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:

Relievers

#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%

STARTERS

#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 GM of the Philadelphia Phillies has been accumulating the players to potentially pull off the greatest single–season heist in the history of baseball.

How will they do this, you might ask?

By utilizing the 3-3-3 rotation.

I will explain why recent rotation alterations by the 1993 Athletics and 2012 Colorado Rockies were not successful. Then I will show how the Phillies version of the 3-3-3 will change the baseball world. But first, let me explain the 3-3-3 rotation and its benefits.

The classic 3-3-3 rotation uses three groups of three pitchers each, pitching once every three games.

Game 1 – Innings 1-3 (Pitcher#1) Innings 4-6 (Pitcher #2)  Innings 7-9 (Pitcher #3)

Game 2 -Innings 1-3 (Pitcher #4)Innings 4-6 (Pitcher #5)Innings 7-9 (Pitcher #6)

Game 3  – Innings 1-3 (Pitcher #7) Innings 4-6 (Pitcher #8) Innings 7-9 (Pitcher #9)

Ideally, each pitcher will throw three innings or 30-50 pitches per appearance. By the end of the season each pitcher will pitch about 162 innings over 54 appearances.

This rotation will help pitchers succeed by:

1) Allowing hitters only one plate appearance against each pitcher

2) Eliminating fatigue by keeping pitch counts down

The more opportunities a hitter has against a pitcher, the better success he has. Dave Fleming of Bill James Online provided statistical evidence from 2008 supporting this fact:

 PA  BA OBP SLG OPS

1st PA in G 108606 .255 .328 .398 .727

2nd PA in G 44505 .270 .334 .431 .765

3rd PA + in G 34520 .282 .346 .453 .800

Notice how every hitting statistic increases with each at-bat. To make a few comparisons, Eduardo Nunez was an All-Star last year, and his OPS was .758. All-Star Xander Bogaerts had an OPS of .802. So if you leave a pitcher in past the third AB (generally 7th or 8th inning) you’re facing a lineup full of 2016 Xander Bogaertses. Not exactly a winning formula.

A similar pattern was echoed in pitch counts:

PA BA OBP SLG OPS

Pitch 1-25 87685 .261 .333 .410 .743

Pitch 25-50 39383 .257 .326 .400 .726

Pitch 51-75 31791 .270 .333 .429 .763

Pitch 76-100 24261 .277 .344 .450 .795

The fact that pitches 1-25 were less effective than 25-50 is due to lineup construction. The rest of the numbers clearly show that pitchers are exponentially worse after the 50th pitch.

In this post, I will explain:

1) Why the 3-3-3 rotation did not work for La Russa in 1993

2) Why the Rockies’ alternative rotation wasn’t accepted in 2012

3) The benefits the 3-3-3 rotation will provide the Phillies in 2017 and beyond

Before we begin, there a few concepts we must accept:

1) Baseball is not archaic; it is ever-changing

2) Categorizing pitchers as only “starters”, “relievers” or “closers” is limiting to the pitchers’ value and abilities. We have to look beyond these inadequate labels. I will use these terms in this article, but attempt to focus on these underlying meanings:

a) Starter – Pitcher trained to throw 5+ innings

b) Reliever – Pitcher trained to throw 1-2 innings

c) Closer – Pitcher with experience throwing the last inning

3) There is no one system that produces winners or losers. You must utilize your personnel to the best of their abilities and limitations

Why the 3-3-3 rotation did not work in 1993

1) The Athletics did not have the personnel to execute the strategy

2) The experiment lasted one week

First, the Athletics had one of the worst pitching staffs in the league in 1993. They were in last place when they implemented the 3-3-3 rotation and had lost nine of their last 12 games. Here is a list of their ERAs in ascending order:

Name                      Training      ERA    Synopsis

Bobby Witt                 SP           4.21     97 ERA +

Goose Gossage          RP           4.53    Age-41 season

Todd Van Poppel      SP           5.04     21-year-old rookie

Ron Darling               SP           5.16       79 ERA+

Bob Welch                  SP           5.29     Age-36 season

Mike Mohler          RP / SP     5.60     Started 9 of 42 appearances

Kelly Downs           RP / SP     5.64     Started 12 of his 42 appearances

Shawn Hillegas      RP / SP      6.97    Started 11 of 18 appearances

John Briscoe             RP            8.03    Started 2 games in 139 IP in career

Only Bobby Witt and Goose Gossage had an ERA under 5.04. Witt was by far their best pitcher and his 97 ERA+ shows he was below average.

The second reason it did not work is the experiment only lasted one week. The public and media backlash from the switch to this rotation was so great that La Russa was forced to abandon the experiment after one week. One week! I don’t care what you do in baseball, if it only lasts one week, then you didn’t give it a real chance. Buster Posey hit .118 in his first week in the MLB in 2009, but the Giants wisely kept him around for 2010.

Why the Rockies’ alternative rotation did not work in 2012

1) They did not have the right personnel

First, let’s describe the specifics of the Rockies’ new rotation. It was a four-man rotation of Jeff Francis, Jeremy Guthrie and rookies Drew Pomeranz and Christian Friedrich. In each start, these four pitchers were given a strict 75-pitch limit. Three rotating pitchers called “piggybacks” would then relieve them.

Game 1 – Francis (75 pitches) Piggyback #1 Reliever #1 Closer #1

Game 2 – Guthrie (75) Piggyback #2 Reliever #2 Closer #1

Game 3 – Pomeranz (75) Piggyback #3 Reliever #3 Closer #1

Game 4 – Friedrich (75) Piggyback #1 Reliever #1/2 Closer #1

Similar to the 1993 A’s, the Rockies made their switch out of desperation. When implemented on June 20th, the Rockies were 18 games below .500 and in a 6-15 slump, on pace to lose over 100 games. Here is a look at the top six Rockies pitcher stats by the end of the year, with ERAs in ascending order:

Name                       Training         ERA       ERA+     IP

Jhoulys Chacin           SP               4.43        105         69

Drew Pomeranz         SP               4.93         94         96.2

Alex White               SP/RP           5.51          84          98

Jeff Francis                 SP               5.58          83          113

Christian Freidrich    SP               6.17          75           84.2

Jeremy Guthrie          SP               6.35          73          90.2

Only one of these starters was even an average pitcher. Three of the four rotation mates were at least 27% worse than the average pitcher in 2012. The issue with the 1993 A’s and the 2012 Rockies are they made these moves in the middle of last-place seasons. They were desperate to change what were the worst pitching staffs in the league. No team heading for a last-place finish is going to respond well to a complete overhaul of the staff in the middle of the summer.

The good news for this particular experiment, however, is that the Rockies pitching staff performed much better after the change was made. In the first 21 games that it was implemented, the starting pitchers improved from a league-worst 6.28 ERA to a league-worst 5.22 ERA. That’s more than an entire one-run improvement! Still the league worst (control your laughter), but that’s a major improvement.

I believe that gives us hope that an alternative and better rotation can be found in the correct circumstances. With the right rotation mates and the correct distribution of pitch counts, I believe there is room for improvement. The key is to train and implement the rotation before the season begins. No pitcher is going to be motivated to try a new system if it is implemented in the middle of a terrible season. It has to be the game plan to begin with, and everyone must be on board. Below you will see why the Phillies have the perfect staff for a 3-3-3 rotation. I have used the 3-3-3 rotation as my basis, but implemented some changes inspired by the 2012 Rockies to ensure success.

How the 3-3-3 Rotation will benefit the Phillies

1) Utilizing the perfect personnel

2) Peak value from assets

3) Health (Physical and Mental)

Personnel

The Phillies have eight middle-of-the-rotation MLB-ready starters who have demonstrated the ability to get MLB hitters out for multiple innings per appearance. The Phillies have five quality relievers who have demonstrated the ability to get MLB hitters out for one inning+ per appearance. Let’s take a look at the 2016 Phillies stats in order of ascending ERAs:

Name             Training    MLB IP 2016    ERA 2016      MLB service

Asher                 SP                27.2                    2.28              0.061 years

Neris                 RP                 80.1                   2.58               1.104 years

Benoit            RP / CP           48                      2.81                Final Year

Neshek          RP / CP            47                     3.06                Final Year

Eickhoff             SP                 197.1                  3.65                1.045 years

Hellickson       SP                 189                     3.71                Final Year

Ramos             RP                 40                       3.83               0.101 years

Buchholz         SP               139.1             Career 3.96          Final Year

Velasquez        SP                131                       4.12                1.086 years

Nola                  SP                 111                      4.78                 1.076 years

Gomez          RP/ CP           68.2             4.85 w/ 37 SV       Final Year

Eflin                   SP               63.1                     5.54                  0.111 years

Thompson        SP               53.2                     5.70                 0.058 years

Asher, Eickhoff and Hellickson were MLB starters with ERAs under 3.71 last year. Buchholz has the ability to be a front-line starter coupled with a career 3.96 ERA. Velasquez and Nola showed great promise despite rather average ERAs in the 4s. Velasquez sported a 10.6 K/9 ratio while Nola’s curveball has the best horizontal movement in the Majors (9.3 inches, beating out Gerrit Cole). The only two pitchers who disappointed were Eflin and Thompson, two young starters getting their first crack at the majors. Let’s count on them performing better next year.

The best reason why this personnel is perfect is because all of the trained starters have generally similar projections. From a projection and performance standpoint, all of these pitchers are middle- to back-of-the-rotation guys with upside. Nola and Velasquez are projected #2/#3 guys while Eflin, Thompson, Asher and Eickhoff are #3 to back-of-the-rotation guys (Though Eickhoff did have an impressive year in 2016). There is no Kershaw or Verlander or Bumgarner or Cueto who are expected to dominate and throw eight innings every start.

By only allowing them up to 50 pitches and one time through the lineup, the numbers listed in the introduction illustrate that the 3-3-3 rotation puts these players in the best possible position to succeed. Since the numbers are now in their favor, pitchers will have a refined focus and confidence. They can make a structured game plan on how they’re going to attack each hitter. This will limit extended innings under duress and ultimately build confidence in the minds of these young pitchers.

You may ask, Kevin, the Phillies aren’t going to contend in 2017. Why go through such a drastic change to get marginally better?

The answer is using the 2017 season as a stage for their assets to increase in value.

Asset Valuation

The Phillies are not in line for a winning season in 2017. They most likely won’t win 80 games in 2018. But 2019 is their year. That amazing 2018-2019 class of Kershaw, Donaldson, Machado, Harper, Pollock, LeMahieu, Keuchel, Harvey, Wainwright, Corbin, Smyly and Shelby Miller will be theirs for the taking, as the only money they have tied up is to Odubel Herrera. Even the 2017-2018 class of Arrieta, Cobb, Darvish, Duffy, Pineda, Tanaka (option), and Cueto (option) could insert an ace or #2 into their staff.

That is why they need to act now. They must increase their pitchers’ values now and acquire better assets with 2019 in mind. The free-agent market will be booming from 2017-2019, thus lowering trade-market value of any player after this year’s deadline. Instead of trading away prospects to get the guys they need, teams will simply open their pocketbooks. Now is the time to trade these middle-of-the-rotation guys away. Especially because they are not all in the 2019 plans.

“Utility Pitchers”

What is the most overpriced asset on the market right now? Relief pitching. More specifically, pitchers who can pitch multiple innings in relief in tough situations. See: Andrew Miller, Kenley Jansen, and Aroldis Chapman. By utilizing the 3-3-3 method, you are training your starters to pitch multiple innings in different scenarios and relieve in later innings. The 3-3-3 method trains your pitchers to achieve the greatest possible value by becoming what I like to call “utility pitchers.”

What makes players like Ben Zobrist, a .266 career hitter, and Ian Desmond, a .267 hitter, worth $60-70 million? They are utility players. Teams these days love utility players and are willing to pay big money for them. They are more valuable now than they have been in all of history. The same can be said for utility pitchers.

If you have ever been to the Arizona Fall League, it is used as a stage for the game’s top prospects. Starting pitchers generally pitch three innings, and relief pitchers will pitch 1-2 innings each for the remainder of the game. They do this to give teams’ top minor-league players exposure to higher competition with an added benefit of raising prospect value in the eyes of other teams. By sending their players to compete with top minor-league competition for all scouts to see, a good showing will raise potential trade interest. For example, this year the Giants sent a young catcher named Aramis Garcia, a former second-round pick. Garcia doesn’t fit into the Giants MLB plans with a player like Buster Posey entrenched at catcher until 2022, but they used him as one of their eight player selections anyway. I can surmise they did this to boost his stock for potential trade scenarios. The Phillies do not have all their current pitchers in their 2018-2019 MLB plans, so why not show them off to other teams?

By using the 3-3-3 method in the MLB as a stage for their abundance of young pitching talent, their pitchers will:

1) Get experience against the top talent in the world

2) Potentially increase their trade value

3) Limit innings to 130 – 160 IP

4) Give young pitching the best chance to succeed at the MLB level

5) Keep their innings down and arms fresh

The Phillies 2017 3-3-3 rotation, which you will notice is a quasi version of the 3-3-3 that I referenced above, would look like this:

1st Group – Hellickson (3) Asher (3) Eflin (2) Neris (1)

2nd group –  Nola (3) Eickhoff (3) Thompson (2) Gomez (1)

3rd Group –  Velasquez (3) Buchholz (3) Benoit (1) Ramos (1) Neshek (1)

Why this particular grouping?

1. Ability to sell three of what we call “closers” at the deadline. They can also switch Benoit and Ramos to the closer role on any particular day, giving Klentak five pitchers with closing experience to sell.

2. Give Eflin and Thompson only 2 IP per appearance because of their struggles last year. This should increase their confidence by decreasing their perceived pressure.

3. Since the Phillies signed two relievers to one-year deals in the offseason, it is apparent that Klentak wants to sell them off at the deadline. This is why I chose the quasi 3-3-3 system.

Imagine Klentak’s bargaining power at the deadline if he has even three of these newly trained utility pitchers pitching well, especially if one is a guy like Asher, Eflin, or Thompson? He could promise 5+ years of control of a utility pitcher who can be a traditional starter or a multi-inning reliever out of the bullpen.

Some people will read this and think that this would be a “demotion” or “devaluation” from being a “starter.” This is not true. All of these pitchers made it to the MLB as what you would call “starters.” They have excelled at pitching 6+ innings per game. This experiment would simply add value to all of them. Just as playing Ben Zobrist at LF, RF and SS doesn’t take away his ability to play 2B.

Most relief pitchers don’t get drafted as closers or relief pitchers. They are given chances at various roles and stick with whichever role suits their strengths best. Look at Chapman and Andrew Miller. Look at Joe Blanton! Terrible pitcher as a labeled “starter” but excelled in a set-up role for the Dodgers last year. General managers won’t trade for a guy for a postseason run if he hasn’t proven that he is going to be a solid contributor in the specific role they need for their team. So by using 2017 as a value-booster, you train all of your pitchers for multiple roles so you can have the leverage to trade any of your guys to any team. Every postseason team needs pitching. The 3-3-3 rotation will give Klentak unlimited options to acquire talent that will help the 2019 team be successful. GMs are most vulnerable at the deadline, and it is time to take full advantage.

Some people might argue that bringing up all of these pitchers at once would be a waste of MLB service time. But what is more important to a GM who has multiple pitchers with middle-of-the rotation ceilings? An option year or service time? This experiment is exactly that, an experiment. It is a trial run for one half of a season to ramp up current asset valuations to acquire a lot of quality pieces for the future. Since all of these pitchers are already on the 40-man roster, sending them to the minors would waste an option year anyway. So why not give this a try? The worst thing you could lose is half a season of MLB service time on a few guys who have served less then 20% of one year in their career.

HEALTH

In an arm-health study by Dr. James R. Andrews the following chart is comprised:

Ages 14 and under – 66+ Pitches (4 days rest) 51-65 (3) 36-50 (2) 21-35 (1) 1-20 (0)

Ages 15 and over – 76+ Pitches (4 days rest) 61-75(3) 46-60 (2) 31-45 (1) 1-30 (0)

These pitchers are prized assets. Millions of dollars coupled with thousands of hours of prep, coaching and playing time are used per arm. Why don’t we take better care of these players?

As a kid, your parents told you to eat your vegetables, sleep eight hours a night and stay in school while getting 60 minutes of exercise a day. But as we grow older we continually skip our vegetables, sleep five or six hours a night, forget to keep our brains active, and rarely exercise. We feel that we can still function this way, but more importantly, we feel we have to function this way. This is because we put too many responsibilities on ourselves at the expense of our own well-being. I’m arguing that we are giving these pitchers too many responsibilities, at a detriment to their peak physical health. Why? Because traditional baseball knowledge tells us that a five-man starting staff is the right way to go in 2017. But look back at history: there used to be one-man, two-man, three-man and even four-man rotations. Those proved to be unsuccessful. I am saying that the five-man rotation isn’t working either. It’s time to make a change.

What if we treated these valuable multi-million-dollar arms with the care that we take with our Little League arms? I propose a hopeful plan of three innings finished for each starter, but an absolute maximum of 36-50 pitches no matter what. These pitchers will then receive two days of rest for every 36-50 pitches, thus receiving the care a child under 14 would receive (see chart above). It is impossible to argue that this wouldn’t be a healthier system than the one we have now. Finally, let’s shift back to trade value. If Klentak is making deals on July 31 and a playoff contender is asking him how his players can help them win a championship, health is another big concern! If he can say that his pitchers have been put on a stricter regimen than any other team in the league, and that his players’ arms are healthier and more fresh than any other team in July in the history of baseball, that is going to increase his bargaining power. Remember, keeping players healthy, putting them in the best position to succeed and increasing trade value all are focused on the 2019 season. Klentak’s initial plan has always been focused on the 2019 season. And this plan will add tremendous benefit to that goal.

Conclusion

Now I am not saying that every team should utilize this strategy. I am not saying this is the future of baseball for eternity. I am saying that with the Phillies assets, at the perfect time in their development, this will be a great strategy to use. A Double-A or Triple-A prospect is worth much less than an MLB-proven prospect. A pitcher who can relieve, start and spot-start is worth more than just a conventional “starter” or “reliever.” More utility is always better than less utility. Healthier arms are better than overused arms.

I am saying the Phillies should give this a try for half of a season in which they won’t win more than 80 games. There is nothing to lose. And hey, if everything goes to plan, maybe this starts a revolution. If not, then they seamlessly revert to a five-man rotation in August. The goal of business is to buy low and sell high, looking for the most reward for the least amount of risk. This is about as high-reward as you can get in a sub-.500 season with about as little risk as I can imagine.

A new idea is always crazy before it makes sense. In the 1920s and 30s it was a rule that star pitchers had to throw 10-20 relief appearances in addition to their normal starting roles. In the 1880s, catching a ball on one bounce was an out. It even used to be legal for a first baseman to grab a runner by the belt so he couldn’t steal second! It is time for a new discussion about the modern-day pitching staff. It is time for rebuilding teams to try new things to get an edge on the competition. It is time for the game of baseball to go through yet another change. We owe it to the fans, to the players, and to the history of our beloved game. We owe it to ourselves to put our reputations on the line for the greater good of baseball.

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

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

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

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

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

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

Jeff Samardzija
2016 ERA: 3.81
2017 Projected ERA: 3.40

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

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

Ivan Nova
2016 ERA: 4.17
2017 Projected ERA: 3.72

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

Wily Peralta
2016 ERA: 4.86
2017 Projected ERA: 4.35

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

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

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

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

Clay Buchholz
2016 ERA: 4.78
2017 Projected ERA: 4.02

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

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

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

It was when I was in sixth grade that I first began to seriously examine baseball.  I made my first annual Top 100 MLB players list that year.  Of course I didn’t know about advanced stats at the time, so Miguel Cabrera was atop that list.  Ironically that was before his Triple Crown.  Brian Kenny had educated me by then, and Trout has been first on every list since.  Anyway, back to the point, I also received the Bill James Historical Abstract that year, and became obsessed with his all-time rankings.  There was his all-time Top 100, and a Top 100 at each position.  Thinking about this the other day, it occurred to me how unusual the second-base rankings were.  Far be it from me to question the Godfather of Sabermetrics, but they seem wrong to me.  Here is the Top 10:

1. Joe Morgan
2. Eddie Collins
3. Rogers Hornsby
4.  Jackie Robinson
5. Craig Biggio
6. Nap Lajoie
7. Ryne Sandberg
8. Charlie Gehringer
9. Rod Carew
10. Roberto Alomar

Again, this seems wrong, but it is Bill James I’m refuting, so some research is probably required.  First, let’s rank the group by career rWAR:

1. Rogers Hornsby 128.7
2. Eddie Collins 122.2
3. Nap Lajoie 104.8
4. Joe Morgan 99.6
5. Charlie Gehringer 79.6
6. Rod Carew 76.7
7. Craig Biggio 65.5
8. Roberto Alomar 65.2
9. Ryne Sandberg 64.2
10. Jackie Robinson 59.4

Career rankings are tricky, because at some point a great peak is better than a long career.  Volume does matter.  Players like Robinson, who played only 10 seasons, suffer in career totals.  Let’s see the players ranked by the total fWAR from their four top seasons.  The group is ranked here by four-year peak:

1. Hornsby 45.6
2. Morgan 38.7
3. Collins 38.0
4. Lajoie 36.4
5. Robinson 33.2
6. Gehringer 30.8
7. Carew 28.7
8. Sandberg 28.1
9. Biggio 26.9
10. Alomar 25.7

That’s nice.  We now know who the best among the group were for their career and for condensed excellence.  However, simply having a long career doesn’t mean a player is the best, nor does having the best brief period of dominance.  Luckily, there’s JAWS.  JAWS is a system used for ranking players that combines career WAR and WAR over a player’s seven-year peak.  It is often used for analysis of Hall of Fame candidacies.  Let’s check out our group when using the JAWS system:

1. Hornsby 100.2
2. Collins 94.1
3. Lajoie 83.8
4. Morgan 79.7
5. Gehringer 65.6
6. Carew 65.4
7. Sandberg 57.2
8. Robinson 56.8
9. Alomar 54.8
10. Biggio 53.4

After seeing these three lists it is evident that only four of the ten are in the running for the title of being the top second baseman of all time:  Collins, Hornsby, Lajoie, and Morgan.  So far all I’ve used to evaluate these players is WAR.  Now, WAR is definitely a great tool, but it is not the only tool.  How about comparing the remaining four players in a few other ways?  Let’s see career wRC+ and Def for starters.

• Collins:  144, 68.3
• Hornsby:  173, 126.5
• Lajoie:  144, 86.3
• Morgan:  135, 14.0

Hornsby is the top-rated player in both wRC+ and Def.  He lead all three lists of WAR metrics.  This doesn’t really look close.  Why then did Bill James have both Morgan and Collins ahead of Hornsby?  He was clearly the best hitter of the three, so then why?  He led both of them in defensive value, so that can’t be why either.  Maybe it’s baserunning?  Let’s check out these three players (sorry Nap Lajoie) in BsR.

• Collins 42.3
• Hornsby -1.8
• Morgan 79.0

Here we go!  Finally, a reason to question Hornsby as the greatest second baseman.  Morgan was first for Bill James, so clearly he believes that the mediocre baserunning of Hornsby and the tremendous baserunning of Morgan makes a huge difference.  Let’s concede hitting to Hornsby, and focus on the two final candidates in just fielding and running the bases.  For their careers the difference in fielding was 112.5 runs, while in baserunning it was 80.8 runs.  Hornsby still wins.  No matter how it is examined, Hornsby always comes out on top.  The greatest second baseman in baseball history is Rogers Hornsby.

Every offseason I do a top 100 MLB players list.  Around the new year is when I start to consider this list seriously, beginning by naming the best player at each position.  Usually, about half of the 10 positions (excluding DH) are close, and the other half are runaways.  This year there is a position that goes beyond even calling it close: third base.

The hot corner currently claims three of the probable top five players in baseball in 2016 NL MVP Kris Bryant, 2015 AL MVP Josh Donaldson, and three-time AL All-Star Manny Machado.  Mike Trout and Clayton Kershaw would of course round out the top five, with players like Mookie Betts and Jose Altuve just missing.  Ranking all of the top players against each other, however, will be discussed in a later article.  For now the focus will stay on the three incredible third basemen.  On the top 100 prior to the 2016 season, Donaldson was the highest-ranked 3B, coming in at #2 overall behind only Trout.  Machado was close behind Donaldson at #9 overall, while Bryant was third at the position in the #18 slot.  But 2016 has now come and gone, and all three of these players had spectacular years.  Now how do they rank?

Let’s start with WAR over the last two seasons, since that’s how long Bryant has been in the league.  For purposes of being fair, we’ll use rWAR.

1. Josh Donaldson 16.3
2. Kris Bryant 14.3
3. Manny Machado 13.5

Well, according to WAR, Donaldson is the clear champion of the position.  He has been worth far more than his competition over the past two seasons.  Just for the record, two players whom I am certain people will try to argue belong with this group in the comments, Adrian Beltre and Nolan Arenado, finish well behind Machado in rWAR.  As useful as WAR is in comparing players, it is not a be-all-and-end-all ranking.  How do the three title players of this article order in OPS+?  This will be the last two seasons as well.

1. Donaldson 151
2. Bryant 142
3. Machado 130

Donaldson wins handily again.  Baseball is about more than just hitting.  How about baserunning?  I’ll rate by XBT% and BsR.

1. Bryant 51% XBT%, 14.4 BsR
2. Donaldson 39%, 4.2
3. Machado 46%, 0.7

Here we go — a list that isn’t topped by Josh Donaldson.  Of course Kris Bryant is a very good baserunner, so this was to be expected.  What’s interesting to me is the edge Donaldson has over Machado despite taking the extra base 7% less of the time.  This can be attributed to Donaldson being on base more often.  Aside from hitting and baserunning, there is defense.  How are these three by the top metrics there?  DRS and UZR/150 should serve this purpose well, again using the past two seasons.

1. Machado 21.0 UZR/150, 27 DRS
2. Donaldson 15.5, 13
3. Bryant 13.1, 7

Bryant is hurt in DRS by his flexibility in positions, but the UZR/150 makes up for that.  Machado is in another world when compared defensively to these competitors.  He is simply incredible on defense.  This, however, does not make up for his being behind both Bryant and Donaldson in hitting and baserunning.

It seems that Donaldson should place first in the position, with Bryant second and Machado third.  One thing is bothering me about this entire analysis, though.  The 2015 and 2016 seasons are being counted as the same in terms of importance.  That should not be.  I’ll re-rank the group by rWAR, weighting 2016 over 2015.  A weight of 1.75:1, or 7:4 in whole numbers.

1. Donaldson 21.88
2. Bryant 20.34
3. Machado 18.50

Well, the order is the same as the original list using WAR, even if the two leaders are much closer.  How about using wRC+?  The weights will remain at 1.75:1.

1. Donaldson 425.3
2. Bryant 396.8
3. Machado 359.8

Donaldson is still the best offensive player.  He still is the best at the position.  One factor is still not being taken into consideration: age.  Donaldson will be in his age-31 season in 2017, meaning he should be entering into a decline.  Bryant will enter his age-25 season, and Machado his age-24.  They should both be improving.  Steamer projections clearly buy into this improvement, at least for Machado, who is projected to have the highest WAR of the three.  Until this actually comes to fruition, however, Bryant’s superior numbers will keep him above Manny Machado.

How to handle the age factor?  In the WAR lists I included, Donaldson’s an average of 10.8% better than Bryant, and he’s 19.5% Machado’s superior.  It seems unlikely that a combined Donaldson decline and Bryant or Machado improvement would make up this gap.  Even if it was more likely, the numbers that have already occurred would take precedence over the numbers that may occur.  Donaldson is still the champion of the hot corner.  The top three third basemen in the MLB right now are:

1. Josh Donaldson, Toronto Blue Jays
2. Kris Bryant, Chicago Cubs
3. Manny Machado, Baltimore Orioles

Despite an influx of information resulting from the advent of Baseball Info Solution’s batted-ball data and the world’s introduction to Statcast, surprisingly little remains known about pitchers’ control over the contact quality that they allow.  Public consensus seems to settle on “some,” yet in a field so hungry for quantitative measures, our inability to come to a concrete conclusion is maddeningly unsatisfying.  In the nearly 20 years since Voros McCracken first proposed the idea that pitchers have no control over the results of batted balls, a tug-of-war has ensued, between those that support Defensive Independent Pitching Statistics (DIPS) and those that staunchly argue that contact quality is a skill that can be measured using ERA.  Although it seems as if the former may prevail, the latter seems resurgent in recent years, as some pitchers have consistently been able to outperform DIPS, hinting at the possibility of an under-appreciated skill.

It is also widely assumed that a hitter’s BABIP will randomly fluctuate during the season, and that changes in this measure often help to explain a prolonged slump or a hot streak at the plate.  Hitters’ BABIPs can also vary drastically from year to year, making it difficult to gauge their true-talent levels.  Research in this field has been done, however, and there have been numerous attempts to develop a predictive model for this statistic, one that projects how a player should have performed, or perhaps more succinctly, his expected BABIP, or xBABIP.  Inspired by the progress, and albeit limited, success of these models, I embarked upon a similar project, instead focusing on the BABIP allowed by pitchers, rather than that produced by batters.  What began as a rather cursory look at exit velocity evolved into a much deeper look, and with this expansion of scope, I achieved some success, though not as much as I had hoped.

My research began with a perusal of Statcast data, and I began to use scatter plots in R to visualize each statistic’s relationship to BABIP.  Most of the plots looked something like this:

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In the majority of plots, it seemed as if there may have been some signal, but there was quite a bit of noise, making it difficult to detect anything of significance.  This perhaps explains the lack of progress in projecting BABIP: after looking at these plots, it appears quite simply difficult to do.  Despite these obvious challenges, I remained hopeful that I could perhaps develop something worthwhile with enough data.  Therefore, I began aggregating information, collecting individual pitcher-seasons from FanGraphs, Baseball Savant, Brooks Baseball, and ESPN, then manipulating and storing the data in a workable format using SQL.  Since Statcast data only became available to the public in 2015, my sample size is unfortunately a bit limited.  I also wanted to incorporate the defense that pitchers had behind them along with park factors when creating my model, so I removed all pitchers that had changed teams mid-season from my records.  This left me with a grand total of 641 pitcher-seasons (323 from 2015, 318 from 2016), and 188 pitchers showed up in both years.  For the remainder of my study, I used the 641 pitcher-seasons to develop the model, but when checking its year-to-year stability and predictive value, I could only use the 188 common data points.

To begin, I fed 29 variables into R: K/9, BB/9, GB%, average exit velocity, average FB/LD exit velocity, average GB exit velocity, the pitcher’s team’s UZR, the pitcher’s home park’s park factor, his Pull/Cent/Oppo and Soft/Med/Hard percentages, and an indicator variable for every PITCHf/x pitch classification.  (Looking back on this, I wish I included more data in my analysis to truly “throw the kitchen sink” at this problem, perhaps including pitch velocity, horizontal and vertical movement, and interaction terms to more accurately represent each individual’s repertoire.  Alas, I plan on keeping this in mind and possibly revisiting the topic, especially as more Statcast data becomes available.)  This resulted in an initial model with an adjusted R-squared of about 0.3; I then ran a backwards stepwise regression with a cutoff p-value of 0.01 to determine which variables were most statistically significant.  Here is the R output:

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For clarity, the formula: xBABIP = -0.157 + 0.005684 * BB/9 + 0.0009797 * GB% + 0.003142 * GB Exit Velocity – 0.0001483 * Team UZR + 0.005751 * LD%

I again obtain an adjusted R-squared of about 0.3, and I don’t find any of these results to be overly surprising, but to be fair, I had little idea of what to expect.  Before examining the accuracy of my entire model, I checked each variable’s individual relationship to BABIP, along with the year-to-year stability of each.  These can be found below in pairs:

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I was most perplexed by the statistical significance of BB/9, and even after completing my research, I still find no entirely compelling explanation for its inclusion.  Typically, BB/9 is considered a measure of control rather than command, but intuitively, these skills seem to be linked, and perhaps pitchers with better command and control are able to paint edges more effectively, thus avoiding the barrel and preventing strong contact.  I was disappointed that its relationship to BABIP appeared so weak, but because of its relative year-to-year stability, I hoped that it would retain some predictive power.

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Previous research has indicated that ground-ball hitters are able to sustain higher-than-average BABIPs, and thus, its inclusion in my model should not come as a shock.  Again, it would have been nice to see a stronger correlation between GB% and BABIP, but there is obviously quite a bit of noise.  However, it does seem that generating ground balls is a repeatable skill, which lends itself nicely to the long-term predictive nature of an xBABIP model.

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Again, as previous research has suggested, the inclusion of GB exit velocity is to be expected.  However, its correlation with BABIP is not as high as I would have hoped; I suspect this may be a result of the unfair nature of ground balls.  In a vacuum, one would expect that low exit velocities are always superior, yet a fortunately-placed chopper may actually have better results than a well-struck ground ball hit right at a fielder, and thus, exit velocity’s signal may be dampened.  There does appear to be some year-to-year correlation though, which offers some promise of an unappreciated skill.

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Here, I’m surprised by the lack of correlation between UZR and BABIP; I collected this data to control for the quality of the defense behind a pitcher, assuming that this could be a pretty significant factor, and although it did remain in my model, the relationship appears to be quite weak.  We should expect a very low year-to-year correlation between UZR, as pitchers that changed teams in the offseason were included in my study, and even if they remained on the same roster, teams’ defensive makeups can change drastically from one season to the next.  Thus, the latter graph is rather useless, but I chose to include it for consistency.

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Unsurprisingly, LD% has the strongest relationship to BABIP, checking in with an R-squared of about 0.15.  I obviously wish that there were a stronger correlation between the two, yet despite the noise, when looking at the data, I think it is fairly evident that there is a signal.  And although I have read that LD% fluctuates wildly from year to year, I was shocked by the latter graph.  It seems as if this is entirely random, and that this portion of a pitcher’s batted-ball profile can be simply chalked up to luck.  This revelation is a bit discouraging, as it suggests that my model may struggle with predictive power, since its most significant variable is almost entirely unpredictable.

I anticipated that more variables would be statistically significant, and I am surprised by their disappearance from the model.  I assumed that Hard% would be highly correlated with BABIP, but it disappeared from my formula rather quickly.  I also assumed that pitchers who generated a high true IFFB% would exhibit suppressed BABIPs, but nothing turned up in the data.  And finally, I thought that K/9 may have been significant; it can be considered a rough estimate of a pitcher’s “stuff,” and I speculated that pitchers with high K/9 probably throw pitches with more movement than usual, perhaps making them harder to square up, but my model found nothing.

After considering each of the significant variables individually, I wanted to examine the overall accuracy of my entire model.  To do so, I plotted pitchers’ xBABIPs vs. their actual BABIPs, along with the difference:

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As mentioned earlier, after incorporating all of the statistically significant variables in my model, I achieve an R-squared of about 0.3, a result that I find satisfying.  I obviously wish that my model could have done a better job explaining some of the variation in the data, and I suspect my model could be improved, although I have no idea by how much.  There is an inherent amount of luck involved in BABIP, and it is entirely plausible that pitching and defense can in fact account for only 30% of the observed variance, and the rest can only be explained by chance.  Despite the lower-than-desired R-squared, I do believe it still verifies the validity of my model, if only for determining which pitchers over- or under-performed their peripherals, saying nothing about why they did so or if they can be expected to do so again in the future.  The lack of correlation in the difference plot indicates that pitchers have been unable to systematically over- or under-perform their xBABIP from year to year, and along with the residual plot, suggests that my model is relatively unbiased and doesn’t appear to miss any other variables that obviously contribute to BABIP.

After determining that my metric had some value in a retrospective sense, I set out to determine whether it had any predictive power.  Because of the lack of year-to-year correlation for most of the statistically significant variables included in the model, I was quite pessimistic, although still hopeful.  I first checked the year-to-year stability of both BABIP and xBABIP:

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It seems that both measures are almost entirely random, although xBABIP is perhaps just a bit more stable from season to season.  Despite this, comparing 2015 BABIP to 2016 xBABIP revealed that, as expected, my model holds little to no predictive power:

View post on imgur.com

Again, although disappointing, this result was to be expected, as the most powerful variable in my model, LD%, fluctuates wildly.  Despite this lack of predictive power, I stand by my model’s validity when considering past performance, and as more data accumulates, perhaps it can be adopted in a stronger predictive form.

Even after concluding that my metric has little predictive value, I thought it would be interesting to look at some of the biggest outliers.  2015’s biggest under- and over-achievers (with their 2016 seasons included as well), along with 2016’s luckiest and unluckiest pitchers can be found below:

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Although the model holds no predictive power after quantitative analysis, anecdotally, it appears to do a decent job.  Each of the 10 pitchers featured as an over- or under-achiever in 2015 saw the absolute value of their difference fall in 2016 (although the sign did change in some cases); in no way am I suggesting that the model is predictive, I just find this to be an odd quirk.  I also find it perplexing that George Kontos appears an over-achiever in both years and can think of no explanation for this.  Along with outperforming xBABIP, his ERA has also beaten FIP and xFIP in each of the last two seasons and five of the last six, suggesting a wonderful streak of luck, or perhaps hinting that the peripheral metrics are missing something.

Ultimately, although it would have been nice to draw stronger conclusions from my research, I am mostly satisfied with the results.  When developing his own model for hitter BABIP, Alex Chamberlain achieved an R-squared of about 0.4 when examining the correlation between BABIP and xBABIP, the highest I have found.  However, his model included speed score, a seemingly crucial variable that I was unable to account for when analyzing pitcher’s BABIPs.  With this in mind, I find an R-squared of 0.3 for my model entirely reasonable, and despite its lack of predictive power, I consider it to be a worthy endeavor.  As the sample size grows and more Statcast data is released, I plan to revisit my formula in coming offseasons, perhaps refining and improving it.

In baseball analysis, we often use comparable players or “comps” to discuss what we think the player is likely to do in the future. Prospects are the most comped players because the general baseball public does not know much about minor leaguers. Comparing these young players to major leaguers allows fans to imagine what these prospects could someday become. Comps are also often used in projection systems. Data analysis has found that similar players often perform similarly throughout their careers. Thus, using former players who compare well with current players aids projection systems in forecasting what a particular player is likely to do in the coming years. Comparable players are also used in contract negotiations and arbitration battles. Players at similar ages with similar careers can expect to get roughly the same contract. In fact, the arbitration process is almost solely interested in comparing similar players and their wages.

Sometimes, players aren’t viewed as being similar when in reality they are actually quite alike. Recently, I found that Julio Teheran and Jose Quintana top each other’s similarity score lists on Baseball Reference. I had usually thought of Quintana as one of the game’s best pitchers and a true ace, while Teheran was at least a rung below that and probably more of a number 2 or 3 starter, so I did some research and found that these two pitchers are more alike than many probably realize.

Both pitchers are from Colombia and they were actually born only miles apart. Colombian-born baseball players are actually quite rare as there have only been 19 such players in MLB history, and this includes at least one set of brothers and a set of cousins. In fact, just this past season Teheran and Quintana became the first Colombian-born pitchers to ever start against each other in the same game. The two are apparently also quite good friends off the field and even work out together in the offseason. They each have also decided that they will pitch for Colombia in the upcoming World Baseball Classic. That will make for a formidable 1-2 punch for the Colombian pitching staff and will be hard for any other team in the tournament to match up against.

These two pitchers also match up quite well statistically, as their numbers look quite similar in a multitude of categories.

You might be able to find two pitchers with more similar numbers, but it wouldn’t be easy. They were both virtually 5-win pitchers according to Baseball-Reference, and the difference there likely comes from Quintana throwing a few more innings than Teheran. Their ERA, FIP, and xFIP are all almost identical and they both achieved their numbers in similar ways, too. Neither pitcher allows many baserunners, and they both strike out about eight batters per nine innings. In 2016, they both also had nearly identical ground-ball rates, and they suppressed homers to the same degree. Both pitchers had incredible seasons in 2016 and were both deserving All Stars, and while Jose Quintana did have a slightly better year and has been the better pitcher for the past several years, Julio Teheran has considerably closed the gap on his fellow statesman.

After seeing how closely the two pitchers’ 2016 stats aligned, I wanted to see how closely their styles of pitching matched up as well. While the approaches are not quite as similar as the statistics, you can see by the pitching styles how the stats could end up so similar. Using PITCHf/x data from Brooksbaseball.com I found that the biggest similarity in their repertoires is their four-seam fastballs. They both rely heavily on this pitch while throwing them about as hard and with similar amounts of movement.

These fastballs are not particularly special for two pitchers with such pedigree. They are each thrown with just average velocity and with roughly an average amount of downward and horizontal movement. They produce roughly the same amount of ground balls as the average pitcher and miss about as many bats as the average fastball. The most unique aspect of either of these pitchers’ fastballs is that Jose Quintana induces an exorbitant amount of pop-ups, which are basically as good as a strikeout. This allows his otherwise average fastball to play up better than the average starter.

After the four-seamer, their repertoires begin to deviate quite a bit. Quintana relies heavily on his sinker and his curveball as secondaries and mixes in a changeup occasionally. He throws his sinker just as hard as his four-seamer, but he gets more movement from the sinker. Julio Teheran uses his slider as his main secondary, throwing it over 26 percent of the time, while he mixes in a sinker, a changeup, and a curveball as his tertiary offerings. His slider is a plus pitch and he uses it to miss bats, while the other pitches are basically used as change-of-pace offerings to keep hitters off of his fastball and slider combination. Both of these guys get by with just average or better stuff, but command of their arsenal coupled with their mastery of the art of pitching have made them two of the upper-echelon pitching talents in the game.

It would only make sense that two players this similar would have similar contracts, but these contracts go way past similar — they are borderline identical. They are each under team control for the next four years. Teheran will make $37,300,000 and Quintana will make just a few hundred thousand more at $37,850,000, assuming that their respective option years are picked up, which is a pretty safe bet. Their yearly salaries are basically identical as well:

Neither player’s salary ever deviates more than just a few hundred thousand dollars in any year under these current contracts. It only makes sense that two players with so many similarities would be compensated so similarly, but should they actually be valued the same?

Probably not; while they did have virtually the same season statistically this year, Quintana’s track record for this level of success is longer. Teheran does also have a successful track record, but he did struggle in 2015, and Quintana just seems to be the surer bet at this point. Steamer projects Quintana to be worth over a win more than Teheran in 2017. However, I do believe that their values should be a great deal closer than public perception. Teheran is two years younger than Quintana and could just be hitting his prime, he is signed to the same contract as Quintana, and his stuff may actually be better. Quintana is currently being aggressively shopped and the asking price is said to be roughly the same as the Chris Sale package. Julio Teheran is not worth that kind of package, but it might be closer than you think.

In “Hardball Retrospective: Evaluating Scouting and Development Outcomes for the Modern-Era Franchises”, I placed every ballplayer in the modern era (from 1901-present) on their original team. I calculated revised standings for every season based entirely on the performance of each team’s “original” players. I discuss every team’s “original” players and seasons at length along with organizational performance with respect to the Amateur Draft (or First-Year Player Draft), amateur free agent signings and other methods of player acquisition.  Season standings, WAR and Win Shares totals for the “original” teams are compared against the “actual” team results to assess each franchise’s scouting, development and general management skills.

Expanding on my research for the book, the following series of articles will reveal the teams with the biggest single-season difference in the WAR and Win Shares for the “Original” vs. “Actual” rosters for every Major League organization. “Hardball Retrospective” is available in digital format on Amazon, Barnes and Noble, GooglePlay, iTunes and KoboBooks. The paperback edition is available on Amazon, Barnes and Noble and CreateSpace. Supplemental Statistics, Charts and Graphs along with a discussion forum are offered at TuataraSoftware.com.

Don Daglow (Intellivision World Series Major League Baseball, Earl Weaver Baseball, Tony La Russa Baseball) contributed the foreword for Hardball Retrospective. The foreword and preview of my book are accessible here.

Terminology

OWAR – Wins Above Replacement for players on “original” teams

OWS – Win Shares for players on “original” teams

OPW% – Pythagorean Won-Loss record for the “original” teams

AWAR – Wins Above Replacement for players on “actual” teams

AWS – Win Shares for players on “actual” teams

APW% – Pythagorean Won-Loss record for the “actual” teams

Assessment

The 1985 Montreal Expos 

OWAR: 55.8     OWS: 320     OPW%: .556     (90-72)

AWAR: 37.5      AWS: 252     APW%: .522     (84-77)

WARdiff: 18.3                        WSdiff: 68  

The “Original” 1985 Expos claimed the National League Eastern division title with a 90-victory campaign, outpacing the Mets by five games. Tim “Rock” Raines swiped 70 bases in 79 attempts, registered 115 runs, batted .320 and set a career-high with 13 triples. Gary “Kid” Carter (.281/32/100) established personal-bests in home runs and placed sixth in the NL MVP balloting. Tim Wallach clubbed 36 doubles and merited the first of three Gold Glove Awards at the hot corner. Andre “The Hawk” Dawson swatted 23 big-flies and knocked in 91 baserunners. Vance Law ripped 30 two-base hits for the “Actuals”.

Gary Carter (catcher) and Tim Raines (left field) ranked eight at their respective positions in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Expos teammates chronicled in the “NBJHBA” top 100 ratings include Andre Dawson (19^th-RF), Tim Wallach (27^th-3B), Andres Galarraga (42^nd-1B), Larry Parrish (53^rd-3B) and Tony Phillips (66^th-RF). “Actuals” first baseman Dan Driessen ranked seventy-eighth while third-sacker Hubie Brooks placed eighty-ninth.

  Original 1985 Expos                                  Actual 1985 Expos

Bob James locked down the late innings for Montreal, saving 32 contests with a 2.13 ERA and a 1.027 WHIP in 69 appearances. Shane Rawley fashioned a 13-8 record with a 3.31 ERA at the top of the rotation. Fellow portsider Joe Hesketh posted a 2.49 ERA to complement a 10-5 mark during his rookie campaign. Bryn Smith (18-5, 2.91) paced the “Actuals” in wins and WHIP (1.052). Tim Burke (9-4, 2.39) and Jeff Reardon (3.18, 41 SV) anchored the “Actuals” bullpen.

  Original 1985 Expos                                Actual 1985 Expos 

 Notable Transactions

Gary Carter 

December 10, 1984: Traded by the Montreal Expos to the New York Mets for Hubie Brooks, Mike Fitzgerald, Herm Winningham and Floyd Youmans. 

Bob James 

June 10, 1982: Sent to the Detroit Tigers by the Montreal Expos as part of a conditional deal.

May 4, 1983: Returned by the Detroit Tigers to the Montreal Expos as part of a conditional deal.

December 7, 1984: Traded by the Montreal Expos to the Chicago White Sox for Vance Law.

Tony Bernazard

December 12, 1980: Traded by the Montreal Expos to the Chicago White Sox for Rich Wortham.

June 15, 1983: Traded by the Chicago White Sox to the Seattle Mariners for Julio Cruz.

December 7, 1983: Traded by the Seattle Mariners to the Cleveland Indians for Jack Perconte and Gorman Thomas.

Shane Rawley

May 27, 1977: the Montreal Expos sent Shane Rawley and Angel Torres to the Cincinnati Reds to complete an earlier deal made on May 21, 1977. May 21, 1977: The Montreal Expos sent players to be named later to the Cincinnati Reds for Santo Alcala.

December 9, 1977: Traded by the Cincinnati Reds to the Seattle Mariners for Dave Collins.

April 1, 1982: Traded by the Seattle Mariners to the New York Yankees for a player to be named later, Bill Caudill and Gene Nelson. The New York Yankees sent Bobby Brown (April 6, 1982) to the Seattle Mariners to complete the trade.

June 30, 1984: Traded by the New York Yankees to the Philadelphia Phillies for Marty Bystrom and Keith Hughes.

Honorable Mention

The 2008 Washington Nationals 

OWAR: 37.2     OWS: 243     OPW%: .500     (81-81)

AWAR: 18.3      AWS: 177     APW%: .366     (59-102)

WARdiff: 18.9                        WSdiff: 64  

The “Original” 2008 Nationals played .500 ball and finished fourth in the division. The “Actuals” dreadful results placed them 22 games off the “Originals” pace. Grady Sizemore (.268/33/90) produced a 30-30 season, successfully stealing 38 bags in 43 attempts while eclipsing the century mark in runs scored for the fourth straight season. Left fielder Jason Bay (.286/31/101) tallied 111 runs and drilled 35 doubles. Vladimir Guerrero (.303/27/91) topped the .300 mark for the 12^th consecutive year and supplied 31 two-base knocks. Milton Bradley (.321/22/77) clubbed 32 doubles, paced the circuit with a .436 OBP and merited his lone All-Star appearance. Orlando Cabrera contributed 33 two-baggers while double-play partner Brandon Phillips blasted 21 dingers and pilfered 23 bases. Cliff P. Lee (22-3, 2.54) achieved Cy Young honors and led the League in ERA. Armando Galarraga (13-7, 3.73) finished fourth in the Rookie of the Year balloting.

On Deck

What Might Have Been – The “Original” 2008 Mariners

References and Resources

Baseball America – Executive Database

Baseball-Reference

James, Bill. The New Bill James Historical Baseball Abstract. New York, NY.: The Free Press, 2001. Print.

James, Bill, with Jim Henzler. Win Shares. Morton Grove, Ill.: STATS, 2002. Print.

Retrosheet – Transactions Database

The information used here was obtained free of charge from and is copyrighted by Retrosheet. Interested parties may contact Retrosheet at “www.retrosheet.org”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive

We all know about Shohei Otani, but in case you are the one baseball fan who doesn’t, he is possibly the best baseball player in the world.  Otani turned 22 years old in 2016.  Although he did not have enough plate appearances to qualify, if he did, Otani’s 1.004 OPS would have led the country (of Japan).  In 382 plate appearances, he posted a slash line of .322/.416/.588, in addition to hitting 22 home runs.  That sounds like a very good player who would draw serious interest from MLB teams if posted.  However, that’s not all.  Otani also posted a 1.86 ERA in 140 IP with an 11.2 K/9.  He owns the NPB record for fastest pitch, at 165 km/h (102.53 mph).  The pitching stats alone would have every team in the MLB drooling.  Combine this with his hitting, and Otani might just be the best baseball player in the world.  And the best baseball player in the world is not going to paid like his title suggests.

The problem is that Otani will not yet be 25 after next season.  The new CBA keeps all international players under 25 from being exempt of the bonus pool system.  A tweet from Jim Allen reported that Otani still wishes to be posted after the 2017 season, when he will be 23 years old.  According to an excellent Dave Cameron article also on FanGraphs, the most money Otani could receive is $9.2 million.  This figure would be equivalent in 2016 to a player worth approximately 1.15 WAR.  Otani would surely be worth more wins than 1.15.

At first I wondered if this would make Otani the most underpaid player in the MLB.  Before that question could be answered, however, I had to answer a more important one: how much would Shohei Otani be worth in wins and, by extension, in dollars?  To make this more interesting, let’s make it a one-year deal, in which Otani would be paid the 2017 projected average price of $8.4 million per win above replacement.

The NPB has no available WAR figure, and no OPS+ or ERA+ was offered either.  Unfortunately, I could not find NPB league totals, so no calculating OPS+ or ERA+ on my own, at least not accurately.  I’ll use MLB league totals to find these numbers, but it is an obvious flaw in my research.  If anyone can find NPB totals for me, post the link in the comments, and I’ll gladly redo the study with those figures.

So, using the MLB totals, here are Otani’s numbers in 2016.  OPS+ 170.  ERA+ 225.

Those numbers look really good.  If these were for an MLB player, he would be by far the best player in the league.  How good were the numbers of other Japanese players before and after they were posted though? Let’s see, using three pitchers’ ERA+ and three hitters’ OPS+.  First the pitchers, including what Otani would hypothetically produce in 2017 by what the others produced.

Masahiro Tanaka:  2013 (NPB) 305; 2014 (MLB) 138

Yu Darvish:  2011 (NPB) 274; 2012 (MLB) 112

Hisashi Iwakuma:  2011 (NPB) 163; 2012 (MLB) 121

Shohei Otani:  2016 (NPB) 225; 2017 (MLB) 113

Now for innings pitched, another component required for the crude WAR I’ll project.

Tanaka:  212.0; 136.1

Darvish:  232.0; 191.1

Iwakuma:  119.0; 125.1

Otani:  140.0; 112.2

The raw numbers of IP and ERA+ can be converted into a metric (PV) that I can change into WAR.

Tanaka:  85.227 PV; 3.3 WAR

Darvish:  103.932; 3.9

Iwakuma:  73.552; 2.0

Otani:  63.911; 1.7

Pitching, Otani would be projected for a 1.7 WAR.  That is worth $14.28 million in real value.  Now for batting, which will be OPS+.

Ichiro Suzuki:  2000 (NPB) 157; 2001 (MLB) 126

Hideki Matsui:  2002 (NPB) 205; 2003 (MLB) 109

Kosuke Fukudome: 2007 (NPB) 155; 2008 (MLB) 89

Otani:  2016 (NPB) 170; 2017 (MLB) 107

That is the quality component of WAR.  Plate appearances now for quantity.  As a side note, because I’m not factoring in defense, oWAR is going to be used instead of WAR.

Ichiro:  459; 738

Matsui:  623; 695

Fukudome:  348; 590

Otani:  382; 540

Now for my metric to convert to oWAR.  I’ll call it OV.

Ichiro:  115.305 OV; 6.1 oWAR

Matsui:  93.179; 3.1

Fukudome:  63.012; 0.6

Otani:  68.180; 0.9

On offense Otani would have a 0.9 WAR.  This translates into $7.56 million.  For a one-year deal using real value, Otani should receive $21.84 million, while producing a 2.6 WAR.  But what about a long-term deal with market value instead of real value?  Using Bill James’ stat of projected years remaining to determine the length of the deal, it would be 10 years.  The first year would not have a salary of $21.84M, but $13.72M.  This year was easy.  Now for the next nine years.  First, we’ll examine his pitching value.  I won’t bore you with all the calculations.  This article is tedious enough without it.  Just the pitching WAR for each year.

2018 2.1; 2019 2.9; 2020 3.9; 2021 4.8; 2022 5.9; 2023 5.7; 2024 3.8; 2025 2.1; 2026 0.7

Now the oWAR for each of the seasons:

2018 1.6; 2019 2.3; 2020 3.0; 2021 3.8; 2022 4.5; 2023 5.3; 2024 4.7; 2025 3.1; 2026 1.7

The total WAR for the years are as follows:

2018 3.7; 2019 5.2; 2020 6.9; 2021 8.6; 2022 10.4; 2023 11.0; 2024 8.5; 2025 5.2; 2026 2.4

Over the course of the 10-year deal, Otani would have a total WAR of 64.5.  This is not what he would likely produce.  My projections are — ahem — optimistic.  These are the numbers he could produce if played as both a pitcher and a semi-regular hitter.  Using real value and these WAR figures, Otani would have a real value of $689.14M.  You can read that number again.  I had to do a double-take.  Go ahead and do one too; it’s still $689.14M.  That is real value — however, not market value.  The market value is the much more important, and interesting, number.  What the market value turns out to be, $249.01M, is still massive, but at least the $24.901M AAV is more reasonable in the market.  In fact, this is likely what he will receive when posted, if he is eligible for this kind of deal.  It will be a shorter deal than 10 years, but the AAV should be in line with what I projected.

However, Otani is a mind-boggling player, so no contract, no matter how mind-boggling it may seem, is out of the question for him.  Even $689.14M.

2016 has been a garbage year. At least, that’s what everyone seems to be talking about right now as the year draws to a close. But here in the baseball world, it’s been a banner year for many reasons, not the least of which is the new era of analysis that has arrived thanks to publicly available Statcast data. I, and I’m sure every other FG reader, have enjoyed following the quality Statcast analysis being developed in these electronic pages, particularly Andrew Perpetua’s “xStats”. In fact, I’m going to go ahead and stake the claim that I may have ‘coined’ (or at least influenced the creation of) the term xStats in the comments section of Andrew’s first xBABIP post. Inspired by the work of Perpetua, along with Alex Chamberlain (BIS-based xBABIP and xISO), and frequent leaguemate and Trevor-Story-lover Andrew Dominijanni (statcast xISO), I’ve decided to spend the offseason digging into xStats a bit deeper.

Perpetua has developed a great set of data using his binning strategy, most recently explained and updated this week, producing xBABIP, xBACON, and xOBA numbers based on Statcast’s exit velocity/launch angle data, along with the resulting ‘expected’ versions of the typical slash-line stats, xAVG/xOBP/xSLG. Throughout the year, I followed these stats fairly closely, often using ‘xStats’ to influence my fantasy baseball decisions. Given the opaque nature of translating a slash-line to actual fantasy stats, I generally went to the spreadsheet with the simple question “over- or under-performing?”, but that was about as far as I got. I found myself coming to probably-wrong conclusions such as “hey, maybe Sandy Leon isn’t actually that bad.” I was frustrated at my inability to turn a seemingly useful tool into actionable numbers for fantasy purposes.

This post serves as a starting point for that translation process. Way back in 2011, Jeff Zimmerman explained a basic approach for projecting R and RBI using only AVG, BB%, and HR% as inputs. I’ll similarly start here by coming up with simple models that translate rate stats (AVG, OBP, ISO) into fantasy-relevant ones, and then finally sub in the ‘x’ versions of those stats to come up with an ‘xFantasy’ line. I’ll stress that these are meant to be simple — I train the models based on all players that reached at least 300 PA in 2016, and I introduce a few team-related factors and shortcuts to improve fits, but I’m not looking to create a new Steamer or ZiPS here, just easy translations.

Home Runs

Starting with the surprisingly easy model, HR per PA is modeled well by ISO alone, with an R² of .902 (excuse my simpleton’s application of statistics here; if you’re hoping for RMSE, p-values, etc., this will be a very disappointing post for you).

HR/PA = 0.2814*ISO – 0.01553

Runs and Runs Batted In

R and RBI per PA are interesting given their strong dependence on lineup position. To de-convolute that a bit, I’ve combined R+RBI into a single category (we can always separate them later). ‘R+RBI’ could be modeled using SLG alone, with an R² of .758, but we can do better by separating SLG into AVG and ISO, and including terms for ‘team R+RBI total’ (player R/RBI totals are influenced by the team’s overall run production) and ‘average batting order position.’ Tanner Bell’s preseason post from this year explains and tabulates the influence of team offense and lineup position on R+RBI production. After doing some work to combine and normalize the data from Tanner’s tables, you can see the dependence of R+RBI/PA on lineup position can be roughly modeled as quadratic:

Average batting order position doesn’t appear to be easily accessible within the FanGraphs leaderboards, but thanks to the new ‘splits leaderboards’, it is possible to calculate with some elbow grease. Integrating all these factors to modify the original SLG model, R+RBI/PA is modeled by ‘SLG_mod’ with an R² of .807.

R+RBI/PA = 0.3292*SLG_mod – 0.04751
SLG_mod = AVG + 1.800*ISO + 2.061e^-4*Team_R+RBI – 2.023e^-3*ABO² + 1.227e^-2*ABO
                    Team_R+RBI = season total R+RBI for player’s team
                    ABO = average position of player in batting order

I mentioned that R+RBI could be separated later. Rather than demand the model predict the breakdown of R vs. RBI for each player, and introduce more sources of variation, I’m taking a shortcut here. The model calculates a value of x(R+RBI), and that is decomposed into R and RBI according to the actual proportion of R vs. RBI accumulated by the player in 2016. For instance, Mike Trout had 123 R and 100 RBI (223 R+RBI), and the model predicts 214.3 R+RBI, so we’ll give him (123/223)*214.3 = 118.2 R, and (100/223)*214.3 = 96.1 RBI.

Stolen Bases

SB per PA is a strange beast, a stat that’s much more dependent upon the whims and opportunities of the player and team than it is on the physical speed of the player. It can be tough to model given the large number of players that never run, or very rarely run. Much like SLG and R+RBI, I found that the SPD metric alone predicts SB/PA well, with an R² of .662 when using a third-order polynomial fit. Is SPD cheating a bit? Maybe. For the uninitiated, it uses SB%, SB attempt frequency, triples percentage, and runs-scored percentage as inputs. You can see how SB/PA would fall directly out of that calculation, especially given the fact that teams tend to only turn runners loose on the basepaths if they are above a certain SB%. In any case, I’ll continue by modifying SPD to improve the fit, though the contribution of xStats to SB/PA will be much smaller than for the other stats.

Two rate stats serve to improve the fit, and they make intuitive sense: OBP, as players need to be on base in order to steal bases, and ISO, as players that hit for too much power tend not to spend as much time standing on first base, trying to steal second. I’ll again include a team factor, ‘team SB/PA,’ to quantify teams’ (or managers’) willingness to send runners, as well as ‘average batting order position,’ as players near the middle of the order tend not to steal as often. In this case I may have failed my initial criteria of a simple model, but it’s nevertheless a nice fit. Integrating it all into ‘SPD_mod’, we can model SB/PA with an R² of .834.

SB/PA = 0.2200*SPD_mod³ – 0.3524*SPD_mod² + 0.2132*SPD_mod – .04170
SPD_mod = SPD/10 + 0.8206*OBP – 0.4670*ISO + 9.180*Team_SB – 9.192e^-4*ABO²
                    Team_SB = average steals per plate appearance for player’s team

Average

Does batting average need its own section? I’m just going to use xAVG.

xFantasy

Now that I’ve reinvented the wheel and created a sort-of-okay way to calculate a 5×5 line based on rate stats, it’s a simple matter of substituting in the Perpetua xStats versions of AVG, OBP, and ISO to arrive at an ‘xFantasy’ line. I’ve also done a quick calculation of 2016 $ values using my normal z-score method, along with x$ values to allow easy comparison (no positional adjustments to either of them, though). The full sheet with 429 players’ 2016 xFantasy stats is found here, and I’ll include below the top-10 and bottom-10 players* whose lines improved/declined most when using xStats:

As one might hope, the top of the list is populated by several of the players that were identified as xStats’ undervalued darlings in 2016, like Mauer and Morales. In Belt, we might be seeing a place where park factors could improve xStats, though the disparity between his 17 HR and 29 xHR is still hard to ignore. Meanwhile, at the bottom of the list, it seems likely that the xSB model fails to adequately predict the SB totals for MLB’s most prolific runners, with Villar, Hamilton, and Nunez all getting hammered in the xSB category. But, it’s also possible that this is a knock-on effect from speedy players getting an unfair shake in xOBP. With Blackmon, it’s certainly possible that this is the other end of the park-factor spectrum, with his 20 xHR flagging way behind the 29 HR he put up.

Finally, one might ask how we solve the ‘Gary Sanchez problem,’ and it’d be quite useful to see what xStats project for players that only played partial seasons, to get an idea of what they ‘should’ have done over a full complement of PAs. Much like the ‘Steamer600’ projections hosted here at FanGraphs, I’ve calculated xFantasy600 values, where each player’s xFantasy line is normalized to 600 plate appearances. Or in other words, in this case, we’re evaluating players on a per-PA basis. Below, we have the top 20 players by xFantasy600 (x$600) in 2016:

Some new names rise to the top here, with Trea Turner, Gary Sanchez, and Trevor Story checking in as the third- (!!!), eighth- (!!), and 16th- (!) best players by xStats in 2016. On the one hand, they all appear to have over-performed in 2016 (check their wOBA vs. xOBA scores), but even regressing back to xStats in 2017 would comfortably land them among the best players in fantasy. The rest of this list is generally a who’s who of the best players in baseball, outside of Rickie Weeks, who was apparently highly effective as a platoon player last year. It’s fun to see that Big Papi went out on top, as the king of xFantasy. Miggy comes in at a very close No. 2, and I’ve seen him kicking around as a second-rounder on some early 2017 rankings – he might be the biggest bargain in drafts this year if that holds up. Overall, I’m very satisfied with this list’s ability to peg the best fantasy players, outside of the potential issue of underrating SBs.

Next time

The next step in this process is to evaluate xStats and xFantasy as a predictive tool. Throughout 2016, I pondered the fact that xStats might tell you more about “what happened” rather than “what will happen.” However, it’s hard to resist the allure of using them to project forward in-season, as they should stabilize faster than their standard statistical counterparts. One thing I have theorized is that xStats might be most helpful in evaluating ‘new swing’ guys, ‘new pitch’ guys, or new call-ups, as we wouldn’t expect traditional projection systems to capture these sorts of things. Craig Edwards has actually released an exceedingly timely look at “Did Exit Velocity Predict Second-Half Slumps, Rebounds?” I’ve now started work on the next chapter of the xFantasy story, comparing first-half and second-half numbers for 2015/2016 (the ‘Statcast era’) using traditional stats, xStats, and Steamer projections (h/t to Andrew Perpetua for updating his sheet to include first/second-half xStats splits).

This first look at xFantasy was a fun exploration of rudimentary projections and xStats. Hopefully others find it interesting; hit me up in the comments and let me know anything you might have noticed, or if you have any suggestions.