Guess Who’s Back, Back Again?
That’s right, I’ve sampled Eminem’s Without Me in the title of this piece. While I’m acutely aware of the ridiculousness of middle-aged white dudes quoting rappers, I’m equally aware that many readers may not be cognizant of the following “we’re getting old” facts. February marked the fifteenth anniversary of the release of Eminem’s first album, The Slim Shady LP and last year he turned (gasp!) 41. So actually there’s nothing wrong with me quoting a lyric of his; we’re both forty-something men whose word choices are sometimes misunderstood.
In the case of my debut project, 11 words was all it took.
11 words consisting of just 60 characters – not even half the length of a full-sized tweet – landed me in hot water with data-inclined readers of my book. I knew that writing a book with a sabermetric-bend was a challenging undertaking. The sabermetric community has a reputation for being fiercely protective of its findings and hostile to outside voices. After all, the leaders in the industry are extremely bright, possess superior analytic skills and like to defend their data-driven positions as passionately as a thesis-submitting doctorate candidate. In short, they’re certain they’re right, can be rough on anyone encroaching on their turf, and quick to pounce on logic errors.
With that in mind, I devoted six early chapters of Trading Bases, specifically Chapters 2-7, to numbers-heavy logic, as opposed to maximum readability, in an attempt to establish credibility with that potential portion of the book’s audience. I acknowledged that when the book was published, there was a decent chance sabermetric scholars might attack my logic. I accepted this; writing about data, modeling and formulas can be fraught with peril, especially on a topic which can arouse as much passion as baseball.
What I didn’t expect was that the first 11 words of the book would get me in trouble. Trading Bases opens with a dedication to my wife as follows:
“To Caitlin, an 8 WAR wife, with a replacement-level husband.”
Cute, right? I thought it hit grateful, self-deprecating, humorous and nerdy notes in a compact manner befitting a dedication that would thrill my wife. And at first, it certainly looked like I’d achieved my goal. Initially, Caitlin appeared to love the dedication, a surprise for her which she didn’t learn about until publication. She proudly showed it to her friends, beamed when my in-laws expressed their gratitude and even teased our daughters that Daddy hadn’t been swayed by their attempted bribes.
I went to the East Coast to promote the book and when I came home two weeks later Caitlin casually asked me one morning about WAR. “Tell me,” she requested after our daughters had gone off to school, “how WAR works.” Completely oblivious to the direction this conversation was going to take I thought to myself, “Wow, you must have really missed me because this is the best avenue of foreplay you could have possibly pursued!”
So, I launched into an explanation of Wins Above Replacement, the ineffable qualities of the “replacement player,” and the attempt to measure and reward skills as opposed to results. I only wish I had surveillance tape of that speech that would have shown her eyes glazing over as I prattled on. At some point, I must have paused to inhale oxygen, and Caitlin interrupted, “Is there a scale?”
“Of course,” I said. “Replacement level players have zero WAR and an average major league player is a 2 WAR commodity. If you have a 4 or 5 WAR season you’re an All-Star and anything over 7 makes you a candidate for a Most Valuable Player Award.”
“Does it go to 10?”
Now, I finally wised up and realized I wasn’t a husband whose wife was showing an interest in his hobby; I was a soldier walking through land mine-infested terrain.
‘Well, there is no upper bound, per se, on the WAR scale,” I started cautiously measuring each word before urgently continuing, “But again, I have to stress, any player who is stringing together a series of 7 or 8 WAR seasons is constructing a Hall of Fame resume.”
The Hall of Fame! Yes, I went there. Surely, I thought, that was an emphatic enough defense of my choice of words in the dedication to defuse the conversation.
“Is anyone a 10?”
I was wrong. So, with my head hung low in resignation I admitted, “Most agree that Mike Trout had a . .”
She cut me off with a “Then why, . . . “
Now it was my turn to cut her off as my resignation turned to exasperation and I shouted, “BECAUSE YOU”RE NOT MIKE TROUT!”
* * *
It’s fitting that Mike Trout’s name emerges as the first Major League player I mention in this piece. For the last two years, by the second week of March I’ve been halfway through my 30 Teams in 30 Days preview series. This year, separate team essays for all MLB teams won’t be possible; I’m once again a full-time member of the workforce.
Even knowing that thirty separately written previews wouldn’t be possible, it didn’t stop me from running the numbers a couple of weeks ago in the same manner as I have since my book took shape in the spring of 2011. My aim was to get two league preview pieces written by Opening Day which would touch on the outlooks of all thirty teams. However, as I started my work, the first total wins markets were posted in Reno and Las Vegas, I started to get fired up for the baseball season, and before I knew it I’d mentally committed to six division previews.
Then, I got to one particular team’s analysis and thought, “this has to be a stand-alone piece.” To kick off the 2014 preview series, it appears below.
Each year I’ve started the 30 Teams in 30 Days series with a defensive discovery, which is where I believe I have the biggest edge between my projection model and the more famous mainstream systems. Two years ago in the Tigers preview for example, I advanced the Maddox/Luzinski Pact in which I explained how team defensive effectiveness was positively not the sum of the individual player ratings. It led to an emphatic under call on the Tigers, which cashed easily. Last year, I led off with the Angels and adamantly challenged the notion that Mike Trout was the best outfielder at any single position on his own team in 2012, in complete opposition to most outlets. In fact, I ridiculed ESPN’s SweetSpot blog for unanimously naming Trout the best defender in all of baseball in 2012. The 2013 Angels were another emphatic under that cashed easily.
Aside from the two easy wins, my decision to feature a defensive-based essay the last two years was future vindicated by the very outlet I mocked in last year’s Angels’ essay. Two months into the 2013 season, on June 7 2013, ESPN SweetSpot ran the following article, Why are Mike Trout’s Defensive Numbers so Bad? Readers of my preview series, and even more satisfying, those who used it to make bets, already knew the answer.
It’s preseason insights like that that get me excited to share my out-of-consensus thoughts on the coming season and, sticking with my defensive theme, I planned to address the incredible value all well-known projection systems, as well as the various flavors of WAR have bestowed on the Atlanta Braves’ shortstop Andrelton Simmons. That was going to be this year’s lead-off piece, but as I alluded to above, another team’s outlook has shelved that plan. Without further ado, let’s get to my highest conviction preview of the 2014 season.
* * *
In an environment in which scoring runs in Major League Baseball has become increasingly more difficult (less runs were scored in 2013 than any non-strike year since 1992) there were a number of fairly wretched offensive teams last year. I’m going to focus on the half-dozen of teams who barely managed to score 600 runs last season. Below are their total runs scored, as well as On-Base Percentage, Slugging Percentage, and Isolate Slugging team readings.
Runs Hi/Ru OBP SLG ISO
Chicago Cubs 602 2.17 .300 .392 .154
Houston Astros 610 2.14 .299 .375 .136
Philadelphia Phillies 610 2.22 .306 .384 .135
Minnesota Twins 614 2.19 .312 .380 .138
San Diego Padres 618 2.18 .308 .378 .133
New York Mets 619 2.13 .306 .366 .129
MLB Average 675 2.08 .318 .396 .143
As regular readers of my work know, OBP, SLG, and ISO are the components needed to determine a team’s expected hits/run ratio. The league average is always about 2, meaning that a team with average OBP, SLG, and ISO is expected to score one run for every two hits they have. Any deviation from this ratio, in either direction, is what I call “cluster luck,” because it’s the result of beneficial or detrimental sequencing, which no team can control over the course of a season.
By the same logic, a team with below-average results in those three categories (or even two out of three) can expect to need more than two hits for each run scored, and vice versa for above-average offenses. This cluster luck calculation is the backbone of my projection work, and above we see why all of those teams had so much trouble scoring runs. With the exception of the Chicago Cubs, who with nearly league-average slugging and well above-average extra base production (which is really what ISO measures) should have scored a materially greater number of runs than 602, we can see why the other teams were so offensively challenged. Largely devoid of cluster-luck, their actual run-scoring matched their skills. Or as noted NLF philosopher Denny Green might express, “they are who we thought they were.”
Just for fun, let’s toss a yet unnamed seventh team into the mix.
Runs Hi/Ru OBP SLG ISO
Unnamed Team 650 2.03 .307 .376 .133
Go back and compare this team to the offensive dregs listed above. Skill-wise, they’re essentially the Padres – just a little bit worse. Or if you’d rather be positive about it, you’d say they’re a mildly more talented offensive team than the New York Mets. So how did they score 30+ more runs than those teams?
But wait, there’s more.
Cluster luck isn’t a one-sided coin. If teams collectively should have scored fewer or greater runs then opposing pitching staffs must be examined as well. Let’s start with our same unnamed team.
Runs Hi/Ru OBP SLG ISO
Unnamed Team 671 2.17 .318 .413 .152
MLB Average 675 2.08 .318 .396 .143
Remember, we’re looking at Runs Allowed here, so this team’s pitching staff is below-league average in terms of skills. They allow runners at a league-average rate, but give up a lot more extra base hits. Yet, their ratio of 1 run allowed for every 2.17 hits is near-elite. (The Detroit Tigers, with the best starting rotation in baseball last year – by far – gave up a run every 2.19 hits allowed.) In short, this team’s pitching staff should have given up runs much closer to a 2.0 rate resulting in about 30+ more runs than they actually allowed.
But wait, there’s more. Remember the comparison to the Padres and Mets, teams that toil half their season in offensive graveyards while our unnamed team . . . . Ah forget it; let’s just summarize their 2013 season.
Restated Runs Scored: 616
Restated Runs Allowed: 702
Expected Wins based on Restated RS/RA 71.3
Actual Wins 85
Ladies and gentlemen, I present to you the luckiest team I have ever come across in my examination of MLB teams over the last decade – the 2013 New York Yankees.
This isn’t a total surprise to baseball observers. The Yankees actually allowed more runs than they scored last year so many fans knew, like the 2012 Orioles, the 2013 Yankees were lucky to have finished above .500, based on their run differential (650 Runs Scored/671 Runs Allowed.) But what people don’t realize is how lucky New York was to have achieved the actual run differential that they did. It should have been much worse. In other words, if the Yankees produced exactly the same output that they did last year, they should expect to win about 71 games. That’s the baseline from which to evaluate marginal changes.
So what changes were made? Well, from the 71 win-talent team their best hitter, by far, Robinson Cano departed via free-agency. Their second best pitcher, Andy Pettitte, who allowed runs at a well-below-AL-average rate (3.74 ERA) over 30 starts and nearly 200 innings retired. And, of course, the 2014 Yankees will be without the services of the greatest reliever of all time, Mariano Rivera, who merely departed with a 2.04 ERA in 2013.
Take away those contributors from the 2013 Yankees and before you make any improvements, you’re starting with the third worst team in all of baseball. Only the Marlins and Astros will be starting from a lower based before marginal improvements.
The Yankees, of course, did make improvements to their roster. Jacoby Ellsbury and Carlos Beltran will join Brett Gardner in the outfield, and Brian McCann equipped with an apparent tailor-made swing for Yankee Stadium represents a vast upgrade at catcher. Derek Jeter and Mark Teixeira figure to get more than 136 combined plate appearances they managed last year. (Sub-.200 batting average, plate appearances it should be noted.) But third base (Scott Sizemore) and second base (Brian Roberts) are still injury-plagued black holes, Teixeira hasn’t been an offensive force since 2009, Alfonzo Soriano (.254 BA/.305 OBP/.487 SLG 2011-2013) is a tenuous solution at DH in a league where DH’s rake (.263/.338/.428 over last three years.) In Soriano’s case, the power has been there, but he’ll be 38 this year and his on-base skills are never coming back to league average.
From the runs allowed perspective, my projection system, which takes age and continuity into effect, essentially broke down when I input all the aged newcomers. It simply projects to be awful. There’s a lot of excitement at the Steinbrenner Complex in Tampa surrounding the arrival of Masahiro Tanaka in camp. The excitement is based on Tanaka’s Cliff Lee-like control of the strike zone during his time in Japan. However, if he has a rookie campaign anything short of Yu Darvish, Tanaka’s going to have trouble improving on the run-suppression results of Andy Pettitte last year. And that’s the thing about cluster luck – Tanaka may post a better walk rate, higher strike out rate, and even induce more ground balls than Pettitte did in 2013, yet still give up more runs.
Finally, there’s the case of C.C. Sabathia. I don’t ever relish reporting on the projected demise of a former Cy Young Award winner, but right now, I’m far more bearish on his prospects this year than any other projection system. My model sees a player who has lost control of the strike zone at the same time he’s losing a significant amount of velocity. In short, it’s the Tim Lincecum, Roy Halladay, Dan Haren, etc. formula. I don’t really want to be right on this particular element of the projection, but going forward I have the once-elite Sabathia residing in a far more modest 4.00+ ERA neighborhood.
With the entire starting lineup on the wrong side of 30, except Scott Sizemore, and at age 29 he’s just barely a ‘youngster’, the Yankees are fielding an unprecedented lineup in terms of average age. Unprecedented in baseball, and maybe even all of sports, that is. However, the concept of throwing together a bunch of aging former stars has been tried in other art forms.
Be warned Yankees fans, Last Vegas was a critical and box-office flop.
Oddsmakers’ expectations: Everyone from baseball insiders to bettors with Las Vegas-based leanings like to make fun of the perceived-less-than-sharp markets that come out of the Reno-based Atlantis Casino, the first shop to post total wins over/under markets on MLB teams. As we’ll see as this series goes on however, I foresee the Atlantis having a smaller forecasting error at the end of the year than any other oddsmaker. The Yankees market is a perfect example. The Atlantis opened them at 83 ½ games. The LVH in Las Vegas, which has always functioned as the official line for my preview series opened them at 85 ½ while most other sportsbooks have since drifted the line higher. My New York-based bookmaker, as well as the overseas gold-standard, Pinnacle Sports has them listed at 87 wins with a slight discount on the under side of the bet.
As you might guess from my preview, I strongly liked the under . . . in Reno. At the current market of 87 wins, an under bet on the Yankees is not only my strongest play this year, it ranks at least as strong as last year’s top-conviction plays, and eventual easy winners, under Toronto and over Cleveland.
79-83 – Fifth in AL East
704 Runs Scored 722 Runs Allowed
Mop Up Duty:
Joe Peta is the author of Trading Bases, A Story About Wall Street, Gambling, and Baseball* (*) Not necessarily in that order, a Dutton Books/Penguin (U.S.A.) publication currently available wherever books are sold. Here are three on-line booksellers you can currently choose from:
He is also the author of Trading Bases, the Newsletter, a companion piece to the book. If you have been forwarded this issue and would like to be placed on the mailing list, please send an e-mail to firstname.lastname@example.org
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