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Which Pitchers Got Burned on the First Pitch in 2016?

It’s always good to get ahead in the count.  The difference in average run expectancy between a first-pitch strike and a first-pitch ball is over .07 runs.  However, trying to get ahead can go horribly wrong for a pitcher.  With 2016 now in the rear-view mirror, I wanted to take a look back at this past season to see which pitchers got hurt more than most on the first pitch.

First, we have to decide on what it means to be “hurt” on the first pitch.  A pitcher could give up a bunch of singles on the first pitch but not get hurt too bad, depending on the situation.  If a starting pitcher is given a five-run lead, he could give up a few first-pitch hits here and there simply from laying fastballs in the zone trying to get some quick outs.  If he gives up a two-out double with no one on up five runs, the win expectancy isn’t going to change much.  Similarly, in a tie game late with a runner in scoring position, a base hit could give the opposing team the lead for good, leading to a huge change in win expectancy.  Since we are interested in context-dependent numbers, we will use a context-dependent statistic.  Win probability added (WPA) will do the trick.  Adding up WPA will tell us the general story of what happened on the first pitch. Mainly, who got burned.

Using 0-0 count data taken from Retrosheet and Baseball Savant, we will rank the top five relievers and top five starters by their WPA to determine who got hurt on the first pitch in 2016.  However, adding the WPA for all pitchers in 2016 is a daunting and unnecessary task as there are over 600 pitcher stats to comb through.  To get a better sense of the worst performers on the first pitch, we will make a simple rate stat of runs allowed on the first pitch divided by the amount of at-bats the ball was put in play on the first pitch. This stat will allow me eliminate a majority of the pitchers for our list.  If a pitcher has a low to average runs to at-bat ratio, they probably didn’t get burned too much on the first pitch. To make our sample size even smaller, we will use the thresholds of 25 ABs for relievers and 50 ABs for starters to qualify for the data set. This gives us 31 qualified relievers and 48 qualified starters.  Additionally, I have included the at-bats throughout the season that I have deemed to have “hurt the most” for a particular pitcher.  These at-bats led to the largest swing in win expectancy last season for each pitcher.  So, for 2016, here are the pitchers that would like more than a few first pitches back:


#5 Tyler Lyons

(Okay, there were relievers with worse WPAs than Lyons, but when you get hit this hard on the first pitch, I have to make you #5. Hey, it’s my list! I can do what I want!)

Lyons actually had a positive WPA on at-bats where the ball was put in play on the first pitch.  However, most of these ABs happened in garbage time which is the major reason why his WPA is close to average.  He lands on this list due to a .480 batting average on the first pitch, including seven extra-base hits.

2016 breakdown

  • ABs: 25
  • Hits: 12 (3 doubles, 4 home runs)
  • Runs Allowed: 8
  • Runs Allowed per AB: .320
  • WPA: 0.075
  • AB that Hurt the Most: May 6th vs Pirates
    • Tasked with keeping the Cardinals close down one run in the 6th, Jung Ho Kang gives the Pirates a commanding 3-0 lead heading into the late innings
    • Cardinals Win Expectancy before AB: 34%
    • Cardinals Win Expectancy after AB: 13.7%
    • Swing in Win Expectancy: 20.3%

#4 Casey Fien

Fien had rough go of things in 2016, finishing with 6.43 FIP and a -0.8 WAR.  Causing most of that damage were the 13 homers he allowed in just 39.1 innings, including five allowed in 25 ABs on the first pitch.  However, teams will continue to take chances on him as he possesses well above-average spin rate (2,504 RPMs) on his fastball, which makes him a cheap potential bounce-back candidate.

2016 breakdown

  • ABs: 25
  • Hits: 15 (3 doubles, 5 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .400
  • WPA: -0.304
  • AB that Hurt the Most: April 4th at Orioles
    • With no score in the bottom of the 5th,  The Orioles get on the board when Adam Jones doubles off the right-center field wall to bring in two runs
    • Orioles Win Expectancy before AB: 72.9%
    • Orioles Win Expectancy after AB: 85.4%
    • Swing in Win Expectancy: 12.5%

#3 Tony Cingrani

There isn’t a whole lot of mystery as to what Cingrani is going to throw toward the plate.  In 2016, he threw his fastball over 89% of the time.  The first pitch of the at-bat was a huge nemesis for Cingrani last season as nearly half of his runs allowed came as soon as the hitter stepped in the batters box.

2016 breakdown

  • ABs: 25
  • Hits: 10 (4 doubles, 1 triple, 1 home run)
  • Runs Allowed: 13
  • Runs Allowed per AB: .520
  • WPA: -0.547
  • AB that Hurt the Most: May 1st at Pirates
    • Down 3-1, Sean Rodriguez gets the Pirates within one with an RBI triple.  The Pirates tie the game two batters later with an RBI single by Matt Joyce.
    • Pirates Win Expectancy before AB: 19.8%
    • Pirates Win Expectancy after AB: 45.3%
    • Swing in Win Expectancy: 25.5%

#2 Justin Wilson

Good news? Wilson posted an average leverage index of 1.48 in 2016, which was a career high. Bad news? Wilson posted a WPA of -0.84, which was a career low.  The first pitch of the at-bat was no exception.

2016 breakdown

  • ABs: 25
  • Hits: 13 (2 doubles, 3 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .400
  • WPA: -1.108
  • AB that Hurt the Most: August 9th at Mariners
    • Three-run homers hurt.  They really hurt when your team is up three runs in the bottom of the 8th with two outs and one swing later, its a tie game.
    • Mariners Win Expectancy before AB: 8.8%
    • Mariners Win Expectancy after AB: 52.4%
    • Swing in Win Expectancy: 43.6%

#1 Tom Wilhelmsen

Like most of the pitchers on this list, 2016 was a year to forget for Wilhelmsen.  He posted a career low 6.38 FIP and was worth a -1.0 WAR.  Its not all bad, though, as Wilhelmsen pitched better upon his return to Seattle in June, but didn’t pitch well enough to warrant a roster spot at this point for  2017 when he was released in November.  He is currently a free agent.

2016 breakdown

  • ABs: 27
  • Hits: 15 (4 doubles, 2 home runs)
  • Runs Allowed: 10
  • Runs Allowed per AB: .370
  • WPA: -1.149
  • AB that Hurt the Most: May 15th vs Blue Jays
    • Tied a 2 in the 6th, Jose Bautista hits a bases clearing, three-run double to put the Blue Jays in a commanding position to win the game.
    • Rangers Win Expectancy before AB: 47.1%
    • Rangers Win Expectancy after AB: 15.2%
    • Swing in Win Expectancy: 31.9%


#5 Dallas Keuchel

After a Cy Young season in 2015 where he was worth nearly six wins, Keuchel regressed a bit in 2016.  Even with his struggles, Dallas was still an above-average pitcher with a 92 FIP-.  Shoulder issues cut his season short in late August but look for Keuchel to come back stronger next year for what projects to be a strong Astros club, hopefully with better results on the first pitch.

2016 breakdown

  • ABs: 81
  • Hits: 27 (6 doubles, 1 triple, 7 home runs)
  • Runs Allowed: 21
  • Runs Allowed per AB: .259
  • WPA: -0.938
  • AB that Hurt the Most: April 21st at Rangers
    • Here’s that devastating three-run homer again.  Given a 1-0 lead in the top of the first, Keuchel gives the lead right back and then some when Ian Desmond hits the first pitch he sees out to give the Rangers a 3-1 advantage.
    • Rangers Win Expectancy before AB: 44.4%
    • Rangers Win Expectancy after AB: 70.1%
    • Swing in Win Expectancy: 25.7%

#4 Felix Hernandez

Sometimes a pitcher can end up on this list simply due to a larger amount of at-bats against him throughout the season.  This could be the case with King Felix as he had 92 at-bats where the batter put the first pitch in play.  Between the starters and relievers, Hernandez had the lowest batting average against him on the first pitch at .326.  Unfortunately, a majority of these hits came at inopportune times for Hernandez, which influenced his WPA more than most.  Ultimately, this is what we are looking for as timely hitting can hurt a pitcher quite a bit.

2016 breakdown

  • ABs: 92
  • Hits: 30 (8 doubles, 4 home runs)
  • Runs Allowed: 23
  • Runs Allowed per AB: .250
  • WPA: -1.087
  • AB that Hurt the Most: July 20th vs. White Sox
    • One out away from getting out of the first with no damage, Todd Frazier gives the White Sox a huge boost early with a first-pitch three-run shot to left.
    • Mariners Win Expectancy before AB: 50.4%
    • Mariners Win Expectancy after AB: 23.7%
    • Swing in Win Expectancy: 26.7%

#3 Sonny Gray

2016 was an injury filled season for Gray as he had two separate stints on the DL before making a final appearance on September 28th after nearly two months on the shelf.  Injury issues to his right trap and forearm clearly played a role in his poor performance last season.  Did those same injuries also play a role in being hit hard on the first pitch?  While I can’t say for sure, it is possible.  Hopefully those injury issues are behind Gray in 2017 as he looks to return to being one of the best starters in the American League once again.

2016 breakdown

  • ABs: 60
  • Hits: 28 (4 doubles, 5 home runs)
  • Runs Allowed: 17
  • Runs Allowed per AB: .267
  • WPA: -1.322
  • AB that Hurt the Most: May 15th at Rays
    • With the score tied at 1, Brandon Guyer gives a huge advantage to the Rays on the first swing with a three-run shot to left.
    • Rays Win Expectancy before AB: 54.4%
    • Rays Win Expectancy after AB: 82%
    • Swing in Win Expectancy: 27.6%

#2 Kyle Gibson

Right shoulder issues early, home runs and a .330 BABIP could sum up Kyle Gibson’s 2016.  As far as the first pitch goes, homers were again an issue as he allowed eight of them throughout the season. I wouldn’t expect the same in 2017.

2016 breakdown

  • ABs: 75
  • Hits: 35 (9 doubles, 8 home runs)
  • Runs Allowed: 23
  • Runs Allowed per AB: .307
  • WPA: -1.962
  • AB that Hurt the Most: August 17th at Braves
    • With Gibson given a 2-0 lead into the 3rd inning, Freddie Freeman altered the game back into Atlanta’s favor with one swing.
    • Braves Win Expectancy before AB: 26.6%
    • Braves Win Expectancy after AB: 51.2%
    • Swing in Win Expectancy: 24.6%

#1 David Price

When David Price signed last winter with the Red Sox for $217 million, he was expected to be the ace of the staff right away.  That didn’t happen as quickly as Red Sox nation would have liked in 2016 as Price posted his highest FIP- since his rookie season in 2009.  This continued into the postseason with another subpar performance against Cleveland in the Divisional Series.  Ten home runs allowed on the first pitch last season surely didn’t help the below-average season — for David Price standards — in 2016.  With Chris Sale now in the fold to take some pressure off, I fully expect Price to return to being the dominant pitcher we have seen in the past in 2017.

2016 breakdown

  • ABs: 109
  • Hits: 43 (6 doubles, 1 triple, 10 home runs)
  • Runs Allowed: 26
  • Runs Allowed per AB: .239
  • WPA: -2.218
  • AB that Hurt the Most: June 8 at Giants
    • What a time for your first career homer!  With the score tied at 1 in the bottom of the 8th, Mac Williamson unties it on the first pitch of the inning giving the Giants a one-run advantage late.
    • Giants Win Expectancy before AB: 59.3%
    • Giants Win Expectancy after AB: 88.5%
    • Swing in Win Expectancy: 29.2%

**Things to keep in mind**

  • I ranked these two lists by WPA. This doesn’t exactly mean that David Price performed more than twice as bad as Dallas Keuchel on the first pitch. Due to its additive nature, Price could have a higher WPA because he had more opportunities to perform worse than Keuchel.  WPA was just a way to rank the pitchers.
  • There is no predictive nature to WPA. If I were to do this list again for 2017, I would expect a completely different list.
  • This was just a fun list to see who got hit hard on the first pitch in 2016. By no means is this an analysis of why a pitcher performed poorly in 2016. While poor performance on the first pitch certainly can aid in an overall poor season, just because a pitcher performs poorly on the first pitch one at-bat doesn’t mean we can expect him to keep performing poorly the next at-bat.
  • It’s not fun getting burned on the first pitch…


The Reds Have a Spin Rate Problem

With baseball’s annual winter meetings taking place this past week near Washington D.C, I want to take a look at the Cincinnati Reds and a potential way of looking to improve upon a historically bad pitching staff in 2016.  While they did just post the worst WAR by a pitching staff since 1900, they were completely average somewhere else, which likely aided them towards the path of history no team wants to make.  The Reds threw the highest amount of average four-seam spin-rate fastballs in 2016.

We are just scratching the surface on spin-rate research.  While we can’t say much for sure about ways to improve spin rate or why it differs from pitcher to pitcher, we do have a pretty good idea it’s good to be different.  The ultimate goal of pitching is to disrupt timing, create mis-hits and have swings and misses.  The more deception a pitcher can create by being further away from average spin on either the high end or low end of the spectrum, the better off they appear to be.  This was a major problem for the Reds last season as the they threw a whole bunch of average towards the plate.

Taking spin-rate data from, I looked at all 30 teams and their four-seam fastball data.  I set a minimum of 50 four-seams thrown by a pitcher to be included in the data set.  Team-by-team totals show that the Reds threw the fifth-most four-seam fastballs in 2016:

  1. Rays: 10823
  2. Diamondbacks: 10667
  3. Marlins: 10606
  4. Rockies: 10102
  5. Reds: 9991

The average spin rate for the four-seam fastball in 2016 was 2241 revolutions per minute.  This season, the Reds pitching staff was pretty close to the MLB mean at 2232 RPMs. Only the Astros, Athletics and Mets were closer to the mean (2240, 2245, 2248 respectively).  Now, let’s create a bucket we will call “four-seams around average” and see what we collect. This bucket will include pitches that were 50 RPMs higher than 2241 and 50 RPMs lower than 2241 for a 100-RPM range of 2191-2291. Next, I’ll use data from the 10 teams closest to the MLB mean, the most “average” spin teams, to determine who threw the most “average fastballs.”  Here are the top five totals:

  1. Reds: 3165
  2. Mets: 2674
  3. Athletics: 2072
  4. Angels: 2056
  5. Braves: 1973

As you can see, the Reds ran away with what we have designated as “average fastballs” with nearly 500 more than the Mets and over 1,000 more than the third-place A’s.  You could be saying to yourself that the Reds may have thrown so many average-spin fastballs because they threw the fifth-most four-seams in the majors this past season.  And you would be right since a larger sample size obviously affords the chance of more average pitches to be thrown (especially if the data follows a normal distribution like ours does). So I’ll bring in another measurement to further support that the Reds were very average in 2016: standard deviation

I’m sure most people are familiar with standard deviation (SD) so I won’t waste time going into formula, but an easy explanation is it’s one way of measuring dispersion in a given data set.  The lower the SD, the closer all the data points are to the mean.  Looking again at our 10 average spin-rate teams and the standard deviation for each team’s data set, here are the five lowest teams in terms of SD:

  1. Reds: 123.99
  2. Mets: 138.56
  3. Angels: 142.838
  4. Astros: 153.105
  5. Cardinals: 157.645

There are the Reds leading the way again!  Let’s attempt to put all 10 teams on an even playing field by taking a sample of 1,000 four-seam fastballs from each group.  The mean of this sample is our random variable.  In R, we will use the replicate function to generate 10,000 of these random variables to learn about its distribution.  After running the simulation, the random variables follow normal distribution which is something we already knew.  What I was interested in is if the team with the lowest standard deviation would have changed after each team had the same sample size. Here are the lowest five teams in SD after 10,000 simulations:

  1. Reds: 3.68
  2. Mets: 4.106
  3. Angels: 4.126
  4. Astros: 4.472
  5. Cardinals: 4.637

No change. By having the lowest SD in the group that was deemed to be the closest to the MLB mean in four-seam spin, and a test of a random sample of 1,000 pitches simulated 10,000 times, this further supports that the Reds pitching staff has a spin-rate problem, and is not just a product of a larger sample size.  In fact, the Reds had the lowest standard deviation of all 30 teams!

So where can the Reds look over the rest of the offseason to improve upon a pitching staff in need of upgrades in spin rate?  Well, a lot of the work in finding spin value from this year’s crop of free agents was done a few weeks ago on this site.  While Cincinnati won’t be in on the top-tier free agents available, there are more than a few options available that shouldn’t cost any more than $5-6 million in annual value that the Reds can afford to not only improve the bullpen, but move further away from the average spin that may have caused them problems all season.

What Reducing the DL from 15 to 10 Days Could Mean

Wednesday night in the 11th hour, MLB owners and players agreed to a new collective bargaining agreement that will cover five seasons through 2021.  While many of the items eventually agreed upon were tweaks and not major overhauls, one of the items that was of interest to me was the reduction of the disabled list from 15 days to 10 days.  On the surface, this could look like a win-win for both the players and the owners.  After all, players get to come off the DL and back on to the playing field five days sooner than they would have in past seasons, and owners can save coaches and fans from having to watch replacement-level players play while a most likely better player is on the shelf.

Using DL data compiled by, I took a look at length of stay on the DL by all players who landed on the list from 2010-2016.  Since 2010, 319 players have spent exactly 15 days  on the DL.  In total, this is 4785 days spent on the DL in seven seasons.  Now, for fun, let’s assume those same 319 players were ready to go after the new minimum of 10 days on the DL.  Simple math here will tell you those players spent 3190 days on the DL.  So in theory, over the course of seven seasons, reducing the DL to 10 days could save players 1595 days on the DL and owners the same number of days using most likely replacement-level players.  On a per-team average basis, reducing the DL by five days could actually save a team 7.6 days of DL time.

Seems like a win-win, right?  Again, players come back sooner, GMs don’t have to call up as many players from the minors and burn options, and owners save money by not having as many extra players come up from the minors accumulating MLB service time.  Not so fast.  In the same seven-season stretch, 3324 players spent 15 days or more on the DL and only 319 came off after 15 days.  So only 9% of all players on the DL spent the minimum amount of time out of action.  Why would this be?  Well, the obvious answer is if a player is hurt, they are hurt.  No one knows a player’s body better than the players themselves and they will return to action when they feel they are ready.

But the other answer is it pays to be on the DL in the majors.  There is protection.  Players still earn their salary and collect service time, so why rush back from an injury?  In the minors it is a different story. If you get hurt it becomes the next man up for a promotion to the big leagues.  There’s a reason there is a saying in the minors: “you can’t make a club in the tub.”  Now, just because there is protection doesn’t mean players want to spend time on the DL.  If they could, they would spend no time on the DL, as time away from the playing field can hurt future earning potential. Injuries are an inevitable part of the game but most seem to prevent players from feeling they are healthy enough to come back sooner than 15 days to compete at their best.  By reducing the DL to 10 days, I can see increased pressure from fans and media to come back quicker.  What we have to remember is this is the new minimum.  Players will return when they and the medical staff feel they’re ready.  I wouldn’t give your hopes up to see players return from the DL sooner than they have in the past.