# Projecting the 2013 Nationals, Part 1: Ground Rules & Starting Line-ups

Spring Training is well underway down in Viera. This is the season, then, of portents and omens–the latest and most amusing of which was the story of an osprey dropping a fish onto the Nats outfield. Not having any expertise in the art of augury, I don’t think I can really comment about the auspiciousness or inauspiciousness of such an omen for the upcoming season.

What I can offer you, however, is the results of my own admittedly crude projection system. Long-time readers will know that I like to think of the baseball season as a single inning of a baseball game writ very very large. In the top of the inning, we see the home team take the field, and see how good the pitching and the defense are at getting opposing batters out. In the bottom of the inning, we watch the home team at bat, and see how well they drive in runs. Then we count the runs allowed in the top of the inning and the runs scored in the bottom of the inning–if the home team scored more runs than the other team, they win.

If you want the nuts and bolts of my projection system, please, read the post I’ve linked above. It describes the general outline of the system as clearly as I can.

This year, however, I’m making a few changes to the Natstradamus projection system.

First, in pitching, I have replaced FIP with xFIP. I don’t know enough about home run/fly ball rates to tell, really, which pitchers are “lucky” or “unlucky” with respect to how many home runs they give up on fly balls. xFIP fixes that for me by normalizing runs allowed by a pitcher to a league-average home run/fly ball rate. Some pitchers get better; other pitchers get worse; but I think over all that might be a more fair way of evaluating pitchers for the purposes of this projection system.

Second, I have tweaked the defensive calculations slightly. Instead of using UZR, I have calculated a UZR/game, and then multiply that  by the number of games in which I expect each player to appear. Again, this is crude, and defensive metrics are highly unstable anyway, but hey, it’s all I’ve got.

Remember, my projections are based on four-year trailing averages for each stat. That is, they’re the averages of the past four years.

With those preliminaries out of the way, let’s start this year’s predictions off by going through the 2013 Nationals’ projected 25 man roster:

Starting Rotation

• Stephen Strasburg, xFIP 2.56
• Gio Gonzales, xFIP 3.81. I do not believe Gio will be subject to a suspension for his alleged involvement in the Biogenesis scandal. I explained my view on the situation here.
• Jordan Zimmermann, xFIP 3.71.
• Ross Detwiler, xFIP 4.44
• Dan Haren, xFIP 3.37.

Starting Position Players

• Danny Espinosa, 2B
• Ryan Zimmerman, 3B
• Ian Desmond, SS
• Bryce Harper, LF
• Denard Span, CF.
• Jayson Werth, RF
• Wilson Ramos, C
• Kurt Suzuki, C. I have Ramos and Suzuki splitting playing time evenly.

Bench

• Tyler Moore, OF/1B
• Steve Lombardozzi, IF/OF

Bullpen

• Rafael Soriano, xFIP 3.6, Primary Closer
• Drew Storen, xFIP 3.46, Primary Set-up, Back-up Closer
• Tyler Clippard, xFIP 3.54
• Ryan Mattheus, xFIP 4.48
• Zach Duke, xFIP 4.34, Left-handed long reliever/Spot starter
• Craig Stammen, xFIP 3.96, Right-handed long reliever/Spot Starter
• Bill Bray, xFIP 4.19, Left-handed one-out guy. This is probably the most controversial pick; others might put Henry Rodriguez or Christian Garcia here instead. But I’m going to assume Bray heads north with the club.

No surprises, then. Stay tuned as we discuss pitching and defense in Part 2 of our projections.

# Looking at the Bullpen: Shutdowns and Meltdowns

Not even in my most optimistic moments would have said that the Nats would win two in a row out of the gate! As I write this on Easter Sunday morning, the Nats are sitting pretty, sharing first place atop the National League’s Eastern Division with the Mets (the Mets!).

And all this despite a lackluster debut for Gio “the Motown Kid” Gonzalez. The Nats won yesterday behind the unexpected heroics of former Hiroshima Carp Chad Tracy, and some absolutely phenomenal pitching from the “B” bullpen, with Craig “Matinee Idol” Stammen in long relief, followed by Ryan “Firework” Mattheus, Tyler Clippard, and some pitching from Hot Rod that was pretty frickin’ bueno.

The Nats’ late-inning heroics aren’t great to my stomach lining, though. I’ve been wondering how I could better quantify the feeling I have when relievers come in. I attempted this earlier, of course, when I introduced my heartburn index–but I’m now convinced that the heartburn index doesn’t give a complete picture.

Fortunately, FanGraphs has ridden to the rescue again, with a new, and, I think, extremely helpful, pair of statistics for measuring relief pitcher performance: Shutdowns and Meltdowns. As the proponent of the new stats explains them:

Shutdowns (SD) and Meltdowns (MD) are two relatively new statistics, created as an alternative to Saves in an effort to better represent a relief pitcher’s value. While there are some odd, complicated rules surrounding when a pitcher gets a save, Shutdowns and Meltdowns strip away these complications and answer a simple question: did a relief pitcher help or hinder his team’s chances of winning a game? If they improved their team’s chances of winning, they get a Shutdown. If they instead made their team more likely to lose, they get a Meltdown. Intuitive, no?

Using Win Probability Added (WPA), it’s easy to tell exactly how much a specific player contributed to their team’s odds of winning on a game-by-game basis. In short, if a player increased his team’s win probability by 6% (0.06 WPA), then they get a Shutdown. If a player made his team 6% more likely to lose (-0.06), they get a Meltdown.

Shutdowns and meltdowns correlate very well with saves and blown saves; in other words, dominant relievers are going to rack up both saves and shutdowns, while bad relievers will accrue meltdowns and blown saves. But shutdowns and meltdowns improve upon SVs/BSVs by giving equal weight to middle relievers, showing how they can affect a game just as much as a closer can, and by capturing more negative reliever performances.

Nats fans are by now intimately familiar with WPA, thanks to the hard work of Federal Baseball. The squiggly-lined graphs he pots after every game show the ebb & flow of the game as measured by WPA. A “Shutdown” happens when a reliever bends the line towards the Nats’ favor. A “Meltdown” happens when a reliever bends the line in favor of the opponent. The Shutdown/Meltdown stat pair thus give us a good indication of whether a reliever is helping or hurting his ballclub–which is kind of neat!

So what does that mean for the Nats bullpen in 2012? Using my standard measuring interval (2008-2011 seasons), here’s how the pitching staff looks:

 Name Holds Saves Blown Saves Shutdowns Meltdowns Heartburn Brad Lidge 9 100 16 92 28 6.85 Tyler Clippard 64 1 18 77 35 5.22 Sean Burnett 54 8 9 63 42 5.62 Drew Storen 13 48 7 59 22 4.34 Henry Rodriguez 13 2 4 13 13 8.51 Tom Gorzelanny 7 1 2 12 5 6.01 Ryan Mattheus 8 0 0 7 6 5.63 Craig Stammen 2 0 0 5 2 4.09

A few things jump out at me at once:

• Since 2008, Brad Lidge is unquestionably the Shutdown King of the current Nats bullpen. The 100 Shutdowns mean that he left his ballclub in a better position to win after his appearance than before one hundred times–and only made them worse 28 times. This makes me wonder whether Philadelphia unloaded him more because of his relatively high heartburn factor than any other measurable quality as a relief pitcher. On the other hand, Lidge’s ridiculous 2008 season may have gone a very very long way towards inflating his stats here. In any case, Lidge was pretty good on opening day this year.
• We all know that Tyler Clippard is an awesome relief pitcher. He was an all-star in 2011. But now we have a clearer idea why. He’s second only to Lidge in shutdowns since 2008, and leads the staff in Holds.
• Sean Burnett has collected 63 shutdowns since 2008–apparently, while I was averting my eyes in terror. The more I study him, the more I am forced to conclude that I have been terribly unfair to Burnett over the past few years.
• We also now have a better idea why Drew “Batman” Storen is such a good reliever. He hasn’t been relieving nearly as long as Lidge, but he’s already accumulated 59 shutdowns. His 2.68 Shutdown/Meltdown ratio is second only to Lidge’s.
• Henry “Hot Rod” Rodriguez is, by this set of measures, not even nearly in the same class as Storen or Lidge. 13 Shutdowns and 13 Meltdowns, giving him an abysmal SD/MD ratio of 1.00–the lowest on the staff. I’m still hoping that he will improve during 2012 and pitch to his potential, though.
• Tom Gorzelanny has a shutdown/meltdown ratio of 2.40. That’s fourth, behind Lidge, Storen and Stammen. I guess he really is better as a reliever than as a starter? Then again, he’s only recorded 12 shutdowns, total–so maybe we don’t know enough about him to judge.
• I was expecting a tighter correlation between high shutdown numbers and low heartburn index numbers. That’s not what we see. Lidge, for instance, ought to give me more heartburn than his shutdown numbers suggest. Mattheus looks pretty bad next to his heartburn near-equivalent Burnett–but then, Mattheus hasn’t had all that many chances yet.

If the Nats’ starting rotation can routinely get through 6 or 7 innings, there are enough high-shutdown arms in the bullpen to keep the game in hand. This is very encouraging news for the rest of 2012.

# Baseball Eve!

## The Boys Are Back in Town!

No real insights for you today on the day before Pitchers & Catchers report to Viera. Federal Baseball already has some early photographic evidence of baseball returning to Viera. Highlights include Jordan Zimmermann and Drew Storen rocking the quasi-official Beastmode T-Shirt introduced to the ’11 Nats by Ian Desmond and made famous by Michael Morse. But who’s that shaking hands with Tyler Clippard? The #tigerbeatbaseball girls want to know. (It’s not Ryan Tatusko, though. I checked that already.)

Two statistically-related things that I’ve been thinking about lately, though:

## Lost in Translation

Given the number of major league players and prospects who play in the Latin American winter-ball leagues in Venezuela, the Dominican Republic, and Mexico, it’s remarkable to me how hard it is to get reliable statistical information out of those leagues. The leagues have their own stats pages, to be sure. For instance, the Venezuelan League’s stats pages are pretty comprehensive. But it’s not exactly easy to find the player you’re looking for. Moreover, calculating advanced statistics like wOBA and wRC is pretty much impossible. The worst has got to be wRC, because it depends on calculating a league average wOBA. To do that for the Venezuelan league, I’d have to key in all the data for all players into another spreadsheet and run the calculations from there. The calculating isn’t too bad, but the data entry will take more time than I’m willing to commit (it’s not like sabermetrics is my job, y’know–and if it were, I’d be pretty terrible at it).

As an aside: reading statistical tables and box scores in Spanish reminded me that my Spanish isn’t as good as it ought to be. Baseball stats are cryptic enough in English, but they can be pretty opaque in Spanish. Glossaries do exist, but I’ve had to bring in an outside consultant for help with a few.

If you’re at all interested in Latin American baseball stats, PuraPelota has the most complete database I’ve been able to find, but they can be a bit slow on the update cycle. I haven’t been able to find anything nearly as complete or helpful for any of the Asian leagues (Japan, Korea, Taiwan). I can’t understand why that would be so–surely the Sabermetric revolution has spread all across the baseball world? Nothing makes you appreciate the excellent work that Baseball Reference and Fangraphs do quite like dealing with the sparse data available for foreign baseball leagues.

## Eye in the Sky

I’ve already written about this post at Línea de Fair, but I can’t help but take a closer look at one of the author’s objections to UZR:

…UZR, the measure employed to determine whether a fielder has more range than his teammates, and whether, on the whole, he can prevent opponents from from creating more runs. Joey Cora used to remind me how an infielder could be better depending on which pitcher was on the mound. This was due not only to the pitches, but also to the control the pitcher has over them. “What happens if a catcher calls for a sinker inside,” Cora asked. The shortstop moves a little, almost imperceptibly, towards the hole if the batter is right-handed. But if the pitcher leaves the ball outside, the roller could go up the middle of the infield. Result? A higher probability that the batted ball goes up the middle of the field and finds the shortstop further away from it–thus raising his UZR.

My initial reaction is that complaining that UZR may not describe that particular defensive alignment and situation like this is like complaining that the Ideal Gas Law won’t tell you exactly where to look for one particular carbon dioxide molecule in a tank full of compressed air.

Part of the problem, I think, is that UZR is the one baseball statistic in (quasi-) common use that is flat-out impossible to derive from other published statistics. As far as I can tell, the whole process depends on individual human beings watching game footage, noting where fielders are positioned, and noting where the fielder meets (or doesn’t meet) the ball.

Because I’m lazy, I figure that there must be a better way to do things–or at least one that isn’t so unbearbly tedious. We already have fairly sophisticated software that can track the location of, say, baseballs and baseball gloves as they move across a camera’s field of view. It should be a fairly simple matter to fix a wide-angle camera (or several) across a baseball field, record the whole game, and only have human intervention whenever the ball strikes the bat. An observer might tap one button when he sees the impact of the ball on the bat, and then tap another when the ball comes to rest (either in the glove of the fielder, or out of play). The end result might look something like the FlipFlopFlyball‘s defensive positioning infographic.

The genius of computing, however, would allow us to track each defensive move as a vector, with an origin point at wherever the defender started when the ball was put in play, and an endpoint at wherever he was standing when the play was over. I’m not so great at mathematics, but I imagine the resulting graphical representations (and statistical inferences!) that could be made from those data would be extremely useful in evaluating the range of any individual defender. Heck, maybe it wouldn’t be too hard to explain– if I only had a brain!

[Something I didn’t notice when I first saw that video in high school: MC 900ft Jesus is wearing a 1926 Washington Senators cap!]

# How Good Does Bryce Harper Have to Be?

Keen readers of this blog–both of you–will have noticed one glaring omission among all of my calculations. I have thus far decided not to include a certain 19-year-old catcher-turned-outfielder who last saw limited playing time at AA Harrisburg.

In a recent column, the Washington Post’s Jason Reid suggested that Bryce Harper needs to grow up. Given that this is the same Jason Reid whose journalistic insight into the Redskins’ quarterback situation early in the 2011 season gave Washington sports fans–and journalism as a whole– the biggest “Doh!” moment since the night Dewey beat Truman, I was moved to tweet:

The fact that @JReidPost raised doubts about @BHarper3407 making the team leads me to conclude Harper WILL make the #nats opening day team

Well, if Harper does make the Opening Day roster, how good does he have to be to do no harm to a squad already projected for 86 wins?

Let’s assume Harper is an everyday player. There’s no indication so far that he can play center field. The Nats don’t have anyone available with a positive UZR as a center fielder except Werth. So let’s put Harper in right field. Here’s the most dangerous assumption of them all: assume Harper is a totally average defender.

## Assuming a Healthy LaRoche

Let’s also assume that Adam LaRoche is healthy and ready to be his usual self at first base. That rounds out the outfield as Morse, Werth, and Harper.

Someone needs to get bumped off the bench. Given that the Nats went out and got DeRosa and Ankiel, that leaves Roger “The Shark” Bernadina the odd man out, so we need to assume that The Shark doesn’t break camp with the Nats.

Assuming everybody’s an every-day type player, we’ll need to cut down DeRosa’s plate appearances, to reflect his status as a real bench player and not half of a platoon. Let’s give him 250 plate appearances. Same with Ankiel.

As constructed and run through my model, this Harper-less squad is good for 83 wins. Were he to join the Nats as the opening-day right fielder, Harper would need to have a wRC of 25–that is “create” 25 runs over 162 games.

What does 25 wRC look like? It looks like an outfielder not much worse than Aaron Rowand of the Giants, who posted 27 wRC in 2011. Rowand batted .233/.274/.347 with 30 extra-base hits (including 4 home runs) in 2011. That’s a pretty low bar to clear.

## But What If LaRoche Isn’t on the Team?

The situation becomes more complicated if LaRoche is not healthy. Morse has to move to first base. Werth slides to center, Harper moves into right. Left field sees a Bernadina/DeRosa platoon. Cameron and Ankiel come along for the ride as bench players. What does this look like now? Not too good, I’m afraid: 73 wins.

To do no harm to the team in this situation, Harper would need to be worth 90 wRC. What does a 90 wRC outfielder look like? Consider Matt Holliday of the Cardinals, who posted exactly 90 wRC in 2011. In 2011, Holliday batted .296/.388/.525 with 36 extra-base hits (including 22 home runs). That’s a much taller order.

To put the sheer magnitude of that task into perspective consider this: in 147 plate appearances with AA Harrisburg, Bryce Harper posted a wRC of 18. Normalizing that to the 600 plate appearances one might expect to see out of an every-day player, that would have given Harper an expected wRC of 83.72. Harper would have to hit major-league pitching better than he hit AA pitching to even have a chance of doing no harm to the team in this situation.

Fangraphs’ RotoChamp projection sees Harper with 259 plate appearances in 2012, projecting a wRC of 36 from those plate appearances. Even if we normalize this to 600 plate appearances, that only gets us to 83.39 wRC–not quite good enough for our purposes.

That’s how good Bryce Harper has to be. The real question is: how good is Bryce Harper? Only he can show us if he’s as good as he has to be. For the sake of Nats fans everywhere, I hope he shows us he’s much better than even that.

# Reason, Passion–and Reasonable Expectations

If you read Spanish at all, read this post over at Línea de Fair. It discusses baseball, the philosophy of science, semiotics, Sabermetrics, and the experience of being a fan all in a single post. One paragraph in particular caught my attention (translation is mine):

The baseball fan and the baseball analyst–sometimes the roles are confused, but both are delighted to see a good ballgame–try to explain the logic of the game and to predict what might happen next in the same way that man used to try to find the reason why the sun rose every day, or why the rain fell. The dynamism and insight of the Society for American Baseball Research (SABR, by its English initials) has generated new explanations, very much in vogue these days, which have been the origin of a feverish debate similar to that between the Apollonians and the Dionysiacs….

The author points to a divide in the philosophy of science between those who believe that reality can be described by the application of reason (Apollonians) and those who doubt that human reason can possibly explain the whole world (Dionysians). This is a tension that I as baseball fan feel very strongly.

On the one hand, there is a certain unknowable, aesthetic quality to baseball. When I see Danny Espinosa leap and pluck a line drive out of the air, turning as he lands to double off the runner taking a lead off first base, I am watching something no less beautiful or graceful as a ballet. But even though I might witness that play at Nats park with twenty or thirty or forty thousand of my closest friends, not one of them will feel quite the same way as I do when I see it. We can communicate those memories to each other, and compare them, but those emotions are really ours alone. And no matter how many times Debbi Taylor (or her successor) asked the Nats’ hero of the day to describe what was going through his mind as he made a game-changing play, neither Debbi nor anyone watching will ever really know how it felt to make that play. That’s a wholly subjective, unknowable experience. Our emotional bond with baseball is made of countless such memories–each of them precious, each of them irreplaceable, and each of them utterly incommunicable.

But then, I spend an awful lot of time perusing statistics. The cynic might suggest that this kills the joy of going down to the baseball game at all. After all, stats don’t tell stories as much as they open windows into specific questions: Which is the most effective pitcher? Who bats better, overall? How good is this player’s defense? Indeed, on this blog, I’ve tried to use my rudimentary grasp of statistics to open a window on the 2012 Nationals season yet-to-be.

All of this mucking about with cold rationality has affected me as a fan–but, I think, for the better. I started my 2012 projections project because I was sick and tired of hearing all the emotional overreactions to the Prince Fielder free agency drama on my twitter feed. The Nationals, so it went, were going to be world-beaters with Fielder and terrible without him. That looked like a proposition I could test, so I did, the best way I could.

As FDR might have said, the only thing Nats fans have to fear is “Fear itself: nameless, unreasoning, unjustified terror, which paralyzes needed efforts to convert retreat into advance.” My calculations put the Nationals anywhere between 84 and 86 wins–on track for their best season since arriving in DC. And, even in a “doomsday scenario” without Adam LaRoche, the Nats look to get anywhere between 79 and 81 wins.

Think about what that means. It means that the worst I can expect from the 2012 Nationals is that they’ll have an even chance of winning any given ballgame on any given night. As a fan, that’s all I can reasonably ask for, anyway. If that’s the worst I can expect, I can put my unreasoning, unjustified terror aside and enjoy the visceral joys of watching Espinosa and his teammates doing beautiful things on a baseball field. It might not be a perfect synthesis of reason and passion, but I’ll take it.

# But You Don’t Have to Take MY Word For It…

I’ve predicted the 2012 Nats to win 84 games, and I feel pretty confident about that prediction. There are other forecasts out there, and, as the great LeVar Burton might say, you don’t have to take my word for it.

By chance, I found these 2012 standings projections over at the Replacement Level Yankees Weblog. You should peruse them at your leisure. But the notable thing is that he has the Nationals winning 83 games in 2012, one under my projection of 84 wins, but close enough.

According to the poster, he used the Marcel the Monkey forecasting system by the legendary Tangotiger. (Who, by the way, seems most insistent that his forecasting engine is a monkey. Definitely not a shell with shoes on).

I haven’t yet had the time to break down and understand the Marcel forecasting system, nor to apply it to the Nationals, but its outputs don’t seem too far out of line.

# The Limits of Prescience

A thread over at the Washington Nationals Fan Forums pushed back against some of my projections here and raised a few points that I neglected to address in my 2012 projections.

## Margins of Error

Interesting projections but the missing piece would be an estimate of how much of a margin of error there would be for both the offensive and defensive estimates that would provide a range for the expected number of wins as opposed to a hard number.

This was a serious omission on my part. All projections have a certain degree of uncertainty built into them, and I really should have discussed the degree of uncertainty built into mine.

I took my method for calculating the projected runs allowed by pitching and defense from this site. The author tested this method against 7 years of complete season data from 2002 through 2008. As he writes:

I found the R^2 value. Not to oversimplify things too much, but this value basically shows what percentage of the variation can be accounted for by the model. The value ranges from 0 (worthless) to 1 (perfect). For my 210 data points, I had an R^2 value of about 0.78 (i.e. 78% of the variation).

That means that my defense and pitching runs allowed projections should be good for plus or minus 22%. That gives a lower bound of 482.84 runs allowed and an upper bound of 755.20 runs allowed.

If we assume that my offensive predictions are correct (a problem I’ll get to in a second), that means the 2012 Nats will win anywhere between 68 and 103 games

I know that’s an immense difference. I’m not sure how I could close that gap. UZR doesn’t account for pitcher or catcher defense, for instance. But even then, I think the method at least gets us in the ballpark.

The offense numbers are a lot more troublesome. I haven’t been able to do any real regression analysis to determine how good my model is–I simply haven’t had the time.

On the other hand our offense has way too many question marks to estimate the total number of runs scored with enough precision to come up with a meaningful value that can be used in a secondary projection as you did in calculating our win total.

Any type of future projection is likely to involve more than a little handwaving. Here, I’ve drawn an arbitrary line: all players included in this analysis are players on the Nats’ 25-man roster as of January 27, 2012, some 23 days before pitchers and catchers are due to report at Viera.

## Individual Players and the Projections

Will Werth stay Werthless?

2011 Jayson Werth was astonishingly bad. I’m going to believe that his 2011 numbers are aberrations and not indicative of a “new normal.” I’m fairly confident that the 4-year average from 2008-2011 is a fair picture of what kind of player Werth is now–somewhere between his Philly days and the debacle of 2011.

Will Desmond, Ramos, and Espi improve or stagnate?

As far as Desmond and Espinosa, I have no idea. I don’t think I have nearly enough data about them to make any predictions going forward. Ramos, however, gets a nice bump from more playing time and more PAs. His wRC/PA isn’t terrible, so that’s to be expected.

Will Morse fall back to Earth?

I’m going to go ahead and say No. As I said in Part 3, Morse’s modest offensive outputs in 2008-2010 might make you think that he’s going to crash down to Earth in 2012. But, remember, I’ve taken a four year average of his wRC/PA over the same period. Giving Morse 600 plate appearances in 2012 gives a projected wRC of 97.00: exactly the same as his breakout 2011 “beastmode” year. Indeed, even if we throw out Morse’s 2011 season, running the same calculation over data from 2008-2010 yields a projected wRC of 90.00: Seven runs short of our prior projection and of the 2011 total, but still enough to make him almost as good as Ryan Zimmerman (projected for 90.69 wRC). Indeed, all of this taken together seems like pretty persuasive evidence that “beastmode” has been lurking inside him the whole time, and only needed to see enough PAs.

Will Zimmm get hurt again? Will LaRoche bounce back?

My response: Dammit, Jim, I’m a baseball fan, not a doctor!. I have really no good way of figuring out La Roche’s prognosis post-surgery, nor can I really know anything about the state of Zimmerman’s joints and muscles. The only real response I have here is that the four-year interval I picked should be fair to both men in terms of their expected production.

Who plays centerfield?

Again, I had to draw an arbitrary line and go with who was in the organization as of the day I began compiling the statistics. That means that for now, we’re looking at a DeRosa/Bernadina platoon in center field. This might not be ideal, but I didn’t want to mix players who weren’t officially in the organization into these projections. Blown Save, Win, however, has attempted to address the center field question in a recent post, where he suggests that perhaps the short-term answer is Rick Ankiel. I’ll have to go back and study this, obviously.

# Projecting the 2012 Nationals, Part 4: Setting Expectations.

In this fourth and final installment of my series on projecting the 2012 Nationals season, we’ll put together everything we’ve learned about the 2012 Nationals so far and make a final, bold prediction of the Nats’ won/loss record.

Actually, what the hell, let’s get the prediction out of the way first: The 2012 Nationals will win 84 games and lose 78, for a winning percentage of .520 on the season.

Remember I said back in Part 1 that a baseball team’s winning percentage can be estimated fairly accurately using the Pythagorean win expectation formula:

$\text{Win} = \frac{\text{Runs Scored}^2}{\text{Runs Scored}^2 + \text{Runs Allowed}^2} = \frac{1}{1 +(\text{Runs Allowed}/ \text{Runs Scored} )^2}$

Plugging the data we collected in Part 2 and Part 3, that comes out to a final winning percentage of .520. Multiply that by a 162-game season, and that gives you 84 wins.

That’s not bad! In fact, it’s 4 more wins than the Nats got in 2011, and it would be more wins than the Nationals have ever gotten in a season since coming to D.C. I don’t know about the rest of you, but I’m very excited by this.

Now all we need to do is convince Ted Lerner to bring back fireworks at Nats Park so we can all hear Charlie Slowes make his signature “Bang, Zoom Go the fireworks! A Curly ‘W’ is in the books!” call as God intended.

You might want to go back and read the rest of the series:

# Projecting the 2012 Nationals, Part 3, Bottom of the Inning: Offense & Runs Scored

In Part 1 of this series, we sketched out one method we can use to project wins and losses for the 2012 Nationals. In Part 2, we sat through the top of our hypothetical season-as-single-inning and watched the 2012 Nats give up 615.72 runs.

Well, now we come to the bottom of the inning. Time for the 2012 Nationals to bat. Do they score more runs than the visiting team? And if they do, how many more? Let’s find out.

## Calculating Projected Runs Scored

As I mentioned back in Part 1, we need to use wRC to figure out how many runs the offense is likely to create.

wRC stands for Weighted Runs Created ( Not World Rally Championship, this blogger’s preferred form of motorsport). The “weighted” bit in the name comes from the fact that a major component in the stat is another advanced offensive statistic, wOBA or Weighted On-Base Average. As Fangraphs explains:

Weighted On-Base Average (wOBA) is based on a simple concept: not all hits are created equal. Batting average would have you believe they are, but think about it: what’s more valuable, a single or a homerun? Batting average doesn’t account for this difference and slugging percentage doesn’t do so accurately (is a double worth twice as much as a single? In short, no). OPS does a good job of combining all the different aspects of hitting (hitting for average, hitting for power, having plate discipline) into one metric, but it weighs slugging percentage the same as on-base percentage, while on-base percentage is more valuable than slugging.

Weighted On-Base Average combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value.

## Using a 4-year Average wRC

With that said, you’d think it would be a simple matter of finding an average wRC for each player during the period under study and then adding all those values together. Doing that yields a shocking result: the 2012 Nats would be projected to score 428.25 runs. As Dave Huzzard over at Blown Save, Win helpfully pointed out, this would make the 2012 Nationals the worst offense in all baseball. In 2011, the San Francisco Giants had the worst offense in the National League, scoring 570 runs.

(Incidentally, according to Fangraphs, the record for fewest runs scored in a single season goes to the hapless 1876 Cincinnati Reds, who scored a paltry 238 runs, earning them a 9-46 record, and plunging them into the National League cellar, 42.5 games behind the Chicago White Stockings. The record for most runs scored goes to the 1894 Boston Beaneaters, who scored 1,220 runs in a 132-game season)

## Accounting for Plate Appearances

A 428.25 total, then, can’t possibly be right. The model must be broken.

Coming back to our imaginary baseball game, I realized something that should have been obvious: the more times that a batter steps up to the plate, the more chances he has to score runs. By dividing a player’s total wRC over the period under study by the number of Plate Appearances that player made, we get the number of runs the player is likely to create, on average, every time he steps up to bat. What happens if we normalize offense to the number of plate appearances each hitter is likely to get? Well, we end up with a table that looks like this:

 Player Position wRC 2008-2011 annual average wRC/PA 2008-2011 annual average 2012 PA (projected) 2012 wRC (projected) Adam LaRoche 1B 65.50 0.132658 600 79.59 Danny Espinosa 2B 22.50 0.116883 600 70.13 Ryan Zimmerman 3B 83.25 0.151158 600 90.69 Ian Desmond SS 33.25 0.102151 600 61.29 Michael Morse LF 37.75 0.161670 600 97.00 Roger Bernadina CF 22.25 0.100112 400 40.04 Jayson Werth RF 95.25 0.154941 600 92.96 Wilson Ramos C 15.75 0.121857 400 48.74 Mark DeRosa RF 44.50 0.129927 400 51.97 Steve Lombardozzi 3B 0.25 0.031250 350 10.94 Jesus Flores C 13.25 0.101727 300 30.52
And, just for kicks, let’s consider the pitchers, too, since this is the National League:
 Player Position wRC 2008-2011 annual average wRC/PA 2008-2011 annual average 2012 PA (projected) 2012 wRC (projected) Stephen Strasburg RHSP -0.75 -0.100000 60 -6.00 Jordan Zimmermann RHSP 0.25 0.010000 100 1.00 Gio Gonzalez LHSP -0.25 -0.111111 100 -11.11 Chien-Ming Wang RHSP -0.75 -0.111111 100 -11.11 John “Long Ball” Lannan LHSP -2.50 -0.042553 100 -4.26 Ross Detwiler LHRP -1.25 -0.104167 20 -2.08 Tom Gorzelanny LHRP -1.25 -0.038462 20 -0.77 Craig Stammen RHRP 1.00 0.048780 20 0.98 Henry Rodriguez RHRP 0.00 0.000000 0 0.00 Brad Lidge RHRP 0.00 0.000000 0 0.00 Tyler Clippard RHRP 0.00 0.000000 0 0.00 Drew Storen RHRP 0.00 0.000000 0 0.00

Added all together, that means the 2012 Nationals score a respectable 640.82 runs in the bottom of our imaginary season-as-single-inning.

One really surprising thing here is that Michael Morse’s “beastmode” 2011 season may not have been a fluke. Morse’s 4-year average annual wRC is 37.75. But when you break it down and look at his wRC per plate appearance, you discover that “beastmode” was always lurking inside Morse, waiting to be unleashed and given enough plate appearances. Multiplying Morse’s wRC per plate apperance by a projected 600 plate appearances (reasonable for an every-day player), his projected wRC jumps to 97.00: exactly what his actual wRC was in 2011’s breakout season.

That does it for the bottom of the inning. The 2012 Nats have scored 640.82 runs. What does that mean, and how should Nats Town feel about the ballclub going into the 2012 season? Tune in tomorrow for Projecting the 2012 Nationals, Part 4: Setting Expectations.

# Projecting the 2012 Nationals, Part 2:Top of the Inning: Pitching, Defense, and Runs Allowed.

In part 1 of this project, I sketched out how we might arrive at a projected win-loss total for the 2012 Nationals by using the Pythagorean win expectation formula. Again, let’s suppose the whole 2012 season is like a day at Nats Park. The visitors get to bat first. As Nats fans, then, the first thing we have to watch is the effectiveness of the home team’s pitching and defense.

# Total Runs Allowed: 615.72

Let’s get this out of the way quickly: I project that the opponents of the 2012 Nationals will score just under 616 runs against the Nats.

To be pedantic, the “visiting” team in our calculations will score 615.72 runs in 2012. Don’t be bothered too much about the fractional runs–they’ll all come out in the wash.

You might ask yourself: “Well, how did I get here?”

The short answer is this: we need to figure out how many runs the pitching staff allows–that means using FIP. In your mind’s eye, imagine the 5-run 9th-inning debacle against the Marlins on July 26th of last year.

Then we need to figure out if the defense can take any of those runs away. In your mind’s eye, think of a happier moment– Roger “The Shark” Bernadina’s unbelievable catch at Nats park, robbing Mike Stanton of at least a couple of runs.

The gist is: Runs allowed is the sum of each individual pitcher’s runs allowed, minus the sum of all the runs saved by each defender.

## Pitching: 619.02 Runs Allowed

You might have noticed that FIP looks an awful lot like the “traditional” pitching effectiveness statistic, Earned Run Average or ERA. This is not an accident. FIP is meant to remove the troublesome “earned/unearned” distinction and get to the question of whether the pitcher “caused” the opposing team to score.

ERA, of course, is calculated like this:

$\text{Earned Run Average} = 9 \times \frac{\text{Earned Runs Allowed}}{\text{Innings Pitched}}$

$\text{Fielding Independent Pitching} = \frac{13HR + 3BB - 2K}{IP} + \text{scaling constant}$

Where “scaling constant” is some constant figure (around 3.20 or so) to normalize things to a league average and make it look like ERA.

Notice that FIP only cares about things that are in the pitcher’s control: Home Runs, Walks, Strikeouts, and Innings Pitched. The rest is up to the defense (which we’ll get to). Notice also that it looks an awful lot like ERA, so we can use it like ERA. FIP tells us how many runs a pitcher is likely to give up, on average, for every 9 innings he pitches.

The only thing we don’t know for sure is the number of innings each pitcher will pitch–that’s what we have to project. But we already know, more or less, how “good” each pitcher is from the FIP data.

To figure out how many runs each pitcher is likely to give up, we calculate an expected runs allowed this way:

$\text{Pitcher's Projected Runs Allowed} = \frac{\text{FIP} \times \text{Projected Innings Pitched}}{9}$

Adding each of those numbers together for each pitcher will give you a total number of runs likely to be given up.

## Starting Pitchers

 Pitcher Name 2012 IP (Projected) FIP (2008-2011 Average) 2012 Runs Allowed per pitcher (projected) Stephen Strasburg 160.00 1.87 33.24 Jordan Zimmermann 180.00 3.59 71.80 Gio Gonzalez 200.00 4.06 90.22 Chien-Ming Wang 180.00 4.35 87.00 John Lannan 180.00 4.57 91.40

## Bullpen

 Pitcher Name 2012 IP (Projected) FIP (2008-2011 Average) 2012 Runs Allowed per pitcher (projected) Ross Detwiler 63.2 4.30 30.42 Tom Gorzelanny 98.1 4.64 50.69 Craig Stammen 61.0 4.23 28.67 Sean Burnett 62.0 4.20 28.93 Brad Lidge 60.0 3.72 24.80 Henry Rodriguez 72.2 3.22 26.00 Tyler Clippard 72.2 3.61 29.15 Drew Storen 73.0 3.29 26.69

## Defense: 3.30 Runs Saved.

Accounting for defense in these projections is, paradoxically, both easier to do and harder to explain.

It’s easier, because there’s not much to be done. We take our UZR data and add them up.

Yup, it’s really that simple. The end result tells us how many of the runs allowed by the pitchers the defense saves. Thus, a positive value means that the defense took away that number of runs that might have scored otherwise. A negative value, on the other hand, means that the defense bungled enough to allow more runs to score than they otherwise would have done.

We can say this, of course, because built into the pitching statistic (FIP) is the assumption that the pitcher would perform exactly the way FIP would expect him to perform in front of a perfectly average defense. UZR measures how much above or below average defense is.

I won’t reproduce the position player tables here–that would be too tedious, and you can read them here anyway. When you add them all together, the 2012 Nats defense will prevent 3.30 runs from scoring that might otherwise have scored.

There are a couple of quirks to this calculation. If you’ve been paying attention, you’ll notice that UZR is a “counting” statistic, not a rate. So over four years, the totals you’ll see in the tables are aggregates: the number of runs, total, in the last four years that that player is responsible for saving (or letting through). For the purposes of this calculation, I’ve had to divide that figure by four, to get a rough estimate of how many runs the player saves, on average, for each year under consideration.

I should note a few things I learned while looking at the defensive statistics:

• Ryan Zimmerman is every bit the defender that I thought he was, apparently. In each of the four years in my study, the Nats could expect Zimmerman to save 7.55 runs, on average. That’s phenomenal.
• As a right fielder, Jayson Werth’s average UZR in the period under study is a respectable 4.35. As a center fielder, he’s perfectly average, with a 0.00 UZR in the period under study. In left field, Werth is less than ideal, but serviceable, with a -1.60 UZR (allowing, on average 1.6 “extra” runs to score).
• By UZR, Roger Bernadina might be the worst center fielder on this roster (-2.10 UZR). He’s much, much better in left field (1.70 UZR). This surprised me. After all, it’s his spectacular diving catch in center field that I linked to above as an example of saving runs.
• On the bench, Mark DeRosa and Steve Lombardozzi are, overall, perfectly average defenders, but they can play an excellent spread of positions. If I were managing the Nats, I’d appreciate the degree of flexibility they can bring to a lineup.

Well, that does it for the top of the inning. The pitchers would have allowed 619.02 runs, but the defense took 3.30 of those away from the opposition. Going into the bottom of the inning, the 2012 score stands with the visiting team at 615.72, with the Nats coming to bat in the bottom of the inning. We’ll find out just how well they bat in the bottom of the inning in Projecting the 2012 Nationals, Part 3, Bottom of the Inning: Offense.