2013 Pod Projections: Johnny Cueto
In what is likely my last Pod Projection post of the year (my eBook teaches you how to forecast players yourself!), Johnny Cueto wins the honor of finishing up the series. Cueto has posted sub-3.00 ERAs two years running now, despite all ERA estimators sitting significantly above those marks. Great fortune (skill?) in the three “luck” metrics we analyze is the explanation for his ERA estimator beating ways. Can it continue?
2013 Pod Projection Index:
Hitter Introduction
Carlos Gomez
Mark Trumbo
Brett Lawrie
Jason Heyward
Desmond Jennings
Pitcher Introduction
Kris Medlen
Jeff Samardzija
Max Scherzer
Chris Sale
IP: 210
Cueto has dealt with various arm and shoulder maladies over the years, but he was completely healthy last season and threw over 200 innings for the first time. Since he has proven now that he could do it and is entering this season with no health concerns, then it would be fair to assume he remains above the 200 IP barrier.
GB%/LD%/FB%: 48%/19%/33%
In 2011, Cueto suddenly morphed into a ground ball pitcher. As expected, some regression did take place, but not enough to return his batted ball profile into his pre-2011 days. Compared to 2010, he has induced a higher rate of ground balls from his four-seam fastball, sinker, cutter and change-up. That’s nearly all of his pitches. Whatever has led to this change, he has been doing for two years now, so it seems safe to expect a repeat.
HR/FB%: 10%
Cueto’s HR/FB rate suppression is one of the ways he has been able to beat his ERA estimators. But even having posted rates below the league average in the past three seasons, his career rate is still right at a league average mark. The GABP significantly inflates home run power, and sure enough, his career home HR/FB is 1.5% higher than his away mark. With no clear explanation of what he has changed these last three years that have resulted in suppressed home run totals, I must assume reversion back to his career mark and a league average rate.
BABIP: .295
Aside from the clear outlier in 2011, Cueto has allowed a BABIP between .290 and .298 every season. He’s a ground ball pitcher now and sports a league average IFFB%, so he shouldn’t possess any special BABIP prevention skills.
BB/9: 2.5
Cueto’s walk rate has declined in every single season, which is quite the trend. Of course, that can’t continue forever. Amazingly, his F-Strike% exceeded the league average for the first time since his 2008 debut season. Now that F-Strike% finally matches with a walk rate in the mid-2.0 range.
K/9: 6.9
Despite possessing a pretty solid fastball velocity-wise and a strong slider which normally leads to lots of swings and misses, Cueto’s stuff simply hasn’t translated into the strikeout rate one might expect. He’s basically league average in generating swinging strikes and that should lead to yet another K/9 around the starting pitcher league average.
Below is my final projected pitching line, along with a smattering of other projection systems for comparison.
| System | IP | W | ERA | WHIP | SO | K/9 | BB/9 | GB%/LD%/FB% | BABIP | HR/FB |
|---|---|---|---|---|---|---|---|---|---|---|
| Pod | 210 | 15 | 3.46 | 1.23 | 161 | 6.9 | 2.5 | 48%/19%/33% | 0.295 | 10% |
| Steamer | 194 | 12 | 4.06 | 1.29 | 143 | 6.6 | 2.6 | ?? | 0.292 | ?? |
| Bill James | 215 | 14 | 3.52 | 1.20 | 169 | 7.1 | 2.3 | ?? | 0.295 | ?? |
| Oliver | 195 | 14 | 3.14 | 1.19 | 147 | 6.8 | 2.4 | ?? | 0.291 | ?? |
| Fans (44) | 210 | 16 | 3.00 | 1.17 | 164 | 7.0 | 2.2 | ?? | 0.294 | ?? |
| ZiPS | 192.2 | 14 | 3.32 | 1.21 | 146 | 6.8 | 2.4 | ?? | 0.293 | ?? |
Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.
While not suggesting that this explains everything, this article is fairly remiss is not mentioning his completely revamped windup since the 2nd half of 2011. He went from fairly standard to Luis Tiant 2.0 – that merits mention.
I just don’t think it’s relevant. What effect did it have on his peripherals? Unless it’s the explanation behind his increased ground ball rate, nothing new is showing up in the results/skills.
Even better was the numerous times he lost his balance in the second half of 2011 doing the Tiant delivery.
Just bought your E-Book, looks great. Thanks Pod!
Any way to include an average of all projections (with Fans regressed) on the player pages?
i don’t think this would be very valuable. An average of all projections would weigh each projection equally, and thus would spit out basically a mush. Some projections make no effort to project playing time while others base their projections substantially on playing time. It would be the equivalent of averaging apples and oranges- you would get a result but it would be basically meaningless.
Seeing a ‘Fans regressed’ projection, though, would be great. Since fans projections are constantly updated I can see this being difficult, but i like the idea a lot.
I thought I read a while back (on this site) that properly weighted averages of numerous projection systems generated the best overall projection, while other individual projection systems tend to be better at projecting hitters or pitchers, the properly weighted projections (i.e. a projection having a pitcher throw 120 IP is weighed less than a projection of the same pitcher throwing 200 IP) were by far the closest for both hitters and pitchers.
Thanks for this, Pod. Seeing this compared to your SP rankings is very interesting; I agree with where you have Cueto ranked generally and compared to the other guys in a similar tier (low end of the top 30; basically a #1 on a deep league or a #2 on a championship mixed league).
Whenever I look at projections I guess I perform a mental evaluation to determine the over/under when ranking two guys who project to be fairly equal in value (Medlen and Cueto, for instance). I like the over on Cueto outperforming his projections, given his consistency and decreasing walk rate. I’d probably want to pair him with someone who could beat his strikeouts, but given his propensity for groundballs and the team around him if I’m in any league ranking wins and ERA I would assume he is more likely to outperform his projections there than most of the guys ranked similarly (Samardzija, Peavy, Gallardo). Wondering if you take any of that thinking into account when comparing projections for your rankings, or if you think it doesn’t really matter when looking at guys in the same tier.
Hmm, I think I’m a little confused about what you’re asking. My rankings are based n the dollar values I calculate, which are derived from my projections. I simply sort in descending order of dollar value and those become my rankings.
I think he’s saying that some players are more likely than others to beat their projections than the average player (improving young players?), and some are more likely to underperform (pitchers with nagging injuries, short track record, etc) than the average player.
Ideally, projections take this into account by providing the middle-of-the-road values around which the player’s stats will fall. But perhaps some players need asymmetrical error bars? I dunno. After I did all my fairly robotic projections, I did nudge values a dollar here and a dollar there based on the player’s reliability, track record, upside, health status, etc.
I think I kind of understand, but all my projections are done manually, so all factors are already taken into account. It sounds like your projections are based on a computer model with manual intervention occurring on a selective basis based on some other factors. So it makes sense to do something like that if mine were developed the same way.
Well to be transparent, I just used the FANs system here, copied the tables into Excel and then made finer adjustments for playing time, save totals, more precise K and BB rates, things like that, before doing z-scores. Then the resulting $xx.xx values were put into my auction sheet by position, with rounding up and down to the nearest dollar, and maybe the one extra dollar, based on those aforementioned subjective factors (e.g. I project Teix at $8.68 — OPS league — after the injury, but I have him at $11 due to the DL slot-ability and potential keep-ability for ’14).
So maybe I’m doubly counting my “gut” input, but I think it works. And it’s a hell of a lot better than what I did in past years…
I can simplify my question thusly:
Given that you’ve taken time and effort into manually projecting certain (or perhaps all) players and calculating value, when you’re in the fire of the draft/auction do you find yourself relying on your calculated values 100% or do you use them as a general guideline? When you have pitchers you value similarly (Cueto, Medlen, and Samardzija, for instance) in an auction are you taking any additional steps to target one type of pitcher over another?
I get perhaps too curious and interested in the minor nuances of draft strategy in case you couldn’t tell.
Tough question. It’s nearly impossible to go 100% by my values in the heat of a draft or auction, as there are always other things popping into my head to sway me one way or another. For the most part, I’ll go by my values, but when comparing players who have near equal value in a draft, other random factors will come into play.
It’s mostly perception. I’ve basically made myself think I dislike Cueto, which isn’t true. I just like him less than others. I’d gladly take him for the right price, but am unlikely to ever have that opportunity. So if that chance did ever come, I’ve already set myself up to feel like I don’t like him and so I’d choose someone else.
This may already be taken into account, but we should expect Cueto’s ERA to continue to outpace his FIP due to his incredible pickoff move. He’s only allowed two stolen bases in the last two years, gets extra outs from pickoffs, and basically makes it much harder for runners to take the extra base pretty much all the time. I’m not sure how much of a difference it should make, but it is a real difference.
Very interesting, thanks for sharing this. That’s the type of stuff not in the stats (at least not on FanGraphs) that certainly does make a difference. Probably provides some of the explanation behind his high LOB%.
If you haven’t, read Dave Cameron’s piece on him from last August: http://www.fangraphs.com/blogs/index.php/johnny-cueto-for-cy-young/ The pickoff move is unreal.