Fluctuation of prospect value during the offseason is a mental exercise. Given the lack of activity to substantiate one’s changing opinion, hype can often be attributed to reputable names in the industry praising players, or the release of top prospect lists into the wild. Nate Pearson’s name has generated helium in the recent months, but instead of dismissing a storyline and citing our historically slow offseason for the surfacing of this hype, I wanted to understand the origin of praise surrounding our budding prospect.

Jim Czajkowski, the Vancouver Canadians pitching coach helped put into perspective how bullish the Blue Jays organization is on their first-round pick from 2017’s draft. Pearson carries a 6-foot-6, 240-pound frame onto the mound, his arm balancing out the offensive firepower Bo Bichette and Vladimir Guerrero Jr. bring to a system loaded with top-end talent.

Having groomed the likes of Aaron Sanchez, Marcus Stroman, and Noah Syndergaard, Czajkowski’s reps with advanced skill sets and assessment of their potential needs no introduction.

“[Nate] is better at his age than any of those guys were…. If I were to rank those guys, Sanchez probably had the best pure arm action and a good curveball, a good sinking fastball too, but Nate has all four [pitches].”

Pearson transferred from Florida International University (FIU) to Central Florida Junior College for the 2017 season for personal development reasons, and the gamble paid off as he posted 118 strikeouts in 81 innings with only 23 walks. Even with his stellar stats, one could assume Pearson may have been passed on last June due to his size.

“It’s a chunky 240 [pounds]. And in high school he was up to 300… he’s thinned down some… It was definitely his workout regiment; it was phenomenal.”

As his time at FIU was largely in a relief role, it was inevitable that discussion arose between Czajkowski and myself regarding how to condition the 6-foot-6 righty to shoulder a progressively larger workload. The focus was more on optimization – the sequencing of Pearson’s innings and coinciding off days – than sheer control of inning quantity.

“He probably pitched once a week [in college], and then he’d have six days to recover… we got him down to one less [recovery] day in Vancouver, and then wherever he goes next year, he’s going to be on a five-man rotation, so he’ll really need to adjust his regiment and take care of his arm care.”

Preparation for the next level is front of mind for Czajkowski and the Blue Jays. Focusing on routine and laying the groundwork to ease Pearson’s adaptation to higher levels lead to necessary and subtle tweaking.

“When we talked to him about his routine, we actually thought he might be overdoing it right after the game with his arm care. We wanted him to tone it down a little bit.”

This restructuring of Pearson’s off-day regiment and arm care was not suggested to his detriment. It became a vital step to eventually ease him into Lansing or Dunedin’s standard, five-man rotation, dealing with less off days in the process.

While any arm possesses the inherent risk of injury, Czajkowski admitted that himself and management are more optimistic with Pearson’s arm health knowing the primary generator of velocity comes from his lower half.

Adding audible intimidation to Pearson’s presence on the mound is a less statistical reason hitters struggled mightily against his offerings.

“There is not a lot of herky-jerky in [Pearson’s] motion, there are times where he pitches and he’ll grunt. And when he does that, he throws 100 [mph]. There are times early in counts where he grunts because he’s trying to make a statement, and he’ll overthrow a couple pitches… he was almost trying to strike guys out early in counts; trying to not let them touch the ball, that’s when he would lose a little bit of command and come out of his delivery a little bit.”

Pearson’s delivery is unique. His 6-foot-6 frame barrels downhill towards a hitter, as the harmony of his kinetic chain capitalizes on the energy stored in his lower half. A strong front leg allows him to stabilize after the energy released from his torso’s aggressive tilt forward finishes his motion. Exceptional is an understatement when describing the extension he achieves; the eye is tricked for seconds as one forgets the amount of mass supporting the big righty.

(Gif from YouTube, video credit to Niall O’Donohoe)

“If you watch him play long toss you know where he gets his power; his power is from his legs.” Czajkowski was quick to confirm what is visually consistent.

Pearson’s work ethic and natural ability, continually touted by Czajkowski in our talk, remain one reason why concerns over inconsistency fell to a simmer from the boil that eclipsed his potential pre-draft. An unusual detriment associated with this level of velocity is how advanced it can be for the pitcher’s level.

“At the lower levels they can’t catch up to his 100[-mph fastball]… The higher Nate goes, to Double-A and Triple-A, his changeup will be able to play because those guys will be able to catch up to his 100.”

Velocity differential between a pitcher’s fastball and changeup remains one of the key factors to predicting the value of the feel-dominant pitch and whether it behaves like a sinker, generating ground balls, or a true changeup, generating whiffs. While Czajkowski rated each of Pearson’s four pitches – fastball, slider, changeup, and curveball – above average, he was quick to disclose his high expectations for a pitch that was hit around for Pearson in his 19 innings with Vancouver.

Pearson’s arm speed is another reason why I’m bullish on his changeup. His body’s aggressive motion towards the plate can deceive hitters from an aesthetic standpoint. Add that to the fade he’ll be able to generate as he evelates his feel for a pitch and his mastery will quickly exceed the talents of his seniors.

But Pearson’s calling card is a two-plane slider; an unfair pitch when backed up with his command. He seamlessly changes the eye level against hitters, leaving most Class A Short Season hitters to guess if they stand a chance of hitting either pitch. The offering below is at this hitter’s belt, which gives a better idea of the pitch’s depth, rather than the late, “fall off the table” break noticeable when he buries the pitch at a hitter’s knees.

(GIF via YouTube, video credit to Blue Jays Prospects)

Is there a point where overuse of such an advanced pitch could hurt a young arm?

“If we think he is overusing his slider, just for strikeouts, we’ll talk about the percentage he throws his pitches. [Nate] gets a breakdown… and I think he did a very nice job this year in utilizing everything.”

Czajkowski reiterated the themes of our talk, bringing up a final thought that adds to his appreciation for the righty.

“He has four major league quality pitches, he has size, but the one thing he doesn’t have yet is stamina. He hasn’t built up the innings to be a starter at the major league level. Roberto Osuna pitched a couple years in the minor leagues as a starter and then became a reliever. So Nate Pearson as a closer at the major league level, I can see that too. Because of his regiment; the way that he throws, and the way that he bounces back tells me that he can handle a relief role, too.”

If the Blue Jays window of contention opens quicker than some anticipate, Pearson’s services may be needed at the major league level sooner than later. With Czajkowski’s suggestion that Pearson could reach Double-A New Hampshire by season’s end if the stars align, opportunity for Pearson to make an impact in 2019 isn’t off the table. His adaptation to higher levels and a five-man rotation are what I consider the largest factors dictating his future role.

Czajkowski’s final words to me on the record epitomize what we’re all thinking about Pearson.

“The sky is the limit for him.”

Special thanks to Jim Czajkowski for allowing me to steal some of his vacation time to chat Canadians baseball and Pearson. I wish the Blue Jays organization, and each pitcher he grooms, the best in the coming season.

I can be found on Twitter – @LanceBrozdow

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

As I’d like to think I’m an aspiring sabermetrician, or saberist (as Mr. Tango uses), I decided to test my skills and explore this research question. How did starters, who had 25 or more starts in one season and an ERA of 6.00 or higher in their final 10 starts, perform in the following season? This explores whether past performance, regardless of intermediary performance, adequately predicts future performance. Mr. Tango proposed this question as a way to explore the concept of replacement level. From his blog: “These are players who are good enough to ride the bench, but lose some talent, or run into enough bad luck that you drop below ‘the [replacement level] line’.” Do these players bounce back to their previous levels of performance, or are they “replacement level” in perpetuity?

To explore this, I gathered game-level performance data for all starters from 2008 through 2017 from FanGraphs, grouped by season. I then filtered out pitchers who had fewer than 25 starts and had an ERA less than 6.00 in their final 10 starts. This left me with a sample of 78 starters from 2008 through 2016 (excluding 2017 as there is no next year data yet). I assumed that a starter with an ERA above 6.00 was at or below replacement level. Lastly, as some starters were converted to relievers in the following year, I adjusted the following year ERA according (assuming relievers average .7 runs over nine innings less than starters: see this thread).

Seems like the 10-game stretch to end each season is a bit of an aberration. The following year’s adjusted ERA is much closer to the first 15+ games than the final 10 games for pitchers in our sample. In fact, the largest difference between any first 15+ game ERA and its following year adjusted ERA is .58 runs, in 2011. The smallest difference between any last 10 games ERA and its following year adjusted ERA counterpart, for comparison, is 1.7 runs, in 2009.

Using adjusted ERA corrects for the potential slight downward bias in our following year totals. Following year games started fell by ~9%, while reliever innings increased from zero to each season’s value. Relievers, on average, have a lower ERA than starters. As mentioned above, I adjusted each season’s following year ERA by .3 runs per reliever inning pitched (my assumed difference in runs allowed between starters and relievers per inning pitched). Another source for potential downward bias is sample size – of the 78 pitchers who fit our sample qualifications, only 69 pitched in the majors the following season. A survivor bias could exist in that the better pitchers in the sample stayed pitching, while the worse pitchers weren’t signed by a team, took a season off or retired.

What is driving these final 10 game ERA spikes? It has been shown that pitchers don’t have much control over batted ball outcomes. Generally, it is assumed pitchers control home runs, strikeouts and walks – the basis of many defense-independent pitching stats. Changes in these three stats could explain what happens during our samples’ final 10 games. Looking at each stats’ rate per nine innings, however, would be misleading, as each season exhibits uniform change (such as the recent home run revolution, or the ever-growing increasing in strikeouts). I calculated three metrics for each subset (first 15+, last 10 and following year) to use in evaluation: HR/9–, K/9– and BB/9–. All three are similar to ERA– in interpretation – a value of 100 is league average, and lower values are better.

Further, not necessary math details: for example, a value of 90 would be read as the following. For HR/9– or BB/9–, a value of 90 means that subset’s HR/9 or BB/9 is 10% lower, or better, than league average.  For K/9–, a value of 90 means that the league average is 10% lower, or worse, than the subset’s K/9. To create these measures, I calculated HR/9, K/9 and BB/9 for each subset and normalized them to the league value for each season – including the next year’s value for the following year’s rates. Then, I normalized these ratios to 100. To do that, I divided HR/9 and BB/9 by the league averages and multiplied by 100. Because a higher K/9 is better (unlike HR/9 and BB/9), I had to divide the league average by K/9 and then multiply by 100, slightly changing its interpretation (as noted above).

As mentioned above, the issue of starters-turned-relievers within our sample likely influences our following year statistics. I was able to adjust the ERA, but I did not adjust the rate stats – HR/9, K/9 or BB/9 – as I have not seen research suggesting specific conversion rates between starters and relievers for these.

Interestingly, our sample of pitchers improved their K/9– across the three subsets, despite having fluctuating ERAs. They were below average, regardless, but improved relative to league average over time. Part of this could be calculation issues, as league K/9 fluctuates monthly, and I used season-level averages in calculations.

Both HR/9– and BB/9– drastically get worse during the 10 start end-of-season stretch. These clearly drive the ERA increase. In fact, despite seven of the nine seasons’ samples having better-than-average HR/9 in their first 15+ starts, every season’s sample has a much-worse-than-average HR/9 in their last 10 starts, where eight of the nine seasons’ samples HR/9 are 40%+ worse than league average. Likewise, though less drastically, our samples’ BB/9 are much worse than league average in the last 10 starts subset. Unlike HR/9–, though, our samples’ BB/9– is worse than league average in the first 15+ starts subset. The first 15+ games’ HR/9– and BB/9– are identical to the following year’s values, unlike K/9–.

It appears that starters with an ERA greater than or equal to 6.00 in their final 10 starts, assuming 25 or more starts in the season, generally return to close to their pre-collapse levels in the following year. This end of season collapse seems to be driven primarily by a drastic increase in home run rates allowed, coupled with an increase in walk rate. These pitchers performed at a replacement level (or worse) for a short period and bounced back soon after. Mr. Tango & Bobby Mueller, in their email chain (posted on Mr. Tango’s blog), acknowledge this conclusion: “they are paid 0.5 to 1.0 million$ above the baseline… At 4 to 8 MM$ per win, that’s probably an expectation of 0.1 wins to 0.2 wins.” We can debate the dollars per WAR, and therefore the expected wins, but one thing’s for sure – past performance is a better predictor of the future than most recent performance.

– tb

Special thanks to Mr. Tango for his motivation and adjusted ERA suggestion.

Roberto Osuna, the Blue Jays young star reliever, has put together a very impressive resume in his 3-year career. Last season Osuna ranked 3rd in RP WAR (3.0) only behind Craig Kimbrel and Kenley Jansen in his age 23 season, and has also posted the highest cumulative WAR among relievers aged 20-23 years old in the last 40 years, while also producing the 2nd best FIP (2.69) and the moves saves (95).

Last July, Jeff Sullivan wrote a very compelling and in-depth article into the pure dominance Osuna was displaying on the mound; he was having a near perfect start to his season. He showed that across the board, Osuna ranked in the top 90 or 95 percentile in all of the major pitching statistics, proving that he had put it all together – matching his control to his skills. A few weeks before Jeff published his article (around June 25th), Osuna had missed some time for personal reasons, which was later disclosed as time away from the team to deal with anxiety issues. Roberto showed great courage speaking out to the public about his own internal struggles, but it was soon after that announcement that Osuna began to struggle on the mound.

It is both a difficult and a delicate analysis to undertake when analyzing the changes to Roberto’s performance last season. It is important to not read too much into certain trends and extrapolate that these derive from mental rather than physical, mechanical or strategic changes; however, this article will explore these changes to see why he suddenly began to struggle and how Roberto can strive to regain his top form for his 2018 season and beyond.

Roberto was at the top of his game in May and June and was putting up ridiculous numbers every time he took the mound. From July onward, Osuna began throwing his cutter and sinker much more frequently and threw fewer four-seam fastballs and sliders, as shown below:

The increase in his FC and SI usage and decrease in his SL and FA usage resulted in a change in his batted ball profile and strikeout potential. Osuna has a devastating slider with one of the best chase rates and swinging strike percentages in the league. He moved away from this pitch in favor of his sinker, which resulted in a lot more groundballs, as shown below. This change affected his BABIP, as it rose from .269 to .298.

Further, the large increase in his cutter usage resulted in a lot more hard-hit balls and he began to use it more often in high leverage situations with runners on base. His cutter usage increased from 15.7% to 37.4% with runners on base and this led to a plummeting left on base percentage. Last season Osuna posted the 2nd worst LOB% in the league among relievers at 59.5%. This is a statistics that jump off the page when juxtaposed with his fellow elite relievers who post metrics above 80 or even 90 percent. Below we can see just how drastic the drop was for him.

Considering that his LOB% was such an outlier compared to his peers, it is important to delve further into how this occurred. Recent history shows how rare it is for a pitcher with such great skills and control to have such trouble with runners on base. Since 2000, there has only been one other reliever who had a FIP under 2.00 who had a lower LOB%. A contributing factor to his struggles with runners on base was his aforementioned change in pitch composition. Increased usage of his sinker increased his balls in play and BABIP, his increased usage of his cutter resulted in harder hit balls and his decreased slider usage decreased his strikeout rate at times where he needed it most. Before June 25th, Osuna had a 2.41 FIP, 29.4% strikeout rate, 0% walk rate and a .304 BABIP with runners on base. After his temporary absence, his FIP actually dropped to 2.02, despite striking out fewer batters (24.1%) and walking more batters (1.8%) but his BABIP increased to .378. His xwOBA of .274 versus his wOBA of .311 with runners on suggests that he got a bit unlucky in the second half of the season, so his high BABIP is likely a combination of poor pitch command or selection, poor defense behind him and bad luck on balls in play.

Osuna enjoyed such great success when getting ahead of hitters (.189 wOBA after 0-1) and especially with 2 strikes (.130 wOBA), that hitters began to be more aggressive earlier the count looking for something to hit hard. A combination of a loss in fastball velocity and poor pitch location, Osuna began to get hit harder in high leverage situations. The top two heatmaps are Osuna’s fastball location and the bottom two are for his cutter. The heatmaps on the left are before June 25th while the ones on the right are after June 25th.

Osuna began to leave his fastball up over the plate in a hittable spot, as opposed to up and in, where he could tie-up right-handed hitters and produce weak contact. His cutter went from a setup pitch or even a waste/chase pitch to a pitch that he threw for strikes. Since Osuna started to throw so many more cutters, of course, he had to throw more of them for strikes, but the problem was he was unable to command the pitch to the better areas of the zone. A likely reason why Osuna began throwing more cutters was because the drop in his fastball velocity, as it was losing its effectiveness.

Pete Walker the pitching coach for the Toronto Blue Jays recently discussed with reporters Roberto Osuna’s offseason and reflected on his 2017 season. He acknowledged that Osuna had a drop in velocity during the season, had some mechanical issues, which impacted his fastball command, and that perhaps he threw his cutter too often during stretches of the season. All of this can be backed up with stats. Both the Jays coaching staff and Osuna are aware of where he can improve to regain with elite form. Walker also alluded that perhaps Osuna’s off of the field issues had an impact on his performance last season. By interpreting some statistics through this lens we can see how it can appear that Osuna lost of a lot of his confidence on the mound, especially in high-stress situations.

In particular, Osuna struggled away from the Rogers Centre as his road ERA was 5.10 versus only 1.85 at home in 2017. Further, Osuna had the 2nd best home wOBA while on the road it was only ranked 48th best.

Osuna was still good in the 2nd half (1.80 FIP and 4.24 ERA) and overall had a great 2017 season, but when the pressure started to grow and the wheels started to spin, they usually fell off (i.e. on the road with runners on base). It is hard to say whether this is the result of a lack of confidence, his decreased velocity on his fastball and his subsequent increased usage of his cutter or if it was a bit of bad luck with runners on base. It is likely a combination of these factors that led to Osuna’s declining second half, but we shouldn’t forget how dominant he can be when he’s at his best. According to Walker, Osuna has put on some muscle this offseason to help him with his durability in maintaining fastball velocity. Just like for most if not all other pitchers, being able to command his fastball is pivotal to Osuna being successful. At the end of last season, Osuna saw a small up-tick in fastball velocity and retired all 15 batters he faced in his last 5 appearances of the season, which is an encouraging sign, but how will he handle adversity, when batters reach base in 2018? With some minor tweaking to his game, Osuna should be on track to bounce back and have another dominant season as the Blue Jays closer.

At the beginning of February, Travis Sawchik wrote a piece about the viability of a team consisting entirely of unsigned free agents. I enjoyed the exercise as it underscored the extent to which players had been waiting to sign deals. Even that late in the year it was easy to field a competitive team, albeit an expensive one. Free agency is always considered to be more expensive than a homegrown team, hence the reason small market teams have to trade free agency bound players and retool, so it was no surprise that to create a decent team, the payroll had to be exorbitant. If you assume one WAR is worth $8-9 million dollars, then you would predict a 40 WAR team to cost over $300 million dollars in payroll. Sawchik’s team ended up costing $245 million dollars for the first year and was projected for 37.1 wins above replacement. While this was good for only $6.6 million dollars per WAR, it would still be the most expensive team in baseball and have to fight to stay in the wild-card race.

At the time I thought this was a good reminder of why the offseason was so slow; it just didn’t make sense for teams to meet players contract demands when it was so inefficient. In the following weeks though, players started signing at a faster rate, and again and again, I was taken aback by how little they were receiving. The $4 million dollar contract for Neil Walker was the most surprising of all for me. With seven straight seasons of at least 2 WAR and no qualifying offer, it seemed obvious to me that he deserved considerably more this. If you were to plug one year of Neil Walker into the surplus value calculators that Dave Cameron used to commonly employ, it estimates that Walker has $16 million in value for 2018, this is nowhere near what he ended up receiving.

Because of this, I decided to do a different spin on Sawchik’s team building exercise, using the contracts players actually signed, as opposed to projected contracts. Here is the team I came up with:

Team of Signed Free Agents

As you can see, the result of this team is much more successful than the team using projected contracts. I managed to stay under the luxury tax threshold ($197 million minus $13 million projected for player benefits) while creating a team that would be eighth in major league baseball in projected WAR just ahead of the Cardinals and behind the Nationals. Think about that, a team with no prospects, no homegrown players, and no assets to trade from could create a competitive baseball team from scratch through one year of free agency. And the team isn’t completely sacrificing its future either, as only four of the contracts are for more than three years. I can’t stress enough how surprising this is.

Instead of showing that free agency is completely inefficient for teams, this shows how easy it would be for a team to be projected for a playoff spot in the year’s offseason. Adding even a couple homegrown players, which every team has would boost this team into the ranks of other division leaders. So why is this the case? How was an entire team of free agents created for $4.2 million dollars per projected WAR? What happened to the accepted value of $8-9 million dollars per WAR?

I found both of these questions kind of perplexing since I could not find a good reason why the price of a win had dropped so quickly, but I decided to compare some of the most surprising contracts with similar players who were traded, and what the return was. All of the following free agents received considerably less than expected salaries based on their projected WAR and the following traded players were traded this offseason and have played a similar position and projection to the selected free agents.

Selection of Signed Free Agents

Selection of Traded Players

While these are only specific examples, all three comparisons, between Longoria and Moustakas or Fraizer, between Gordon and Gomez, and between Walker and Solarte, show a higher value placed on traded players than on free agent ones. Even with Longoria’s $68 million owed, he still returned three prospects in a trade. The Rays did take on Denard Span’s contract, but this salary dump was made up for by Arroyo alone, who is worth $20.2 million according to the Updated Version of MLB Prospect Surplus Values. Despite this, Moustakas received a tenth of the guaranteed money, and Frazier, who didn’t have a qualifying offer, a quarter of Longoria’s contract. So while Longoria had surplus value in a trade to the Giants, Frazier, and Moustakas, whose projections are very close to Longoria’s, couldn’t get anything close to his contract.

Similar situations occur with the other two comparisons. Dee Gordon was worth two prospects in surplus value, but Carlos Gomez, who is only projected to be a half-win worse, couldn’t even get 15% of Gordon’s guaranteed money. Also, the Blue Jays traded two prospects for Solarte this winter, even though he is making slightly more money than Neil Walker, and is projected to be only half as valuable. These both seem to be huge gaps between the value on the trade market, and value on the free agent market.

Then there is Jonathan Lucroy. While there were no significant catcher trades this offseason, comparing Lucroy’s Fangraphs projection to his contract is absurd.He has the fourth highest projected WAR for catchers in all of baseball, meaning that 27 teams could have upgraded by signing him, and yet he received $6.5 million. Granted, this projection seems high, but it’s easy to forget that two years ago he was a 4.6 win player. It seems to me that he would be worth much more than $6.5 million.

It is hard to know what all of this means. Do our WAR models overestimate mid-level talent? Do teams have projections very different from what is in the public sphere? Does it really have a lot to do with what teams think they can do with a player as opposed to their present value as was brought in a recent piece by Jeff Sullivan? Was the $8-9 million per win phase really just a contract bubble that has burst? While it is true that the gap in value between trade candidates and free agents is what you would expect if there were to be collusion from general managers, I think there is little other evidence of that, but it is still confusing why some player values seem to have dropped so quickly when compared to previous markets. I feel like I have brought up more questions than I have answered, but one thing is clear to me. If the trends from this offseason continue, looking to free agency for mid-level players will be much more efficient than it once was for teams. It seems unlikely that contracts for these types of players stay this low, however, since as any economics textbook can tell you, demand drives the price up. I would guess that plenty of teams will once again realize the value of Neil Walker for $4 million a year in the coming weeks, months and years ahead.

Last season was the year of the three true outcomes specialist.  Aaron Judge’s dominant three true outcomes season was the most prominent example of this: he ranked second in home runs (52) and walks (127) and first in strikeouts (208).  In total, 57% of his plate appearances resulted in one of the three true outcomes.  He was the American League Rookie of the Year and in the running for the 2017 American League Most Valuable Player award, finishing second.  His performance helped the Yankees reach the American League Division Series.

We know that the three true outcomes rate has been increasing.  In part, this is due to the average player increasing his rate of home runs, strikeouts and walks.  But there is also the unusual player in the mold of Judge who takes an extreme approach at the plate resulting in dominant three true outcomes seasons.  The number of these hitters has been increasing over time.

Figure 1. Three True Outcomes Specialists per Season, 1960-2017

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Figure 1 shows the number of dominant three true outcomes player seasons over time.  To get here I examined all players since 1913 with at least 170 plate appearances in a season.  I considered a dominant season one with a three true outcomes rate of at least 49%.  There have been 132 player seasons with a three true outcomes rate of at least 49%.  All of them have taken place after 1960.

The graph shows that the number of dominant seasons has been increasing over time.  Since Dave Nicholson first did it in 1962, most years have had at least one player cross the threshold.  Since 1994, every season has had at least one.  From 2001 to 2010 there were four seasons with five three outcomes hitters.  There was six in 2012 and eight in 2014.  The trend is currently peaking with 13 in 2016 and 16 in 2017.  The trend is a bit more extreme but similar to the average increases in three outcomes rates over time.  It seems that more players pursue (and teams tolerate) an approach to hitting that includes extreme rates of the three outcomes.

It is worth pointing out that those 16 players in 2017 account for about 4% of all players with at least 170 at-bats.  Three true outcomes specialists are more common but still rare.  Who are those 16 players?  Table 1 lists them including the home run, walk and strikeout rates, and the combined three true outcomes rate for the year.

Table 1. Three True Outcomes Specialists, 2017

The list includes many of the unique player stories of the year.  Aaron Judge’s rookie year was historic.  Joey Gallo made waves, particularly for his extreme three true outcomes rates.  Miguel Sano was an All-Star who helped lead the Twins to a bounce back year and a wildcard spot.  Eric Thames was a surprise story of the year, returning from a year in Japan and sparking the Brewers to an early lead in the National League Central.

Notable about this list is the young cohort of hitters who have consistently taken the all or nothing approach of the three true outcomes specialist.  Judge, Olson, and Blash all made their MLB debut in 2017.  Gallo still qualified as a rookie despite making his debut in 2016.  Keon Broxton, Ryan Schimpf, and Kyle Schwarber are in their second year.  Sano has been a specialist for three years running.  Sure, there are old hands like Napoli and Carter, and Davis who take the all or nothing approach, but the record number of specialists the last couple years have been due to this young cohort of three true outcomes specialists.  A new record will come down to 2018 rookies who practice this all or nothing approach heading into their major league debuts, and the number of teams willing to tolerate the strikeouts that come with this approach.

I ran some queries over at Baseball Savant and came across this tidbit of information. Since 2015, no other pitcher froze hitters on strike three more than Cleveland Indians’ Corey Kluber.

I decided to write an article on Kluber’s caught looking data along with how he’s able to be the best at getting hitters held up on that third strike.

Sifting through the last three years of Statcast data, and filtering the results down to a 5000 pitch minimum, Kluber ranks second overall to Clayton Kershaw (2.38%) in called third strike ratio to total pitches (2.28%).

So, why am I not writing about Kershaw? Well, I’m not concerned with ratio because, in this case, the ratio is independent of the number of times Kluber is able to deal that third strike. Kershaw might be better at working over hitters (thereby throwing less) but that doesn’t necessarily lend itself to more swing-less third strikes.

Kluber has thrown with two strikes nearly 1500 more times than Kershaw has in the last 3 years. But, Kershaw his pitched much less (mainly due to injuries), so we’re not going to ‘punish’ Kluber for this. And, we’re talking about a difference in the ratio that’s a tenth of a percent.

Moving on, I wondered if there is any advantage pitching in the American League? First, I looked at the overall plate discipline numbers for the entirety of Major League Baseball from 2015-2017.

So we have a 3-1 ratio of swings, as well as contact, in verses out of the zone. Now I’ll compare the AL vs NL three-year average.

We’re talking about fractions of a percent difference, with the only real disparity (if you can call it that) is the out of zone contact where the AL has a nearly 1% difference. So, there is no advantage to pitching in either league in terms of the type of at-bat you’ll experience.

Using a minimum of 1000 pitches each year, I found that Kluber finished first in 2015, third in 2016, and 2nd in 2017 in strikeouts looking. Furthermore, in context of plate appearances with two strikes, Kluber is ahead in the count (1-2/0-2 count) 24% of the time, even at 45%, and behind (or, a 3-2 count) 31% in those three years. Nearly a quarter of every two-strike situation, hitters are forced to be aggressive at the plate; and just under a third of the time, the batter has to make a mandatory choice.

Before I proceed,  I need to point out that there is some discrepancy as to what Kluber actually throws. He uses something of a sinking fastball that is hard to classify; it goes either way but my main source of research indicates it’s basically a sinker. And with his breaking pitches, which some sites call it a slider, some call it a curve, but it may be a slurve.  For argument’s sake, we will refer to both of them as a sinker and a slider.

So what is it that Kluber is using that’s laying waste to hitters on strike three? His sinker, which he’s thrown for strike three 108 times (50%) since 2015.

The above graph is his pitch selection after strike two the last three seasons.

His sinker location when he throws regardless of the count. Good luck telling a hitter where to concentrate his swing when he throws it.

However, something changed in 2017; he cut back on his bat-confining sinker by 7% and increased his change-up and slider/curve/slurve usage 1.5% and 7.3% respectively.

Just for curiosity’s sake, Kluber’s release points are nearly identical on all three pitches. So the hitter may not know whats coming at him with the intention of ending up as strike three (until its too late).

OK, so he leaned more on his slider last year. What can we make of that using his last three years’ run values in the context of runs above average?

The sinker, his bread and butter pitch for strikeouts, seems to hover around league average in terms of run value. Upping his change and slider usage appears to have paid dividends; Kluber seems to believe those are better suited to set the batter up for the strikeout. I would also venture to guess his sinker isn’t nearly as effective when thrown earlier in the count, hence the negative run value.

To note, Kluber’s two-strike stats: .136 BA/.392 OPS/10-1 K-BB

His sinker is clearly working when he needs it to.  Overall, it’s his least-effective pitch as hitters eat it up for a .300 average. Nevertheless, according to the data, it’s a tough pitch to gauge when used for that third strike.

Maybe Kluber will start using his slider more with two strikes. However, if he does so, that could cause him to be dethroned as the ‘King of Caught Looking’; his slider is swung at more than any other pitch he has, thereby causing a swinging strikeout.

Regardless, Kluber should still be able to put batters away with that devastating sinking fastball; opponents have 2-to-1 odds they’ll be dealing with it when the count has their backs are against the wall.  It usually doesn’t end well.

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.