Archive for Player Analysis

Mookie Betts went to an arbitration hearing. Marcus Stroman went to an arbitration hearing. George Springer and Jonathan Schoop did not. Other than the obvious differences between these players, there are others— related to the arbitration process itself— that may have affected these outcomes. Particularly, the differences and qualities of their filings.

To those unfamiliar with the arbitration process, eligible players and teams who are unable to come to a settlement ahead of the given deadline, submit salary filings which reflect either party’s evaluation of the player’s worth. Even after filing, teams and players are able to negotiate a one-year contract, but in some cases, a panel of arbitrators will decide a salary: either the player’s bid or the team’s bid, but not any number in between. This “final-offer arbitration” system is designed to create compromise and negotiation between bargaining parties as the threat of losing a large amount of money increases the incentive to settle early while a midpoint is still available. By extension, teams and players are encouraged to moderate their bids as an outlandish one is surely to be challenged and lost.

But, two different theories exist as to how the difference in bids itself affects the likelihood of hearing. Some argue that higher differences between teams and players in valuation would increase the likelihood of an arbitration hearing as the difference in bids reflects differences in valuation. However, others— namely Carell and Manchise in Negotiator Magazine (2013)— argue that differences in bids increase the risk of heading to a hearing and incentivize teams and players to hammer out a settlement.

Using two separate probability models and data on all players that filed for arbitration between 2011 and 2017, I examined the likelihood that a player goes to an arbitration hearing based on the differences in bids between the player and the team. The models both control for the player performance— by incorporating the effect of WAR— and utilize a dummy-variable for Super-Two status— controlling for the effect of players granted a “bonus year” of arbitration eligibility. The only difference between the two models is the variable of interest. The first uses the ratio of the absolute bid differences to the midpoint between the two salaries in order to measure the effect of a growing gap between filings relative to the actual size of the filings. The latter model separates the two effects to understand whether absolute gaps and absolute filing size have an effect on arbitration hearings. The model specifications and regression results are shown below. The table below essentially shows the marginal effect on likelihood to go to hearing due to a 1 unit change in the corresponding variable.

Model 1:

Model 2:

Results:

Both models demonstrate highly significant coefficients indicating that players with large gaps in salary filings are less likely to enter hearings. In fact, in the aggregate sample of players an increase of $100,000 in bid differences reduces the likelihood of a hearing by 2.7% and a 1% increase in Bid Difference to Midpoint Ratio decreases the likelihood of a hearing by 1.1%. This stands as an incredibly significant effect considering only 16.73% of players in the sample even made it to a hearing. Quite evidently, teams and players are incredibly risk-averse and fear losing the arbitration hearing and being forced to agree to a suboptimal salary. Thereby, the incentive to settle is driven up by higher bid differences.

Another interesting result shows that in all samples, an increase in filing midpoint by $100,000 increases hearing likelihood by 0.56%. As such, all else equal, players with higher filing midpoints are more likely to head to a hearing. The intuition behind this is best explained considering this with the negative coefficient on WAR, as both WAR as Midpoint are highly related but have opposite and significant signs. While WAR indicates that better players are less likely to head to a hearing, the positive coefficient on Midpoint states that “better” players are more likely to head to a hearing.

Though these indicate opposite effects, considering the effect of a high midpoint with WAR constant and vice-versa, the theory provides explanatory qualities. A more aggressive salary bid— given an exogenous and fixed level of production— is easier to dispute for a low-value player than a high-value player. Thus, independent of the player’s production level, a higher Midpoint leads to a higher likelihood to enter an arbitration hearing. As such, the positive coefficient on Midpoint likely reflects bad players bargaining for extra money rather than good players— whose effects on hearing likelihood are captured by the WAR coefficient. Considering the WAR coefficient independent of the filing midpoint as well, teams are more likely to focus their negotiation efforts on their better players, thereby reducing the likelihood high WAR players end up in hearing.

The final variable of interest in these regressions is the dummy-control for Super-Two status. As mentioned earlier, Super-Twos represent young players with substantial playing times who are rewarded with an extra year of arbitration eligibility. The models predict that Super-Two status increases the likelihood of hearings by 14.3%-16.9% depending on the model. As such, these young players seem more likely to challenge their teams in salary evaluations. This too comes as no surprise since challenging a team in your first (and bonus) year of arbitration eligibility can lead to significant level effects in subsequent arbitration hearings. A salary increase from the league minimum to $545,000 to even $1M can snowball into much larger raises in the following years with an arbitration victory. As such, these players may have a higher incentive to enter hearings and capture these multiplicative effects.

Now, revisiting the four cases above— Betts, Stroman, Springer, and Schoop— some interesting cases do pop out. Betts may not have been the most likely candidate to head to an arbitration hearing, the $3M difference between Betts and the Red Sox was incredibly high and reflected an enormous risk for either party entering a hearing. The predicted path for Betts was likely closer to George Springer’s contract extension or Jonathan Schoop’s 1-year deal. By contract, Stroman may represent the classic arbitration case, a low-risk hearing for either party, bargaining over a small fraction of their bids. And while Stroman expressed his frustration— or lack thereof— following the hearing, history shows that the Stromans of the world will likely end up there again. Ultimately, the final offer arbitration system does its job: those who disagree significantly tend to work toward compromise, while those who disagree a little take a change and roll the dice.

Eric Hosmer is one of those guys you either love or hate. His career, which includes one World Series championship and two American League pennants, has been just as polarizing.

First, who Hosmer is. Consider his WAR each season since 2011:
1.0
-1.7
3.2
0.0
3.5
-0.1
4.1

Interesting pattern; let’s look into that. The chart below is Hosmer’s career plate discipline (bolded data are positive WAR seasons).

Nothing appears to be out of sorts, no obvious clues to suggest a divergent plate approach.

Moving on, I noticed his BB/K rate did relate to his productive seasons; that alone can’t possibly explain his offensive oscillations. While his strikeout and walk rates did vary, the differences were a matter of two or three percentage points, at best.

So, I decided to look at his batted ball contact trends and found that his line drive rate directly correlated with his higher WAR seasons; 22%, 24%, 22% in 2013, 2015, and 2017 respectively with 19%, 17%, 17% in 2012, 2014, and 2016 accordingly.

OK, so his launch angle must be skewed. But, like his plate discipline, no outliers were demonstrated; his 2017 season should be easy to pick out. The below animation is a glance at Hosmer’s three-year launch angle charts, in chronological order.

How about his defense? Well, something seems off about that, too.

He’s won a Gold Glove at first base four out of the last five years. He looks great on the field, but unfortunately, his defense reflects the same way as a skinny mirror; his UZR/150 sits at -4.1 and his defensive runs saved are -21. Since 2013 (the first year he won the award) he ranks 13th in DRS and 12th in UZR/150 out of all qualifying first basemen. So, middle of the pack basically but worth four gold gloves? Probably not.

As we could have surmised, he’s simply an inconsistent player. Falling to one side of the fence yet?

One thing is a certainty; his best season was, oddly enough, his walk year with the Kansas City Royals in 2017. Now, I’m not about to speculate that Hosmer played up his last year with the Royals to get a payday (which he most certainly got). Looking back at his WAR in the first part of the article, you can see his seasonal fluctuations suggest he was due for a good year.

Keeping with the wavering support of Hosmer, is the contract he acquired to play first base with the San Diego Padres. His eight-year deal (with an opt-out in year five), will net him $21 million each season. He will draw 25.8% of team payroll. When his option year arrives in 2022, he’s due for a pay cut of $13 million in the final three years.

A soundly contructeed contract as, according to Sportrac’s evaluation, his market value is set at $20.6 million a year. To note, the best first baseman in baseball, Joey Votto, signed a ten-year deal in 2015 for $225 million dollars (full no-trade clause). Starting in 2018, Votto is slated to make just $4 million more than Hosmer will in the early portion of his deal. Did San Diego overspend? It all depends on what their future plans are for him.

In any case, Hosmer will join a team that, following his arrival, is currently 24th in team payroll. In 2019, they will hop to 23rd. It could go down further upon the arrival of their handful of prospects who look to be the core of the team.

So who will the Padres have going forward? Using wOBA, probably the most encompassing offensive statistic, I decided to forecast what the coming years will look like for Hosmer. It goes without saying that defense is nearly impossible to project. So, for argument’s sake, we’ll continue to assume Hosmer will be an average defender at first.

Since Hosmer’s rookie year in 2011, the league average wOBA is approximately .315. Hosmer should stay above that through the majority of the contract. But, let’s be more accurate. Using both progressive linear and polynomial trend line data (based on both Hosmer’s past performance and league average wOBA by age), I was able to formulate a projection for Hosmer through age 35 (no, I’m not going to lay out any of my gory math details).

OK, I lied. Here is the equation I used to come to my prediction :

From age 28 on is what we want to look on from. Hosmer is expected to take a dive offensively in 2019 with a bounce-back year in 2020, sticking with his past trends. A year before his opt-out clause (where he’s slated to make $13 million), his wOBA is expected to regress at a stable rate. He’ll continue to be league average or better during the twilight years of his career.

Prognosis

Hosmer seems to be appropriately compensated. You could argue that he’s making too much, but the Padres had the money to give him and they are banking on Hosmer to be highly productive at Petco. But, chances are (according to his history), he won’t maintain (or exceed) his 4.1WAR in 2018. He’ll be labeled as a bust but ought to have a few good years in him during the $21 million salary period. And, as my forecast chart shows, his 2022 pay cut comes at just the right time.

*This posts and more like it can be found over at The Junkball Daily

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.

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.

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).

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

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.

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.

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

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.

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.

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.

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.

It’s been about several weeks since the Pirates parted ways with both Gerrit Cole and Andrew McCutchen, the former to Houston, and the latter to San Francisco. Most fans and analysts expect Josh Harrison to be next, and by the looks of it, that’s what he’d prefer.

Some would consider the Pirates to be rebuilding, while others suggest it may be somewhat of a retooling, hoping that some names that were expected to work out, but suffered setbacks either last year or culminating throughout the last several years (like Marte and Polanco) will bounce back or reach expectations.

That, coupled with players breaking through and reaching their potential (like Bell and Taillon), along with other young players (as though the Pirates have any other type of player now) like Trevor Williams, who showed a lot of promise last year, or Steven Brault, who pitched very well at AAA Indianapolis, perhaps the Pirates can field a winning team. It doesn’t hurt that they inked one of the top relievers in the game, Felipe Rivero, who emerged with a breakout season last year, to a four-year deal.

But most Pirates fans aren’t buying it. There was even a petition started on Change.org for “MLB to force Bob Nutting to sell the Pirates”, and to this date it has reached 59,456 signatures. Of course, there is basically no chance that this petition will actually result in anything.

Before both trades, the Pirates projected win total by FanGraphs was a whopping 81. After the Cole trade, it went to- er- stayed at, 81. It did move, though, once McCutchen was dealt, dropping from 81 to 78, which would still be three wins better than last year’s club, which might cause some to say that technically the team is improving, even if it’s by the most basic metric; of course, most would say that’s nonsense.

Last year, the Pirates were plagued by a multitude of problems, from Marte’s PED suspension, a plethora of injuries to, well, everyone, and even to Taillon missing time due to testicular cancer (which he brilliantly rebounded from, appearing as a starting pitcher just five weeks after surgery). Not to mention Jung-ho Kang’s off-field issues and inability to return to the team. The Bucs suffered a six-game setback from last year’s projections where they were expected to go 81-81. They finished six games below that total, winning 75 contests.

The Pirates were basically destined to fail last year. Now many believe that the Pirates are in store for the same fate this year after departing with two of the franchise’s marquee players.

A lot of the Pirates roster will look strikingly similar to last season, except for those received in the trades, which includes: 3B Colin Moran, P Joe Musgrove, P Kyle Crick, P Michael Feliz, among several other pitchers not involved in either of those trades, Nik Turley, Jack Leathersich, and Jordan Milbrath. It is unlikely that those players will make that big of an impact.

It’s possible, without any major injuries, the contributions the Pirates expected to receive last year will be more likely to reach fruition this year. If Gregory Polanco has the kind of breakout season people felt like he might have when the Pirates first acquired him, it’s possible for him to be a 5.0 WAR player. A litany of injuries prevented him from coming anything close to that last year, registering a 0.5 WAR, but with glimpses of power in his minimal contributions.

The same is true for Starling Marte. In 135 games for the Pirates in 2013, Marte posted a 4.8 WAR and 122 wRC+. We are all aware of Marte’s 80 game suspension following him testing positive for performance-enhancing drugs prior to the 2017 season. When he returned, he failed to be the player the Pirates hoped he’d be, and of course, he’ll have a lot to prove after his suspension in his first full season, but it isn’t completely insane to think he might experience a resurgence.

Josh Bell had a breakout season for the Bucs in his rookie campaign, perhaps positioning himself to be the next face of the franchise. Bell registered a 1.4 WAR last year and 113 wRC+, a .338 wOBA, and an OPS of .800. He hit for significantly more power than was expected of him, blasting 26 cannonballs, which was 12 higher than his 2016 total in AAA Indianapolis, playing nearly every game (159) in 2017. If Bell continued to grow this offseason, it’s entirely possible he’ll repeat in some statistical categories, like home runs wOBA, and OPS, and improve in others, like BABIP (.278), making him a very legitimate threat in the middle of the order.

Joe Musgrove, whom the Pirates acquired from Houston, showed that he may have the stuff for a solid third in the rotation type pitcher. Musgrove appeared in 38 games for Houston last year, starting 10 of them, posting a FIP of 4.38, an ERA- of 113, and an xFIP of 4.03. Those numbers are about in-line for a 5 starter, most likely, but PNC is one of the most “pitcher friendly” parks in baseball. Also, I’m not one to chalk up occurrences to magic, but Ray Searage has worked some serious voodoo in the past, and that could likely be the case here, especially with Musgrove who is by no means a lost cause pitcher to begin with. Additionally, Musgrove throws pretty hard, last year registering his fastball around 93.5 mph, his cutter a tick over 90, and a slider around 92.

Colin Moran will likely see the most time at 3B this season, as David Freese’s production levels just don’t quite reach what they should to warrant starting everyday, especially with a young player like Moran waiting in the wings. Jeff Sullivan wrote an article highlighting Colin Moran’s swing change, and some of the numbers were glaring. During seasons 2013-2016, Moran sat around 50% ground balls, and with the way baseball’s evolved, that’s not really a good thing. But in 2017, that number was strikingly different. Moran hit a ground ball only 34% of the time. With his decrease in ground balls came an increase in home runs. He had a previous high of 10 in AAA with far more at bats than his 18 in 2017 during his AAA campaign.

Lastly, Michael Feliz, another piece from the Astros, comes to Pittsburgh after having posted interesting numbers in 2017. Firstly, Feliz throws hard, reaching the high 90s with his fastball, averaging nearly 97 mph in 2017. He posted a FIP of 3.78, an xFIP of 3.58, and an xFIP- of 81. Feliz will likely be a strong complement to Felipe Rivero out of the pen.

Help will have to also come from players being called up from AAA for the first time (Meadows, who suffered setbacks last year on the DL, Keller, perhaps Bryan Reynolds, among others), but if some things break the right way, the Pirates may experience more success than originally anticipated. Don’t misunderstand me, I’m not saying the Pirates will be in contention for the NL Central this year, or even a Wild Card spot. I’m saying the potential is there for them to rebound from last year and finish the season above .500 at 82-80, especially if they can capitalize on a flailing Reds team, as well as in games against the largely inept NL East.

But barring a major outbreak by a lot of guys, the Pirates will likely be an average to below average team (somewhere along the lines of 75-87 to 79-83). It wouldn’t surprise me for them to finish better than last year, if not just for the sheer manpower versus last year, and hopefully not having to deal with such setbacks.

But when is it most likely the Pirates will be able to actually contend? The front office will say 2019 at the earliest, and there’s some credence to that.

Mitch Keller is projected to make his debut sometime this season, ranked 16th overall, and is the Pirates best prospect. Austin Meadows, ranked 45th, is expected to make his debut this season, as well. The last of the Pirates top 100 prospects, Shane Baz (67th) isn’t expected to make his debut until 2021, and hopefully, the Pirates are competing before then.

From the Pirates own top 30 list, several potentially important players are expected to debut in 2018, including Nick Kingham and Bryan Reynolds (the latter of whom came over in the McCutchen trade). 2019 will see a string of more players, and if they make an impact right away could yield a winning ball club, like Ke’Bryan Hayes and Cole Tucker. If you combine their potential productivity with the progression of guys that are already there and guys that are debuting this season, the Pirates could be returning to a similar place as their winning years, 2013-2015, in as little as two seasons.

Although it should be noted, the Pirates most successful years weren’t necessarily fueled by prospects. When Gerrit Cole debuted in 2013, one of the Pirates most successful seasons, he was really the only major prospect getting to play at that time, while the majority of the roster was comprised of veteran holdovers from the season before.

What that could potentially mean is that perhaps 2019 isn’t necessarily a possibility in terms of being competitive. Perhaps a more realistic timetable is 2021. By that time, Starling Marte will be in the final year of his contract at age 32, and likely his last year as a Pirate, and assuming he’s able to rebound, will be in the latter part of his most productive years. Gregory Polanco, if he’s able to reach his potential, will be in his Age 29 season and possibly at the peak of his ability. Moreover, by 2021, most of the players we’ve discussed will have had time to fully develop, like Josh Bell and Jameson Taillon, plus any guys coming up over the next two seasons.

All of these guys won’t pan out, but there’s a pretty good chance some of them will, and that’s the best an organization and fan base can hope for (except for the Astros who have seemed to hit the jackpot in every regard). The team will also need to be supplemented by veterans, and not just the cheap ones. For the Pirates to make a run and win between 2019-2023, the front office is going to need to spend more money than they were willing with Gerrit Cole and Andrew McCutchen.

There are a lot of hypotheticals in the Pirates future, but there truly is a lot to be excited about. I know it’s a difficult thing to request of Pirates fans, but this transition will require patience. That, and the front office attempting to provide more from the outside in free agency or big trades, and probably both. There is a lot the front office has done right in the past; unfortunately, though, there is also a lot its done wrong. We’ve seen the front office make some truly good trades, having the insight to know when guys have passed their peak and flipped them at the right time (like the acquisition of Rivero). But there will have to come a time where they send prospect packages for big-time players; if not, the Pirates may not see a real contender until ownership changes.

I don’t need to sell this all that hard. You come to FanGraphs. You’ve seen the articles about Eric Hosmer, his wildly fluctuating value, and how that stacks up next to his big free agency ask. The horse is dead already — rest in peace, horse. And yet, here it is. Another caution label to throw on Eric Hosmer, who is beginning to look more caution label than man now.

Statcast has been wonderful in both expanding the breadth and the depth of baseball analysis among both professionals (unlike myself) and hobbyists (hey, like myself!). Where PITCHf/x allowed us deep inside the world of pitching, many aspects of hitting were largely a black box until recently. With the aid of launch angles, exit velocity, and xBA we can judge not only the hitter’s results, but the process by which he arrived at them — is the hitter making quality contact? For Hosmer, his 25 home runs in 2017 might lead you to believe that he is. Statcast, as we’ll see, respectfully disagrees.

When it comes to types of contact, barrels are the crème de la crème. MLB’s glossary has the in-depth details, but in short — hit ball good, ball do good things. Statcast captures every batted ball event and allows us to take a closer look at who’s clobbering the ball on a regular basis. The leaders in barrel rate (Barrels per batted ball event, min. 200 batted balls) — Aaron Judge (25.74%), Joey Gallo (22.13%), and J.D. Kong (19.48%). Nothing out of place here. The laggards will surprise you just as much as the leaders did (in that they will not surprise you at all) — Dee Gordon (0.18%), Darwin Barney (0.36%), and Ben Revere (0.37%).  Hosmer’s 6.99% barrel rate ranks 121st out of 282 players, just above the average of 6.83%.

This not-terrible barrel rate is being masked by a well-above-average home run rate. Hosmer’s 22.5% HR/FB% ranks 30th in that same sample of 282 players. How do barrel rate and HR/FB% correlate?

Very well, actually. It seems my “hit ball good” theory has legs. Highlighted in red is Hosmer, and from a glance, it’s clear he’s pretty outlier-y. Using the equation from the best fit line and plugging in Hosmer’s barrel rate yields a pedestrian 14.34% xHR/FB%. The difference between his HR/FB% and xHR/FB% ranks 3rd out of 282. Yikes.

You might be wondering if HR/FB%-xHR/FB% even means anything. What good is knowing the difference if we don’t know the standard deviation or the distribution of the sample? Let the following bell curve assuage your concerns. Highlighted in red, again, is Hosmer.

I don’t have a very good conclusion for this. I’ve seen people mention his worm-killing tendencies. I’ve seen concerns about his defense. I’ve seen mentions of his BABIP-inflated career year. What I hadn’t seen yet was just how out of line his power numbers looked to be with his contact quality, and for a player seeking as much money as he is, that’s one more thing to be concerned about.

A few recent researchers have been doing some fascinating work on the relationship between strikeouts and clutch and leverage performance. Some good work has been done and there has even been good content added to the comment sections of the respective articles. To start a talk on anything that has to do with clutch performance, there are a few things that need to be settled first.

What is clutch?

The stat called ‘clutch’ has aptly been called into question recently. Does it measure what it is intended to measure, is the main issue. Clutch is namely one’s ability to perform in high leverage situations vs. their performance in not-high leverage situations. If someone is notably poor in important PAs compared to their relative performance in lower leverage situations, clutch will let us know. However, if someone is a .310 hitter in all situations, that hitter is very good, but clutch is not really going to tell us much.

I think the topic has been popularized partly because of Aaron Judge, who had a notoriously low ‘clutch’ number last season. Many have blamed his process to striking out, which indeed could very well be a factor in the relative situational performance gap. However, Judge helped his team win last year despite his record-setting strikeout process. Still, Judge wasn’t even top 40 in WPA last year, but then again neither were a lot of good players. But are high strikeout guys really worse off in high leverage spots? The rationale with putting a strong contact hitter up to the plate in high leverage game-changing spots is intuitively obvious, but all else equal, is someone like Ichiro really better in game-changing situations than someone like Judge?

Many have been using clutch to compare relationships with other stats. To be quite honest, I can’t seem to get much of a statistical relationship between anything and ‘clutch’ so I am opting for a different route. We know that a player’s high leverage PAs are worth many times more to the importance of their team as low leverage situations, by about a factor of 10. If we assume WPA is the best way of measuring a player’s impact to their team winning in terms of coming through in leverage spots, then we can tackle the clutch problem, in the traditional sense of the word.

WPA is not perfect, like every other statistic that exists or will exist. There are a lot of factors that play into a player’s potential WPA. Things like place in the batting order, strength of teammates among other factors all play a part. But in terms of measuring performance in high leverage, it works quite well.

Examining the correlation matrix between WPA and several other variables tells is some interesting things.

**K=K% and BB=BB%

We assume already that a more skilled hitter is going to better be able to perform in high leverage situations than a not as skilled hitter. What we see is that K% appears to have a negative relationship with WPA, but not a strong one, and not as strong as BB%, which has a positive relationship. Looking at statistics like wOBA, K% and BB% along with WPA can be tricky because players with good wRC numbers can also strike out a lot. See Mike Trout a few years back. Those same players can also walk a lot. I like this correlation matrix because it also shows the relationship between stats like wOBA and K%, which you can see are negatively correlated but also very thinly. The relationship between stats like these will not be perfect. Again, productive hitters can still strikeout a lot. Those same players again can also walk a lot. This helps to lend evidence to confirm that a walk is much more valuable than a strikeout is detrimental.

I’ll add a few more variables to the correlation matrix without trying to make it too messy.

We see again that WPA and wOBA show the strongest relationship. The matrix also suggests that we debunk the myth that ground ball competent hitters lead to better performance in high leverage situations.

So why do we judge players like Judge (no pun intended) so much for their proneness to striking out, when overall, they are very productive hitters who still produce runs for their teams? The answer is that we probably shouldn’t. But it wouldn’t be right just to stop there.

So how exactly should we value strikeouts? One comment in a recent article mentioned that when measuring clutch against K% and BB%, he or she finds a statistically significant negative relationship between K% and clutch. However, that statistical significance goes away when also controlling for batting averages. Interestingly, I found the same is true when using WPA as the dependent variable but instead of using batting average, I used wOBA.

To further test this, I use an Ordinary Least Squares Linear regression to test WPA against several variables to try to find relationships. I run several models based mainly on some prior studies that suggests relationships with high leverage performance and other variables. Before I go into the models, I feel I need to talk a little more about the data.

More about the data:

I wanted to have a large sample size of recent data so I use a reference period of 10 years, encompassing the 2007-2017 seasons. I use position players with at least 200 PAs for each year that they appear in the data, which seems to allow me to capture other players with significant playing time besides just starters. This also gives me a fairly normal distribution of the data. The summary statistics are shown below.

There aren’t really abnormalities in the data to discuss. I find the standard deviations of the variables to be especially interesting, which will help me with my analysis. All in all, I get a fairly normal distribution of data, which is what I am going for. The only problems I found with observations swaying far from the mean were with ISO and wOBA. To account for this, I square both the variables, which I found produces the most normal adjustment of any transformation. The squared wOBA and ISO variables is what I will be using in the models.

I use multiple regression and probability techniques to try to shed light on the relationship between strikeouts and high leverage performance. First I use an OLS linear regression model with a few different specifications. These specifications can be found below.

For the first equation, I find that wOBA, BB% and K% all have statistically significant relationships with WPA at the one percent level. I know that is not exactly ground breaking, but we can get a better idea of the magnitudes of the relationship. The results of the first regression are below.

I find that these three variables alone account for about 60% of the variance in WPA. Per the model, we find that a one percentage point increase in K% corresponds to about a 1.14 percentage point decrease in WPA. Upping your walk rate one percent has a greater effect in the other direction, corresponding to about a 5-percentage point increase in WPA. Also per the model, we find that a one percentage point increase in the square root of wOBA corresponds to about a 35.50 percentage point increase in WPA. These interpretations, however, are tricky, and do not really mean much. Since WPA usually runs from about a -3 to +6 scale, looking at percentage point increases does not really tell us anything tangible, but it does give a sense of magnitude.

To account for this, I convert the measurement weights into changes by standard deviation to help us compare apples-to-apples on a level field. The betas of the variables shown below.

We see that wOBA not surprisingly has the greatest effect on WPA while K% has the smallest. All else equal, a one standard deviation increase in K% corresponds with just a -0.04 standard deviation decrease in WPA. A one standard deviation increase in BB% has more an upward effect on WPA than K% does a downward one, albeit by not much. Though the standard deviations for these variables are not very big, so the movement increments will be small. Nevertheless, we still see level comparisons across the variables in terms of magnitude.

We go back to the fact that good hitters still sometimes strike out a good portion of the time. We like to think that strikeout hitters are also just power hitters, but Mike Trout was not that when he won his MVP while striking out more than anyone in the league. Not completely gone are the days where the only ones who were allowed to strike out were the ones who hit 40+ round trippers a year. I’m not necessary trying to argue one way or another, but getting comfortable with high strikeout yet productive players could take some getting used to. We value pitchers who can rack up high numbers of strikeouts because it eliminates the variance in batted balls, but comparing high K pitchers and high K batters is not exactly the same. Simply putting the ball in play is not quite enough in the MLB when you’re a hitter, but eliminating the batted ball variance through strikeouts is important for pitchers.

Speaking of batted ball variance, we can account for that in the models. I add ISO, hard hit ball%, GB% and FB%. I would have liked to add launch angle to the sample but I do not have the time to match the data right now, but that would likely improve the sample. I do my best and account for exit velocity with Hard%. I do not account for Soft% or Med% because some preliminary tests showed no statistical significance. Same goes for LD%, which was a bit surprising. I am mainly looking for how K% changes while controlling for these new variables, and if I can get any better account for the variance in the model.

When controlling for the new variables, the magnitude of the K% shows a stronger negative relationship. We find that despite some other popular belief, ground balls seem to be negatively correlated with WPA, but not as much as fly balls. wOBA and BB% show the strongest positive relationship with WPA. Hard% shows a positive relationship with WPA but is only significant at the 10% level. This model accounts for about 65% of the variation in WPA.

Batted ball profiling for WPA is still a little tricky. Running F-tests for significance on GB and FB, I find that indeed both of them together are significant in the model. However, when controlling for season to season variance, GB and FB percentages are not significant and don’t help the model. I think it’s likely the case that extreme fly ball hitters, all else equal, will not be as strong in high leverage situations.  Kris Bryant seems to fit the profile of a guy who constantly puts the ball in the air yet struggled in high leverage spots last year. On the opposite end of the spectrum, extreme ground ball hitters were not WPA magicians either. It is likely that when looking at the entire sample, FB and GB rates play a part, but when looking at an individual season level, the variance in these rates doesn’t really tell us much.

The explanation may be as simple as that MLB fielders are good. Yes, batted ball variance is very real, but simply making contact, all else equal, does not much change your ability at adding to your team’s chances at winning as striking out. Do not get me wrong, putting a ball in play is always better, but the simple fact of putting the ball in play in itself is not much more helpful. In addition, striking out a lot could suggest mechanical issues with a player’s swing, timing issues etc, though I do not believe it should be a blanket generalization. Mike Trout (I like mentioning Trout, but there are many more who fit this profile) may strike out a lot (not so much anymore) but he also has a great controlled swing where he hits the ball at optimal launch/speed angles, making him good at performing in high leverage situations.

Perhaps the shift has hurt the ability of extreme pull hitters to produce enough to the point where it hurts their WPA. A better idea would probably be to look at platoon splits to see if extreme pull lefties are hurt more than extreme pull righties, since lefties get shifted on much more often. The next explanation is more of an opinion gathered from my playing days and could easily be debated, but the ability to use the whole field is a sign of a better well-rounded hitter. Being an extreme pull hitter often means you lock yourself in to one approach, one swing, and one pitch. But again, I have no statistical evidence to back that up, but that is what I have gathered while being on the field. I think it is good to sometimes throw the eye test into statistical analysis to keep the study grounded.

It seems that performance in high leverage situations is more a mentality and ability to adjust approaches given the situation. The overall conclusion I gather is that K% is detrimental to one’s ability to perform in high leverage situations, but not by much. There are good hitters who strike out a bit, but those good hitters are still good hitters, as demonstrated but the strong relationship between stats like wOBA and WPA. Yes, Aaron Judge struck out a lot last season and had a big dip in relative performance in high leverage situations as seen by his Clutch metric, but all 29 other teams wish they had him. However, even when looking at BB/K rate, the leaders at the very top also show the highest WPAs, but the other leaders beyond that do not follow suit.

To see a more visual relationship between K% and WPA, below is a scatter plot comparing the two metrics with a line of best fit.

Looking a scatter plot of WPA vs. K%, we can see a slight downward relationship with WPA, but the data is mostly scattered around the means, helping confirm my aforementioned conclusion. We can see that there are not as many high K guys with high WPAs as there are high K guys with lower WPAs, but that doesn’t really tell us much because there are obviously going to be more average and below average players than above average. I’ll let you guess the player who had an over 30% K rate yet had a WPA of well over 5.

I know the matrix graph is a little overwhelming, but we can see that K% does not show much of a strong visual relationship with anything. We see a slight upward tick in the slope of measuring K and ISO together, but still predominantly scattered around the means. We also see a slight downward tick in the slope of GB% and K%. Besides the obvious strong relationship with wOBA and WPA, BB% does indeed show positive visual relationship with WPA. The fact that ISO shows a relationship with both K and WPA is interesting. Perhaps ISO helps explain the quality of batted ball variance that I have been trying to capture. The 2s after wOBA and ISO indicate their squared variables.

It seems that no one trait makes a hitter good in high leverage situations or not. Exceptionally well-rounded hitters, such as Joey Votto and Mike Trout, seem to constantly be ahead of everyone else in high leverage situations. Even still, they are not the same types of hitters exactly, though both walk a lot and make quality contact with the baseball. I believe that performance in high leverage situations is a mentality and the ability to keep a solid approach in the face of pressure. Using the Clutch metric itself is probably better when looking at how batters deal with pressure, but players know what is high leverage and what is not and respond accordingly.

Interestingly enough, though I won’t go into much detail here, I took O-Swing and Z-Swing rates and measured them both independently against WPA as well as with the full model. What I found was that O-Swing’s effect on WPA is statistically significant from zero while Z-Swing’s is not. O-Swing% of course showed a negative relationship with WPA. Disciplined batters who have the ability not to chase pitches, thereby recognizing good ones, indeed are poised to do better in big spots (if that is not stating the obvious). I don’t think anyone will pinpoint the exact qualities of a good situational hitter. The best pure hitters will have the edge on WPA, even if they are prone to striking out.

Oh BABIP, the stat of luck. For those wondering what the baseball BABIP is – it stands for Batting Average on Balls in Play. So basically a player’s batting average excluding home runs and strikeouts. It’s often viewed as a stat of luck.

So who was lucky in 2017? Who are the 2017 BABIP All-Stars? Here are the qualified (unless noted otherwise) BABIP leaders at each position.

Catcher: Alex Avila, .382 BABIP *Min 300 PA

Whoa, .382! Yeah, that’s not going to happen again, at least not in 300 plate appearances. Alex Avila had a nice bounce back in 2017, his best season since his career year in 2011. But what do we make of it considering he had such a high BABIP? Well for starters, Avila had the second-highest hard-hit rate of all players with at least 300 plate appearances behind only J.D. Martinez. Yes, Alex Avila’s ridiculous 48.7% hard-hit rate was better than Aaron Judge, Giancarlo Stanton, Joey Gallo, Miguel Sano – everyone but Martinez (which was 49% if you’re wondering). A high hard-hit rate does generally relate to a higher BABIP, but we have no reason to believe he’ll even sniff a 40% hard-hit rate again, and with limited speed it’s hard to imagine his BABIP being anywhere near .382.

2018 Expectations: .320

First Base: Trey Mancini, .352 BABIP

2017 was Trey Mancini’s first big-league season so we have to look back to his minor-league numbers for comparisons. A .352 BABIP seems pretty high for a lumbering first baseman, but Trey Mancini actually posted a high BABIP regularly in the minors. He held a BABIP above .344 in five different 52+ game stints at different minor-league levels, including a .400 BABIP over 84 games at AA in 2015. Even in his largest sample, 125 games at AAA in 2016, he posted a .351 BABIP!

He holds a decent hard-hit rate at 34.1% and was able to avoid a lot of infield fly balls. So, while .352 may seem high, I’d expect Mancini to consistently achieve an above-average BABIP. I do anticipate his norm being a little lower – around .335, but overall I don’t think this an out of the ordinary BABIP.

2018 Expectations: .335

Second Base: Jose Altuve, .370 BABIP

Look, Jose Altuve is one of the best in the game, and a perennial first-round pick in fantasy baseball. There’s no questioning his talent, but a .370 BABIP should be viewed as really high for any player. And for Altuve, this was the highest mark of his career, although not by much. Altuve achieved a BABIP of .360 back in 2014 and hit the .347 mark in 2016.

Altuve is a high-contact player with a lot of speed. His BABIP will generally always be higher than most, but .370 is pushing it. I’d peg his expectations at .340-.350 for 2018.

2018 Expectations: .345

Third Base: Chase Headley, .341 BABIP

A .341 BABIP is quite a bit higher than Chase Headley’s career BABIP of .328, but not that extreme. His career high, albeit in only 113 games, was .368 back in 2011. But what really stands out to me here is his .303 BABIP in 2016. Headley’s 2016 and 2017 seasons were nearly identical when you dig into the numbers. Similar hard-hit rates, strikeout and walk rates, and an identical ISO. Even down to the infield fly-ball percentage, the stats show a very similar season, but the results were very different for BABIP. So what the baseball gives?

Well, BABIP is generally viewed as luck, and I think this is a case where Headley had some bad in 2016 and some good in 2017. I’d put his BABIP expectations below that of even his career, somewhere around .320.

2018 Expectations: .320

Short Stop: Tim Beckham, .365 BABIP

I feel like Tim Beckham has been in the game for years, but 2017 was really his first full season in the bigs. A former first overall draft pick, Beckham finally started to break out last year. His strikeout rate continues to be an issue, but he showed promise in several areas. We don’t have great data to compare his BABIP to, but Beckham has good speed and hits it hard when he makes contact. One of the best numbers to support a high BABIP is his extremely low infield fly-ball percentage, 3.7%. Regardless, a .365 BABIP isn’t going to happen again. I think FanGraphs’ projections of .330 nails it right on the head.

2018 Expectations: .330

Left Field: Tommy Pham, .368 BABIP

Tommy Pham, what a season! Where did this come from, what the baseball Tommy? Well, Pham had shown strong signs in recent years at AAA, but struggled mightily with strikeouts in 2016. Wow, what a difference some vision correction can do! For those unaware, in 2008 Pham was diagnosed with a degenerative eye condition, which has recently been treated. There are numerous articles on this, but here is one from the St. Louis Post-Dispatch to check out. So what do we do here? Well, while Pham did strike out a ghastly 38.8% of the time in 2016, he still maintained a strong BABIP of .342. His hard-hit rate remains strong and he has a nice line-drive rate. And let’s not forget, Pham does have some wheels, too.

There’s not a great answer for this one, but we have to expect a dip in 2018. Numbers are supportive of a higher BABIP, but not at .368.

2018 Expectations: .340

Center Field: Charlie Blackmon, .371 BABIP

This guy just keeps getting better. Sure, Charlie Blackmon enjoys the Coors Field effect, but his numbers are still very impressive. I’m going to make this one simple. Blackmon is a great player with speed and has increased his hard-hit rate by almost 5%, but even Coors Field won’t help him to a BABIP of .371 again. I do, however, believe he can repeat his mark from 2016, .350.

2018 Expectations: .351

Right Field: Avisail Garcia, .392 BABIP

I’ve actually written about Avisail Garcia in more detail elsewhere, but to summarize – this isn’t going to happen again. This was the highest BABIP by a qualified hitter since 2013, and Garcia has never been anywhere close to this in his big-league career. Yes, he has shown improvements in numerous ways, but expect this BABIP to come crashing down to earth and landing at around .320.

2018 Expectations: .320

Designated Hitter: Domingo Santana, .363 BABIP

Did you know Domingo Santana had a .359 BABIP in 2016? Right off hand, it would seem .363 isn’t too far off expectations for the young slugger who is finally showing his potential. A .363 BABIP shouldn’t be expected for anyone, but I have a hard time arguing against it for Santana. Take a look at some of his AAA BABIP totals – 2014: .408 in 120 games, 2015: .429 in 75 games with the Astros and .467 in 20 games with the Brewers. Crazy! He has good speed and hits the ball hard. Did you know he had the second highest line-drive rate of all qualified hitters in 2017 at 27.4%?

2018 Expectations: .345

And just for fun – Pitcher: Robbie Ray, .433 BABIP *Min 50 PA

Who doesn’t like to talk about pitcher hitting stats! With a qualifier of 50 minimum plate appearances, Robbie Ray takes the cake for pitchers with a whopping .433 BABIP. What else do we even need to say here?

2018 Expectations: It doesn’t matter