Archive for November, 2016

Hardball Retrospective – What Might Have Been – The “Original” 1979 Mets

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

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


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



The 1979 New York Mets 

OWAR: 50.7     OWS: 262     OPW%: .479     (78-84)

AWAR: 24.8      AWS: 188     APW%: .389     (63-99)

WARdiff: 25.9                        WSdiff: 74  

The “Original” 1979 Mets ended the season in the cellar, yet the club outpaced the “Actuals” by fifteen victories! Ken Singleton earned runner-up status in the MVP balloting on the strength of a .295 BA with 35 circuit clouts and 111 ribbies. Lee “Maz” Mazzilli (.303/15/79) nabbed 34 bags and merited his lone All-Star appearance. Tim Foli set personal-bests in batting average (.288), base hits, runs and RBI. John “The Hammer” Milner contributed a .276 BA with 16 jacks while splitting time between left field and first base. “Actuals” right fielder Joel Youngblood posted a .275 BA and raked 37 doubles. Richie “The Gravedigger” Hebner added 25 two-base knocks and drove in 79 baserunners.

Tom Seaver and Nolan Ryan rated sixth and twenty-fourth, respectively, among pitchers in the “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” Mets teammates registered in the “NBJHBA” top 100 ratings include Ken Singleton (18th-RF) Paul Blair (66th-CF) and Bud Harrelson (88th-SS). “Actuals” third baseman Richie Hebner ranked fifty-sixth while center fielder Jose Cardenal placed seventh-sixth.

  Original 1979 Mets                                  Actual 1979 Mets

John Milner LF 1.8 13.03 Steve Henderson LF 2.18 11.79
Lee Mazzilli CF 3.56 24.14 Lee Mazzilli CF 3.56 24.14
Ken Singleton RF 4.49 31.68 Joel Youngblood RF 3.75 17.31
Mike Jorgensen 1B -0.09 2.56 Willie Montanez 1B -1.71 2.45
Bud Harrelson 2B 0.55 3.1 Doug Flynn 2B -1.92 6.85
Tim Foli SS 1.88 17.19 Frank Taveras SS -0.83 11.83
Ted Martinez 3B -0.34 1.38 Richie Hebner 3B 2.32 14.43
Alex Trevino C 0.36 5.04 John Stearns C 1.28 10.89
Joe Nolan C -0.02 3.57 Alex Trevino C 0.36 5.04
Jerry Morales RF -1.96 3.43 Elliott Maddox RF 0.67 4.88
Duffy Dyer C 0.11 3.21 Dan Norman RF -0.1 2.22
Benny Ayala LF 0.3 3.01 Jose Cardenal RF 0.36 1.99
Paul Blair CF -1.12 1.41 Ron Hodges C -0.24 1.14
Ron Hodges C -0.24 1.14 Ed Kranepool 1B -0.58 0.86
Ed Kranepool 1B -0.58 0.86 Kelvin Chapman 2B -0.7 0.67
Kelvin Chapman 2B -0.7 0.67 Gil Flores RF -0.36 0.34
Bruce Boisclair RF -0.88 0.29 Bruce Boisclair RF -0.88 0.29
Ike Hampton 1B 0.03 0.19 Sergio Ferrer 3B -0.1 0.16
Roy Staiger 3B 0.06 0.17 Tim Foli SS -0.08 0.1

Jerry Koosman reached the 20-win plateau for the second time in his career. Tom “The Franchise” Seaver (16-6, 3.14) led the National League with 5 shutouts and finished fourth in the Cy Young Award balloting. Nino Espinosa delivered 14 victories with a 3.65 ERA. Nolan Ryan aka the “Ryan Express” tallied 16 victories and struck out 223 batsmen. Craig Swan augmented the “Originals” and “Actuals” rotation with 14 wins and a 3.29 ERA after securing the National League ERA title during the previous campaign.

  Original 1979 Mets                                  Actual 1979 Mets 

Jerry Koosman SP 6.06 22.76 Craig Swan SP 3 15.36
Tom Seaver SP 3.68 16.04 Kevin Kobel SP 1.16 7.87
Craig Swan SP 3 15.36 Pete Falcone SP 0.49 6.15
Nino Espinosa SP 2.15 14.6 Tom Hausman SP 1.69 5.95
Nolan Ryan SP 2.88 13.52 Andy Hassler SP 0.54 4.87
Neil Allen RP 0.19 6.26 Skip Lockwood RP 1.89 6.86
Tug McGraw RP -1.53 4.62 Neil Allen RP 0.19 6.26
Jeff Reardon RP 0.29 2.33 Ed Glynn RP 0.67 4.5
Roy Lee Jackson RP 0.43 1.77 Jeff Reardon RP 0.29 2.33
Dwight Bernard RP -0.51 0.44 Dale Murray RP -1.34 1.87
Steve Renko SP 2.68 11.18 Pat Zachry SP 0.28 2.94
Jim Bibby SP 2.85 11.06 Juan Berenguer SP 0.35 1.84
Ed Figueroa SP 0.98 5.38 Roy Lee Jackson RP 0.43 1.77
Jon Matlack SP 0.81 4.31 Ray Burris SP 0.13 0.85
Juan Berenguer SP 0.35 1.84 Wayne Twitchell RP -1.31 0.84
John Pacella SP 0.05 0.33 Jesse Orosco RP -0.33 0.57
Kim Seaman RP 0.05 0.29 Dwight Bernard RP -0.51 0.44
Jackson Todd RP -0.64 0.01 John Pacella SP 0.05 0.33
Mike Scott SP -0.83 0 Dock Ellis SP -1.6 0
Mike Scott SP -0.83 0

 Notable Transactions

Ken Singleton 

April 5, 1972: Traded by the New York Mets with Tim Foli and Mike Jorgensen to the Montreal Expos for Rusty Staub.

December 4, 1974: Traded by the Montreal Expos with Mike Torrez to the Baltimore Orioles for Bill Kirkpatrick (minors), Rich Coggins and Dave McNally. 

Jerry Koosman 

December 8, 1978: Traded by the New York Mets to the Minnesota Twins for a player to be named later and Greg Field (minors). The Minnesota Twins sent Jesse Orosco (February 7, 1979) to the New York Mets to complete the trade. 

Tom Seaver

June 15, 1977: Traded by the New York Mets to the Cincinnati Reds for Doug Flynn, Steve Henderson, Dan Norman and Pat Zachry.

Nino Espinosa

March 27, 1979: Traded by the New York Mets to the Philadelphia Phillies for Richie Hebner and Jose Moreno.

Nolan Ryan

December 10, 1971: Traded by the New York Mets with Frank Estrada, Don Rose and Leroy Stanton to the California Angels for Jim Fregosi.

Honorable Mention

The 2012 New York Mets 

OWAR: 27.7     OWS: 262     OPW%: .492     (80-82)

AWAR: 24.1       AWS: 221      APW%: .457    (74-88)

WARdiff: 3.6                        WSdiff: 41

The “Original” 2012 Mets placed third, fourteen games in arrears to the Nationals. David “Captain America” Wright (.306/21/93) raked 41 two-base hits and received his sixth All-Star invite. Angel “Crazy Horse” Pagan topped the circuit with 15 triples and set career-highs with 38 two-baggers and 95 runs scored. Jose B. Reyes swiped 40 bags and rapped 37 doubles while double-play partner Daniel Murphy contributed a .291 BA with 40 two-base knocks. Nelson R. Cruz nailed 45 doubles and jacked 24 round-trippers. First-sacker Ike B. Davis established personal-bests with 32 taters and 90 ribbies. A.J. Burnett paced the starting staff with 16 victories along with a 3.51 ERA and 180 strikeouts.

On Deck

What Might Have Been – The “Original” 2013 Marlins

References and Resources

Baseball America – Executive Database


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 “”.

Seamheads – Baseball Gauge

Sean Lahman Baseball Archive


The Real Best Reliever in Baseball

The best relief pitcher in baseball is not who you think he is. Most of you probably would not even include him in the top 10. If I were to take a poll on who is the best relief pitcher in baseball, the top voted would likely be Zach Britton, Dellin Betances, Aroldis Chapman, Kenley Jansen, and Andrew Miller. I will say that it is none of them. To illustrate my point, I will compare this mystery pitcher’s numbers to all of their numbers. Nothing too scary, just xFIP, K/9, and ERA. I also will not just tell you which pitcher produced which numbers. Where would be the fun in that? I will compare the numbers of all six pitchers and walk you, the reader, through determining which one is the best.

Pitcher A: 1.18 xFIP; 14.89 K/9; 1.45 ERA
Pitcher B: 1.92; 13.97; 1.55
Pitcher C: 1.17; 16.84; 1.16
Pitcher D: 1.75; 15.53; 3.08
Pitcher E: 2.41; 13.63; 1.83
Pitcher F: 2.09; 9.94; 0.54

At first glance, Pitcher F’s ERA of 0.54 is likely what stands out most. Alas, even calling him only by a letter cannot mask Britton. He has the lowest K/9 by far and the second-highest xFIP, so Britton is effectively taken out of consideration.

Pitcher D has an ERA over a run higher than any of the others. His K/9 and xFIP fit in the range but do not stand out. Thus, Dellin Betances is out as well.

Of the remaining four, Pitcher E rates the worst in each of the three categories. Goodbye, Kenley Jansen.

That leaves us with Pitcher A, Pitcher B, and Pitcher C. In this group, B is the worst across the board. Aroldis Chapman leaves the conversation.

Pitcher C is better than Pitcher A in all three statistics. Andrew Miller bows and exits.

Carter Capps stands victorious.

Yes, I know Capps did not pitch in 2016. I used his 2015 numbers. They stack up just as well against the elite relievers from that year as well. It is true that Capps pitched only 31 innings in 2015, but the stats I used are rates. Maybe a larger sample would have dragged him into mediocrity, but I doubt it. Capps was ahead of the field by such a large margin that even with regression in his 2017 return he would be #1.

I am crazy for saying Carter Capps is the best relief pitcher in baseball. Or am I, really? If Capps pitches as well in 2017 as he did in 2015, just over a larger sample, I believe many of you will agree with me. Some of you may even agree with me after reading this.

So, let me be the first to say it: Carter Capps is the real best relief pitcher in baseball.

Do Teams that Strike Out a Lot Steal More Bases?

This is a question that intuitively would seem to be answered by: Sure, why not?  The assumption was recently made in the comments section of this article by an FG writer:

Think about it — if you are Rougned Odor and you are on first base and, say, Joey Gallo is at the plate, there’s a good chance he’s going to cool down the stadium with some high-powered fanning.  He’s not exactly known as a high-contact guy.  There’s a roughly one-in-three chance that his at-bat is going to end in a backwards K sign being held up by someone in the stands.  So ‘Ned might decide this is a good time to steal because the ball isn’t likely to be put into play in the air, where, if caught, he would have to double back to tag.  Maybe he’s also thinking that, like Brad Johnson alluded, the break-even point for a steal (famously ~75% success rate as calculated by Bill James in Moneyball, ~66% in this more recent FG article) is lower if the guy at the plate is likely to cause an out, specifically a strikeout which normally doesn’t allow a runner to advance like a bunt, grounder or long fly might.

On the other hand, maybe Odor doesn’t have such a cynical view of Gallo, and doesn’t change his mindset on the basepaths.  Maybe he doesn’t try to assume what Gallo might do, so he doesn’t go for any more risky of a steal than he otherwise might.  So maybe he isn’t stealing at a higher rate than normal if the guy at the plate is a K machine.  Heck, maybe Joey Gallo is a specifically bad example here, because, though he does whiff a lot, he also hits a lot of home runs, which might cause a runner to take fewer risks when waiting on the outcome of his plate appearance.

So, let’s looks at what the numbers have to say.  I ran a simple correlation analysis between team stolen-base totals and team K%.  Here’s what I got:

So, no real correlation to be seen here.  But perhaps that shows that it could be a market inefficiency.  In 2016, the Brew Crew led the league in both K% and stolen bases.  Even without John Villar’s big SB season, they are a top-five SB team.  Below is a chart from last year — in yellow are the top five teams in both total SBs and K%.

Perhaps the Rays should have been trying to steal some more?  Though some of these anomalies could just simply be explained by personnel issues — maybe teams like the Orioles just have no one who can steal on the entire squad?

Here’s the same chart, for 2015, just for sugar and giggles:

For the Astros, this is starting to look like a trend — Orioles too.  I think my final answer to the question posited by this post is — Hmm, not sure exactly.  But maybe?

Don’t Tread On Dyson

Browsing through the unqualified FanGraphs WAR leaders for 2016, one may come across what seems like an anomaly at No. 69. Just ahead of certified breakout stars such as Jonathan Villar and Trevor Story, grizzled veterans such as Asdrubal Cabrera and Troy Tulowitzki, and an All-Star catcher in Yasmani Grandal, sits Royals back-up outfielder Jarrod Dyson, at 3.1 fWAR in just 337 plate appearances. If a well-educated-baseball individual were asked to name this mystery outfielder who placed just above these solidly above-average everyday players, Jarrod Dyson wouldn’t be one of the first 30 outfielders most would name. How did Dyson make it so far up this list? And what is he doing rotting on the bench behind the likes of Paulo Orlando, or even the corpses of Alex Gordon and Lorenzo Cain for that matter?

Jarrod Dyson ended up being the most valuable Royal this year. Even more valuable than Danny Duffy and Salvador Perez, despite having the ninth-most plate appearances on the team. So what’s the problem? The problem is Dyson’s profile is far from sexy. He owns a career .325 OBP and only seven home runs in over 1500 plate appearances. His wRC+ is below average for an American League outfielder at 86. Where Dyson extracts his value is in his defense and baserunning, two ways of evaluating a player that are still slow to catch on.

Since Dyson starting seeing semi-regular playing time in 2012, he ranks fourth in FanGraphs BsR behind Mike Trout, Billy Hamilton, and Rajai Davis, all of whom had more plate appearances than Dyson. If stolen bases are your cup of tea, Dyson ranks sixth since 2012, behind five guys who all had more plate appearances. The numbers are there to show how great of a baserunner Dyson is; the problem is getting front offices to realize just how valuable baserunning can be, especially when it comes to a player like Dyson who owns a decent, but not great career OBP.

It doesn’t stop at Mr. Dyson’s baserunning. If the Royals don’t use him as a pinch-runner off the bench, he is used as a defensive replacement. Obviously the Royals think highly of Dyson’s defense, and the numbers agree. Of the outfielders with at least 1000 innings, Dyson ranks fourth since 2012 in FanGraphs’ UZR/150. Even in limited playing time, competing against some who have played twice as many innings, Jarrod ranks 15th in FanGraphs’ defensive value. Jason Heyward, the man who just signed an eight-year, $184-million contract last offseason, is the only other player who ranks in the top 15 in both baserunning and outfield defense according to FanGraphs. What’s perplexing about this is that it’s not as if Heyward is a slugger on top of his outstanding defense and baserunning; he would only be considered a slightly above-average hitter by most measurements. So why isn’t Dyson considered in the same vein as Heyward? Sure, Jason Heyward, former first-round pick and All-Star, has more of a track record, but Jarrod Dyson should at least have been given a chance to start by this point.

Jarrod Dyson shows there is still progress to be made on the analytics front. The inexplicable handling of Dyson can be attributed to a mistrust in advanced statistics. If we are going to consider Mike Trout to be the best player in baseball based on metrics such as WAR, then players such as Dyson need to be given the same consideration. What separates Mike Trout from David Ortiz, Miguel Cabrera, and Josh Donaldson is what makes Jarrod Dyson at least an above-average starting outfielder, if given the chance.

Bucking the Trends

As Cubs fans and non-Cubs fans alike celebrate the end of the 108-year drought, we have overlooked the fact that in winning, the Cubs also bucked two trends in major league baseball:

  1. 100+ win teams struggle in the postseason and rarely win the World Series, especially since the wild-card era began in 1995
  2. Losers of the ALCS and NLCS (Cubs lost 2015 NLCS) historically decline the following season, both in win total and playoff appearance/outcome

Below is a table to quantify a team’s performance in the playoffs:



Playoff Result Score
Win WS 4
Lose WS 4-3 3.75
Lose WS 4-2 3.5
Lose WS 4-1 3.25
Lose WS 4-0 3
Lose LCS 4-3 2.75
Lose LCS 3-2* 2.666666667
Lose LCS 4-2 2.5
Lose LCS 3-1* 2.333333333
Lose LCS 4-1 2.25
Lose LCS 4-0 or 3-0* 2
Lose LDS 3-2 1.666666667
Lose LDS 3-1 1.333333333
Lose LDS 3-0 1
Lose Wild Card Game 0.5
Miss Playoffs 0

*The LCS was a best-of-five-game series from 1969 through 1984

It is important to acknowledge how close a team comes to winning a particular round. Based on a 0 to 4 scale, with 0 indicating the team missed the playoffs and 4 indicating the team won the World Series, the table credits fractions of a whole point for each playoff win. For example, in a best-of-seven-game series, each win (four wins needed to clinch) is worth 0.25. In a best-of-five-game series, each win (three wins needed to clinch) is worth 0.333 (1/3). Any mention of playoff result or average playoff result in this article is derived from this table.


Playoff baseball, due to its small sample size and annual flair for the dramatic, historically has not treated exceptional regular season teams well. Jayson Stark recently wrote an article for ESPN titled, “Why superteams don’t win the World Series.” He noted that only twice in the first 21 seasons of the wild-card era had a team with the best record in baseball won the World Series (1998 and 2009 Yankees). Those two Yankee teams are also the only two 100-win ball clubs in the wild-card era to win the World Series. Research in this article will span the years 1969 to 2015, with 1969 being the first year of the league championship series (LCS).

Entering the 2016 season there had been 47 100+ win teams since the start of the 1969 season. Of those, 10 (21.3%) won the World Series. Other than those 10 World Series winners, how did 100+ win teams fare in the post-season?

Below are the average playoff results for 100+ win teams in each period of the major league baseball playoff structure from 1969 to 2015. The playoff structures were as follows:

1969-1984: LCS (best of 5 games) + World Series (best of 7 games)

1985-1993: LCS (best of 7 games) + World Series (best of 7 games)

1995-present: LDS (best of 5 games) + LCS (best of 7 games) + World Series (best of 7 games)

The wild-card game (2012-present) is omitted because a 100+ win team has yet to play in that game, although it certainly would be rare if we ever see a 100+ win team playing in the wild-card game.

Teams Average Playoff Result WS Titles % WS Titles
1969-1984 18 3.07 7 38.9%
1985-1993 7 2.75 1 14.3%
1995-2015 22 2.27 2 9.1%
1969-2015 47 2.65 10 21.3%

As the data shows, 100+ win teams during the 1969-1984 period on average made a World Series appearance. This could be partly due to the fact there was only one round of playoffs (the LCS) ahead of the World Series, with the LCS being a best of five games. It was certainly a much easier path to the World Series once a team made the playoffs, yet on average 100+ win teams were finishing with a World Series sweep.

Changing the LCS from a best-of-five-game series to a best-of-seven-game series had a negative impact on team post-season performance, as 100+ win teams during the 1985-1993 span on average lost a deciding Game Seven in the LCS.

When the league added the wild card and LDS in 1995, it expanded the opportunity to make the playoffs but made the path to a World Series title more difficult, for a team now had to win 11 games to hoist the trophy. In the wild-card era, 100+ win teams are on average losing 4-1 in the LCS. This period also has the lowest percentage of 100+ win teams winning the World Series.

Average Playoff Result Likelihood to Win WS
1969-1984 3.07 25.3%
1985-1993 2.75 19.4%
1995-2015 2.27 6.8%
1969-2015 2.65 17.1%

Using average playoff result standard deviation and a normal distribution, we can also see that the likelihood of a 100+ win team to win the World Series has had a significant decrease over the past several decades, left at under 7% during the wild-card era. The longevity of 100+ win teams in the playoffs has been trending downward over the past several decades. Despite being on the verge of a World Series defeat, the Cubs were able to successfully break through and buck a trend that had haunted outstanding regular-season teams for decades, especially since the wild-card era began in 1995.


The 2015 Cubs lost to the Mets in the NLCS yet bounced back in 2016 to have an even better regular season and win the World Series. This, however, was a rare feat. Teams that lose in the LCS historically win fewer regular-season games and perform worse on average in the post-season (if they make it) the following year. Below are two charts (1969-2015 and 1995-2015) that display average win differential, average playoff result, likelihood win differential is greater than +5 (2016 Cubs were +6), and the likelihood of winning the World Series.

1969-2015 American League National League MLB
Average Win Differential -7.27 -5.73 -6.5
Average Playoff Result 1.02 1.07 1.05
Likelihood Win Differential is >(+5) 13.7% 13.7% 13.8%
Likelihood to Win WS 2.9% 2.7% 2.8%
1995-2015 American League National League MLB
Average Win Differential -5.42 -2.32 -3.87
Average Playoff Result 1.00 1.46 1.23
Likelihood Win Differential is >(+5) 18.1% 21.6% 20.0%
Likelihood To Win WS 1.4% 5.2% 3.2%

Due to the 1981 and 1994 strikes, a few data points for win differential and playoff result are not included in the calculation. The data set includes 82 LCS losers for win differential and 88 LCS losers for average playoff result. The 1980-81, 1981-82, 1993-94, 1994-95, 1995-96 win differentials are not included for LCS losers in both leagues. The 1994 and 1995 playoff results are not included for LCS losers in both leagues because there was no post-season in 1994, hence no LCS loser. Regardless, there is a notable trend among LCS losers to perform worse the following season.

The 2016 Cubs not only won six more regular-season games than in 2015, but they became only the seventh team in history to lose the LCS one season and win the World Series the following season (1971 Pirates, 1972 Athletics, 1985 Royals, 1992 Blue Jays, 2004 Red Sox, 2006 Cardinals). Two of the previous six teams repeated as champions: 1973 Athletics and 1993 Blue Jays. Most recently, the 2005 Red Sox lost 3-0 in the ALDS and the 2007 Cardinals failed to make the playoffs.


The Cubs have already been pegged favorites to win the 2017 World Series, which isn’t surprising given the fact nearly every key player is under team control. Is history on their side? Winning back-to-back titles is difficult in today’s competitive league, as new baseball thinking has somewhat evened the playing field and the small sample size of post-season baseball has the ability to lend unexpected results.

The 10 100+ win teams who have won the World Series since 1969 historically have not been successful in their attempts for back-to-back titles. Below are the average win differentials and average playoff result for these teams in the season following their championship:

Win Differential From 100+ Win WS Team Playoff Result
1970 Mets -17 0
1971 Orioles -7 3.75
1976 Reds -6 4
1977 Reds -14 0
1978 Yankees 0 4
1979 Yankees -11 0
1985 Tigers -20 0
1987 Mets -16 0
1999 Yankees -16 4
2010 Yankees -8 2.5
Average -11.5 1.83

Only three of these 10 teams (1975-76 Reds, 1977-78 Yankees, 1998-99 Yankees) have repeated as champions. Can the 2017 Cubs be the fourth? No matter the numbers, the 2017 Cubs still have to perform on the field. They were on the brink of losing the World Series in 2016, so we must not take anything for granted. But despite this, there’s no doubt the 2017 Cubs will be in a good position for a repeat. The Cubs are expected to be MLB’s best regular season team in 2017, according to FanGraphs and Jeff Sullivan’s analysis in his November 11, 2016 article. Only time will tell.

Should the Best Team Win Each Year?

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

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

Read the rest of this entry »

The Case for Alex Avila

With the offseason in full swing, there are a number of contenders looking to fill their vacancies at catcher. After surveying the market, the most common names featured have been Brian McCann and an injured Wilson Ramos. After that, you hear rumblings of the Athletics dangling Stephen Vogt and another exercise in how teams value pitch-framing with Jason Castro. There is a player, however, I feel is being overlooked and could provide value to a contender. Alex Avila isn’t the sexiest name on the free-agent market but could be a sleeper candidate for a team willing to roll the dice.

When combing through the free-agent leaderboards, I discovered a couple of interesting data points that show how Avila could break through with the bat. First, I calculated “Good Contact %” by adding together Medium and Hard Contact %. Alex Avila was the leader at 91.3%. Other notable players on this list include Justin Turner, fifth at 87.9%, and the recently-signed Kendrys Morales, seventh at 87.2%.

So we have a 29-year-old left-handed-hitting catcher who makes good contact and plays the toughest position on the defensive spectrum. So why isn’t there more chatter about Alex Avila? The two biggest culprits lying in the stat line would be his groundball and strikeout tendencies. In 2016, Avila ran a 52% groundball rate and a 37% strikeout rate. Even then, Avila still managed to produce a 104 wRC+ which, given the low bar for catchers, is excellent. Diving into Statcast, we find that Avila had an average exit velocity of 92 MPH, which groups him in the same bucket as J.D. Martinez and Chris Davis. The disclaimer here is Martinez and Davis had over 300 batted balls and Avila had fewer than 100.

This is where there may be hidden value waiting to be unlocked. Avila has an average launch angle of 7.5 degrees, which is suboptimal for a slow-footed catcher. Given his exit velocity, if he could increase his average launch angle into the 15-30 degree range he could exponentially improve his offensive production. If he shifted his approach to drive balls in the air to his pull side, he could unlock additional power and maximize the contact he does make. Last season, Avila ran a pull percentage of 38%. Given his left-handedness and groundball tendencies, he is easily shiftable, which depresses the value of his bat. Avila only managed to hit seven homers last year, but with an offseason to work on a change in approach, I firmly believe he could unlock additional power.

Avila would definitely benefit from being the strong side of a catching platoon. If I am Dan Duquette and the Baltimore Orioles, I am moving to sign Avila to be the strong side of a catching platoon in hopes he could undergo a Trumbo-esque transformation by maximizing contact in a hitter-friendly environment. At present, Steamer projects Avila for 1.2 WAR in 2017, which on the open market should garner a commitment of just under $10 million on a one-year deal. If nothing changes, Avila can still be a serviceable option in a platoon, but if these changes were to take hold, we could be looking at the steal of the offseason.

A Different Look at the 2016 NL Cy Young

The National League Cy Young Award race is looking like it is going to be closer than the 2016 presidential election. Kyle Hendricks has the sparkling ERA and solid peripherals while Max Scherzer has the sexy strikeouts and the innings pitched of a workhorse. Jon Lester, meanwhile, was the ace of the Cubs and got the first start over Hendricks in the playoffs despite having slightly worse numbers because of his reputation as a big-game pitcher. All three candidates are deserving and have a legitimate chance to win the election; let’s just hope the 30 voters all show up.

With starters getting the hook quicker and quicker over the past few years, pitching is increasingly becoming a race to the bullpen rather than a one-pitcher marathon. In light of this, we’re going to compare these pitchers through their first five innings pitched each start. This will show how the pitchers pitched while they were at their peaks in each game rather than while they were tired or overworked from being left in too long. In theory, this should give a statistical boost to Max Scherzer because his manager Dusty Baker is notorious for leaving starters in too long (see 2003 Cubs pitchers). Scherzer and Lester should also get a slight edge because Hendricks was given a pretty short leash this year. He has not had to pitch under excessive conditions as often as Scherzer or Lester have.

Innings 1 – 5
Kyle Hendricks 150.0 1.68 3.03 3.45 0.256 0.302 0.246 0.6 24% 6% 0.246 8% 51%
Max Scherzer 169.0 2.93 3.15 3.39 0.251 0.350 0.260 1.1 32% 7% 0.247 15% 33%
Jon Lester 151.0 2.50 3.33 3.47 0.272 0.321 0.262 0.8 25% 7% 0.259 10% 48%

Through innings 1-5 this year, the edge actually ends up going to Kyle Hendricks with the lower FIP, HR/9, wOBA, and a sparkling 1.68 ERA. Although Scherzer has that ugly 1.12 HR/9, it is mainly due to the high number of fly balls given up. The 47.2% of automatic outs via pop-outs and strikeouts should allow him to continue as one of the best pitchers in the NL for a couple more years. Lester hangs in with solid numbers across the board, although his FIP and xFIP are the highest of the group.

Innings 6 – 9
Kyle Hendricks 40.0 3.83 3.87 4.12 0.280 0.379 0.283 1.1 20% 6% 0.265 13% 40%
Max Scherzer 58.1 3.03 3.48 3.31 0.262 0.406 0.285 1.5 31% 4% 0.278 7% 34%
Jon Lester 51.2 2.26 3.63 3.48 0.254 0.372 0.269 1.2 24% 5% 0.248 2% 45%

After the fifth inning this year, Hendricks really hit a wall, supporting a 3.87 FIP and 4.17 xFIP. Scherzer takes a small hit overall but still pitches at a Cy Young level late in the game. Lester continues to pitch solidly as well, although the 2.26 ERA is suspiciously low considering his 3.63 FIP in the late innings. Hendricks’ poor performances after the fifth inning help explain why Joe Madden decided to go with Lester in Game 7 of the World Series in the fifth inning against the Indians rather than Hendricks. Although the playoffs do not count towards Cy Young voting, the fact that Maddon brought in Lester on short rest because he did not trust Hendricks in the biggest game of the year shows how cautious Maddon has been with his ERA-title winner in late-game situations this year.

Another thing to consider is that Lester and Scherzer are considered the “aces” of the staff. They know going into each game that they are expected to pitch to the seventh, eighth, or ninth inning. They have to pace themselves while Hendricks has the luxury to empty his tank through five and allow the bullpen to close out the contest. Or, since each pitcher threw under 60 innings after the fifth, this may be like the presidential polls and is just too small of a sample size to matter.

Before we decide who should ultimately serve the one-year term as the National League Cy Young Award winner, we should look at one more thing. The Cubs defense. Yes, Hendricks has a great GB% and is fantastic at limiting contact, as his adjusted contact score is 75 (Lester and Scherzer are 88 and 92 respectively). However, Hendricks and Lester had one of the best defenses to ever be assembled behind them doing work on all the balls in play. The Cubs as a team allowed a .255 BABIP, which is .042 points better than average and .033 points better than the Nationals. Their FIP-ERA gap is 0.62 while the Nationals are right around league average with an FIP 0.06 higher than the team ERA. So, while Hendricks and Lester both had a hell of a season with 2.13 and 2.44 ERAs respectively, the top-notch defense the Cubs deployed behind them deserves a lot of the credit.

If I had to pick who I thought deserves the Cy Young award, I would pick Scherzer, followed closely by Hendricks. Through the first five innings of games, his FIP is comparable to Hendricks’, so it comes down to whether I would take the longevity of Scherzer, or the contact management of Hendricks. While the 75 adjusted contact score is fantastic, he doesn’t quite get to the 1.07 gap in FIP-ERA without fantastic fielders and a little luck behind him. Scherzer threw the most innings in the NL this year and was the undeniable ace of the Nationals’ staff. The two Cubs pitchers may lose the Cy Young race, but they will be just fine with the hardware that they already earned this year.

“Pitchers Never Bat Strategy” Now Worth Seven Wins Per Year

The case for never letting pitchers bat in the NL has just gotten a whole lot better. I now estimate that if a NL team were to always pinch-hit for their pitchers they would expect to pick up a whopping 7.2 wins per year. And that, my friends, is a game-changer.

In my initial post two weeks ago I laid out a strategy in which a National League manager pinch-hits for his pitchers every time their turns come up in the batting order. I called it the “Pitchers Never Bat” strategy. The manager would keep a pitching staff of 11 “relievers” and no “starters.” The major benefit of doing this, I estimated, would be an improvement in the team’s offense.

I addressed what I considered the two major “components” of the analysis and estimated that the impact of this strategy was worth an extra 3.6 wins per year if the team was the only team in the National League to implement it. I also identified four other components of the analysis that could possibly add to, or take away from, my initial estimate of 3.6 wins per year.

In this follow-up post I will do two things. First, I will make some improvements by estimating the impact of two of the four components that I previously left unaddressed. And second, I will address some concerns raised by some members of the FanGraphs community via their thoughtful comments on my initial post.

Here is where I left off at the end of my original post:

Estimated Change in Wins Per Year by Component –

Component #1:   +3.6

Change in Runs due to pinch hitters batting for all pitchers


Component #2:   +0.0

Change in Runs Allowed due to using pitching staff in a new way


Component #3: Not Evaluated

Change in Runs Allowed due to added flexibility in selecting pitchers based on how they are warming up prior to or during a game


Component #4: Not evaluated

Change in Runs Allowed due to opponents’ inability to “stack the lineup” to take advantage of the starting pitchers “handedness” (i.e., lefty or righty)


Component #5: Not evaluated

Change due to reducing size of pitching staff by 1-2 men


Component #6: Not evaluated

Change in Runs Allowed due to the “times through the order” effect


TOTAL:                +3.6 Wins per Year



So now, let’s make some improvements to the prior analysis. Here, I’m going to add estimates for the impacts of Components #4 and #6:

Component #4 – Handedness

In my “Pitchers Never Bat” strategy, the starting pitcher leaves the game when his turn in the batting order comes up, as a pinch-hitter takes his place. In this approach the starting pitcher will typically throw 1-3 innings, averaging two innings per start. Compare this to the conventional starting pitcher who will throw six innings, on average. If the opposing manager were to “stack” (or “tilt”) his batting order to have more lefties (LHB) to face a righty starting pitcher (RHB), or more RHB to face a LHP, as they do now, the value of his tilt would only be in effect for two innings, not six. The manager of the team using the “Pitchers Never Bat” strategy would most likely bring in the next two relievers with the opposite hand of his starter. Example: A lefty starter goes two innings, and is replaced by two consecutive right-handed relievers who would pitch two innings each.

After reviewing league averages for wOBAs for each of the four “handedness combinations” (i.e., LHP/LHB, LHP/RHB, RHP/LHB, and PHP/RHB) as well as how much managers “tilt” their line-ups to take advantage of the starting pitcher’s hand, I estimate that the opponent would lose his current handedness advantage for, on average, four PAs per game, with each of these PAs reducing his batters’ expected wOBA by 18 points for these PAs. Over 162 games, that amounts to 648 PAs per year. Using the rule of thumb that a decrease of 20 wOBA points decreases team run production by 10 runs per every 600 PAs, I estimate that the opponents will lose 9.8 runs/year (that is 18/20 * 10 * 648/600). And since every 10 runs is worth a win, on average, that’s a positive impact to the team implementing the “Pitchers Never Bat” strategy of about 1.0 wins/year (= 9.8/10).

But, since opponents will quickly catch on to the new strategy that they are facing, they should immediately stop trying to “stack” or “tilt” their line-ups. If the opposing manager puts up a line-up that is set up with absolutely no regard to lefty or righty pitching, he can reduce the negative impact to his offense by about 25%, down to a loss of 7.3 runs/year, or a loss of 0.7 wins/year. Since I assume that the opponents will take this less damaging approach, I will use +0.7 wins/year as a conservative estimate for Component #4.

Component #6 – Times Through the Order

Times Through the Order (TTO) refers to differences in pitcher performance due to how many times pitchers have faced the opposing lineup. I recently read an excellent piece on this topic by Mitchel Lichtman, published on Baseball Prospectus on 11/5/13, entitled “Everything You Always Wanted to Know About the Times Through the Order Penalty.” I will draw on one of his many key findings to estimate the impact of TTO on the “Pitchers Never Bat” strategy.

Lichtman presents data (drawn from 2000-2012) which shows that starting pitchers are, on average, at their best the first time through the line-up, are worse the second time through, and even worse the third time through. Using “wOBA against” statistics (adjusted appropriately for batter quality), he shows that pitchers suffer a decay of about 10 points in wOBA against when going from the first TTO to the second TTO, and then decay another 10 points when going from the second TTO to the third TTO. He also estimated the wOBA against statistic for the second TTO is equal to the pitchers’ overall wOBA against. So, in other words, starting pitchers are about 10 points better than average for the first TTO, about average for the second TTO, and about 10 points worse than average for the third TTO.

In the “Pitchers Never Bat” strategy, starters will occasionally work into the beginning of the second TTO, so I’ll assume that 80% of the batters they face will be in the starter’s first TTO, and 20% will be in the second TTO. This means that their wOBA against should be about eight points better (=10 points * 80%) than they would see if they were used in the conventional six-plus inning approach. This advantage will be repeated again by the relievers who replace the starter and pitch through the sixth inning, or until the time that the starter would typically be pulled when using a conventional pitching staff. Think of it this way – instead of a starter throwing a wOBA against of .320 for the first six innings, you get a starter plus two relievers each throwing a wOBA against of .312 for the first six innings. And this benefit is strictly due to the TTO effect.

Improving your wOBA against statistic by eight points for the first six innings of every game means that these pitchers will face about 4,374 batters per year (= 27 PA per game X 162 games.)  Again, using the rule of thumb of 20 woBA points equates to 10 runs per 600 PA, I estimate the impact of this improvement to be a decrease in Runs Allowed of 29.2 runs per year (=8/20 * 10 * 4,374/600.) And using the rule of thumb that 10 runs per year equates to one additional win per year, I can finally estimate that the positive impact of the TTO effect to be 2.9 additional wins per year (=29.2/10).

Now, let’s revisit where we stand with our six components:

Estimated Change in Wins Per Year by Component –

Component #1:   +3.6

Change in Runs due to pinch hitters batting for all pitchers


Component #2:   +0.0

Change in Runs Allowed due to using pitching staff in a new way


Component #3: Not Evaluated

Change in Runs Allowed due to added flexibility in selecting pitchers based on how they are warming up prior to or during a game


Component #4:   +0.7

Change in Runs Allowed due to opponents’ inability to “stack the lineup” to take advantage of the starting pitchers “handedness” (i.e., lefty or righty)


Component #5: Not evaluated

Change due to reducing size of pitching staff by 1-2 men


Component #6:   +2.9

Change in Runs Allowed due to the “times through the order” effect


TOTAL:                 +7.2 Wins per Year



Commenters to my original post raised no objections with my estimated value of +3.6 wins per year due to Component #1, which is the expected change in runs due to pinch-hitters batting for all pitchers. Their two primary concerns were regarding Component #2, which is the change in runs allowed due to using the pitching staff in a new way. Commenters were concerned that my proposed staff of 11 pitchers, averaging 130 innings pitched (IP) per year each, would not be able to handle that large a workload, and therefore the pitchers’ performances would be worse than they would be as part of a traditional pitching staff.

On the issue of workload I see it as follows: Say half of the new staff comes from current relievers who are used to throwing 50-80 IP per year. The new strategy would ask them to average 100-130 IP per year. And let’s say that the other half of the new staff comes from current starters who are used to throwing 160-200 IP per year. The new strategy would ask them to throw 130-160 IP year. So, yes, one would expect that the old relievers would probably pitch worse if they were asked to throw an extra 50 IP per year. But, by similar logic, the old starters would be expected to pitch better if they were asked to reduce their workload by 30 or 40 IP per year. Do these two effects offset each other? Does one dominate the other? I don’t know. Even if Component #2 resulted in a negative net effect, how big could it be? Could it be large enough to outweigh the +7.2 wins estimated from Components #1, #4, and #6? I don’t think so.

And what if, instead, the GM hired 11 guys for the staff that were all starters previously? Would that lead to a net gain to the staff’s performance due to reduced workloads per person? Potentially. Also, note that the impact we are talking about here is solely due to workload and has nothing to do with Handedness (Component #4) or Times Through the Order (Component #6).

For those still concerned that an average of 130 IP for each of 11 pitchers is still a big negative, here are three ways to reduce the average workload:

First, due to call-ups from the minors, visits to the DL, and expanded rosters in September, team workloads are actually shared by far more than the current 12-13 pitchers on the roster at any one point in time. In 2016 the median number of pitchers used by NL teams was 27. If you ranked each team’s staffs at year-end by IP, and then added up the IP thrown by their top 12, you’d find the top 12 typically account for about 80% of their team’s total IP. So you could safely reduce my 130 IP per person that I required for the “Pitchers Never Bat” strategy by 10% to adjust for that. That brings the average workload required down to 117 IP (= 130 * 90%).

Second, some commenters suggested I keep a 12-man staff, not 11 as I proposed. Doing this would decrease the average workload per pitcher by another 8%, or about 9 IP. That would bring down the average workload from 117 IP to 108 IP. (Of course, this would require that the number of position players be reduced by one, and there would be some negative impact because of that.)

Third, as I mentioned in my first post, a team could keep an ace starter that is allowed to bat for himself. He would be used exactly as an ace is used now, pitching 6+ innings every fifth day. In this variation, the “Pitchers Never Bat” strategy would only be used on the four days that the ace is resting. So, here the ace would pitch about 180 innings, reducing the workload for each of the other pitchers by another six innings per year, bringing their average workload down to about 102 IP. (By the way, I roughly estimate that the ace would need to have an expected WHIP of 1.05 or lower to justify allowing him to bat. At a WHIP of 1.05, the added benefit of letting him pitch 6+ innings would just offset the benefits from Components #1, 2, 4, and 6.)

So, to recap, with all three of these changes incorporated, the staff would consist of an ace throwing 180 IP, plus 11 others averaging about 102 IP, and another 15 or so pitchers that come and go throughout the season to support the 12 “primary” guys by sucking up the remaining 20% of the entire team’s IP. This should alleviate the concerns about pitcher workload.

I’m still not totally comfortable quantifying the impact of Component #2 yet, but I’m going to go out on a limb and say that if the staff was developed from 11 guys who were previously starters throwing 180 IP, the smaller workload should improve their average performance. My hunch is that the impact might be slightly positive, whereas the commenters thought it was negative. At this point I’m still going to leave the impact of Component #2 at 0.0, or no change, pending further evaluation.



By adding estimates for the impacts of “Handedness” (Component #4) and “Times Through the Order” (Component #6) my total estimated value of the “Pitchers Never Bat” strategy has jumped dramatically from +3.6 wins (in my initial post) to +7.2 wins per year. If this were to hold up, this would be an astounding gain to any NL team that implemented the strategy. At the going rate of $8 million per year that teams currently pay per win, this equates to about $58 million per year. I look forward to hearing your comments regarding this analysis.

Oh, and by the way, if any NL team would like to discuss additional analysis and/or implementation of this strategy please feel free to contact me at

WAR and Eating Innings

A WAR Carol

Winter has come, baseball season is over, and Ebenezer finishes his analysis and goes home to his cold bed and DVD of Game 4 of the 2004 ALCS since there are no longer any current games to watch.

The Ghost of Pitcher Wins appears and informs him that he will be visited by the Ghosts of Relievers Past, Present, and Future, who will explain to him the errors of his ways.

Mike Marshall appears. “In 1974 I pitched 208.1 innings in relief during the regular season and 12 more in the post season. I accumulated 4.4 WAR. It would have been about 2 wins higher, but I was penalized for being a relief pitcher. It seems that giving my manager over 200 innings of good pitching becomes less valuable if I do it out of the bullpen when and where he needs it rather than as a starter on a schedule. I’m not alone — in MLB history there have been 393 pitcher seasons with over 100 regular season innings and no starts. I don’t even hold the record for most relief innings in a season. Why must I suffer for being a reliever when I carried a starter’s load?”

Next is the Ghost of Relievers Present. Tony Watson appears. “In 2016 I went 67.2 innings as a lefty with a large platoon split (.049 difference in wOBA between lefties and righties). But because I wasn’t totally hopeless against righties I faced well over twice as many righties as lefties (195 to 77) and ended the season with –0.1 WAR. Had I been a worse pitcher so Clint Hurdle used me less I’d have had a positive WAR. Would any manager have actually preferred a LOOGY who faced fewer batters and was inferior against both righties and lefties? Why am I penalized for being too good to be used only as a LOOGY?”

Next comes a group of six — it’s the Ghost of Relievers Future, and they say, “In the distant future a team attempts starting by committee. They have nine pitchers who typically go 18 batters each on a three-day rotation so as to avoid the third time through the order penalty. We come in in the middle of a game, at an unpredictable time, and do the same job for the same length of time as the starters. Occasionally we’re asked to cover additional outs if an earlier pitcher melts down or is injured so we enter early and go long. The starters have a lower replacement level than we do despite having the easier job with greater certainty about both when they will enter and leave a game. How is this fair?”

They fade from view, and The Ghost of Pitcher Wins reappears and says, “Seriously; the reason relievers have a higher replacement level is because their usage is different than that of a starter in ways that affect their value. But different relievers can have drastically different usage, and that also affects their value. Fix this, Ebenezer, or in the long run WAR for relievers will suffer my fate and be superseded by a better tool for reliever evaluation.”

Why the Problem Exists

Why does the replacement level differ between starters and relievers? That’s easy — replacement level is different because it’s easier to find a reliever with a given xFIP, wOBA, RA/9, ERA, or pretty much any other rate stat than it is to find a starter that good. Starters improve when sent to the bullpen; relief is an easier job, so it has a higher replacement level.

But if that were all there was to it then pretty much everyone would do nothing but have bullpen games. Relievers are better and the goal is to win games. So why employ starters at all, much less pay them lots of money?

I’m going to assume that a team has seven roster spots for relievers and five for starters. I’m going to exclude September and October from this analysis as the limit to a 25-man roster doesn’t apply in those months.

In 2016, prior to September, a starter roster spot averaged 151.8 innings (decimal fraction rather than outs obviously). A reliever roster spot averaged 60.8 innings. A team averaged 1184.6 innings.

With those utilization numbers a team would need 20 roster spots of typical relief pitching to get to September. This is not viable. A reliever is less valuable than a starter because eating innings has real value. Getting lots of outs has value beyond simple run prevention, because the team not only needs to prevent runs per at-bat for one or two lefties a game, but someone also needs to get through a large number of innings, and most relievers provide far less of this value than a starter.

The problems with reliever WAR in the fable above all come from the fact that we’re using reliever or starter status as a proxy for the ability to eat innings and changing replacement level to reflect this, rather than giving an explicit adjustment for being able to eat innings as a thing of value in its own right and otherwise evaluating pitcher results on a common basis.

Not all starters are equally good at eating innings, not all relievers are equally bad at it, and the ability to eat innings per roster spot used on the pitching staff has value.

When Steve Carlton went 346.1 innings of 11.1 WAR ball in ’72, he not only pitched quite well on the batters he faced, but he also gave his managers a lot of added flexibility by eating far more than his share of the innings. This is a source of substantial value not captured in the current methods. When Mike Marshall ate over 208 innings in relief that was again a source of substantial value not captured in the current methods. Marshall is in fact penalized on the assumption that he is failing to do exactly the thing that he clearly did.


One problem with what I’ve been saying is that the value added depends on innings/roster spot over time, and I don’t have good information about roster usage. Even if I did have good information about exactly how long each pitcher spent on a roster, I don’t want to give a pitcher a negative WAR for being called up and never used. For that matter I also don’t want to have to change the formula in September when roster spots drop in value.

I’m going to use appearances as a proxy for roster-spot usage. Appearances are readily available and this doesn’t penalize a pitcher just for sitting on the bench. Outs/appearance gives an indication of how good a pitcher is at eating innings, or at least of how good his manager thinks he is. Once a pitcher is in, he typically stays in until the manager has a reason to take him out or the game ends. Closers put in only at the end see such short appearances because the manager doesn’t want to use him for longer appearances.

Note that this is all extremely preliminary; I’m mostly hoping someone else will come up with a better solution than I have below.

Cut to the Chase

I had a bunch of stuff typed up, and reading it puts me to sleep.

I ended up convincing myself that I wasn’t going to do better than a simple linear approach. I’m using outs/appearance as a proxy for efficiency at eating innings; I split this into two terms.

The proposed replacement formula for pitcher WAR is:

WAR = (Runs above League Average)/(R/W) + (C1 × total outs recorded) – (C2 × total appearances)

That’s familiar enough — the first term is wins above (or below) average, the C1 × total outs recorded term is simply adding in a replacement level, the C2 × total appearances is a penalty to represent eating a roster spot.

What I’m actually doing is reducing the replacement level and adding a small penalty based on number of appearances. Unless you think there is something magical about being the “starter,” the different replacement levels for starters and replacements already add such a penalty. They simply do so in an ad-hoc way by adjusting the replacement level and assuming that relievers are the ones with short appearances.

The elephant in the room is that relievers and starters record different numbers of appearances over the same amount of time and I’m using appearances as a proxy for roster usage. This is where the math I removed comes in. On June 17, 1915, George Washington “Zip” Zabel came in for 18 1/3 innings in relief in a single game. I don’t think he needed less rest than a starter. Outs/appearance is being used as a stand-in for the ability to eat innings, and rest requirements would also be reasonably modeled as a term dependent on Outs/appearance. I don’t need a separate term for the things already being accounted for.

Let’s run the numbers for 2016. I’m going to assume that the total WAR given to starters and relievers in each league is at least approximately correct, and that all I’m doing is redistributing that WAR slightly.

Player Type

WAR xFIP Total Outs Total Appearances
AL starter 155.3 4.34 41,450 2,428
AL reliever 58.8 3.94 23,383 7,301
AL pitcher 214.1 4.22 64,833 9,729
NL starter 171.6 4.14 40,788 2,428
NL reliever 43.8 4.18 24,298 8,002
NL pitcher 215.4 4.16 65,086 10,430

I don’t have league-specific runs/win handy; 9.778 is the combined value, so I’ll use that. I also don’t have a good way to correct for the fact that some fraction of reliever WAR is due to leverage concerns and won’t apply to the average values I’m using here.

155.3 = outs/27×(4.22−4.34)/.92/9.778 + 41,450×C1 −2,428×C2

58.8 = outs/27×(4.22−3.94)/.92/9.778 + 23,383×C1 −7,301×C2

And if follows that for the AL the value of C1 is 0.004906 and C2 is 0.01135.

The AL C1 value gives a replacement level of 0.1345 below league average, or replacement of 0.3655, slightly less than the .38 currently used for starters. Then the AL C2 value penalizes a pitcher an 88th of a win for each time he comes into a game.

The same calculation for the NL comes out with a C1 of 0.004856 and C2 of 0.009520; or replacement of 0.3689, and a penalty of one 105th of a win per appearance.

Call it a replacement level of 1 win less than average per 200 outs recorded and a penalty of 1 win per 100 appearances and you’d be close enough for a first cut.

I strongly suspect that more detailed analysis with better starting numbers and taking leverage effects into account would work better, but the basic method will give long relievers some credit for what they’re doing, and give exceptionally long or short starters a small amount of credit for their ability (or inability) to eat innings also.