’s porous defense, the Twins gave up on pitching to contact, too. They just hadn’t realized it yet.

Hopefully, with the new management in place, pitching to contact is forgotten. While it is also important to keep a viable defense behind the pitcher, I still can’t trust the pitch-to-contact ideology. It had a good run, but seriously, when was the last time the Twins were able to produce a consistent pitcher out of a highly-praised prospect? Liriano wasn’t consistent, Kyle Gibson has yet to dominate, and Jose Berrios has looked shaky is his brief appearances. I think Scott Baker might be the answer to my question, but if not him, then maybe Johan Santana?

Clearly, the Twins need a new philosophy for grooming pitching. It’s a team riddled with questions, and this is not the lone answer, but it can be one step in the right direction for the team currently pegged at the bottom of the AL barrel.