## Derek Norris, 2016 — A Season to Forget

While it may not be the most exciting Nationals story of the offseason, Wilson Ramos signing with the Rays and the subsequent trade for Derek Norris to replace him is a very big change for the Nats. Prior to tearing his ACL in September, Ramos was having an incredible 2016, and he really carried the Nationals offense through the first part of the year (with the help of Daniel Murphy, of course) when Harper was scuffling and Anthony Rendon was still working back from last season’s injury. Given Ramos’ injury history it makes sense to let him walk, but Nationals fans have reasons to be concerned about Norris.

After a few seasons of modest success, including an All-Star appearance in 2014, Norris batted well under the Mendoza line (.186) in 2016 with a significant increase in strikeout rate. What was the cause for this precipitous decline? Others have dug into this lost season as well, and this article will focus on using PitchFx pitch-by-pitch data through the pitchRx package in R as well as Statcast batted-ball data manually downloaded into CSV files from baseballsavant.com, and then loaded into R. Note that the Statcast data has some missing values so it is not comprehensive, but it still tells enough to paint a meaningful story.

To start, Norris’ strikeout rate increased from 24% in 2015 to 30% in 2016, but that’s not the entire story. Norris’ BABIP dropped from .310 in 2015 to .238 in 2016 as well, but his ISO stayed relatively flat (.153 in 2015 vs. .142 in 2016). Given the randomness that can be associated with BABIP, this could be good new for Nats fans, but upon further investigation there’s reason to believe this drop was not an aberration.

Using the batted-ball Statcast data, it doesn’t appear that Norris is making weaker contact, at least from a velocity standpoint (chart shows values in MPH):

Distance, on the other hand, does show a noticeable difference (chart shows values in feet):

So Norris is hitting the ball further in 2016, but to less success, which translates to lazy fly balls. This is borne out by the angle of balls he put in play in 2015 vs. 2016 (values represent the vertical angle of the ball at contact).

The shifts in distance & angle year over year are both statistically significant (velocity is not), indicating these are meaningful changes, and they appear to be caused at least in part by the way pitchers are attacking Norris.

Switching to the PitchFx data, it appears pitchers have begun attacking Norris up and out of the zone more in 2016. The below chart shows the percentage frequency of all pitches thrown to Derek Norris in 2015 & 2016 based on pitch location. Norris has seen a noticeable increase in pitches in Zones 11 & 12, which are up and out of the strike zone.

Norris has also seen a corresponding jump in fastballs, which makes sense given this changing location. This shift isn’t as noticeable as location, but Norris has seen fewer change-ups (CH) and sinkers (SI) and an increase in two-seam (FT) & four-seam fastballs (FF).

The net results from this are striking. The below chart shows Norris’ “success” rate for pitches in Zones 11 & 12 (Represented by “Yes” values, bars on the right below) compared to all other zones for only outcome pitches, or the last pitch of a given at-bat. In this case success is defined by getting a hit of any kind, and a failure is any non-productive out (so, excluding sacrifices). All other plate appearances were excluded.

While Norris was less effective overall in 2016, the drop in effectiveness on zone 11 and 12 pitches is extremely noticeable. Looking at the raw numbers makes this even more dramatic:

2015                                                     2016

So not only did more at-bats end with pitches in zones 11 and 12; Norris ended up a shocking 2-for-81 in these situations in 2016.

In short, Norris should expect a steady stream of fastballs up in the zone in 2016, and if he can’t figure out how to handle them, the Nationals may seriously regret handing him the keys to the catcher position in 2016.

All code can be found at the following location : https://github.com/WesleyPasfield/Baseball/blob/master/DerekNorris.R

## Kinda Juiced Ball: Nonlinear COR, Homers, and Exit Velocity

At this point, there’s very little chance you are both (a) reading the FanGraphs Community blog and (b) unaware that home runs were up in MLB this year. In fact, they were way up. There are plenty of references out there, so I won’t belabor the point.

I was first made aware of this phenomenon through a piece written by Rob Arthur and Ben Lindbergh on FiveThirtyEight, which noted the spike in homers in late 2015 [1]. One theory suggested by Lindbergh and Arthur is that the ball has been “juiced” — that is, altered to have a higher coefficient of restitution. Since then, one of the more interesting pieces I have read on the subject was written by Alan Nathan at The Hardball Times [2]. In his addendum, Nathan buckets the batted balls into discrete ranges of launch angle, and shows that the mean exit speed for the most direct contact at line-drive launch angles did not increase much between first-half 2015 and first-half 2016. He did observe, however, that negative and high positive launch angles showed a larger increase in mean exit speed. Nathan suggests that this is evidence against the theory that the baseball is juiced, as one would expect higher mean exit speed across all launch angles. I have gathered the data from the excellent Baseball Savant and reproduced Nathan’s plot for completeness, also adding confidence intervals of the mean for each launch angle bucket.

Figure 1. Mean exit speed vs. launch angle.

At the time of this writing, I am not aware of any concrete evidence to support the conclusion that the baseball has been intentionally altered to increase exit speed. This fact, combined with Nathan’s somewhat paradoxical findings, led me to consider a subtler hypothesis: some aspect of manufacturing has changed and slightly altered the nonlinear elastic characteristics of the ball. Now, I’ve been intentionally vague in the preceding sentence; let me explain what I really mean.

Coefficient of restitution (COR) is a quantity that describes the ratio of relative speed of the bat and ball after collision to that before collision. The COR is a function of both the bat and the ball, where a value of 1 indicates a perfectly elastic collision, during which the total kinetic energy of the bat and ball in conserved. The simplest, linear, approximation of COR is a constant value, independent of the relative speed of the impacting bodies. It has long been known that, for baseballs, COR takes on a non-linear form, where the value is a function of relative speed [3]. Specifically, the COR decreases with increasing relative speed, and can vary on the order of 10% across a typical impact speed range. My aim is to show that, for some reasonable change in the non-linear COR characteristics of the baseball, I can reproduce findings like Alan Nathan’s, and offer yet another theory for MLB’s home-run spike.

In order to explore this, I first need a collision model to incorporate a non-linear COR. I want this model to be relatively simple, and also to be able to account for different impact angles between bat and ball. This is what will allow me to explore the effect of non-linear COR on exit speed vs. launch angle. I will mostly follow the work of Alan Nathan [4] and David Kagan [5]. I won’t show my derivation; rather, I will include final equations and a hastily drawn figure to explain the terms.

Figure 2. Hastily drawn batted-ball collision.

The ball with mass is traveling toward the bat with speed $\dpi{300}&space;v_{ball}$, assumed exactly parallel to the ground for simplicity. The bat with effective mass is traveling toward the ball with speed $\dpi{300}&space;v_{ball}$, at an angle $\dpi{300}&space;\theta&space;_{1}$ from horizontal. We know that in this two-dimensional model, the collision occurs along contact vector, the line between the centers of mass, which is at an angle $\dpi{300}&space;\theta&space;_{2}$ from horizontal. This will also be the launch angle. Intuition, and indeed physics, tells us that the most energy will be transferred to the ball when the bat velocity vector is collinear with the contact vector. When the bat is traveling horizontally and the ball impacts more obliquely, above the center of mass of the bat, the ball will exit at a lower speed. These heuristics are captured with the following equations, where COR as a function of relative speed will be denoted $\dpi{300}&space;e\left&space;(&space;v_{R}&space;\right&space;)$, and the exit speed $\dpi{300}&space;v_{f}$.

$\dpi{300}&space;v_{ball}^{'}=v_{ball}\cos&space;\left&space;(&space;\theta&space;_{2}\right&space;)$                                                             (1)

$\dpi{300}&space;v_{bat}^{'}=v_{bat}\cos&space;\left&space;(&space;\theta&space;_{2}-\theta&space;_{1}\right&space;)$                                                   (2)

$\dpi{300}&space;v_{R}=v_{bat}^{'}+v_{ball}^{'}$                                                               (3)

$\dpi{300}&space;r=\frac{m}{M}$                                                                              (4)

$\dpi{300}&space;v_{f}=\frac{e\left&space;(&space;v_{R}&space;\right&space;)-r}{1+r}\cdot&space;v_{R}+v_{bat}^{'}$                                           (5)

Now all we must do is choose a functional dependence of the COR on relative speed. Following generally the data from Hendee, Greenwald, and Crisco [3], and making small modifications, I produced the following models of COR velocity dependence:

Figure 3. Hypothetical non-linear COR.

Note that, for the highest relative bat/ball collisions, the “old” and “new” ball/bat collisions will result in similar amounts of energy transferred, while in the “new” ball model, slightly more energy will be transferred to the ball in lower-speed collisions. This difference seems to me quite plausible given manufacturing and material variation of the baseball. It is also worth emphasizing that this difference need only be on average for the whole league; some variation ball-to-ball would be expected.

Taking the new and old ball COR models from Figure 3 and plugging into equations (1)-(5) allows us to simulate the exit speed across a range of launch angles. I have assumed a bat swing angle of 9 degrees. Calculations and plots are accomplished with Python.

Figure 4. Exit speed as a function of launch angle for non-linear COR.

The first thing to note about Figure 4 is that the highest exit speed is indeed at 9 degrees, which was the assumed bat path. The second is the remarkable likeness between Figure 4, the model, and Figure 3, the data. Clearly, I have cheated by tweaking my COR models to qualitatively match the data, but the point is that I did not have to make wildly unrealistic assumptions to do so. I have not looked deeply into the matter, but this hypothesis would also suggest that from ’15 to ’16, a larger home-run increase would be expected for moderate power hitters than from those who hit the ball the very hardest. In fact, Jeff Sullivan suggests almost exactly this [6], although he also produces evidence somewhat to the contrary [7].

There is certainly much complexity that I am ignoring in this simple model, but it is based on solid fundamentals. If one accepts that baseball manufacturing could be subject to small variations, and perhaps a small systematic shift that alters the non-linear coefficient of restitution of the ball, it follows that the exit speed of the baseball is also expected to change. Further, the exit speed is expected to change differently as a function of launch angle. That a simple model of this phenomenon can easily be constructed to match the actual data from suspected “before” and “after” timeframes is at least interesting circumstantial evidence for the baseball being juiced. Perhaps not exactly the way we all expected, but still kinda juiced.

References:

[1] Arthur, Rob and Lindbergh, Ben. “A Baseball Mystery: The Home Run Is Back, And No One Knows Why.” FiveThirtyEight. 31 Mar. 2016. Web. 30 Aug. 2016.

[2] Nathan, Alan, “Exit Speed and Home Runs.” The Hardball Times. 18 Jul. 2016. Web. 23 Aug. 2016.

[3] Hendee, Shonn P., Greenwald, Richard M., and Crisco, Joseph J. “Static and dynamic properties of various baseballs.” Journal of Applied Biomechanics 14 (1998): 390-400.

[4] Nathan, Alan M. “Characterizing the performance of baseball bats.” American Journal of Physics 71.2 (2003): 134-143.

[5] Kagan, David. “The Physics of Hard-Hit Balls.” The Hardball Times. 18 Aug. 2016. Web. 23 Aug 2016.

[6] Sullivan, Jeff. “The Other Weird Thing About the Home Run Surge.” FanGraphs. 28 Sept. 2016. Web. 4 Dec. 2016.

[7] Sullivan, Jeff. “Home Runs and the Middle Class.” FanGraphs. 28 Sept. 2016. Web. 4 Dec. 2016.

## Examining Net Present Value and Its Effects

Going back to January 2016, Dave Cameron wrote an article detailing the breakdown of money owed to Chris Davis over the life of the deal he signed last year. For myself, this provided insight into how teams value long-term contracts, but more importantly it led me to more questions about how money depreciates over time. Fast-forward to the present and we start to see some articles and comments with people speculating about how much money teams are going to throw at Bryce Harper when he reaches free agency in a few years. The numbers have been pretty incredible; $400 million?$500 million? Even $600 million? Then someone threw out an even larger number:$750 million.

The best thing to do is ignore these numbers because we are still a couple of years away from free agency and he just had a down year where he was “only” worth 3.5 WAR, which gave the team a value of $27.8 million. At some point the numbers don’t even make sense because the contract values are getting so inflated. But at the same time, good for him, maybe he’ll buy a baseball team once he retires, or a mega-yacht. But unfortunately we will need to wait until after the 2018 season before we find out the value of this contract. In the meantime, speculation will run rampant and the media will throw out inflated numbers for the amusement of the masses. Now, the purpose of this article is not to predict the value of Bryce Harper’s future contract, but to examine a few scenarios as to the actual value in present-day dollars. To do this I will use the concept of Net Present Value (NPV) from Dave Cameron’s Chris Davis article and then use some of the numbers from his article predicting a contract for Bryce Harper. Let’s set a couple rules; (1) Match the length of contract given to Stanton — 13 years, (2) use nice round numbers and get as close to the total values as possible, (3) use a discount rate of 4%, (4) this is an exercise in futility and not to be taken too seriously and finally (5) to estimate NPV for a massive contract. Here are the scenarios for a 13-year contract totaling in excess of$400M, $500M and$600M.

 13 Year Contract Structure Year Age 2019 26 $31,000,000$38,500,000 $46,500,000 2020 27$31,000,000 $38,500,000$46,500,000 2021 28 $31,000,000$38,500,000 $46,500,000 2022 29$31,000,000 $38,500,000$46,500,000 2023 30 $31,000,000$38,500,000 $46,500,000 2024 31$31,000,000 $38,500,000$46,500,000 2025 32 $31,000,000$38,500,000 $46,500,000 2026 33$31,000,000 $38,500,000$46,500,000 2027 34 $31,000,000$38,500,000 $46,500,000 2028 35$31,000,000 $38,500,000$46,500,000 2029 36 $31,000,000$38,500,000 $46,500,000 2030 37$31,000,000 $38,500,000$46,500,000 2031 38 $31,000,000$38,500,000 $46,500,000 Total$403,000,000.00 $500,500,000.00$604,500,000.00 NPV $309,555,083.25$384,447,442.10 $464,332,624.87 Over the life of this contract, the value of each in NPV is significantly less than the actual amount signed. That’s because$5 today won’t buy you as much five years down the road. To get a little more numerical, 13 years from now currency will lose ~40% of its value. Quoting the Chris Davis article again, the league and the MLBPA have agreed to use a 4% discount rate to calculate present-day values of long-term contracts. Since important people within the industry take this into account, that’s likely why we don’t see too many contracts with a significant amount of deferred money.

Since players are taking — and I use this term very lightly — a “hit” when they sign a long-term deal, I wondered what kind of contract structure would benefit a player the most. Again, I wanted to use nice round numbers, so I settled on a 10-year, $100M contract, looking at an equal payment structure, a front-loaded contract, and a back-loaded contract. Here’s what I came up with:  Hypothetical 10 Year$100M Contract Year Equal Front-loaded Back-loaded 1 $10,000,000$14,500,000 $5,500,000 2$10,000,000 $13,500,000$6,500,000 3 $10,000,000$12,500,000 $7,500,000 4$10,000,000 $11,500,000$8,500,000 5 $10,000,000$10,500,000 $9,500,000 6$10,000,000 $9,500,000$10,500,000 7 $10,000,000$8,500,000 $11,500,000 8$10,000,000 $7,500,000$12,500,000 9 $10,000,000$6,500,000 $13,500,000 10$10,000,000 $5,500,000$14,500,000 Total $100,000,000$100,000,000 $100,000,000 NPV$81,108,957.79 $83,726,636.52$78,491,279.06

There’s not a huge difference, but a player would gain just over $5M by signing a front-loaded contract as compared to a back-loaded contract. It seems as though the agents and the MLBPA are more concerned about total dollars rather than NPV since they probably want to drive up total contracts. And in case you’re wondering what those annual salaries would look like in NPV from the table above, I’ve created another table to show what those salaries actually look like in NPV over the life of our hypothetical 10-year contract.  NPV Of Hypothetical 10 Year$100M Contract Year Expected Equal Front-loaded Back-loaded 1 $10$9.62 $13.94$5.29 2 $10$9.25 $12.48$6.01 3 $10$8.89 $11.11$6.67 4 $10$8.55 $9.83$7.27 5 $10$8.22 $8.63$7.81 6 $10$7.90 $7.51$8.30 7 $10$7.60 $6.46$8.74 8 $10$7.31 $5.48$9.13 9 $10$7.03 $4.57$9.48 10 $10$6.76 $3.72$9.80

## Eric Thames: The Ideal Gamble

It was in November, yet we may already have the most fascinating free-agency signing of the offseason. Traditionally, free agency is for contending major-league clubs looking to overpay players in hopes that they can deliver a championship. The Milwaukee Brewers went off the beaten path and may be using free agency as a vessel to help their rebuild.

This year’s free-agent class, headlined by Edwin Encarnacion (34) and Carlos Beltran (39), has a shortage of quality bats. The 2016-2017 free-agent class will more than likely be defined by complementary players rather than typical studs who will impact a pennant race. This lack of possible assets forced the Milwaukee Brewers to get creative. The Brewers’ signing of KBO baseball star Eric Thames, four years removed from his last MLB at-bat was…genius?

First, let’s see how we got here.

The Brewers were unhappy with Chris Carter manning the first-base position. It is not often a team will cut a player after he hit 41 home runs, but that is exactly what happened. Carter’s overall lack of production outweighed the power output. Posting a .218 batting average, coupled with a 33.1% strikeout percentage, Carter performed slightly better than a replacement-level player. After cutting ties with Carter, Milwaukee looked at its free-agent options.

With his coming off a 47-home-run season, it is unrealistic for the Brewers to sign All-Star Mark Trumbo (30). The only other impact bat would be Mike Napoli (35). Napoli should benefit from the scarcity of sluggers this offseason. In 2016, Napoli had a nice bounce-back campaign, launching 35 home runs and making headlines such as “Party at Napoli’s.” However, the party stops at first base. Napoli is a below-average baserunner and defender, causing his VORP (Value Over Replacement Level Player) to total just 1.0.

The Brewers would have to be in love with Napoli’s ability to swing the stick for the club to decide to pull the trigger. But a 35-year-old slugger with poor defense is likely not a good fit for any National League team, let alone the rebuilding Brewers.

As for the rest of the free agents, there is a theme of mediocrity. Moreover, each of them will be over the age of 30 by opening day. Even if the remaining players are able to defy the odds and maintain their levels of performance, it will be nothing more than a stop-gap signing.

After a 73-89 campaign in 2016, the Brewers are not in “win now” mode. Over the past two years, the Brewers have sold, sold, and sold some more. Each trade Milwaukee made brought in quality talent, and according to MLB.com Milwaukee now has MLB’s #1 farm system. Milwaukee has eight players cracking the top-100 prospect list that will be making themselves known as soon as next year. So for a team in rebuilding mode, why sign Eric Thames? Low risk; high reward.

Per Adam McCalvy, Thames will make $4 million in 2017,$5 million the year after, and $6 million in 2019. The team also holds an option on his contract for 2020 for$7.5 million, with a $1-million buyout. That totals out to$16 million guaranteed. Fiscally, it boils down to this: Approximately $25 million for two years of Carter or 3-4 years of Thames for$16-$24.5 million, including bonuses. In 181 major-league games, Thames posted a .250 batting average with 21 home runs. He had a respectable .727 OPS in that time. This bodes well in comparison to recent Cubs signee Jon Jay who had a similar .774 OPS in his first two seasons. Thames found himself out of the league, while Jon Jay continued his successful career. After 2012, Thames found work in the aforementioned KBO. Over three seasons, Thames averaged 42 home runs while hitting .347 and earned an MVP award in 2015. Oh, and there’s a 30-minute highlight reel of just home runs. Pitching in Korea cannot be compared to the talent in Major League Baseball. There is a big difference between putting up numbers in Korea and doing so in MLB. However, Jung Ho Kang and Hyun Soo Kim are supporting evidence that succeeding can be done. One thing is evident when watching Thames swing: he has raw power to all fields. If Thames performs similarly to his 2011-2012 form, then Milwaukee has lost nothing. The deal would simply mean they swapped two replacement-level first basemen while simultaneously saving money. But if Thames shows that he truly is a new player, Milwaukee will once again be front and center during the trade deadline. Thames could be the premier left-handed bat on the trade market while also having a dream contract for contending clubs. The value of his bat along with contractual control over him through 2020 at only$16 million guaranteed could bring in multiple top prospects. This is the dream scenario of course, but hey, it can’t hurt to dream.

## Hardball Retrospective – What Might Have Been – The “Original” 2013 Marlins

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 2013 Miami Marlins

OWAR: 33.0     OWS: 255     OPW%: .468     (76-86)

AWAR: 18.5      AWS: 185     APW%: .383     (62-100)

WARdiff: 14.5                        WSdiff: 70

The “Original” 2013 Marlins tied with the Phillies for last place, yet the ball club managed to school the “Actuals” by a 14-game margin. Miguel Cabrera seized MVP honors for the second consecutive season and notched his third straight batting title. “Miggy” produced a .348 BA, dialed long-distance 44 times and knocked in 137 baserunners. Adrian Gonzalez swatted 22 big-flies and reached the century mark in RBI for the sixth time in his career. Matt Dominguez drilled 25 two-base hits and blasted 21 round-trippers. Giancarlo Stanton supplied 26 doubles and 24 four-baggers as a member of the “Originals” and “Actuals”.

Original 2013 Marlins                              Actual 2013 Marlins

 STARTING LINEUP POS OWAR OWS STARTING LINEUP POS OWAR OWS Josh Willingham LF 0.23 9 Christian Yelich LF 1.34 8.34 Marcell Ozuna CF/RF 0.16 6.68 Justin Ruggiano CF 1.11 9.23 Giancarlo Stanton RF 3.14 16.66 Giancarlo Stanton RF 3.14 16.66 Adrian Gonzalez 1B 4.12 21.17 Logan Morrison 1B 0.32 6.16 Josh Wilson 2B -0.11 0.54 Donovan Solano 2B 0.44 6.95 Robert Andino SS -0.26 0.82 Adeiny Hechavarria SS -2.33 4.28 Miguel Cabrera 3B 6.8 33.13 Ed Lucas 3B 0.42 7.2 Brett Hayes C 0.17 1.01 Jeff Mathis C -0.17 3.22 BENCH POS OWAR OWS BENCH POS OWAR OWS Matt Dominguez 3B 0.84 11.34 Marcell Ozuna RF 0.16 6.68 Gaby Sanchez 1B 1.91 10.36 Placido Polanco 3B -0.35 5.41 Christian Yelich LF 1.34 8.34 Chris Coghlan LF 0.32 5.35 Logan Morrison 1B 0.32 6.16 Derek Dietrich 2B 0.63 5.29 Chris Coghlan LF 0.32 5.35 Juan Pierre LF -0.27 4.38 Jim Adduci LF 0.03 0.59 Rob Brantly C -0.98 2.61 Alex Gonzalez 1B -0.94 0.32 Greg Dobbs 1B -0.6 2.5 Mark Kotsay LF -1 0.17 Jake Marisnick CF 0.13 1.54 Kyle Skipworth C -0.05 0.01 Miguel Olivo C 0.17 1.17 Scott Cousins LF -0.06 0 Nick Green SS -0.01 1.05 Chris Valaika 2B -0.13 0.58 Joe Mahoney 1B -0.04 0.54 Koyie Hill C -0.55 0.54 Austin Kearns RF -0.13 0.25 Matt Diaz LF -0.14 0.15 Casey Kotchman 1B -0.25 0.06 Kyle Skipworth C -0.05 0.01 Jordan Brown DH -0.06 0 Gil Velazquez 3B -0.01 0

Jose D. Fernandez (12-6, 2.19) merited 2013 NL Rookie of the Year honors and an All-Star invitation while placing third in the NL Cy Young balloting. Portsider Jason Vargas contributed 9 victories with a 4.02 ERA to the “Originals” rotation and Henderson “The Entertainer” Alvarez fashioned a 3.59 ERA and 1.140 WHIP for the “Actuals” in 17 starts. The Marlins’ bullpen featured Steve Cishek (2.33, 34 SV). A.J. Ramos whiffed 86 batsmen in 68 relief appearances.

Original 2013 Marlins                             Actual 2013 Marlins

 ROTATION POS OWAR OWS ROTATION POS OWAR OWS Jose D. Fernandez SP 5.57 16.22 Jose D. Fernandez SP 5.57 16.22 Jason Vargas SP 2 7.04 Henderson Alvarez SP 1.89 6.19 Tom Koehler SP 0.46 3.96 Nathan Eovaldi SP 1.39 5.63 Brad Hand SP 0.4 1.43 Ricky Nolasco SP 1.13 4.92 Alex Sanabia SP -0.33 0.6 Jacob Turner SP 0.87 4.56 BULLPEN POS OWAR OWS BULLPEN POS OWAR OWS Steve Cishek RP 1.62 12.99 Steve Cishek RP 1.62 12.99 A. J. Ramos RP 0.34 5.23 Mike Dunn RP 1.06 6.64 Ronald Belisario RP -0.9 2.61 Chad Qualls RP 1.22 6.22 Sandy Rosario RP 0.24 2.53 A. J. Ramos RP 0.34 5.23 Dan Jennings RP 0.08 1.95 Ryan Webb RP 0.6 5.02 Ross Wolf SW 0.14 1.92 Tom Koehler SP 0.46 3.96 Arquimedes Caminero RP 0.16 0.95 Kevin Slowey SP 0.46 3.15 Logan Kensing RP 0.02 0.1 Dan Jennings RP 0.08 1.95 Josh Johnson SP -1.25 0.04 Brad Hand SP 0.4 1.43 Josh Beckett SP -0.81 0 Arquimedes Caminero RP 0.16 0.95 Chris Hatcher RP -0.93 0 Alex Sanabia SP -0.33 0.6 Chris Leroux RP -0.17 0 Brian Flynn SP -0.59 0.14 Edgar Olmos RP -0.68 0 Steve Ames RP -0.02 0.02 Chris Resop RP -0.6 0 Duane Below RP -0.19 0 Chris Volstad RP -0.49 0 Sam Dyson SP -0.59 0 Chris Hatcher RP -0.93 0 Wade LeBlanc SP -0.41 0 John Maine RP -0.66 0 Edgar Olmos RP -0.68 0 Zach Phillips RP -0.03 0 Jon Rauch RP -0.71 0

Notable Transactions

Miguel Cabrera

December 4, 2007: Traded by the Florida Marlins with Dontrelle Willis to the Detroit Tigers for Dallas Trahern (minors), Burke Badenhop, Frankie De La Cruz, Cameron Maybin, Andrew Miller and Mike Rabelo.

July 11, 2003: Traded by the Florida Marlins with Will Smith (minors) and Ryan Snare to the Texas Rangers for Ugueth Urbina.

January 6, 2006: Traded by the Texas Rangers with Terrmel Sledge and Chris Young to the San Diego Padres for Billy Killian (minors), Adam Eaton and Akinori Otsuka.

December 6, 2010: Traded by the San Diego Padres to the Boston Red Sox for a player to be named later, Reymond Fuentes, Casey Kelly and Anthony Rizzo. The Boston Red Sox sent Eric Patterson (December 16, 2010) to the San Diego Padres to complete the trade.

August 25, 2012: Traded by the Boston Red Sox with Josh Beckett, Carl Crawford, Nick Punto and cash to the Los Angeles Dodgers for players to be named later, Ivan De Jesus, James Loney and Allen Webster. The Los Angeles Dodgers sent Rubby De La Rosa (October 4, 2012) and Jerry Sands (October 4, 2012) to the Boston Red Sox to complete the trade.

Matt Dominguez

July 4, 2012: Traded by the Miami Marlins with Rob Rasmussen to the Houston Astros for Carlos Lee.

Gaby Sanchez

July 31, 2012: Traded by the Miami Marlins with Kyle Kaminska (minors) to the Pittsburgh Pirates for Gorkys Hernandez.

# On Deck

What Might Have Been – The “Original” 1985 Expos

# 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”.