One frequent complaint of baseball fans attempting to quantify the performance of relievers is the lack of a metric that provides an adequate representation of how a reliever impacts the game. ERA is faulty for players who frequently enter with runners on base, and other stats like inherited runners scored and WHIP tend to give only a partial picture. The save is the least helpful indicator, as it does not differentiate between a save with a 3-run lead and a much more difficult 4 or 5 out save in a one run contest. Attempting to remedy this situation, Tom Tango and Fangraphs have created a new metric for evaluating relievers, based on WPA. For those who are not familiar with WPA, here is a quick primer:

WE (win expectancy): The percent chance a particular team will win based on the score, inning, outs, runners on base, and the run environment.

WPA (win probability added): WPA is the difference in win expectancy (WE) between the start of the play and the end of the play. That difference is then credited/debited to the batter and the pitcher. Over the course of the season, each players’ WPA for individual plays is added up to get his season total WPA.

Using WPA, they have devised a way to credit relievers for particularly good performances, called shutdowns, and give demerits for poor performances, called meltdowns:

A Shutdown is when a reliever accumulates greater than or equal to 0.06 WPA in any individual game.

A Meltdown is when a reliever’s WPA is less than or equal to -0.06 in any individual game.

This is simply a more precise way to evaluate the impact the reliever had on the game, and considers the context of the situation, such that 2 runs allowed in a 12-2 game are weighed differently than the same 2 runs in a 4-3 game. In the comments, Tango notes that the ratio is 1.6 Shutdowns for every Meltdown, and that a poor reliever would have a ratio of 1 to 1. Let’s take a look at the Yankees bullpen and how it has performed in this area thus far:

The last two columns represent Shutdowns (SD) and Meltdowns (MD). It all seems fairly intuitive to me. Alfredo Aceves has been used in a number of tight spots, and his ability to eat 2-3 innings in an outing contributes to his good showing. Mariano and Joba have also been excellent, with the two of them combining for just one poor outing. Meanwhile, Boone Logan has been neither great nor poor, while Chan Ho Park and David Robertson have shown wild inconsistency. Finally, Damaso Marte has been atrocious, allowing a number of inherited runners to score. The metric seems to fit with the observed performances, and gives a quick and dirty way to measure impact on the game. I hope it catches on.

From the NY Post:

Reggie Jackson’s belief that Robinson Cano has passed Dustin Pedroia as the premier second baseman in the American League isn’t simply Mr. October’s bias because he works for the Yankees.

“After this season he will be the best second baseman in the American League and then chase Chase [Utley],” Jackson told The Post. “He is a better player than Pedroia and I think Pedroia is a great player, an MVP.”

Jackson has company from the fraternity that scouts everything from tools to makeup.

The Post contacted six scouts and asked them who was better. Three clearly favored the sizzling Cano, another said it was close but went with Cano and while the fourth picked Pedroia, he admitted Cano was the better hitter. The sixth said Cano had better skills but Pedroia’s all-out effort every game made it a push.

I started this post to dispel the notion that Robbie is better, as I was certain that Pedroia has been the better player since he entered the league. However, I always try and go where the data takes me, and now I am not certain that a definitive evaluation can be made on this question. Here are the WAR numbers from baseballprojection.com, with Cano first and then Pedroia:

Using Fangraphs WAR gives a slightly different picture in 2009, with Pedroia edging Cano, but the general point holds true. Cano was better in 2007, they were about equal in 2009, and Pedroia was vastly superior in 2008. Cano also had solid seasons in 2005 and 2006 while Dustin was toiling in the minor leagues. Another important variable is the home road splits:

Cano Home: .307/.336/.485 wOBA: .350
Cano Road: .310/.345/.485 wOBA: .352

Pedroia Home: .326/.384/.501 wOBA: .382
Pedroia Road: .288/.354/.420 wOBA: .341

Cano does not have a noticeable split, while Pedroia clearly benefits greatly from playing in Fenway Park.

I think the choice between Cano and Pedroia hinges upon how heavily you weigh Pedroia’s lesser numbers on the road and Cano’s poor 2008. If you see 2008 as a fluke and consider the home-road splits to be vitally important to this analysis, then you will likely take Cano. If you believe that 2008 is indicative of Cano’s inconsistency and find that the splits are not that significant for a guy who is a pretty good player away from home, you will choose Pedroia. I’m torn on this, although if I was forced to make a choice, I would probably take Dustin. 2008 scares me a bit, and Pedroia is a slightly more well-rounded player. But a choice for Cano would be equally valid, and I’m sure I’ll vacillate on this one as their careers progress.

Who do you think is the better player? Why?

This is a question I have asked before, and Bill Simmons touched on some relevant answers in a recent mailbag:

We knew something shifted in baseball a few years ago; it’s definitely happening in basketball right now. Whether it transforms the other sports remains to be seen. I do think we could reach a ceiling with performance-related formulas some day soon — if we’re not getting there already — and complicated analysis will shift to less definable quantities like injury recovery and behavior. But that’s a few years away. As I mentioned at the conference, the big challenge for sabermetricians this decade will be learning how to educate a mainstream audience in a relatable and entertaining way. Easier said than done.

There are some quantifiable areas that have yet to be fully explored, with defensive metrics still waiting for technologies such as Hit f/x to help take them to the next, more accurate level. However, there are some elements of the game, particularly offensive production and pitching, where the innovation seems to be about building upon existing ideas and adding a higher degree of accuracy rather than reinventing the wheel.

Outside of defense, where might we see some revolutionary ideas? Simmons mentions behavior and health, and I think health in particular will become a new frontier for statistical analysts, as we try and predict injuries based on workloads, pitch and swing types, and other observable factors. Teams that can find some measure of predictability in terms of player durability will find themselves at a strong advantage when it comes to building an effective, consistent team. Injury projections represent a logical evolution of the “Moneyball” philosophy that encourages teams to exploit market inefficiencies.

Where do you think the sabermetric revolution will take us next?

Nick Swisher, by all measures, had an excellent 2009. After a terrible 2008 in Chicago that lead to him being traded for practically nothing, Swisher bounced back in a big way and helped the Yankees to their 27th championship. The question now is whether he can repeat his performance. On the surface, his numbers suggest that he is not due for a major regression:

Swisher’s numbers in 2009 are similar to those from 2006 and 2007, and a look at his Fangraphs page shows that most of his other indicators (such as batted ball data) have remained consistent and steady as well. However, two recent articles suggest that if you dig deeper, you might find some reasons to believe that Swish is due for at least a bit of a regression regarding both his walk rate and his power. First, Jeff Zimmerman of Beyond The Boxscore used swing data to compute plate discipline, and then extrapolated an expected walk rate for each player. Swisher’s estimated walk rate was 12.5%, while his actual walk rate was 16%. This suggests that he is likely due for a correction in his walk rate in 2010.

Another important element of Swisher’s game, his power, may also be facing a bit of a downturn. Mike Axisa explains:

“Just Enough” homers are those that cleared the fence by less than ten feet vertically or that landed past the wall by less than the fence height (so if it’s an eight foot wall, it landed no more than eight feet deep)…..
As you can imagine, Just Enough homers are the most volatile year-to-year because they’re so close to the fence. As Rybarczyk chronicled at ESPN’s TMI blog, players who’ve hit 30 total homers in a season with at least 40% of them qualifying as Just Enoughs have seen their homer totals fall 23% on average the next season. That’s a problem for Swisher and the Yankees, because he led the American League with 14 Just Enough homers, 48.3% of his total big flies.
This isn’t the first time Swisher has been in the Just Enough danger zone either. His 14 Just Enoughs were second in the league back in 2006, exactly 40% of the career-high 35 homers he hit for the A’s. What happened in 2007? Swish regressed back to just six Just Enoughs and 22 total homers, a 37.1% drop. This isn’t to say Swisher is guaranteed to see a drop off in his homerun – and thus overall offensive – production in 2010, but it’s not looking good.

Basically, Swisher’s knack for sneaking balls over the wall last year may have overinflated his home run totals to an unsustainable level. As such, it would not be surprising to see him back around 20-25 home runs, rather than increasing past 30 as he enters his prime.

Now, stating that Swisher will lose production in the walk and home run categories sounds like pretty bad news for a three true outcomes (HR, BB, K) type player. That said, neither study sees Swish losing enough in either category to sap him of his effectiveness, and you could make the argument that a player at his age is likely to improve. Furthermore, Swisher was terrible at home last year, which is something that is uncharacteristic for players in general and Swisher in particular. While he is unlikely to repeat his road performance, the room for improvement at home should overcompensate for any loss of effectiveness on the road. In all, I expect Swisher to be very similar in 2010 to what he was in 2009, but would not be surprised to see a modicum of regression in terms of walks and power.

This is a guest post from friend of the blog Jamal Granger. It is a meticulous piece of research and we are proud to be running it here at TYU.

Endless thanks to Eric Seidman of Baseball Prospectus, who devoted his valuable time to supplying with me with the essential data for this post, and introduced me to the wonders of SQL (though, as I begin to immerse myself, I question whether “thanks” is the appropriate term …).

The 1975 Cincinnati Reds were the topic of a recently published novel by celebrated sports journalist Joe Posnanski. In the book, titled The Machine: The Story of the 1975 Cincinnati Reds, Posnanski “… captures all of the passion and tension, drama and glory of this extraordinary team considered to be one of the greatest ever to take the field,” says Amazon.com; however, based on a recent discussion that Mike Francesa had with his listeners on his radio show – Mike’d Up – about the greatest infield-plus-catcher units in baseball history, I decided to take a statistical look at things and discovered how the ’75 Reds arguably boasted the greatest quintet of players to ever take the baseball diamond.

Using weighted Equivalent Average (EqA), total Equivalent Runs (EqR) and Rally’s Wins Above Replacement (WAR) data that dates as far back as 1969 for the former two, a likely indubitable argument can be made that Hall of Famers Johnny Bench, Joe Morgan, Tony Perez, and All-Stars Dave Concepcion and Pete Rose combined to not only lead their 20 teammates to a 108-win season and a World Series victory over the Boston Red Sox but, statistically, became the greatest infield-plus-catcher unit, or Diamond Unit,  in the past four decades.

While the aforementioned Reds squad may very well be the greatest Diamond Unit in the past forty years, arguments can be made for almost a handful of other teams. If you go by EqA, the 2009 Yankees are the best; EqR says that the 1974 Reds – with third basemen Dan Driessen replacing Pete Rose of the ’75 team – beats the bunch; Rally’s WAR has the ’75 version of The Big Red Machine as the alpha dog since 1969. While your opinions may vastly differ from mine, I say that the 1975 Reds are the top unit because WAR factors in all aspects of a player’s production – which is something that EqA and EqR do not.

By WAR, here is the leader board for the best Diamond Units since 1969:

The Year of the Green Wood Rabbit: The 1975 Cincinnati Reds – Morgan’s Magnificence

The 1975 Cincinnati Reds – led by a 12-win season by second basemen Joe Morgan – hit to the tune of a .305 EqA and 504.9 EqR, and produced a grand total of 29.4 WAR, a full three wins above the next closest quintet, the 1976 Reds. Morgan, posting career highs in batting average (.327), stolen bases (67, tied with his ’73 mark), on-base percentage (.466), weighted Runs Created (138.2) and wOBP (.463), was the near-unanimous winner for the first of consecutive NL MVP awards (Charlie Hustle stole two votes), and actually stole more bases (67) than he struck out (52). Also, not only did Morgan’s .360 EqA and 136.9 EqR pace the majors, the next closest qualifier (at least 300 plate appearances) for EqA was the Royals’ John Mayberry (.329).

Following Morgan’s stupefying campaign, Hall of Fame backstop Johnny Bench produced an astounding 6.5-win season, which, amazingly enough, is just the fourth-highest mark of his career. Bench, a MVP candidate in any other year (well, more on that later), did not produce any career-high marks but was part of a tremendous offensive trio of catchers that included Oakland’s Gene Tenace (.316 EqA and 107.4 EqR; why is he not in the Hall?) and St. Louis’s Ted Simmons (.311 and 106.4; another questionable HOF exclusion). Although Bench’s .308 EqA trailed both Tenace and Simmons for the lead amongst MLB catchers, he trailed only Joe Morgan for the team lead in what made a devastating two-three combo in Cincinnati’s lineup.

Pete Rose put up a .317/.406/.432 vital in 1975 and his 4.4-win season was just a stepping stone in a 12-year period from 1965-1976 that saw him produce at least four wins above replacement in every season but his 3.6-win campaign in 1970. Rose, known for his trademark hustle on the base paths, produce just two runs above replacement in that regard; and it makes you wonder: how much of that storied hustle actually helped his teams instead of just showing a lot of heart? Earning All-Star and Gold Glove (Total Zone had him as ten runs below replacement, but whatever) honors in 1975, Mr. Hustle was the lone National League player earn any first-place votes in the MVP race, as teammate Joe Morgan deservedly ran away with the title.

In terms of his non-offensive production, Dave Concepcion was a stalwart – his base running and defense made him produce to a level approaching that of a league-average player (17 RAR). However, Concepcion came to the plate 762 times in 1975, and as his .257 EqA and 64.5 EqR will tell you, he was a below-average hitter in every sense of the term. The beauty of analysis is that everything is relative, and in Concepcion’s case, he was among a group of shortstops (Larry Bowa of the Phillies; Bert Campaneris of the Athletics; Chris Speier of the Giants) that could lay claim to being the best offensive performers of that position in the non-Toby Harrah (.398 wOBA) division.

After enjoying a six-year stretch from 1968-1973 in which his WAR ranged from 4.2 to 6.7, Tony Perez’s 1975 campaign saw him deliver a 3.1-win campaign as the weakest link of the Machine’s Diamond Unit. Although this was in the midst of quite a prolonged decline phase, Perez’s 83.7 EqR and .288 EqA placed him in the top 33 percentile in an environment that saw the Royals’ Mayberry pace the field with a .329 EqA, 124.9 EqR and a robust .427 wOBA.

John Walsh of THT did an interesting study recently in which he looked at trends in OBP at the leadoff spot over time. What he found was a bit strange:

As the data shows, teams have been placing players with below average on-base skills in the leadoff spot for much of the last decade. This exhibits a failure to properly optimize the lineup, as it tends to result in fewer runners being on base for a club’s big hitters. Joe Pawlikowski at RAB touched on the issue of optimizing the top of the lineup yesterday in explaining why Nick Johnson should hit second:

To illustrate this point, let’s take an ideal scenario. Jeter and Johnson both hit in front of Teixeira for all of Teixeira’s plate appearances, and they OBP somewhere around their 2009 totals, .400 and .420. Running a quick percentage check, this means that Teixeira would come to bat with both runners on 16.8 percent of the time, and at least one runner on about 65 percent of the time. Given Teixeira’s 707 plate appearances from 2009, that means he’d come to bat with at least one runner on 460 times, and two runners on 119 times…..

Last year, with Jeter’s .400 OBP and Damon’s .365, Teixeira had a 14.6 percent chance of coming to the plate with both runners on, or 62 percent with at least one runner on…..If Granderson recovers to his 2008 form, he’s essentially a clone of Damon. While that’s good, and while he’ll be able to take extra bases that Johnson will not, I think that the added plate appearances give the Yankees a bigger advantage. It means more opportunities for Tex and A-Rod.

To sum up, Johnson batting second means more opportunities with runners on for Teixeira and Rodriguez. The Yankees need to keep this in mind and avoid the problem Walsh discusses in his study, whereby teams are placing fast players who do not reach base frequently in lineup slots ahead of their big boppers. Rather, they should stack as many high-OBP players in front of Tex and A-Rod as possible. In fact, Dave Pinto suggested that the Yankees should consider batting Johnson 9th as a second leadoff man. This would allow Johnson and Jeter to reach base for power hitters such as Granderson (who would hit second), Tex, and A-Rod. A similar option would be to put Nick Swisher or Granderson 9th and keeping Johnson at #2, which might be a good way to further optimize the lineup and provide as many opportunities as possible for the middle of the order hitters to bat with men on base.

How would you optimize the lineup?

The headline is an obvious statement, but I had yet to see an actual number put on the gap between starting and relieving until now. Tom Tango said the following:

The replacement level pitcher as a starter has a .380 win%. Move that starter to relief, and his win% goes up by about .09, or .470 win%. That’s it.

The average starter has a win% of .490 and the average reliever has a win% of .520 (more or less, and by win% I mean based on his pythag component ERA). As you can see, the average reliever is not that much better than the replacement-level pitcher as reliever. That’s why we say relievers are a dime a dozen. So, the average starter is +.11 wins per 9 IP and he uses up two-third of the innings. The average reliever is +.05 wins per 9 IP and he uses up one-third of the innings. If you follow along, the average starter gives you twice the value, per inning, as the reliever, and he gives you twice the innings. That sets the value of the average reliever of 25% of the average starter (1/2 times 1/2). This number goes up a little when you add in the leverage impact of relievers.

When people bring up Joba Chamberlain and suggest he belongs in the bullpen, I frequently explain that starters are significantly more valuable than relievers, such that it makes sense to give him every chance to succeed out of the rotation. Even if Joba is a top reliever and simply an average starter, his value is almost certainly going to be greater taking the ball every five days. Unless he tanks entirely in that role, the “bull in a china shop mentality” and all of that psycho-babble garbage that gets spewed to support moving him to the pen should be viewed as largely irrelevant. The job of the team is to extract as much value as possible from Joba, and having him in the rotation is the best way to do so.

Matt wrote an excellent post this morning about bringing statistics into the mainstream, and I think Chris began to follow through on that with his fascinating post on breaking down UZR. Both posts illustrated that fans now have more information at their hands than ever before, and that we can educate ourselves about the very essentials of the game. However, an interview that I heard this morning on WEEI, with Theo Epstein, reminded me that as fans, we still do not have all of the information:

I think that he (Ellsbury) is an above-average center fielder now, who is going to be a great center fielder. I know there is a certain number we don’t use that is accessible to people online that had him as one of the worst defensive center fielders in baseball last year. I don’t think it’s worth anything. I don’t think that number is legitimate. We do our own stuff and it showed that he is above average.

I think Theo is posturing here a bit, as every single method available to fans for statistically evaluating defense had Jacoby as a poor centerfielder in 2009. He is likely protecting his player and avoiding the talk radio firestorm that would ensue if he called Ellsbury a poor defender. That said, this did bring into focus the fact that clubs do have proprietary systems to determine player value, such that fans do not have equal information to that of the clubs. While proprietary does not necessarily mean better, these clubs have been attempting to hire those at the cutting edge of the industry, such that you would expect them to be at least slightly ahead of the field.

What does this mean? Put simply, it means that the numbers that we use as fans are imperfect, and should be utilized with that in mind. That does not mean that we should not use those numbers to craft our arguments, or that conclusions based on those numbers are faulty. Rather, when the numbers provide shades of grey, it is important to note that they are likely inexact and far from absolute. Furthermore, because the data is imperfect, subjective judgments and evaluations of players should have a place in the discussion. We can argue about how large that place should be, and I would say that it should be minor, but visual observation can occasionally pick up on nuance that is lost in the statistical breakdown.

I recently had the opportunity to talk to the GM of a team that uses sabermetrics extensively, and he told me that the gap between the information that the clubs have and that which the fans have is rapidly closing. That said, the data that the clubs use is far from perfect in of itself, and the information available to us is certainly no better. We need to be prudent in how we use these numbers, and be careful not to depend on them past their level of reliability. If we do, we become just as ignorant as those who choose to deride sabermetrics.

This morning, John Sickels posted an article in which he suggested that sabermetric analysis has become too granular to be interesting and fresh:

The newest stuff is becoming so granular that I’m having problems making sense of it. I’m a humanities guy, and the most advanced math is beyond my ability to completely comprehend. My personal opinion is that the many of the newest metrics (at least in regards to hitting and pitching) are just more complicated ways to say the same basic truths…..

But I’m finding that as I read the most advanced sabermetric stuff regarding major league players, my eyes glaze over and I start to get the grad school feeling again: why am I reading this? I’m not enjoying it. I want to watch a baseball game.

So am I just entering my dotage prematurely? Or is advanced sabermetric analysis becoming so specialized that no one but physics and math majors can understand it, leaving us humanities majors behind, let alone the average fan? If that is true, what can be done about it? I don’t mean stopping research; obviously it needs to go forward. But I mean, how do we find ways to disseminate the new knowledge and make it comprehensible for the non-math folks among us? How do we integrate and explain the new knowledge?

This article has garnered plenty of interest in the sabermetric community, with two writers at THT responding. First, Pat Andriola:

So when you say that they are “more complicated ways to say the same basic truths,” you are, to an extent, 100% correct. However, the questions that remain are: 1) how much an improvement are we gaining over the basic truths and 2) how valuable are those marginal improvements? Maybe you find these advances boring and trite, but many others (such as myself) don’t. I’m sure there are front offices and analysts that clamor over the newest posts at Fangraphs and The Hardball Times, just like I’m sure you find the latest breakdown of a hot prospect’s swing riveting. These are, ultimately, questions of what gives us the most utility (or satisfaction), and are completely subjective.

Pat is right on the money here, as I have spoken to a number of people within front offices, including one GM, who said that they follow Fangraphs and THT religiously, attempting to get an edge in data analysis and evaluation. These teams find these marginal improvements important, hoping that they provide even the slightest edge over their competition. If the clubs find this sort of analysis important, then it makes sense for an interested fan to be interested as well.

The second article, from Dan Novick, does a fantastic job addressing the idea that sabermetric analysis is boring and too technical:

Baseball writing on the internet is a meritocracy. Sabermetrics isn’t spreading because we say it is. It is spreading because there is an increasing number of fans that find it useful. There is no such thing as “required reading.” If you don’t find a particular aspect of sabermetrics useful anymore, there won’t be any negative repercussions should you choose not to read it.

I could not have said it better myself. If you are a Yankee fan and do not like sabermetrics, you can skip over that sort of article here or at RAB, or ignore those sites entirely. There are so many options and forums for discussion that a fan could likely stick to the most basic of sabermetric precepts and still find a place where he or she can have a reasonable discussion about the sport, and have a fairly decent understanding of value and related concepts. If you are a creator of content, you can ignore sabermetrics as well, and cater to a less stat-obsessed crowd. No one is being forced to use sabermetrics. If you do not like them, just ignore them. It really is that simple.

Sickels is not “anti-stat,” and I doubt that he would suggest people ignore sabermetrics entirely. He was simply raising a reasonable point. Do you agree with him?

Hey all; I’ve put together a fantasy baseball league for the writers and readers of TYU and I’d love for you to join. Right now, E.J. and I are in and the maximum for the league is 12. The draft is currently scheduled for March 15th (Monday). It’s a league from Yahoo! and the ID# is 171636 and the password is simply “tyu” without the quotes.

The hitters’ categories are: R, RBI, HR, AVG/OBP/SLG/OPS, SB%.

The pitchers’ categories are: IP, W, L, S, K, ERA, WHIP, K/BB, BS.

If you’re interested, join on up, we’d love to have you!

While we’re on the subject, I’d like to talk about fantasy baseball. Some people may think that Fantasy Baseball is more of a detriment to the game, that it takes people away from the “reality” of the game and puts the focus on the numbers rather than on the players. There is nothing farther from the truth in my experience. Fantasy Baseball has done a ton to help me get even more into baseball. It makes me research players and try to learn something about players and teams that aren’t the Yankees. Because of Fantasy Baseball, I watch MLB.tv to see other players perform. The game requires that the owner of the team find something out about a variety of players and get a more “global” view of Major League Baseball, instead of keeping a focus on his or her own team.

Fantasy Baseball also helped get me more into the analytical side of the game. Through Fantasy Baseball, I became more and more interested in the world of sabermetrics. Curious, I walked down that path and I’m glad that I did. Some may argue that this gets me farther away from the game, but I obviously disagree. To me, the numbers tell the story of every game in incredible detail. I can see exactly what some player did at exactly a certain time. I can match that numerical story to the one I experienced when watching, listening to, or attending the game. The numbers make the story of the game complete; they fill in the blanks. Since getting deeper and deeper into Fantasy Baseball and advanced baseball metrics, my love for the game has only grown.

Fantasy Baseball has made me a more educated fan who’s been able to experience the wonderful game of baseball from a variety of angles. It has made me become even more engrossed in my favorite sport, favorite team, and favorite players. I’d recommend it to anyone who wants to get immersed in the sport. So, please, join up and enjoy.