Miguel Cabrera and the Inevitable Decline

Miguel Cabrera had a tough 2017. Could his decline be due to regression? Age? Could it be the back problems he allegedly played through? Or, was he just plain unlucky?

Knee-jerk assumption is health issues. From Jon Tayler (Sports Illustrated):

…it’s clear that, at age 34, his body is breaking down. On (September 24th 2017), Detroit learned that Cabrera, who had to leave Saturday’s game early with back pain, has been diagnosed with two herniated discs in his lower back, with manager Brad Ausmus telling reporters that his star may not play again this year. Back issues have been a problem for Cabrera since he played for Venezuela in the World Baseball Classic back in March and are the latest in a litany of aches and pains he’s dealt with since turning 30; as Ausmus put it, “This has probably slowly been developing for years.

Baseball players break down. Some sooner (and more drastically) than others.  A player with Cabrera’s skill set can regress and still be above average.

So, let’s delve into regression and luck.

A quick overview of the last four seasons for Cabrera.

mCabrera1417

2017 was likely worse than anyone could have reasonably expected.

We’ll mostly work with Weighted On-Base Average. wOBA is a great tool that helps determine how productive a hitter has been.

It’s more informative than OBP as it uses weights to determine where a hitter ended up and what he accomplished when reaching base. OBP only tells us that the batter got on base. That might be enough for others, but some of us would like a little more context; no judgment on which you prefer.

For regression sake, let’s look at Cabrera’s career wOBA against the league average in terms of age.

chart (11)

As we can see, Cabrera had a wOBA well above average for a player his age. According to the chart data, at ages 29 and/or 32 is when wOBA seems to peak; .319 for 29 year olds, .320 for 32 year olds.

Once Cabrera hit 34, he crashed back to earth and managed a league average wOBA.

Perhaps 2017 was an anomaly; a result of bad luck? Here’s a glance at his batting average on balls put in play. His career BABIP is .344 and 2017 he posted a .292; slightly worse than league average.

chart (12)

Cabrera’s BABIP remained steady, save for a few fluctuations, then plunged into mediocrity. So he was just unlucky…right?

We can look at this graph and see from age 19 to 23 it continually climbed, then dove down at 25. The chart follows a similar trend, with a bit more volatility, from age 26 to 32. Can we infer it will trend upward again? Since it would be quite a feat for his BABIP to go on another positive run, I’d venture to guess that, at this point, it will stabilize.

So, can we blame the injury now?

Well, we can’t measure how much his back problems affected his hitting. We can take it into consideration but we don’t know how much it was actually bothering him. He managed to play in 130 games, so its hard to say it was that much of a problem for him. I would presume, depending on the pride of the player, that as you got older you’d want to protect your body more; give it more rest. Obviously, Cabrera is a tough guy as he averages something like 150 games a season. Knowing that the organization was sliding down into obscurity, maybe he felt it was his duty to keep playing for the fans.

Those are a lot of ‘maybe’s’.

Other than his rookie year, he’s never played less than 100 games each season. He had injuries in 2015; listed as day-to-day with a back soreness on September 23rd and ended up on the 15-day DL July 4th with a calf injury.

Regardless, the sharpness of the wOBA decline is what I find disconcerting. His biggest drop in wOBA occurred between the ages of 29 and 30; about a .070 drop. Then, going from age 33 to 34, it dropped .086 points. To note, the average wOBA actually increases two-hundredths of a point from 33 to 34.

So why did this happen? We’re going to investigate Cabrera’s wOBA versus his xwOBA for 2017.

To summarize xwOBA: Based upon the type of contact, it’s what was expected to happen versus what actually happened.

*Already know xwOBA? Skip down to the chart

Aaron Judge drives a ball into the left-field gap, under a certain launch angle and exit velocity. Let’s say he hits it into an average outfield and it drops in for a double. Alternately, Mike Trout drives a ball under the same conditions but Billy Hamilton is playing center field. Since Hamilton has elite speed and is a good defender, he caught the same type of hit Judge dropped between inferior defenders.

One other thing I want to point out about xwOBA. It takes speed into account. Albert Pujols is not a fast runner; much slower than average. That being said, he’s more inclined to hit into double plays and/or unable to leg out an infield single. A ball hit with the same trajectory by other players might be beaten out.

I understand that speed is a factor in a game but given the likelihood of that ground ball being hit for an infield single, xwOBA would adjust for a player like Pujols because it would be expected that he could leg it out. That aspect could be seen as a flaw depending on your point of view.

This might be oversimplifying the concept…or making it even more confusing. And it might not be an exact science, but its pretty darn close.

Here’s a chart of comparison to other hitters who saw a variance from xwOBA to (actual) wOBA in 2017.

2017XWOBA

We can see one thing standing out; Cabrera, by two-hundredths of a point, is well ahead of the other nine in terms of the difference. It took a little bit of a dip from Brandon Moss to Logan Forsythe but not as drastic.

Going back to 2016, Billy Butler had the biggest drop at -.058; Cabrera finished fourth with -.050 (.459 xwOBA/.409 wOBA).

So, two reasonably big differential drops over the course of two years. The caveat here is in 2016, Cabrera was much more productive; a 4.8 WAR with a 152 wRC+.

Consider this contact visual, from ‘16-‘17, of Cabrera’s xwOBA and wOBA.

mCabrera1

Quite a distinction in contact as well as balls in play. Yet, his launch angles remained in the same sphere, between roughly 40 and -20 degrees. Cabrera clearly isn’t having a problem with his swing. Mechanically, anyway.

Let’s move onto contact and exit velocity during the drop-off years of ’16 and ’17. In 2016 Cabrera had a total of 238 ‘good contact’ hits (barrels/solid/flares) on 9.5% of pitches seen:

Miguel Cabrera (6)

In 2017, 161 with a 7.8% ratio:

Miguel Cabrera (5)

 

And how about Cabrera’s exit velocity?

mCabreraEV

Was he swinging in pain? How much and to what detriment isn’t quantifiable, especially because he still managed 500-plus plate appearances. Pain to you isn’t necessarily pain to someone else.

Cabrera is on the decline. His xwOBA data makes a case for that. I’m going to infer it was simply a coincidence that his injury occurred the same year. While he wasn’t hitting the ball as hard (humans do lose strength), he maintained his launch angles; something that would have changed (at least a little) if you’re burdened from back pain.

You can’t play at a high level, like Cabrera has, forever. Even during his not-so-great years, he was still so much better than an average player. Regression is inevitable. Last season appeared to be the year that it happened to one of the best hitters the game has ever seen.

*Statcast data courtesy of Baseball Savant


Try to Catch Corey Knebel Upstairs

There is a good chance you didn’t know who Corey Knebel was until last season. As a mediocre middle reliever for a bad Milwaukee Brewers team, he did not receive much attention in 2015 and 2016. Most are familiar with him now after he posted 39 saves for a contending Brewers team. Knebel ranked third in the league in strikeout rate in 2017 with a 40.8% rate, sitting behind only the far-and-away best relievers in baseball: Craig Kimbrel and Kenley Jansen. Let’s take a look at what he has to offer.

Knebel will do one of two things.

He will blow past you with 98+ mph, or:

… he will drop this nasty breaking ball on you.

Knebel really doesn’t do anything else, and, as 2017 proved, he doesn’t need to. He has two incredible pitches, and that’s all a reliever needs to be successful. That curveball is exceptional, but let’s focus on Knebel’s fastball for a little.

Velocity doesn’t make you good, but velocity is generally good. Spin doesn’t make you good, but spin is generally good. Most pitchers would say they would rather have more of the two than less of the two. Neither of them guarantees success, and the two combined don’t guarantee success either. However, it’s unlikely you will find a pitcher with an abundance of both categories who isn’t marginally successful IF they can command their pitches. And that’s a big if. (See Betances, Dellin)

Knebel’s fastball has an abundance of velocity and spin. In his career, the pitch has averaged 96.4 mph and 2375 RPM (2245 is roughly league average). Here is a plot of spin rate and exit velocity of all the relief pitchers who have thrown at least 200 four-seam fastballs since Knebel’s first full season in 2015. Knebel is highlighted in yellow.

Knebel ranks near the top in both categories. Using Z-scores (which measure how much something differs from average, positively or negatively) to standardize velocity and spin rate, his combined score ranks 41 out of 347. Impressive, of course, but not outstanding. So why did Knebel’s fastball plummet from a 117 wRC+ against in 2015-16 to a 76 wRC+ last season, despite the same high velocity and spin? Watch the fastball GIF again and pay attention to how high in the zone, or out of the zone, rather, Knebel throws the ball.

Now, here are two pitch heat maps. The first, Knebel’s fastball location in 2015-16. The second, his fastball location in 2017.

The first is essentially middle-middle. High-heat pitchers are less afraid to throw in the strike zone, as they want to force hitters to catch up to the pitch. Hitters caught up to Knebel. While the spray in 2017 is not as compact, the shift up in the zone is obvious. With how hard he throws his fastball and how much spin it has, it’s quite difficult to hit Knebel high in the strike zone.

Even when his fastball was not successful prior to last season, it was still troublesome for hitters up in the zone. In 2015-16, Knebel caused opponents to whiff on just over 40% of their swings at fastballs high in the zone, ranking 12th among relievers. That figure was even more spectacular in 2017, as he posted a 46.4% rate, which placed him third among relievers. Hitters can’t contact that velocity and spin that high up. The change in pitch location doubled his swinging strike rate from 8% in 2016 to 16% last season, along with shooting his chase rate up from 22% to 32.6%.

Knebel always had the stuff for a great fastball, he just had to figure out how to command it. Now that he has, he looks like a terror of a pitcher. Knebel can finish hitters off with his curveball or dare them to catch up to his fastball upstairs.


Curveballing with Cleveland

Last season led the MLB to the extremes of curve-balling rates.

Since 2002, the first season of pitch tracking, four of the top six teams in single-season curveball usage came from last season (starters only). In third was the 102-win Cleveland Indians, in fourth was the 104-win Los Angeles Dodgers, in fifth was the 101-win Houston Astros, and in sixth was the…66-win Philadelphia Phillies. It’s probably just coincidence that the three best teams threw the most curveballs. But that’s not what this article is about.

The Indians led the league curveball rate in 2017, with their starting pitching staff throwing 20.6% curveballs. They were historically extreme last season, but they weren’t all that extreme last season compared to last season. The Dodgers starters threw curveballs 20.1% of the time and the Astros starters threw curveballs 19.4% of the time. But no matter what time period you want to look at, they were extreme in curveball effectiveness.

Pitch values are not the best evaluator of success, but they can give you a good indication of success with enough of a sample. Atop the starting pitcher curveball pitch value leaderboard for 2017 sits the Indians, accumulating 56.3 runs of value. The Diamondbacks come in at second with a run value of 25.6. The difference between those two teams is the same as the difference between the Diamondbacks and the 13th place Reds. Going back to 2002, no team comes even close, with the 2003 Cubs ranking second with 30.9 runs. Obviously, though, if they threw curveballs that much, they are going to rank highly. Using standardized pitch values, which measure run value per every 100 pitches, the Indians ranked first last season with a 1.80 mark. The Yankees were second, posting a not very close 1.35 figure. Since that 2002 season, Cleveland’s standardized pitch value from last season ranks third.

It may be easier to visualize the ridiculousness. Below is a plot of the curveball rate (as a decimal) of every individual team single-season since 2002, along with their standardized pitch value. The 2017 Indians are in orange.

Almost no team has matched their curveball run value in general, but accounting for the frequency with which they throw it, 2017 Cleveland is on another planet. No team that threw curveballs at least 15% of the time comes close to touching the Indians pitch value from last season. How were they this good?

Corey Kluber’s absurd breaking ball is up to debate whether it is a slider or a curveball, but Fangraphs pitch type classifies it as a curveball, so it is a curveball for this purpose. Since 2014, Kluber has accumulated 97.9 runs of pitch value on his curveball. In that same time period, no other team as a whole has accumulated more than 40.9 runs. Which isn’t that surprising when the pitch looks like this:

It’s not all Kluber though. If you remove his 37.8 run value from the Indians 2017 total, the team would still rank fourth overall with 18.5 runs. Let’s look at what the rest of their rotation was putting on hitters.

When you think of Carlos Carrasco and his breaking ball, you probably think slider, but he’s jumped on the curveball train. After throwing it less than 10% of the time in 2013-15, his curveball usage climbed all the way 16.2% last season. It didn’t lose any effectiveness:

For Trevor Bauer, his curveball is how he’s found success for most of his career. So why not throw it more? That’s just what Bauer did, bumping it’s usage from 19.4% in 2016 to 29.8% in 2017 en route to a breakthrough season. Who knows how to get a pitch to move like this:

Josh Tomlin never threw his curveball more than 15% of the time in any previous season, but used it 24.1% of the time in 2017. The Indians may be on to something:

In his sophomore season, Mike Clevinger shaved more than two runs off his 2016 ERA of 5.26. He also doubled his curveball usage to 11.6%. Here it is:

All of these guys were in Cleveland in 2016, yet the starting rotation ranked “only” seventh in the league with a 4.08 ERA. In 2017, the starters ranked second with a 3.52 ERA. The trend here is clear. All of these guys, including Kluber (who went from 19.7% to 27.4%), significantly increased their curveball usage. All of them had career years.

It’s evident that Cleveland came together as a staff and made a decision to throw their breaking balls as much as possible. It appears to have worked. As noted by Jeff Sullivan, the Indians may have had the best pitching staff of all time last season. That includes their incredible bullpen, but the starting rotation held up their end of the bargain as well.

The team did something unbelievable in 2017. Cleveland combined historical curveball usage with historical curveball effectiveness. All five of these pitchers return next year. Don’t be surprised if the Indians stretch the bounds of curve-balling even more in 2018.


Jim Thome: First and Last Three Outcomes Hall of Famer

Jim Thome was elected to the Hall of Fame on January 24th.  Given my recent obsession with the three true outcomes, I immediately recognized the significance of this event.  I believe Jim Thome is the first, and likely the last three true outcomes Hall of Famer.

Table 1 shows Thome’s home run, walk, and strikeout rates along with his three true outcomes rate for each season.  The final column is the MLB average three true outcomes rate for the season.  Thome was a three true outcomes machine from 1996 until his retirement in 2012.

Table 1. Jim Thome, Three Outcomes Hall of Famer

Season Team PA HR/PA BB/PA SO/PA TTO Avg TTO
1991 Indians 104 1% 5% 15% 21% 26%
1992 Indians 131 2% 8% 26% 35% 25%
1993 Indians 192 4% 15% 19% 38% 26%
1994 Indians 369 5% 12% 23% 41% 27%
1995 Indians 557 4% 17% 20% 42% 28%
1996 Indians 636 6% 19% 22% 47% 28%
1997 Indians 627 6% 19% 23% 49% 28%
1998 Indians 537 6% 17% 26% 48% 28%
1999 Indians 629 5% 20% 27% 53% 28%
2000 Indians 684 5% 17% 25% 48% 29%
2001 Indians 644 8% 17% 29% 54% 28%
2002 Indians 613 8% 20% 23% 51% 28%
2003 Phillies 698 7% 16% 26% 49% 28%
2004 Phillies 618 7% 17% 23% 47% 28%
2005 Phillies 242 3% 19% 24% 46% 27%
2006 White Sox 610 7% 18% 24% 49% 28%
2007 White Sox 536 7% 18% 25% 49% 28%
2008 White Sox 602 6% 15% 24% 45% 28%
2009 2 teams 434 5% 16% 28% 50% 29%
2010 Twins 340 7% 18% 24% 49% 29%
2011 2 teams 324 5% 14% 28% 47% 29%
2012 2 teams 186 4% 12% 33% 49% 30%

Thome was part of a small group of specialists with multiple dominant three true outcomes seasons.  Table 2 provides a list of players with 4 or more of these dominant seasons.  I consider a season with at least 170 plate appearances and a 49% three true outcome rate as a dominant season.  The casual three true outcomes observer will recognize the players on this list as notable specialists.  Rob Deer, of course, is the iconic three true outcomes hitter.  I used Deer’s career three true outcomes rate of 49% and 4 dominant season to construct the table.

Table 2. Dominant Three True Outcomes Specialists

Player Career Seasons
Jim Thome 1991-2012 10
Adam Dunn 2001-2014 9
Russell Branyan 1998-2011 8
Mark McGwire 1986-2001 6
Jack Cust 2001-2011 5
Chris Carter 2010-2017 5
Rob Deer 1984-1996 4
Chris Davis 2008-2017 4
Alex Avila 2009-2017 4

Thome’s 10 dominant seasons are more than any other player.  He is also the only Hall of Famer on the list.

Maybe Mark McGwire should be in the Hall of Fame (depending on your PED era position).  Already past eligibility to be inducted by the Baseball Writers Association of America (BBWAA), perhaps he will have a chance in the future with the Veterans Committee.

Adam Dunn will be on the 2020 ballot.  He was a consistent three true outcomes specialist, but we will see if the BBWAA consider him a dominant player over the course of his career.

Russel Branyan and Jack Cust are interesting players to see on this list.  Branyan makes the list because of my 170 plate appearance requirement.  Cust was a dominant three true outcomes hitter for five straight years, 2007-2011.  Neither are on the Hall of Fame ballot.

Carter and Avila do not have contracts for 2018, but could land somewhere.  Davis is signed with Baltimore through 2022.  Joey Gallo and Aaron Judge are two young hitters in the three true outcomes mold not yet on the list.  So maybe it is too soon to make a judgement on the Hall of Fame potential of three true outcomes hitters in the future?

But I am going out on a limb to say that despite the trend towards three true outcomes baseball, we have seen our first and last three true outcomes Hall of Famer in Jim Thome.


Maybe the Brewers Are Stepping Up Because of Zach Davies?

Not entirely, no. Before acquiring Christian Yelich and signing Lorenzo Cain, the Brewers still had a host of other talent scattered throughout their lineup that made them interesting, if not necessarily intimidating. But Davies occupies a particular space that their more established players have surpassed and that their other younger players have yet to enter.

He’s going into his third full season and has provided nearly six wins for the team, and his approach could make you believe he could keep doing that in the middle of the rotation. He’ll play 2018 at just 25 years old.

Travis Sawchik detailed why Davies is an outlier toward the end of last season. At 6’0, 155, he creates his success by combining guts, guile, and execution. Of those three characteristics, guile may pique the most interest.

Davies Sequencing

From his start on July 19 and after, Davies was a different pitcher. He made a big adjustment to his sequencing as he adapted the usage of his secondary pitches to better complement his two-seamer.

Davies’s two-seamer and changeup fade to the same quadrant of the strike zone. If he was working off the fastball, it may have been giving batters a better opportunity to tee up a changeup because of how tunneling works. They would have looked the same to batters by the time they had to decide to swing, generating a similar bat path.

Meanwhile, his cutter and curve could have come out of the same tunnel as his two-seamer, but ended up working the opposite side of the plate. Batters would have had a much more difficult time picking up what was coming next.

Davies heatmaps

Given how Davies’s two-seamer and changeup work the same area of the plate, it’s interesting that their drop in wOBA was identical after July 19, and equally interesting that the cutter and curveball became so much more effective.

These differences after Davies’s change in sequencing speak to the possible impact a tunneling effect could have had on his overall game. While tunneling isn’t necessary for pitchers to have success it may be especially productive for a guy who throws in the low 90s and lives on the edges of the zone.

Sequencing better in any count is bound to help performance, but it appears to have helped Davies in one particular situation. When he was behind 3-1 before July 19 — 38 times in 19 starts — hitters were smacking the ball into play at over 100 miles an hour. After that date — 26 times in 14 starts — he coaxed hitters’ exit velocity down to 87.3 mph. Once or twice a game, Davies turned absolute screamers into much more average balls in play. That’s critical when a pitcher isn’t going to generate outs on his own with whiffs, as Davies won’t with his career 6.55 K/9.

We can probably fairly consider this adjustment as deliberate, based on another quote from him in Travis’s article:  

“I think it just comes down to the other side of the game that not a lot of people pay attention to…[t]he thinking part of the game…[smaller guys]  have to rely upon smaller details about their game that can give them an edge.”

What if that also explains, at least partially, how the Brewers decided to go big by trading for Yelich and signing Cain? Teams have billions of data points to measure player performance. Davies’ subpar raw skills apparently haven’t kept him from being able to make adjustments and providing tangible value, despite falling outside what those data points might influence teams to prioritize. In this respect he’s a player who gives his team a unique edge.

We’ll have to wait to see an encore from Milwaukee’s holdovers from 2017, as well as the impact their newcomers make. But Zach Davies finds himself at the heart of a team looking to make some noise in a challenging NL Central.

All data from Statcast.


Who Obtains the Most Assistance in Pitcher Welfare?

Nobody’s perfect, especially umpires. This is the case at any level of the game. Be it softball, tee ball, or baseball, from Little League to the Big Leagues, you will have undeniably disagreed with a call that an ump has made.

Given the movement, velocity, and the newly anointed skill of pitch framing, it’s becoming more difficult for umpires to get the calls right. The robo ump has been discussed quite a bit but I’m not sure how I feel about a machine making decisions in lieu of accepting the concept of human error. We did it for decades before instant replay was instituted.

Umpires get balls and strikes wrong a lot. It’s the way it goes. Given that understanding, I wanted to know which pitcher has in recent years been the beneficiary of favorable calls.

And, like the umpires, not all (strike zone) charts are 100% accurate; leave a little room for error here.

I’ve parsed data on which pitchers have had the most declared strikes that were actually out of the zone. I decided to stop at 2014 because I felt that four years of information was sufficient for the study.

First, the accumulated data.

From 2014 to 2017, the amount of pitchers with phantom strikes has been increasing at fairly high rate; the biggest leap was from 2014 to 2015 (36 pitchers).

chart (4)

Interestingly, the pitchers with at least 100 ‘phantom strike’ calls has actually decreased.

chart (6)

And, despite the jump in total pitchers involved from ’14 to ’15, the pitchers with <=100 strikes called decreased at the highest rate.

Should we go tin foil hat and infer that umps are no longer favoring certain pitchers as much as they used to? Doubtful, but I’m not investigating integrity here.

So who is getting the most benefit from the perceptively visually impaired? First, I took the last four years of pitching data for our parameters. Then, I cut final the list down to a minimum of 10,000 pitches thrown. Lastly, I included only the top 20 pitchers in the group.

20PhantomStrikes

As we can see, Jon Lester of the Chicago Cubs has been the most aided overall; 562 non-strikes in four years.

For the optically minded, here is the pitch chart of Lester’s data.

Jon Lester
That’s A LOT of Trix!

Now, lets see if the percent of pitches has any impact on our leader(s).

20PhantomStrikesPercent

Not a whole lot of variance, at least near the top. Lester clearly wins The MLB Umpires’ “Benefit of the Doubt Award”.

OK, so now we’ve got our man. Case closed, right?

Oh…that little caveat of ‘pitch framing’. Perhaps its that Lester has had great framing from his catchers. Let’s look into that.

For the moment, we are going to focus on Lester and his primary catcher from 2014-2016, David Ross.

dRossLester

Clearly 2014 was Lester’s most favorable year with Ross. That year, Lester ranked third in total pitches called favorably out of the zone (156) and 11th in ratio of calls (4.47).

The subsequent years with Ross are as follows:

2015- 6th (141), 10th (4.43)
2016- 5th (125), 7th (3.95)

Here’s where things get a bit intriguing. Recapping 2017, things appear to fall apart completely for the Cubs in the context of pitch framing.

2017CubsFraming

The only catcher who was able to garner a positive framing rating was Kyle Schwarber, who caught just seven innings that year. But even his stats are far from impressive.

And how did Lester fair in terms of ‘phantom strikes’ that year? He ranked first in overall strikes called out of the zone (150) and fourth in total call ratio to pitches thrown (4.46).

He wasn’t all that far from the top under Ross, but was basically the frontman of the metrics in 2017.

Some things are hopelessly lost in the sphere of the unexplained. But, the research didn’t set out to find reasoning. In this case its more fun to be left with subjective theories. However, it’s a bit silly to think that there is actually an umpire conspiracy allowing Lester to succeed when he apparently shouldn’t.

My best guess is maybe they feel sorry for him since he can’t accurately throw the ball in the infield anywhere other than to the catcher (which did changed a bit in 2017)?

Regardless, Lester is our guy, here; receiving a sizable edge in terms of missed calls. It will be interesting to see if this trend continues this season.


The MLB Prize Money Solution

First things first, I’m an Englishman and baseball is my favourite sport [I’m English, so Americans, you’re getting an extra “u” there]. This makes me rather unusual in my home country. Most popular in English sport is soccer, a ball game you play with your foot [let’s not go there]. I’m guessing there may be a few Premier League fans here who read Fangraphs, but with the assumption that most of you won’t have much interest it, I have a suggestion to fix some of MLB’s financial problems by importing an EPL solution. MLB needs proper prize money.

I’ll address the elephant in the room. It might sound a bit mad to try and fix MLB’s financial problems by importing strategies from the Premier League, where free-market capitalism is stronger than in MLB and player contracts on small teams can be bought out by rich teams in player transfers. It made it hard to compete if you weren’t a rich team, until recently. In 2016, Leicester City became the only team in 20 years to break the strangle-hold the four richest teams have had on the Premier League title. One reason they managed it might be the way the EPL shares its money out.

Most money made in sport today comes from the TV rights deals made so fans can watch their teams play without needing to go to the stadium. The EPL does this better than any other sport, so despite coming from one of the world’s middle nations in terms of population, it brings in more money per team than almost any other. It does this by the league selling all the commercial rights collectively. This income gets shared out as prize money between the teams based on their records at the end of each season, with a good baseline for all. This meant that last year, Sunderland, finishing as the worst team in the league, got £93.5m ($132m – I’ll convert all figures to dollars from now on) and Chelsea, who won the league, got $212m in prize money. The worst team received around 62% of the prize money the best team did.

Baseball’s TV rights money is (mostly) negotiated separately team-by-team and is one major cause of the variance between incomes of the rich teams and the small teams. One reason the Yankees and Dodgers have the highest payrolls is because of the huge chunk of TV money they collect. The reason the Rays, Pirates and Marlins are at such a disadvantage is because they can’t collect anything like as much. The result is, in order to be competitive at all, they might need to consider losing more, draft well and return to competitiveness in a few years with good, cheap, young players. Financially, they don’t really lose out much by doing so and there are great risks in spending beyond their means in free agency to try and improve (both in results and financially).

It might also lead small and mid-market teams to be conservative in the free agent market. A lower income team might not risk signing a player to a big contract as one big mistake prevents them having the money to fund a good team with their financial burden. A common belief amongst Braves fans was their rebuild in 2014 was in part precipitated by the two big contracts that started producing little on-field value in Melvin Upton and Dan Uggla (together $25m for around zero WAR in 2013 and 6 years left on their contracts). With their good performance, the Braves might have made the playoffs. Without it, they didn’t have the money to replace them, leading to mediocrity or a rebuild. The risk of a big contract failing to provide value dissuades small-market teams from signing free agents much more than big market teams. This off-season’s cold stove has also seen high-income teams saving rather than spending. But there may be a way to encourage the lower-income teams to spend more:

Distribute significant prize money based on winning percentage at the end of each season, funded by collectivizing the TV rights money for all MLB teams. [You could also add a bonus pool for playoffs]

This would do a number of things. Assuming a breakdown of prize money like in the EPL, outlined above (worst team gets 62% prize money of the best team), it would give a higher baseline income to the very low-income teams like the Marlins/Rays/Pirates/A’s. The Marlins new management claimed the club’s losses required them to lay off their expensive contracts this winter. They flooded the market with good players which helped suppress the FA market. The Rays wouldn’t need to trade Longoria for money reasons. The A’s wouldn’t need to trade any good player they have who gets too expensive in arbitration.

The prize money would also provide better financial incentives for teams to try to win more games. The Royals made some significant payroll increases when they started winning in 2014-15 because of the extra revenue it generated. But the majority of that revenue came from their playoff appearances. This method would also provide some incentive to the mediocre teams. The last few years have seen mediocre teams like the Braves, Padres, White Sox and now the Marlins and Pirates trade away some decent core players through having little incentive to remain mediocre and aim upwards. Having good players on the trade market every offseason has (in part) led to the “super-teams” and “rebuilding-teams” dynamic and is likely helping suppress free agent contracts. Let’s say that being mediocre led to $30m more in prize money than being awful. That money might motivate some middling teams to spend and push upwards to the playoffs rather than trade downwards to a rebuild. Middling teams spending more might also threaten the positions of the “super-teams”, pushing them into spending more in the free agent market.

Coda:

The EPL still hasn’t fully overcome its problem with rich teams winning the league every year and poor teams having no chance. Manchester City is amongst the league’s richest teams and are top of the league by a long way this season. Chelsea (another rich team) won last year. The original winning/financial inequality came about because the top four teams (Man U, Arsenal, Liverpool, Chelsea) qualified for the Champions League every year so got significantly more income (through its TV rights) than those who didn’t qualify. This led to the same four rich teams filling the top four positions in the league from 2003 to 2009, often because they could afford to buy other team’s best players. That aspect hasn’t really changed; only five teams have won the title in 20 years. However, as the prize money in recent years went above $80m for the bottom placed team, the mid- to lower-income teams were able to afford really great players too. If you watch every week you can’t help but notice how much more competitive every game is now. Rich teams are often beaten by poorer teams; Manchester United dropped out of the top four, so did Liverpool, replaced by Spurs and Manchester City; Leicester won the title. It made the league more competitive and better to watch. Perhaps until this year when the transfer market went bananas and suddenly $132m might not buy much anymore. This year’s Man City team could be like this year’s Yankees team. Rich and dominant. I’m sure fixes will be needed in future to stop the dominance of money. But baseball could still learn a thing or two from the Premier League.

Resource:

http://www.telegraph.co.uk/football/2017/05/16/premier-league-2017-prize-money-much-club-line-earn-season/

http://www.bbc.co.uk/sport/football/40125394

https://www.forbes.com/teams/kansas-city-royals/

https://en.wikipedia.org/wiki/List_of_professional_sports_leagues_by_revenue

https://www.fangraphs.com/blogs/estimated-tv-revenues-for-all-30-mlb-teams/


Is ‘Tanking’ in Baseball Worth It?

With all the blabbing about the fire sale of the Miami Marlins, and less so with the Pittsburgh Pirates, does the philosophy of ‘tanking‘ in Major League Baseball work? Can it come to fruition the same way it does in the National Football League or the National Basketball Association?

The biggest and most obvious difference in those sports is the vast majority of players you’ll draft in the NFL or NBA are ready to play (even start) the following season. Not only that, players are more of a ‘sure thing’ in those leagues; you’re more likely to hit on a player since the pool is much more shallow than it is in baseball.

While in MLB, there are several levels to break through before you’re actually ready to play in the top-level.

Now, I understand the angle of ‘tanking’ to accumulate funds and eventually splurge on some free agents or wanting to make your team younger. I can follow that train of thought (sort of) but we are going to go on the premise that teams are doing it to grab top-level draft picks through each round.

Yes, the Houston Astros and Chicago Cubs, after many seasons of horrid baseball, are now World Champions thanks to patience and a great analytics department. Let’s just break even and apply this to your average front office.

According to research done by Cork Gains of Business Insider back in 2013:

“After three years, we will probably only see about 15% of this year’s draftees in the big leagues. And for most players, it will take 4-6 years to make it to the highest level”.

Let’s take a more recent peek at draft success within the first five rounds of the MLB draft. We only go that deep because, honestly, after that point (and even five rounds is a reach) it’s mostly a crap shoot and I’d venture to guess no team has any sort of advantage over the other.

I’m using five years as its reasonable to expect, even with a high schooler, to reach the big leagues within that amount of time.  Of players drafted in 2017, none have reached the majors; no surprise there. In 2016 just one player drafted, third-round pick Austin Hays of the Baltimore Orioles, has made it to the majors. We ought not to reference that year, either.

The 2015 draft is when we start seeing results.

2015Draft

Of first rounders in the 2015 draft, first overall pick Dansby Swanson debuted in 2017. Alex BregmanAndrew Benintendi, and Carson Fulmer all came up in 2016.

In the 2015 draft, out of 165 players picked in the first five rounds, 7% have made it to the big leagues.

It goes without saying the list grew considerably in 2014 and it follows that it would in subsequent drafts. Out of rounds one through five in 2014, we have 19% currently in the majors.

2104Draft

Let’s investigate the success rate by round, referencing research done by Mike Rosenbaumof Bleacher Report.

draftSuccess

 

So the first round, you’re likely to get two out of every three players in the majors, then 50/50 in the second round. However, this chart is no reference for success or failure for the player once they do reach MLB.

The standard deviation of success rate drop-off is 4.96%, with a variance of 24.6%

Here, we’ll observe the first overall picks since 2006 and their yearly average WAR from all MLB seasons.

no1WAR

The first-overall pick in this era yielded about 50% averaging WAR over 3.2 (we’ll get to the context in a bit).

Lastly, let’s look at the cumulative WAR of rounds one through five, starting at 2011 to 2015. As mentioned before, there was just one player who reached MLB that was selected in the first five rounds during the last two years.

avgWARDraftRd

*Mookie Betts 24.1 WAR

So the chart lends itself to logic; the older the year, the higher the cumulative WAR. But, there is still a lot we don’t know yet as there are still players in the 2011 draft toiling in the minors, yet could break into the big leagues in the next year or so.

Yet, something funny happens. There is a spike in WAR once we get to round 5. As noted, the majority of WAR from that round in 2011 comes from Mookie Betts. Could we infer that later rounds will increase as well? Probably not, as the random variation would likely be all over the place player to player. But it’s not a stretch to assume that you can find just as much value in later rounds as you can in the first couple.

Obviously, the bigger success stories come from the first round. But, keep in mind that’s just one player. On a team of 25 guys, it’s less likely that this player can turn an entire franchise around by themselves. In the NBA its possible, or the NFL where a quarterback can pull a team out of mediocrity within a year or so.

I’ll average the first round pick WAR to get an idea of what a team who continually ‘tanks’, could expect to get out of first rounders for the next several years.

2011- 2.8
2012- 1.7
2013- 1.2

Again, this isn’t concrete information but it’s enough data to get a rough inference. If you ‘tank’ for several years, and get a high first round pick, you can expect to get a players who will average a WAR of about 3 (after about five years); the average WAR of a number one pick (using the data in the ’06-’16 chart) is a little better than 2.

So you’ve got a good shot to get a player considered decent or, at best, above average.

The following chart give some context on WAR for those unfamiliar.

WARvalues

Is it really worth ‘tanking’ in baseball? In, say, five years of mediocrity, how often can you expect to hit on a player in the early rounds (average 2.5+ WAR)? Again, in the first round (’06-’16) chart above, you’ve got a 50/50 shot. Is it worth driving your franchise into the ground with those kinds of odds?

Like I’ve said before, we are using a small amount of data that is on a sliding scale (the older the draft, the higher the WAR). Since it would take roughly 3-5 years for an organization to acquire draft picks that could break into the league and help push the team into championship contention, it’s not too far of a reach. Meaning you can expect your first couple of picks per year to start normalizing WAR after a couple of seasons…if they reach the majors at all at all.

So is ‘tanking’ worth it? Allegedly to the Marlins and Pirates, it seems to be. They have highly paid analysts and I’m a lowly blogger, so they know better than I do. But, with the information I’ve been able to acquire, it doesn’t seem as though stockpiling high picks will benefit an organization enough to risk losing fans, revenue, and respect in MLB in the short term.


Lorenzo Cain Follow-Up: Market Value and 2018 Projections

As one of the remaining top free agents, Cain agreed to a 5yr/$80M deal with the Milwaukee Brewers. Reports indicate that Cain will earn $75M from 2018 to 2022 ($13M/$14M/$15M/16M/$17M) plus incentives, in addition to a deferred yearly payment of $1M for years 2023-2027 ($5M total). Based on Cain’s projected Market Value, he should be worth close to 9.6 sWAR for the next 3 years ($84M approx. Assuming 5% inflation per year). Understandably, Cain’s history regarding injuries and durability is a considerable factor moving forward.

A new home should benefit Cain’s offensive output. For the past 3 years, Miller Park has averaged a 106 HR Factor for RHB; which is significantly (P = 0.03) higher than Kauffman Stadium’s 88 (RHB) in the same period. Moreover, Cain has not “slowed” down, his sprint speed for last year (29.1 ft./sec) ranked him in the top 20 overall, in addition to being ranked in the top 10 among Center-Fielders as well.

Based on the aforementioned factors, Cain’s 2018 updated projections represent an increase in both OPS and ISO from last season. He should be able to get on-base at an above-average rate (0.358 OBP) in addition to an increase in SLG from last year. Although his wOBA is projected to regress marginally, Cain’s updated projections still aim toward a 3.7 sWAR (as a CF) for this upcoming season. Please find Cain’s updated 2018 projections in the chart below.

2018 Projections: Lorenzo Cain

YEAR AGE sWAR wOBA OBP SLG OPS ISO AVG K% BB%
2015 29 5.5 0.360 0.361 0.477 0.838 0.170 0.307 16.2% 6.1%
2016 30 2.7 0.322 0.339 0.408 0.747 0.121 0.287 19.4% 7.1%
2017 31 4.1 0.347 0.363 0.440 0.803 0.140 0.300 15.5% 8.4%
2018 32 3.7 0.336 0.358 0.457 0.815 0.157 0.300 16.9%

7.4%

———————————————————————————————————————————————————Disclaimer: SEG projections are computer-based projections of performance based on the “SEG Projections System” framework. Regarding Wins Above Replacement (WAR), sWAR is the “SEG Projection System” calculation of WAR. sWAR will always stand for WINS ABOVE REPLACEMENT (“WAR”), unless noted otherwise.

 


Michael Conforto Had a Unique 2017

A few days ago, I decided to start my 2018 Fantasy Baseball List. My process this year is about categorizing all players into groups that defines each player’s positive and negative traits, all based on league average stats. In this process, while linking players with similar traits, I found something interesting about Michael Conforto’s 2017 season.

So, without further ado, here is the process and the math disclaimer:

First, I took all the players who had at least 900 PA between 2015-2017 (Averaging 300 PA per season), and while I evaluated a lot of the stats, the one who I’m talking about right now is Hard Contact %. Then, I found out the league average Hard Contact % of those three seasons altogether. After that, I took all the players who were league average and ran another average to know who were the ones on top. (I wanted to make this benchmarking process as easy as possible). I used the trait ”Ball Murderer” on those players.

After that, I evaluated a lot of other stats, but the focus right now is on FB%, LD%, GB% and wOBA. I took all players with at least 200 PA on both 2016 and 2017, and found out the players who made the biggest changes in each batted ball-type, using a similar process of average as before. For those players, I decided to use the trait ”Substantial Line Drive Increase”, ”Substantial Fly Ball Increase”, ”Substantial Ground Ball Increase” and ”Substantial wOBA Increase”. The same is true for decreasing values.

Then, I decided to see which players had a sustainable ”Substantial wOBA Increase”. What I wanted was to link every positive baseball process that I know that could derive into an increase of wOBA.

So, between all those links, it came the moment to answer a pretty easy question, which was: ‘‘Which players who hit the ball relatively hard on the last three seasons have made a batted ball type adjustment in order to increase their wOBA production”?

With this question, I thought I would get a narrow list of 5 – 10 players whose wOBA Increase would be backed up by this adjustment. To my surprise, I only found one name on it: Michael Conforto.

Conforto had a great season last year. He upgraded his BB%, upgraded his batting line to .279/.384/.555 and had a great .392 wOBA. Also, he became less pull happy and slightly upgraded his Hard Contact %. What most people could point out as a step back was that he lifted the ball less than last year.

While this might be true, Conforto showed an impressive upgrade on his LD%, which is a major factor behind his production upgrade. Going back to the name of this post, he was the only player who has hit the ball hard for the last three seasons (which I call ”Proven Hard Contact%), to upgrade substantially his LD% and wOBA. Another interesting aspect of this analysis is that no player who was tagged with the ”Ball Murderer” trait showed a substantial increase on his FB% and wOBA.

Conforto had a breakout season last year. He was characterized for lifting the ball in his career, and he lifted it less last year. But his great increase on LD% indicates that his results of 2017 were valid, and that he is a great and unique choice for your 2018 fantasy baseball team.