In the 2018 Hall of Fame balloting, Omar Vizquel received 37% of the vote in his first year on the ballot.  This implies strong voter support, and a high likelihood of being inducted into the Hall in the coming years.  The problem, as has been noted by many writers including Craig Edwards here at Fangraphs, is that Omar Vizquel was not a good offensive player.  Edwards compares Vizquel to other below-average offensive producers already inducted into the Hall and concludes:

“It seems necessary to point out that Vizquel’s [offensive] deficiency wasn’t a run-of-the-mill weakness. If elected to the Hall of Fame, he might be the worst offensive player there.”

Of course, Vizquel is not getting support for the Hall of Fame based on his offensive reputation.  He’s known as a great defender.   Yet, advance stats seem to indicate in no uncertain terms that the value Vizquel provided with his glove was not nearly enough to make him a Hall of Famer.  According to JAWS, a system developed by Jay Jaffe to evaluate Hall of Fame worthiness, Vizquel is about as strong of a candidate as Hanley Ramirez, Dave Concepcion or Rafael Furcal i.e. he is not particularly worthy and it’s not particularly close.  But those 37% of voters seem pretty insistent.  What are they seeing that the statistics aren’t?

Vizquel was a mediocre offensive player, and that can’t be disputed.  The ability of offensive statistics such as wRC+ and BsR to quantify historical offensive value and adjust for historical context are firmly established.  Defensive statistics, on the other hand, remain controversial.  Since 2003, when granular fielding data became available through Baseball Info Solutions, Baseball-Reference has used Defensive Runs Saved (DRS) in their WAR calculations, and Fangraphs has used Ultimate Zone Rating (UZR), both statistics derived from the BIS data.  I believe that both are good metrics for evaluating defense, but are far from perfect.  Even further from perfect is the statistic used to calculate defensive WAR for both Baseball-Reference and Fangraphs for seasons prior to 2003, Total Zone (TZ), which is calculated using Retrosheet play-by-play data.  There has been criticism of the use of these statistics for historical comparison, including by Bill James, who argues against Andruw Jones‘ defensive-value based case for the Hall by stating that older defensive metrics such as TZ are more conservative in their allotment of value due to the limitations of the data to quantify exceptional performance.  He argues that comparing players evaluated by new metrics to players evaluated by old metrics is comparing apple-to-oranges, that the methodologies are too different, and their accuracy too poorly understood for strong arguments about players to be based off of them.

Vizquel was 36 when UZR and DRS 2003, and as such his prime years are all being evaluated by TZ.  Here are the defensive runs valuations across his career, per Fangraphs, bucketed into ranges of years where the statistics are stable:

So the metrics here are telling us that early in his career, Vizquel was a top-of-the-league defender, then dipped to a slightly above average defender for this late-20’s early 30’s.  Then he pops back up to great for his late 30’s when UZR kicks in, and dips back to slightly above average for his 40’s.  This is odd, especially with how Vizquel falls off a cliff in his late 20’s, then returns to form in his late 30’s.  Important to note is that that over half of the defensive runs accumulated in the 2002-2007 interval are credit of a 23-run 2007 season, his best single-season total of his career.  Did Omar Vizquel have far-and-away his best defensive season as a 40-year-old on the Giants?  Maybe.  Things happen.  But probably not, right?  Was Omar Vizquel a much better defensive infielder in his late-30s than in his late 20’s?  Maybe.  It’s possible.  But that doesn’t really make sense, does it?

I’m not showing this to discredit defensive statistics.  I’m just trying to illustrate that there’s a wide margin of error that we’re dealing with here, and the further complication of a change in metrics half way through Vizquel’s career.  Is it possible that Omar Vizquel’s Hall of Fame case is being lost in all that?  Let’s see.  Let’s say we don’t trust Vizquel’s defensive metrics at all.  Let’s say that all we trust are the distributions of valuations defensive metrics assign to each year’s pool of players.  Let’s give Omar Vizquel as many defensive runs as he needs to be a Hall of Famer, and then let’s look at what that implies about how good he would have had to have been, relative to the league.  For instance, if Vizquel with his added value now has the career defensive numbers of Mark Belanger, and you want to argue that he was actually as good as Mark Belanger defensively, then you can also argue Vizquel is Hall-worthy.

For this exercise, I’m going to define Hall of Fame worthiness as the average JAWS of Hall of Fame shortstops, 54.8.  JAWS is calculated by averaging a player’s career WAR and best 7 seasons worth of WAR.  I needed to get Vizquel’s 34.2 JAWS up to 54.8 by adding only fielding runs.  To accomplish this, I threw away Vizquel’s metrics and assumed that he produced fielding runs at a constant per-inning rate throughout his career.  I then took into account aging by adding a linear 3% decrease in this rate starting at age 33.  Then, using the values of his other WAR components provided in his Value table on Fangraphs, I was able to calculate his career and peak WAR for different per-inning fielding runs rates.  To be clear, I kept all of his career values estimated by Fangraphs the same, including his positional adjustment.  I have him playing the exact same number of innings that he did in real life.  The only thing changing here is the rate at which he produced fielding runs.  The rate that got him to 55 JAWS turned out to be 0.019 Fielding Runs/Inning.  Here’s what that looks like in terms of WAR:

Did I just give Omar Vizquel 29.3 more career WAR?  Yes, it appears so.  Here is what my “proposed”, hypothetical Vizquel fielding runs totals look like compared to his actual runs.

That seems like a whole lot of extra fielding runs, doesn’t it?  An unrealistically high amount, perhaps?  Well, let’s see.  Below, I plotted the proposed and actual defensive runs (with the positional adjustment added) on top of violin plots of the distribution of defensive runs for all players in the league each year.  The proposed Vizquel seasons are red triangles, while the actual Vizquel seasons are the blue squares.

What we’re seeing here is that for my proposed Vizquel defensive seasons, he would be or near the top of the league nearly ever year for about 20 straight years, apart from two seasons where his playing time was down due to injury.  So, it looks like Vizquel needs to have been pretty damn good at defense to be Hall-worthy.  Here is where he would rank among the league each year with my proposed defensive runs totals, along with where he actually ranked, and the proposed and actual runs totals.

My proposed Vizquel seasons puts him as a top-10 defender in the league 17 times, and at number one four times.  That’s a lot of times!  One might say way too many to realistically expect!  Hmmm…  Now let’s look at how my proposed Vizquel’s career defensive value stacks up against all post-War non-catchers.   This table was taken from the Craig Edwards piece cited at the start of my article by the way.

Yowza! That’s a lot of runs!

If the conclusion of this analysis isn’t obvious by now, here it is:  To make Omar Vizquel a Hall of Famer by boosting his fielding numbers, you have to make him really, REALLY good at defense.  Like capitalized, bolded, italicized REALLY good.  Twice as good as the metrics say.  182 runs better than Ozzie Smith.  You have to believe that he performed as a top-10 defender in the league from age 22 to age 40.  You’re saying he was peak-Andrelton Simmons for nearly two decades.  To argue Vizquel is worthy of the Hall of Fame, given his offensive value is what it is, you’ll have to argue that he was, by a considerable margin, the greatest defender of all time.

There are ways I could have made these proposed numbers a little more plausible.  I could’ve concentrated Vizquel’s defensive value more into his seven peak seasons, which would’ve meant he needed less career WAR to achieve the same JAWS score, but that would’ve made the value of those peak years absolutely absurd.  I could’ve lowered the bar, just trying to get him to, say, one standard deviation below the mean Hall of Fame shortstop JAWS score.  But that puts his value in the territory of the Joe Tinkers, Hughie Jenningses and Dave Bancrofts of the world, who’s own inclusion in the hall is questionable.  And I can’t see how doing any of these things would even get my proposed values down near Ozzie Smith. Ozzie Smith! Y’know, like,the greatest defensive shortstop of all time?

If you want to make the argument that Omar Vizquel is underrated by fielding metrics, that could very well be the case.  He was a great player who played on some phenomenal teams, and it’s plausible the metrics aren’t getting his fielding numbers quite right.  But just bumping up Vizquel a few runs here and there still isn’t going to get him anywhere near the Hall of Fame.  The bottom line is that a player who runs a 83 wRC+ over 24 years in the majors has an enormous amount of ground to make up with his defense if he is going to be Hall-worthy.

If you want to make the case that he is a Hall of Famer based on his fielding, as 37% of Hall voters seem to have, you are also going to have to inflate the value of his fielding to the point of absurdity.  It’s important to note just how good you’re implying he was.

When making evaluations on player ability in terms of their quantifiable actions, there comes a point when you have to take into consideration sample size to determine the validity of the numbers you’re seeing.

Take a batter who comes up 100 times and gets 27 hits. That’s a .270 batting average. Not bad. Another batter comes up 1000 times and gets 270 hits for the same .270 average. So, are both hitters the same? On the surface, yes. However, can you expect the hitter who came up 100 times to continue to hit .270? Is that a reliable amount of at-bats to make an inference? Can we assume the batter with 1000 at-bats is more likely to continue to hit around .270 going forward? I believe we’d all agree, since this is pretty basic-level statistics, that the higher at-bats, the more reliable the batting average.

Statcast has a new-ish measurement of balls hit on the barrel of the bat, or ‘barrels’. This is useful because now we can see how well batters are squaring up on pitches.

Let’s say you have two different batters. One that bloops singles off end of the bat or sneaks grounders past the infield may have a similar batting average as a guy who regularly rips hits into the outfield. So how would you judge the better hitter? They both (with exceptions) produce the same result. Would you go with the guy who regularly squares up on pitches; a hitter that is likely to produce more ‘effective’ hits? Or a batter who tends to hit the ball off the end of the bat, in on the hands, etc. who tends to produce weak contact that could result in groundouts, pop-ups, etc?

If you have to pick one to pinch hit, who would you rather have walking to the plate?

Before I roll up my sleeves, glance below at the type of contact MLB hitters have been producing on average the past three years.

What I’m going to do is determine if three years of data is enough to make an inference on what we can reasonably expect an average hitter to produce in terms of barrels per contact; have we reached a point where the three-year sample size is reliable to make inferences going forward?

First, I looked at the collection of batted ball events since 2015. Each year had roughly 900 hitters with at least one batted ball event. All together it accumulated a total ‘population’ of about 2700 hitters. I decided it would be easier and more educative to try and break it down year by year.

Using the 900-something batters per year, I wanted to develop a sample size from that group with a confidence interval no higher than five. Using the entire three-year ‘population’ of hitters would show results all over the board; the data became very volatile as the batted ball events decreased.

By taking no less than 100 occurrences of contact, it’s more reasonable to scale. The average batted ball event (BBE) per qualified hitter (with at least one event) is roughly 40% of the overall average of 253 events per hitter. This is closer to the overall ratio of hitters that had several dozen BBEs instead of batters with a few events, which produced large fluctuations.

You could ask “Why didn’t you take ALL the data and average it out?” Well, I could have. The problem I had was the variation is incredibly high; too many of the 2700+ had a very small amount of events (and barrel rate) which cannot lend itself to fidelity. On a scatter plot, it tells us almost nothing.

Instead, I cut the ‘population’ down and required at least 100 BBEs. That gave me a total of 1170 players, or a little more than half of the entire 2015-2017  hotter population.

This is the scatter plot, based upon BBEs (Y-axis, horizontal) and total barreled hits (X-axis, vertical) that was produced using that criteria.

In the above chart, the coefficient of determination (or, r2) equaled 0.161; not a great, but certainly not menial, expectation of correlation between BBE and total barrels.

In layman’s terms, the more events you produce, the higher the expectation of having more barrels becomes. You could have made that inference without the chart, however, I was curious to see if the increase was as sharp as I expected it to be (it wasn’t).

So I wanted a more reliable correlation, as it is logical to assume that the more you do something, the higher the amount of times you achieve your goal.

I took all of those BBEs and compared them to the percentage of barrels (X-axis) to BBEs (Y-axis). I feel that ratio produces a much more accurate relationship.

This time, the r2 equaled a much more stable 0.006 with several outliers present. The further you look down from those outliers, the more concentrated the chart. For the most part, roughly 80% of the plot points are 10% or below. The amount of hitters above that 10% mark would be baseball’s elite power hitters.

It appears we may have concrete proof of normalization.

So, for now, we can assume that your average batter can expect to have maybe 5%-7% barrels per contact; slightly more as your contact events increase.

But, let’s break it down a bit so we can say with certainty that this ratio is dependable for hitters going forward. I wanted to keep the sample size the same throughout the three years of collected Statcast data; 66%, or 395 batters.

We’ll start with 2015.

Below I took the total population of 915 batters in 2015 and used a confidence interval of 4.89 to get the sample size of 395. And, as with all subsequent charts, I worked with a 99% confidence level.

-With all remaining charts, the X-axis is the percent of BBEs to barrels and the Y-axis is the BBEs.

For 2015, the coefficient of determination is 0.032 with maybe nine outliers. There is a minor amount of regression but mostly a stable trend line. And, we see the line staying within a 7%-9% ratio of barrels to BBEs.

Here is 2016’s data; a population of 909 hitters with a 5.00 confidence interval.

Now, even with a similar r2 as 2015 (0.039) we are starting to get larger variation and a few more outliers. Yet the trend line again regresses, this time at a slightly sharper scale.

For 2017, 905 total hitters and a confidence interval of 4.88.

2017 comes across as a mess of variation with dozens of outliers. The trend line produced an r2 of 0.007. And, in contrast to the previous years, there wasn’t a regressive trend as BBEs became more frequent; it actually shows a slight increase.

What does that mean? No idea. Could it be, now we have this information available, that hitting coaches are working with batters to improve their contact? Shot in the dark but I can’t come up with a better inference.

Now, lets use each year sample size combined (1175), use a confidence interval of 4.9 (average CI of the three years of study) to come up with a sample size of 66%, or 552 batters.

Now we have a very stable (with a negligible increase) trend, 0.003 coefficient of determination, with some variation and exceptions at a rate of 10%.

Most of those outliers from the graphs are represented in the following chart. And, of those aberrations, several appear in all three groups.

So, the question is whether or not the available Statcast data on barrels is considered stabilized after three years; can we reliably scale a batter’s barrel rate? Do we have a reliable sample size for hitters?

It looks as though we do.

After three years, the overall trend line(s) appear to be somewhat stable in the 5-8% window for an average batter; we can expect most hitters to be at or below 10% barrels per batted ball event.

Last Tuesday, Baseball Prospectus rolled out three new metrics for evaluating pitcher performance – Power (PWR), Command (CMD) and Stamina (STM). I was particularly drawn to the PWR metric, which is described as a way of evaluating how much a pitcher fits into the “power pitcher” archetype. It’s an intriguing and novel approach to evaluating and classifying pitchers, I think it’s great new lens for looking pitchers. But when I looked a little closer at the 2016 PWR scores, something jumped out at me.

Oh no.

Oh no.

Bartolo Colon is not a power pitcher. Bartolo Colon is the exact opposite of a power pitcher. Bartolo’s peak fastball velocity by Baseball Prospectus’s metrics was 72nd out of 84 pitchers with 150 IP in 2016. Bartolo does not blow anyone away with his 90 MPH fastball, he relies on pinpoint placement to generate whiffs and mixes in offspeed stuff to generate weak contact (indeed, Colon’s 2016 ranked 7th in CMD).

PWR is still an effective measurement: look at all of the other pitchers it (correctly) classifies as power pitchers. But there does not exist an interpretation of the phrase where one could think of Colon’s 2016 as emblematic of a power pitcher. So what gives? Can PWR be adjusted to relieve it of its Bartolo Colon problem?

Like a computer program, if I want to debug this, I have to know how PWR works. Fortunately, BP tells us how PWR is calculated in a fairly straightforward manner:

As of right now, our Power Score is comprised of these three identifiable parts: Fastball velocity (three parts), fastball percentage (two parts), and the velocity of all offspeed pitches (one part). There are some other factors that we considered when developing this metric—such as the tendency to work up in the zone, and to lean on fastballs in put-away counts—but the current version of this metric only includes the three main components discussed above.

While I don’t have access to BP’s exact numbers used for calculating the PWR, I rigged up a rough approximation using the PITCHf/x numbers available on FanGraphs by normalizing each of the above components and weighing them as described above. I plotted my values (xPWR) against BP’s (PWR) and they look reasonable, so I’ll try to use xPWR to mess around and see if I can resolve PWR’s Bartolo Colon issue while maintaining their current level of accuracy for evaluating actual power pitchers.

2016 Colon has a xPWR score of 54, not 59, and he’s only 20th in xPWR, which doesn’t seem so bad until you realize that Colon’s xPWR puts him squarely between Jose Fernandez and Max Scherzer. Colon needs dramatic adjustment, and hopefully, the adjustments I make in terms of xPWR can be translated to PWR as well.

The best way to fix a problem is to address the cause, so why is Colon registering an abnormally high PWR score? The main culprit is likely his Fastball%. Here are the leaders in FB% from 2016:

I know that FB% is worth about one-third of PWR, and Bartolo is in a league of his own when it comes to FB%. Hence, the most likely culprit appears to be Colon’s insane FB%. There have only been two seasons where pitchers threw 2000+ pitches in a season and posted an FB% above 89%, and both belong to Bartolo Colon – 2012 and 2016. Starters (and to a large extent, relievers) do not typically rely upon their fastballs so much, and since Colon is such an outlier, using normalized scores makes him stand out in a big way. The closest any starter came to Colon’s crazy FB% values was Henderson Alvarez in 2014 (82.7%), so Colon receives a (rather unfair) bonus in PWR scores for throwing so many fastballs, one that makes up for his lack of velocity. Colon cheats the PWR metric by throwing pitches that are technically fastballs and are classified as such but aren’t nearly fast as a traditional fastball. The flaw in PWR is that it assumes that any pitch classified as a fastball is, well, fast – but this isn’t the case for Bart, and so he presents an anomaly.

Perhaps I can rectify giving Bart such an advantage by reducing the weight of FB% – if I drop the weight on FB% to one part instead of two, our top pitchers (min 150 IP) by xPWR (v2) look like this:

And here are are our best xPWR scores for relievers (min 40 IP):

Note that I scaled the original values to best match the scale of PWR.

Colon has — rather ignominiously — dropped out of the top ten, with his xPWR (v2) falling all the way to 45 the same as John Lackey and Jake Odorizzi. The leaders in xPWR (v2) all fit the profile of a power pitcher — hard throwers, fast offspeed stuff, rely heavily on the fastball — and Colon can’t cheat the metric as much. But at the same time, we’re still committing the same mistake as the originally PWR metric in assuming that fastballs are thrown hard, just to a lesser degree. Maybe we should revamp our approach to the PWR metric.

Perhaps we can simply use average pitch speed across all pitches. This approach rewards pitchers for simply throwing hard and doing so frequently. If I use total average pitch velocity and normalize those values to fit with PWR, Bart’s exploit of FB% can’t work. At the same time, taking a straight average of pitch velocity and normalizing it incorporates all of the tenets of PWR (fastball velocity, FB%, and offspeed velocity), so we’re staying true to the spirit of the original metric. Let’s use this approach for xPWR (v3).

Here are the leaders for 2016 in xPWR (v3) among pitchers with 150+ IP…

… and relievers with 40+ IP.

And what of our good friend Bartolo? Colon’s xPWR (v3) score falls around 47, the same range as Kyle Gibson and Felix Hernandez.

This third method gives us a lot less range in terms of scores, so it’s more difficult to differentiate between players – but at the same time, it does just as good of a job of identifying pitchers who fall into the power-pitcher archetype while leaving out those who are not.

Is PWR “broken” in its current state? Of course not. Almost every metric has a few players who can cheat it one way or another. Colon happens to be extremely good at cheating the PWR metric. With a couple changes, however, BP might be able to keep Colon from breaking into the top ten with a ridiculous PWR score while maintaining the integrity of the metric as a method of evaluating how well pitchers fit into the PWR archetype.

There is no need to emphasize how bizarre this off-season has been. By this time last year, the best available free agents were Matt Wieters and Jason Hammel. This year, there are enough available free agents to create an all star team. With that in mind, I began to wonder if a team could actually be competitive by signing 25 free agents. A super-team of current free agents would undoubtedly contend this year. However, it would also require a payroll in the range of $300MM. If such a team had to stay within the luxury tax threshold, it would need to make significant cuts.

To satisfy my curiosity, I made a spreadsheet of WAR and salary projections for all of the remaining free agents. I attempted to construct the best teams possible within a variety of budgets, and compared my projected WAR totals to teams with similar payrolls. Constructing a great team was more challenging than I expected. I encourage readers to give it a try.

Note: Most contract values are based on a combination of reported offers, the MLBTR free agent predictions, and recent signings. It is likely that many of these players will sign deals that are far off my projections.

Download the Team Builder:

Click Here or the link below to download the team builder spreadsheet. The file should be titled “Free Agent Team Builder 2018”. I suggest using Excel, I haven’t tested it on other programs.

https://www.dropbox.com/s/aii5ewmhabpna8q/Free%20Agent%20Team%20Builder%20February%202018.xlsx?dl=0

Create your own free agent super-team, or see if you can build a competitive roster on a budget. Feel free to comment or share your team, and see how your team stacks up against mine.

Here are two examples of teams I created:

Small Market Team ($90MM Payroll)

Big Market Team ($197MM Payroll)

My Analysis: 

Based purely on WAR projections, my small market team would be the worst team in baseball. It’s worth noting that this spreadsheet has some flaws. Projected WAR would increase with a full 40 man roster, but so would payroll obligations. With that in mind, this team would still doubtfully have a winning record. There is some potential upside throughout the roster, but my lineup is heavily reliant on veteran players returning to old form. The bullpen is probably the biggest weakness, but spending my budget on relief pitching would have been a tough decision to make.

The big market team is much more promising. However, my projected WAR would still rank them among the bottom tier of teams. With some added depth, I think this team would have a winning record, but contending for a World Series would be a bit of a reach. With such a high payroll, this team would likely also rank among the teams getting the lowest amount of value for each player.

In a way, I believe this project exemplifies one reason why the free agent market has been so stagnant. While these teams are respectable, they would project poorly compared to others. I knew beforehand it would be tough to build a team paying 25 players for past performance, but attempting to put these teams together helped me further appreciate the value of homegrown talent.

Now it’s your turn to build a team, download the spreadsheet and give it a shot! If you have any thoughts or anything to add, please feel free to comment below.

Cost-controlled, 4-WAR players have the ability to revamp a farms system. The Brewers confirmed that notion by paying a hefty price to nab a piece that pushes the National League Central into a clear three-team race.

Reacting to trades can be redundant, especially after nearly a week for shock and awe to simmer down. Instead of reaction, I choose to consider how Yelich’s environment might affect his swing.

I’ve seen a lot of buzz, on the fantasy side of the industry and elsewhere, about how much this change from Miami to Milwaukee helps Yelich’s value. If we crudely compare the 2017 Marlins and Brewers, there isn’t much of a difference on the offensive side of the baseball. The Marlins actually outscored, outwalked, and outhit the Brewers, with the nine-win difference between the two teams attributed largely to the difference in pitching.

Yelich also hit between Giancarlo Stanton and Marcell Ozuna for the majority of 2017, the early movers out of the Marlins’ new regime. The lefty will now hit between some mixture of Lorenzo Cain, Eric Thames, and Ryan Braun – a clear downgrade.

The key element of any argument for Yelich’s performance and resulting value increasing is rooted in the change of scenery – literally.

What we do know is that Miller Park in Milwaukee is a substantially for home runs off the bat of left-handed hitters.

What we don’t know is by exactly by how much.

Varying methods exist for calculating park factors. Guessing Yelich’s new level of production becomes slightly difficult to peg the further into this rabbit hole one digs. I used Stat Corner’s methodology along with Baseball Prospectus to find a balance between what seemed to be aggressive and conservative ratings between Miller and Marlins Park. Where the two disagree is on how much, regarding home runs, Miller Park inflates the longball; it’s clear they both see Marlins Park as below average in everything (even home run sculptures).

To stop your eyes from glazing over as show off my skills in google sheets, focus on the two boxes highlighted yellow. Here exists the greatest discrepancy between park factors from 2016 to 2017.

2016 was a robust year for left-handed home runs in Milwaukee, but such inflation fell off in 2017; Baseball Prospectus believes more than Stat Corner. This is likely due to a difference in methodology – a topic for another day.

As any good arbitrator would, I want to split the difference, and naively predict the park factor for Miller to fall somewhere in the middle of 112 and 132. If we assume Marlins Park stays consistent on its park factor for 2018, we’d expect a 30 percent increase in home run totals (92 HR factor for MIA to 120 for MIL) for a left-handed hitter going from Marlins Park to Miller Park.

As a player sees half their games at home, in a vacuum, a 30-home-run bat with even home-road splits would see roughly 4.5 more home runs in his home games. A 20-home-run hitter would see an uptick of three home runs. Factor in everything else a park could help or hurt with and I’m confident saying, yes, this change will impact Yelich’s statistics. Thankfully, the difference between Marlins Park and Miller Park isn’t immaterial, meaning my crude math and assumptions can largely be forgiven in favor of a general consensus.

***

Giving Yelich 21 home runs for 2018, roughly three more from his 2017 total seems reasonable. The question is if you think Yelich’s 2017 is the more representative body of work than his 2016, where he hit 24 home runs with a home run to fly ball rate above 24 percent.

Favoring Yelich’s impressive 2016 and providing an aggressive home run prediction could tie to a few factors.

• Miller Park inflates Yelich’s home run total more than we think (and more than my crude numbers say)
• Yelich is entering a prime window for power according to aging curves
• Yelich changes his swing

The last of my trio above is the most interesting, given how beautiful and fluid his swing currently is.

This is where statistics and scouting clash.

I asked two of my most trusted baseball information resources (Kevin Black- @Kevin_Black_ and Richard Birfer – @RichardBirfs) what they’d do with Yelich given the knowledge that Milwaukee is a substantially better park for left-handed power. They both differed their response, mentioning how little should change for Yelich given the success with his current approach.

I probably agree, but speculating on something that might be far from Yelich and his hitting coach’s mind is more entertaining than agreeing with my reputable contacts.

Yelich’s batted-ball profile isn’t something often tied to praise. He sits near the bottom of the league in pull percentage (33%) and average launch angle (only 5.6 degrees in 2017). You might convulse at the thought of batted balls below a 7-degree launch angle, but there is misconception around that as well. Andrew Perpetua mentions how balls hit between 0-10 degrees are often hard to achieve because of how perfectly lined up the barrel of one’s bat has to be with the ball to result in this angle. As a result, balls in this window are very productive, resulting in a batting average of .472 and slugging percentage of .522 in just under 50,000 batted balls.

Sure, some of the balls he lifts to right field will have a better chance to carry out, but it’s even less convincing to suggest drastic change if Yelich sprays low line drives across the field successfully.

Yelich is an extremely productive, unique hitter, but his profile doesn’t “fit” with the kind of production that benefits substantially from life in Miller.

A wiseman once said, don’t break what isn’t broken. But as I remember the band Meat Loaf saying, “If it ain’t broke, break it.” In layman’s terms, if a more productive alternative exists… why not?

When I started mulling over what to do with Yelich in order to embrace Miller Park, a swing comparison came to mind: Alex Gordon. The Royal put up career spray and batted ball data that hangs right around league average, with a slight tendency for fly balls.

You’ll notice their swings are pretty similar. Yelich starts his hands further back and goes into a higher leg kick, but both these balls looks like they’re hit to left-center, with the inside-out approach Yelich uses to push his opposite field hit percentage near 30, five percent above league average.

Compared to Yelich, Gordon is willing to open up on pitches. Veering from Yelich’s inside-the-ball approach, Gordon generated a lot of his home run power to “true” right field. Yelich’s home run spray chart shows us that “true” right field pull power is something the former Marlin has turned to only sparingly from 2016 to 2017. Gordon’s spray chart across his most productive three years shows power that skews itself heavily to right field; a noticeable difference from Yelich even as these two hitters are a fair comparison mechanically.

The issue? I believe Yelich has more power than Gordon. Going to a slightly pull-happy approach for Yelich and mimicking Gordon deviates too much from his current approach. Balance, if we are to entertain breaking Yelich’s current poise, is key.

So how about Joey Votto? He’s a player with a somewhat-similar swing (as we’ll see in a second) and his career batted ball distribution is nearly even to all fields, with a fly ball rate lower than Gordon’s, but higher than Yelich’s. Here is that same clip of Yelich next to a younger Votto (2015).

The biggest difference I notice – aside from hand placement – is how centered Votto’s weight stays from his stride to front-foot plant. Yelich is comparable, but you’ll notice how much more Yelich uses his lower half to generate momentum towards the ball. This isn’t a fault of Yelich. It’s actually just me praising Joey Votto.

While I’d love for Yelich to one day possess the power Votto does and sit back so well, it’s tough to expect that kind of change. What I’d love to see Yelich do in Milwaukee is take the lift aspect of Votto’s game and embrace it, even with the knowledge of how productive the 0-10 degree launch angle window can be. I don’t want to see Yelich open up as much as Gordon and I can’t expect him to evolve into Votto’s power profile. So the balance would be to keep the same all-fields approach, but make a conscious effort to tweak and embrace a slight uptick in fly balls. Votto is able to do so with a fantastic line drive rate. Instead of taking the Yonder Alonso approach and shooting for the moon, a marginal tweak to “unlevel” Yelich’s swing, similar to the rotational path Votto possesses, could be extremely beneficial.

If no change occurs in Yelich’s batted ball profile come 2018, while I still love his move to Milwaukee for various other reasons (he is happy and has the incentive to win), I wouldn’t expect a noticeable inflation of statistics simply because of Miller Park.

And at then end of the day, we all need to be more like Votto.

A version of this post can be found on BigThreeSports.com.

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.

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.

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.

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.

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.

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:

In 2017, 161 with a 7.8% ratio:

And how about Cabrera’s exit velocity?

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

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.

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 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

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

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.

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.

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.

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.