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Well, it’s that magical time of year. The season is winding down, and here we sit preparing to begin our annual stare into the frozen, dead abyss of a fantasy-baseball-less winter. Fortunately, those of us in keeper and dynasty leagues are somewhat exempt from this situation because, as the saying goes, there is no offseason. This is actually, legitimately one of my favorite times of the year for those formats, as it’s the time when we can start to analyze the breakouts and the disappointments of the past season and begin to construct rudimentary target lists for upcoming trade talks over the winter and, in shallower formats, drafts next spring. I play primarily in head-to-head leagues, and particularly in this format I’ve found the following exercise to be quite valuable as a quick and dirty starting point. Since head-to-head championships are determined by player performances during weekly format playoff matchups the it tends to open up opportunities for acquiring players on the relative cheap who performed poorly down the stretch and may have contributed to a league-mate losing his or her final matchup. This “recency bias” plays off exactly the kind of negative memory impact my colleague Jeff Quinton took a long and engaging look at last offseason, and I highly recommend the read for context.

One of the primary places I like to direct my efforts at the start of each off-season is the FIP bin of second-half pitching performances. Particularly given that starting pitchers will usually only get one shot, maybe two, to contribute in a head-to-head matchup a poor finish to the year can sting that much more. And the metric also provides a valuable opportunity to sniff out some emerging talent among guys who may have started to come into their own at the big league level but might not quite have the production (or, subsequently, the hype) to back it up just yet.

To this end, here are the top 20 pitchers with the biggest gap between their second-half ERA and FIP numbers:







Nathan Eovaldi





Scott Kazmir





Trevor Cahill





Clay Buchholz





A.J. Burnett





Kyle Gibson





Mark Buehrle





John Danks





David Price





Hisashi Iwakuma





Marcus Stroman





Gio Gonzalez





Jake Arrieta





Justin Verlander





Drew Hutchison





Brett Oberholtzer





Bartolo Colon





Danny Salazar





Rubby de la Rosa





Tim Hudson




It’s an interesting list, to be sure. On the one hand you’ve got a few of the higher end starters that you’d expect to see posting strong FIP numbers. Guys like Price, Iwakuma, Gio Gonzalez, and even one of the year’s big breakouts in Jake Arrieta posted solid independent numbers even down the stretch when they may have gotten the short end of the luck stick in one way or another to ding their overall effectiveness. You obviously aren’t as likely to be able to use the numbers here for leverage in trade negotiations, as we’re dealing with higher-priced assets and helium guys who aren’t likely to come at a discount going forward.

And on the other end of the spectrum there are some older veteran guys who pitched pretty poorly but still probably deserved better. Guys like A.J. Burnett, John Danks, Bartolo Colon, and Mark Buehrle aren’t generally all that interesting for a conversation like this. They’re pretty well-known quantities, and while their general stability at the back of your deep-league rotation or as streaming options in shallower formats has a place for appreciation, there’s really no upside or surplus value to be found in targeting any of them.

But there are a few noteworthy names here, starting at the very top of the list with Nathan Eovaldi. Eovaldi entered the season as a somewhat under-the-radar sleeper pick on account of his high-octane fastball, but his inconsistency with secondary pitches and general inability to get lefties out consistently had held him back. And for the first month of the season it looked like he might just pay off with a legitimate breakout, as he posted a 2.19 FIP and 35-to-5 K:BB ratio over 38 innings to open the season. The wheels started to fall off in May, however, and he never really did regain his April footing outside of a four-start stretch in August. It’s tempting to look at his .356 second-half BABIP as a sign of impending regression and lump Eovaldi into the “target” list, but at the same time that inflated was well-earned on account of a 26 percent second-half line-drive rate. Both his fastball and slider—the pitches he throws a combined 85 percent of the time—have yielded line drives on over 30 percent of balls put in play, and despite their high-octane velocity, both rate as below-average pitches per PITCHf/x calculations. It’s frustrating given the arm strength, but I’d just as soon avoid putting too much stock in an Eovaldi breakout for next season.

On the flipside, Kyle Gibson is a name that actually looks pretty interesting that I probably wouldn’t have otherwise bothered to check in on without this exercise. He’s a former three-time member of the BP101, most recently checking in at number 64 heading into 2013. Since coming back from Tommy John surgery in late 2012 he’s added a couple miles an hour to his fastballs, and despite generating less-than-average whiffs from his secondaries, he’s shown an elite ability to induce weak ground-ball contact in his career. Two interesting things about Gibson’s second-half struggles: on one hand, he’s been terrible at stranding runners. For whatever reason he’s pitched very poorly out of the stretch, and it’s an issue that has haunted him throughout his big league career. And second, he’s seen a wholesale collapse in the effectiveness of his slider. The pitch rated as the eighth-best slider among all starters for the first half of the season, yet has regressed to a slightly below-average offering since the break. The negative value is largely the produce of a vicious spike in the line drive rate he allowed with the pitch in July and August. Insert small sample size warning here, but in September he appears to have cured what was ailing him, as he’s burning worms at a staggering 78 percent clip (though in turn getting brutalized to the tune of a very unlucky .333 average against the pitch). While he’s never going to be a big-time strikeout guy, it is worth noting that his whiff rate has risen about three percentage points in the second half as well. If he can maintain his whiff rate above 15 percent—that is, somewhere more or less north of 6.0 K/9 per—with his elite ground-ball tendencies and a dose of normalized batted ball luck, he has the makings of a cheap, attainable mid-rotation AL-only guy, and someone with the profile of a Kyle Lohse or Henderson Alvarez for the back end of mixed-league rotations. I wouldn’t get too excited until he shows he can maintain his effectiveness better with runners on, but the raw materials are there for a nice undervalued asset.

Marcus Stroman is probably the most exciting name on this list in terms of upside, and this winter may just be your last chance to get him on the (relative) cheap. He’s shown some typical inconsistencies from start to start that have led to five second half starts in which he’s allowed five or more runs. Those clunkers have helped to inflate his baseline ERA despite independent metrics that suggest he’s been one of the best starters in the American League during that time. His combination of limiting walks and inducing groundball contact has been a potent one, and it has only gotten better as he’s begun relying more heavily on his sinker as his primary fastball since the beginning of July. It’ll probably always be an open question if his small frame can hold up to a 200-inning season until he shows he can do it, but he’s at 160 and counting this year and hasn’t shown any decreased velocity or increased arm-angle variation thus far in September. If you want him the time to pounce is probably now, while his slightly inflated ERA may help mask just how dominant he’s become.

And finally, Stroman’s rotation-mate Drew Hutchison makes for another tantalizing option for those in search of potential breakout targets. Hutchison generated some buzz this past spring after showing off a velocity bump in the Grapefruit League, and the increase has largely held to the tune of about a mile and a half on his fastball this year. His three pitch mix has induced a strong double-digit swinging strike rate, leading to nearly a strikeout an inning and his continued deployment in mixed leagues throughout the season despite poor ERA and WHIP returns. I don’t think the strikeouts are a fluke, and his whiff rate has actually risen to an exceptional 26 percent rate in the second half despite his overall struggles. But what may very well turn out to be a fluke is the combination of a poor 68 percent strand rate and gross 12.6 percent HR:FB clip in the second half. His BABIP has decreased by 14 points, the whiff rate has taken a sizeable step forward, he’s lopped a percentage point off his walk rate… and yet, his ERA is almost a full run higher in the second half on account of that ugly combination. It doesn’t add up given the skillset he’s shown, and given how uneven the topline production has been he might just make for the best option on this list for buying on the cheap this off-season.

Thank you for reading

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The presence of three Jays on the list doesn't seem like a coincidence. Their defence this year has been terrible. While they may regress, without defensive upgrades, Jays' starters may continue to have higher BABIPs.
Not sure raw FIP is that useful (vs. ERA) over such small a small sample:

FIP still uses HR rates, which are highly erratic over a half-season. Better would be to use xFIP after adjusting for park HR rates.
I went back and forth about this very question when I started including this particular exercise in my annual end-of-season itinerary a couple years ago. The way I came down on it is that HR rate as a variable is something I'm more interested in looking at secondarily with these pitchers. I've never entirely loved xFIP as a blanket stat because certain players are more consistently prone to homeruns than others, and it's an element of pitcher performance I like to look at on a case-by-case basis rather than normalizing. Ultimately this is only meant as a quick and dirty starter activity in the long process of a compiling an off-season target list. It's been helpful for me, and hopefully using it (or similar baseline derivatives like xFIP or FRA) can help jumpstart the offseason for others as well.