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Anthony Rendon was ejected from a game earlier this month for … not contesting the strike zone. He flipped his bat down, faced away from the umpire, and did not visibly open his mouth. He was tossed by Marty Foster, for, what crew chief Joe West described incorrectly as ‘throwing equipment.’ (The pathologization of a non-white player’s actions after the fact to justify an ejection by a white ump is the subject of an entirely different set of analyses.)

After the game, Rendon actually went on record to say that umpires, like players, should be held to specific standards and demoted if they fail to meet those standards. This statement is remarkable for a couple of reasons. One, as most Nats fans know, getting Rendon to say anything, particularly anything of substance, to the media is pretty tough. He is, to forgive the pun, a pretty close-mouthed guy. For another, he points out that umpires, like players, are now doing their jobs in the Statcast era – we know, to a pretty refined degree, how well or not well they’re performing.

Players can be subject to replays that will tell them if their hand left the bag for the fraction of a fraction of a second, such as what happened to Jose Lobaton for the last out in the 8th in 2017 NLDS Game 5 (stay salty, my friends). But a home plate umpire’s word, particularly about the strike zone, is law. I understand ball vs. strike calls not being subject to replay. Even as someone who thinks most of the league’s pace-of-play ‘innovations’ are utter nonsense, I can’t see a good system in which every pitch could be subject to review. (Though, if the manager could make it one of their challenges, that’d be a start.) Umpires, therefore, should be held to the same standards, including performance reviews, as the players whose games they call.

The other thing that makes Rendon’s statement noticeable is that he’ll be facing the same umpiring crew in the final game against the Mets of this series and is likely to face them again this season. Saying that an umpire isn’t, in effect, doing their job commensurate with how Rendon is doing his is putting a pretty wide target on his own, and his team’s, back.

But beyond this instance, Rendon’s relatively mild approach to being struck out looking was disproportionately punished. He was ejected for not doing a whole heck of a lot, a punishment that seems incredibly disproportionate to a ‘crime’ that didn’t seem to go against MLB rules, written or unwritten.

I quickly tweeted out asking for an analysis of non-white versus white players in similar circumstances, because I had a hard time picturing a white player (like, say, Kris Bryant) being tossed for the same thing. Since no analysis existed, I did my own.

My analysis of available player ejection data from 2015-2017 led to the unmistakable conclusion: Non-white players, and Latino players in particular, are tossed at rates completely disproportionate to their representation in the league.

Methodology

Here’s a spreadsheet of data I compiled, mostly using Umpire Ejection Fantasy League data. I decided to limit it to 2015-7, in part because of use of Statcast and relatively consistent replay rules.

I also came at this analysis assuming any particular non-white and Latino player was as likely as any white, non-Latino player to be ejected, and so compared player ejections with league representation percentages for particular ethnicities. However, in doing analysis on position players only – that is, excluding pitchers – I didn’t have the league representation percentages adjusted for position players.

A major limitation in my data is having to hand-assign players as being white or non-white, and Latino or non-Latino. This was done using country of origin and knowledge of US-born players, and therefore is limited by my personal knowledge, particularly for US-born players. For instance, Marcus Stroman’s mother is from Puerto Rico and he was offered the chance to pitch for Team PR in the WBC. For the purposes of this analysis, he was classified as ‘nonwhite’ and ‘Latino.’

I also don’t know how players self-identify; I’m assuming Anthony Rendon, whose family is from Mexico and who was offered the opportunity to play for Team Mexico, self-identifies as Latino, but I don’t know if he’s stated that specifically. For non-US-born players, I also classified all players born in Latin American countries as Latino, but again, that’s not the same as asking for someone’s self-identification and that’s not the same as how any particular umpire perceives any particular player. For example, Francisco Cervelli, who is Italian and Venezuelan, was classified as non-white and Latino for this analysis.

I also classified Latino players as ‘non-white’ for the purposes of this analysis. While many Latinos self-identify as white, the Racial and Gender Report Card for Major League Baseball, where I got the league demographic data, identifies them as non-white and calculates them in the total of ‘players of color.’ So I maintained this classification for the purposes of this analysis. Any mistakes are unintentional; I welcome comments with suggestions for re-categorization.

Lastly, the umpiring corps has, as far as I know, not changed dramatically year to year. It’s a notoriously narrow pipeline and one almost entirely composed of white men. Analysis showed that some umpires toss players more than others, but this hasn’t been controlled for brawls. Additionally, the numbers of players tossed is a reflection of the number of games worked, which I haven’t controlled for.

This analysis isn’t meant to ascribe ejecting non-white and Latino players to any particular bad actor within the umpiring corps but to show a pattern of behavior.

The data:

Non-white players being ejected accounts for almost 50 percent of total ejections, despite players of color never being more than 42.5 percent of the league. Latino player ejections account for 38 percent of ejections, despite Latinos never being more than 31.9 percent of the league. Non-white, non-Latino players (of whom most are African-American), accounted for about 11 percent of ejections, fitting with representation in the league, except that no Asian players were ejected in this time period, and Asian players made up between 1.2 and 1.9 percent of the league. So, non-white, non-Latino, non-Asian players make up about 9-10 percent of the league and 11 percent of the ejections.

2017 is, therefore, a bit of a fluke. Of total players, nonwhite and Latino players were actually not tossed any more often (relative to their representation in the league) than their white peers.

I then controlled for two things:  pitcher ejections and ejections by non-home plate umpires, figuring that most pitcher ejections were as a result of beaning batters (which, yep, keep tossing them), and non-HP ejections might result from ejections during brawls, arguing slide calls, or in circumstances dissimilar to Rendon’s.

A few things became noticeable. One, overall ejections seem to be dropping, but non-pitcher, HP umpire ejections are holding pretty steady. Two, in 2015 and 2016, non-white position players comprised the majority of ejected players. Not only were non-white position players being ejected at a rate disproportionate to their representation in the league, they were being ejected more often than their white peers.

Latino position players were also being ejected by home plate umpires at rates disproportionate to league representation in 2015 and 2016 – 40 percent of ejections in 2015, despite Latino players being 29 percent of the league, and 42 percent of ejections in 2016, despite being 28.5 percent of the league.

For 2017, non-white and Latino players were ejected slightly more frequently than representation would account for.

So, is 2017  a step in the right direction or a flukey year or something else? No idea, and with the 2018 season being nascent, it’s hard to say. If there have been interventions on the part of the MLB or the umpire’s union, fantastic, but those interventions have not, as far as I’m aware, been made public.

I also know we won’t know for a while about 2018 ejections because ejections aren’t all timed equally. One of the weird things about this data is that white players tend to be ejected in early months, and non-white and Latino players make up the majority (or at least a disproportionate percentage) of ejections after May.

What this means is that the early ‘eye test’ for white and non-white players being ejected at similar rates won’t bear out in later months.

What if they deserve it?

None of this has addressed a fundamental question in considering ejections: Some guys have it coming. I tried to control for this in considering repeat offenders – that is, if there are certain players who, by virtue of reputation and absent any racial dynamics, just get tossed a lot.

Of the guys who’ve been tossed more than three times, the results are … very unsurprising:

Of these repeat offenders, Escobar and Kemp are non-white, and the former is Latino. The rest are the kind of love-’em-or-love-to-hate-’em white guys you might expect to make up such a list. So again, the eye test of ‘Bryce gets tossed too,’ doesn’t bear out when you look at the number of different players tossed total.

For ‘three-peaters’ – guys tossed 3 times in the past three seasons – of the 13 tossed three times, only two, Joey Votto and Justin Turner, aren’t Latino. And for players tossed once or twice – so not for having a rep as a showboat or arguer or ‘disrespectful’, 57 Latino and 115 non-Latino players have been tossed in 3 years. So, 33 percent of ejections have been for Latino players, despite the fact that Latinos averaged at 30 percent of the league’s players during this time. For non-white players, 76 non-white and 96 white players were ejected once or twice, meaning 44 percent of players ejected once or twice weren’t white, with the league averaging 41.5 percent non-white players during this time.

In totality, 36 percent of the players being tossed are Latino, and 46.5 percent of the players being tossed are non-white, both higher than their representation in the league.

If ejections are the league’s way of dealing with argumentation at the plate, we should consider that Latino players and non-white players are already disproportionately disciplined by their fellow players – and brawls are more likely to break out between players of different ethnicities.

We should also consider why players are perceived to ‘have it coming’ to them for arguing, ‘showboating,’ or other displays of either enthusiasm or disrespect, depending on your perspective, and why Latino and non-white players are dinged for it so more than their white peers for what are likely similar behaviors.

Umpiring by largely white umpires on increasingly non-white players is a cross-cultural conversation, one that’s monitored by 40,000 fans, TV viewers, and the ever-watchful eye of Statcast. The league has a vested interest in solidifying its presence in Latin American countries and in trying to encourage African-American players – who are a decreasing percentage of players overall – to continue with the game.

I don’t pretend to know what’s in an umpire’s heart (I assume pine tar and certificates for failed eye exams). I didn’t do this analysis to say that any particular umpire is actively thinking that they should eject a non-white or Latino player because they are non-white or Latino. What I discovered in doing all of this is that there is a very clear pattern of behavior among umpires when it comes to player ejections when the Statcast era is taken in its totality. An action may cause harm – in this case, an ump being more likely to throw out a non-white player – without any specific racist intent.

Additionally, the idea that umpires are enforcing ‘respect’ (and Joe West said that was Foster’s intent in tossing Rendon – “You have to do something or he loses all respect from the players.”) on non-white and Latino players is particularly galling. If non-white and Latino players are disproportionately perceived as ‘disrespectful’ of the game for similar actions as their white peers, such as tossing a bat after a strikeout, then the issue is perceptions and not players.

This, of course, is a societal issue beyond baseball. Analyses of behavioral perception by white teachers show that they tend to ascribe disrespectful, aggressive behaviors to non-white students at higher rates than they do to white students or than black teachers do with black students. Analysis of school punishments shows that black and Latino students are suspended and expelled at much higher rate than their white peers without any evidence they’re misbehaving more. So this is not a problem unique to player-umpire dynamics, but instead is one indicative of broader structural societal dynamics.

To work on addressing this as a structural issue, the league can change how it handles ejections. A few proposals:

• All plays over which a player is ejected are automatically reviewable, including balls vs. strikes. If an umpire ejects a player on a strike call that, on review, is revealed to be a ball, the player isn’t ejected. If a player makes contact with an umpire, they should be ejected but if players see that there is a clear and objective appeals process to an ejection, my guess is that they’re more likely to calmly walk off than explode.
• All ejections should be reviewed as part of a rigorous rating process for umpires. Umpires who repeatedly eject players for calls that, on review, they should not have made (such as a bad ball vs. strike call) should experience some form of penalty – by being demoted, retrained, or fined.
• Umpires’ ability to call balls vs. strikes compared with what Statcast determines is in or out of the zone should be made publicly available. If an umpire is consistently below a certain percentage of accuracy, they should be demoted or retrained.
• Player strikeout rates should be adjusted for umpire accuracy the way player defense is adjusted for particular ballparks.
• Diversify the umpire corps. Currently, umpires are generally older white men who feel tasked with enforcing ‘respect’ from young, increasingly non-white players. I’m not saying that simply hiring more people of color (including women of color) is a cure-all for these kinds of issues, but diverse perspectives may mean a decrease in unintended slights between players and umpires, and a general change in player-umpire dynamics.
• Radically, I would also like strike calls to be reviewable. They would cost a manager a challenge if incorrect like any other play. If a manager challenges – and is correct in challenging – strike calls repeatedly, then the umpire, and not the player or manager, should be held at fault.

If this sounds like we can replace a home plate umpire with Statcast for calling balls vs. strikes, then I’m for it. As the cliche goes, I didn’t watch the game for the umpiring, and if a computer can do what the umpires are doing in a fashion that doesn’t disproportionately penalize players of color, then I don’t see a downside.

I co-host Resting Pitch Face, a bi-weekly baseball podcast with a Nationals bias. I can be reached on Twitter at @sydrpfp.

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.

Cody Bellinger was called up by the Dodgers to the big leagues on April 25^th of this year. Coming in at only 21 years of age, Bellinger was looking to make a name for himself. Toward the beginning of the season he would split starts between left field and first base. Eventually Adrian Gonzalez would go down to injury, giving Bellinger the opportunity of being an everyday first baseman. Bellinger rose to the occasion, cementing himself in the history books, as he will be the National League Rookie of the Year. Not only will he achieve this award, but he helped bring his team to the World Series. Before Bellinger’s arrival to the team, the Dodgers were 9 for their first 20 games. The Dodgers would go on to win 104 of their 162 games.

During the course of the season, Bellinger put up incredible numbers. He played in 132 games throughout the year, driving in 97 runs, scoring 87 times, and belting an astonishing 39 home runs, finishing only behind the powerful Giancarlo Stanton (with 59). Bellinger had a respectable .267 batting average while maintaining a .352 on-base percentage and .581 slugging percentage. He was a force at the plate, putting fear into the eyes of many pitchers. Although he didn’t walk so much — only 11.7% of the time — he still managed to have a wOBA of .380, staying in the top 30 for the MLB. On average, he would draw a walk for about every two strikeouts; not the best, but still better than most players belting over 30 homers. His plate discipline was above average for power hitters throughout the season, but come postseason, this would all change.

Throughout much of the postseason, most people were reflecting on Aaron Judge’s struggles, after having himself a historic season at the plate. Judge would break the record for strikeouts in a postseason until Bellinger would then beat this unfavorable record with 29. Through Bellinger’s 15 postseason games, he would belt three home runs, driving in nine runs and scoring 10 times while walking only three times. Most of these statistics happened during the NLDS and NLCS. His wOBA would fall to .295, with a .219 batting average, walking 4.5% of the time, while striking out in an astounding 43.3% of his plate appearances. In fact, in the World Series alone, he would achieve 17 of his 29 strikeouts. Bellinger would struggle immensely at the plate throughout the World Series, with the exceptions of Games 4 and 5.

During the series, the Astros pitching staff would focus on beating Bellinger in on the hands with curveballs falling out of the zone, and with fastballs tailing up and away. Amazingly, Bellinger during the regular season only chased pitches out of the zone 29.7% of the time. This would change immensely as the Astros pitching staff’s effective deception would often pull Bellinger’s bat out of the zone.

In Game 4, Bellinger would face Astros pitcher Charlie Morton in the top of the 5^th with no outs in a 1-2 count. Bellinger’s stance is in a more upright position with his bat also in a vertical position. This makes creating torque through his hands a little more awkward, as he rolls his hands into a hitting position. When this curveball begins to spin further in on his hands, it becomes too difficult to bring his hands in further, leading to this awful swing and follow-through shown. His approach on this pitch looks as if he’s trying to hit the ball 500 feet over the right-field wall; not an optimal mindset in a 1-2 count when you know the curveball is coming. His head was nowhere near the zone; he may as well have swung with his eyes closed. This is the position we often saw Bellinger in throughout the World Series when thrown an inside curveball. However, Bellinger would use this at-bat for his next plate appearance.

Now we see later in the game Bellinger is in a 1-1 count facing Morton in the top of the 7^th. He knows he’s going to see a curveball in on his hands and adjusts accordingly. His body is in a lower position with his bat in a more angled approach, with his hands staying back, anticipating curveball, looking to stay in on the ball with his hands and drive it to right field. Bellinger manages to fight this pitch off, fouling it back, showing his adjustment helped. His follow-through is also in a significantly better position, with his head staying back looking at the ball, and his body stays in a more balanced stance. This approach, showing that he’s able to make even a small adjustment to making contact with the low and in curveball, led pitchers to start targeting the outside upper half of the zone with the fastball again.

Here we see in Game 4, Bellinger faces Astros pitcher Charlie Morton with a 1-1 count and 0 outs in the top of the 5^th. Bellinger’s body is not in an effective hitting position for hitting this outside fastball. His body is falling out away from the zone, his pivot foot is not providing any power, and his hands reach out from his body too far. Bellinger would acknowledge this issue and had this to say before Game 4:

“I hit every ball in BP today to the left side of the infield,” Bellinger said. “I’ve never done that before in my life. Usually I try to lift. I needed to make an adjustment and saw some results today. I’m pulling off everything. Usually in BP I just try to lift, have fun in BP. But today I tried to make an adjustment. I needed to make an adjustment, and so I decided I’m hitting every ball to left field today.”

This is exactly what Bellinger would do.

In the top of the 9th in Game 4 with a 1-0 count and no outs, Bellinger faces Astros closer Ken Giles with runners on. Bellinger has his eyes locked in on the ball as he’s seen this pitch before. He’s using his approach from batting practice earlier to drill this ball into the gap. He keeps his body in an athletic hitting position, keeping his hands in and generating all his power through his lower half, creating torque through his strong hands. We see him drive this ball into the left-center gap, keeping his eyes on the ball the whole way and maintaining a strong follow-through. Bellinger did exactly what he said he would do and helped his team win this game. He would then carry on this adjustment into Game 5, showing people why he will be this year’s NL RoY.

Although Bellinger would fall into his old habits in Games 6 and 7, his ability to recognize where the problem is and the ability he has to adjust is what makes him an effective hitter. Through this, Bellinger will only continue to become better and will continue to become one of the most feared hitters in the league this next season. At only 22 years old now, Bellinger will become the next big star in this great sport we call Baseball.

Throughout the baseball season, I like to estimate teams wins, but I don’t do it in the traditional way. Some time ago, I discovered that I could use innings pitched to get a close estimate. Here’s what I do:

1) Take team games played and divide by 2;

2) Take the team’s innings pitched and subtract the team opponents’ innings pitched;

3) Add 1 and 2.

For example, the Washington Nationals, as of the All-Star break, have played 88 games. They have 789.33 IP, and their opponents have 781.33 IP. So I take 88 divided by 2, which gives me 44. Then I take 789.33 minus 781.33, which gives me 8. Then 44 plus 8 gives me an estimate of 52 team wins. Checking the standings, I see that Washington indeed has 52 wins.

How does my method compare with the traditional Pythagorean? (The Pythagorean method, of course, takes runs scored squared and divides by runs scored squared plus runs allowed squared.) I’ve set up some charts to demonstrate. First, let me present the relevant statistics for all teams as of the All-Star break (all statistics courtesy CBS Sportsline):

Now let me present a chart showing how many teams wins are predicted by my method and the Pythagorean method (for the Pythagorean method, I’m using 1.82 as my exponent, as shown by MLB on their Standings page):

My method appears in the second column, and the Pythagorean method appears in the third column, with actual team wins in the last column. My method, as shown above, gives estimated wins directly. The Pythagorean method actually computes winning percentage. To get the estimated wins for the Pythagorean method, I multiplied the team’s estimated winning percentage by the team’s games played.

The methods are pretty close! On a couple of teams, though, the methods miss by a wide margin. I’m way off on the Angels, for example, while Pythagoras is off on the Giants. But which of these methods is closer overall? I did an r-squared between each of the estimated win columns and the actual wins and got these results:

Mine’s a little higher, but let’s use mean squared error (MSE) as a cross-check. Here are my numbers:

I’m not a numbers person, so if I’ve made made errors in my calculations, please let me know, and I will never, ever trouble you fine readers again with another post. But I’ve published previous studies of both methods (in other places, under other names) and have found each time that my method edges out the Pythagorean in both r-squared and MSE.

If my method works at all, it’s because better teams typically have to get more outs to finish off their opponents. If the Dodgers, say, are at home against the Phillies, chances are they’re already winning when they go to the bottom of the ninth, and so the Dodgers don’t have to come to bat. That means the Dodgers had to get 27 outs and the Phillies had to get only 24. Conversely, on the road, if the Dodgers are leading the Phillies, the Phillies have to come to bat in the bottom of the ninth, and the Dodgers have to get the full 27 outs to end the game.

One caveat: my method tends to be more descriptive than predictive, so it’s a better measure of how a team has performed, not a good predictor of how a team will perform in the future. The Pythagorean method is much better as a predictive tool.

So there it is! My estimated team wins method. I hope you find it useful.

The Mets are the talk of the town…for all the wrong reasons. They currently sit at a 31-41 record and are 12 games behind the Washington Nationals in the NL East, which as of now seems to be theirs for the taking. The Mets boast one of the worst bullpens in the majors and have been plagued by injuries as well as underperformance from the bulk of their lineup. With the results of this season, many are beginning to wonder if it’s time to turn the page on this current pack of Mets players, many of whom were on the 2015 team that lost to the feisty Kansas City Royals in the World Series. I will attempt to go group by group in an effort to determine whether or not the Mets should begin a new rebuilding process, the most dreaded phrase in sports.

Starting with the outfield, Yoenis Cespedes is locked in for three more years in his current contract. It’s understandable why the Mets were looking to sign him in the offseason based on his performance in 2015 and 2016. However, injuries and poor performance have contributed to the current record that the Mets have. Cespedes still won’t lose his spot in left. Curtis Granderson, due to his age, will most likely not be re-signed, as well as Jay Bruce who, if he is not traded before the deadline, will most certainly test free agency. Juan Lagares has been injury-prone the last couple years but the one piece of good news is that Michael Conforto has seen a resurgence since coming back from Triple-A Las Vegas. Also, one of their top prospects, Brandon Nimmo, should receive regular playing time in the outfield, if not this season, then definitely in 2018.

Next, we have the infield, which has been decimated by injuries. Neil Walker and Asdrubal Cabrera have struggled through injuries (and who knows if/when David Wright will ever step on a baseball field again). Jose Reyes and Lucas Duda have mightily underperformed. The good news for the Mets is that Cabrera, Walker, and Reyes will be gone after the season, which means that the infield can get much younger. Top prospects Dominic Smith and Amed Rosario will be September call-ups and, if all goes well, can be regulars in the lineup next year. T.J. Rivera and Wilmer Flores have proven to be reliable pieces in the lineup. Despite some injuries from Flores, he has made up for it with his versatility in both the field and in the lineup, giving manager Terry Collins options to choose from. While Flores and Rivera may not be long-term solutions, they are the best options that the Mets have at the moment. As far as catching is concerned, Travis d’Arnaud is probably the Mets’ best option right now, although he has severely underperformed since being traded to them. The Mets should try to get another catcher in free agency.

Finally, the best pitching staff is a huge question mark, but also a big concern among scouts. Matt Harvey clearly no longer has any interest in remaining with the team and Noah Syndergaard, Zack Wheeler, and Steven Matz are just injuries waiting to happen. Even Jacob deGrom, who has been I believe the best starter this season, has a history of arm injuries that makes Mets front-office personnel nervous. Even Robert Gsellman and Seth Lugo are recovering from injuries sustained during this season. The bullpen has been just as bad. The bullpen so far has logged 257 innings to the tune of a 4.97 ERA. Not to mention they have not had a reliable closer since Jeurys Familia has been both suspended and injured this season, and the rest of the bullpen outside of Addison Reed and Jerry Blevins has been downright horrendous.

Overall, the Mets need to begin the next phase of the rebuilding process. With aging veterans and current players underperforming, it’s clear that the time for a championship has come and gone for this group. The Mets need to get younger and it starts with the old addition-by-subtraction technique. By dumping aging veterans with big contracts, the Mets will be able to allocate their resources and maybe pick up some pieces in free agency while simultaneously giving their top prospects playing time and allowing them to develop. As the great Cosmo Kramer once said on Seinfeld, “I think it’s time that we shut down and re-tool.”

Yankees right fielder Aaron Judge is, to say the least, an imposing figure in the batter’s box. Judge is one of only three position players in baseball history with a height and weight of at least 6’7” and 255 pounds, respectively – the other two, for those curious, being 1960s power hitter Frank Howard and current Tigers minor league Steven Moya – and with his enormous size comes enormous strength. According to Statcast, 59.5% of Judge’s batted balls last season left the bat with an exit velocity of at least 95 miles per hour, a mark that trailed only those of the Brewers’ Domingo Santana and the Mariners’ Nelson Cruz. Further, Judge’s average exit velocity ranked second among the entire league, with only Cruz ahead of him. However, the player comparison that most swiftly comes to mind is the Marlins’ Giancarlo Stanton, who, incidentally, finished third in average exit velocity last season. When Judge truly barrels up the ball, as exemplified here, his raw power tends to elicit the type of awe usually reserved for Stanton.

Unfortunately for the Yankees, Judge was largely unable to capitalize on this strength in 2016. Although he only saw 95 plate appearances, he batted an uninspiring .179 with an astronomical 44.2% strikeout rate. Even his ISO, above average at .167, was still disappointing for a player claiming raw power as his most prominent attribute.

The Yankees, of course, were fully aware that their right fielder’s approach at the plate needed an adjustment. Said Yankees assistant hitting coach Marcus Thames during spring training:

“I thought [Judge] started expanding a little too much… At the big-league level, the game’s a little bit more physical, it’s a little bit faster and I thought it sped up on him a little bit and he started expanding.”

A cursory look at Judge’s 2016 batting statistics plate surprisingly suggests that plate discipline may not be as big a problem as one would expect based on Thames’ comments. Among 451 position players with at least ninety plate appearances in 2016, Judge’s O-Swing percentage was tied for 119^th at 33.6% (27^th percentile), and his Z-Swing percentage of 63.5% ranked nearly identically, at 112^th (68^th percentile).  Judge, surprisingly, rated fairly well in both measures: he chased far fewer balls than the average hitter, and he swung at a healthy percentage of strikes.

His contact rates, on the other hand, did not inspire quite the same sanguinity. Last season, Judge ranked dead last in overall contact percentage, as well as on pitches outside the strike zone. On pitches inside the strike zone, his contact percentage saw a slight improvement relative to his peers, but still ranked 42^nd from the bottom. BaseballSavant’s pitch heatmaps suggest that Judge seemed to have the most difficulty with low and away pitches, both in and out of the strike zone. The following graph displays the locations of Judge’s swinging strikes from 2016 (not including foul balls):

As the preceding heatmap illustrates, the crux of Judge’s contact problems occurs in the low-and-away portions in and around the strike zone. However, a heatmap of Judge’s hardest-hit balls (exit velocity >= 100) shows that Judge’s best contact occurs on pitches that aren’t located anywhere near the low and away sections of the zone. In fact, the pitches Judge hits best are on the inside half of the plate:

Now, let’s see where Judge’s weaker contact (exit velocity <= 99) falls in the strike zone.

So, low and away pitches not only induce a league-leading whiff rate for Judge, but even when he does manage to connect, he connects with his weakest exit velocity. Marcus Thames’ comments, therefore, may require a slight adjustment: Judge didn’t necessarily expand the zone in 2016, but he certainly didn’t make the most efficient use of it. Courtesy of Brooks Baseball, the following graph illustrates Judge’s 2016 whiff rate by zone:

From these charts, we can observe Judge’s whiff rate slowly rising from left to right (inside to outside) across the strike zone. To cut down on his high swinging-strike rate, which was the third-highest in the league among those 451 batters, Judge should reduce his swing rate on low and outside pitches – at least, until the count or game situation demands a more aggressive approach. Ahead in the count, however, Judge should look primarily for the middle-in pitches that have produced better and more frequent contact. He shouldn’t even consider swinging at anything on the outer sections of the plate, as he did last season while ahead in the count (heatmap from FanGraphs):

As of Tax Day afternoon, the Yankees are only 11 games into the season, so it’s admittedly a bit early to draw any major conclusions. Even so, we should note that Judge has shown signs of legitimate improvement over last year’s campaign. In 33 at-bats, Judge is slashing .276/.364/.621, and although a 175 wRC+, .345 ISO, and 50% HR/FB rate are all but guaranteed to decline, there’s still reason to believe that Judge has made significant strides in his approach at the plate. Last year, Judge saw the 18^th lowest percentage of fastballs in the league at 49.8%, a percentage that this season has dipped even further, to 45.5%. Pitchers, expecting Judge to flail as in 2016, have fed him a steady diet of low and away breaking balls. The following chart reflects all off-speed pitches Judge has faced to date in 2017:

Even with this steady diet of low and away breaking balls, Judge has managed to cut his O-Swing% from 34.9% to 23.9%, and his swinging-strike percentage has fallen from 18.1% to 12.0%. This is especially impressive considering that, like last year, pitchers have thrown him a fairly low percentage of strikes (about 41%).

The Yankees have lots of reason for optimism regarding their young slugger. As the starting right fielder in Yankee Stadium’s less-than-spacious right field, Judge’s value to his team will derive mostly from his batting output. If Judge can consistently lay off of the low and away pitches that gave him problems last year, he’ll have more opportunity to mash the balls that find the inner half of the plate – like this beauty from last Wednesday. If his early 2017 performance is any indication, Judge’s offseason adjustments have the potential to transform him into a Giancarlo Stanton-caliber power hitter.

A little while ago, I created a post here about finding undervalued pitchers by looking at improvements between the first and second halves of the season. I had created a linear regression model for the predictions using data from 2002 to 2015, but when trying to use the same model to find overvalued pitchers, it didn’t exactly work as expected (I use the word “work” loosely here — in all likelihood, my predictions will fail as badly as the new Fantastic Four movie). It did find pitchers who suffered massive setbacks, but the majority of those were primarily due to increased — and probably unsustainable — home-run rates.

For example, Matt Andriese had an extremely successful first half of 2016. He put up a 2.77 ERA in 65 innings, backed up by a 2.85 FIP. But those numbers were much like my ex-girlfriend: pretty on the surface, but uglier once you get to what’s underneath. He struck out a lower percentage of batters than the average pitcher during that time while giving up more hard contact. The biggest sign, though, was his deflated home-run rate. He allowed just 0.28 home runs per nine innings, with only 3.2 percent of his fly balls going over the fence. This righted itself in the second half, where his HR/9 increased to 2.15 and his HR/FB to 17.4 percent. On the other hand, he improved his strikeout and walk rates, actually leading to a drop in his xFIP from 4.04 to 3.92 from the first half of the season to the second.

So then what should we expect from Andriese in 2017? The model I created predicts a 5.56 ERA from Andriese, leaning toward his 6.03 ERA from the second half of last season. While it’s unlikely he will allow fewer than 0.3 home runs per nine innings next year, it’s equally as unlikely that he’ll allow over 2 — after all, no qualified pitcher did so over the course of the 2016 season. Andriese’s full-season FIP of 3.78 actually closely aligned with his xFIP of 3.98, so it’s fair to guess that his home-run rates will level out and his ERA in the coming year will be in that range. That would signify an improvement from his 2016 season, rather than his decline predicted from the model.

So, instead of using the model, I took a simpler approach. Here are the players with at least 50 IP in each half of the 2016 season whose xFIP increased the most from the first half to the second:

For the purposes of this article, I’ll ignore Santiago and Shields since it’s unlikely that either of them will be relevant in 2017. That leaves four other pitchers whose skills declined dramatically over the course of the season and who you might want to avoid in your drafts.

Tanner Roark

Believe it or not, Roark’s already 30 years old. He’s actually had pretty decent success in his four years in the majors, with a 3.01 career ERA in over 573 innings. On the flip side, over that same time he has a 3.73 FIP, 3.96 xFIP and 4.06 SIERA. That’s not to say he’s a bad pitcher — just perhaps not as good as his ERA would have you believe. The same can’t be said for his second half of 2016. Despite actually bringing his ERA down from 3.01 to 2.60, his already-inflated FIP and xFIP numbers got even worse. His strikeout rate declined by 2.5 percent while his walk rate rose by about the same amount, leading to just a dismal 1.87 K/BB in the second half. His HR/9 nearly doubled as well, but not due to a substantial increase in his HR/FB rate — rather, his fly-ball rate rose from 26 to 37.6 percent, more in line with his pre-2016 career average of 33.9 percent. Why, then, was he able to continue to be successful? A .230 BABIP and a 86 percent strand rate offer an answer. Don’t expect another sub-3 ERA season from Roark — instead, look more toward his Steamer projection of 4.15.

Drew Smyly

For many last year, Smyly was a popular target. He was a high-strikeout guy who was able to limit walks and generate infield flies, prompting Mike Petriello to write this ringing endorsement for him. In his 114 1/3 innings for Tampa Bay before 2016, Smyly had maintained a 2.52 ERA and was among the best at generating strikeouts. But it all went wrong last year. As Tristan Cockcroft points out, Smyly’s season was marked by a first half of bad luck and a second half of deteriorated skills but better luck. His first-half 5.47 ERA was likely undeserved, as he continued getting strikeouts and limiting walks, but was plagued by a .313 BABIP, 63.2 percent strand rate and a 15.0 HR/FB rate, which corresponded to a 4.45 FIP and 4.07 xFIP. His ERA dropped to 4.08 in the second half, but nearly all of his peripheral stats worsened. A move to Seattle won’t fix all his problems, as Safeco Field was actually more hitter-friendly than Tropicana Field in 2016. The sky is the limit for Smyly, but there’s reason to be cautious. It’s possible he bounces back, but this could be who he is now.

Aaron Sanchez

This guy is good, don’t get me wrong. It took a while for some people to catch on, but I was always on his bandwag…all right, so I was one of the guys who didn’t buy in right away. That’s why I don’t do this for a living. Anyway, seeing his name on this list surprised me. After some digging though, it turns out that in my ignorance, I may have been onto something. In 2015, in Sanchez’s trial run as a starter, he was all right. A 3.55 ERA hid a 5.21 FIP and 4.64 xFIP before he got injured and was subsequently moved to the bullpen. When he returned on July 25, he was a completely different pitcher. This time, while he may not actually have deserved his 2.39 ERA, a 3.10 FIP and 3.33 xFIP showed he had made some kind of improvement. Or had he? After all, he only threw 26 innings in the second half of last season. And while there was undoubtedly a huge improvement for him in strikeout and walk rates, something else caught my attention. Take a look at Sanchez’s batted-ball type percentages from 2015:

Pretty clearly, Sanchez improved his batted-ball profile after becoming a reliever. His 2015 second-half ground-ball percentage of 67.6 percent would be the greatest of all of the 1281 qualified pitcher-seasons since 2002, when the statistic started being tracked. His fly-ball percentage of 18.3 percent, while not as extreme, would still rank as the ninth-lowest since 2002. That begs the question: would he be able to sustain those rates when he moved back to the rotation? The answer, as it always is with historically extreme rates, was no:

Both of his rates came crashing back to historically-accurate norms pretty much right away, and they continued to trend in the wrong direction as the season progressed. This, consequently, caused Sanchez’s xFIP to skyrocket. His strikeout and walk rates got worse from the first half of the 2016 season to the second, but only slightly. What really moved his xFIP was his fly-ball rate, which soared (pun intended — maybe I should do this for a living) from 21 percent to 31.8 percent. It’s difficult to say where Sanchez will go from here — after all, this was his first full season as a starter. If he can keep his fly-ball rate at last year’s 25.1 percent — which ranked fourth-lowest among qualified starters — he could still be a pretty decent starting pitcher, even with regression to a league-average HR/FB rate. What’d be even more impressive, though, is if he could keep his batted-ball rates at his numbers from the first half of 2016, which were among the league’s best. Perhaps with a full season under his belt, Sanchez may now have the stamina and endurance to achieve this feat. If he does, look out. If he doesn’t, you’re looking at an average guy.

David Price

Now that I’ve written nearly an entire article’s worth about one guy, let’s talk about another player from the AL East. Price, for much of his career, has been among the elite at the position. Before last season, the only time he had had an ERA above 3.50 was his first season as a starter back in 2009. Every year of his career, he’s been an above-average strikeout guy, but he topped even his own lofty standards when he struck out 27.1 percent of the batters he faced in the first half of 2016. He was unable to sustain that rate, and in the second half of the season he managed to strike out just 20.3 percent of batters, which would have been his lowest full-season rate since 2009. So what changed? Actually, it might have been the first half that was the fluke. Price allowed a 74.2 percent contact rate in the first half, contrasted with a 79.1 percent rate in the second. Those numbers don’t necessarily mean much on their own, but the difference is easy to spot when looking at his career rates:

Price’s whiff rate was higher than ever in the first half of 2016, but it’s tough to figure out why. Per Brooks Baseball, Price was generating swings and misses on his changeup at a career-best rate in the first half, but I couldn’t find any obvious changes to his velocity or movement on the pitch or any other. It’s fair to wonder, then, if his second-half numbers are what we should expect from Price at this point in his career, since his contact rates during that time were much more sustainable. He probably won’t be as bad as his 2016 3.99 ERA, but I wouldn’t be shocked to see it end up above 3.50 for the second year in a row.

Of course, this is not a comprehensive way to find overvalued pitchers. It’s a crude approach, but one that’s meant to highlight guys who fell off in the second half, as they’re the ones more likely to carry over those declined skills into 2017. That being said, xFIP obviously isn’t perfect, and these players all showed that they were capable of posting above-average results over half a season. Take a risk on them if you want, but be warned that they may not be worth the price.

When looking to the future, there are countless ways to try and find undervalued pitchers.

One such way is to look at which pitchers’ FIPs outperformed their ERAs last year. This is a good approach, but it isn’t enough. For one, there will be players who consistently underachieve on their metrics, like the ever-teasing Michael Pineda. He sits second on the 2016 leaderboard in ERA-FIP, but his ERA is more than half a point greater than his FIP for his career and over a point greater each of the past two seasons.

The other problem with this approach is that FIP has become mainstream enough that everyone will be doing this same thing. Players who outperformed their FIP will be be common targets on draft day, driving up their prices and eliminating any sleeper potential that they had. This, too, is the downside of projections and other easily accessible data.

A different approach is then needed. In that spirit, I decided to create a linear regression model to predict a subsequent year’s ERA based on the difference in first- and second-half splits from the previous year, as well as that year’s ERA. This would help find the players who improved the most from the beginning of the year to the end, and perhaps players who are likely to carry over those improvements into the next season.

The model was generated using data from 2002 to 2015 obtained from FanGraphs’ splits leaderboard, with only pitchers with at least 50 IP in each half-season being considered so as to remove potential outliers. Non-significant variables were removed, and a final model was created. The resulting model was then used with 2016 data to predict ERA in 2017. The following graph shows those predictions, after being rescaled, plotted against 2016 ERA:

For the most part, the predictions line up with their 2016 counterparts. The labeled data points, though, are the ones I want to focus on. Based on this model, each of them are expected to see their ERA drop significantly from last season to this one and could help provide value in the latter rounds of drafts.

Jeff Samardzija
2016 ERA: 3.81
2017 Projected ERA: 3.40

The Shark has had a rough career. Since becoming a starter in 2012, he’s only had one season in which he’s beaten last year’s mark of a 3.81 ERA. He’s played for four different teams in those five years, he’s on the wrong side of 30 and his name is at the same spot on the pronunciation scale as Jedd Gyorko’s. But he does have a few things going for him. He’s struck out over 20 percent of the batters he’s faced in all but one year since 2011, and he’s pitching in a park where home runs go to die. His average fastball velocity is holding steady above 94mph and it was only two years ago where he had a sub-3 ERA with the estimators to back it up. He’s proven he can put up solid numbers, so the predicted improvement isn’t unreasonable. He had a 3.66 FIP in the second half of 2016 that exactly matched his ERA, a substantial drop from his first half numbers. The biggest contributors were his strikeout rate, which rose from 18.9 to 21.9 percent, and his HR/9, which dropped from 1.15 to 0.94. There’s no reason to think the rates are unsustainable either — his HR/FB dropped to a near-league average (in a normal year) 10.8 percent, and his strikeout rate improved almost directly with an increase in his O-Swing%:

Samardzija was able to get batters to swing at pitches out of the zone more frequently as the season went on, and consequently was able to produce more strikeouts. Steamer projects him for a 3.66 ERA, which isn’t all that far off this model’s prediction. If he can bring his strikeout rate back to what it used to be, and AT&T Park does its job, Samardzija could provide some sneaky value in 2017.

Ivan Nova
2016 ERA: 4.17
2017 Projected ERA: 3.72

Moving to the NL seemingly agreed with the former Yankees second-round pick. After posting an unsightly 4.90 ERA in 21 games (only 15 starts) in pinstripes, he turned his season around in Pittsburgh with a 3.06 ERA and 2.62 FIP in his final 11. Switching leagues undoubtedly helped, but there are more reasons behind his improvement. For one thing, he increased his strikeout rate while decreasing his walk rate — just doing those two would be reason to expect a lower ERA. Perhaps more significant, though, is that he halved his HR/9. Much of this is due to a change in scenery — his HR/FB dropped from 21.3 percent before his trade to just 7.8 percent afterward. Of course, he can’t be expected to repeat his performance. He walked just three batters in 64 2/3 innings, good for a 1.1 percent walk rate and a 17.33 K/BB. While Nova is probably better than Phil Hughes, it’s unlikely that even he can replicate that kind of walk rate. Look for Nova to improve on his ERA from last year, but don’t expect him to be as good as his second half. He’ll fall somewhere in the middle, but even that will be more than useful.

Wily Peralta
2016 ERA: 4.86
2017 Projected ERA: 4.35

Don’t look now (unless you promise to come back), but Peralta had a 2.92 ERA in the second half of 2016. Part of this was admittedly due to an inflated 81.7 percent strand rate, but even accounting for that, he managed a 3.75 FIP and 3.59 xFIP during that stretch. His success can be due largely in part to his increase in strikeout percentage, which jumped from 13.6 percent to 20.8 percent. It’s difficult to determine the exact reason behind this, but one explanation might be his increase in velocity. At the start of the year, his fastball was only averaging under 95 mph, a continuation of his 2015 trend and a disgrace to fireballers everywhere. By August, he was closing in on 97 mph, and presumably striking out batters as a consequence. Here’s his velocity by month since 2014, via BrooksBaseball:

Not only did Peralta see an increase in his strikeout rate, but his walk rate improved as well from 8.7 percent to 6.5 percent, which is the lowest to reasonably expect given his career numbers. His WHIP dropped from 1.88 to 1.15, his HR/9 from 1.64 to 1.02 and his wOBA against from .421 to .295 — seemingly everything improved except his age, but I’ll give him a pass on that account. The secret behind his success? His ability to limit hard-hit balls and induce soft contact. Take a look at the trends for each type of contact rate:

In case that doesn’t do it for you, here’s his Statcast exit velocity broken down by game date, via Baseball Savant (with a linear regression line added for those last few skeptics who aren’t convinced):

Peralta’s not an ace, but he has the potential to help out teams this season. Monitor his velocity during spring training, and buy him for a discount on draft day.

Clay Buchholz
2016 ERA: 4.78
2017 Projected ERA: 4.02

Of all pitchers who threw at least 50 innings in each half of the season, Buchholz improved his FIP the most — his first-half FIP was 6.02, so he gave himself quite an advantage, but he still brought it down to 3.74 following the Midsummer Classic. He’s already proven himself to be a capable pitcher, with four sub-3.50 ERA seasons in his past seven seasons, and now he goes to Philadelphia, where pitchers go to be reborn (see: Hellickson, Jeremy). Also, he moves from the AL East to its NL counterpart. Besides going up against a pitcher instead of a designated hitter, he will be facing the likes of the Braves and Marlins instead of the Blue Jays and Yankees.

Despite the difficulty of his former division, Buchholz still managed to improve as the 2016 season wore on. He marginally increased his strikeout and walk rates, doubling his K-BB% to a still-mediocre 9.3 percent in the second half of the season. While that’s not exactly comforting, it’s worth noting that his walk rate in the first half the season was higher than anything he’s put up since 2008, so it’s not likely to approach that number anytime soon. Furthermore, he was able to bring down his bloated HR/FB rate, despite the league’s general struggle to do so. In the first half of the season, 15.9 percent of Buchholz’s fly balls resulted in home runs, which would have been higher than any single season in his career. In the second half that number improved to 5.1 percent, which was much more reasonable given his average rate of 6.5 percent over the previous three seasons. Steamer projects him for a 4.07 ERA, but it’s not difficult to envision a scenario where he does better than even that.

With all that being said, not all of the pitchers on this list are going to live up to their projections. No model is perfect, and none of these guys have exactly had exemplary careers. But they all showed significant improvement over the course of last year, and that’s a strong indication for what to expect from them in 2017.

The Seattle Mariners finished second in the AL West with an 86-76 record. With a strong offense — they scored the sixth-most runs per game during 2016, led by Robinson Cano, Kyle Seager, and Nelson Cruz — the Mariners starting pitching lagged behind. With fans hoping for a bounce-back performance from Felix Hernandez, the King waned further, seeing an increase in ERA, FIP, and walk rate with decreasing number of strikeouts, first-pitch strikes, and swinging strikes. Hernandez was worth only 1 win above replacement, and at 30, it is unlikely the King will ever become the dominant pitcher he once was.

Despite logging only 121 innings, James Paxton pitched well, leading to a 3.5 fWAR, the highest among all Mariner pitchers. Paxton has always shown some upside, having strung along a 3.43 ERA and 3.32 FIP in 50 starts across four seasons. The 28-year-old has struggled to remain healthy, having only pitched 286 innings since 2013. Throughout the 2016 season, Paxton showed his best form.

Paxton averaged the highest fastball velocity for left-handed pitchers at 96.7 MPH. It was almost 3 MPH faster than the lefty ranked second, Robbie Ray. Among pitchers with 100 innings pitched, Paxton had the fifth-best FIP-, 17^th-best SIERA, and 21^st-best strikeout-minus-walk percentage. Furthermore, Paxton threw strikes. This was evident in his first-pitch-strike rate — 62.4% — and with the Mariners pitcher posting an elite 4.7% walk rate. Throughout the season, Paxton was unlucky, with a .347 BABIP and a strand rate hovering close to 66%. Paxton’s average exit velocity on line drives + fly balls was slightly above average. Couple that information in with a Deserved Run Average (DRA) of 3.09, and it is fair to say Paxton pitched fairly well and should have an impressive 2017 campaign.

One of the reasons for Paxton’s success? He changed his release points:

James Paxton Release Point Changes

In addition, Paxton’s cutter became one of his main pitches. Having reluctantly thrown it in years past, Paxton’s cutter was his second-most-used pitch and was quite effective. Among pitchers who threw 200 cutters, Paxton’s had the best whiffs per swing rate. Batters kept swinging, and they kept missing. It also boasted the lowest wOBA allowed in his arsenal.

James Paxton Cutter Vertical Movement

The big change in Paxton’s cutter, aside from the 1-mile increase in velocity: less rise (In 2014, Brooks Baseball classified the cutter as a slider). As the season wore on, Paxton also got more rise in his fastballs, leading to a greater induction of pop-ups. Paxton’s curveball was second in velocity among left-handed pitchers who threw at least 200. It featured an above-average ground-ball rate and swinging-strike rate.

Paxton showed significant growth during the 2016 season. With Felix Hernandez unlikely to return to his previous form, Paxton has the tools and ability to become the Mariners’ ace. The key for him will be to stay healthy in a pivotal season for both the Mariners and the 28-year-old pitcher.

The Cubs won the 2016 World Series. Though that hopefully isn’t news to anyone, it is still interesting for a variety of reasons. Notably, it was the Cubs’ first World Championship since 1908. I have nothing new or interesting to add to the conversation about the Cubs’ accomplishment. The reason I want to talk about the Cubs now is because not only are they World Champions, they were also clearly the best team in the MLB this year.

Most fans recognize that those two statements are saying vastly different things. The Cubs won more games in 2016 than any other team, had the greatest run differential and had the highest team WAR total, so it is fairly safe to say that they were, in fact, the best team in 2016. But in 21 seasons from 1995-2015 (wild-card era) the team with the best regular-season record (or tied) has only won the World Series four times: the Red Sox in 2007 and 2013 and the Yankees in 1998 and 2009. That’s a 19% success rate. Also since 1995 only three teams that have led the major leagues in team WAR have won the World Series: again the 2007 Red Sox and 2009 Yankees, and also the 2010 Giants. That’s 14%. So that raises the question: is this a problem? Should the World Series champion more frequently be the best regular-season team? Should MLB change things to fix this problem?

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