Archive for Strategy

The NL West: Time Zones, Ballparks, and Social Investing

I think the National League West is the most idiosyncratic division in baseball. Note that I avoided a more disparaging term, like odd or weird. That’s not what I’m trying to convey. It’s not wrong; it’s just…off. Not bad–it’s home to 60% of the last five World Champions, right?–but different. Let me count the ways. (I get three.)

Time zones

EAST COAST BIAS ALERT!

It is difficult for people in the Eastern time zone to keep track of the NL West. Granted, that’s not the division’s fault. But 47% of the US population lives in the Eastern time zone. Add the Central, and you’re up to about 80%. That means that NL West weeknight games generally begin around the time we start getting ready for bed, and their weekend afternoon games begin around the time we’re starting to get dinner ready. The Dodgers, Giants, and Padres come by it naturally–they’re in the Pacific time zone. The Diamondbacks and Rockies are in the Mountain zone, but Arizona is a conscientious objector to daylight savings time, presumably to avoid prolonging days when you can burn your feet by walking barefoot outdoors. So effectively, four teams are three hours behind the east coast and the other team, the Rockies, is two hours behind.

Here’s a list of the number of games, by team, in 2015 that will be played in each time zone, ranked by the number of games in the Mountain and Pacific zones, counting Arizona among the latter:

Again, I’m fully on board with the idea that this is a feature, not a bug. But it’s a feature that means that a majority, or at least a solid plurality, of the country won’t know, for the most part, what’s going on in with the National League West teams until they get up in the morning.

Ballparks

OK, everybody knows that the ball flies in Coors Field, transforming Jose Altuve to Hack Wilson. (Check it out–they’re both 5’6″.) And the vast outfield at Petco Park turns hits into outs, which is why you can pencil in James Shields to lead the majors in ERA this year. But the other ballparks are extreme as well: Chase Field is a hitter’s park; Dodger Stadium and AT&T Park are pitchers’ havens. The Bill James Handbook lists three-year park factors for a variety of outcomes. I calculated the standard deviations for several of these measures (all scaled with 100 equal to league average) for the ballparks in each division. The larger the standard deviation, the more the ballparks in the division play as extreme, in one direction or the other. The NL West’s standard deviations are uniformly among the largest. Here’s the list, with NL West in bold:

  • Batting average: NL West 10.1, AL West 7.2, AL Central 6.5, AL East 5.8, NL East 5.2, NL Central 1.6
  • Runs: NL West 26.5, NL Central 7.9, NL East 6.9, AL East 4.0, AL Central 2.8, AL West 2.7
  • Doubles: AL East 20.3, NL West 11.3, NL East 6.2, NL Central 5.9, AL Central 5.1, AL West 2.9
  • Triples: NL West 50.6, AL Central 49.5, NL East 33.6, AL West 28.3, AL East 27.8, NL Central 11.1
  • Home runs: NL Central 30.2, NL West 23.9, NL East 20.0, AL East 18.7, AL Central 11.3, AL West 11.2
  • Home runs – LHB: NL Central 31.6, AL East 27.4, NL West 25.6, NL East 21.7, AL West 14.7, AL Central 11.7
  • Home runs – RHB: NL Central 32.1, NL West 24.0, NL East 20.0, AL East 14.4, AL Central 13.6, AL West 10.2
  • Errors: AL East 17.7, NL West 12.2, NL Central 11.6, NL East 11.5, AL West 11.2, AL Central 8.2
  • Foul outs: AL West 36.2, AL East 18.3, NL West 16.0, NL Central 15.2, AL Central 13.8, NL East 6.2

No division in baseball features the extremes of the National League West. They ballparks are five fine places to watch a game, but their layouts and geography do make the division idiosyncratic.

Social Investing

You may be familiar with the concept of social investing. The idea is that when investing in stocks, one should choose companies that meet certain social criteria. Social investing is generally associated with left-of-center causes, but that’s not really accurate. There are liberal social investing funds that avoid firearms, tobacco, and fossil fuel producers and favor companies that offer workers various benefits. But there are also conservative social investing funds that don’t invest in companies involved in alcohol, gambling, pornography, and abortifacients. This isn’t a fringe investing theme: By one estimate, social investing in the US totaled $6.57 trillion at the beginning of 2014, a sum even larger than the payrolls of the Dodgers and Yankees combined.

Here’s the thing about social investing: You’re giving up returns in order to put your money where your conscience is. That’s OK, of course. The entire investing process, if you think about it, is sort of fraught. You’re taking your money and essentially betting on the future performance of a company about which you know very little. Trust me, I spent a career as a financial analyst: I don’t care how many meals you eat at Chipotle, or how many people you know at the Apple Genius Bar, you can’t possibly know as much about the company as a fund analyst who’s on a first-name basis with the CEO. So there’s no sense in making it even harder on yourself by, say, investing in the company guilty of gross negligence and willful misconduct in a major oil spill, if that’d bother you.

Note that I said that with social investing, you’re giving up returns. Some social investing proponents would disagree with me. They claim that by following certain principles that will eventually sway public opinion or markets or regulations, they’re investing in companies that’ll perform better in the long run. That’s a nice thought, but social investing has been around for decades, and we haven’t yet hit that elusive long run. The Domini 400 Index, which was started in 1990, is the oldest social investing index. It started well in the 1990s, but has lagged market averages in the 21st century. Now called the MSCI KLD 400 Social Index, it’s been beaten by the broad market in 10 of the past 14 years. It’s underperfomed over the past year, the past three years, the past five years, and the past ten years, as well as year-to-date in 2015. The differences aren’t huge, but they’re consistent. Maybe for-profit medicine in an aberration, but acting on that meant that you missed the performance of biotechnology stocks last year, when they were up 47.6% compared to an 11.4% increase for the S&P 500. Maybe we need to move toward a carbon-free future, but stocks of energy companies have outperformed the broad market by over 100 percentage points since January 2000. I think that most social investing investors are on board with this tradeoff, but some of the industry proponents have drunk the Kool-Aid of beating the market. That’s just not going to happen consistently. In fact, a fund dedicated to tobacco, alcohol, gambling, and defense (aka “The Four B’s:” butts, booze, bets, and bombs) has outperformed the market as a whole over the past ten years.

OK, fine, but what does this have to do with the National League West? Well, two of its members have, in recent years, made a point of pursuing a certain type of player, just as social investing focuses on a certain type of company. The Diamondbacks, under general manager Kevin Towers and manager Kirk Gibson, became a punchline for grit and dirty uniforms and headhunting. (Not that it always worked all that well.) The Rockies, somewhat less noisily, have pursued players embodying specific values. Co-owner Charlie Monfort (a man not without issues) stated back in 2006,  “I don’t want to offend anyone, but I think character-wise we’re stronger than anyone in baseball. Christians, and what they’ve endured, are some of the strongest people in baseball.” Co-owner Dick Monfort described the team’s “culture of value.” This vision was implemented by co-GMs (hey, Colorado starts with co, right?) Dan O’Dowd and Bill Geivett. (OK, O’Dowd was officially GM and Geivett assistant GM, but the two were effectively co-GMs, with Geivett primarily responsible for the major league team and O’Dowd the farm system).

Now, there’s nothing wrong with players who are also commendable people. You could do a lot worse than start a team with Clayton Kershaw and Andrew McCutchen, to name two admirable stars. Barry Larkin was a character guy. So was Ernie Banks. Brooks Robinson. Walter Johnson. Lou Gehrig. All good guys.

But holding yourself to the standards set by the Diamondbacks and Rockies also means you’re necessarily excluding players who are, well, maybe more characters than character guys.  Miguel Cabrera has proven himself to be a tremendous talent and a somewhat flawed person. Jonathan Papelbon has a 2.67 ERA and the most saves in baseball over the past six years, but he’s done some things that are inadvisable. Carlos Gomez, a fine player, second in the NL in WAR to McCutchen over the past two years, has his detractors. Some of the players whom you’d probably rather not have your daughter date include Babe Ruth, Ty Cobb, Rogers Hornsby, Barry Bonds, and many of the players and coaches of the Bronx Zoo Yankees.

I want to make a distinction here between what the Diamondbacks and Rockies did and the various “ways” that teams have–the Orioles Way, the Cardinals Way, etc. There’s plenty of merit in developing a culture starting in the low minors that imbues the entire organization. That’s not what Arizona and Colorado did. They specified qualities for major leaguers, and, in the case of the Diamondbacks at least, got rid of players who didn’t meet them. I don’t know what’s wrong with Justin Upton, but for some reason, Towers didn’t like something about him, trading him away. The Braves make a big deal about character, but of course they traded for Upton, so the Diamondbacks went way beyond anything the Braves embrace.

In effect, what the Diamondbacks and Rockies have done is like social investing. They’ve viewed guys who don’t have dirty uniforms or aren’t good Christians or something the same way some investors view ExxonMobil or Anheuser-Busch InBev. Again, that’s their prerogative, but it loses sight of the goal. Investors want to maximize their returns, but as I said, most social investors realize that by focusing on only certain types of stocks, they’ll have slightly inferior performance. They’ll give up some performance in order to hew to their precepts. Baseball teams want to maximize wins, and there really isn’t any qualifier related to precepts you can append to that.

The Rockies and Diamondbacks were living under the belief that by focusing on only certain types of players, they could have superior performance. It’s like the people who think they can beat the market averages through social investing. It hasn’t happened yet. And, of course, the Diamondbacks and Rockies were terrible last year, with the worst and second-worst records in baseball. Just as social investing doesn’t maximize profits, the baseball version of social investing didn’t maximize wins in Phoenix or Denver.

I’ve used the past tense throughout this discussion. Towers, Gibson, O’Dowd, and Geivett are gone, replaced by GM Dave Stewart and manager Chip Hale in Arizona and GM Jeff Bridich in Colorado. (The Monforts remain.) Last year, the Diamondbacks created the office of Chief Baseball Officer, naming Tony LaRussa, a Hall of Fame manager who’s been less than perfect as a person and in the types of players he tolerates. These moves don’t change that these are both bad teams. But by pursuing a well-diversified portfolio of players going forward, rather than a pretty severe social investing approach, both clubs, presumably, can move toward generating market returns. Their fans, after all, never signed on to an approach that willingly sacrifices wins for the sake of management’s conscience.


The Method to the Yankees’ Madness

Last week Miles Wray examined an emerging spending pattern of the New York Yankees, suggesting that the club’s approach to free agent spending varies, depending possibly on how many dollars had recently come off their books: They appear to spend each offseason either signing seemingly every premium free agent available (2008-9, 2013-4) or they limit themselves to the bargain bin, focusing on late-offseason signings, reclamation projects, and trades.

While this description is certainly accurate, at least since 2008-9 when this pattern began to emerge, there’s little discussion of why a team would choose to invest in free agency this way.  Presumably, teams like the Yankees, the Dodgers, or the Red Sox, which are capable of fielding significantly higher payrolls than any other team in the league, would prefer to do the opposite: Selecting from a much more limited subset of free agents would limit the advantage gained over other teams.  It’s also not inconceivable that a team with as much money as the Yankees might have concerns that they’d be driving up the entire market, increasing their own cost of acquiring talent.  This approach also has very real impacts on team age and roster flexibility as an entire free agent crop begins to enter their decline years together.

Moreover, the Yankees may very well not be the only team taking this approach.  An argument can be made that the Boston Red Sox are following a similar strategy, albeit at a pace accelerated by their shorter duration contracts they signed in the 2012-3 offseason and their salary-dump trade with the Dodgers four months earlier.  The team signed both Hanley Ramirez and Pablo Sandoval, arguably the two top hitters available in free agency, only a year after they signed precisely one free agent, Mike Napoli, who was their own.

So what does a team gain by going on spending sprees followed by (relative) austerity?  I submit they pursue this approach to gain one thing: draft picks.

Consider for the moment what happens in the case where the Yankees are not following this feast/famine strategy in free agency, and instead they sign a premium free agent each year.  In 2009-10 they might’ve signed Matt Holliday or Jason Bay.  2010-1, Carl Crawford or Jason Werth.  In 2011-2, Fielder/Pujols/Reyes.  In 2012-3 Upton/Hamilton/Bourne/Grienke.  All were free agents tied to compensation, meaning in addition to the dollar-cost of signing that player, the signing team also forfeited a draft pick.  (It’s probably also worth noting how godawful most of those signings look today, but that’s the nature of free agency – The last couple of years are almost always ugly.)  The mechanics of where those picks go have changed since the 2012-3 offseason but the cost to the signing team remains the same: A first round draft pick, or a later round pick if the first round pick is already spoken for.

Instead of signing those players, over that span the New York Yankees signed only a single draft-pick-compensation free agent, Rafael Soriano, 2010-11, and it was over the objections of Brian Cashman.  They kept their first-round draft picks in 2010, 2012, and 2013, and picked up a few compensation picks from departing free agents like Nick Swisher, Javier Vázquez and Soriano.

As Miles points out, however, the Yankees simply can’t stockpile picks and rebuild like a normal team.  This restraint is made possible by lavish spending in the 2008-9 offseason, where the Yankees signed pretty much everybody and then went out and won the World Series.  Signing Teixeira, Sabathia, and Burnett means the Yankees not only forfeited their first round draft pick, but their second and third round draft picks as well.

When viewed in the whole, however, this doesn’t appear to be that bad of a deal for the Yankees.  By moving their spending forward into the 2008-9 offseason instead of spreading it out over four years, they essentially traded their second and third round draft picks in 2009 for first-round draft picks in 2010, 2012, and 2013.  They repeated this approach 2013-4, signing Brian McCann, Jacoby Ellsbury, and Carlos Beltran, and while the early returns from those transactions are not promising, it should be noted that the McCann and Ellsbury deals, at least, were considered sound at the time they were signed.  Beltran?  Not so much.  With the last free agent with draft pick compensation attached off the board, they’re keeping their 2015 first round pick as well.

At a time when the aging curve for older players have suddenly become unforgiving, the value of young players is certainly up, and the Yankees appear to be maximizing their chances of acquiring young talent in the draft by minimizing the draft pick cost of signing free agents.  This approach is remarkably similar to their strategy in the international market, where they’ve determined the best way to acquire talent is not to stick to a limited bonus pool each year, but to sign ten or eleven of the top thirty international free agents, (and possibly one more.)  This approach costs them a great deal of money in luxury tax and international bonus pool “overage” tax, but may make sense given how much surplus value an above-average, cost-controlled young player generates.  Now, if only they could do something with all those draft picks


How to Use LABR Mixed Draft to Your Benefit

The 15-team LABR Mixed Draft is the most exciting of the expert fantasy drafts each year. Amateur fantasy owners from all over the globe tune into the live spreadsheet broadcast and debate each one furiously on social media.

Most of these amateurs are looking for expert guidance to help them in their own draft. They see a player getting drafted well above their ADP and they often move the player up on their own personal big board.

I do not think this is the best way to approach and absorb the most information out of LABR. When one expert reaches on a pick, we have no idea if there is a consensus. It could have been just one expert making a stand on a player he himself feels strongly about, or there could have been several owners who felt the same way about that player. We just don’t know.

What we do know is that when certain players drop well below their public rankings, there is an agreement of pessimism. That is the information that could be significant for the rest us. Every owner in the league letting a player fall well below their ADP is the expert consensus we should be looking for.

Here’s a quick look at nine players who the experts are cool on.

Read the rest of this entry »


The Disappearing Downside of Strikeout Pitchers

In 1977, Nolan Ryan was in the midst of his dominant tenure pitching for the California Angels. Four years before, he had broken Sandy Koufax’s modern strikeout record, and his stuff wasn’t going away. The 30 year-old finished the ’77 season three outs shy of 300 innings, and struck out 10.3 batters per nine innings. Those 341 strikeouts came with a home run rate 60% lower than league average.

Yet, somehow, Ryan was not the best pitcher in baseball that season. He finished 3rd in AL Cy Young voting. In the majors, he was 4th in pitcher WAR, 10th in Wins, 7th in ERA, and 9th in FIP. So how could such an unhittable season be so clearly something other than the best in baseball?

In 1977, Nolan Ryan walked 204 batters. That is 5.5 walks per start. With Tom Tango’s Linear Weights, we can say that Ryan’s walks cost the Angels over 60 runs, which is ~30 runs worse than if he had a league-average walk rate. Batters were fairly helpless against Nolan Ryan, but what help they did get, they got from him.

In the 1970’s, this phenomenon was not unheard of. Pitchers who struck the most hitters out tended to walk the most as well. (Note: for this article, I’m including pitchers who threw 140+ innings)

K BB 1970s

For every additional 5-6 strikeouts, you could expect an additional walk from a pitcher. This is not surprising for a few reasons. The main two that come to my mind are:

1) If a pitcher strikes out a lot of hitters, then GM’s and managers will be more willing to tolerate a lack of control, and
2) Harder throws, nasty movement, and a focus on offspeed pitches can lead to strikeouts and make balls harder to locate.

It seems natural that there would be a positive relationship here, and it goes along well with the idea that flamethrowers are wild.

But could that relationship be going away? Here’s the same chart, but instead of being the 1970’s, this is for the year 2010 and on:

K BB 2010s

In this span, it takes 20 strikeouts to expect an additional walk. There’s still a relationship, but it’s much looser.

And while it’s possibly irresponsible to look at sample sizes this small, the relationship was almost completely gone last year. If we only look at 2014 pitchers, we see the following:

K BB 2014

Given that the model here suggests that 300 strikeouts lead to one walk, I think it’s safe to say there wasn’t a meaningful relationship between strikeouts and walks last year.

It’s important to note that this is a continued trend. There has not been a specific time when strikeout pitchers decided to stop walking people. Broken up by decade, this is something that has constantly been occurring over the last 40 years.

K BB Correlation Decades

I’m not exactly sure what the big takeaway from this is, but I’m more curious about what is causing this shift. As far as the results from such a change, I do not believe this explains the drop in offense, since the trend continued through the booming offense of the late ’90s and early 2000s.

Maybe player development is better than it used to be. If coaches can better address player weaknesses, it would be possible for pitchers to be more well rounded.

Perhaps teams are less willing to tolerate players with large weaknesses, even if they are strong in another area. I find this theory unlikely in an age when almost any strength can be valued and measured.

It’s possible that pitchers try to strike batters out differently than they used to. Maybe they used to be more likely to try to get hitters to chase balls out of the zone to get a third strike, leading to more walks.

Most likely, it’s something that I am missing. But regardless, we are no longer in an era where a pitcher like Nolan Ryan leads the league in strikeouts, and you simply have to deal with his astronomical walk numbers. The modern ace is tough to hit and can command the zone, and there are plenty of them.


A Look at SGP-Based Rankings Using Different Projection Sets (Part 1)

The bulk of the work I do pre-draft and in-season is essentially based on an SGP (standings gain points) projections and ranking system. I use SGP data from leagues that match the format and settings of the league I’m ranking for (ideally from 10+ years of data from the actual league, where possible). While I usually do my own projections for 30-40 players of specific interest, in general I’m happy to utilize the projections published by experts that actually know what they’re doing and do it for a living. Specifically (and in no particular order) I use Steamer, Pecota, and Baseball HQ.

These lists may not be useful in ‘absolute’ terms – again, the data I’m using here reflect the SGP settings I use that reflect the league I play in. However, I still believe the lists offer an interesting way to notice a) how each projection system differs on its view of individual players, and b) general overall differences in each projection system. Blindly following a projection set is probably going to be better than randomly picking players by throwing darts at the wall. But you can squeeze a lot more value out of these rankings and the projections you use by gaining a deeper understanding of how each set of projections work, and what ‘biases’ and tendencies might be part of the numbers.

What I like to do each year is generate ‘top X’ lists of players at each position for each projection set I use, then play around in the results to spot any glaring differences.  Is one projection overly conservative on expected ABs? Is one projection set basically expecting a repeat of last year’s career year? I can use that as a starting point to drill down into some of the numbers to see what might be behind the differences. Personally, I find it all too easy to get overwhelmed at all the different numbers available to be looked at – far too often I find myself deep down the rabbit’s hole, spending three hours looking at average fly ball distance on balls hit on the second Wednesday of the month on even-numbered days or something. I find this approach helps me narrow in on specific players or numbers of interest. And the benefit of doing this by SGP, broken down by category, is that it is easier to see specifically how each player is projected to impact each category. Player stats will not win your fantasy league, roto points will win you your fantasy league: I get a better understanding of the player’s ‘value components’ and how it impacts the particular league I play in.

First, a quick overview of SGP. Standings Gain Points is a way to measure the contribution of each player to your overall roto league standings. Larry Schecter’s excellent book, ‘Winning Fantasy Baseball’ is a great primer on the subject. Other places to read about SGP online are here and here. In a nutshell the system looks at the average stats needed to gain one point in the standings for a particular rotisserie category. For example, suppose in your league over the past 10 years, you needed 10 HRs to gain one point in the HR category standings. A player projected to hit 30 home runs would be credited with 3 SGPs for the HR category. Tally up all the SGPs the player is expected to add (or subtract) for all categories, and you get a total SGP score.

There’s a ton more to it, but that’s the basics – ever tried to figure out if the guy hitting a lot of HRs but no average was more valuable (and if so, by how much) than the guy hitting for a decent average and some SBs but no power? Now you have an idea.

In this first article, I look at at Catchers. I’ll add reports on all the hitter positions over the next couple of weeks. A reminder that these rankings are based on SGP values which are basically unique to my specific league, so your numbers will differ if you play in a different league format, but again, we’re looking a relative differences, not absolute numbers (For the record, the league format for the SGP rankings here: Standard 12-team 5×5 roto, 1 catcher, three OF and two util, 1250 innings cap).

Here is the list of top 12 catchers ranked by my league’s SGP, based on Baseball HQ projections:

Figure 1. Top 15 catchers by SGP & BHQ projections

Rank MLBAMID Full Name RSPG HRSPG RBISPG SBSPG AVGSPG Total
1 457763 Buster Posey 4.22 2.66 4.60 0.15 1.24 12.87
2 543228 Yan Gomes 4.22 3.08 4.28 0.15 0.12 11.84
3 519023 Devin Mesoraco 3.73 3.78 4.33 0.15 -0.31 11.67
4 594828 Evan Gattis 3.48 4.33 4.12 0.00 -0.48 11.45
5 518960 Jonathan Lucroy 3.98 2.24 3.84 0.73 0.62 11.40
6 431145 Russell Martin 3.54 2.52 3.84 1.02 -0.12 10.80
7 521692 Salvador Perez 3.54 2.38 4.01 0.00 0.46 10.39
8 435263 Brian McCann 3.42 3.50 4.01 0.15 -0.68 10.38
9 425877 Yadier Molina 3.66 1.54 3.74 0.58 0.71 10.23
10 467092 Wilson Ramos 2.86 2.52 3.84 0.00 -0.16 9.06
11 446308 Matt Wieters 3.11 2.38 3.41 0.15 -0.14 8.90
12 444379 John Jaso 3.85 1.54 3.19 0.44 -0.32 8.70
13 572287 Mike Zunino 3.66 2.80 3.68 0.00 -1.52 8.63
14 519083 Derek Norris 3.29 1.96 3.14 0.73 -0.72 8.40
15 425900 Dioner Navarro 2.61 1.96 3.25 0.29 0.05 8.16

Nobody should be surprised to see Buster Posey at the top of any catchers list; he’s there because he has such a huge advantage over everyone else at the position in Batting Average. And he has a full point advantage over the next tier of players. Gomes and Mesoraco at 2nd and 3rd? Probably more of a surprise. Gomes has legit power, and the batting average isn’t a fluke (career BABIP: .323). Mesoraco had a career year last year – his 25 HRs in 440 PA is only 6 fewer than he hit in 1,100 PAs in 2013, 2012, 2011 combined. Yes, he plays in a tiny crackerjack box of a park. But his FB% jumped 10ppt (33.8% to 43%) from 2013 and 2014, while his HR/FB rate more than doubled, from a constant 10% or so in 2011-2013 to 20.5% in 2014. Color me less than convinced. And with only .44 points separating them, the next four players (Gomes, Mesoraco, Gattis and Lucroy) are basically interchangeable.

Russell Martin’s ranking gets a big boost from expected SB contribution; if those SBs dip he falls quite a bit. Would anyone be surprised if a catcher that turns 32 in February and was only 4-of-8 in stolen base attempts last year doesn’t run that much in 2015?

Conversely, if Zunino can boost his average a bit, he could be excellent late-round value. He gets a massive -1.52 hit to his SGP total after hitting less than his weight last year. On the one hand, one could possibly expect a bit of an uptick in the batting average; his BABIP last year of .248 was the lowest mark he’s recorded at any point for a full season going back to 2012 and his days in the Arizona Fall League. On the other hand, he struck out 33% of the time last year, so…yeah.

Finally – what’s surprising about this list is who’s not on it – no d’Arnaud, no Rosario.

Figure 2. Top 15 catchers by SGP & Steamer projections

Rank MLBAMID Full Name RSPG HRSPG RBISPG SBSPG AVGSPG Total
1 457763 Buster Posey 4.29 2.66 4.06 0.15 0.87 12.02
2 594828 Evan Gattis 4.22 3.92 4.28 0.15 -0.88 11.68
3 435263 Brian McCann 3.85 3.36 3.79 0.15 -0.54 10.61
4 518960 Jonathan Lucroy 4.04 1.96 3.47 0.73 0.36 10.55
5 431145 Russell Martin 3.79 2.24 3.19 0.87 -0.81 9.28
6 518595 Travis d’Arnaud 3.29 2.38 3.25 0.29 -0.54 8.67
7 521692 Salvador Perez 3.23 1.96 3.14 0.15 0.10 8.58
8 446308 Matt Wieters 3.35 2.38 3.03 0.44 -0.68 8.52
9 543228 Yan Gomes 3.17 2.24 3.09 0.29 -0.36 8.42
10 519023 Devin Mesoraco 2.98 2.52 2.98 0.44 -0.60 8.31
11 467092 Wilson Ramos 2.86 2.24 2.98 0.15 -0.03 8.19
12 425877 Yadier Molina 2.92 1.40 2.76 0.44 0.35 7.86
13 501647 Wilin Rosario 2.30 1.96 2.44 0.29 0.16 7.14
14 518735 Yasmani Grandal 2.98 1.82 2.71 0.29 -0.69 7.11
15 455139 Robinson Chirinos 2.73 1.68 2.49 0.29 -0.80 6.39

The first thing to notice about this list – in general the total ‘SGP’s provided are considerably lower than for the BHQ group above. At 8.90 total SGPs, Wieters wasn’t even in the top 10 in the BHQ list; 8.90 SGPs almost makes him a top-5 pick on this list. The numbers suggest that Steamer is a bit more conservative (or BHQ overly optimistic) in its forecasts, particularly for HR and RBIs. My understanding is that BHQ’s projections are largely based on playing time projections, so perhaps the numbers will change as we get closer to spring training and the start of the season and jobs are won/lost etc. It will be interesting to see how (if) these numbers change.

Looking at the list itself, Posey and Gattis again in the top five, no surprise there. McCann in the top five looks somewhat surprising (despite a rather big gap between Gattis and McCann). Maybe Steamer remembers that McCann still hit 23 HRs last year and still plays in a favorable park? His LD% was stable last year, GB% down a tick, FB% up a tick. His HR/FB rate was down quite a bit from 2013, which is surprising given that the conventional wisdom suggested he was moving to a more favorable ballpark…but his 2014 HR/FB rate was almost exactly in line with his average since 2008. Steamer might also be expecting an uptick on that awful .231 BABIP from 2014, although not sure if it’s factoring in the increased defensive shifts he saw last year. Less than .50 points separate d’Arnaud at #6 and Ramos at #11. Of the group, Wieters is now the grizzled veteran of the bunch and looked like he was on his way to a career year before getting hurt last year. If he’s healthy, he ironically could be the ‘safe’ pick of the bunch.

Grandal makes an appearance. Interestingly, Steamer is forecasting almost exactly the same number of Runs, RBIs and HRs this year – in the same number of at-bats – as last year, despite Grandal moving from a horrible Padres team (last year at least) to a much better Dodgers team (last year at least). I’d normally expect a bit of an uptick in those numbers.

Spoiler alert, but this is the only projection where Chirinos comes in the top 15; Steamer appears to be a bit more optimistic in projected at-bats, giving him a bump in Runs and RBIs that he doesn’t enjoy in the other projections.

Figure 3. Top 15 catchers by SGP & Pecota projections

Rank MLBAMID Full Name RSPG HRSPG RBISPG SBSPG AVGSPG Total
1 594828 Evan Gattis 4.41 4.19 4.82 0.0 -0.6 12.82
2 457763 Buster Posey 4.47 2.66 4.33 0.15 0.90 12.51
3 435263 Brian McCann 4.10 3.36 4.12 0.15 -0.78 10.93
4 431145 Russell Martin 4.85 2.38 3.30 1.16 -1.13 10.56
5 518960 Jonathan Lucroy 3.98 1.96 3.68 0.87 0.06 10.54
6 518595 Travis d’Arnaud 3.91 2.66 3.68 0.15 -0.57 9.83
7 521692 Salvador Perez 3.54 1.96 3.68 0.0 0.33 9.51
8 446308 Matt Wieters 3.66 2.38 3.57 0.29 -0.77 9.14
9 425877 Yadier Molina 3.42 1.54 3.19 0.58 0.39 9.12
10 572287 Mike Zunino 3.79 3.08 3.74 0.29 -1.79 9.1
11 543228 Yan Gomes 3.23 2.24 3.09 0.15 -0.03 8.67
12 518735 Yasmani Grandal 3.66 2.10 3.09 0.29 -0.56 8.58
13 519023 Devin Mesoraco 3.23 2.38 3.25 0.29 -0.68 8.47
14 455104 Chris Iannetta 4.04 1.96 3.19 0.44 -1.46 8.16
15 467092 Wilson Ramos 3.11 1.96 2.92 0.0 -0.26 7.73

Pecota loooooves it some Gattis, putting him in the top spot over Posey. The Pecota rankings for catchers have fairly clear tiers: Gattis and Posey at the top, a substantial gap to McCann, Martin, and Lucroy, then another gap, followed by only a point or so between d’Arnaud at #6 and Iannetta at #14. Iannetta actually only shows up here because Pecota is significantly more bullish on Iannetta across the board vs the other projection sets; this almost certainly is due to differing views on ABs; Pecota’s AB projection for Iannetta is about 80 ABs higher than the BHQ projection, and over 150 more than the Steamer projection.

The difference between the Pecota numbers for Yan Gomes and the BHQ numbers are interesting – BHQ projects Gomes as one of the top 3-4 HR hitters at the catcher spot; here he’s projected to be 8th.

Martin again gets a big SB bump, which just manages to offset a rather large Avg hit (particularly compared to, say the BHQ projection, where the Avg hit was minor). Pecota is probably looking at his .290 average last year and figuring it’s a .336 BABIP-fueled fluke; Martin hadn’t had a BABIP over .290 since 2008.

Zunino again projects to have great all-around numbers except for the black hole at Batting Average. If he somehow is able to hit even .250, Zunino would likely be a top-five fantasy play behind the plate.

Looking at all three rankings, the projections differ – sometimes significantly – on some players. The BHQ-based SGP rankings loved Yan Gomes and Mesoraco; Steamer and Pecota, not so much. At the other end of the spectrum: Salvador Perez was ranked 7th in all three projection systems, largely because he’s one of the few catchers expected to make a reasonably-sized positive contribution to batting average. Although we saw last time that maybe targeting batting average wasn’t all that important…


Replacing Replacement Value in Fantasy Auctions

With the baseball season rapidly approaching and recent posts by FanGraphs authors converting projected statistics into auction values, I thought I would share my approach towards valuation I have used in a long-standing A.L. league with 12 teams, 23 player rosters selected through auction (C, C, 1B, 3B, CI, 2B, SS, MI, 5 OF, 1 DH), a $260 budget, a 17-player reserve snake draft and the ability to keep up to 15 players from one year to the next, an attribute that inflates the value of the remaining pool and can further distort disparate talent across positions and categories.

We have traditionally used a 4×4 format, and while I have persuaded my co-owners to switch to a 5×5 for the coming year, what follows is my process for a 4×4 league.

There was a distant time when I was a whiz at math but my utter lack of a work ethic for advanced math collided with university-level calculus and I crumbled as surely as a weak-kneed lefty facing Randy Johnson. So my understanding of some key statistical processes is compromised. And by some I mean most.

But what I lack in math I hope I make up in approach:

(1) For categories over multiple years in this league, teams finish in a standard bell-shaped curve, with two or three teams well ahead, two or three well behind and six to eight clumped more closely together.

(2) In a 12-team league, a third-place finish in a category bets you 10 points. Across eight categories, averaging a third-place finish gets you 80 points, which is enough points to win out league between 80% and 90% of the time.

(3) Given both (1) and (2), my goal is to finish in third in every category, because doing do will far more often than not win my league, and because that target is a comfortable space above the pack in the middle, creating a margin for error within which I can still secure a win.

(4) I calculate what totals I need for each category to finish third based upon the specific history of our league, giving greater weight to more recent and relevant trends.

(5) I calculate the totals needed to finish dead middle in the pack for each category, again based upon the specific history of our league, giving greater weight to more recent and relevant trends.

(6) The difference between the third-place totals and the median totals become my spread, in a sense, the yardstick against which I then measure all projected player performance.

(7) I don’t weight pitchers and hitters evenly because my league does not – the marketplace of my league places significantly less value on pitchers, spending between $70 and $100 on them, and I adjust values to account for that. Perhaps that is also justified by either greater volatility or more injuries for pitchers. In any case, I divide the total value for hitters by 14 and for pitchers by 9 to come up with the average value for hitters or pitchers.

(8) I calculate what each of 14 hitters and 9 pitchers would need to contribute per player for each category for both the top and the bottom of the spread.

(9) For each category, I divide the median production per player by the difference in the gap to find the incremental value of each unit of production.

(10) For each player and for each category, I start with the median value of median production for all four categories, than add or subtract the incremental value depending upon if their projected production is above or below the median.

(11) I do the same for keepers to calculate inflation value, then list both the value and inflated value next to each player, broken down by position, so I can track both availability and the ebb and flow of inflation in real time.

(12) Finally, my league is mostly inelastic except for dumping trades. That means it is not easy to trade surplus categories for deficit categories. So I create a running tally of my projected production, starting with my keepers and adding players I gain in the auction with the goal or at least reaching each of the target levels needed for projected third-places finished in each category.

(13) I don’t adjust assigned value based on the position played but of course I consider position as I bid in order to reach my targets in an inelastic league. I may deliberately pay somewhat more than inflation cost for a good player if the likely alternatives is paying over inflation value for a poor player and being left with more money to spend then there is talent to spend it on. I do so knowing my keepers will produce to much surplus value that I can win simply getting players close to inflation value.

At least in my league, my projected values, adjusted for inflation, are pretty close to the mark notwithstanding the outliers that will come in any marketplace, both for individual players and for more systemic biases (my league overpays for closers, for example). I don’t win every year, but when I fall short, it is not because my valuations were off but because of too many failures in projecting specific players.

Is there a statistical basis for tossing replacement value as a baseline for creating auction values or statistical benefit to instead using league-specific gaps between middling and winning teams? Frankly, I don’t know, however intuitive my system seems to me. But I’d welcome feedback on my approach, statistical arguments for and against it, and whether it warrants further exploration.


Fantasy Baseball: Are Some Categories More Important Than Others?

While doing some work on my pre-season projections sheet, I came across a link to complete data from Razzball – complete full-season data for 48 12-team 5×5 fantasy baseball leagues[1]. I’ve been using this as a handy cross-reference in doing some SPG (Standings Points Gained) calculations, but I decided to try and use the data to do an exercise on something I’d been thinking about: are some categories more important than others?

First, I looked at the by-category scores for all 48 first place teams, then all the second place teams, etc:

R

HR RBI SB Avg W Sv K ERA WHIP Avg score
1st pl teams

10.8

10.4 10.2 9.8 8.3 10.7 10.3 11.1 9.8 9.9

10.11

2nd pl teams

9.8

9.0 9.9 8.3 8.2 9.5 9.8 9.9 9.6 9.1

9.31

3rd pl teams

9.0

8.4 9.1 8.5 7.6 8.9 8.9 9.1 8.1 7.8

8.56

4th pl teams

8.5

8.0 8.2 7.8 7.7 7.7 7.7 7.8 7.6 7.6

7.86

5th pl teams

7.9 7.5 6.9 7.4 6.8 7.3 7.2 7.5 7.1 6.8

7.24

The 48 first place teams, on average, scored 10.11 in the 5×5 categories. So basically a top-3 finish in all categories. Not that surprising.

Digging a bit deeper, I looked at the average score in each category for 1st place teams, then for 2nd place teams, and so on. I included the standard deviation (a measure of variability) and how often a team was in the top 3 for that category:

1st Place teams R HR RBI SB Avg W Sv K ERA WHIP
Average score 10.8 10.4 10.2 9.8 8.3 10.7 10.3 11.1 9.8 9.9
Std Dev 1.6 2.1 2.3 2.3 2.9 1.7 1.8 1.2 2.2 2.0
% in top 3 77.1% 72.9% 70.8% 62.5% 41.7% 79.2% 75.0% 87.5% 64.6% 66.7%
2nd place teams R HR RBI SB Avg W Sv K ERA WHIP
Average score 9.8 9.0 9.9 8.3 8.2 9.5 9.8 9.9 9.6 9.1
Std Dev 2.0 2.6 2.0 3.0 3.2 1.9 2.3 1.9 2.4 2.6
% in top 3 58.3% 52.1% 68.8% 41.7% 43.8% 60.4% 68.8% 66.7% 62.5% 56.3%
3rd place teams R HR RBI SB Avg W Sv K ERA WHIP
Average score 9.0 8.4 9.1 8.5 7.6 8.9 8.9 9.1 8.1 7.8
Std Dev 2.5 3.1 2.3 2.8 3.2 2.5 2.6 2.1 2.8 2.7
% in top 3 54.2% 47.9% 54.2% 47.9% 33.3% 52.1% 50.0% 50.0% 39.6% 37.5%

A quick glance seems to suggest that the most important categories were Runs on the batting side, and Ks on the pitching side: the average score for the team that won their league was highest – by quite a margin, and also varied less – for those two categories. Winning teams were also more likely to be at least in the top 3 in Runs and Ks compared to any of the other batting and pitching categories, respectively.

Conversely, Batting Average did not appear to be that important – less than half of the teams that won their league were in the top 3 in Batting Average, and it had the lowest average score for champion teams of all the 5×5 categories. It was also the most volatile – with a standard deviation of 2.9, around 67% of teams that won their league would have had a Batting Average score ranging from 11.2 down to as low as 5.3!

What about second-place teams? Ks and Runs were important here as well, but without the gaps seen for winning teams. The highest-scoring category on the pitching side was again Ks, but at 9.9, this was only 0.1 higher than the second category (Saves). On the hitting side, RBIs had the highest average score at 9.9, with Runs at 9.8

There’s another way to look at the data – if you were the leader in, say, Home Runs, how likely is it that you won your league? Here’s another breakdown:

1st in category
R HR RBI SB Avg W Sv K ERA WHIP
Avg Finish 2.1 3.0 3.0 3.4 5.2 2.5 3.1 2.2 3.2 3.6
% in top 3 75.0% 58.3% 56.3% 50.0% 31.3% 60.4% 58.3% 75.0% 60.4% 54.2%
2nd in category
R HR RBI SB Avg  W Sv K ERA WHIP
Avg Finish 3.4 4.3 3.3 4.3 4.9 3.5 3.0 3.3 4.5 4.2
% in top 3 39.6% 35.4% 56.3% 31.3% 31.3% 43.8% 41.7% 43.8% 27.1% 35.4%
3rd in category
R HR RBI SB Avg  W Sv K ERA WHIP
Avg Finish 4.3 4.3 4.1 4.7 5.5 4.1 3.8 3.5 4.6 4.9
% in top 3 20.8% 31.3% 25.0% 22.9% 22.9% 31.3% 43.8% 35.4% 39.6% 29.2%

This table tells us, for example, that once again, teams that finished tops in Runs or K’s, had an average overall finish of 2.1 and 2.2, respectively: basically, they finished 1st or 2nd overall in their league, and fully 75% of teams that were first in Runs or K’s had a top-3 overall finish. (15 teams were first in both Runs and Ks – of those, 14 won the league; the lone exception came in third).

Conversely, teams that had the best Batting Average only finished 5th on average, and only 30% of teams with the best batting average were in the top 3.

I’m not showing the data here, but the reverse was also true: of the teams that were in the bottom half in the league in Runs, or in K’s, exactly none of them won the league. None. Only four teams (for both Runs and K’s) even managed a 2nd place overall finish!

On the flip side, there were 26 teams that were in the bottom half in Batting Average but 1st or 2nd overall, including 14 overall winners.

So the data appear to be telling us that we need to focus on Runs and Ks, and not worry quite as much about Batting Average. There may be some logic behind this: players scoring lots of runs are, perhaps, coming to bat more often, which means more opportunities for HRs, SBs and RBIs. Pitchers generating lots of Ks are perhaps more likely to be in position to pick up Wins and Saves and have better ratios.

While I don’t think anyone would recommend ignoring a category altogether – even Batting Average – I think the key takeaway is that in looking at roster construction, you might benefit by paying closer attention to Runs and K’s – for example, by letting those two categories be the tie-breaker if two players appear to be close in value.

Obviously, none of this is particularly new or revolutionary. And of course the usual caveats apply: 48 leagues from one particular year may or may not be a sufficient sample size to draw conclusions from. Results will almost certainly differ in some way or another for leagues with different settings (1 catcher leagues vs 2 catcher leagues, 5 outfielders & 1 util vs 3 OF and 2 util, etc). My knowledge (or lack thereof) of statistics and such could make the entire exercise completely worthless, etc.

But I, at least, found it interesting – that’s all that matters, really – and I am looking to incorporate this as I do my projections this year.

[1] 12-team, standard 5×5, 5 outfielders and one utility spot; max 180 games started for pitchers, and – at least according to Razzball – the Razzball leagues are supposed to be generally more competitive that more casual leagues.


7 Reasons Why the A’s Will Win the AL West in 2015

The A’s winning the West after a huge offseason makeover in 2015 might seem like an unlikely achievement, but here are seven reasons why this is not at all unachievable:

 

1. The New-Look Infield

In 2015 the Athletics will be throwing out a fresh face at each of the four starting infield positions. Here’s a quick look:

2014 2015
1B: Brandon Moss 1B: Ike Davis (Mets)
2B: Eric Sogard 2B: Ben Zobrist (Rays)
SS: Jed Lowrie SS: Marcus Semien (White Sox)
3B: Josh Donaldson 3B: Brett Lawrie (Blue Jays)

Especially from an Athletics fan’s perspective, the left side of this chart looks very nice. The names Moss and Donaldson are very important and dear to you; however, the right side of this chart is actually more productive overall. While Moss and Donaldson have the highest wOBA of the eight players at .351 and .339 respectively, Jed Lowrie and Eric Sogard have the two lowest at .300 and .262 respectively. This averages out to be a wOBA of .313. The Average wOBA for 2015’s infield is .320.

You might be thinking that Lawrie does not compare to Donaldson, and you could be right. The fact of the matter is that Lawrie is a downgrade from Donaldson, but not by all that much, meanwhile, Zobrist is a huge upgrade from Sogard at 2B. And even Sogard is an upgrade from Punto as the UTIL infielder.

Other important categories that favor the 2015 infield are BB%, K%, FB%, Contact%, OPS, OBP, etc. Also, the new infield got quite a bit younger and faster.

The 2015 infield also has a higher average wRC+ at 104 in comparison to 2014’s 102.5. These aren’t huge differences, but the A’s are expecting better years from Lawrie, who was injured a lot in 2014, Davis, who hit 32 HR in 2012, and Semien, who hasn’t really had much of a chance in the majors yet. These moves were necessary, not only to save money, but because the 2014 team didn’t actually win the AL West. I’m now going to compare this new INF to a team that did win the West, the 2012 A’s.

The 2012 INF consisted of Josh Donaldson, Stephen Drew, Cliff Pennington and Brandon Moss. There were other guys in the mix earlier on in the season, i.e. Jemile Weeks, Brandon Inge, however, these were the guys that got it done down the home stretch.

2012 A’s INF WAR wOBA wRC+ 2015 A’s INF WAR wOBA wRC+
Brandon Moss 2.3 .402 160 Ike Davis 0.3 .324 108
Cliff Pennington 1.0 .263 65 Ben Zobrist 5.7 .333 119
Stephen Drew 0.0 .310 97 Marcus Semien 0.6 .301 88
Josh Donaldson 1.5 .300 90 Brett Lawrie 1.7 .320 101
2012 AVG 1.2 .319 103   2014 AVG 2.1 .320 104

These numbers are almost identical, however the 2015 team has a slight edge in every category. That is despite the fact that the A’s expect growth from the incoming players this season. Even after the significant losses of Josh Donaldson and Brandon Moss the A’s infield is more than capable of pushing them toward another Western division title.

 

2. The Designated Hitter

The Athletics’ DH numbers from 2014 are not where you want them to be. Yes, Melvin will still use this spot as a “half-rest” day for players like Crisp, Reddick and Lawrie, but the newcomer Billy Butler will most likely fill the spot the majority of the time. Butler is a huge upgrade from the A’s team DH numbers last season in which Callaspo, Moss, Norris, Jaso, Vogt, Dunn, among countless others had at bats. Let’s take a look at the 2014 A’s DH numbers vs. Billy Butler’s 2014 numbers. (he also had a down season):

Player WAR wOBA wRC+
2014 Team DH -1.3 .284 82
Billy Butler -0.3 .311 97

This chart shows that Butler is a significant upgrade at the DH spot, as he will bring a lot more production to the middle of this lineup. I should also bring up his career numbers, which are a wOBA of .351 and wRC+ of 117. If Butler can get back to his career form, the A’s offense is looking at a huge boost, but even if he doesn’t and repeats his 2014 performance, the DH spot is still getting a nice upgrade.

 

3. The Rotation

The starting rotation for the A’s no longer consists of Jon Lester, Jeff Samardzija or Jason Hammel, but it is still a very strong group with huge potential. I’m going to compare the projected 2015 group to the 2012 and 2013 rotations that led the A’s to division titles.

2012

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Tommy Milone 190 6.49 1.71 1.14 3.74 1.28 2.8
Jarrod Parker 181.1 6.95 3.13 0.55 3.47 1.26 3.5
Bartolo Colon 111 5.38 1.36 1.00 3.43 1.21 2.4
Brandon McCarthy 82.1 5.92 1.95 0.81 3.24 1.25 1.8
A.J. Griffin 79.1 7.00 2.08 1.09 3.06 1.13 1.4
Team Average  / 6.35

2.05

0.92 3.39 1.23

2.4

 

2013

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
A.J Griffin 200 7.70 2.43 1.62 3.83 1.13 1.5
Jarrod Parker 197 6.12 2.88 1.14 3.97 1.22 1.3
Bartolo Colon 190.1 5.53 1.37 0.66 2.65 1.17 3.9
Tommy Milone 153.1 7.10 2.29 1.41 4.17 1.29 1.3
Dan Straily 152.1 7.33 3.37 0.95 3.96 1.24 1.4
Team Average  / 6.76 2.47 1.16 3.72 1.21 1.9

 

Projected 2015 (2014 STATS)

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Sonny Gray 219 7.52 3.04 0.62 3.08 1.19 3.3
Scott Kazmir 190.1 7.75 2.36 0.76 3.55 1.16 3.3
Jesse Chavez 125.2 8.52 2.94 0.93 3.44 1.30 1.7
Jesse Hahn 70 8.36 3.73 0.51 2.96 1.13 0.8
Drew Pomeranz 52.1 8.6 3.44 0.86 2.58 1.13 0.7
Team Average  /

8.15

3.10

0.74

3.12

1.18

2.0

As you can see, the 2015 rotation wins four out of the six categories. They won the majority of the categories already, but this 2015 staff has the potential to be better than these numbers show. In past years, the A’s success had a lot to do with their strong pitching staff — this is a big reason why I believe they will win the west in 2015 — however, we need to take a look at the projected rotations of the four other teams in the division to see how the A’s compare to each of them.

Here are the five teams’ projected rotations for 2015:

 

Angels

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Jered Weaver 213.1 7.13 2.74 1.14 3.59 1.21 1.5
C.J. Wilson 175.2 7.74 4.35 0.87 4.51 1.45 0.6
Garrett Richards 168.2 8.75 2.72 0.27 2.61 1.04 4.3
Matt Shoemaker 121.1 8.16 1.56 0.67 2.89 1.07 2.6
Andrew Heaney 24.2 5.84 2.55 2.19 6.93 1.50 -0.4
Team Average  / 7.52 2.78 1.03 4.11 1.25 1.7

 

Mariners

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Felix Hernandez 236 9.46 1.75 0.61 2.14 0.92 6.2
Hisashi Iwakuma 179 7.74 1.06 1.01 3.52 1.05 3.2
Roenis Elias 163.2 7.86 3.52 0.88 3.85 1.31 1.4
J.A. Happ 153 7.53 2.71 1.24 4.12 1.31 1.5
James Paxton 74 7.18 3.53 0.36 3.04 1.2 1.3
Team Average  / 7.95 2.51 0.82 3.33

1.16

2.7

 

Rangers

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Colby Lewis 170.1 7.03 2.54 1.32 5.18 1.52 1.6
Yu Darvish 144.1 11.35 3.06 0.81 3.06 1.26 4.1
Nick Tepesch 125.2 4.01 3.15 1.07 4.30 1.34 0.4
Derek Holland 34.1 6.29 1.05 0 1.31 1.02 1.3
Ross Detwiler   /   /   /   /   /   /   /
Team Average   / 7.17

2.45

.8 3.46 1.29 1.85

 

Astros

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Colin McHugh 154.2 9.14 2.39 0.76 2.73 1.02 3.3
Dallas Keuchel 200 6.57 2.16 0.50 2.93 1.18 3.9
Scott Feldman 180.1 5.34 2.50 0.80 3.74 1.30 1.6
Brett Oberholtzer 143.2 5.89 1.75 0.75 4.39 1.38 2.4
Brad Peacock 122 7.97 4.57 1.48 4.50 1.52 -0.1
Team Average   / 6.98 2.67 0.86 3.59 1.28 2.2

 

Athletics

Player IP K/9 BB/9 HR/9 ERA WHIP WAR
Sonny Gray 219 7.52 3.04 0.62 3.08 1.19 3.3
Scott Kazmir 190.1 7.75 2.36 0.76 3.55 1.16 3.3
Jesse Chavez 125.2 8.52 2.94 0.93 3.44 1.30 1.7
Jesse Hahn 70 8.36 3.73 0.51 2.96 1.13 0.8
Drew Pomeranz 52.1 8.6 3.44 0.86 2.58 1.13 0.7
Team Average   /

8.15

3.10

0.74

3.12

1.18 2.0

The Mariners and the Athletics both have really solid pitching staffs. The Mariners have arguably the best pitcher in the American League in Felix Hernandez. The Angels also have a good young ace in Garrett Richards, but he is coming off an injury; it will be interesting to see how he bounces back. Sonny Gray proved that he is a true ace last season, going over 200 innings and pitching extremely well in big games. The numbers do give the A’s a slight edge; they won three of the six categories and the Mariners won two of them. King Felix, Iwakuma and the solid supporting cast are hard to bet against, but 1-5, the A’s have a better staff according to last year’s numbers.

 

4. Speedee Oil Change

Anytime manager Bob Melvin calls on the bullpen, the A’s should be confident. There are so many capable arms out there that it’s really not fair. Honestly, a starter could go four innings with a lead and that would be enough for this bullpen with Otero, Abad, Cook, O’Flaherty, Clippard and Doolittle in the mix. There are plenty of other options as well that might not get a shot because it’s already crowded with talent out there. The starters, however, are very capable of giving you six or seven innings consistently, which makes this bullpen even that much more deadly, allowing Melvin to create left-on-left matchups or vice versa. The fact of the matter is, if you can’t score, you can’t win. While the starting staff is very solid, getting to the bullpen might not be the opponent’s best option when facing the A’s. Another positive for the A’s has been their ability to fight their way back into ballgames the last few years. With a bullpen like this who can keep the deficit where it is, the probability of achieving a comeback is that much greater.

As shown by the Royals on the successful end and the Dodgers on the opposite end, the strength of your bullpen can make or break your season.

Let’s compare the A’s bullpen to the other teams in the division by highlighting the projected top six bullpen arms for each team:

 

Angels

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Joe Smith 74.2 8.20 1.81 0.48 1.81 0.80 18 15
Huston Street 59.1 8.65 2.12 0.61 1.37 0.94 0 41
Mike Morin 59 8.24 2.90 0.46 2.90 1.19 9 0
Fernando Salas 58.2 9.36 2.15 0.77 3.38 1.09 8 0
Cory Rasmus 37.0 9.24 2.92 0.73 2.68 1.16 0 0
Vinnie Pestano 18.2 12.54 2.41 1.45 2.89 1.23 1 0
Team Average  / 9.37 2.39 0.75 2.51 1.07  /  /

 

Mariners

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Tom Wilhelmsen 75.1 8.12 2.7 0.72 2.03 1.00 8 1
Danny Farquhar 71 10.27 2.79 0.63 2.66 1.13 13 1
Dominic Leone 66.1 9.50 3.39 0.54 2.17 1.16 7 0
Fernando Rodney 66.1 10.31 3.80 0.41 2.85 1.34 0 48
Yoervis Medina 57 9.47 4.42 0.47 2.68 1.33 21 0
Charlie Furbush 42.1 10.84 1.91 0.85 3.61 1.16 20 1
Team Average  /

9.75

3.17

0.60

2.67 1.19  /  /

 

Rangers

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Robbie Ross 78.1 5.86 3.45 1.03 6.20 1.70 2 0
Shawn Tolleson 71.2 8.67 3.52 1.26 2.67 1.17 7 0
Roman Mendez 33 6.00 4.64 0.55 2.18 1.12 10 0
Neftali Feliz 31.2 5.97 3.13 1.42 1.99 0.98 0 13
Tanner Scheppers 23.0 6.65 3.91 2.35 9.00 1.78 1 0
Phil Klein 19 10.89 4.74 1.42 2.84 1.11 0 0
Team Average  / 7.34 3.90 1.34 4.15 1.31  /  /

 

Astros

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Luke Gregerson 72.1 7.34 1.87 0.75 2.12 1.01 22 3
Pat Neshek 67.1 9.09 1.2 0.53 1.87 0.79 25 6
Josh Fields 54.2 11.52 2.80 0.33 4.45 1.23 8 4
Chad Qualls 51.1 7.54 0.88 0.88 3.33 1.15 2 19
Tony Sipp 50.2 11.19 3.02 0.89 3.38 0.89 11 4
Jake Buchanan 35.1 5.09 3.06 1.02 4.58 1.50 0 0
Team Average   / 8.63

2.14

0.73 3.29 1.10  /  /

 

Athletics

Player IP K/9 BB/9 HR/9 ERA WHIP HLD SV
Dan Otero 86.2 4.67 1.56 0.42 2.28 1.10 12 1
Tyler Clippard 70.1 10.49 2.94 0.64 2.18 1.00 40 1
Sean Doolittle 62.2 12.78 1.15 0.72 2.73 0.73 5 22
Fernando Abad 57.1 8.01 2.35 0.63 1.57 0.85 9 0
Ryan Cook 50 9.00 3.96 0.54 3.42 1.08 7 1
Eric O’Flaherty 20 6.75 1.80 1.35 2.25 0.95 3 1
Team Average   / 8.62 2.29 0.72

2.41

0.95

 /  /

The Mariners and Athletics each won two out of the five categories. The Athletics also came in second in two other categories. Although this chart shows the Mariners and the A’s as pretty evenly matched, the Mariners have a lot of aging players in their pen, so we cannot be sure if they will keep up the good numbers. The Astros got a lot better by adding Luke Gregerson and Pat Neshek, but that still wasn’t enough to make them the best in the division, especially after the A’s went out and traded for the two time All-Star, Tyler Clippard. All of these teams except Texas have a very strong bullpen, so trying to come back from a deficit is going to be a tough feat in this division.

The A’s also have a lot of other options past these six players, probably more so than the other four teams, making injuries less of a factor for them.

 

5. Coco Crisp

When Coco Crisp is at the top of the lineup, the A’s are a better team. Over the past three seasons there’s no player who has had as much of an overall impact on this team than Coco. Whether it’s at the plate, in the field or in the clubhouse, Crisp’s impact is significant. Despite losing a lot of star players, the A’s will not take a step backward because they still have their most important piece in Crisp. If Crisp would have been traded away this offseason, I don’t believe the A’s would be ready to compete for the AL West title in 2015. There would be too long of an adjustment period, someone else would need to step up big time and fill his shoes. Luckily, the A’s don’t have to worry about that yet. Bottom line: the A’s need Coco Crisp.

 

6. Depth and Versatility

Having a deep roster is always important in a 162 game season. You will have players go on the DL, it is unavoidable. Being able to replace the injured players with capable major leaguers is key to a team’s success in the long run. Billy Beane has constructed a 40-man roster with tremendous depth, especially with pitching. The A’s have eight or nine guys capable of making the starting rotation, not to mention two others (Jarrod Parker and A.J. Griffin) due back this summer. There are upwards of ten players competing for a spot in the bullpen as well. It will be interesting to see who makes it on to the 25-man roster, but I wouldn’t be surprised if Triple-A Nashville has a stacked opening day roster. Having great options in the minor leagues is key for any team, and the A’s will definitely have that this season with Kendall Graveman, Chris Bassitt, Sean Nolin and Brad Mills, four starters likely to be starting in Triple-A. Also, RJ Alvarez, Eury De La Rosa and Evan Scriber, three above-average bullpen arms will likely be starting down there as well.

The A’s lineup is a very versatile group this season. Eric Sogard, A’s second baseman the last few seasons, has moved into a utility INF role; he plays excellent defense, and for a defensive replacement, he can handle the stick pretty well. Ben Zobrist is known for his ability to play all over the diamond with above-average defense, and also for getting the job done from both sides of the plate; his career wOBA is .344. Craig Gentry and Sam Fuld can play all three outfield positions with ease while providing speed off the bench in pinch running situations. Marcus Semien will likely be the everyday SS, but he can play all over the infield as well. Stephen Vogt will mostly catch, but he can play first base and corner outfield if the A’s need him to. The amount of options the A’s have, if injuries do occur, are limitless. It will be entertaining to see how Bob Melvin constructs his lineup card every day.

 

7. The Manager

Bob Melvin is the perfect manager for a team of misfits and players who have never played together previously. He will bring this group to play for each other, as a unit, one day at a time. Melvin is great at creating matchups that benefit the team and give them the best chance to succeed. The roster that has been assembled this season is perfect for just that. It is loaded with skilled, versatile players. Bob Melvin has done it before and he will do it again.


Which Center Fielders Made the Plays that Mattered Most?

Jeff Zimmerman posted an interesting article on Friday. It prompted me to try to analyze the relationship between (i) an outfielder’s ability to make plays, and (ii) an outfielder’s ability to save runs. From my analysis below, the relationship is not as hand-in-glove as I initially would have thought.

From what I understood about Jeff’s article, he advanced a new defensive metric called “PMR,” which stands for Plays Made Ratio. Jeff calculated this ratio using data from Inside Edge, which categorizes every ball in play into one of six buckets. Jeff explains:

Most of the fielding data falls into two categories. The zero percentage plays are just that, impossible plays, and make up 23.2% of all the balls in play. Balls in this bucket are never caught and always have a 0% value. The other major range is the Routine Plays or the 90% to 100% bin. Defenders make outs on 97.9% of these plays, which make up 64.0% of all the plays in the field; the 2.1% which aren’t made are mostly errors. In total, 87.2% of all plays are graded out as either automatic hits or outs; it is the final ~13% which really determine if a defender is above or below average.

Between almost always and never, four categories remain. Even though each category has a defined range, like 40% to 60%, the average amount of plays made is not exactly in the middle of each range. Here are the actual percentage of plays made in each of the four ranges.

Range

Actual Percentage

1% to 10%

6%

10% to 40%

29%

40% to 60%

58%

60% to 90%

81%

With these league average values and each individual player’s values, a ratio of number of plays made compared to the league average value can be calculated. To have the same output of stats like FIP- and wRC+, I put Plays Made Ratio on a 100 scale where a value like 125 is 25% better than the league average. Here is the long form formula and Jason Heyward’s value determined for an example.

Plays Made Ratio = ((Plays made from 1% to 90%)/((1% to 10% chances * .063%)+( 10% to 40% chances * .289)+ (40% to 60% chances * .576) + (60% to 90% chances * .805))) * 100

Heyward’s Plays Made Ratio = ((1+10+9+26)/((14*.063)+(16*.289)+(9*.576)+(27*.805)))*100

Heyward’s Plays Made Ratio = (46/32.4)*100

Heyward’s Plays Made Ratio = 142

Heyward had a heck of a season. Of the 66 playable balls hit to him, normally only 32 of them would have been caught for an out. Heyward was able to get to 46 of them, or 42% better than the league average. He has consistently had above league average values with a 133 value in 2012 and 125 in 2013.

Jeff posits that the new PFM metric gives us new insight that FanGraphs current go-to defensive metric (Ultimate Zone Rating) does not:

Now remember this stat [PMR] only looks at how often a fielder would have made the play considering their position on the field. The team could be playing its outfielders back to prevent a double or their infielders in for a bunt which could put the defender out of position. Additionally, it doesn’t look at the final results of the play (at least for now). If Sir Dive Alot is playing in the outfield and he loves to try to catch every ball hit his way, then he will get to a few extra flyballs by diving all the time, but those he doesn’t get to will pass him by for more doubles and triples. Also, an outfielder could be good at making plays while coming in versus going deep; balls which fall in over his head would be more damaging than those which fall for shallow singles. While his Plays Made Ratio may be high, the number of runs he saves, as seen by UZR or Defensive Runs Saved, may be lower by comparison.

This got me thinking about the relationship between a player’s PMR and his UZR, and, more specifically, his RngR. As I understand RngR, it is the component of UZR that estimates the number of runs a player saves, or surrenders, due to his range. RngR isolates the contribution a player’s range makes to his Ultimate Zone Rating by ignoring the contributions from his arm and his ability to limit errors.

Intuitively, it would make sense that a player’s PMR and his RngR would be strongly correlated. In other words, a player whose range allows him to make more plays than average would also be the same type of player whose range would allow him to save more runs than average. A simple two-by-two matrix, with RngR along the left side and PMR along the top would show the following quadrants:

Below Average PMR Above Average PMR
Above Average RngR (1) Poor range/saves runs(?) (2) Good range/saves runs
Below Average RngR (3) Poor range/surrenders runs (4) Good range/surrenders runs(?)

My intuition is that players would fall in either quadrant (2) or quadrant (3). The interesting questions arise with players that would fall in quadrant (1) (those who exhibit poor range, but whose range saves runs), and in quadrant (4) (those who exhibit good range, but whose range does not save runs). There are several explanations for why a player may fall into quadrant (1) or (4).

Jeff noted three possible explanations.  First, a player may be overly aggressive, which would may lead to more outs (a higher PMR) but also more misplays resulting in doubles and triples (a lower RngR). Second, “an outfielder could be good at making plays while coming in versus going deep; balls which fall in over his head would be more damaging than those which fall for shallow singles. While his Plays Made Ratio may be high, the number of runs he saves, as seen by UZR or Defensive Runs Saved, may be lower by comparison.” Third, a player (or his team) may be particularly well adept at positioning himself, which would amplify his RngR rating, but not necessarily his PMR (as Jeff noted when discussing Nick Markakis).

How does the relationship between PMR and RngR look if it is applied to actual players? To find out, I looked at all center fielders who between 2012 and 2014 had at least 70 “total chances” (defined by Inside Edge as balls hit to that fielder where there is between a 1% and 90% likelihood that the ball is caught). That provided me a list of 18 center fielders. Next, I calculated each player’s rate-based RngR/150 (calculated by his total RngR divided by the innings he played in center field, multiplied by nine, multiplied by 150). That revealed the following table:

Name PMR RngR/150
Jacoby Ellsbury 128 11.5
Lorenzo Cain 127 19.5
Mike Trout 126 3.9
Michael Bourn 122 4.4
Ben Revere 122 -3.0
Andrew McCutchen 120 -1.5
Denard Span 116 4.0
Carlos Gomez 114 11.2
Dexter Fowler 114 -12.0
Juan Lagares 108 18.7
Coco Crisp 106 -2.3
Jon Jay 105 3.2
Adam Jones 90 -5.7
Leonys Martin 89 0.6
Austin Jackson 88 -1.2
Colby Rasmus 87 2.7
Angel Pagan 87 -2.4
B.J. Upton 80 -0.6

A scatter chart of this information looks like this. I also added a best-fit line to the scatter plot. My intuition that a player’s RngR/150 would be strongly correlated with his PMR is contradicted by this data. In fact, according to this data, (and based on my very limited skillset at statistical analysis, which may be completely incorrect), only 15% of the runs saved due to these 18 center fielders’ range can be explained by their Plays Made Ratio.

Even more interesting than the two-by-two matrix characterization introduced above, are the points on the scatter plot that are either way above (Juan Lagares and Lorenzo Cain) and way below (Dexter Fowler) the linear trendline.

The data suggest that Lagares/Cain and Fowler have similar range in center field, but that the former use their range to save more runs than the latter. One possible implication of this information is that Fowler is not optimizing his ability and that through better decision-making (such as being more aggressive or less aggressive on fly balls) or better positioning he could save more runs. As discussed earlier, it could also mean that Fowler is not (relatively) adept at playing balls hit over his head or in the gap, which leads to more doubles and triples.

On a larger scale, a possible implication of this data is that teams could significantly improve the amount of runs their center fielders save by (i) coaching their center fielders to make optimal decisions regarding their aggressiveness and (ii) properly positioning their center fielders. I would be curious to analyze what is the optimal amount of aggression a center fielder would have in going after balls hit to the outfield, the optimal way to position himself. For example, is it better to play shallow and be aggressive in cutting off singles (which Lagares has a reputation of doing) or to play deep? Those questions are best answered in a follow-up post/article.


The Future is Bright, But Will the A’s Compete in 2015?

The Oakland Athletics may have finally completed their roster turnover on Wednesday with their most recent deal sending Yunel Escobar to Washington for RP Tyler Clippard. However, you can never know if Billy Beane is finished making moves. With that being said, I’d like to break down the roster from last year to this year and assess whether or not the team will actually regress in 2015. The fact is that the Athletics got quite a bit younger this offseason and acquired many players with a lot of team control remaining. The distant future appears brighter now than it did prior to this offseason, but the main question is, will the Athletics be able to compete in 2015 as well as they would have prior to the roster turnover? Lets take a look at the numbers:

STARTING LINEUP

I will start by comparing the most common nine players in the A’s lineup last year to their projected starting nine this year, using WAR and wRC+:

[All stats give on the chart will represent the 2014 season in the MLB only. In further commentary I may bring up career numbers or minor league numbers for some players.]

2014 WAR wRC+ 2015 WAR wRC+
C – Derek Norris 2.5 122 C – Stephen Vogt 1.3 114
1B – Brandon Moss 2.3 121 1B – Ike Davis 0.3 108
2B – Eric Sogard 0.3 67 2B – Ben Zobrist 5.7 119
3B – Josh Donaldson 6.4 129 3B – Brett Lawrie 1.7 101
SS – Jed Lowrie 1.8 93 SS – Marcus Semien 0.6 88
LF – Yoenis Cespedes 3.4 109 LF – Sam Fuld 2.8 90
CF – Coco Crisp 0.9 103 CF – Coco Crisp 0.9 103
RF – Josh Reddick 2.3 117 RF – Josh Reddick 2.3 117
DH – Alberto Callaspo -1.1 68 DH – Billy Butler -0.3 97

2014 AVG WAR = 2.1 / Total wRC+ = 929

2015 AVG WAR = 1.7 / Total wRC+ = 937

As shocking as it may seem, this displays that the A’s should in fact score more runs with their lineup in 2015 than they did with Donaldson, Moss and Cespedes in the heart of their lineup last season. Although, this chart only accounts for 2014 stats, in which Billy Butler (among others) had an off year. If the A’s can get him back to, or even near his 2012 form, in which his WAR was 2.9 and his wRC+ was 139, they could be in for a significant upgrade on offense as a whole. One of the reasons why this lineup has the potential to be more successful even after losing a guy like Donaldson is because of the acquisition of Ben Zobrist. While Brett Lawrie is -4.7 to Donaldson in WAR and -28 to Donaldson in wRC+, Zobrist is +5.4 to Sogard in WAR and +52 to Sogard in wRC+, more than making up for the loss of Donaldson. While the A’s did use a lot of other DH besides Callaspo in 2014, he totaled the greatest amount of plate appearances from that spot, which might lower the 2014 numbers a little.

The average WAR is down slightly from last season, but with Stephen Vogt behind the plate and Marcus Semien most likely getting the every day job at SS, the A’s feel they are upgrading defensively. Semien’s numbers represent his slim 255 plate appearances in the majors last season, but in TripleA his wRC+ was 142. You cannot expect that out of Semien at the major league level, but it shows that he has potential to improve in 2015. The A’s did use a lot of players at each position last season and they will again in 2015; that is why it is important to also take a look at the bench players from last year and the projected bench for this year.

BENCH

While the 25-man roster is not set in stone for 2015 just yet, here is last year’s most commonly used bench players versus next year’s projected bench.

2014 WAR wRC+ 2015 WAR wRC+
Nick Punto 0.2 73 Craig Gentry 1.4 77
Craig Gentry 1.4 77 Josh Phegley 0.2 92 – 132(AAA)
John Jaso 1.5 121 Eric Sogard 0.3 67
Sam Fuld 1.3 73 Mark Canha N/A 131(AAA)

2014 AVG WAR = 1.1 / TOTAL wRC+ = 344

2015 AVG WAR = .48 / TOTAL wRC+ = 367(407)

While these numbers are a bit skewed due to the fact that Canha has not yet reached the majors and also because Jaso was actually a starter while he was healthy, they do give a good idea of what to expect in 2015. Sogard takes over for Punto as the reserve infielder. Fuld and Gentry will most likely platoon in LF, same goes for Vogt and Phegley at C. Since Fuld and Vogt are LH, they will see more time in the starting lineup, leaving Gentry and Phegley on the list of bench players for 2015. Gentry and Phegley will see most their time against lefties, which will likely help their overall numbers. The A’s always do a great job shifting their lineup to create the match ups they want, expect more of the same with platoons and late pinch hitting in 2015.

STARTING ROTATION

The starting rotation is an area where a lot of people say they A’s have question marks. This may be due to the fact that they lost Jon Lester and Jason Hammel to free agency and traded away Jeff Samardzija to the White Sox earlier this off season. However, the A’s held the best record in baseball for months in 2014 with a rotation featuring Sonny Gray, Scott Kazmir, Jesse Chavez, Drew Pomeranz and Tommy Milone. Four of those guys will be returning in 2015, with a slew of other young arms fighting for a spot in the rotation. Anyone from Chris Bassitt, Jesse Hahn, Sean Nolin or Kendall Graveman would be an upgrade or at worst an equal replacement of Milone. Let’s take a look at the numbers for the five players who started the most games for the Athletics last season VS the A’s projected rotation for next season using ERA, WHIP and WAR from the 2014 season:

2014 ERA WHIP WAR 2015 ERA WHIP WAR
Sonny Gray 3.08 1.19 3.3 Sonny Gray 3.08 1.19 3.3
Scott Kazmir 3.55 1.16 3.3 Scott Kazmir 3.55 1.16 3.3
Jesse Chavez 3.44 1.30 1.7 Jesse Hahn 2.96 1.13 0.8
Jeff Samardzija 2.99 1.07 4.1 Jesse Chavez 3.44 1.30 1.7
Tommy Milone 4.23 1.40 0.4 Drew Pomeranz 2.58 1.13 0.7

2014 AVG: ERA = 3.46 / WHIP = 1.22 / Avg WAR = 2.56

2015 AVG: ERA = 3.12 / WHIP = 1.18 / WAR = 1.96

Keep in mind that ERA and WHIP are better when they are lower and WAR is better if it is higher. While this list does not consist of Jon Lester, the A’s were at their best when they still had Chavez and Milone in their rotation. Also, it was a small sample size for Pomeranz, so we cannot expect numbers quite that solid again in 2015. However, with all that being said, the A’s, despite losing All-Stars, should not take more than a tiny step back in 2015. This rotation is still very solid and is in fact younger this year than last. Not only that, the A’s now have a lot more depth with three other pitchers not on this list that could fill a rotation spot, Chris Bassit, Sean Nolin and Kendall Graveman. Also, we cannot forget about the Tommy John rehabbers Jarrod Parker and AJ Griffin, who could make their way back into this rotation before the All-Star break. Both Parker and Griffin were huge contributors to the A’s success in both 2012 and 2013.

BULLPEN

There are a lot of similar faces coming back to the Athletics’ bullpen in 2015. So, instead of continuing with the format I’ve used for position players and the starting rotation I’m quickly going to compare Luke Gregerson and Tyler Clippard, the one main difference in the bullpen for 2015.

Player ERA / WHIP / WAR

Luke Gregerson 2.12 / 1.01 / 0.9

Tyler Clippard 2.18 / 1.00 / 1.5

These numbers are very similar, making Clippard a perfect replacement for Gregerson, taking over the 8th inning duties in front of incumbent closer Sean Doolittle. I don’t think many people expected the A’s to make a move to acquire another back end of the bullpen piece. Even after losing Gregerson, they seemed to have a very solid bullpen, but now it is even more solidified with a proven set-up man in Tyler Clippard. Another important thing to note about Clippard is his ability to create fly balls. His FB% in 2014 was 49.4% also, his IFFB% was 19.3% and that will likely increase mightily with him now pitching in Oakland. He is the perfect pitcher for the o.Co Coliseum. The A’s will pay Clippard more than they would have paid Escobar in 2015, but they are saving money in the long run due to the fact the Escobar is owed 14 million over the next two seasons and Clippard becomes a free agent after this season (in which he will make around 9 million).

Now let’s take a look at 12 potential options for the Athletics bullpen in 2015. Some of them are locks, but the others will either gain a spot due to the fact that they did not make it into the rotation or if they have a solid showing in spring training.

Name Team (2014) IP ERA WHIP WAR
Sean Doolittle Athletics 62.2 2.73 0.73 2.4
Tyler Clippard Nationals 70.1 2.18 1 1.5
Dan Otero Athletics 86.2 2.28 1.1 0.7
Chris Bassitt White Sox 29.2 3.94 1.58 0.7
Fernando Abad Athletics 57.1 1.57 0.85 0.6
Ryan Cook Athletics 50 3.42 1.08 0.3
Eury De la Rosa Diamondbacks 36.2 2.95 1.39 0.2
R.J. Alvarez Padres 8 1.13 1 0
Kendall Graveman Blue Jays (AAA) 38.1 1.88 1.02 N/A
Sean Nolin Blue Jays (AAA) 87.1 3.5 1.25 N/A
Eric O’Flaherty Athletics 20 2.25 0.95 -0.1
Evan Scribner Athletics 11.2 4.63 0.94 -0.2

There are a lot of very solid options for the A’s bullpen in 2015. I’d expect to see, Doolittle, Clippard, O’Flaherty, Cook, Otero and Abad for sure, but I expect all of these guys to make an impact at some point, if not this season then in 2016.

TAKEAWAY

The Athletics have a very deep pitching staff. With Sonny Gray and Scott Kazmir headlining the rotation, they have a plethora of options to fill the remaining three spots. Pomeranz, Hahn and Chavez look to be the leading candidates, although Billy Beane himself has mentioned Kendall Graveman as someone he sees making the rotation out of spring training. The A’s also have a very strong bullpen, especially after the recent acquisition of All-Star set-up man Tyler Clippard. After losing Josh Donaldson, Brandon Moss, Yoenis Cespedes and Derek Norris (four All-Stars), the A’s lineup for 2015, according to wRC+ actually got better. It’s not always the big name All-Stars that make a team successful. Oakland has proven this many times in the past, most recently in 2012, right after an offseason makeover similar to this year’s. The one piece that has remained since before the 2012 makeover and after this 2015 makeover, is Coco Crisp. There cannot be enough said about the value of Crisp to the A’s organization. With Crisp healthy in CF and the newly acquired pieces filling in around him, I expect the A’s to be back competing for another American League West division title in 2015.