In a fairly surprising turn of events, the Rockies demoted Jon Gray Saturday. Gray has arguably been baseball’s most enigmatic pitcher this year, posting a career-worst 5.77 ERA supported by career-best peripherals — e.g., a 13.4% swinging strike rate (SwStr%) underpinning a 28.9% strikeout rate (K%), and fielding independent metrics of 2.78 xFIP, 3.08 FIP, and 3.15 SIERA. Given our most basic sabermetric understandings of baseball, Gray should be a very good pitcher, even if he pitches half his starts at hitters’ paradise Coors Field.

I have written about how a common-breed Rockies pitcher’s peripherals might be penalized for calling Coors Field home (Gray inspired this bit of research as well). FIP metrics generally underestimate ERA by anywhere from 0.8 to 1.3 runs for home starts (compared to 0.0 to 0.2 runs for road starts), suggesting that Rockies pitchers may underperform (a) their FIPs by 0.35 runs or (b) their SIERAs by 0.65 runs — given error bars, maybe more.

Still, that doesn’t explain why Gray’s ERA is nearly 6 right now. I shed light on the ridiculousness of the move; his strand rate (LOB%) is suppressed and his batting average on balls in play (BABIP) is elevated, even compared to his uniquely bad baselines. I’m not sure there’s much more to it.

Nick Mariano of RotoBaller noted here that Gray’s fastball has been incredibly hittable since his debut and especially this year. Despite my thoughts on the inevitability of regression in Gray’s favor, I wanted to pursue Mariano’s train of thought a little further. Gray’s fastball is bad, but how bad? And why?

Read the rest of this entry »

There are 10 qualified starting pitchers with an xFIP under 2.80 in the last 30 days.

Among them are predictable names like Chris Archer (2.52), Clayton Kershaw (2.53), Chris Sale (2.64), Johnny Cueto (2.74), and Zack Greinke (2.76).

The other five aren’t as well known, and therefore, they are more intriguing.

Being in the top 10 xFIP leaderboard for a month is not necessarily a huge accomplishment. However, xFIP has one of the highest correlations with future ERA of all pitching metrics, so it’s among the most relevant numbers to examine when searching for potential breakouts or analyzing the legitimacy of poor or plus performance.

Below is a table sorted by the top 10 qualified starting pitchers in xFIP over the last 30 days*, with the best statistic in each category highlighted in yellow, and the worst statistic in each category highlighted in red: Read the rest of this entry »

If you follow me on Twitter, you know how this ends. Statistically speaking, though, you probably don’t follow on me Twitter, so you probably don’t know how this ends. Then again, maybe you really do know how this ends, because when you clicked this link, you probably had to read the title first. Or maybe you didn’t! Honestly, I don’t want to pigeonhole you. Maybe you’re the kind of person who clicks links all willy nilly with zero regard for content. I’m sure SEO folks love you but also lose their minds trying to understand you.

No matter. Let’s pretend you didn’t read the title. Now you’re presented with blind résumés. Can you guess who Players A and B are?

Did you have to cheat? It may actually be more difficult than you thought. You know the names already, but perhaps you got them out of order: Kyle Hendricks is Player A and Tanner Roark is Player B. But look at that! Hendricks and Roark are almost perfectly identical within every metric. Roark even edges Hendricks in xFIP, innings per start, and WAR per start. It’s kind of a big deal, given Hendricks is owned in more Yahoo! leagues than Roark (85% to 78%).

Read the rest of this entry »

A pitcher’s excellent performance early in the season can obscure poor performance later on, as his stats slowly converge toward expectations. If you held onto Jordan Zimmermann too long, you suffered the consequences. The same applies for the opposite scenario; Matt Shoemaker has been one of the baseball’s better pitchers since mid-May after an atrocious start to the season.

Alas, the deeper we get into the season, the more important it becomes to check recent leaderboards. With the way player performance ebbs and flows during a season, a span of five or six starts can probably be considered a small sample size. Still, keeping an eye on these small samples can illuminate interesting trends.

For whatever reason, there seem to be a lot of unfamiliar or unexpected names on FanGraphs’ xFIP leaderboards under the “last 30 days” split. Let’s break them down!

Read the rest of this entry »

I like a lot of pitchers who have unperformed this year. With strikeout and walk rates (K%, BB%) of 20.5 percent and 7.0 percent, respectively, Drew Hutchison delivers everything I want from a mid-rotation fantasy starter. With a 5.19 ERA and a 1.47 WHIP, however, he delivers a flaming bag of feces to my doorstep.

The same can be said for Taijuan Walker who, after a terribly rough start to the season, dazzled for seven straight starts before recently tossing three stinkers. With plate discipline ratios better than Hutchison’s and just 22 years old, Walker demonstrates the skill set and ceiling that have earned him consensus top-20 honors on prospect lists from 2012 through 2014. Yet his 5.06 ERA and 1.29 WHIP have left fantasy owners not only disappointed but also reeling.

Hutchison and Walker share a common trait: their ERAs dwarf their fielding independent pitching (FIP) statistics. FIP was designed to demonstrate a pitcher’s true performance in light of the events he can control — that is, events independent of balls put into play at the mercy of the defense supporting him (among other things).

Read the rest of this entry »

It took a dominant 11-strikeout, zero-walk performance from Taijuan Walker for the fantasy world to finally take notice of him. I don’t have ownership trend data to exemplify this, but I do have an anecdote: he was available in every league I’m in before the start, and he was owned in every league I’m in shortly after it.

The truth is Walker had demonstrated progress, described here by Eno Sarris, in his prior four starts, notching 27 strikeouts to three walks in 29 innings. Someone who hadn’t been paying attention to Walker probably wouldn’t have noticed: his ERA prior to the recent five-game surge stood at 7.33, and he had completed the sixth inning only twice in nine games. Once a hyped prospect, he looked like a 22-year-old who still needed seasoning to reach his potential.

No longer, as you will probably have to give up an asset of value to acquire Walker from a fellow owner now. The price may not be too steep given his poor ratios (4.94 ERA, 1.39 WHIP), but this is likely the highest they’ll be for the rest of the season.

Read the rest of this entry »

Contributing to RotoGraphs’ Bullpen Reports last year brought me much joy: opening infinite Brooks Baseball player cards for sabermetric outcomes and watching glorious GIFs. Oh, Marcus Stroman’s Two-Seamer:

GIF: Marcus Stroman’s Two-Seam Fastball from last week’s outing. It may be our favorite of the year – http://t.co/vyJM8ltUir

— Pitcher List (@ThePitcherList) August 31, 2014

Mmm.

My last Bullpen Report from October looked at possible closers through outcomes and presented BABIP differentials (actual BABIP versus expected BABIP using Inside Edge data).

Read the rest of this entry »

Last year at the All-Star break I did a piece that collected all the pitchers who had a difference of at least 0.50 between their FIP and xFIP. At the end of the season I collected the 2nd half ERA for each pitcher in the survey to see if FIP or xFIP did a better job of predicting the results. The raw results favored xFIP, as that metric did a better job predicting 20 of the 34 pitchers. However, FIP did a better job of predicting how the best pitchers in the group would fare.

The results were definitely interesting, but it was hard to make any proclamations off one year of data. So, now is the time to assemble this year’s chart of pitchers with a discrepancy of at least half a run between their FIP and xFIP. Like last year, this list was crafted by hand, so please let me know if you see any omissions. I am looking for pitchers who had 70 or more innings pitched at the All-Star break.

This year there are 38 pitchers in our survey. Five pitchers are repeats from a season ago – Kershaw, Lee, Blackburn, Verlander and Bannister. However both Blackburn and Bannister had HR/FB rates below average last year at the break while they are both above average this year. Kershaw, Lee and Verlander are the only ones who “beat” the average HR/FB rates in both seasons. As far as predicting 2nd half ERA goes, Kershaw and Lee were wins for FIP while xFIP did a better job of predicting Verlander.

To determine which metric is better at forecasting 2nd half ERA, I am going to take the midpoint between their FIP and xFIP and compare it to their real life ERA in the second half of the season.

Using Kennedy as an example, 4.57 is the midpoint between his FIP and xFIP. So, if Kennedy’s ERA in the second half is 4.44, I will count that as a “win” for xFIP. On the flip side, if Kennedy’s second half ERA is 4.66, I will count that as a “win” for FIP.

Like last year, I will check in on this list after the end of the regular season.

Earlier today, in Part I of the series, I published a chart of 34 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.

Zack Greinke – His xFIP was 101 points higher than his ERA, making Greinke a sell high guy. This was a big win for FIP.

Joel Pineiro – After allowing just three home runs in 17 first half games, Pineiro served up eight home runs in 15 games after the break. This was a big win for xFIP.

Tim Lincecum – It was a very good second half of the season for Lincecum, just not as good as the first half. He did have a slightly higher HR/FB rate in the second half, and xFIP did a better job predicting his post-break ERA. Still, those fantasy owners who kept him based on his FIP did not end up disappointed.

Dallas Braden – Made just four starts after the break due to a foot infection. Officially a win for xFIP, but one we should probably dismiss due to lack of playing time.

Paul Maholm – His second half ERA was lower than his first half one, despite more HR allowed. Still, this was a pitcher that FIP would have identified as a potential buy candidate at the break, so a win for xFIP.

Tim Wakefield – Made just four starts in the second half due to leg and back injuries. Officially a win for xFIP, but one we should probably dismiss due to the lack of playing time.

Clayton Kershaw – He had a 5.0 HR/FB rate at the break and was even better in the second half, as he finished the year with a 4.1 mark. His ERA finished two full runs below what xFIP predicted. This was a big win for FIP.

Derek Lowe – Opponents posted an .888 OPS versus Lowe in the second half of the season, including 10 HR in 331 ABs. This was a big win for xFIP.

Cliff Lee – Everyone thinks the move to the NL turned things around for Lee but he was 3-0 with a 1.44 ERA in his first three games with Cleveland after the break. His HR/FB rate has been below 11 percent the past five seasons. This was a big win for FIP.

Carlos Zambrano – This was the closest one, as Zambrano’s second half ERA of 4.14 was just barely closer to his first-half FIP than his xFIP. Zambrano pitched worse in the second half than in the first, but it had nothing to do with his HR rate, which declined slightly from the 5.8 he posted in the first half. This was a slight win for FIP.

Jair Jurrjens – Both FIP and xFIP predicted Jurrjens’ ERA to rise in the second half and instead he pitched even better after the break. If you went strictly by FIP at the break, you would have listened to offers for Jurrjens. If you went by xFIP you were in the sell/cut area. This was a win for FIP.

Jeff Niemann – As with Jurrjens, both of our metrics predicted an ERA rise from Niemann in the second half. FIP had him as a sell while xFIP had him as a sell or cut guy. This was a slight win for FIP.

Nick Blackburn – Yet another pitcher that both metrics forecasted a rise in ERA. Except this time, the actual rise was more drastic than even the more pessimistic xFIP predicted. Since you might have kept him if you used FIP, this was a big win for xFIP.

Edwin Jackson – Pretty much the same thing as with Blackburn above, except you were even more likely to keep Jackson if you used FIP. This was a big win for xFIP.

Mike Pelfrey – The spread with our two metrics was not nearly as great with Pelfrey as it was for Blackburn and Jackson, but the end results were the same. This was a big win for xFIP.

Jon Garland – FIP projected Garland to be virtually the same in the second half as he was in the first half while xFIP had him being noticeably worse. The trade to Los Angeles invigorated Garland, or perhaps it was simply leaving a bad home park, as he finished the year with a 5.29 ERA at Chase Field and a 1.67 ERA at Dodger Stadium. This was a win for FIP, but probably not a pitcher anyone was targeting at the break.

Felix Hernandez – Again, both metrics predicted an ERA rise in the second half, although xFIP was more pessimistic, making him a sell high guy with a difference of 94 points. Hernandez pitched even better after the break, making this a big win for FIP.

Justin Verlander – Both metrics predicted an ERA drop in the second half for Verlander, with FIP being the most optimistic. Verlander pitched well, but saw his ERA go up, making this a win for xFIP.

Brian Bannister – A 3.66 ERA in the first half made Bannister look like a useful pitcher. Both metrics saw an ERA increase, but xFIP was the most pessimistic. This was a big win for xFIP.

C.C. Sabathia – Our two metrics were split on how Sabathia would fare in the second half. With a predicted decrease from his first half ERA, this was a big win for FIP.

Brad Penny – Our two metrics were split again. But Penny’s ERA went up in the second half. This was a win for xFIP, but not many people who used FIP were angling to acquire Penny.

Vicente Padilla – His HR/FB rate went up significantly in the second half, yet Padilla produced a lower ERA after the break, thanks to a move to the NL. Neither metric identified Padilla as a pitcher to target, although FIP came very close to hitting his actual mark.

Jarrod Washburn – Both metrics identified Washburn as a sell candidate as his ERA was 92 points lower than his FIP and 150 points lower than his xFIP. Officially a win for xFIP, although you likely would have made the same decision regardless of which metric you used.

Jered Weaver – A win for xFIP, which had him as a sell, while FIP had him as a listen. There are also extra points for xFIP for exactly predicting his second half ERA.

Joe Blanton – The metrics were split on how Blanton would fare in the second half. This was a big win for xFIP, which forecasted him to be a useful pitcher and he ended up better than that.

Bronson Arroyo – Technically a win for xFIP but not many fantasy players were running out to acquire Arroyo based on his 4.99 first half xFIP.

Jamie Moyer – Repeat the comment from Arroyo, except sub in 5.06 xFIP.

Trevor Cahill – Same as the above two, except with a 5.18 FIP.

Chris Volstad – Our two metrics were split on Volstad. FIP saw him continuing to be a sell/cut guy while xFIP saw him being a useful pitcher with a sub-4.00 ERA. This was a win for FIP.

Rick Porcello – Both systems predicted a rise in ERA but FIP elevated him to cut status. This was a win for xFIP.

Braden Looper – The two metrics were split on Looper, with xFIP predicting a drop in ERA. Looper actually pitched worse in the second half but neither system would have advocated acquiring him at the break.

Josh Geer – Made just three starts after the break due to lousy pitching. Not one that either system would have suggested to add.

Rich Harden – While most of the players with above average HR/FB rates have been of little or no value in regards to fantasy, Harden is the exception. Both systems saw him improving on his first half ERA but xFIP was much more bullish. And Harden exceeded those expectations. This was a big win for xFIP.

Randy Johnson – Appeared in just four games after the break due to a rotator cuff strain. Officially a win for FIP, but one we should probably dismiss due to lack of playing time.

*****

If you made your fantasy decisions this year based on xFIP, you would be feeling very good about your choices with Pineiro, Lowe, Blackburn, Jackson, Pelfrey, Bannister, Blanton, Harden and to a lesser extent Maholm, Verlander, Weaver and Porcello.

If you made your fantasy decisions based this year based on FIP, you would be feeling very good about your choices with Greinke, Kershaw, Lee, Hernandez, Sabathia and to a lesser extent Jurrjens and Volstad.

From a pure bulk standpoint, you were better off in 2009 using xFIP at the break. But those who relied on FIP were more likely to make the right call on four of the five pitchers with the lowest ERA in the second half among the 34 pitchers in our sample.

We really cannot make any inferences for the future based on this one small sample. What we can say is that judging strictly from results in 2009 it would be a mistake to ignore FIP completely and absolutely while making fantasy decisions at the All-Star break. This year if you used xFIP you would have made the wrong decisions on some of the best pitchers in the game.

Last month, my friend and colleague Derek Carty of The Hardball Times (THT) wrote a provocative article questioning the utility of FIP. Carty wrote, “While the original, underlying premise for FIP is sound, and while it’s absolutely better to use than simple ERA, and while there are certainly uses for FIP in some circumstances, for 99 percent of fantasy purposes, I ignore FIP completely and absolutely.”

Carty proceeded to list pitchers he believed were under and over valued by FIP, mainly due to their HR rate. He suggested that instead of FIP, we use LIPS (Luck Independent Pitching Stats). The problem with LIPS is that it takes a lot of work to calculate and is not freely available on a regular basis.

Since the main beef with FIP is HR rate, it should be relatively similar to use xFIP, a stat invented by THT which they describe as: “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 (sic) of a pitcher’s future ERA.”

As a general rule, most starting pitchers will have a HR/FB rate around 11 percent in a full season’s worth of pitching. However, there are always going to be exceptions to the rule. In 2008, Cliff Lee had the lowest HR/FB rate with a mark of 5.1 percent while Brandon Backe checked in with the highest at 16.1 percent. In 2007, the low was 4.1 percent while the high was 17.7 percent.

So, from a fantasy owner’s point of view, when evaluating pitchers should you look to normalize HR rate and use xFIP or are you just as likely to come out with a correct answer if you use FIP?

Here at the All-Star break, I have gone through and compiled a list of pitchers who have a difference 0.50 or greater between their FIP (taken from FanGraphs) and their xFIP. This list was done by hand, so it is possible I omitted someone by mistake. Please alert me if you come across someone I missed.

We have 34 people with a 0.50 or greater difference between their FIP and xFIP. Unfortunately, these are not all people you would want to have in a standard 12-team mixed league but the vast majority of these are roster worthy.

At the end of the year, I am going to come back to this list and see which one of these metrics was better for fantasy purposes. I am going to take the midpoint between their FIP and xFIP and compare it to their real life ERA in the second half of the season.

Using Greinke as an example, 2.55 is the midpoint between his FIP and xFIP. So, if Greinke’s ERA in the second half is 3.33, I will count that as a “win” for xFIP. On the flip side, if Greinke’s second half ERA is 2.22, I will count that as a “win” for FIP.

I am curious to find out what the raw score will be. My guess is that it will be fairly close to 50-50, with neither metric enjoying a huge advantage. Perhaps more importantly, I will also look to see if either metric does a better job of predicting a certain class of pitcher.

Regardless of what the results are in 2009, it is only one season’s worth of information.