Archive for Johnny Cueto

Streaming Starters: September 21, 2019

Down the stretch, we’ll be covering the probable starters and highlighting our favorites to stream as you chase down your fantasy titles. The pitcher in question must be available in 50% or more of leagues according to FantasyPros.com, which combines ESPN and Yahoo! roster rates (sometimes exceptions just over 50% will be mentioned if they are really good and should have a much higher roster rate).

Note: * denotes that combined ESPN/Yahoo! ownership rates were unavailable on FantasyPros.com, so ESPN’s rate was used.

Pick Him Up!

Zac Gallen (3-6, 2.81) at SD | 51%

Gallen and his 28.7 percent strikeout rate take on the Padres at PETCO Park? It’s a matchup made in Strikeout Heaven. He’s a must-start and a must-add wherever available.
Read the rest of this entry »


Starting Pitcher Walk Rate Over- and Under-Achievers

Here in the final week of April, sample sizes are starting to build up, but there is still a lot of statistical weirdness out there. Just in the walk rate leaderboard alone there are some confounding data for fantasy owners to ponder. Jose Quintana and Michael Wacha with double digit rates? Vince Velasquez among the lowest one-fourth? It’s like I hardly know these guys.
Read the rest of this entry »


The Mystifying Reds Rotation

The 2014 Reds rotation ranked third in ERA but just 23rd in WAR, which is a bit strange given that Cincinnati got more innings out of its starters than any other team. Alfredo Simon played a fairly large role in that discrepancy as he threw just shy of 200 innings with a 3.44 ERA but just 0.9 WAR. Simon’s low strikeout rate doesn’t exactly rack up WAR. If Mat Latos and/or Homer Bailey had been able to throw a full season’s worth of innings, the discrepancy surely would have been less pronounced.

The good news is that Simon will be staving off the regression monster elsewhere and Bailey will hopefully make at least 30 starts this year. The bad news is that Latos is gone and the Reds didn’t do much in the way of replacing Latos or Simon’s numbers. They’ll go with internal options, which isn’t assured to go wrong, but the back half of the rotation is iffy at best. Read the rest of this entry »


Building a Closer Through Outcomes

There are a sundry of middle/set-up relievers that can succeed in the closer role if given the opportunity. A few obvious: Wade Davis if something happens to Greg Holland… Wil Myers for James Shields and who?!; Ken Giles if the Phillies can somehow find a trade partner for the grundle-grabber; and Brad Boxberger if not Jake McGee. I assume David Robertson signs elsewhere and Dellin Betances steps in.

Lets’ look at potential closers using reliever outcomes. Here are the average contact and balls in play-related outcomes for all relievers that qualified and specifically relievers with 10+ saves:

Command: K% BB% K-BB% Ct% SwStr% Zn% F-Str%
AVG for RP w/ > 10 SV 0.27 0.07 0.19 0.75 0.12 0.46 0.63
SD for RP w/ > 10 SV 0.08 0.03 0.08 0.06 0.03 0.04 0.04
AVG for all qualified RP’s 0.22 0.09 0.14 0.77 0.11 0.45 0.60
SD for all qualified RP’s 0.07 0.03 0.07 0.05 0.03 0.04 0.05
Balls In Play: GB% FB% IFFB% GB/FB HR/FB LOB% BABIP
AVG for RP w/ > 10 SV 0.43 0.37 0.11 1.40 0.08 0.78 0.275
SD for RP w/ > 10 SV 0.11 0.10 0.04 1.02 0.05 0.08 0.042
AVG for all qualified RP’s 0.45 0.35 0.09 1.50 0.09 0.75 0.289
SD for all qualified RP’s 0.10 0.09 0.05 0.81 0.05 0.08 0.044

Our Filters: 

  • Contact-related outcomes: For K-BB%, Ct% and SwStr%, I filtered simply by the general relief pitcher averages. Everyone below average in these 3 categories was filtered out.
  • Command-related outcomes: For zone% and first-pitch-strike%, I used 1 standard deviation below average and 1.25 SD’s below average in BABIP as filters. 1.25 SD allowed me to omit only the relievers that had career BABIP’s higher than we would like to see for a closer or in general. I didn’t want to screen out Jenrry Mejia (1.24 SD below the mean) or Tim Stauffer (1.19 SD below), because there’s a possibility for BABIP regression.
  • Balls In Play-related outcomes: I was lax on the balls in play outcomes. I went with a 40% Grounder rate and 45% Flyball rate as my filters versus the averages that you see above because below average fly-rates don’t mean much in places like Tampa (where Boxberger is elite but below average in fly-rates); and above average grounder-rates don’t mean as much with atrocious defense behind you (hence Corey Kluber’s unlucky BABIP, which should have been closer to .299 per end-of-season xBABIP/Inside Edge data), but I digress.

Using these filters, we’re left with a robust list of above-average relievers beyond just closers (scroll down for the noted filters):

What happens if we use the command (K-BB%)/contact (Ct% and SwStr%) related averages for relievers with more than 10 saves this year? 

…We’re left with some elite closers and then a few interesting names.

Last year, Danny Farquhar (just missed the list this year) had a top 20 swinging-strike rate – about a percent better than Fernando Rodney, but it was masked by his BABIP and left-on-base rate that killed his surface stats (4.20 ERA vs. 2.40 xFIP). This year, he actually outperformed his xFIP with a 2.66 ERA. After an early season MASH Report on Rodney’s velocity, I eyed Farquhar. At least keep him in mind next year if anything does happen to Rodney.

Josh Edgin (Mets for those of you that don’t know) has a top 65 contact-rate sandwiched between Mark Melancon and Jake McGee and even induced grounders 50% of the time. He has a pretty extensive repertoire as well. In order of usage: Fourseamer, Slider, Curve, Change, Cutter and Sinker. According to his Brooks Player Card, he has great swing and miss rates on his Curve (>56%), Cutter (50%), Slider (>42%) and Sinker (33%). Even his Change approaches 30%. This isn’t the case on his Fastball, but at 93+ MPH, it induces a decent amount of grounders (1.8 GB/FB). Keep in mind he had late-season elbow issues which effected his velocity by a MPH or so, but he could be called upon to get Mejia out of a jam. I like him better than an unhealthy Bobby Parnell and Jeurys Familia from a command perspective for another year.

Zach Duke did his best Craig Kimbrel impression prior to the R2M monster hitting him in August. Prior to 8/1, Duke had a 34.9% K-rate and 27.3 K-BB%. Kimbrel ended the year with a 38.9% K-rate and 28.3 K-BB%. I think August and September brought him back to his realistic value (~2.50 ERA, 1.15 WHIP). It will be interesting to see who closes for the Brewers if they let Francisco Rodriguez go. Both Duke and Will Smith have above average (even for RP w/ 10+ saves) swing-and-miss. Will Smith should have additional command next year, but Zack Duke induces grounders better which I like in my closers/in Milwaukee. They also have Jonathan Broxton. The hierarchy seemed to be K-rod-Broxton-Smith late last season. If that’s the case, they should use Duke more (former starter) and in higher leverage situations. He was equally solid against both lefties (.258 wOBA) and righties (.262 wOBA).

Oliver Perez everybody! I thought I could filter him out by his splits being a lefty, but like 2012, he was more effective vs. righties (and faced 44 more of them). The D-backs have Addison Reed, up-and-comer Evan Marshall as well as Daniel Hudson caught touching 97 MPH so if not by outcomes or splits, we can filter Perez out by opportunity.

Darren O’Day and Andrew Miller is part of one dominating Baltimore bullpen – one that gets referenced by anyone who thinks the Orioles can beat the Tigers in the ALDS. Notice that Zach Britton didn’t make either of the above lists! He was filtered out by his below average K-BB% (13.70%). It’s his 75+% grounder rate (hence the 81+% left-on-base rate and .215 BABIP) that keeps him elite in Baltimore. The only concern you can have with O’Day is a fastball velocity almost 2 standard deviations below the mean for relievers, but his arm angle combined with that slider still induces a 30+% whiff-rate on both pitches. Miller’s slider though is a world apart from O’Day’s: only Pedro Strop, Will Smith, Jake Diekman, Greg Holland and Oliver Perez induces more whiffs than Miller’s 55% according to Baseball Prospectus’ Pitchf/x Leaderboards.  I doubt we’ll see a closer-transition next year in Baltimore unless Britton’s GB/FB ratio takes a drastic dive because his HR/FB ratio, which approached 18%, could be an issue.

An xBABIP review

On the last day of the season, @jeffwzimmerman provided me with Pitch xBABIP based on inside edge data. Let’s look at some of the bigger xBABIP differentials to keep in mind:

The last column depicts the z-score for BABIP differential. Francisco Rodriguez was expected to have a BABIP about 120 points above his actual BABIP. I highlighted (red/bad; green/good) the xBABIP z-scores as well so that you know whether or not to actually be concerned meaning sure Aaron Sanchez has the 5th biggest BABIP differential (over 2 standard deviations from the mean), but a .239 xBABIP is still utterly elite (3.34 SD’s from the mean). On the other side of the equation, it’s nice to see Evan Marshall, Carlos Martinez and Adam Ottavino (albeit in Colorado) with large BABIP differentials. Marshall and Martinez even have xBABIP’s over .5SD from the mean.

The last bit of fun

It was a very fun year to be doing bullpen reports for RotoGraphs. Aroldis Chapman broke the single-season strikeout rate of 2012 Craig Kimbrel (50.2%). He struck out 52.5% of the hitters he faced. Andrew Miller (42.6%) and Brad Boxberger (42.1%) also made the top 10 seasons ever. Dellin Betances (39.6%), Wade Davis (39.1%) and Craig Kimbrel (38.9%) made the top 20. Chapman’s swinging-strike% of 20% beat ’12 Kimbrel by .8%, but he couldn’t pass ’04 Lidge, ’03 Gagne, ’04 Gagne, ’02 Gagne or ’05 Lidge. Chapman, Miller, Doolittle, Boxberger, Betances (Wade Davis and Kenley Jansen close behind) all had historical, top 20 K-BB rates. Relievers dominate this list: only ’99 Pedro Martinez (#12), ’00 Pedro Martinez (#21), ’01 Randy Johnson (#27) and ’01 Pedro Martinez (#28) make it into the top 30, but it’s clear that we have a growing list of elite relievers.

From a fantasy perspective, thanks to 45+ saves totals out of Holland and Kimbrel, we had two relievers ranked in the top 20 pitchers. If Chapman didn’t miss time and Betances and Davis consumed the closer role, we would have had 3 others. Last year, Craig Kimbrel and his 4 wins, 50 saves, 98 SO’s, 1.21 ERA and .88 WHIP campaign made him the 3rd most valuable pitcher. This year with Kershaw, Cueto, Felix and Kluber, it would have taken even more.

If we combined the 3 more dominating performances exclusive of saves this year: Aroldis Chapman’s K-rate (52.5%) and saves total (36), Dellin Betances IP (90) – who was dominating in his own right, Wade Davis’ ERA (1.00) and Wins total (9) and Sean Doolittle’s WHIP (.73) – let’s call this guy Aroldellin Dooldavis, we would wind up with a 10.95 z-sum…just above Corey Kluber (10.72), but under Clayton Kershaw (13.74), Johnny Cueto (12.95) and Felix Hernandez (12.50). Even 50 saves wouldn’t have done the trick (12.43 z-sum):

Name Age IP WHIP zWHIP ERA zERA W zW SO zSO SV zSV 5×5
Clayton Kershaw 26 198.1 0.86 4.23 1.77 3.53 21 3.38 239 2.95 0 -0.34 13.74
Johnny Cueto 28 243.2 0.96 3.91 2.25 3.22 20 3.16 242 3.01 0 -0.34 12.95
Felix Hernandez 28 236 0.92 4.29 2.14 3.37 15 2.06 248 3.12 0 -0.34 12.50
Aroldellin DoolDavis 25 90 0.73 2.50 1 2.25 9 0.74 177 1.77 50 5.17 12.43
Corey Kluber 28 235.2 1.09 2.14 2.44 2.69 18 2.72 269 3.52 0 -0.34 10.73
Adam Wainwright 32 227 1.03 2.79 2.38 2.72 20 3.16 179 1.80 0 -0.34 10.13
Jon Lester 30 219.2 1.1 1.88 2.46 2.46 16 2.28 220 2.59 0 -0.34 8.86
David Price 28 248.1 1.08 2.40 3.26 0.89 15 2.06 271 3.56 0 -0.34 8.56
Chris Sale 25 174 0.97 2.68 2.17 2.43 12 1.40 208 2.36 0 -0.34 8.52
Madison Bumgarner 24 217.1 1.09 1.97 2.98 1.35 18 2.72 219 2.57 0 -0.34 8.27
Zack Greinke 30 202.1 1.15 1.18 2.71 1.78 17 2.50 207 2.34 0 -0.34 7.46
Max Scherzer 29 220.1 1.18 0.94 3.19 0.93 18 2.72 252 3.20 0 -0.34 7.45
Jordan Zimmermann 28 199.2 1.07 2.02 2.66 1.85 14 1.84 182 1.86 0 -0.34 7.22
Julio Teheran 23 221 1.08 2.13 2.89 1.57 14 1.84 186 1.94 0 -0.34 7.13
Stephen Strasburg 25 215 1.12 1.61 3.14 1.01 14 1.84 242 3.01 0 -0.34 7.12
Garrett Richards 26 168.2 1.04 1.96 2.61 1.64 13 1.62 164 1.52 0 -0.34 6.39
Greg Holland 28 62.1 0.91 1.11 1.44 1.28 1 -1.02 90 0.10 46 4.73 6.21
Craig Kimbrel 26 61.2 0.91 1.09 1.61 1.16 0 -1.24 95 0.20 47 4.84 6.06

 


Guarantee Fairy: Deep League Options

I’ve stolen from the movie before. I’ll do so again…

Guarantee? If you want me to take a dump in a box and mark it guaranteed, I will. I got spare time. But for now, for your fantasy teams’ sake, for your daughter’s sake, ya might wanna think about listening to quality content from me.

If you don’t know where this reference is from, then well…just ring your call button, and Tommy will come back there and hit you over the head with a tack hammer.

I actually will play guarantee fairy here, specifically for deep leagues since there are no uber-exciting names that jump out in my below grid. So here goes…

So long as they pitch to a qualifying level of innings without getting hurt or losing velocity (not ballsy enough to leave out these contingencies), I GUARANTEE these starters won’t be any worse next year (although in the grid below I highlighted in different strengths of green/red both starters and relievers):

Read the rest of this entry »


Way Too Early Top 10 SP for 2015

I spent an hour trying to find something relevant to say about the last two weeks of the season but was unsuccessful. Or at least there was no topic worth spending several hundred words on. If you’re looking for a two start streamer in what is likely the last week of your H2H playoffs, look at Blue Jays Marcus Stroman and Drew Hutchison. They’re the most talented pitchers owned in less than 50% of ESPN.com leagues in terms of K-BB% and OPS allowed on balls in play (aka limiting hard contact). Instead, let’s spend a few hundred words quickly running through a way too early top 10 SP list for next year. Read the rest of this entry »


Trying to Measure Contact Management

This past weekend I had the pleasure of attending SaberSeminar in Boston. This is the second consecutive year I have been able to go, and I would highly recommend that you attend in future years if at all possible.

There were many great presentations, but one in particular stood out to me because of the potential relevance to fantasy baseball. Our very own Tony Blengino gave a spectacular presentation on the best and worst pitchers in the history of baseball at contact management (aka inducing weak contact). As far as I can tell, Tony took the HITf/x data, to which us normal people don’t have access, and calculated how each pitcher performed when allowing the various batted ball types. He then combined the performance on various batted ball types and scaled to 100 like we do here with things like ERA- and wRC+. I’m positive I’m simultaneously butchering the methodology while leaving significant portions of it out. Forgive me. For a little more insight, read Blengino’s recent posts on limiting hard contact for AL pitchers and NL pitchers.

This got me all fired up to get back home and see if I could calculate something like what Tony came up with so that we could use this as a fantasy tool. I was thinking this could be a new mechanism by which we could determine a player’s ability to induce weak contact. That’s a drum that Michael Salfino has long been beating by looking at ISO allowed. Salfino has rightly pointed out that hit quality (measured by ISO allowed) is more meaningful than the hit itself (measured by BABIP). Read the rest of this entry »


An Enviable Cincinnati Reds Rotation

Believe it or not, the Cincinnati Reds’ Starters contributed more total innings than any other National League rotation in 2013. In fact, there aren’t too many other teams with fewer question marks going into 2014 than the Reds, and that’s despite letting their #3 starter walk in free agency (or so it seems). The Reds, by my count, have a very solid four contributors in any fantasy format, and even though they play in one of the friendliest places to hit, you’d be pretty fortunate to have a pair of them on your squad headed into 2014.

Read the rest of this entry »


Johnny Cueto’s Continued Success

If Cincinnati’s Johnny Cueto had managed to pitch just six more innings in a 2011 that started late because of right shoulder inflammation and ended early because of a strained lat, he’d have finished second only to NL Cy Young Award winner Clayton Kershaw with a 2.31 ERA. (Assuming that in those six innings, he pitched to a consistent level of production as he had before, of course.)

Ending up behind only Kershaw and just ahead of Roy Halladay is pretty impressive company, yet I can’t say I was completely buying into Cueto simply based on that. His sparkling 2.31 ERA was hardly backed up by a 3.45 FIP and a 3.90 xFIP; along with declining velocity and strikeout rates, it seemed that Cueto’s nice season was more a mirage of a career-low .249 BABIP than anything else. I believe that prior to the season on another site, I named him my “most overrated” pitcher headed into 2012, figuring that the ERA would likely to return back to his previously established rates.

Three months into the season, Cueto is outdoing himself with a 2.21 ERA despite having his BABIP indeed return to almost exactly his career average, and clearly I couldn’t have been more wrong about him. How is he doing it? Read the rest of this entry »


2010 FIP Challenge Results Part II

Earlier today, in Part I of the series, I published a chart of 38 pitchers who had a difference of 0.50 or greater between their FIP and xFIP at the All-Star break and their 2nd half ERAs. Here I want to go into more detail rather than just giving a raw score for the two metrics.

In rating the two systems, I considered the metrics to recommend keeping a pitcher if at the All-Star break they were at 3.50 or lower, to listen to a trade if they were between 3.51 and 4.00, to actively look to sell the player if they were between 4.01 and 4.50 and to either sell or cut a pitcher if they were above 4.51.

Of course, we also have to consider what the pitcher’s actual ERA was at the break, too. A pitcher could still be a sell candidate if one of the metrics was significantly higher than his ERA. For these extreme cases, I considered a difference between 50-75 points to be a “listen” candidate, while above 75 to be a “sell high” guy.

Felipe Paulino – pitched in just 5.2 innings after the break. Officially a win for xFIP, but one we should probably dismiss due to a lack of playing time.

Francisco Liriano – His second half ERA was better than his first half mark, but both metrics thought he was outstanding before the All-Star break. This is a clear win for xFIP.

Anibal Sanchez – His 2nd half ERA (3.44) was a near-perfect match for his first half FIP (3.46).

Clay Buchholz – Both systems thought Buchholz was not nearly as good as he was in the first half. FIP had his as a keep while xFIP said he was an active sell. Since Buchholz did even better in the second half of the season than the first, this was a clear victory for FIP.

Josh Johnson – Both systems had Johnson as a keeper, but xFIP did a better job predicting his 3.50 post All-Star break mark.

Daisuke Matsuzaka – After allowing 4 HR in 71 IP in the first half, Matsuzaka served up 9 HR in 82.2 IP after the break. Big win for xFIP.

Johan Santana – His 3.00 ERA in the second half almost identical to his first half mark of 2.98. xFIP had Santana as a cut, so an easy win for FIP.

Jason Vargas – Eight of his 14 starts after the break came on the road and he allowed eight of his 10 second half HR away from Safeco. Big win for xFIP.

Justin Verlander – In the last three years, Verlander has posted an ERA over 5.50 in the month of April. He was terrific from May 1st through the end of the season again in 2010. Easy win for FIP.

Barry Zito – A lousy second half of the season made Zito a spectator for the Giants in the post-season. xFIP did an outstanding job predicting Zito’s collapse.

Ubaldo Jimenez – Just like with Zito, xFIP was just about perfect predicting Jimenez in the second half.

Tom Gorzelanny – Both systems saw Gorzelanny as a pretty good pitcher, but xFIP came closer to his second-half collapse.

John Danks – Again, the crystal ball for xFIP was right on target for Danks.

Tommy Hanson – In both seasons in the majors, Hanson has outperformed his xFIP. He turned it up a notch in the second half of 2010, thanks as much to his .233 BABIP as his 7 HR in 100.1 IP.

Matt Cain – Another pitcher with a history of outperforming his peripherals, Cain beat his FIP by nearly a run and his xFIP by nearly two runs in the second half of 2010.

Clayton Kershaw – Just as good in the second half of the season as he was in the first.

Cliff Lee – Many people wanted to eliminate Lee from this study last year, as he went from a pitcher’s park to a hitter’s park. But Lee outpitched his xFIP after the break in 2009. No such luck for Lee this year while following a similar story line of moving to a tougher park for pitchers.

C.J. Wilson – Like Kershaw, he was remarkable consistent between halves and ended up as a win for FIP.

Livan Hernandez – Those of us who kept predicting the bottom to fall out for Hernandez in 2010 are still waiting. Meanwhile, his second half ERA of 4.02 was a perfect match for his first half FIP.

Doug Fister – I imagine even the staunchest FIP supporters were shopping Fister every chance they could.

Fausto Carmona – After back-to-back seasons with a BB/9 over 5.00, Carmona allowed just 29 BB in 94.0 IP after the break last year. That had more to do with it than HR rate (10 HR in 94 IP) for why FIP was a clear winner.

Gavin Floyd – Fantasy owners did not know start from start what to expect from Floyd, but xFIP did a nice job of predicting his second half ERA.

Mark Buehrle – Like Wilson and Kershaw, Buehrle was a model of consistency with his ERA between halves this year. However, I worry about his K/9 rate and would be shocked if he was on any of my teams next year.

Brandon Morrow – With the Blue Jays out of the race, they decided to shut down a healthy Morrow after his first September start, which limited him to 46.1 IP after the break. A polar opposite to Buehrle, Morrow posted a 10.95 K/9, up from 8.14 a season ago.

Johnny Cueto – Cueto’s second half ERA was in the range predicted by his first half FIP and xFIP. But it was just 0.05 away from his FIP.

John Lackey – His second half ERA of 3.97 was a nice match for his 3.89 career mark but I doubt that makes too many Red Sox fans happy about his season and the team’s remaining obligation to him.

Kevin Correia – The first player on our list to have a big discrepancy between his FIP and xFIP due to a high HR rate, Correia did not show much regression in the second half of the year. Those who thought he would rebound, especially considering his home park, were disappointed. Correia allowed 13 HR in 82.1 IP in Petco this year.

Cole Hamels – Just like in 2009, one pitcher from the high HR rate side completely turned things around to become one of the best pitchers in the second half. Hopefully, Hamels has more luck in 2011 than 2009’s entry did. Rich Harden had a 5.58 ERA in 20 games with the Rangers this year after having a tremendous post-break performance (2.55 ERA) in 2009.

Nick Blackburn – Both systems would have advised cutting Blackburn, who responded with a 3.94 ERA in the second half, as he showed a big across-the-board improvement, including a microscopic 1.83 BB/9.

Kevin Millwood – Most of the players with a high HR/FB rate come back as not worth the risk by both systems. But xFIP said Millwood was significantly better than he showed in the first half and did an excellent job projecting his post-break ERA.

Jeff Karstens – An sore shoulder led to just one appearance in September. I regret the pain suffered by Mr. Karstens but it’s probably just as well that it played out that way.

Zach Duke – Hard to believe he made the All-Star team in 2009. Since then he is 11-23 with a 5.52 ERA.

James Shields – Both systems predicted a big bounce-back performance in the second half by Shields but that never materialized. He continued to give up HR by the basket, saw his K/BB ratio drop by over a full point and saw his BABIP increase to .362 after the break.

Brian Bannister – Limited to 25.2 IP in the second half due to rotator cuff tendinitis.

Randy Wolf – Both systems saw Wolf as waiver wire fodder but he had a 2.67 ERA in his final 13 starts, which was right after I placed him on waivers in a dynasty league.

Doug Davis – Elbow tendinitis kept Davis from pitching after the All-Star break.

Ricky Nolasco – FIP was nearly perfect with its Nolasco forecast, a marked departure in recent history, as he has generally underperformed his peripherals the past two seasons. Of course, Nolasco pitched just 47 innings after the break due to knee surgery.

Ian Kennedy – Neither system thought much of Kennedy going into the break but xFIP had a brighter outlook. Meanwhile, Kennedy put it altogether after the All-Star game, with Quality Starts in seven of his last nine outings. He finally started pitching well in his home park. In his last three games in Chase Field, Kennedy allowed 4 ER (0 HR) in 19 IP.

*****

When I started this comparison in 2009, my belief was that you would be just as well off using either system. After last year, there was a definite raw advantage for xFIP but now with two years worth of data, the two systems are basically even. Overall, there have been 72 pitchers who’ve had a 0.50 or greater difference between their FIP and xFIP at the All-Star break. Here’s how they did if you used their first half FIP or xFIP to project their second half ERA:

xFIP – 37
FIP – 34
Push – 1

Both systems have strengths and weaknesses. Generally speaking, xFIP does a better job with non-elite pitchers with low HR rates while FIP does a better job with elite hurlers. So, if a Tom Gorzelanny is cruising along with a sub-7.0 HR/FB rate, it appears you should look to sell high. But if it’s Justin Verlander, perhaps you should hold onto him.

We know that over the long haul that xFIP is the better metric to use for most pitchers. The issue here is that for one season (or one partial season) there may not be enough time for regression to fully kick in. Let’s look at Tim Lincecum. In the first half of 2009, he had a 3.9 HR/FB rate. In the second half of the season he had a 7.5 HR/9. This year he had a 9.9 HR/FB ratio. He has been regressing towards a normal HR/FB rate since the first half of 2009. But it did not all come in the same season.

Readers have suggested using first half ERA, or a mid-point between first half FIP and xFIP or an average of all three to see which one best predicted second half ERA. I think these are worthwhile suggestions and perhaps ones that we can use in the future (going back retroactively, too) as our sample size increases. We can also eliminate pitchers who did not pitch substantial innings and look for other trends and anomalies as our population gets bigger.

This started with a claim by my friend and colleague Derek Carty that FIP was basically useless for fantasy purposes with other metrics like xFIP available. Right now it appears FIP is making a case not to be tossed into the trash can by fantasy players.