Last year, Manny Acta made a splash by dropping Grady Sizemore to second in the batting order. This year, he’s considering moving him back to leadoff. Is either the right move? And how should the rest of the lineup look?
In Part 1 of this article, I looked at the ability of individual projection systems to forecast hitter performance. The six different projection systems considered are Zips, CHONE, Marcel, CBS Sportsline, ESPN, and Fangraphs Fans, and each is freely available online. It turns out that when we control for bias in the forecasts, each of the forecasting systems is, on average, pretty much the same. In what follows here, I show that the Fangraphs Fan projections and the Marcel projections contain the most unique, useful information. Also, I show that a weighted average of the six forecasts predicts hitter performance much better than any individual projection.
Forecast encompassing tests can be used to determine which of a set of individual projections contain the most valuable information. Based on the forecast encompassing test results, we can calculate a forecast that is a weighted average of the six forecasts that will outperform any individual forecast.
In spite of a questionable off-season approach to their designated hitter situation and a deadline deal that didn’t fill that vortex of suck, the Chicago White Sox are in first place on the backs of Alex Rios, Alexei Ramirez, Paul Konerko, and most of the pitching staff. After facing Baltimore for one more tonight, they will be going into a critical 3 game series against division rival Minnesota. It doesn’t get us anywhere to look at the past, so the question is what can the White Sox do to maximize the value of the players they have going forward?
The most glaring weakness is still the DH spot. Mark Kotsay has received the majority of the playing time here, and he has also been the team’s least valuable hitter. Kotsay has posted a slash line of .228/.305/.378, with a wOBA of .299. Kotsay’s –0.7 WAR is not only the worst on the team, but tied for fourth worst in the MLB among players with 280 or more PAs. It’s clear that Kotsay isn’t getting the job done, but who is the most viable choice to replace him?
Young Cuban slugger Dayan Viciedo is an interesting option. He’s posting a .361 wOBA (.310/.310/.521) in his first 71 PAs. On the surface, that looks great, but his .333 BABIP is unsustainable for someone as… um… husky as Viciedo. Also, his walk rate of 0% is going to be exploited soon enough (Viciedo’s already swinging at 39.8% of balls outside of the strike zone). It’s clear that even with his incredible power, he’s just not ready for the Majors, and would likely be eaten alive in the playoffs.
Mark Teahen is nearing the completion of his rehab stint in Charlotte, and could be back with the club in the next week. The .255/.340/.387 (.317 wOBA) line he put up while starting at third base isn’t the most stunning, but against RHP he’s hitting .287/.376/.444 (.363 wOBA).
Andruw Jones has played in the DH role some, while also serving as the fourth outfielder. Andruw’s .204/.312/.444 line gives him a .336 wOBA, placing him in the neighborhood of being a league average hitter. Jones benefits from facing lefties, against whom he posts a line of .235/.350/.515 (.376 wOBA).
So based on those numbers, the answer to the DH scenario appears to be a Teahen/Jones platoon, right?
Wrong.
Well, half right. The White Sox currently have a right fielder who, while being a good hitter, is just terrible defensively. I’m of course referring to Jermaine Dye Carlos Quentin. Quentin’s line of .232/.328/.488 (.352 wOBA) is solid in it’s own right, but Quentin’s a great candidate to improve that line, thanks to the impending regression of his .213 BABIP. Quentin’s defense in the past two seasons has been quantifiably terrible. Back to back UZR/150s of –25.2 and –34.2 (the former in LF) have shown that Quentin can’t get the job done, and that he’s a DH (or maybe a first baseman, but that’s a discussion for 2011).
Teahen, meanwhile, has a UZR/150 of just –2.0 in 261 games in right. Jones, in 42 games this year, has a UZR/150 of 8.6. A platoon of these two players also would help the oft-injured Quentin stay healthy, keeping his dangerous bat in the lineup.
The White Sox are in a position that most didn’t think they could be in after the first two months of the season. The team has had some breaks, but if they’re going to compete with a very good Twins team, they have to utilize their players effectively. Getting Quentin out of the outfield and Mark Kotsay out of the lineup? Well, that’s just smart baseball.
The World Baseball Classic is certainly a noble idea. I mean, what’s not to like about it on paper? You take the best players from each baseball-playing nation and have them battle it out to see which country reigns over the rest of the globe. Can anyone trot out a more thunderous lineup than the USA? Who has the more dynamic pitchers: the Dominican Republic or Venezuela? Does Japan really produce the most fundamentally sound players? Fans all over the world have shown their support for this, as have many players.
All of this would be fine if baseball were like basketball, hockey or soccer; sports where you could wake up, trip over your dog, tumble down the stairs into a pair of cleats, skates or sneakers and play. Those sports employ bio-mechanics the body was designed to handle like running, jumping, kicking and swinging. Baseball, specifically pitching, is not like that. The human arm was not designed to handle the stress and torque put on it by pitching. If you don’t believe me, then I have a few thousand shoulder and elbow scars to show you, including my own.
The lucky few who are able to withstand such actions and be successful are kept on a yearly routine: start throwing in mid-February, build strength and stamina through March before turning up the intensity at the beginning of April. But just like it isn’t wise to turn the ignition on a new Mustang and instantly floor it, it doesn’t seem right to take a pitcher conditioned to ease into a season during Spring Training and tell him to pitch with October-like intensity in March. Unfortunately, this is the case with the WBC.
After looking through the statistics of those who appeared in both WBC tournaments, it is my belief that pitchers who participate in the WBC, especially starters, are far more likely to see a regression in their performance, get hurt or both than pitchers who do not play in the WBC. I reason that the most likely cause is the tournament’s timing disrupts the normal routine of pitchers and their arms are not yet ready to handle the stress and intensity then. With data collected from various sources, I will demonstrate the stark differences between WBC pitchers and their counterparts who did not participate in the tournament, using spreadsheet data and graphs included in this analysis.
***
The pitchers who were included in this study had to satisfy a few conditions. First, pitchers in the WBC group had to have pitched primarily in Major League Baseball in 2005, 2006, 2008 and 2009[1]. Players who played in one year but not another (spent one year in the minors or injured; or retired after a WBC) were not included. For the baseline of starters and relievers, a pitcher who made 10 or more starts for the year was counted as a starter while a pitcher who made 25 or more appearances with nine or fewer starts was counted as a reliever. The “all pitchers” category includes every pitcher who made an appearance during the 2005, 2006, 2008 and/or 2009 seasons.
***
At the heart of it, the key to successful pitching is how good you are in preventing runs from scoring, with ERA and component ERA (ERC)[2] being the primary statistics used to measure this aspect. The MLB’s ERA usually falls between 4.25 and 4.45 in most years, with only small differences from season to season. The last four groups saw small-to-moderate increases in their ERA between 2005 and 2006, but WBC starting pitchers saw a dramatic jump, from 3.75 to 4.48 while the ERC inflated from 4.09 to 4.79. WBC relievers also saw a significant jump in their collective ERAs (3.15 to 3.51), but not only is that only roughly half of what starters experienced, WBC relievers saw their ERC drop from 3.86 to 3.41. Compared against the league-wide ERA/ERC jumps of 0.24 (4.29 to 4.53) and 0.25 (4.18 to 4.43), respectively, the WBC starters’ jumps look even more like one of Superman’s single bounds. A major factor for this spike may be the above-average rise in HR/9 ratios. The average MLB starter showed no increase in his HR/9 rates and all other groups had increases of 0.1, but the HR/9 rates of WBC starters rose by 0.2 (0.9 – 1.1).
Home runs aren’t so bad, just as long as there isn’t anyone on base, but WBC starters were putting more and more runners on in 2006. Starting pitchers saw the highest rise in WHIP out of the five groups. The major league-average increase in WHIP between 2005 and 2006 was 0.04 (1.37 to 1.41), but the average WBC-participating starter saw his WHIP rise double that amount (0.08) from 1.29 to 1.37. Part of that increase was fueled by an up-tick in their BB/9 rates, which climbed from 2.9 to 3.1 (0.2). The most startling changes, though, were with the starters’ rising H/9 rates and falling K/9 rates . While all other groups saw a 0.2 increase in their H/9 ratios, WBC-participating starters’ ratios shot up by 0.5, going from 8.7 in 2005 to 9.2 in 2006. This may be attributed to a pitcher’s prematurely tired arm or improper mechanics from being rushed along during what normally is Spring Training. Either way, the pitches became more hittable, which also showed a decrease in these pitchers’ ability to strike batters out.
Every group I collected data on showed an improvement in their K/9 ratios by 0.2…except for WBC-participating starters. Their K/9 ratios actually fell, going from 7.0 in 2005 to 6.7 in 2006—a drop of 0.3 whiffs per nine innings. A good K/9 ratio shows both how good a pitcher is at retiring a batter without the help of his fielders and how dominant his repertoire is. The higher, the better. When I see that one group’s ratio is regressing while all others are improving, that would make me a little curious as to what may be causing such a downturn, especially with a group as valuable as starting pitchers. If I were in a team’s front office, it would make me wonder if this little event that is supposedly good for baseball is actually harming my pitcher and my team’s playoff chances.
***
Now, this wouldn’t so much of a concern if the pitchers who saw this decline in performance were just hurlers on the wrong side of 30 and/or at the tail-end of their contract, but that’s not a case. Pitchers like Jake Peavy and Dontrelle Willis saw their performances take a dive after participating in the 2006 WBC, while promising up-and-comers like Francisco Liriano and Gustavo Chacin suffered major injuries that year. Two of the more alarming examples are Peavy and Willis, two National League hurlers from pitcher-friendly ballparks who use complicated or violent deliveries.
Peavy seemed out of sorts during the first half of the 2006 season, posting ERAs of 5.17 or worse in three of the first four months. It was during this time that Peavy was also prone to the long ball, serving up 14 of his 23 home runs in April, May and June. The “gopher-itis” lessened once July hit, but then Peavy had a little more trouble finding the strike zone. After issuing no more than eight free passes in each of the first three months, Peavy walked 12 or more batters in every month during latter half of the season. Peavy eventually straightened himself out in 2007, but the same cannot be said for Willis. After nearly winning the Cy Young in 2005, Willis never could establish any consistency in 2006. His WHIP climbed an astonishing 0.29 points from 1.13 (sixth in the NL) to 1.42 (outside the top 30). At the same time, his HR/9 rate doubled from 0.4 to 0.8 while his opponents’ OPS climbed from .644 to .745. Since then, Willis’ regression went from bad to worse and is now viewed as little more than a reclamation project for the Arizona Diamondbacks.
***
Whereas 2006 saw a decline in WBC pitchers’ performance, the 2009 tournament participants saw an even more disturbing trend: a steep drop in their time on the mound. There were only negligible decreases in innings pitched following the 2006 WBC—10.1 percent for starters, 2.6 percent for relievers—but those figures worsened dramatically following this past tournament. WBC starters pitched, on average, 21.1 percent fewer innings in 2009 than they did in 2008 while relievers saw their innings totals drop by 27.2 percent. Houston ace Roy Oswalt saw his streak of five consecutive 200-inning seasons come to an end due to chronic back problems. Cincinnati’s Edinson Volquez appeared in one WBC game, then made only nine starts during the regular season before undergoing “Tommy John” surgery[3].
A second trend I noticed involved those pitchers who were in the playoffs the previous season. Out of the 11 pitchers who appeared in both the ’08 playoffs and the ’09 WBC, eight of them missed time due to injury (or, in the case of Javier Lopez, demotion) or saw an overall regression in their performance. The pitchers from this group who spent time on the disabled list pitched anywhere from 13.5 percent to 80.3 percent fewer innings than they had in ’08. Some of the more notable examples include Red Sox right-hander Daisuke Matsuzaka, whose 59.1 innings in ’09 were the fewest he’s pitched in either Japan or America, and Angels set-up man Scot Shields, who had never been on the disabled list for his entire nine-year big league career.
***
There are more examples of pitchers seeing their fortunes change for the worse after either of the two WBCs, like Bartolo Colon’s shoulder falling apart after rushing through rehab and Esteban Loaiza’s collapse in Oakland in 2006 or how Volquez’s elbow went kaput in the middle of 2009. I won’t list every pitcher who suffered, but my point is clear: the WBC increases the chances for pitchers to suffer injuries, see an across-the-board decline in performance or both. As I stated earlier, I feel the biggest reason for these unfortunate trends is the timing of the tournament. Holding this tournament in the early spring can only damage the health and careers of the players who wish to represent their countries and, in turn, hurt the player’s team both on the field and their long-term organizational plan. I feel the best possible resolution would be to hold the tournament at two different times: have the preliminary rounds during the week of the All-Star Game—while giving MLB, the Japanese leagues and all other leagues a mid-season break—and the final two rounds shortly after the World Series. This way, not only would the careers and health of the pitchers be better preserved, but it would also be highly beneficial to MLB as a whole.
Under the current scheduling, the WBC and MLB has to battle against the NCAA men’s basketball championship tournament for ratings and coverage. Since all other major professional and collegiate leagues are inactive in July, it would allow MLB a better opportunity to drum up interest in the tournament and give less well-known baseball-playing nations a bigger platform to perform. The week off would also benefit the players who are not in the WBC, as they would have had time to recover from injuries and spend invaluable time with family and friends. Lastly, the buzz over a recently completed World Series could carry over to the final stages of the WBC, with story lines from the first phase being built up prior to the resumption of the tournament. Playoff-participating players could have the option of continuing in the tournament or allow other players, who spent most of October resting and re-energizing, to go in their places. Those fresh bodies would also improve the quality of play seen by the fans.
The bottom line is this: the World Baseball Classic is an excellent idea, but is poorly executed in its current form, with pitchers suffering the most damage. Pitchers are the most valuable and volatile commodity in baseball and MLB should do its very best in order to protect that commodity. Even though there have been only two tournaments to study, the numbers are very clear and the logical decision to change should be made.
Michael Echan is a freelance sports writer from New Jersey. Please contact him if you would like to see the compiled spreadsheet data and graphs. He may be reached at mcechan@hotmail.com
[2] ERC is a statistic created by Bill James. It takes the number of hits, walks, home runs, hit batters and total batters faced by a pitcher to give an “alternate” ERA that better reflects his performance.
[3] Volquez did pitch a career-high 196 innings in 2008, his first full season in the big leagues, but has had his workload gradually increased during his career. His combined innings progression: 140 in ’05, 154 in ’06, 178.2 in ’07, 196 in ’08.
There are a number of ERA estimators out there and just as many opinions on which one is the best. Among the more well-known estimators are FIP (Fielding Independent Pitching, developed by Tom TAngo), xFIP (FIP, with a normalized HR-rate), SIERA (created by Matt Swartz and Eric Seidman at Baseball Prospectus), tRA (created by Graham MacAree), QERA (created by Nate Silver), Component ERA (created by Bill James), and DIPS, which was developed by Voros McCracken and was the first ERA estimator to attempt to use the three true outcomes (strikeouts, walks, home runs allowed) to separate the things pitchers have control over from other factors, such as defense, sequencing of hitting events, and luck. Ultimately, that’s what an ERA estimator attempts to do: they allow us to evaluate pitching performance based on the things pitchers actually control.
For this article, the three estimators that will be used are FIP, xFIP, and SIERA. A quick refresher on the three:
FIP—“Fielding Independent Pitching, a measure of all those things for which a pitcher is specifically responsible. The formula is (HR*13+(BB+HBP-IBB)*3-K*2)/IP, plus a league-specific factor (usually around 3.2) to round out the number to an equivalent ERA number. FIP helps you understand how well a pitcher pitched, regardless of how well his fielders fielded. FIP was invented by Tangotiger.” (from The Hardball Times glossary).
xFIP—“Expected Fielding Independent Pitching. This is an experimental stat that adjusts FIP and “normalizes” the home run component. Research has shown that home runs allowed are pretty much a function of flyballs allowed and home park, so xFIP is based on the average number of home runs allowed per outfield fly. Theoretically, this should be a better predicter of a pitcher’s future ERA.” (from The Hardball Times glossary).
SIERA—Skill Interactive Earned Run Average. This is the most recent entry into the field and is more complex as it incorporates a number of adjustments to the basic three true outcomes formula. From the introductory essay at BP, there are things that SIERA takes into account that other ERA estimators do not: it allows for the fact that a high ground ball rate is more useful to pitchers who walk more batters, a low fly ball rate is less useful to high strikeout pitchers, adding more strikeouts is more useful to low strikeout pitchers, and adding ground balls is more useful for high ground ball pitchers. SIERA also uses ground balls per plate appearance rather than ground balls per balls in play.
For background information on FIP, xFIP, and SIERA, please see the following web pages:
http://www.hardballtimes.com/main/statpages/glossary/
http://www.baseballprospectus.com/article.php?articleid=10027
Ultimately, we want an ERA estimator that will tell us how well the pitcher is pitching after you take away the defense and luck elements. Also, we want our ERA estimator to be able to most accurately predict future performance. If you have Dan Haren and his 4.56 ERA on your fantasy team, you want to know if he’s going to improve or if you should part ways with your expected Ace, so you look at an ERA estimator as a clue to his expected future performance. Which ERA estimator you choose can give you very different expectations.
Here at Fangraphs, I’ve noticed a recent backlash against xFIP from commenters on articles that use the metric in their analysis. These commenters feel that pitchers do have control over their HR-rate, whereas xFIP normalizes all pitchers to a league average rate. Often, they will point out that a pitcher’s home ballpark could be a factor in a pitcher’s high home run rate and that it isn’t likely to come down as long as the pitcher continues to play for that team. For them, FIP is the metric to use. This can obviously make a big difference in predicting future performance. I’m not going to weigh in on that particular debate, but I did want to highlight some pitchers and their respective ERA, FIP, xFIP, and SIERA numbers to illustrate the different expectations based on which ERA estimator you choose to use.
All pitcher data is as of June 30 and only pitchers with 75 or more innings were included. This produced a sample of 115 pitchers.
ERA Leaders
| Rank | Pitcher | ERA | FIP | xFIP | SIERA |
| 1 | Josh Johnson | 1.83 | 2.47 | 3.16 | 2.99 |
| 2 | Ubaldo Jimenez | 1.83 | 3.07 | 3.68 | 3.49 |
| 3 | Jaime Garcia | 2.27 | 3.47 | 3.84 | 3.77 |
| 4 | Roy Halladay | 2.29 | 2.78 | 3.06 | 3.05 |
| 5 | Adam Wainwright | 2.34 | 3.11 | 3.27 | 3.12 |
| 6 | Tim Hudson | 2.37 | 4.37 | 4.29 | 3.94 |
| 7 | David Price | 2.44 | 3.73 | 4.07 | 3.97 |
| 8 | Cliff Lee | 2.45 | 2.34 | 3.30 | 3.09 |
| 9 | Clay Buchholz | 2.45 | 3.47 | 4.28 | 4.37 |
| 10 | Yovani Gallardo | 2.56 | 2.97 | 3.46 | 3.32 |
Generally, the league’s top 10 ERA leaders have had some good fortune to go along with their good pitching. In the case of these pitchers, the first place to look is their BABIP. In 2010, MLB hitters have a .299 BABIP. Eight of the ten pitchers in the list above have BABIPs lower than .299 and the other two pitchers are at .304 and .305. The lowest is Tim Hudson’s .234. Left On Base Percentage (LOB%) is another key area. Eight of the ten pitchers have a LOB% of 79% or higher, with the other two at 71.6% and 76.2%. Ubaldo Jimenez leads the league with a LOB% of 86.2%. Finally, HR-rate (HR/FB) is a key factor for a pitcher keeping his ERA low. Nine of the ten pitchers have a HR/FB rate at 9% or lower, with Clay Buchholz leading the pack at 3.6%.
FIP Leaders
| Rank | Pitcher | FIP | ERA |
| 1 | Francisco Liriano | 2.19 | 3.47 |
| 2 | Cliff Lee | 2.34 | 2.45 |
| 3 | Josh Johnson | 2.47 | 1.83 |
| 4 | Roy Halladay | 2.78 | 2.29 |
| 5 | Tim Lincecum | 2.88 | 3.13 |
| 6 | Jered Weaver | 2.93 | 3.01 |
| 7 | Yovani Gallardo | 2.97 | 2.56 |
| 8 | Jon Lester | 3.01 | 2.86 |
| 9 | Ubaldo Jimenez | 3.07 | 1.83 |
| 10 | Adam Wainwright | 3.11 | 2.34 |
When we shift over to look at FIP leaders, we have four pitchers who fall out of the top 10 based on ERA: Jaime Garcia, Tim Hudson, David Price, and Clay Buchholz. Joining the remaining six in this list of FIP leaders are Francisco Liriano, who surges to the top, along with Tim Lincecum, Jered Weaver, and Jon Lester. Francisco Liriano has a solid 3.47 ERA, but his FIP shows he could be much better going forward. The main culprit is a .355 BABIP, which should come down. All ten of these pitches have great HR/FB rates. Adam Wainwright has the highest rate, at 9.0%. The other nine pitchers are at 8.7% or lower, with six pitchers sporting a rate below 7.0%.
xFIP Leaders
| Rank | Pitcher | xFIP | ERA |
| 1 | Francisco Liriano | 3.01 | 3.47 |
| 2 | Roy Halladay | 3.06 | 2.29 |
| 3 | Josh Johnson | 3.16 | 1.83 |
| 4 | Jered Weaver | 3.21 | 3.01 |
| 5 | Tim Lincecum | 3.22 | 3.13 |
| 6 | Adam Wainwright | 3.27 | 2.34 |
| 7 | Cliff Lee | 3.30 | 2.45 |
| 8 | Ricky Romero | 3.43 | 2.83 |
| 9 | Dan Haren | 3.43 | 4.56 |
| 10 | Jon Lester | 3.44 | 2.86 |
The usual suspects remain on the list, with two additions in Ricky Romero and Dan Haren, while Yovani Gallardo barely drops out of the top 10, falling to 11 here, and Ubaldo Jimenez drops to 16. Romero had placed out of the top 10 in ERA (17th) and FIP (11th), so he receives just a slight bump up based on xFIP, where he places 8th. Dan Haren is the high-riser, though, as he’s allowed a HR/FB rate of 13.5%. Haren is 78th based on ERA and 47th based o FIP, but moves up to 9th based on xFIP. If you believe that HR-rates normalize over time, then Haren is a pitcher to target. If, however, you think Haren will continue to be plagued by the long ball, whether that’s due to his home park or his actual skill, then you might want to steer clear of him (his career rate is 11.0%, by the way).
SIERA Leaders
| Rank | Pitcher | SIERA | ERA |
| 1 | Jered Weaver | 2.55 | 3.01 |
| 2 | Francisco Liriano | 2.91 | 3.47 |
| 3 | Josh Johnson | 2.99 | 1.83 |
| 4 | Roy Halladay | 3.05 | 2.29 |
| 5 | Cliff Lee | 3.09 | 2.45 |
| 6 | Adam Wainwright | 3.12 | 2.34 |
| 7 | Dan Haren | 3.14 | 4.56 |
| 8 | Tim Lincecum | 3.17 | 3.13 |
| 9 | Jon Lester | 3.28 | 2.86 |
| 10 | Yovani Gallardo | 3.32 | 2.56 |
The SIERA leader list and xFIP leader list have nine common names. The difference is Yovani Gallardo at #10 according to SIERA and #11 according to xFIP, and Ricky Romero (#11 based on SIERA, #9 based on xFIP). Looking at the entire list shows that xFIP and SIERA produce similar ERA estimates. I ran a correlation for all 116 pitchers between their xFIP and their SIERA and it produced a 0.96 correlation. I then took the absolute difference between each metric for each pitcher and found that, on average, the difference was 0.17. Seventy-seven of the 116 pitchers (66%) had xFIPs and SIERAs within 0.20 of each other and four pitchers had identical xFIPs and SIERAs.
Pitchers the ERA Estimators Agree On
Some pitchers have FIPs, xFIPs, and SIERAs that are near matches for their actual ERA. It might be said that these pitchers are the easiest to predict going forward, simply because all three ERA estimators agree that their current ERA is likely to be a legitimate estimate of their ability. Below is a top 10 list of pitchers who’s ERA estimators agree most closely with their actual ERA. The final column, “AVG”, shows the average of the three ERA estimators. To create the top 10 list, I found the absolute difference between each estimator and actual ERA, then divided by three to get an average absolute difference for each pitcher.
| Rank | Pitcher | ERA | FIP | xFIP | SIERA | AVG |
| 1 | Freddy Garcia | 4.66 | 4.69 | 4.60 | 4.66 | 4.65 |
| 2 | Kyle Kendrick | 4.88 | 4.89 | 4.90 | 4.98 | 4.92 |
| 3 | Roy Oswalt | 3.55 | 3.51 | 3.55 | 3.39 | 3.48 |
| 4 | Zack Greinke | 3.72 | 3.74 | 3.76 | 3.52 | 3.67 |
| 5 | Kenshin Kawakami | 4.48 | 4.23 | 4.52 | 4.52 | 4.42 |
| 6 | Chris Volstad | 4.40 | 4.21 | 4.47 | 4.47 | 4.38 |
| 7 | Felix Hernandez | 3.28 | 3.38 | 3.49 | 3.33 | 3.40 |
| 8 | Tim Lincecum | 3.13 | 2.88 | 3.22 | 3.17 | 3.09 |
| 9 | Scott Kazmir | 5.42 | 5.27 | 5.46 | 5.15 | 5.29 |
| 10 | Jeremy Bonderman | 4.36 | 4.02 | 4.42 | 4.23 | 4.22 |
Now, some of these pitchers are better than others. In Joe Morgan terms, these are the most “consistent” pitchers when looking at how they fare according to advanced metrics but consistent doesn’t mean good (something Joe never seems to mention). You can be consistent like Scott Kazmir and be of no use to anyone. Or you can be consistent like Felix Hernandez or Tim Lincecum and be a top starting pitcher. These pitchers generally have BABIPs within 10 points of the league average and HR/FB rates close to league average.
Most Volatile Pitchers
The following list shows the pitchers who’s ERA estimators disagree with their actual ERA by the largest amount. These are the pitchers who advanced metrics suggest will either greatly improve or who are headed for heaping dose of reality in the future.
| Rank | Pitcher | ERA | FIP | xFIP | SIERA | AVG |
| 1 | Tim Hudson | 2.37 | 4.37 | 4.29 | 3.94 | 4.20 |
| 2 | Livan Hernandez | 3.10 | 4.40 | 4.91 | 5.18 | 4.83 |
| 3 | Clay Buchholz | 2.45 | 3.47 | 4.28 | 4.37 | 4.04 |
| 4 | Ubaldo Jimenez | 1.83 | 3.07 | 3.68 | 3.49 | 3.41 |
| 5 | Jeff Niemann | 2.72 | 4.39 | 4.29 | 4.16 | 4.28 |
| 6 | Jason Vargas | 2.80 | 3.71 | 4.81 | 4.45 | 4.32 |
| 7 | David Price | 2.44 | 3.73 | 4.07 | 3.97 | 3.92 |
| 8 | Jaime Garcia | 2.27 | 3.47 | 3.84 | 3.77 | 3.69 |
| 9 | Justin Masterson | 5.21 | 4.04 | 3.94 | 3.55 | 3.84 |
| 10 | Matt Cain | 2.93 | 3.60 | 4.70 | 4.49 | 4.26 |
Of note here is that nine of these ten pitchers are expected to perform much worse going forward, with only sabermetric favorite Justin Masterson expected to improve. Some of these names are sure to cause controversy. Matt Cain, for example, consistently out-performs his FIP and xFIP. He has a lifetime ERA of 3.44, with a lifetime FIP of 3.66 and xFIP of 3.97. Every year, his HR/FB rate is below the league average (7.7% for his career), and in five of his six years in the league his BABIP has been below league average (.285 for his career). At some point, we must conclude that Matt Cain is better than the ERA estimators think he is. Another pitcher on this list, Tim Hudson, has a career ERA of 3.43, with a FIP of 3.82. He’s done it with a better-than-expected career BABIP (.287). This year, that BABIP is .234, so he should regress, but he has a history of bettering his FIP, so he has a good chance of not regressing as much as the ERA estimators believe he will.
The ERA Estimator “Get Them If You Can” Official List
For this list, I limited the pitchers to those for whom the average of the three ERA estimators suggest a 3.80 ERA or below. I don’t think it’s particularly helpful to know that the ERA estimators suggest Kyle Davies should have an ERA around 5.04 rather than the 6.06 he currently sports. The “AVG” column is the average of the ERA estimators. The “DIFF” column is the difference between that average and the pitcher’s actual ERA.
| Rank | Pitcher | ERA | FIP | xFIP | SIERA | AVG | DIFF |
| 1 | Randy Wells | 4.96 | 3.47 | 3.77 | 3.94 | 3.73 | -1.23 |
| 2 | Dan Haren | 4.56 | 3.90 | 3.43 | 3.14 | 3.49 | -1.07 |
| 3 | James Shields | 4.76 | 4.13 | 3.55 | 3.41 | 3.70 | -1.06 |
| 4 | Gavin Floyd | 4.66 | 3.41 | 3.81 | 3.73 | 3.65 | -1.01 |
| 5 | Brandon Morrow | 4.50 | 3.45 | 3.90 | 3.55 | 3.63 | -0.87 |
| 6 | Tommy Hanson | 4.50 | 3.45 | 4.10 | 3.54 | 3.70 | -0.80 |
| 7 | Francisco Liriano | 3.47 | 2.19 | 3.01 | 2.91 | 2.70 | -0.77 |
| 8 | Jason Hammel | 4.32 | 3.69 | 3.81 | 3.85 | 3.78 | -0.54 |
| 9 | Justin Verlander | 4.02 | 3.38 | 4.10 | 3.74 | 3.74 | -0.28 |
The top eight pitchers on this list have BABIPs at .328 or higher. The top six have LOB% below 70%. Dan Haren and James Shields sport HR/FB rates of 13.5% and 14.4%. Obviously, some of these pitchers are better than others and you can see for yourself the disagreement between the ERA estimators. Haren and Shields, with their high HR/FB rates, have much higher FIPs than the others. If you believe he can remain healthy, I’d say the #1 target would be Francisco Liriano, as his ERA is 40th among starting pitchers, while he’s ranked #1, #1, and #2 according to the ERA estimators.
The ERA Estimator “Sell! Sell! Sell!” Official List
For this list, I limited the pitchers to those who currently have ERAs below 3.50 and a K/9 great than 6.0. Tim Hudson and Livan Hernandez, with K-rates around 4.0, are not likely to be easy to unload, despite their shiny ERAs. The pitchers below have good ERAs and solid strikeout rates, but the ERA estimators suggest they are not as good as their performance so far.
| Rank | Pitcher | ERA | FIP | xFIP | SIERA | AVG | DIFF |
| 1 | Clay Buchholz | 2.45 | 3.47 | 4.28 | 4.37 | 4.04 | 1.59 |
| 2 | Ubaldo Jimenez | 1.83 | 3.07 | 3.68 | 3.49 | 3.41 | 1.58 |
| 3 | Jeff Niemann | 2.72 | 4.39 | 4.29 | 4.16 | 4.28 | 1.56 |
| 4 | David Price | 2.44 | 3.73 | 4.07 | 3.97 | 3.92 | 1.48 |
| 5 | Jaime Garcia | 2.27 | 3.47 | 3.84 | 3.77 | 3.69 | 1.42 |
| 6 | Matt Cain | 2.93 | 3.60 | 4.70 | 4.49 | 4.26 | 1.33 |
| 7 | Ted Lilly | 3.12 | 4.21 | 4.61 | 4.27 | 4.36 | 1.24 |
| 8 | Andy Pettitte | 2.72 | 3.76 | 4.04 | 4.05 | 3.95 | 1.23 |
| 9 | Wade LeBlanc | 3.25 | 4.19 | 4.60 | 4.57 | 4.45 | 1.20 |
| 10 | Trevor Cahill | 2.88 | 4.18 | 4.03 | 4.02 | 4.08 | 1.20 |
These pictures have a mixture of low BABIPs, high LOB%, and low HR/FB, which makes them candidates to perform worse from here on out. Of course, Matt Cain, as mentioned before, always seems to defy expectations of ERA estimators. Also, Ubaldo Jimenez, currently #2 in ERA, is #9 in FIP, and #16 in xFIP and SIERA, so he’s still a top pitcher, just not as good as he’s shown so far. Depending on your confidence in these advanced metrics, there are moves to make as the baseball season reaches its halfway point.
Daric Barton, first baseman for the Oakland Athletics, is currently tied for the Major League lead in sacrifice bunts. And a lot of people really do not like that.
Over at Athletics Nation, an A’s fan site, statistics-savvy contributors have been calling for manager Bob Geren’s head for months. Joe Posnanski agrees. He wrote a column the other day suggesting that, among other things, “[s]omebody tell that man to stop doing that immediately.” Matt Klassen at FanGraphs also agrees, arguing that every single one of Barton’s bunts has been a bad idea. How could the team that led baseball’s statistical revolution in the late-1990s and early-2000s be so stupid? How can Billy Beane sit back and let his manager throw away out after out by allowing Barton, a good on-base hitter, to sacrifice his plate appearances?
As Tom Tango explains, it is not so simple. Tango makes two points: 1) Barton may have a chance to reach base when he bunts; and 2) all the bunting may force infielders to play in, giving him more hitting room and making him more successful when he does choose to swing.
The latter point is difficult to measure, but Tango has provided help with the former. His run expectancy calculator is a wonderful tool that allows some analysis of Barton’s bunts. It is based on the idea that every combination of baserunners and outs has a certain average “run expectancy.” There can be zero, one, or two outs in the inning, and there are eight possible configurations of baserunners (empty, first, second, third, first and second, first and third, second and third, loaded). Multiply the three out states by eight baserunner states, and there are 24 different situations that can come up in an inning. For each of these states, a team can expect to score, on average, a certain number of runs to the end of the inning — the run expectancy. Input a batting line into the calculator, and you get a table that shows the run expectancy for all 24 states.
One more consideration before we plug in some numbers: the current A’s team is not good at hitting. Since they score fewer runs per game than most teams (in other words, fewer runs per 27 outs), each out is worth a little less than it would be for an average team. Their lack of offensive punch also magnifies the value of a runner moving 90 feet closer to home.
I plugged the A’s season batting line through Monday into the calculator, and all run expectancy numbers come from the resulting tables. Let’s first look at the numbers when Barton bunts with a runner on first and no outs. On average, the A’s should expect to score 0.873 runs between this situation and the end of the inning. If Barton successfully bunts the runner to second, the state changes to a runner on second and one out — a situation which yields an expectation of 0.648 runs. So by successfully bunting in this situation, it would appear that Barton has cost his team, on average, about a quarter of a run. However, a successful sacrifice bunt is not the only possibility. Barton could reach base, resulting in runners on first and second with no outs (run expectancy: 1.493). The bunt attempt could also fail, resulting in a runner on first and one out (run expectancy: 0.499). Barton is a good bunter and always bunts with the speedy leadoff batter on first, so his chance of failure is probably very low. For the sake of argument, let’s say he can expect to pop his bunt up or fail in some other way only about two percent of the time. What about reaching base? Using all of these numbers, a little algebra can tell us how much of a chance Barton needs to have to make this a good play.
P(Bunt Fails) * .499 + P(Bunt Succeeds) * .648 + P(Barton Reaches) * 1.493 = .873
I suggested that P(Bunt Fails) is perhaps .02, so we can set P(Barton Reaches) = X and P(Bunt Succeeds) = .98 – X to make the probabilities add up to one. Solving for X gives about .27, or 27 percent. This means that if Barton has a greater than 27 percent chance of reaching base when he bunts with a runner on first with no outs, then he is actually increasing the number of runs his team should expect to score. If he has a less than 27 percent chance of reaching base, he costs his team runs and would be better off simply swinging away.
Reaching base could include a bunt hit or a fielder error, but a 27 percent chance still seems like a stretch. How about when there is a runner on second and no outs, the situation in which Barton has most often been successful? Posnanski specifically blasted the decision to bunt in that situation, but the numbers are actually a bit better. Here is the equation:
P(Bunt Fails) * .648 + P(Bunt Succeeds) * .895 + P(Barton Reaches) * 1.715 = 1.044
With a runner on second and no outs, again assuming a two percent chance of total failure, the threshold is 19 percent — if Barton has better than a 19 percent chance of reaching, he is helping his team score more runs. The number still seems high, but, contradicting Posnanski, it appears that bunting in this situation is a better play than when there is a runner on first.
Barton has appeared to be bunting for a hit on many of his sacrifices, and though he has not succeeded, he must believe there is some chance he will get on base. And there are two other factors at work. First, the fielders must play further in if he is likely to bunt, making his non-bunt appearances in these situations far more valuable. Second, Tango’s tool also gives the chance of scoring at least one run for each state, and this value stays constant at about 41 percent when Barton successfully bunts a runner to second, and actually rises from 58 percent to 65 percent when he bunts a runner from second to third.
Indeed, Barton’s bunts are far more complicated than some commentators have made them out to be. As Mitchel Lichtman explained during the playoffs last year, when a few Yankees sacrifices left viewers baffled, we cannot simply analyze the before and after state of a “successful” sacrifice bunt. The range of possible outcomes includes the bunter reaching safely; the effect on the fielders should the batter choose to swing is also a factor. The A’s may actually know what they are doing here.
This post originally ran at Ball Your Base.