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 LaRussa 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 1999 Chicago White Sox 

OWAR: 45.1     OWS: 289     OPW%: .504     (82-80)

AWAR: 28.5      AWS: 225     APW%: .466     (75-86)

WARdiff: 16.6                        WSdiff: 64  

The “Original” 1999 White Sox tied the Royals for second place in the American League Central, eight games behind the Indians. Robin Ventura (.301/32/120) established career-highs in batting average and RBI while earning his sixth Gold Glove Award at the hot corner. Randy Velarde (.317/16/76) rapped 200 base knocks and set personal-bests in almost every offensive category. Mike Cameron drilled 34 doubles and pilfered 38 bags. Harold Baines (.312/25/103) topped the century mark in RBI for the third time in his career during his age-40 season. Ray Durham registered 109 tallies and swiped 34 bags. Magglio Ordonez (.301/30/117) scored 100 runs and merited his first All-Star invitation. Frank E. Thomas clubbed 36 two-baggers and delivered a .305 BA. Chris Singleton (.300/17/72) placed sixth in the AL Rookie of the Year balloting and Paul Konerko contributed 24 dingers and 81 ribbies for the “Actuals”.

Frank E. Thomas rated tenth among first basemen according to “The New Bill James Historical Baseball Abstract” top 100 player rankings. “Original” White Sox chronicled in the “NBJHBA” top 100 ratings include Robin Ventura (22^nd-3B) and Harold Baines (42^nd-RF).

  Original 1999 White Sox                          Actual 1999 White Sox

Mike Sirotka (11-13, 4.00) and James Baldwin (12-13, 5.00) labored through their second seasons in the Sox rotation. Alex Fernandez supplied a 7-8 record with a 3.38 ERA after missing the entire 1998 campaign due to injury. Bob Wickman notched 37 saves with an ERA of 3.39 for the “Originals” while Keith Foulke (2.22, 9 SV) and Bob Howry (3.59, 28 SV) secured late-inning leads for the “Actuals”.

  Original 1999 White Sox                       Actual 1999 White Sox 

Notable Transactions

Robin Ventura 

October 23, 1998: Granted Free Agency.

December 1, 1998: Signed as a Free Agent with the New York Mets. 

Randy Velarde

January 5, 1987: Traded by the Chicago White Sox with Pete Filson to the New York Yankees for Mike Soper (minors) and Scott Nielsen.

December 23, 1994: Granted Free Agency.

April 12, 1995: Signed as a Free Agent with the New York Yankees.

November 2, 1995: Granted Free Agency.

November 21, 1995: Signed as a Free Agent with the California Angels.

October 23, 1998: Granted Free Agency.

December 7, 1998: Signed as a Free Agent with the Anaheim Angels.

Mike Cameron

November 11, 1998: Traded by the Chicago White Sox to the Cincinnati Reds for Paul Konerko. 

Harold Baines

July 29, 1989: Traded by the Chicago White Sox with Fred Manrique to the Texas Rangers for Wilson Alvarez, Scott Fletcher and Sammy Sosa.

August 29, 1990: Traded by the Texas Rangers to the Oakland Athletics for players to be named later. The Oakland Athletics sent Joe Bitker (September 4, 1990) and Scott Chiamparino (September 4, 1990) to the Texas Rangers to complete the trade.

January 14, 1993: Traded by the Oakland Athletics to the Baltimore Orioles for Allen Plaster (minors) and Bobby Chouinard.

November 1, 1993: Granted Free Agency.

December 2, 1993: Signed as a Free Agent with the Baltimore Orioles.

October 20, 1994: Granted Free Agency.

December 23, 1994: Signed as a Free Agent with the Baltimore Orioles.

November 6, 1995: Granted Free Agency.

December 11, 1995: Signed as a Free Agent with the Chicago White Sox.

November 18, 1996: Granted Free Agency.

January 10, 1997: Signed as a Free Agent with the Chicago White Sox.

July 29, 1997: Traded by the Chicago White Sox to the Baltimore Orioles for a player to be named later. The Baltimore Orioles sent Juan Bautista (minors) (August 18, 1997) to the Chicago White Sox to complete the trade.

October 29, 1997: Granted Free Agency.

December 19, 1997: Signed as a Free Agent with the Baltimore Orioles.

Alex Fernandez 

December 7, 1996: Granted Free Agency.

December 9, 1996: Signed as a Free Agent with the Florida Marlins. 

Bob Wickman 

January 10, 1992: Traded by the Chicago White Sox with Domingo Jean and Melido Perez to the New York Yankees for Steve Sax.

August 23, 1996: Traded by the New York Yankees with Gerald Williams to the Milwaukee Brewers for a player to be named later, Pat Listach and Graeme Lloyd. The Milwaukee Brewers sent Ricky Bones (August 29, 1996) to the New York Yankees to complete the trade. Pat Listach returned to original team on October 2, 1996.

Honorable Mention

The 1932 Chicago White Sox 

OWAR: 21.5     OWS: 205     OPW%: .380     (58-96)

AWAR: 17.0      AWS: 147     APW%: .325     (49-102)

WARdiff: 4.5                        WSdiff: 58  

The cellar-dwelling “Original” 1932 White Sox fared better than their “Actual” counterparts in terms of team WAR, Win Shares and winning percentage. Although the “Actuals” recorded only 49 victories, the team finished in seventh place ahead of the miserable Red Sox (43-111). Willie Kamm clubbed 34 doubles, delivered a .286 BA and drove in 83 baserunners for the Pale Hose. Second-sacker Bill Cissell posted career-bests in batting average (.315), runs (85), hits (184), doubles (36), home runs (7) and RBI (98). Rookie right fielder Bruce Campbell (.286/14/87) contributed 36 two-baggers and 11 three-base hits. Smead “Smudge” Jolley (.312/18/106) drilled 30 doubles while outfield mate Carl Reynolds produced a .305 BA. Luke Appling aka “Old Aches and Pains” rewarded the Chicago brass with 20 two-base hits and 10 triples after achieving full-time status. Ted Lyons completed 19 of 26 starts and furnished an ERA of 3.28.

On Deck

What Might Have Been – The “Original” 2001 Rangers

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

There’s no shortage of people who think Michael Conforto should never have been demoted last season. The thinking among members of this group is that the Mets messed around with Conforto’s development by twice transporting the 23-year-old outfielder to Las Vegas rather than allowing him to work through his struggles in the majors.

Whether you agree with this sentiment or not, there is no arguing that Conforto did struggle, especially against LHP. There is also no arguing that the acquisition of a left-handed RF at last year’s trade deadline was directly related to said struggles.

The presence of that left-handed RF, Jay Bruce — and maybe more importantly the $13-million 2017 salary associated with Bruce that has scared off potential trade suitors to date — leaves the current state of the 2017 Mets outfield quite complicated.

As it stands now, hundred-millionaire Yoenis Cespedes has permanent claim in left, and Curtis Granderson seemingly has permanent claim in center, leaving Bruce or Conforto to man right. The defensively-superior Juan Lagares could spell Granderson against lefties (if he makes the roster, that is), which would leave one of Granderson/Bruce/Conforto in right. Further, there is some talk of Jose Reyes getting time in the outfield. So yeah, pretty complicated.

A 2015 summer addition, Conforto never had a chance of being Super Two-eligible post-2017. But last season’s two Vegas vacations have left his current service time at 1.043. Stated another way, if Conforto starts 2017 in Vegas and spends the first 48 games there, the Mets gain another year of team control. Given the superfluous state of the Mets current OF, this scenario, which would have sounded outlandish in March of 2016, is now worth considering.

Michael Conforto Salary Chart

While it’s impossible to predict Conforto’s future arbitration salaries, I arrived at this estimate using the general rule of a 50% increase in salary each year of arbitration. A low-ish estimate of $2 million for Conforto’s Year 1 arbitration salary would yield a $6.75 million Year 4 arbitration salary, while a high-end estimate of $4.5 million for Conforto’s Year 1 arbitration salary would yield a $15.2 million Year 4 arbitration salary. $10.7 million is not only pretty close to the exact middle of these two numbers, but also conveniently lines up with the value of 1 win in 2022 when using 5% inflation.

If Conforto turns out to be just an average regular, the Mets would still gain $10.7 million in 2022 surplus value. If he’s a lot better than average while in the heart of his prime, then the Mets’ 2022 surplus value would be much greater. If you think Conforto will be below average in 2022, then what the Mets do with him in 2017 is mostly irrelevant. Any way you slice it, the potential long-term financial advantage is discernible.

It’s no surprise that Conforto’s playing time projections are all over the place — Depth Charts projects 245 PAs, Steamer projects 319 and ZiPS projects 558. 48 games equal 29.6% of the season, which equates to 73 PA using Depth Charts projections, 94 PA using Steamer projections, and 165 using ZiPS projections. Take the average of those three and you get 111 PA for Michael Conforto over the first 48 games.

Which brings me back to my original title of this piece — is the value of 111Michael Conforto PA in 2017 worth more than a one-year deal for ~$10.7mil in 2022, Conforto’s age-29 season?

There are plenty of variables to consider when answering this, but the most important is probably comparing the 2017 versions of Conforto and Bruce. While Conforto projects as a better hitter, fielder and runner than Bruce, Bruce did run a 124 wRC+ against RHP 2016 and holds a 115 career mark. No one is confusing Bruce for Bryce Harper, but he’s a perfectly suitable platoon option in RF.

Also relevant is the Mets’ schedule over the first 48 games.  Using FanGraphs projections, the weighted projected win percentage of the Mets’ first 48 opponents is .477 — roughly the equivalent of a 77-win team. Now of course these 48 games won’t count any less than the 114 that will follow, but if you truly think Conforto is a better option than Bruce AND you had to choose 48 games to play Bruce over Conforto, the first 48 would be pretty ideal.

While Conforto looked miserable at times last year, it’s impossible to ignore that he posted a 152 wRC+ from July 2015 – April 2016 at the ages of 22-23. While his 2016 Barreled Balls May Not Have Been Ideal, he continued to hit the ball hard amidst his struggles.

I hope Michael Conforto is in RF when Noah Syndergaard throws his first 100mph fastball against Julio Teheran and the Braves on Monday, April 3. But if he’s not, then he must be 2000 miles away, getting at-bats in Las Vegas, rather than a matter of feet away, wasting away in the dugout in Queens. The latter simply doesn’t pay.

Inspiration struck me after reading Jeff Sullivan’s piece yesterday on how Christian Yelich could morph into Joey Votto with continued changes, or shall we say improvements, to his batted-ball profile. Namely, hitting the ball in the air more. As Jeff rightly pointed out, Yelich hammers the ball as well as anyone in baseball; it’s just that, to date, he’s done so much more often on the ground. You know who doesn’t have Christian Yelich’s problem?  Nick Castellanos.

Castellanos has driven changes in his batted-ball profile, which were covered last May by Eno Sarris when he documented the change in Castellanos’ launch angles. Why should you care? Because he’s slowly morphing into Nolan Arenado, and now is the time to buy.

There have been only 10 players with at least 250PA each season since 2013 to grow their FB% year over year.

To be fair to Arenado, hitting more fly balls isn’t the only thing that’s made him the home-run king of the NL (now that Chris Carter has departed to the AL). It’s been his meteoric rise in HR/FB rate as well. There are 10 other players that would fit nicely on this table with Castellanos, but I’ll leave that as an exercise for the reader. Chances are, you’re already well aware of the other players that would join him on the list — I’m looking at you, Justin Turner.

For fun, if we were to project out a full season of at-bats with some growth for Nick Castellanos, we get an interesting range of outcomes for his HR totals:

Much like the Yelich-to-Votto comparison, there are some things that keep Castellanos from becoming Nolan Arenado — namely his strikeout rate, which is 24.6% to Arenado’s 14.6%. This limits the number of balls he puts in play and thus the number of fly balls and homers he can hammer. However, with a little bit of health, growth and maturation in approach, we could see a 30HR season out of Castellanos this year.

For many years, the undisputed king of velocity in Major League Baseball has been Aroldis Chapman, with his fastball that averages around 100 mph and regularly reaches higher. Few pitchers have even been able to approach the level of Chapman’s fastball since he came into the league, and none have surpassed him. However, in 2016, one pitcher finally did it. Mauricio Cabrera of the Atlanta Braves averaged nearly 101 mph on his fastball in 2016 and he regularly touched 103; but yet there was still a major difference between Cabrera and the incredible Chapman. Chapman struck out over 40% of the batters he faced last year, while Cabrera struck out less than 20%. Strikeouts are intuitively related to fastball velocity. The faster that a pitcher can throw the ball, the less time a batter has to react, making it harder to make contact. So how does a pitcher such as Cabrera, who throws as hard as anyone in the game, strike batters out at a well below-average rate?

I first thought that maybe his perceived velocity is not as great as his actual velocity, and sure enough Cabrera does gets very little extension toward the plate when he delivers the ball. He only extends about six feet toward the plate before he releases the ball, which is a full foot shorter than fellow reliever, Zach McAllister, and several inches shorter than average for fastball-heavy relievers. This lack of extension means that the velocity that the batter perceives is slower than the actual velocity coming out of Cabrera’s hand, because it has farther to travel before it gets to the plate. However, this is only a minor difference, as Cabrera’s perceived velocity is still above 100 mph. This is not a huge drop, but it does bring him closer to the pack, as many relievers get good extension that increases their perceived velocities above their actual velocities. Chapman, for instance, gets great extension toward the plate on his already incredible fastball, which results in his excellent perceived velocity of over 101 mph. Cabrera’s lack of extension is likely a contributing factor to his low strikeout numbers, but it does not seem to be the main culprit.

Next, I wanted to see if there was something about the spin rate on his fastball that doesn’t lend itself to strikeouts. Spin rates correlate quite strongly with strikeout rates. Pitchers with high spin rates on their fastballs typically generate more swings and misses, and thus more strikeouts. It turns out that Mauricio Cabrera does have a low spin rate on his fastball. His fastball spin rate of 2300 rpm is well below average for fastball-heavy relievers, which is probably a major reason why he doesn’t miss many bats.

While it makes intuitive sense that something like the amount of spin on his fastball could be the reason for his low strikeout totals, it is still puzzling to see that his spin rate is so low, because spin rate is typically correlated with velocity. For most pitchers, the harder you throw, the more spin you will put on the ball. Aroldis Chapman, for example, has one of the highest spin rates in the sample. In order to single out the spin rate from the velocity, I divided the spin rate by the velocity to find the Bauer Unit, named after Indians pitcher Trevor Bauer. Cabrera’s average Bauer Unit of 22.85 is one of the lowest in the entire sample of fastball-heavy relievers. This means that he has some of the lowest spin per MPH in the game. There must be something inherent in how Cabrera throws a baseball that just doesn’t allow him to generate the amount of spin that is typically commensurate of how fast he throws.

Cabrera’s low spin is not all bad, though. Just as high spin rates lead to strikeouts, low spin rates lead to ground balls. An average spin rate is really where you don’t want to be, as those are the pitches that get squared up more often. While Cabrera actually has an above-average spin rate for the entire population of major-league pitchers, his spin rate is one of the lowest in the league compared to his velocity. This effectively makes him a low-spin pitcher, and last year’s batted-ball numbers bear that out. Nearly 50% of the batted balls Cabrera gave up last season were on the ground, and he didn’t surrender a single home run all season despite giving up the hardest average exit velocity in the game last year on his fastball. Cabrera got away with that extreme exit velocity by only allowing an average launch angle of 5.9 degrees, which was one of the lowest among the fastball-heavy relievers. It is hard to do much damage on balls hit on the ground, even if they are hit 95 mph. While the myth that the harder the ball is thrown the harder the ball can be hit has largely been disproved, it is interesting to see that the pitcher who throws the hardest also gave up the highest average exit velocity.

Of course, strikeouts aren’t just about swinging strikes; you have to get called strikes as well. Throughout Cabrera’s minor-league career, he struggled to throw strikes consistently. So much so that many thought his strike-throwing ineptitude might prevent him from ever even reaching the big leagues. However, once he started pitching in the majors, he suddenly discovered how to find the strike zone. Of course, walking four and a half batters per nine innings is still poor, but that mark represented his lowest walk rate since rookie ball in 2012. Even with the high walk rate last year, he actually threw strikes at an above-average rate. His Called Strike Probability, according to Baseball Prospectus, was 47%, which is slightly above league average. For a guy like Cabrera who has always struggled with control, it is probably a good thing to see him filling up the strike zone at an above-average clip. However, the tendency to pitch within the zone could result in more contact and thus bring his strikeout numbers down. Since he doesn’t command his pitches well, he cannot nibble at the corners or trust himself to throw his pitches just off the plate to generate swings and misses. This allows hitters to either lay off pitches that are safely outside, or lock in to the pitches that are squarely in the zone. This could be another significant cause for his lack of strikeouts.

Another reason Cabrera doesn’t strike out many batters is because he doesn’t possess a bat-missing secondary offering. His secondary pitches are all used primarily to get hitters off of his fastball. He throws the hardest change-up in baseball at 91 mph, and a mid-80s slider with good depth. The change-up got squared up pretty often in 2016, which makes sense, seeing that he throws the pitch with the velocity of a league-average fastball. The slider also does not get many whiffs, but hitters were not able to do much damage off of it in 2016. Batters only slugged .136 off of his slider last season, and the pitch generated the highest rate of fly balls of any slider in the game. Perhaps what is even more significant is that hitters had an average exit velocity against his slider of 85 mph and an average launch angle of 30 degrees. For reference, hitters that hit the ball with an exit velocity of 85 mph at a 30-degree launch angle went 4 for 72. His slider may not be a swing-and-miss offering, but it sure seems to be a good out pitch for him.

It looks like Cabrera’s low spin rate on his fastball relative to its velocity is the main reason for his lack of strikeouts. However, it is also likely that that same low spin rate allows him to induce an extreme amount of ground balls, which helps him limit the damage from the opposing batter. His lack of extension toward the plate and his tendency to live in the strike zone are also contributing factors. He also doesn’t have a secondary offering that gets many swings and misses. His slider, however, does produce a great deal of pop-ups, which is another way he limits damage on his batted balls. A major reason for his success last season despite his low strikeout totals and high walk numbers was that he didn’t give up any home runs. While a complete lack of dingers is very unlikely to persist, the types of batted balls he allows on his fastball and slider make it difficult for batters to hit it deep off of him.

Cabrera walks too many batters, and while I wouldn’t be surprised to see some progression in his strikeout rate, I don’t expect him to ever strike out batters at the same rate as someone like Chapman. He should be able to persist for several years as a good late-inning reliever, but he probably will never reach the elite levels that his fastball might suggest.

Before spring training started, Scott Lauber at ESPN explored whether Rick Porcello could match his 22-win season from 2016. The short answer? No. Probably, almost definitely, not.

Conventional wisdom would swiftly say that, too, though. Three pitchers netted 20 wins last year, two in 2015, and three in 2014. And over those three years, none of the pitchers repeated the feat.

With wins speaking to much more than simply the pitcher on the mound, there are two things to consider when digging into the question: What could Porcello repeat, and what could the Red Sox offense?

Let’s start with the offense. Lauber’s article acknowledges that the Sox scored a league-leading 5.42 runs per game last year, and 6.83 per Porcello start. The biggest difference between this year’s and last year’s team is Mitch Moreland replacing David Ortiz. You could close your eyes and dip your hand into a bowl of cold spaghetti like it’s a Halloween Horror House and pull out the contrast between their production. As is, Moreland is projected to be worth about half a win next season. Alone, that suggests how the Sox could have struggles producing the same way in 2017.

But there are other questions to answer, too. How will top prospect Andrew Benintendi fare? Will Pablo Sandoval make any difference or continue to be negligible? I’m not suggesting the Sox won’t be good. It would be hard for them not to be. But they have enough variables going into the year that Porcello getting another 20+ wins is largely on him, which could be difficult for reasons beyond conventional wisdom.

These numbers tend to feed into each other, which is why they’re useful in seeing just how good Porcello was, and how well things broke for him last year. His pitching profile was relatively similar to past seasons, though. It’s not like Drew Pomeranz discovering a new pitch or Brandon Finnegan changing a grip. Porcello’s sinker (or two-seamer, depending which stat site you reference) gets a lot of the credit for his exceptional performance, but differences in his curveball may reveal reasons for it, too.

None of these changes are insignificant. The h-movement tells us Porcello’s curve ran away more from right-handed hitters and in on lefties. The v-movement tells us it dropped more. Add in how it was three mph slower and it rounds out how the pitch fell off the table more. He worked the zone more up and down over the plate than he did side to side in the two years prior, so it could have messed with batters more when the rest of his pitches moved as they have.

According to Lauber, Porcello mimicking anything close to 2016 will come down to “keeping hitters honest with his off-speed pitches.” Opponents hit .190 against his slider and .174 against his changeup. That could concern pitch-sequencing. Take a look at how he distributed his offerings in general, and then when ahead or behind in the count.

While the numbers don’t detail specifically when each pitch was thrown, they indicate that Porcello was eerily similar no matter what the count was. Sequencing isn’t about finding a magic combination of pitches; it’s about making sure a hitter can’t tell what’s coming. It certainly seems he was successful at it.

This data shines light on the tiny changes that might make a big difference in the game, which is one of the most fascinating aspects of baseball. But even more interesting is a quote from Dave Dombrowski in the ESPN piece, where he said, “I don’t think [Porcello] will try to do too much anymore.”

By itself, that reads like a generic sports-interview statement. But think about what the concept of “trying to do too much” really means in baseball: trying to do too much of one thing. A guy tries to hit a five-run homer or hit 100 on the gun every time; really tries to impose his will over the game by doing something impossible. Porcello wasn’t relying on any one pitch in 2016. And what Dombrowski is hinting at here, intentional or not, is there’s a certain amount of surrender that’s necessary for faring well in baseball.

Lauber tells how Porcello best explains his 2016 success by saying he “better understands what makes him effective.” Maybe that has to do with knowing how much the game controls versus how much he can, which let him harness his own abilities more.

I fear a lesser 2017 from Porcello could be called a disappointment by some, but an advanced understanding doesn’t always mean advanced success. The reality is it was a great year aided by good luck, probably buoyed by the cognizance that has allowed Porcello to be a contributing major-leaguer since he was 21. Maybe he isn’t as good this coming season, but it doesn’t take away from the player he is.

—

career and pitch movement data from FanGraphs; pitch usage from Baseball Savant

The Nationals have had what seems to be a perpetual issue at closer. They have churned through Drew Storen, Tyler Clippard, Rafael Soriano, Jonathan Papelbon, and now Mark Melancon. Some people have touted Koda Glover as the solution for the next half century, but he remains mostly untested. For a team with a great record of developing starting pitchers such as Jordan Zimmermann, Tanner Roark, and Stephen Strasburg, and a general manager in Mike Rizzo whose list of faults is one name long — Jonathan Papelbon (I’m still hopeful about Adam Eaton) — it is somewhat surprising that they have not been able to address the omnipresent glaring issue at the end of games. The potential solution might be in the starting rotation: Joe Ross.

It may not seem obvious, but Ross is a perfect candidate to be moved to the bullpen. Ross has never pitched a full season as a starter. He pitched 105 innings this most recent season, and missed the middle of the season sidelined with a shoulder ailment. The slider that he threw 39% of the time this season is known to wear down a pitcher’s arm, and it did wear down his brother Tyson’s. A move to the ‘pen might save Joe Ross’s arm.

Ross’ numbers are far superior his first time through the order. As Eno Sarris detailed in his article “Who Needs a New Pitch the Most,” Ross’s velocity decreased a full mile per hour during his average start, his strikeout rate dropped by over 10 percent, and his wOBA against shot up from .248 his first time through the order to .371 his second time through.

Most importantly, Ross really only throws two pitches, a slider and a sinker. Two pitches are typical of a reliever, but a solid third option is often required to stick in the rotation. His sinker currently averages about 93 mph, so a move to the end of games could see that number rise to 95. He might also be able to get away with throwing his slider, which batters have hit just .173 against, more often. That combination is tantalizing.

It doesn’t make sense to give up on Joe Ross as a starter just yet, but if his arm fizzles out yet again this season, the Nats should give him a shot in the ‘pen.

The player that all Milwaukee Brewers fans, and baseball fans for that matter, should be watching most closely this spring is Eric Thames. Thames, after three incredible seasons in the KBO, signed a three-year, $16-million deal to man first base for the Brewers. The front office likes what they see from the 2015 KBO MVP, but admittedly did not scout him in person while he was playing overseas; instead, they relied on video to make their assessment of his game. I’ll admit, I can’t wait to see Thames play this year; the mystery, concerns, and potential all make for great theater, but there is one question that keeps haunting me at night: How do former MLB payers fare when they play overseas and then return? As much as this post is about Thames, it is also about those few players who have done what he is doing.

I approached this by looking at all the major-league players who have played in both Korea and Japan over the past 10 years. I could have gone further back to the days when Cecil Fielder was playing in Japan, but the game, both in North America and across the Pacific, has changed significantly since then. The argument could be made that the game has changed significantly over the past 10 years — it changes every season — but that is the beauty of baseball.

I wanted to isolate Korea only, but, perhaps not surprisingly, there were too few players to make anything of that. Out of the several hundred total players in both these leagues over the past 10 years, only a total of 11 players who began their career in MLB returned to MLB after an overseas hiatus. That’s 11 between the KBO AND NPB. 11! Four players from the KBO and seven from NPB. Here’s a graph that shows their names and WAR before and after their careers in Japan and Korea:

(Numbers courtesy of baseball-reference.com)

The outcome for these players is, well, not good. A select few players like Lew Ford and Chad Tracy carry the “pre-Japan/Korea WAR” section thanks to longer, successful careers in MLB before they changed leagues. It also seems unfair to compare these players to each other due to their careers, or lack thereof, upon their return. For example, Ford’s 79 plate appearances are incomparable to Wilson Valdez’s 966. But, in every case, the story arch is the same: Begin their professional baseball career in North America, make it to the majors as a 20-something, decline at the major- and minor-league level, go to Japan/Korea, return to North America in a very limited capacity and fail to make an impact with a major-league-affiliated team.

If the careers of these 11 players is a trend, then Eric Thames is in for a lot of trouble.

But there is reason to believe that Thames is the exception to the rule. Will Franta wrote a convincing Community Research article about the reason to believe that Eric Thames will do well. Additionally, various projections believe that Thames could be anywhere from a 1.2 to 2.2 WAR player with mid- to high-20 home-run totals and an above-average wRC+. Dave Cameron wrote an article analyzing the projections for Thames and concluded that he has the potential to be “the steal of the winter,” and for three years and $16 million, that could very well be true.

But there are factors going against Thames. It isn’t all too often professional players find their footing at the major-league level in their 30s (Thames will be 30 on Opening Day). Plus, with several other corner infielders in the form of Hernan Perez, Travis Shaw, Jesus Aguilar and others who could fill in at first if need be such as Ryan Braun and Scooter Gennett, a team in the middle of a rebuild might not completely be opposed to disposing the incumbent starting first baseman if another star emerges. Even comparing career KBO and NPB players to their transitions to MLB, we can see that there are a lot more Tsuyoshi Nishiokas than Jung-ho Kangs, which is why players like Kang, Ichiro Suzuki, Hideo Nomo, and Yu Darvish are lauded when they succeed in the majors.

I believe that Eric Thames will not be like the 11 others who, by and large, failed in their returns. Thames is intriguing and there is a lot to like about him — and a lot to worry about with him. There are pros and cons to his game. I believe that he will be a great addition to a team that, honestly, could afford to wait for him to assimilate completely to the game.

So far, we’ve developed some mathematical principles for visualizing appearance data for relief pitchers, and for measuring how apart they are. The goal has been to say something about how pitchers are being used, not only in a vacuum, but in the context of the way in which the team has chosen to divide up its relief innings for the season. We’ve only partially gotten there so far, but today let’s take a slight detour to ask: Is the underlying data conveying the most useful information?

Inning and score differential at the time of entering the game are the critical data elements in answering questions related to usage. The numbers and tables in my previous articles all focused on using these two elements. Here’s an example of the underlying data being used, in the form of three Daniel Hudson appearances which appear identical.

Inning and score differential are critical; however, as data elements are concerned, they are somewhat raw. Fortunately, those aren’t the only data elements we can look at. The next-most impactful data, I would argue, is the base-out state at the time that the pitcher enters the game.

Let’s establish a baseline: It’s the norm for relief pitchers to enter the game in a clean inning (no outs, no runners on base). Among pitchers with 20+ relief appearances in 2016, this was the situation in 68.1% of appearances. That’s a very high percentage, considering that there are 24 base-out states. It’s also very intuitive when we think about the game. Among other reasons, pitchers need time to warm up, and mostly, they do so while their own team is batting. It’s also the only base-out state which is guaranteed to happen every inning.

In my first article, I created a definition for what I feel like constitutes a quality at-bat. I also examined a few test cases¹ and hypothesized different ways in which this data could be used going forward. As a reminder, my definition of a quality at-bat (QAB) is an at-bat that results in at least one of the following:

1. Hit
2. Walk
3. Hit by pitch
4. Reach on error
5. Sac bunt
6. Sac fly
7. Pitcher throws at least six pitches
8. Batter “barrels” the ball.

To calculate a QAB percentage I divided the player’s total number of QABs by his total number of plate appearances. I then dove a little deeper into QABs to see what conclusions I could draw from this statistic.

The first thing I did was run every hitter in 2016 who had more than 400 at-bats and created a leaderboard. I displayed the players with the best QAB% and the worst QAB% below. The average QAB percentage in 2016 was 48.54%.  Not surprisingly, Mike Trout leads all hitters and is followed closely by Joey Votto — a player who always finds a way to get on base. The player that stuck out to me most on this list was Chris Carter. This is a player who had a lot of trouble getting a contract this offseason, despite leading the league in homers. In fact, he had so much trouble that he considered going to Japan before finally signing with the Yankees. However, he had the 10^th highest QAB percentage. Mike Napoli’s QAB% also surprised me because I do not view him to be a particularly elite hitter; yet he ranked number four between two of baseball’s best hitters.

One commenter on my last post pointed out that OBP could be highly correlated with QAB%. They were right. In fact, there is a strong correlation of r²=.82 between OBP and QAB%, which makes sense since they share many of the same parameters. After this finding, I decided to create an interactive scatter plot of OBP and QAB% to see what the data looked like and to see if I could find any interesting patterns. If you interact with the graph you can see that the five players who seem to be a little above the data between .3 and .35 OBP are Chris Carter, Mike Napoli, Michael Saunders, Miguel Sano, and Jason Werth.

Click here for an interactive version

Why does QAB% seem to favor this group of players more than others? By investigating the other parameters in my definition of QABs, I found that these five hitters were taking a lot of pitches. In fact, all five of these hitters were in the top 15 last year in pitches per plate appearance, with Jason Werth and Mike Napoli being numbers one and two, respectively. Additionally, Chris Carter’s score was likely higher since he barreled the 8^th most balls last season. This leads me to believe that QAB% tends to favor or distinguish hard-hitting, patient sluggers.

Is QAB% another way in which we should be evaluating hitter performance? Probably not. As much as I love seeing Chris Carter on a list with the best players in baseball, this statistic uses an old-school mindset that does not show true value. That being said, it can still be helpful. It is a good way to show which hitters are taking a lot of pitches. It also helps quantify what coaches and broadcasters mean when they say a player had a  “good at-bat.” Finally, perhaps you watched a lot of Indians games last season and you couldn’t help but feel like Mike Napoli was the best hitter ever. His QAB% may identify why you feel that way. Mike Napoli is a good hitter, but not nearly as good as former MVP Josh Donaldson despite the fact that they both have a very similar number of at-bats that a coach would call “quality”.  Overall, I think this statistic does a good job of quantifying something that used to be a lot harder to quantify. At the very least, QAB% has given me a reason to be excited about Chris Carter joining the Yankees, my favorite team. Opening day cannot come soon enough.

1. In my first article I made a mistake with my test cases. Barrels, a Statcast statistic, did not start being counted until 2015. I had provided QAB numbers starting in 2014. With the way I wrote my code this actually caused the barrels in 2015 and 2016 not to be counted. I should not have provided 2014 numbers at all, and the numbers for 2015 and 2016 were a little lower than they should have been. All of my calculations have been corrected for this article.

Background

Back on 2016-Nov-11 I posted WAR and Eating Innings.

Basically, I was looking at reliever WAR and concluded that giving a lower replacement to relievers isn’t quite correct. Inning for inning, a replacement reliever needs to be better than a replacement starter, because eating innings has real value. But reliever/starter doesn’t actually capture the ability to eat innings, and I gave several examples where it fails historically.

I don’t have roster-usage numbers and don’t want to penalize a pitcher for sitting on the bench, but outs per appearance makes a nice proxy for the ability to eat innings; and in a linear formula that attempts to duplicate the current distribution of wins between relievers and starters, this gives roughly 0.367 win% as pitcher replacement level (as opposed to the current 0.38 for starters and 0.47 for relievers), and then penalized the pitcher roughly 1/100^th of a win per appearance.

The LOOGY needs to be pretty good against his one guy to make up for that penalty, but for a starter it will make almost no difference.

That’s pretty much the entire article summarized in three paragraphs. By design, this doesn’t change much about 2016 WAR — it will give long relievers a modest boost, and very short relievers (LOOGYs and the like) a very modest penalty, and have an even smaller effect on starters.

So why did I bother?

Well, first, there are historical cases where it does matter; but more to the point, I was thinking that relievers are being undervalued by current WAR, and to examine this I needed a method to evaluate a reliever’s value compared to a starter’s value, and different replacement levels complicate that.

Why Do I Think Relievers Are Undervalued?

You could just go to this and read it; it shows that MLB general managers thought relievers were undervalued as of a few years ago. But that’s not what convinced me. What convinces me is the 2016 Reds pitching staff. 32 men pitched at least once for the Cincinnati Reds in 2016. Their total net WAR was negative.

Given that the Reds did spend resources (money and draft picks) on pitching, if replacement level is freely available, then that net negative WAR is either spectacularly bad luck, or spectacularly bad talent evaluation.

32 Reds pitchers were used; sort by innings pitched, and the top seven are all positive WAR, accounting for 5.6 of the Reds’ total of 6.7 positive WAR. Of their other 25 pitchers, only three had positive WAR: Michael Lorenzen (reliever, 50 innings, part of the Reds’ closer plans for the coming year), Homer Bailey (starter, coming off Tommy John and then injured again, only six appearances), and Daniel Wright (traded away mid-season, after which he turned back into a pumpkin and accumulated negative WAR for the season).

It sure sounds like the Reds coaches knew who their best pitchers were and used them. Their talent evaluation was not spectacularly bad. But they had 17 relievers with fewer than 50 innings, and not one of them managed to accumulate positive WAR for the year.

Based on results, we can list the possible mistakes in who they gave innings to: Maybe they could have used Lorenzen a bit more. That’s it; otherwise it’s hard to improve on who they gave the innings to. They also usually gave the high-leverage innings to their best relievers.

So, if replacement level is freely available, why did the Reds coaches give a total of 574.2 innings to 22 pitchers who managed between them to accumulate no positive WAR and 7.1 negative WAR?

If that’s just bad luck, it is spectacularly bad luck; and spectacularly consistent, as the Reds seem to have known in advance exactly who was going to have all this bad luck.

I don’t really believe it is bad luck. Thus, I don’t really believe that the Reds pitchers were below replacement, and the alternative is that replacement (at least for relievers) is too high.

GMs Still Agree: Relievers Are Undervalued by WAR

The article I referenced above was from the 2011-2012 off season; maybe something has changed.

As I write this (2017-Feb-24), FanGraphs’ Free Agent Tracker shows 112 free agents signed over the 2016-2017 off-season. 10 got qualifying offers and thus aren’t truly representative of their free-market value. 22 have no 2017 projection listed, and most of those went for minor-league deals (Sean Rodriguez and Peter Bourjos are the exceptions, and they aren’t pitchers). I’m going to throw those 32 out.

That leaves a sample of 80 players, 28 of them relievers or SP/RP. A fairly simple minded chart is below:

(Hmm, no chart. There was supposed to be a chart. Don’t see an option that will change this. Relief pitcher Average $/Year=5.7105*projected 2017 WAR with an R² of 0.585; everyone else Average $/Year=4.6028+1.401*projected 2017 WAR with an R² of .5917. Note that the “everyone else” line, if you could see it, is below the relief pitcher line at 0 WAR, and then slopes up faster from there.)

R² values aren’t great, and overall values per WAR are low because most of the big paydays are on multiyear contracts where value can be assumed likely to collapse by the end of the contract (I’m not including any fall-off). But the trend continues — MLB general managers think relievers are worth more than FanGraphs thinks they are.

The formula I give above (replacement of 0.367 win% with a −0.01 wins/appearance) is based on trying to reproduce the FanGraphs results. But if the FanGraphs results are wrong, then so is my formula.

Why the Current Values Might Be Wrong

I’ve shown why I think the current values are wrong, but what could cause such an error?

Roster spots change in value over time. That’s all it takes; the reliever is held to a higher (per-inning) standard because historical analysis indicated that he should be. But if roster spots were free, then it would be absurd to evaluate starters and relievers at all differently. The difference in value depends on the value of a roster spot; or, if using my method, the “cost” imposed per appearance needs to be based on the value of a roster spot.

Prior to 1915, clubs had 21 players, and no DL at all. In 1941, the DL restrictions were substantially loosened, and a team could have two players on the DL at the same time (60-day DL only at that time). In 1984, they finally removed the limits to the number of players on a DL at a time; in 2011, a seven-day concussion DL was added, and a 26^th roster spot for doubleheader days; in 2017, the normal DL will be shortened to 10 days.

21 players and no DL makes roster spots golden. You simply could not have modern pitcher usage in such a period.

Not to mention the fact that, in 1913, you’d never have been able to get a competent replacement on short notice. Jets and minor-league development contracts both also dropped the value of a roster spot.

25-26 roster spots, September call-ups to 40, and starting this year you can DL as many players you want for periods short enough that it’s worth thinking about DLing your fifth starter any time you have an off day near one of his scheduled starts. Roster spots are worth a lot less today; it’s not surprising that reliever WAR seems off, when it was based on historical data, and the very basis for having a different reliever replacement level is based on the value of a roster spot.

Conclusion

When I started this, I was hoping to produce a brilliant result about what relief-pitcher replacement should be. I have failed to do so; there’s simply too little data, as shown by the low R² values on the chart I tried to include above, to make a serious try at figuring out what general managers are actually doing in terms of their concept of reliever replacement level.

But the formula I suggested back in November has an explicit term acting as a proxy for the value of a roster spot, and that term can be adjusted for era. If you drop the cost of an appearance from 0.01 WAR to some lower value, raising replacement a bit to compensate, you’ll represent the fact roster spots have changed in value over time.

Given any reasonable attempt to estimate the cost per appearance based on era, I don’t see how this could be worse than the current methods.