As an avid reader of FanGraphs, I’ve been following the ongoing mock draft and thought it would be interesting to compare the results to the dollar value rankings I created using Steamer’s 2015 projections.

I downloaded the draft spreadsheet partway through the 16^th round, just after pick 183 (Chase Headley). Here is a breakdown, position-by-position. I’ve included the overall pick and the dollar value for that player based on 2015 Steamer projections in parentheses.

First Base

Four first basemen were taken in the first round: Miguel Cabrera (4^th–$48), Paul Goldschmidt (6^th–$36), Jose Abreu (8^th–$35), and Edwin Encarnacion (10^th–$36). After Cabrera is off the board, the next three guys are almost identical in value, according to Steamer. At this point, it becomes the preference of the owner. Goldy and Abreu are younger than Encarnacion and should hit for a better average with similar production in the HR/RBI department, so it makes sense to see them go ahead of Edwin, but there really isn’t much difference among them.

In the second round, Freddie Freeman (18^th–$24) went before Anthony Rizzo (21^st–$29) in what could be considered a questionable selection. Steamer likes Rizzo over Freeman by $5 in value. Below are their respective projections for 2015:

560 AB, 81 R, 24 HR, 83 RBI, 3 SB, .284 AVG—Freeman ($24)

541 AB, 85 R, 30 HR, 89 RBI, 6 SB, .271 AVG—Rizzo ($29)

Rizzo has Freeman beat in every category except for average.

Let’s look at their average stats over the last two years (since Rizzo has only played full seasons over the last two years):

579 AB, 91 R, 20 HR, 94 RBI, 2 SB, .303 AVG—Freeman

565 AB, 80 R, 28 HR, 79 RBI, 6 SB, .258 AVG—Rizzo

This tells a different tale, as Freeman now has the edge in runs, RBI, and average, with Rizzo leading in homers and steals. Another factor is the outlook for their respective teams. The Cubs look to have a much better offense than the Braves, which should allow Rizzo to score and drive in more runs. I would have gone for Rizzo before Freeman.

Five more first basemen were taken in rounds 4, 5, and 6: Albert Pujols (47^th–$22), Victor Martinez (48^th–$26), Adrian Gonzalez (53^rd–$22), Joey Votto (61^st–$16), and Prince Fielder (72^nd–$23).

Based on past history, I believe you have to take Pujols, Martinez, and Gonzalez before Votto and Fielder. Votto played in just 62 games last season and doesn’t have the power you’d like to get from a first baseman. Fielder played in just 42 games and is coming off major surgery that included having his neck bones fused together. His production was already dropping before the injury, so he really is a question mark for 2015.

Back to Pujols, Martinez, and Gonzalez: Steamer likes V-Mart the best of the three and he is coming off a terrific season (.335, 87 R, 32 HR, 103 RBI), but is also heading into his age 36 season (Pujols will be 35, Gonzalez will be 33).

Let’s look at what they’ve done over the last three seasons (seasonal averages):

544 AB, 74 R, 25 HR, 91 RBI, 5 SB, .273 AVG—Pujols

569 AB, 77 R, 19 HR, 96 RBI, 1 SB, .321 AVG—Martinez

601 AB, 76 R, 22 HR, 108 RBI, 1 SB, .290 AVG—Gonzalez

It’s close. There’s enough of a range of outcomes with all three hitters that they could finish the season in any order.

Rounds 7 through 9 saw four more first baseman get drafted, starting with Carlos Santana, taken with the 77^th pick. Here I’m not sure of the league specifications. For my dollar values, I have Santana only eligible at first base (94 game played in 2014) or third base (26 games played). He did play 10 games at catcher. If he’s only eligible at first base or third base, I have him worth $8. If he’s eligible at catcher, his value jumps to $21 based on positional scarcity. Anyway, the four first baseman taken here were Santana (77^th–$8), Chris Davis (79^th–$13), Lucas Duda (105^th–$5), and Steve Pearce (106^th–$17).

Steamer 2015 projections:

490 AB, 74 R, 21 HR, 73 RBI, 4 SB, .245 AVG—Carlos Santana ($8)

483 AB, 71 R, 30 HR, 79 RBI, 3 SB, .242 AVG—Chris Davis ($13)

534 AB, 69 R, 24 HR, 75 RBI, 3 SB, .234 AVG—Lucas Duda ($5)

514 AB, 77 R, 23 HR, 74 RBI, 6 SB, .270 AVG—Steve Pearce ($17)

Again, Santana is much more valuable if you can slot him at catcher. Davis is a big risk considering he had by far the worst year of any of these players in 2014 (.196, 65 R, 26 HR, 72 RBI, 2 SB), but he also has the highest ceiling, having hit 53 homers with a .286 average in 2013. Duda had a breakout 2014 season, hitting 30 homers and driving in 92 runs last year. Steamer sees regression to 23 and 74 in 2015. Of these four players, Steve Pearce had the best rate stats in 2014 (.293/.373/.556) and best wRC+ (161). He’s projected for a career-high 586 plate appearances in 2015. Consider the Orioles have an open spot for him to be an everyday player after losing Nelson Cruz and Nick Markakis in the offseason, if you expect Pearce to get the playing time, he’s your guy. My order for these four players would be Pearce, Davis, Santana, and Duda (unless Santana has catcher eligibility).

The next four first basemen could be right up there with the previous group, based on Steamer projections: Ryan Zimmerman (120^th–$11), Mark Trumbo (135^th–$12), Justin Morneau (163^rd–$14), and Eric Hosmer (166^th–$17). The projections:

508 AB, 70 R, 19 HR, 71 RBI, 3 SB, .275 AVG—Ryan Zimmerman ($11)

526 AB, 67 R, 29 HR, 81 RBI, 4 SB, .246 AVG—Mark Trumbo ($12)

479 AB, 68 R, 19 HR, 74 RBI, 2 SB, .295 AVG—Justin Morneau ($14)

573 AB, 76 R, 19 HR, 77 RBI, 7 SB, .278 AVG—Eric Hosmer ($17)

With Zimmerman, you have to worry about his health, as he only played in 61 games last year. He also had the lowest HR/FB% of his career, at 7.8%. In 2012 and 2013, he hit 25 and 26 home runs, so he could bounce back and be just fine. Trumbo played in just 88 games last year and hit 14 homers after back-to-back seasons of 30 or more. Steamer expects him to bounce back to 29 homers, albeit with a low batting average. Morneau is the oldest of this bunch, at 34 years old, but has the Coors Field advantage and should hit for the best batting average. Hosmer is the youngest of this bunch (25), but is also coming off a bad year rate-stat wise (.270/.318/.398).

The interesting thing to notice is that this group of four, taken in rounds 10-14, is projected to be similar to the previous group of four, taken in rounds 7-9. There’s a difference of 90 picks between Santana at 77 and Hosmer at 166, but little difference in their projections, with Hosmer actually projecting better.

Final Notes: The top four of Miggy, Goldschmidt, Abreu, and Encarnacion are a tier above Freeman and Rizzo. Then you have Pujols, Martinez, and Gonzalez, with the wild cards of Votto and Fielder fitting in just below them. Beyond that, I’d expect diverse opinions when it comes to Santana, Davis, Duda, Pearce, Zimmerman, Trumbo, Morneau, and Hosmer. Davis is the most volatile. Pearce could be a late-bloomer, like Jose Bautista. Santana is likely the most predictable but is much more valuable if he can be played at catcher than first base, while Zimmerman and Trumbo are coming off injury-shortened years.

There are different ways to rank players for fantasy baseball. These rankings will depend on the league settings and your personal beliefs as to what is the best method. Currently, there are two main methods that immediately come to mind. Here at FanGraphs, Zach Sanders has posted his method to determine rankings and dollar values using z-scores, which takes into account the standard deviation for that player’s statistics compared to the set of players in the sample. Others prefer a method called Standings Gain Points. With Standings Gain Points, you will need to have an expectation of the end-of-year standings in the different categories based on previous year’s data.

I wanted to find a quick-and-easy way to rank starting pitchers and I remembered reading a post at Tom Tango’s site about the usefulness of “strikeouts minus walks” for pitchers (K-BB). Similarly, some writers here at FanGraphs have begun to use K%-BB% in their posts. I decided to look at a few options to see which is the best “quick-and-easy” way to rank starting pitchers.

For each of the last three seasons, I used the z-scores method to determine the top 100 starting pitchers for comparison purposes. More specifically: I found the sum of the standard deviations for four traditional categories for starting pitchers: wins, strikeouts, ERA, and WHIP (saves were not included because I was just looking at starting pitchers). I’ll use Clayton Kershaw as an example. I divided Kershaw’s wins (21) by the standard deviation of all pitchers’ wins in this sample (3.5) to get the z-score for Kershaw in the wins category (6.0). I did the same for strikeouts (239/41.9=5.7).

For ERA and WHIP, I had to do a little more work. Again, using Kershaw as an example. I took the ERA of the group of pitchers (3.27), subtracted Kershaw’s ERA (1.77), multiplied by Kershaw’s innings pitched (198.3), then divided by nine. The result was 33. This gave me Kershaw’s number of runs saved (runs allowed below what a pitcher with a league average ERA would allow in that many innings). This may be confusing to some. That number, 33, is how many more earned runs Kershaw would have to allow to have a league average ERA. In 2014, Kershaw pitched 198.3 innings and allowed 39 earned runs for an ERA of 1.77. Had he allowed 33 more earned runs, he would have allowed 72 earned runs. Allowing 72 earned runs in 198.3 innings would give him an ERA of 3.27, which is the average of this group of pitchers I’m working with.

I did a similar thing for WHIP. I took the WHIP of the group of pitchers (1.18), subtracted Kershaw’s WHIP (0.86), then multiplied by Kershaw’s innings pitched (198.3). This gave me the number of base runners saved for Kershaw (64). This means had Kershaw allowed 64 more base runners (walks or hits) in the same number of innings pitched, his WHIP would have been league average.

Once I found runs saved and base runners saved for each pitcher, I found the standard deviation of the group of pitcher for each metric. I then divided that pitcher’s runs saved and base runners saved by the standard deviation of the group of pitchers to get the z-scores for ERA and WHIP. Because I was only dealing with starting pitchers, I did not use saves, but if relievers had been included, saves would be done the same way as wins and strikeouts. Once I found the z-scores for wins, strikeouts, ERA, and WHIP, I added them together to get a total number for each pitcher. The pitchers were ranked by this total number for fantasy purposes.

Once I had this total for each pitcher, I ran correlations with each pitcher’s total number based on z-scores and some potential “quick-and-easy” methods to rank these pitchers. I started off with four potential methods: raw strikeouts, K-BB, K%, and K%-BB%. The following shows the correlation for these four methods with the total number figured above (the sum of the z-scores for the four fantasy pitching categories for starting pitchers).

For 2014:

0.80    K-BB

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.78    K-BB

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.77    K-BB

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

Of these four methods, K-BB has the highest correlation, followed by raw strikeouts. This makes sense because starting pitchers in fantasy baseball get some of their value from the innings they pitch. They need to pitch to get those wins and strikeouts. The other two options (K% and K%-BB%) don’t factor in playing time, so it’s not surprising that they don’t correlate as well as K-BB and raw strikeouts.

With this in mind, I took K% and multiplied by innings pitched for an additional metric, along with (K%-BB%)*IP for another. Below, I’ve included these two options.

For 2014:

0.83    (K%-BB%)*IP

0.80    K-BB

0.78    K%*IP

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.81    (K%-BB%)*IP

0.78    K-BB

0.77    K%*IP

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.80    (K%-BB%)*IP

0.77    K-BB

0.76    K%*IP

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

As you can see, (K%-BB%)*IP comes out on top, but the more simple K-BB is close and the point is to find a “quick-and-easy” method. The next idea I had was to incorporate home runs allowed. Keeping it simple, I created K-BB-HR and compared it to the others.

For 2014:

0.83    (K%-BB%)*IP

0.82    K-BB-HR

0.80    K-BB

0.78    K%*IP

0.72    Strikeouts

0.65    K%-BB%

0.58    K%

For 2013:

0.81    (K%-BB%)*IP

0.81    K-BB-HR

0.78    K-BB

0.77    K%*IP

0.72    Strikeouts

0.67    K%-BB%

0.56    K%

For 2012:

0.80    K-BB-HR

0.80    (K%-BB%)*IP

0.77    K-BB

0.76    K%*IP

0.71    Strikeouts

0.55    K%-BB%

0.44    K%

This method (K-BB-HR) is right there with (K%-BB%)*IP. It’s not quite as simple as K-BB, but it is quite simple. Using K-BB is very simple and will get you close to the more complex methods to rank starting pitchers. If you want to take it one step farther, use K-BB-HR.

So, without further ado, here are the top 20 pitchers ranked by K-BB-HR using Steamer projections for 2015:

1. Clayton Kershaw (164)
2. Chris Sale (152)
3. Max Scherzer (151)
4. Felix Hernandez (141)
5. Stephen Strasburg (137)
6. Yu Darvish (136)
7. Madison Bumgarner (135)
8. Corey Kluber (132)
9. David Price (123)
10. Matt Harvey (121)
11. Zack Greinke (118)
12. Jon Lester (116)
13. Masahiro Tanaka (111)
14. Cole Hamels (110)
15. Adam Wainwright (106)
16. Johnny Cueto (106)
17. James Shields (105)
18. Jeff Samardzija (102)
19. Jordan Zimmermann (101)
20. Ian Kennedy (100)

That’s a pretty good-looking list and easy to figure using three basic statistics and subtraction.

Adjusting to the New Reality

The level of offense in baseball has been dropping for some time now. In the 1980s and into the early 1990s, teams scored around 4.3 runs/game (with the exception of 1987, when offense jumped up to 4.7 runs/game for one year, then went right back down in 1988). Offense started to rise in 1993 and first jumped over 5 runs/game in 1996. Run-scoring peaked at 5.1 runs/game in 2000, then leveled off to around 4.8 runs/game through 2007. Since 2008, offense has gone down steadily, with 2014 seeing an average of 4.1 runs/game. You have to go back to 1981 to find fewer runs per game in baseball (4.0 runs/game).

This has implications in the world of fantasy baseball. Consider the table below that shows the ERA in Major League Baseball by year, going back to 2001:

Some would point to PED testing for the lower level of offense, some would blame a bigger strike zone, some would peg it on the increasing number of relievers throwing 95+ for an inning or two. Whatever the reason, this is the new reality and sometimes it can be hard to adjust to new realities.

Let’s look at the numbers shown above in more detail.

Over the stretch of years from 2001 to 2009, MLB had an ERA of 4.39. Over the three-year stretch from 2010 to 2012, ERA dropped to 4.01. The last two years have seen big drops each year, from 4.01 to 3.87, to 3.74.

This has repercussions in fantasy baseball. With ERA dropping quickly, we need to reevaluate the pitchers we take on draft day and during the season.

Let’s go back to 2009, when MLB had an ERA of 4.32. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.54. The median ERA for this top 60 was 3.77. There were 11 pitchers with an ERA under 3.00.

Fast forward to 2014. Last year, MLB had an ERA of 3.74. The top 60 starting pitchers in ERA (minimum of 160 IP) combined for an ERA of 3.14. The median ERA for this group was 3.33. There were 22 pitchers with an ERA under 3.00.

In 2009, the median guy in the top 60 was someone like John Danks (3.77) or Jarrod Washburn (3.78). Last year, the median guys in the top 60 were Jose Quintana (3.32) and Chris Archer (3.33). [Caveat: I know ERA isn’t the only way to judge a pitcher in fantasy baseball. I’m keeping it simple.]

Six years ago, when scouring the waiver wire, that pitcher with a 4.00 ERA was a potential pick-up. These days, you don’t want to look at that guy, he’ll just hurt your team. This may seem obvious, but it really is a change in mindset when you’re looking to improve your team. What we once thought was good is no longer good.

One of the side effects of a big drop in the run environment is the difficulty for projection systems to keep up. If we go back to the 2010 season, we can see a stark example. If a pitcher had league average ERAs in 2007 (4.47), 2008 (4.32), and 2009 (4.32), we could do a simple 3/2/1 weighted average for his three seasons and project an ERA of 4.35 for 2010. League-wide, though, ERA dropped from 4.32 in 2009 to 4.08 in 2010. Most projection systems will project ERAs that will be in line with the previous few seasons’ run environment. In this case, the projections will be well above what the actual ERAs were for the 2010 season (unless a projection system can anticipate such a drop in offense).

Let’s do the same for more recent seasons. If we take a pitcher with league average ERAs in 2011 (3.94), 2012 (4.01), and 2013 (3.87), and do a simple 3/2/1 weighted average, we get a 2014 projection of 3.93. The actual ERA in MLB in 2014 was 3.74, so pitchers as a group are going to be forecast with ERAs around 0.20 higher because the drop in offense was so drastic.

With this in mind, I looked at last year’s projections from four systems: Steamer, ZiPS, Davenport, and Oliver. I looked at all pitchers who were projected by each of the four systems who pitched 30 or more innings in 2014. There were 326 pitchers in this group and they finished 2014 with a combined ERA of 3.58. You can see how each of the projection systems forecast these players prior to the 2014 season:

When looking at the data, what you shouldn’t do is say that Davenport had the best projections. What is true is that Davenport best anticipated the run environment. Looking at the table, it would be easy to assume that Davenport and Oliver had the best projections, as they were closest to the actual ERA of this group of pitchers. In reality, if you are trying to assess which system better projected individual players, you would first want to adjust them all to the actual run environment, then compare the differences between projected ERA and actual ERA for individual pitchers.

In the case of the 326 pitchers used above, the table below shows the average absolute difference in actual ERA and projected ERA for each individual pitcher, using projections adjusted to the run environment of this group of pitchers.

Looking at it this way, it’s easy to see that the different projection systems were very close on this group of 326 pitchers and Davenport and Oliver are in the middle of the pack, with Steamer moving from the bottom to the top.

What does this mean for 2015? If you’re the type of fantasy baseball player who likes to create your own projections by combining projections from other sources, you will first want to know what level of offense those projections are expecting (ERA in this example). If you think 2015 will be much like 2014 (3.74 league-wide ERA) but the projections expect an ERA much higher or lower, you should adjust all pitchers by the amount the projections are high or low. With these new adjusted projections, you can now combine your projections.

As an example, I took those same 326 pitchers from above and compared their actual combined ERA from 2014 to their 2015 Steamer projections. This group of pitchers had a combined ERA of 3.58 in 2014. Steamer is projecting them to have a 3.84 ERA in 2015. The difference is 0.26 in ERA. I don’t know the run environment Steamer is basing their projections on, but this would suggest that it’s higher than what we saw in 2014.

Based on the disclaimer that accompanies each team’s ZiPS projections, we know that ZiPS is projecting based on the AL having an ERA of 3.93 and the NL having an ERA of 3.75. This would be a slight increase from the 2014 season (AL: 3.82 ERA, NL: 3.66 ERA) and is, essentially, a 3/2/1 weighted average from 2012, 2013, and 2014.

I looked at the starting rotations for the five teams that we have ZiPS projections for so far. There are 25 pitchers and they are projected by ZiPS to pitch 3985 innings with a 3.73 ERA. These same 25 pitchers are projected by Steamer to pitcher 4039 innings with a 3.98 ERA. Steamer is high by 0.25. Steamer projects higher ERAs for 23 of these 25 pitchers. This is a small sample of just 25 pitchers, but it would appear that you will want to adjust the Steamer pitching projections down if you do any sort of combining of projections in your fantasy baseball prep.

In addition, if you’re in a keeper league and have access to last year’s data for your league, you may want to project your keepers and potential additions for 2015 and compare your team projections to last year’s stat categories. This way, you will have an idea of how competitive your team will be. For example, I’m in an 18-team, 25-man roster league. We have nine starters on offense, four starting pitchers, and two relievers in our active lineups, and a 10-man bench that can be made up of players from any position. Teams in this league averaged around 1000 innings last season, so when I create projections, I can plug in the stats for my keepers and potential additions to see how my team looks for the upcoming season. In order to compare my projected 2015 team to 2014 stat categories, I want my projections to be adjusted to the level of offense of 2014 (in this case, ERA).

Offense in baseball has been dropping for a few years now. Successful fantasy players will have to adjust to this new reality when doing their pre-season prep work, on draft day, and when adding players from the waiver wire.

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

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

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

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 (17^th) and FIP (11^th), 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 78^th based on ERA and 47^th based o FIP, but moves up to 9^th 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

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.

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.

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.

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 40^th 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.

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.