It’s no secret that the Cubs have struggled to draft and develop pitching, so it’s nice that they have been willing to spend freely to fill in the gaps. Sahadev wrote about the team’s struggles in December (subscription required, h/t Fangraphs), with a pretty remarkable chart.

The problem with this chart is pretty simple: contenders are not particularly interested in developing “MLB-quality” pitching, they are aiming for good pitching. Note the quality and rank order of teams on the list. In general, contenders tend to have fewer innings available for pitchers that are not expected to be good, but could surprise. Case in point, if Cub draftees Zack Godley (2013, 10^th round) and Paul Blackburn (2012, supplemental round) had pitched the same number of innings with the Cubs instead of the Dbacks and A’s, the team would rate out right in the middle, at 15^th instead of 30^th on this chart. Would that make you feel any better about the Cubs’ pitcher development? It really wouldn’t move the needle for me.

This doesn’t seem to be a new issue for Epstein. If we set the bar for a decent career at 6-7 WAR, the Red Sox under Epstein produced only Jonathan Papelbon, Clay Buchholz, and Justin Masterson. (Lester was drafted in 2002, pre-Epstein). That’s not terrible, but it’s a pale shadow of what they accomplished on the positional side of things.

On Chatwood

It is pretty remarkable that Tyler Chatwood has, at the moment, signed the second- largest pitcher free agent contract of the offseason. Chatwood’s upside has been covered pretty exhaustively around the web, so allow me to make the opposite case:

1. There have been cases of pitchers getting better after leaving Colorado, but it is by no means a given. On the one hand, we have Drew Pomeranz, Jason Hammel, and Juan Nicasio (if you squint). On the other, we have pretty much everyone else, notably Ubaldo Jimenez and (for our purposes) Eddie Butler.
2. Here is a list of K-BB% laggards over the past two years, minimum 300 innings. It is much better to be on the non-Chatwood side of that list.
3. Much has been made of Chatwood’s elite spin rate, however, the same force that causes breaking pitches to move less at Coors should also cause them to spin faster (or more accurately, slow down less during flight).

I don’t hate the signing, and hope it works out. It is definitely more comforting now that he will be competing for the fifth spot. Every year since they became competitive, the Cubs have shelled out to build on their young core: Lester, Heyward, Quintana, and now Darvish.

State of the Farm

If you are worried about the Cubs having given away a lot of positional talent to fill-in pitching gaps over the last two years, never fear. According to the KATOH projection system, the Cubs have not one, but two better prospects than Eloy Jimenez. Those two are, of course, Victor Caratini and Charcer Burks.

It’s typically not that difficult to figure out what projection systems are “thinking,” but this one has me scratching my head. Eloy made it to AA and raked as a 20-year old, while Burks was only decent there at 22. The former must grade out as a complete statue in Clay Davenport’s defensive ratings.

Last spring, Myles had the idea that instead of just compiling our dumb predictions about the upcoming season, we should do something a bit more interesting. The idea was for each of the OV authors to pick a group of teams and track those teams throughout the season to see whose group did the best. Originally, we were going to pick teams through a sort of combination draft/white elephant gift exchange, but we ended up settling on an auction. The six of us (Berselius, DMick, IL424, Myles, Myself, and Sitrick) decided to meet up at OV headquarters, use an imaginary budget, and auction off the teams.

Everything was going smoothly until Sitrick decided that not only did he not want to participate in this silly exercise, he didn’t want to write about the Cubs or see any of us again for as long as we all should live. Thus far, history has smiled on that decision. Unfailing steadfast in the face of rejection, however, the rest of us soldiered on and auctioned off 6 teams a piece rather than 5, with a budget of 126 fake dollars per person.

My strategy going in was to avoid all the worst teams. I wanted at least two good teams in the hope that one would finish above 90 wins, and four mediocre teams that would collectively finish above .500. Everything went according to plan except that one of my “good” teams ended up being the Red Sox. (That and the fact the DMick laid waste during the auction. Bleh…)

As you can see, DMick managed to win comfortably despite buying the DBacks, who ended up as the worst team in baseball.

The one thing I was curious about going into the auction was whether we, as a group, would add any value over and above information in the public sphere. The short answer is: not really. The results were pretty stratified. Nine teams went for $30 or more, which translates into an 89-or-better win projection, and 10 teams went for $10 or less, a projection of less than 70 wins. Only two teams went for between $11 and $21 (71 to 81 wins), whereas 9 teams actually finished in that range. In fantasy terms, the group went with a stars-and-scrubs approach. As a result, you would have been much better relying on PECOTA or some other system to predict win totals.

But what about the identity of our stars and our scrubs? Were the teams we picked as stars more likely to exceed their projections, and the scrubs more likely to tank? To answer that question, I looked at BP’s projections at the time. I compared the difference between our effective projections and BP’s, and compared that to the actual difference at the end of the year. Take the Tigers for example. Dmick bought them for $35, which translates into a projection of approximately 94 wins, whereas BP had them at 88. So our auction had them at +6 relative to BP. They finished at 90 wins, or +2 wins relative to BP. In that case, the group was right to be optimistic. Repeating that for all teams and running a correlation for the 2 values revealed a slightly positive result (r = 0.07). That is, teams that we valued above or below BP’s season win projection were slightly more likely overperform or underperform, respectively. When I ran the same exercise using Vegas over/unders as a baseline, however, things didn’t work out as well. The correlation for those results was negative (r = -0.11).

On the whole, I think all that was probably random. Listening to us was not a great idea if you were headed to Vegas. We will try to do better next year.