History of Pitchers as Position Players

The various projection systems are the closest we can come to predicting future. I was thinking of what they currently lack, and the first thing that came to mind was pitchers as batters. I then checked how each team did with their pitchers last season. It turns out that the spread from the best team, the Dodgers, to the worst team, the Pirates, is less than three wins. The true talent level is much narrower than that, and there does not seem to be much advantage gained by including pitcher batting in projections. Instead, I decided to look at the history of pitchers as position players.

Brett Gardner and Positional Adjustment: CF vs COF

Brett Gardner is the typical center fielder, with speed and range in the field. The New York Yankees just signed him for a four-year extension of 52 million dollars, but to play left field alongside Jacoby Ellsbury instead of center field. There are concerns that Gardner’s bat may not play in a corner outfield spot, that his value would be lower at LF than at CF. This is the effect of the positional adjustment. As a player’s fielding contribution is compared to other players of the same position, we have to adjust our evaluation of a player based on where he plays in the field. The established positional adjustment has a CF getting a boost of +2.5 runs over a full season while a LF or RF gets a penalty of -7.5 runs. In theory, a CF moving to LF would gain 10 runs in the field to make up the difference, as they are now compared to worse fielders. I will be testing whether this statement holds true in reality.

Predicting LOB%

In my article last week, I developed xLOB% as a descriptive statistic to estimate a pitcher’s LOB%. In this article, I will attempt to predict LOB% of a pitcher using his statistics from the previous season. Despite its fairly weak predictive results, pLOB% explains 12.7% of the variation in a pitcher’s LOB% in the following season, better than Steamer’s projection and kLOB%.

Estimating LOB%

Luck has been the explanation whenever a pitcher has a significantly lower ERA than his FIP. There are two statistics where luck plays a huge role, BABIP and LOB%. Using Steve Staude’s pitching stat correlation tool, we can see that BABIP only has a correlation of 0.156 from one season to the next, while LOB% has a correlation of 0.205, for pitchers with a minimum of 30 innings pitched from 2007 to 2013. These numbers are much lower than the correlation of K% or BB%, suggesting that a large portion of BABIP and LOB% are subject to random variation and independent of a pitcher’s skill. However, the correlation is not 0. They are not completely random, and a pitcher can still play a small role in controlling their BABIP and LOB%. Many writers, including Steve, have tackled the issue of BABIP using batted ball data. In this article, I will be estimating a pitcher’s LOB% for the current season. This is not supposed to be a predictive stat, but a descriptive one. Think of it as FIP. While FIP estimates the pitcher’s ERA using strikeouts, walks and homeruns, xLOB% estimates the pitcher’s LOB% given his other pitching statistics for the same season. I will be introducing pLOB% in the next article, which attempts to project LOB% of a pitcher for the following season.

Random Thoughts from Georgetown-Marquette

(Small sample size caveat: This is the second time I have watched a Georgetown game this season)

Let me run through the last minute of regulation, with Georgetown up by four with the ball:

The Value of a Compensation Draft Pick

            If the magnitude of deals for free agents like Robinson Cano and Jacoby Ellsbury already seem too high to you, the market rate might be even higher than you see on the surface. Both Cano and Ellsbury were signed with draft pick compensation attached. If MLB teams are evaluating the draft picks accurately, the valuation of Cano and Ellsbury are even higher than just their contracts on surface. How much should the draft picks discount these contracts? To answer the question, we have to determine how much the draft picks are worth by themselves.

Predictions for 2013 NBA playoffs

OKC 4-1 HOU (4-2)
SAS 4-1 LAL (4-0)
DEN 4-2 GSW (2-4)
MEM 4-3 LAC (4-2)
MIA 4-0 MIL (4-0)
NYK 4-3 BOS (4-2)
IND 4-1 ATL (4-2)
CHI 4-2 BKN (4-3)
MEM 4-2 OKC (4-1)
SAS 4-2 GSW (4-2)
MIA 4-1 CHI (4-1)
IND 4-2 NYK (4-2)
SAS 4-3 MEM (4-0)
MIA 4-2 IND (4-3)
MIA 4-2 SAS (4-3)

Giving Up Too Much for Igoudala

The Golden State Warriors have decided that Andre Iguodala (48 million dollars over 4 years) is worth the assets it has to give up to dump the contracts of Andris Biedrins, Richard Jefferson and Brandon Rush on the Utah Jazz, namely two first round picks, including one in the coveted 2014 draft. However, the Warriors are likely as far from contention as they are from the lottery, and that is not a point where the team should be trading the future for the present.

Clippers Shore Up, but Questions Remain

The Los Angeles Clippers have used their most valuable chip in Eric Bledsoe to acquire two wing players they desperately need, Jared Dudley from the Phoenix Suns and J.J. Reddick via sign-and-trade. They also give up Caron Butler’s expiring contract to the Suns in the process. The Milwaukie Bucks will pick up two second-rounders in the trade, but the focus should be on the Clippers side. Dudley and Reddick solve one of the Clippers’ wing positions, but the other remains in doubt unless Matt Barnes re-signs far below his market value.

A Minimal Upgrade at Minimal Cost

Let the trade rumors begin to fly! The first significant trade of the season has occurred, as the Baltimore Orioles acquire  Scott Feldman and Steve Clevenger from the Chicago Cubs for Pedro Strop, Jake Arrieta and international bonus slots.