Reviewing 2015 Pod’s Picks: Starting Pitcher
It’s the final Pod’s Picks recap of the season! Finally, we get to my favorite position for nerdy analysis, the starting pitchers. Refresh your memory with my original starting pitcher Pod’s Picks post.
Bullish
Consensus Rank: 65 | My Rank: 20 | Actual Rank: 55
How does Dickey pitch for a team with far and away the best offense in baseball, and yet finishes with just 11 wins? Because he had poor luck, of course. The team averaged 5.5 runs per game, yet Dickey received just 4.45 runs of support. Of course, it wasn’t only the lack of wins that hurt his value. His strikeout rate also collapsed, something I didn’t foresee. And yet even given the lack of wins and just 126 punchouts, he still earned positive mixed league value.
Verdict: Consensus
Consensus Rank: 72 | My Rank: 36 | Actual Rank: 53
Despite missing much more time than initially anticipated, Verlander still earned positive value from an overall season perspective, and was obviously worth even more if you started the clock from the time he made his 2015 debut. Of course, his ERA suggests Verlander enjoyed a full rebound, but a look under the hood reveals that this wasn’t necessarily the case. His SIERA only dropped marginally and ultimately it was simply a reversal of BABIP luck that pushed his ERA down by more than a full run. His fastball remained below 93.0 mph and he just posted the highest FB% of his career. He’s now riskier than ever.
Verdict: Pod
Consensus Rank: 60 | My Rank: 25 | Actual Rank: 224
Arm injuries limited Cain to just 60.2 innings and he was terrible when he actually took the mound. His strikeout rate hit a career low, BABIP and HR/FB rate both jumped for a third straight year to set new career highs, and his average fastball velocity slid below 91.0 mph for the first time. Clearly, his health was a major factor and it’s impossible to say what to expect from him now moving forward.
Verdict: Consensus
Consensus Rank: 83 | My Rank: 53 | Actual Rank: 111
The intrigue of a strikeout rate spike in 2014 and a move to what promised to be a strong defensive team didn’t actually result in any value here. His strikeout and walk rates fell right back to his 2013 level, while the Red Sox defense was awful. His skills improved in the second half and ERA declined, and I can’t help but think things have to get better next year. I think he’ll be a nice value in AL-Only leagues.
Verdict: Consensus
Consensus Rank: 57 | My Rank: 32 | Actual Rank: 76
A strong strikeout rate for the second year in a row, but this time a ridiculous 17.2% HR/FB rate marred his performance. That’s going to drop right back down again, but I remain leery of his increased strikeout rate, unless it’s all thanks to a velocity spike in 2014 that was mostly sustained.
Verdict: Consensus
Consensus Rank: 54 | My Rank: 30 | Actual Rank: 144
I would have loved to see McCarthy pitch a full season and see what ends up happening given his elite underlying skills versus that gruesome home run rate.
Verdict: Consensus
Consensus Rank: 53 | My Rank: 31 | Actual Rank: 39
Finally, I didn’t choose a major bust! I was fairly certain that Kazmir’s poor second half in 2014 would reduce his draft cost and since I acquired him in several leagues, I was right. And although his skills slid this season, he lucked his way into a better ERA. Though that luck was offset by some poor win fortune, as he managed to garner just seven of those. Once again, his skills eroded in the second half, and his ERA jumped as well. Perhaps he simply doesn’t have the stamina to last all year. Since this has happened for two straight seasons, it might be wise for his 2016 owners to look into trading him if he’s once again outperforming his skills near the all-star break.
Verdict: Pod
Bearish
Consensus Rank: 24 | My Rank: 58 | Actual Rank: 21
Wow, even in just 154 innings and with an ERA well above his 2014 mark, he still outperformed both our preseason rankings. Oddly, my projection called for just 140 innings, so I’m very surprised he ranked so much better than I valued him. The 0.99 WHIP had a lot to do with that, thanks to a .242 BABIP. How does one explain a .242 BABIP paired with a 16.9% HR/FB rate?!
Verdict: Consensus
Consensus Rank: 38 | My Rank: 62 | Actual Rank: 147
Fister missed a month with a right forearm injury, while his skills took a downturn and his luck reversed course. It was fairly obvious this was going to happen after he posted a 2.41 ERA, but 3.93 SIERA in 2014.
Verdict: Pod
Consensus Rank: 20 | My Rank: 44 | Actual Rank: 9
This looks ridiculously wrong now, but my ranking was strictly due to my innings projection of 175. While an extra 15 innings shouldn’t have resulted in a difference of 35 places in the rankings, I obviously didn’t project a 2.54 ERA or 0.98 WHIP. I believed that Degrom was for real, but my forecast had to assume some regression. That didn’t happen.
Verdict: Consensus
Consensus Rank: 62 | My Rank: 85 | Actual Rank: 215
Once again, injuries ruining a good comparison.Alvarez was limited to just four starts due to right shoulder issues that eventually required surgery. Due to his pathetic strikeout rate, he’s a better real life pitcher than fantasy one.
Verdict: Pod
Consensus Rank: 30 | My Rank: 51 | Actual Rank: 30
This was all about the innings pitched projection. I projected just 165 innings due to the knee injury he was recovering from, which severely cut into his value, but instead he made 32 starts and threw over 200 innings for the first time in his career. But my ratio projections proved close, as he regressed from his breakout 2014 performance as expected.
Verdict: Consensus
Consensus Rank: 39 | My Rank: 59 | Actual Rank: 157
Some good fortune in 2014 obscured declining skills and fastball velocity, and sure enough, his performance went down the tubes this season. Funny though, his skills are velocity actually rebounded somewhat, but a career worst BABIP and LOB% ruined his results.
Verdict: Pod
Consensus Rank: 36 | My Rank: 56 | Actual Rank: 148
After his breakout that I saw coming back in 2013, I have been surprisingly down on his prospects ever since because of a subpar strikeout rate and excelled BABIP fortune. This year was no different, as he landed in my Bearish category. Finally though, I was right to be pessimistic. Even though his strikeout rate rose, the combination of an inflated BABIP and suppressed LOB%, pushed his ERA up nearly two full runs from 2014. Perhaps now he’ll be undervalued next year?
Verdict: Pod
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.
“How does one explain a .242 BABIP paired with a 16.9% HR/FB rate?!”
Pardon my ignorance, but why is that surprising?
Because, to use hyperbole, it means everything hit in the air is a home run and everything hit on the ground is an out. Not everything, obviously, but way more than you’d expect on both counts.
Also, the HR rate implies that Tanaka was giving up lots of hard contact, while the BABIP says the opposite.
But homeruns don’t factor into babip.
The author may be shining a light on two different luck factors and wondering how a pitcher can be lucky in one and unlucky in the other.
I don’t think BABIP and HR/FB are explicitly linked in any way, so I understand your confusion at the linking of the two stats in this way.
I explain it by the fact that you only need to hit the ball ~ 300 ft in Yankee Stadium to get a homerun.
Tanaka @ home: 19.1%
Tanaka @ away: 13.6%
Against LHB @ Yankee Stadium he had a 25.7% HR/FB ratio. That’s moderately crazy.
Great pull.
Verdict: Still over-weighting differences in IP projections
Huh? No. I was explaining why my ranking was off, because I underprojected innings. That’s not the valuation system’s fault, it’s mine! If I had a crystal ball, I’d be able to perfectly forecast a pitcher’s innings pitched. Unfortunately I don’t.
Also an issue is that I’m using my valuation system for my rankings, but then using a different system to compare end of season ranks. It’s kinda apples to oranges. But I’m not running end of season values for my system, so it’s the best I got.
Ahh right, sorry for snark. I think I’ve commented on your preseason SP picks before about this but it seems like you always have older guys like Dickey and Cain way higher than consensus, and younger guys like DeGrom and Richards lower than consensus. IMO if you flatten out your IP projections a bit these guys wouldn’t jump out so much. Like you said, we don’t have a crystal ball for innings.
It definitely could be a difference in valuation system though. If you aren’t using z-scores like Zach, I guess it’s likely that playing time matters more in your system.
You are probably right, as perhaps I’m a bit too conservative with my young pitcher IP forecasts. To me, I need them to prove they could handle a 200 IP workload before I project one.
Cain’s a bad example because he was coming off injury and was a total crapshoot. Dickey, yeah, somehow always end up too bullish because his value is from counting stats and not ratios.
“his value is from counting stats and not ratios”
I think this is actually where most people have a problem with your pitcher projections and valuations. Maybe it works in super deep 10+ leagues, but in the shallower leagues we mostly play in there’s significant opportunity to find valuable pitching via free agency and trades – especially for the kinds of owners who read Fangraphs. So a guy throwing 200+ mediocre innings (“value is from counting stats and not ratios”) isn’t actually all that valuable.
It’s not 200 innings of mediocrity versus 140 innings of a great young pitcher and 60 innings of no value. It’s 200 innings of mediocrity versus 140 innings of great young pitching and 50 innings of pretty decent pitching (maybe even along the lines of Dickey).
That should have been 20+ team leagues.
Pod: As much as I love reading your columns, readers would have been much better off sticking with the consensus than Pod picks. However, being an “expert”, you should have outperformed the consensus by a significant margin and in many cases, you were “WAY” off. You should re-evaluate how you pick your “picks”. Better luck next year…
This very website has shown that going with blended projections (fantasy or otherwise) is usually better than going with any individual one. I don’t think Pod would disagree!
I would disagree, though it depends on what you’re using the projection for. I think for fantasy purposes, it’s a bad idea. If you just want a projection set that overall is best, then it probably is better. But that’s not what you’re going for unless you’re submitting it into a projection contest.
You do realize that the “consensus” is composed of 4 other RotoGraphs authors, right? You could quibble with calling them experts, sure, but the ranking wasn’t just pulled from mock drafts or some random website!
Second, not sure why I have to even bother saying this, but this is one year at one position. Whether I did better than the consensus or not on these particular rankings means absolutely nothing. It’s like comparing projections on 10 players, being off on 7 of those, and declaring my projections suck. I ranked a whole lot more than just this group of pitchers.
Sweet, man. You should do some of your own blurbs then and compare with Pod’s at year’s end.
Regardless of whether or not he was spot on is secondary to the fact that this wasgood, offseason intellectual exercise.