This post was co-written by John Edwards. If you’re not already familiar with his work, you can (and should!) follow him here. 

Launch angle and exit velocity became a big deal when MLB released them through Statcast at the start of the 2016 season. They instantly told an old story in a new way. It wasn’t surprising to see Nelson Cruz, Giancarlo Stanton, or Miguel Cabrera at the top of the leaderboards. We knew they knocked the snot out of the ball. But now we knew that they knocked the snot out of the ball in excess of 110 mph or better, and at 34 degrees or better.

Two years later and the terms are nearly ubiquitous, even speckled through broadcasts. But they’re often provided without context as colorful notes in single instances. Do we really care how fast the ball went out in the moments we’re watching, or at what angle, as long as it went out? It doesn’t tell us how the dinger or double or snagged liner happened, just that it did; and we just saw it with our own eyes.

So, what about that how? What’s contributing to a player generating that record exit velo or optimal launch angle?

Swing speed.

Swing speed could help inform us of how well a player is tuned into their timing at the plate and where they’re making contact, both of which tell more of our old story in an exciting new way than launch angle or exit velo alone. But the problem with swing speed is we don’t have that data. It’s simply unavailable: while Baseball Savant used to feature bat speed it no longer does.

Fortunately, enough data exists that we have approximations to work with. David Marshall reverse-engineered the formula Baseball Savant used in calculating swing speed – and now we can play around with those numbers!

Let’s look at who the best hitters were by bat speed last season, with a minimum of 100 batted ball events.

Since the formula for predicted bat speed is essentially average exit velocity (AEV) accounting for pitch speed, and AEV is the majority factor in the equation, the leaders in bat speed are also among the leaders in AEV. But there are still some differences, and they’re some very important differences! The speed at which a pitch comes in affects how fast it goes out, so players facing pitchers who throw harder might register lower average exit velocities than a player with comparable bat speed facing pitchers who throw slower.

But bat speed isn’t consistent from plate appearance to plate appearance. Sometimes you check your swing, other times you let loose. But there’s an important trade-off: many of the hitters with superb bat speed strike out frequently, and hitters with low bat speed (Mallex Smith, 50.0 MPH or Billy Hamilton, 51.0 MPH) make a lot of contact without striking out. Low bat speeds allow for more contact and fewer whiffs, but high bat speeds allow for better contact at the expense of greater whiffs. As a result, bat speed is loosely correlated to contact% (R^2 of 0.09), and better correlated to contact% than exit velocity (R^2 of 0.08).

MLB hitters are aware of this loose correlation. Since 2015, they’ve swung .4 MPH slower than average with two strikes, collectively opting for more contact and foul balls so they can stay alive longer in at-bats. But the guys who are the best at managing this are among the best in the game at generating offense, and they don’t all necessarily slow down their swings at the same rate. However, each had a wOBA with two strikes roughly 15% better than overall league average in 2017.

The leaders here show us multiple paths to success with two strikes in regard to players slowing down or speeding up their bats. Beyond that, we get a few bands of players worth noting. Anthony Rendon and Francisco Lindor were really in a class of their own last year when it came to generating offense. They’re the only two players who were about 80% better than the average player overall, and lost less than 60 percentage points of wOBA with two strikes. Rendon only swung .2 MPH slower in those instances while Lindor swung .9 MPH slower.

That’s not to say they were the best, though. Joey Votto (2.1 MPH slower), Austin Barnes (1.0), Mike Trout (.4), Bryce Harper (.4 faster), and Rhys Hoskins (.5 faster) generated the most offense with two strikes. Collectively, they were so much better than most of their peers that they were able to absorb a bigger drop in effectiveness with two strikes in the count and still pose a considerable threat.

Whether swinging slower or faster than average with two strikes, the way these players optimized their swing speed with two strikes informs us of their approach more than their launch angles or exit velos alone. But what about the guys at the other end of the spectrum?

Giancarlo Stanton and JD Martinez had the largest differences in offense created with two strikes of anyone in the league. You can see all the data here. Per John’s own Statcast database, they each had dips in wOBA of more than 160 points when their backs were up against the wall, implying that the way they sold out for power when they were down to their final strike really didn’t work in their favor. They both swung slower than the .4 MPH average drop in those instances, and a peak at their heat maps suggests they were way more willing to hack at offerings out of the zone, too.

A lot of their peers actually acted in a similar manner, too. It turns out that 40 of the 50 players who saw the biggest drop in wOBA with two strikes slowed down their bat in those counts. They’re even more diverse of a group of players than the ones who saw the least drop. There might not be another offensive context where you’ll see Carlos Correa ranked with Lonnie Chisenhall, or Jose Altuve with Michael A. Taylor, or Josh Donaldson with Patrick Kivlehan.

Examining players in this light provides a unique perspective to some of the game’s most critical moments. Despite the variance in the quality between these players, the 2017 approximations suggest that they didn’t exhibit much of a two strike approach at all. Slowing down your bat but expanding your strike zone to chase pitches that are inherently less hittable seems like a recipe for Ks. 

If swing speed can tell us who’s optimizing their approach at the plate — or who isn’t — can it also help us predict a outbreak? We compiled hitters with at least 100 batted ball events in 2016 and 2017 (using batted ball events since our predicted bat speed equation uses exit velocity), and saw which hitters saw the most improvement from 2016 to 2017. Unsurprisingly, swinging harder resulted in much better production at the plate.

Conversely, most hitters who declined in bat speed declined in production (except for Delino DeShields, curiously enough).

But having a slow bat speed isn’t necessarily a bad thing, nor should all players strive to increase their bat speed. We discussed previously how bat speed and contact% are inversely related — not swinging out of your shoes every at-bats means that you have better time to react to pitches and make contact.

For hitters like DeShields, Suzuki, and Gordon, they want as much contact as possible – their maximum bat-speed isn’t comparable to guys like Gallo and Judge, so there isn’t really a way to sell out for power here. Judge and Gallo can get away with striking out so much because the few balls that they put in play frequently go yard, but if someone like Gordon adopted that approach, the increase in power wouldn’t compensate for the increased strikeout rate.

Instead, Deshields, Suzuki, and Gordon produce by making as much contact as possible and relying on their speed to beat out hits on their weak contact. By relying on their speed and balls-in-play for production, it’s beneficial for these hitters to not swing out of their shoes.

Using bat speed to predict breakouts is similar to looking at exit velocity changes to predict breakouts, but has its trade-offs: it’s better in that it accounts for differences in pitch velocities faced, but it’s worse in that our bat speed predictions are only approximations.

They’re still something, though, and they give us more of a predictive look at what goes into making a great hitter than hearing about their launch angle or exit velo in isolated instances.

                                      HOW MUCH DOES APRIL MATTER ?

April provides the first hard evidence of what may be in store for the new season. But like any other single month, it usually has some conspicuously extreme results.  Typically 40% of all teams start notably well (.600 +)  or poorly (.400 minus) where by season’s end only 12% of all teams are at those outer edges of win percentage.  With 85% of the season to go, plenty of time remains for fates to change.  Or do they really change that much ?        This study focuses on the April records of all teams from 2000-2017 compared to their post-April results and odds for making the playoffs.  Two issues are addressed.  First, how closely teams’ remaining five months have corresponded to their Aprils and second, what effect April has had on teams’ playoff chances.

              PREDICTICE VALUE OF APRIL FOR REMAINDER OF SEASON (ROS) RESULTS

April records of all teams from 2000-2017, were divided into six win % categories: Excellent (.650+ win % ), Good (.550 – .650), Slightly Positive  (.500 – .549), Slightly Negative (.450-.499),  Weak (.350 -.449) and Poor (below .350).   Teams in each April win category were compared to their post-April and full season win/loss percentages. The percentage of teams in each April category who played at playoff level (.580), contention level (.540+) or near-contention (.500) after April were also measured as well as the percentage of teams in each April win category who made the playoffs.  Following are the results:

Each level of April performance has had better last 5 months performance and playoff chances than the level immediately below it. Contrasts between the top 1/3 and bottom 1/3 April teams are quite significant.  Two thirds of teams who’ve started well (.550 +) remain near contention by playing  .500 + ball afterward and roughly half make the playoffs.  Only one third of teams with .449 or worse Aprils play .500+ thereafter and just 9 % of these early strugglers have made the playoffs. Nearly 4 of every 5 of all playoff teams were .500+ or better in April and less than 1 in 10 playoff teams started .425 or worse. April has done a good job of quickly identifying contenders and non-contenders.

Much of that is due to April records strongly relating to prior season results. Current full season records also have had solid resemblance to prior full season records. So April simply confirms that many (if not most) teams are headed for the same general fate as last year. While these are generally reliable maxims, they’re hardly infallible. Many teams do change fortunes and sometimes April wrongly suggest things will stay the same.   Likewise April often may indicate a change is coming in the current season and it doesn’t materialize.   That will be explored next – just how often does April “fool” ?

HOW MANY TEAMS ARE APRIL “FOOLERS” ?

Roughly 3 in 8 teams had more than .100 pt change in their April v. rest of season (ROS) win percentage.          For teams who start extremely well or poorly, even huge April v. ROS differentials haven’t change their season’s destinies. 2003 Yankees started the year at .769 win %  but played “only” .596 ball the rest of the way and won over 100 games.  Conversely a .260 point jump in win % for the 2000 Tigers over the last 5 months still didn’t push them over .500 on the season. But for a significant number of teams the last 5 months can wash out much of the good or bad April does. Hot starters fall apart and teams buried in April can rise from the ashes.

For determining both the amount and type of April deception the logical starting point is prior year records. Fully 70 % of teams with 90+ wins the prior year have .500 + Aprils v. 37% for teams with 69 minus wins. Since so many teams tend to have Aprils which are “characteristic” of their prior season , measuring the true amount of “April deception” can be done from two angles: 1 – How many Aprils “characteristic” of last year give true v. false signals of another similar season ?  2 – How often do “uncharacteristic” Aprils end up being true v. false signals of a better or worse year ?

 1^st – How reliable are “characteristic” Aprils.  The following chart illustrates this

As can be seen, prior year good teams (90+ wins) with good Aprils have had twice the chances of making the playoffs as an average team (30%). On the other side, prior year sub-.500 teams with sub-.500 Aprils have had very little chance of making postseason. Only 9 of 138 such teams have overcome their bad starts.   The percentage of teams finishing above .500 is also remarkably different.   80 % of good teams with good Aprils end up plus .500 on the year, where a mere 14 % of bad teams with bad Aprils do. “Characteristic” Aprils are highly reliable indicators of either continued contention or non-contention.    

“Highly reliable” does not mean “perfect”. The 90+ game winner/good April formula didn’t work for  defending world champions 2004 Marlins, 2013 Giants nor defending NL West champion 2008 D-Backs or 2005 Dodgers.  Nor did bad team/bad April deter the 2015 Rangers, 2011 D-Backs, and two Rockies teams (2009, 2007) plus the 2007 Cubs from rising up and making the playoffs.  But these are exceptions.

Applying these principles of “characteristic” Aprils to 2018 would bode well for the Red Sox, Yankees, Astros, Cubs and Diamondbacks. It would not for the Tigers, Rangers, Marlins, Padres, White Sox, Orioles, Royals and Reds.  With only 1 in 15 former sub-.500 /sub-.500 April teams having made the playoffs historical odds would indicate that none of the above eight teams will either.  Of course these clubs weren’t expected to contend but neither were the Braves, Phillies, Pirates, and Mets who were also bad teams from last year.  The difference is that latter four teams have had good Aprils.  So what do their “uncharacteristic” Aprils mean ?

 How often do “uncharacteristic” Aprils send true or false signals of change ?

After a surprising start in April many teams revert to their “true selves” (good or bad) in the last 5 months. But some percentage of uncharacteristic Aprils often correctly signal changes in a team’s fortunes. This time the data is parsed more finely to show where false and true indicators of change may appear.

“Uncharacteristic” April at the extreme ends tend to be fairly accurate in signaling real change, particularly changes for the worse.  Very bad sub-.400 Aprils by former 81+ winners have shown that most are in trouble that season.    Nearly 60 % fail to achieve even a .500 season and only 15 % make the playoffs.  Some 90+ winners (21%)  with below .400 Aprils get off the mat and still make playoffs.  But that contrasts sharply with their prior year 90+ winning brethren who start April at .520 + and have made the playoffs 62% of the time.

What about extremely strong Aprils from previously bad teams ? Prior 70-79 winners who play .600 + in April have done quite well as 44 % have made the playoffs.  One caution, however, is small sample size as only 14 teams fall into this category. For really bad prior year teams (70 wins and below)  who start .600+ most achieved .500 seasons and 1 in 5 made the playoffs. This includes some notable turnarounds:  2000 Cardinals, 2000 White Sox, 2006 Tigers, 2012 Orioles, 2013 Red Sox, 2015 Astros, 2015 Cubs, 2017 Rockies, 2017 D-Backs.    Of course, excitement over great Aprils by prior bad teams should be tempered by the fact that most still have missed the playoffs. This may apply to expectations raised by the start of the 2018 Mets.  Although the 2015-2016 Mets were playoff teams so it’s quite possible their strong April 2018 could be a legitimate sign of revival.  Teams who gained 15 or more wins over the prior year showed an average of 120 points jump in April v. prior year win %.  The Mets have jumped 220 points.

Mildly uncharacteristic Aprils send wrong signals of change more often than not. Which is logical as a 65 game winner who goes 14-13 in April has shown less transformative evidence than one who’s 18-9. Nor has a former 100 game winner with a 12-13 April shown the same reason for concern as a 7 -18 start might.   Only 22 % of “below 70” teams with .500-.599 Aprils finished over .500. Former 70-79 winners with decent .500-.599 Aprils have upped their playoff odds but only to a subpar 20%. Seven teams fit this description in  2018 – Mariners, Pirates, Phillies, A’s, Braves, Blue Jays, Giants.  History says 1 will make it, 2 would be very optimistic.

However, one type of mildly uncharacteristic April is noteworthy. While previous 90+ winners with modestly bad .400-.499 Aprils still have a high recovery rate (77 % end up over .500) they’ve had a big drop in playoff odds. Only 29% make it after such starts.  That is not good news for 2018 Dodgers, Nationals or Twins whose goal is to make the playoffs.  Which brings up the next issue – how does April influence playoff chances ?

   WHAT EFFECT DOES APRIL HAVE ON MAKING THE PLAYOFFS  ?

One old adage is “you can’t win the pennant in April but you can lose it”. While there is definite truth in this, April has the fewest games of any month, and still leaves ample time to recover. The 2001 Oakland A’s had a miserable 8 -17 April but went on to win 102 games.  That said, they were the only sub-.400 April team to win 100 + games and only one of 8 teams who started that poorly and still made the playoffs.   Another 10 such bad start teams played the rest of the season at contender levels (.540+ ) yet failed to make the playoffs and their poor Aprils were instrumental in that.

While it is clearly possible to recover from a weak start, bad Aprils leave a diminished margin of error. Two-thirds of all playoff teams start the year solidly (.530+ ), the vast majority (78%) are at least .500 + and fully 95 % of all playoff teams have avoided disastrous (sub. 400) Aprils.   Teams who’ve stumbled early can’t count on fellow playoff contenders being in the same underwater boats.  They have to play serious catch-up with rivals whose yachts have begun to float away.  An “average” playoff team has a .564 April win percentage.  So a club with a 9 -15 April is trying to catch teams who’ve gone 14-10 or better.  If the 15-9 April teams plays at “only” .537 ROS and gets 88 wins, it takes a .580 ROS from the poor April starter to overcome that.  Playing at .580 + level ROS has been done by only 15% of all teams, which equates to being one of the top three teams in one’s league after May 1^st.

Those 8 playoff teams who started sub-.400 averaged a .604 ROS win percentage.  Five of the eight were .580 + ROS and the lowest ROS win percentage was .572 which translates to 93 wins over a full season.    In addition, there were four teams with a .580+ ROS that missed the playoffs.   Despite being better ROS than their key divisional or wild card rivals those four teams (2004 Giants, 2005 Indians, 2011 Red Sox, 2012 Angels) lost out on the playoffs because of their inferior April records.

The other side is that very strong Aprils can provide a cushion to play at less than .550 ROS. There are even occasions where teams with less than a .520 ROS have made it in due to strong Aprils: 2016 Mets, 2015 Astros, 2014 A’s, 2006 Cardinals, and 2000 Yankees.  Ironically there are two World Series winners in that group (Cardinals and Yankees).   The Cardinals were particularly unusual as they were the only team to make the playoffs with a sub-.500 ROS record.

The lone .600 + ROS who missed the playoffs (2005 Indians) provide a classic example. They were the second best team in the AL after April, outplaying their division rival and subsequent World Series winner White Sox   84-55  to  82-56  ROS.   However, with the 17-7 April of the Sox, and Indians’ poor 9 -14 start, Sox gained a 7.5 game cushion.  The double whammy was that the Tribe’s poor April also cost them the wild card to Boston.   Had the Indians played even 12-13 instead of 9-14 they’d have ousted Boston.   Of course Cleveland’s 13-16 July didn’t help either, nor did going 1 -5 the last week of the season (including a 3 game sweep by the Sox) after the Indians had whittled the lead down to 1.5 games on Sept. 24.  But the April cushion built by the Sox allowed them to withstand an incredible Aug/Sept run by Cleveland and the Tribe was forced to play unbelievably well to stay in the hunt.   This all happened before the second wild card was introduced in 2012, and if it had applied back then Cleveland would have made it as that second wild card.   So has this second wild card now made it easier for April stumblers to recoup ?

HOW THE SECOND WILD CARD HAS CHANGED PLAYOFF ODDS

Adding a second wild card team has changed the odds in some meaningful ways as the following illustrates.

Clearly 87 – 91 wins has had twice the chances of making the playoffs than before. Before 2012 teams needed to be stoking the engine every month to get to 92 wins. However with the bar now lowered to 88 wins since 2012, this allows more margin for error. That margin can go two ways. It can benefit teams who stumble in April and need a lesser ROS to get in.  In fact for teams who started less than .450 and then made the playoffs prior to 2012, the average ROS was .604.  For the three teams that have done that since 2012, the average ROS is .574.   But the post-2011 lower win threshold can also help teams who come out strong in April who don’t have to tear it up in the last 5 months to get in.   So which has it helped most ?  Following is a breakdown of the percentage of playoff teams after 2012 who’ve played at given ROS levels comparing those who had .600 + Aprils v. below .600 Aprils.

Those who started .600 or better had a much lower burden to meet ROS.   Only 26% of the 600+ April teams who made playoffs achieved .580 ROS where over half of the below .600 Aprils had to meet that burden.  While having a .600+ April is no assurance of making the playoffs, it virtually is if teams play at least decently thereafter.   Here is what has happened to the 34 teams with 600+ Aprils from 2012-2017.

As can be seen, 600+ April and 520+ ROS has been a successful formula 20 of the 22 times. The only two to miss were 2013 Rangers (91 wins) and 2012 Bucs (90 wins), who also happened to be the only 2 of 42 teams who’ve won 90+ since 2012 and missed the postseason. Playing .520 is hardly a torrid pace as it equates to 84 wins over a full season.   Playing at .500- .520 ROS isn’t playoff caliber yet 3 teams still made it, all helped by .650+ Aprils (2016 Mets, 2015 Astros, 2014 A’s).   The 7 teams who collapsed to sub-.500 after hot Aprils clearly didn’t deserve to get in.

One final cut of the data can establish who has been helped more by adding 2 more wild cards – the April surgers or the April stragglers. .565 ball equates to 92 wins and .520 ball equates to 84 wins . Teams who play at .565 levels ROS should make the playoffs.  Teams between .520-.564 should contend.

This defines the task confronting a good team with a sub-.500 April. Every year on average there are 4 such teams who rebound with .500+ ROS at a typical year looks like. However, they have to find a way to better several of the 11 teams who’ve started .500 + in April and are still +.500 and there are only 10 playoff spots.   Their only remedy is to play .565 + ball ROS and for one team (2012 Angels) even that wasn’t enough due to a terrible 8 -15 April.     But the .565+ April starter has it easier as a .520-.564 ROS gives him a 2/3 shot of still making it where only one team of 11 who started below .500 (2016 Giants) was able to eek into playoffs with less than .565 ROS.    The April .565 starter still has a shot with a tepid .500 – .520 ROS, but those are all teams as noted before that were red-hot in April (.650+).

Due to the abundance of teams starting well or decently (in April 2018, 13 teams were .565+ and 19 were .500+) the poor starters simply have to be a lot better ROS than their rivals to shove their way through the crowd.   The second wild card has benefitted the good April starters moreso than the good teams with bad Aprils. Of course the same math could be applied to any month, good or bad, so to paraphrase the classic Passover question:  why is April so different from all other months ?

PSYCHOLOGICAL EFFECTS OF GOOD OR BAD APRILS

Despite the fact that April is only 15% of the overall season, when early-mid season personnel decisions are being made, April results can still have considerable impact.   April’s record can influence decisions such as: the patience a team has for a younger player with early season struggles or whether the team tries for a mid- May trade to replace an injured starter and/or considers promoting a top notch AAA player despite his arb clock issues.  When trading season starts in June, 35% – 40% of the team’s record at that point has been baked in by April’s wins and losses.  Even by the late July deadline, April still comprises 25% of the season.  If early results have affected fan attitudes and attendance, ownership may be either more or less willing to commit dollars to bigger name players at the deadline.  These factors may give April importance beyond its mathematical impact on the standings.   That teams tend to mirror their April win-loss %’s as the season progresses may be in part that April can create a sort of self-fulfilling prophesy.

The 2014 Cleveland Indians were 75-60 after April 30^th, the Oakland A’s were 70-63 over the same time. Yet Oakland got the wild card by 3 games over Cleveland due to a 18-9 April v. the Indians 10-17.   Early season results were still impacting July decisions as the A’s were buyers and the Indians sellers.   To be sure, the A’s record worsened post-July and the Indians got better.  But without Jon Lester (2.35  ERA with A’s) and Jeff Samardzija (3.14) who knows how much worse it might have been for the A’s .  While ridding themselves of Justin Masterson may have helped the Indians and trading Cabrera didn’t hurt, how much better would they have been had they gotten an outfielder and starting pitcher in July instead of being sellers ?

Conversely, the 2016 White Sox benefited from a 17 -8 April despite a May tailspin which left them with a 29 -27 record on June 4^th and only 2 games behind in the AL Central. They then traded a very talented younger prospect named Fernando Tatis Jr.  for James Shields.  Needless to say this is a trade that has not worked well short or long term.  Despite the fact that the Sox had 3 straight losing seasons prior to 2016,  management seemed to believe that April/early May represented the success they felt the team was capable of as opposed to the more recent reality of losing.  Successful Aprils can sometimes keep wishful thinking alive for too long.

CONCLUSIONS

Clearly April has proven to be a good proxy for the team’s chances going forward that season. But April records, also have to be viewed in context of all the evidence.  How much of a factor were injuries, over or under performances, or new offseason acquisitions?  Thoughts of dumping contracts and rebuilding, while too premature for May 1^st, are still quite logical particularly for poorer prior year clubs who are off to bad starts.

For clubs in the playoff hunt, April can have real impact since 3-5 wins one way or another can be make/break. Since 2013 teams with 87-88 wins have made the playoffs in 10 of 11 cases where only 2 of 14 teams with 85-86 wins have. Clearly these margins apply to other months’ results too, but as noted before, a very good or bad April can affect team decisions in June and July.   Having some breathing room afforded by a 16-10 April instead of the catch-up pressure of a 10-16 start can play into the psychology as well.

Winning the division winner is far preferable to having to win a one-game playoff as a wild card and April provides a good checkup on division rivals. Both Boston and NY look like they will fight it out all year although Toronto can’t be ignored. In the Central, the Indians’ are in a strong position with their chief rival, the Twins, are 4.5 games back already and the rest of the Central bad teams who’ve started with bad Aprils.   Houston’s good start helps especially since all of their closest chasers (Mariners, Angels, A’s) were sub-.500 teams last year, but the Angels improved through offseason acquisitions.  In the NL East, even though the Nationals are 5 games back they’re chasing teams who were all sub-.500 last year (Mets, Braves, Phillies).  Where the Dodgers, who are 8 games back, are pursuing a D-Back team that won 93 last year.  Turner’s absence hurt in April but so did the lack of offense from the rest of the team which may continue particularly since Seager is lost for the year.    Plus the bullpen woes cannot be overlooked. So April’s 12-16 record cannot be easily dismissed as an aberration.  Nor can the historical evidence of diminished playoff odds (20-25% range) of good teams who’ve had the Dodgers’ kind of April.   We shall all see soon enough.

All stats and tables from Baseball Reference. All batted ball data from Baseball Savant.

After Gerrit Cole’s magnificent start on Friday, and indeed, his string of magnificent starts to open the season, it follows that we would hear a lot about him and his resurgence (some might say his breakout). Lost in the buzz around Cole is the start-of-season performance of another former teammate of his, Francisco Liriano. Liriano has not been otherworldly, but has so far looked more like he did in 2013 than in 2017. That is to say, he’s looked quite good!*

*Before you start playing the Small Sample Size Song, I know this is a small sample size, but I wanted to write about it, so stow your tunes at the doorstep.

Here’s a summary of his 6 starts so far, including Friday’s 7 innings of 1-run ball:

April 2

Pretty good for a guy that ran a 5.66 ERA last season! As you can see, Liriano pitched quite well in his first start, allowing only 6 baserunners over 6.2 innings, making for a WHIP just under 1. He also allowed only 4 line drives, and pitched the Tigers to a 6-1 victory over Kansas City. A pleasant surprise to open the season for the more pessimistic among us (such as me).

April 9

Another surprising start for our friend Francisco. This time, he only went 6 innings, and allowed 2 earned runs instead of 1, but I’d take that from my projected #5 any day. His WHIP here is exactly 1, and he allowed just 2 line drives this time around. More good results from the 34-year old.

April 17

Liriano didn’t look as good this time around, but he was never supposed to be an ace in Detroit. We presumably signed him for depth, and while the deal seemed a head-scratcher at the time, Al Avila is looking pretty smart now that Daniel Norris has gone down. This start, Liriano went 5 innings and allowed 8 baserunners along with 2 runs. He had his highest strikeout total of the season, but aside from fly balls allowed, everything else was worse. The thing is, this still isn’t a bad start. It is not, by definition, a Quality Start™, but it’s still a relatively okay one, and it’s certainly still above what we expected from Liriano.

April 22

Another passable outing from Francisco. 2 of his 3 earned runs came on the next pitcher allowing inherited runners to score, but that doesn’t change the box score, or the fact that he allowed 6 baserunners in 5.1 innings. His line drives were down, but if you’ve been paying attention, you’ll notice that he’s allowed a home run in every start except his first. I’d hesitate to call allowing a home run three starts in a row a definite trend, but it’s certainly starting to look like one. Hopefully Franky can turn it around soon.

April 28

This is more like it! This qualifies as a quality start. Again, 2 of the 3 earned runs were from inherited runners scoring, and again, that doesn’t change the box score, but it’s always nicer to see a 6 in the IP column than a 5. What’s more, Frenchy did not allow a home run for the first time in almost a month. The worries begin again when you take a look at the number of baserunners allowed (8), and are slightly heightened when you see that he only struck out one batter, but on the plus side, his 2 walks are the lowest he’s allowed since his first start with the Tigers.

May 4

Finally, we come to yesterday’s start, which by every metric is the best. Liriano pitched 7 strong innings, allowing 1 earned run on 5 baserunners. What’s more, not only did he not allow a home run — he didn’t allow a single line drive! With only 2 walks again, Francisco seemed to recapture the magic he’d shown in a majority of his 2018 starts.

This article was supposed to be uplifting for Tigers fans. I went into this thinking I’d be able to write nice things about Francisco Liriano and demonstrate that while he’s no Gerrit Cole, he’s still much better than people are giving him credit for.

To my great despair, this does not seem to be the case. Liriano sports a lovely 2.97 ERA and a very respectable WHIP of 1.073. These, plus his H/9 and BB/9, are down a striking amount from 2016, and all but his BB/9 are his best since 2006(!). Unfortunately, his peripherals tell a different story. Liriano’s FIP is 4.13, which is mostly attributable to his paltry 6.4 K/9, itself a point of concern — it’s his lowest ever, by 1.1. But the batted ball data paints an even more dismal picture for Francisco’s future.

Since 2016, Liriano’s hard hit percentage has remained pretty stable, from 33 to 33.7 to 32.7 this year. That puts him in the company of such luminaries as Jeff Samardzija, Brandon Morrow, and Sean Newcomb. That’s a little harsh; this year, he’s closer to guys like Justin Verlander, Clayton Kershaw, and Masahiro Tanaka. But it’s early in the season, and I’d still say Liriano comps closer with JC Ramirez than Corey Kluber.

The more worrying data lie in Liriano’s expected outcomes. Statcast measures expected wOBA (xwOBA) based on batted ball profiles and compares it to actual wOBA. Since Statcast began tracking batted balls in 2015, Liriano’s wOBA and xwOBA have remained within 15 points of each other. This season, there is a 109 point difference. A sobering number, to say the least. Couple that with the fact that his xSLG is a frightening .513, it seems our ostensibly-resurgent pitcher has just been exceedingly lucky. I haven’t watched a Liriano start yet this year, but I have listened to a few, and I distinctly recall hearing quite a few exclamations of astonishment from Dan Dickerson directed toward our middle infield.

What originally started as a post meant to proclaim the newfound prowess of a dubious offseason acquisition ended up as a bleak prediction for his future. But we must remember, in our unexpected despair, that this is baseball, and hope spring eternal for the simple reason that we really have no idea what could happen. Nobody could have predicted Andruw Jones’ death spiral, or Rick Ankiel’s conversion from pitcher to outfielder.

And so I remain foolishly optimistic that Liriano’s success is for real. If he starts to pitch poorly, I will probably appeal to small sample size until at least July, while ignoring the massive amount of cognitive dissonance required to hold that position and still write this article. Luckily for me, I don’t care. I am a Tigers fan first, and I am duty-bound to have faith in our players until their last breath, or at least their last breath in a Detroit uniform.

My realistic prediction is that Liriano will pitch to a 4.5-5 ERA for the rest of the season, and I won’t be disappointed. My homer prediction is that he continues to showcase his recaptured abilities and pitches to a 3-3.5 ERA. I will be pleasantly surprised if that happens. If it doesn’t, well, this team was supposed to suck anyway.

At the end of April, Mike Petriello wrote on the most interesting rookie you need to know more about, Padres outfielder Franchy Cordero. The way Cordero hits the ball, paired with how he runs and can defend, make him more than intriguing. However, Petriello detailed that the margin of error within Cordero’s game could turn him into just about anything — be it Keon Broxton or Aaron Judge.

The two potential comps couldn’t possibly represent further opposite ends of the spectrum. Broxton was demoted to the minors last season and Judge was a Rookie of the Year winner and MVP candidate. So where will Cordero end up shading himself within that vast spectrum? Consider those three players in their first extended stint in the Majors.

Can you tell who’s who?

Players A and C are Judge and Broxton in 2016. The two had largely similar plate discipline. The biggest differences came in Broxton’s reluctance to chase out of the zone, which fueled a lower strikeout total and a high amount of walks. But in between them, as player B, is Cordero. He swung more, chased nearly as much as Judge, made the least contact in the zone by a big margin, and whiffed way more than any of the three. While these are just descriptive numbers — things we can look at after the fact — it’s easy to see how Cordero approaches the batter’s box similar as these two other guys whose difference in success could fill the Grand Canyon.

The real interesting part comes in looking at their plate discipline after their first extended stint in the Majors. It gives us a sense of how each player bought into their skill set, possibly based on the success they did or didn’t have in their debuts.

Notice anything? Broxton went one way in the season after his debut, while Judge and Cordero have gone the other, more productive way. Broxton simply did the things you don’t want a player to do. He reached out of the zone more, made less contact doing it, and created fewer free passes for himself. Judge reached out of the zone less, made more contact, and shrunk his K-BB rate to Rick Moranis levels. It’s funny how one decision can impact so many results.

So far, Cordero’s second extended stint in the Majors has mimicked Judge’s. He’s trending in all the same ways and generating monster power while he’s at it. Petriello noted how he’s part of the roughly one percent of hitters in the entire league to have hit a ball 115+ mph in 2018. That single data point alone is enough to project a pretty positive profile. What he’s really doing to generate that kind of exit velocity, though, is optimizing his mechanics with his contact point. It’s even more impressive when you combine it with how he hits homers. Since Statcast went live in 2015, the greatest average home run distance from any single player is Carlos Gonzalez, at 421 feet. Giancarlo Stanton is second at 420 feet. Cordero has averaged 438 feet per bomb after six home runs so far in 2018.

The Padres seem to believe that Cordero isn’t a finished product just yet, but that he’s good enough to learn on the job. The biggest truth to that is probably most easily visible in his free swinging ways. When he came up last year, he struck out in more than 44% of his plate appearances while drawing a walk in only six percent. But this year, his improved discipline at the dish has resulted in a jump in walks of four percent and an 11% decrease in whiffs. He’s not quite stepping up to Aaron Judge levels, but he’s demonstrating that he’s learned two things. One is that he can let tempting pitches out of the zone go because the contact he does make is strong enough to wait for. The other is that just because a pitch is in the zone doesn’t mean he has to swing. In this sense, it’s like a pitcher sequencing his stuff. By letting pitches go that don’t necessarily play into his strengths, Cordero is giving himself more opportunities to meet the ones that do.

If you’ve been wondering about sample sizes for all the examples above, that’s fair. Most of them are relatively small, potentially opening them to scrutiny because they don’t provide us the stability we crave when evaluating players. But that doesn’t mean they’re useless. In this context, they act in two ways: as indicators of aggression in each of Judge, Broxton, and Cordero; and what they’ve each learned once they had a chance to stay up in the Majors.

Franchy Cordero has proven to be more than intriguing, and he’s found himself in a unique situation that many clubs wouldn’t likely provide a young player. But the Padres are in a unique spot, too, and Cordero, unheralded as he may be so far, may be critical in helping to elevate them in the standings as time moves on.

—

Plate discipline data from FanGraphs. Heat maps from Baseball Savant; gif made with Giphy. 

There is beauty in the penultimate. While hit number 3000 will be the moment that is played at Albert Pujols’ inevitable Hall of Fame ceremony, that milestone could only be reached due to the 2,999 victorious battles waged before it. This is the story of Miguel Castro vs. Albert Pujols. The following article focuses on the complicated beauty of everything that surrounded the penultimate hit of a cherished milestone. The following piece is also showcase of how being in touch with batting analytics can and should help managers make the correct bullpen calls.

Miguel Castro is a young, below average reliever. Since his trade from the Rockies to the Orioles in 2017, Castro has posted an ERA of 3.25 and a WAR of -0.1. These numbers are far superior to the ones posted during his stint with the Rockies, but they are not anything particularly special. During his development, Castro has all but ditched the fastball, as he initially (2015) threw it 63% of the time. By 2017, when he would first duel with the aging Pujols, batters saw a fastball from Castro a mere 1.7% of the time, with even less of a fastball dish rate so far in 2018. Castro now makes his career on Changeups, Sliders and especially Sinkers. Castro threw batters a Sinker 58.8% of the time in 2017, this puts his Sinker rate at 6th among 2017 relievers. These numbers have stayed relatively the same so far in 2018, although Castro has thrown slightly less Sinkers in favor of more Changeups. As baseball writers have lamented the death of the Sinker, Castro has been one of the few pitchers that still rely heavily on the dying pitch.

The Albert Pujols of St. Louis needs no introduction, he is one of the most prolific hitters of all time, and a future Hall of Famer. The Albert Pujols of Anaheim is a different player altogether. Much has been written recently on FanGraphs about the decline of Pujols, so I will spare those details here. Instead, I want to focus on how Castro allowed hit number 2,999 to occur against a batter that had been unable to get on base in all their previous meetings. 

In 5 meetings at the plate that span from August 18th 2017 to May 3rd 2018, Pujols has hit on Miguel Castro one time. On May 3rd, Pujols hit a 96 mph sinker (Castro’s average sinker speed this year) and in doing so acquired his 2,999th hit. In all of their three previous meetings Pujols hit into an out, and on their subsequent meeting Albert was hit by an inside Changeup. So what was different about their 4th meeting? For the first and only time, Castro threw a sinker close to the center of the strike zone. In their previous 3 meetings, Castro threw Sinkers on the inside and outside of the plate, as well as mixing in Sliders that got looking strikes on multiple occasions. On Thursday night however, after a Slider that got called a ball and, just like in previous encounters, a Slider that got Albert looking, Castro threw a Sinker down the middle-right, and paid the price.

From 2016 to 2017, Pujols’ Batting Average slid across the board against every single pitch but two. One of those pitches just happens to be Miguel Castro’s specialty, the Sinker. (The other is the Curveball.)  In fact, of all the pitches that Albert sees on any given day, he has the best chance to get on base while facing a Sinker by a wide margin. In 2017, Pujols batted .338 against the Sinker, compared to .250 against the Changeup, his next highest batting average against a given pitch. Average is not the only thing Albert was better at while facing a Sinker. His stats across the board are at their highest in 2017 and now 2018 when facing the Sinker. Pujols has a higher SLG% and more HRs when facing a Sinker. He had the most doubles in 2017 against the Sinker compared to any other pitch. One of the three Triples in his entire career came against a Sinker. In short, Albert undoubtedly likes to see a pitcher that throws Sinkers.

Analyzing Pujols’ batting average in the strike zone with and without the data for Sinkers since June 1st 2016 shows just how effective Albert has been against the afformentiond pitch. Almost every area of the strike zone saw an increase in average when attempts at Sinkers were factored in. Of special note is the mid to upper right quadrant, where averages increased in every sector. This is the area in which Castro threw the Sinker that would create Pujols’ 2,999th hit.

To futher analyze Pujols’ batting preference for Sinkers, I also compared the heatmaps of Albert’s average against Fastballs compared to Sinkers.

Unsuprisngly, we again see a great disparity between Pujols’ performance when facing Sinkers and when facing other types of pitches.

The conclusion here is that on Thursday night Buck Showalter replaced Chris Tillman with the worst possible choice. With runners on and Pujols’ soon coming up to bat, Showalter subbed in Castro, a pitcher whose main pitch was the favorite of the upcoming batter, who then summarily hit the Sinker into play and scored runs on a breezy double. An event that would put the former St. Louis slugger one hit way from history. If Baseball Clubs would have teams of analytics people, those who could have warned Showalter before he sent out Castro, teams could make more informed decisions about who to put out in relief in high risk situations as seen on Thursday night.

• Data was sourced from Fangraphs and BaseballSavant

Thank you for reading! This is my first piece in the whole baseball analytics realm, and chances are this thing has logical fallacies or something of the like. Any helpful comments/critcism/pointers are much appreciated.

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.

Everyone is looking for keys to get players to elevate the ball. One important point is certainly the so called attack angle. The attack angle is the angle of which the bat attacks the ball (uppercut, level or down). Baseball used to teach swinging down but now you actually want a small uppercut. Players use different cues to achieve that. Common cues are for example leaning slightly back to the catcher and work up with the front elbow.

Up in the zone elevating is pretty easy. The league average launch angle (LA) in the upper third of the zone is 20 degrees. Even Christian Yelich averages 15 degrees in the upper part oft the zone. In a prior analysis I also found out that LA in the upper part of the zone has little influence on wOBA, the 20 lowest average LA guys in the upper third actually had a slightly better wOBA than the 20 highest LA guys (.402 vs .393). 170 out of 182 hitters last year averaged 10-plus degrees.

That is very different low in the zone. The league average LA in the lower third was just 5 degrees and over 30 guys actually had a negative LA. Here the wOBA for the high LA guys is 80 points higher than the low LA guys. The difference is made low in the zone.

View post on imgur.com

So the key for the low LA guys is definitely still to lift the low pitch. So how can this be achieved? You definitely need to swing up and you also need to avoid rolling over and hitting a grounder to pull field which is what the sinker-ballers try to achieve.

One theory is that on low pitches you tilt the shoulders more down and hit with the bat pointing more to the ground. The cue is that for high pitches the bat turns more like a merry go round and on low pitches more like a ferris wheel.

@thepainguy created that pic about the lead arm couple weeks ago pic.twitter.com/LHC28pO8n3

— dominikk85 (@dominikkeul) October 23, 2016

This Ferris Wheel like path makes sure that the bat comes through more straight through from below rather than going across the ball which leads to rolling over.

Mike Trout is so good at this that he is able to sometimes even hit down and in pitches  to dead center for a bomb while most have to pull that ball. Jeff wrote a nice article about this: https://www.fangraphs.com/blogs/jabo-mike-trout-has-a-new-trick/

Of course this Ferris Wheel path also has his disadvantages, for example Trout used to be very bad on high pitches the first 4 years of his career. Still he got away with that because most pitchers would only pitch up like once per at bat and not live up in the zone so Trout would just take but ideally a batter would flatten out the bat up in the zone and swing steeper down which Trout actually did last year causing him to improve up.

But the traditional level bat, level shoulders cue is definitely hurting on low pitches and made the sinker so popular. Now that more guys learn the new swing path the sinker doesn’t work as well anymore but there are still hitters who struggle down (like Hosmer and Yelich).

The pitch up is getting more popular but it can not suppress launch angle. The high pitch lifts itself, when a pitcher pitches up he needs to compensate for the higher LA by more pop ups, lower EV and more Ks.

It is a good sign that Hosmer now thinks about swinging up more but if he wants to increase his LA he either needs to stop swinging at pitches in the lower third and target pitches up or change the rotation axis of his bat to more vertical on top of his attack angle because if you swing up but across the ball on low pitches all you do is hitting your grounders with more topspin.

I measured the vertical angle of some good and bad low ball hitters. On the left of the picture you have Yelich and Hosmer and the other pics are Ortiz, Trout and Votto who are all excellent low ball hitters. All pitches I chose were about knee high and on the inside of the plate because that affects the bat angle.

What you can see is that Hosmer and Yelich have an angle in the mid 20s while the other three are in the low to mid 40s.

View post on imgur.com

That is important because on low pitches the flat barrel will naturally rotate to the left causing top spin similar to a tennis top spin while the steep barrel will rotate up and on the line to CF. https://www.youtube.com/watch?v=MJHTQncT-AA

So to learn to elevate getting a positive attack angle by leaning slightly back and keeping the head over the rear hip during the turn and working the front elbow slightly up definitely is important, but you also need to match the rotation axis of the bat around its long axis with the height of the pitch. The old cue of not dropping the back shoulder and hit with a level bat has its merits on pitches above the waist where a too steep bat angle is indeed bad (see young Trout) but on pitches mid-thigh to knee height this cue is very destructive. In the upper third of the zone the bat angle will be relatively flat ,but in the lower third you need to drop the back shoulder and tilt the rotation axis oft he bat down to around 40-45 degrees.

That means changing the swing isn’t that easy, you have to account for several things. It can be done but it is some work, will we see Hosmer and Yelich making all those adjustments? If they don’t make it they could also adjust less and try to just avoid hitting the low pitch but of course, that would eventually give the pitchers an opening to exploit.

So far there is no improvement for Hosmer. It is early but his GB rate is 58%. He either needs to stay away from the low pitches and target pitches up (and away in his case) or make more changes to his swing.

Introduction

Much has been made of the ‘bullpen revolution’ over the past couple of years. Andrew Miller and Chris Devinski represent relievers on the forefront of the revolution, on teams at the forefront of innovation. The Astros routinely use Devinski in the middle innings, and for multiple innings. Devinski, a skilled pitcher who could close on many teams, provides a bridge to the ‘high leverage relievers’ , and affords the Astros bullpen flexibility that is usually unseen with conventional bullpen management. Conventional bullpen management calls the starter to pitch at least 6 innings, with high leverage relievers then entering to close the game. However, as pointed out in recent articles by Russel Carleton at Baseball Prospectus, starting pitchers often fail to reach the sixth inning. Starters are pitching less every year, and real evidence has been found of starters performing considerably worse towards the end of outings.

Carleteon, and others around the baseball community, have examined different possible rotations constructs that would make starters more efficient. Rotation ideas include tandem starters, four-man rotations, six-man rotations, and others. What I propose is something slightly different and more radical.

My method proposes a group of seven pitchers capable of handling a starters workload. The goal of these 7 pitchers is to maximize the amount of games the team enters the 7th inning with a lead. The traditional model would have the 5 best pitchers pitch every fifth day, and if they are unable to complete six, they are relived, usually by a mediocre reliever. Instead, my system proposes a tandem-type method where decisions are made based on the leverage of the game in different innings. If your goal is to reach the 7th inning with the lead, having a good pitcher available to bridge the gap and conserve the lead makes sense. Specifically, my method calls for a four-man rotation, with each starter going anywhere from three to give innings, rarely six. Here’s the kicker; the ace is not one of the 4 starters. Instead, the ace will often relieve a starter in the fourth, fifth or sixth innings in high leverage situations.

I believe this simulation hones in on one important question. How valuable is a pitcher throwing 6 innings of two run baseball, but doing so once every five days? Is he more valuable pitching three times a week, two important innings at a time, with his runs allowed more likely spread over three games? Many will say that a starter who can go deep into games and keep the your team in games is indispensable. It keeps the bullpen fresh and gives your team a great chance to win. I don’t disagree. But I think the notion that this is the most efficient way to manage a whole rotation is short sighted. By having the best starter available to come in in the third, fourth, fifth, or sixth of almost any game, you’re creating the opportunity to win games you might otherwise lose had you let the inferior pitcher remain in the game. I propose that it’s likely that six innings pitched over three games provide no disadvantage when compared to 6 innings pitched in one game.

And finally, the question of if pitchers can pitch on four days rest if they are only going three-five innings at a time is important to consider. Russel Carleton showed here that pitchers going on three days of rest are largely unaffected in their performance. Previous game pitch count has a much greater effect on current game performance than days of rest. However, the effects of pitching a couple innings every other day or every three days could catch up to a pitcher over the course of a season. Then again, it could be beneficial to the pitcher by allowing them more opportunity to work on their craft. The truth is, a a strategy that calls for pitching 3 innings at a time multiple times a week hasn’t been seen in decades, and the exact effect it would have on todays pitchers is unknown.

Simulation Specifics

I created a Monte Carlo simulation in Python with the ultimate goal of seeing if two teams, comprised of the same exact pitchers, may achieve different results using different pitching management strategies.

I started by gathering pitcher data from FanGraphs. I got ERA data for starters and relievers who qualified over the past three years. Then, using random sampling in Python, I randomly sampled 150 times from the starter data. These 150 samples represent the 150 starters in my simulation, and each starter was placed on one of 30 arbitrary teams. I did the same for relievers, generating seven per team.

Now, with 30 teams, each of differing skill levels, I could simulate a season. While each team had high leverage relievers, for the sake of this model, I only looked at the five starters and the 2 worst relievers ( the mop-up men) for each team. I also insured that the two mop-up men always had worse ERA’s than any starter on their team.

I first simulated the season using traditional rotation management. Each pitcher went as far as he could, and was removed based on simplistic criteria that relied on the amount of runs he had given up and the amount of innings he had pitched. Of course, more goes into deciding weather to pull a starting pitcher, but for the sake of this simulation, I kept the criteria simple. Innings were simulated all at once, with the amount of runs determined by a random number generator which incorporated the pitchers ERA. No offense was used in the run generation, only the pitching talent level was considered. After each game is simulated through six innings, the winner and loser is recorded; in the event of a tie, the away team gets credited with a win.

For the second simulation, the starting rotation is made up of the number two, three, four and five starters. The ace never starts!!

In the second simulation, the starter always pitches at least three innings, regardless of his performance. He goes out for the fourth inning only if he’s given up less than 3 runs and he pitched less than 5 innings in his previous start. He goes out for the fifth inning only if he’s given up less than 2 runs and he pitched less than 4 innings in his previous start. He goes out for the sixth inning only if the ace and the two mop-up men are not available. If the starter is pulled, either the ace or one of the mop-up men is brought into the game, depending on the leverage of the situation.

The criteria above is one instance in which the starter is removed due to rest or runs given up. There is another instance in which the starter can be removed. If the Leverage Index is greater than 1.1 heading into the fifth or sixth innings, and the ace is sufficiently rested (determined by other criteria), the ace will be brought in.

Results

For each of the thirty teams in my fictional league, I simulated 100 seasons where that team used the new pitching strategy, and every other team used the old pitching strategy. On average, teams added .6 wins a season. The max wins added was 1.32, the min wins added was -.666. There does seem to be a very slight advantage to be had from saving your ace for the big moments.

Conclusion

The future of the five-man rotation is in question. As teams and analysts explore alternate strategies, the question posed by this project will certainly be raised. Through this analysis, it seems the value of a start by an ace once a week can be matched, and beaten, by 2 or 3 separate two inning high leverage outings by that same ace. Furthermore, it is known that pitchers moving to the bullpen perform better because they are able to exert more energy per outing, since they pitch less than starters. A question remains, however, on whether pitching less per outing but pitching in more games allows pitchers to exert more energy per outing, despite pitching the same amount over a given time, say a week.

The practicality of this simulation is lost a bit in simplicity and in the unknown. Runs are modeled using a random number generator, and pitching changes are ruled by a small series of if-and-elif statments. Not to mention the simulation only allows pitchers to be subbed before an inning starts. Certainly, real baseball is more complex. Regardless, I believe the simulation provides a framework to understand different pitching strategies. Future work could involve an examination of other pitcher management strategies as well as added complexity.

All in all, a strategy such as the one proposed here calls for very short rest and short outings throughout the year, something that hasn’t been seen in decades. Furthermore, a team moving their ace to the bullpen to add marginal wins would face an uproar from the fans, the media, and the ace himself. It’s fun to imagine aces pitching this way in a simulation, but the reality of a strategy like this is a little far fetched. Nonetheless, as starters start to pitch less, good pitchers are going to be needed to bridge the gap to the late innings. There is value to be had in shortening outings and insuring good pitchers pitch in important situations.

Although posted by Jack Marino this was a truly collaborative effort by Grant Carr, Justin Clark, Jake Fisher, Jack Marino, and Noah Nash.

The introduction of Statcast technology in 2015 has allowed analytics departments around the MLB to quantify aspects of the game that until the last few years were impossible to measure. One of the previously unanswerable questions that Statcast has allowed us to examine is the optimal launch angle for each hitter in the MLB. If the free agent market this winter has told us anything, it is that teams are now becoming more sabermetrically savvy with their checkbooks and are understanding the value a player adds to their roster in a far more analytical sense. For example, Mike Moustakas may have hit 38 bombs last year, but the fact of the matter is that he is a two WAR player with a below average glove and minimal range. Moustakas’s late signing for just $5.5 million plus incentives after declining a $17.4 million qualifying offer indicates that the market seems to have a much better understanding of his value than it has in years past. Since optimizing launch angle is defined as adding the greatest possible value per at bat, finding the right launch angle is undoubtedly a smart decision for a player trying to put himself in the best possible position to break the bank during free agency.

What makes this optimization problem so difficult is that simply knowing a launch angle on a certain ball in play very rarely tells us anything definitive about the outcome of that ball. The reason for this is that batted ball outcomes are extremely dependent on other variables such as exit velocity and the positioning of the opposing team’s defense. For example, a 25° launch angle hit above 100 mph is in most cases a home run; however, a ball hit at that same angle at 80 mph is almost surely a flyout. To gain a complete understanding of this relationship, we think the following visuals can be extremely helpful, but this relationship can also often make a lot of intuitive sense.

Never shying away from a challenge, we decided to dive into this problem and see what sort of algorithm we could develop to take a hitter’s batted ball data in 2017 and calculate an optimal launch angle for that hitter in 2018. The data we used for this project are from baseballsavant.com.

Since calculating an optimal launch angle will most likely result in an adjustment of a player’s swing, it is important to understand the possible repercussions of that change. For example, to increase launch angle, one definitely will need to adjust swing path to a more uppercut swing, which could in theory lead to a higher strikeout rate. For this reason, before recommending any changes, we wanted to make sure we understood the relationship between launch angle and strikeout rate. Using players with over 100 at bats during the 2017 season, we constructed the following plot and built a linear regression model trying to predict strikeout rate from launch angle. What we found was an R-squared value of approximately .05, meaning that only 5% of the variability in strikeout rate was accounted for by launch angle.

Following this conclusion, it seemed fair to move on and continue our analysis under the assumption that any tweaks we make to a player’s swing will not cause a drastic change in strikeout rate or quantity of balls put in play.

We think at this point, major strides have already been made in understanding launch angle, especially the possibly unexpected result of our linear model above. However, the problem still has not been solved and our methodology for solving it has not yet been revealed!

The method we came up with was to use simulation to increase the sample size of exit velocities based off the distribution of our hitter’s and calculated comparable players’ batted ball data, take these simulated exit velocities and fix a launch angle to them, use k-nearest neighbors on our hitter and comparable players’ to get a likely outcome for that batted ball type, then see what launch angle maximizes a hitter’s expected weighted On Base Average (wOBA) given the simulated distribution of exit velocities and k-nearest neighbor outcomes.

That may be a lot to throw at a reader all at once, so let’s examine a case analysis of this study using San Francisco Giants outfielder Andrew McCutchen. McCutchen’s 2017 season saw him have an average exit velocity of 88.4 mph and an average launch angle of 14.2°. Optimal launch angle is extremely player specific, so the first thing we have to do is gain a complete understanding of McCutchen’s batted ball profile. The chart below does an excellent job of helping us to do exactly this. For example, it appears McCutchen never surpassed a launch speed of 110 mph off the bat in 2017, had a pocket of homeruns between 23-30° and 95-110 mph, had a band of doubles at similar exit velocities but lower launch angles, and a group of singles at low launch angles and an even larger distribution of exit velocities than before. Now this is a great plot for understanding comparable players, but the fact of the matter is that there are entirely too many players to compare on a plot by plot basis.

To combat this problem, we first narrowed down the field to players who took over 100 at-bats during the 2017 season and then used the technique of Principal Component Analysis to narrow down the field of comparable players even further. For the variables in our PCA, we chose many different metrics using the Baseball Reference play index including home runs, triples, doubles, and singles per at bat, fly ball rate, ground ball rate, WPA, RC, and oWAR amongst others. After completing the analysis, we chose our first four principal components, which accounted for 76% of the variability in the original variables. We squared and summed the differences of each player’s first four principal component scores and created a list of the top 20 players whose four squared distances were the smallest. From here, we removed players who did not bat righty to try to account for the lefty/righty splits a righty batter like McCutchen may have. Then we went plot by plot trying to match the pattern of hits and exit velocities to McCutchen’s plot above. After this qualitative piece of our analysis was complete, we came up with Adrian Beltre, Alex Bregman, Brian Dozier, and Eugenio Suarez as our four comparable players. Their distribution of hits graphed with McCutchen’s can be found below and are remarkably similar.

When we considered how to create this optimal launch angle, we knew we wanted to somehow incorporate different areas of the strike zone, as the optimal launch angle on a ball up and in is likely not the same as on a ball down and away. To combat this potential problem, we divided the strike zone into 9 sections and created the following heat maps for both McCutchen and McCutchen and his comparable players. To understand these heat maps, it is important to note that the first number in each zone is the average launch angle on balls in play for that player or groups of players during the 2017 season in that zone, while the second number is the average exit velocity on balls in that zone.

Looking at McCutchen’s heat map, we saw clear variation in exit velocity, launch angle, and offensive outcome (in this case wOBA) by zone, which confirmed our belief that we would have to take zone-specific differences into account. We decided to find the optimal angle for each of our nine zones, planning eventually to combine those angles into a single, optimal number unique to McCutchen. Looking at zone-specific data for McCutchen and his comparable players, we ran into the same challenge that motivated finding those comps in the first place: lack of data. There was simply not enough data on launch angle, exit velocity, and wOBA between McCutchen and his comps to perform the kind of verifiable analysis that comes with a larger sample size.

To overcome this challenge, we turned to simulation. Specifically, we searched for a distribution that would allow us to generate reasonable launch velocities for a given zone. With this distribution, we could test possible combinations of launch angle and exit velocity to explore which zone-specific angles might be optimal. Looking at a histogram of launch velocities for McCutchen and his comps, we observed a pronounced left skew across all nine zones. With this trend in mind, the Weibull distribution made sense for its flexibility in modeling real-life processes that feature multiple varieties of skew. Implementing maximum likelihood estimation on the zone-by-zone data used to generate the heat maps gave us the parameters for nine Weibull distributions that closely characterized the trends in exit velocity we observed for each zone. For example, the fit of our Weibull distribution in zone 1 shows the clear left skew, but also the excellent job of the flexible Weibull to fit the data.

In all, this process allowed us to generate any number of exit velocities for each zone that might reasonably approximate the kind of speeds we see on actual batted balls, leaving us with finding a range of launch angles that could be optimal for a given zone. While looking at the distribution of launch angles for McCutchen and his comparable players, we decided to consider only the launch angles between the 25th and 75th percentile for each zone. This gave us a number of discrete angles to test in conjunction with each zone’s launch velocity distribution for optimal offensive performance.

For each possible angle within a given zone, we generated 1000 exit velocities from that zone’s respective Weibull launch velocity distribution. Next, we used k-nearest neighbors to assign a wOBA value to every launch angle, exit velocity pair by examining similar pairs of launch angles and exit velocity and their associated wOBA within the McCutchen and comps dataset. This procedure gave us 1000 wOBA values for every launch angle that might be observed in a particular zone. By taking the mean of those wOBA values for each possible launch angle, we gained a more complete sense of what kind of offensive performance might be associated—on average—with the various launch angles for each zone. To identify which angle in each zone was optimal, we simply chose the launch angle with the highest associated wOBA.

Now that we had our nine optimal launch angles in each of the nine zones, we wanted to come up with a way to get to one optimal launch angle. When coming up with this angle, we knew it would be important to incorporate how often a player faces pitches in each zone as well as some measure of his talent level in each zone. To incorporate these two factors into our analysis, we started in zone one and took the product of the proportion of pitches McCutchen saw in zone one and his contact percentage in zone one, then repeated this process for the other eight zones and took the proportion of each of these products to create linear weights. Once we had our linear weights, we simply multiplied each zone’s weight with our previously calculated optimal launch angle in that zone and took the sum of these products. A visualization of this process can be seen below:

To finalize our findings, while Andrew McCutchen finished his 2017 regular season with an average launch angle of 14.2°, our advice based off our model is that he lower his average launch angle to 13.0°.

Well, there’s our methodology, not saying it’s perfect, but we’re certainly happy with our results.

About the Authors:

Grant Carr is a mathematics and economics double major at Kenyon College.

Justin Clark is a mathematics major at Kenyon College.

Jake Fisher is a history major at Kenyon College.

Jack Marino is a mathematics and economics  double major at Kenyon College.

Noah Nash is an english major and art history minor at Kenyon College.

The group can be contacted at marinojc@kenyon.edu with any further questions.

The same model as before, basically.

No two batters are the same. Identical, perhaps, but not exactly the same. Two identical stat lines can be produced in a myriad of ways. However, batted ball contact can be telling – it’s difficult to confuse a barreled ball to a soft-hit ball. I wanted to apply my Statcast hit prediction model to 2017 batters, to see which batters were easily predictable and which had outside factors beyond batted ball statistics impacting their hits and outs.

First, I would like to mention I made a major change to my model. Initially, I trained the model on a portion of 2017 data and applied the model on the rest of the season’s data. Between then and now, I added 2016 data to my SQL database and chose to train my model on all of 2016 for use on all of 2017. Here are the updated results:

Identical. My model got slightly worse at dealing with outs (a percentage point increase I misclassifying outs as hits, corresponding with the same decrease in predicting outs as outs) and slightly better at predicting hits (a .6 percentage point increase in predicting hits as hits and the same decrease at predicting hits as outs). The purpose behind retraining the model was that now I can apply it to the entirety of 2017 instead of merely 80% of 2017 – which, when dealing with small sample sizes (such as batter-specific analysis), helps.

My model did surprisingly well at predicting the correct batted ball outcome for hitters. I limited the sample of hitters to those with 100 or more balls in play, removing pitchers and many part-time players (though, in complete honesty, I don’t think sample size matters much here, as my model predicts each batted ball in isolation – please convince me either way in the comments).

My model did pretty well! The model’s predictions ranged from 72-89%. Above are the top and bottom five players in terms of prediction accuracy (Correct%) as well as the top and bottom 10% averages. I included some statistics I thought would correlate with prediction accuracy – batting average on balls in play (BABIP), isolated slugging (ISO), pull to opposite batted ball ratio (Pull/Oppo), fly ball to grounder ratio (FB/GB), hard hit percentage (Hard%) and speed score (Spd).

My hypothesis was that higher speed and a more even spray distribution (a Pull/Oppo ratio closer to one) would decrease the performance of my model. Through comparing the top and bottom 10% averages, we see some correlations with some variables and model accuracy. BABIP, Pull/Oppo, FB/GB, Hard% and Spd all appear to impact my model’s accuracy. One thing these all have in common is that they impact BABIP – better spray charts increase BABIP, fewer ground balls decrease BABIP, harder hit balls can be harder to field (lower reaction times) and increase BABIP, and faster players can beat out infield hits at a higher rate. Clearly, some of these stats work independently of the others- for example, Buster Posey and Victor Martinez have low speed scores yet higher sprays (lower Pull/Oppo).

This has been a fun model to explore. Essentially, the ability to predict hit outcomes from batted ball statistics depends on BABIP – or, if you rather, factors that influence BABIP. Until deeper fielding data is publicly available, such as initial start position, catch probabilities, etc., I don’t think I can improve this model. Ideally, I hope to develop it into a tool to evaluate batters’ contact abilities sans luck. Let me know if there are any batters specifically that you’d like to see, or other ways I can explore the model!

– tb