Friday, May 16, 2014
Sunday, May 4, 2014
Saturday, April 19, 2014
Friday, March 7, 2014
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.
Since the first professional league in 1871, pitchers have never hit above the league average. Their wRC+ has steadily declined over the years, all the way to negative since the 1980s. Since the adoption of the designated hitter in AL in 1973, pitchers have never had a wRC+ over 10, except for 1974.
Given their terrible performance at the plate, it is good for fans that pitchers have come to the plate less and less over the years. The sharp decreases in 1981 and 1994 are results of shortened seasons. The reason behind this is the increased usage of relief pitchers. The slight downward trend in recent years might also be a result of managers realizing the importance of each plate appearance and the diminished performance by the starter as he goes through the lineup.
Fangraphs has an opaque way of calculating WAR for pitchers as position players. While Baseball-Reference forces 0 WAR onto the pitchers as a whole no matter how well they hit, pitchers can have positive to negative WAR on Fangraphs, as long as all the position players, including pitchers, add up to 570 WAR a year. After hovering around 0 WAR from 1973 to 2001, pitchers suddenly experienced a sudden drop in value in 2002 and have not recovered since. This is where Fangraphs’ non-transparent method confuses me. From 2001 to 2002, pitchers actually improved in terms of batting (from wRC+ of -11 to -7). The majority of the difference stems from positional adjustment. While pitchers received a boost 660 runs in positional adjustment in 2001, they only gained 546 runs in 2002 in about the same number of plate appearances. It seems that pitcher positional adjustment is not constant, though there is nothing on the site that explains how it is calculated.
This is not an article that is meant to explain anything. I am simply looking at the history of pitchers as batters in a graphical and pointing out the lack of clarity behind Fangraphs calculation of WAR for pitchers.
All statistics courtesy of Fangraphs.
Thursday, March 6, 2014
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.
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%.
Saturday, February 15, 2014
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.
Tuesday, January 21, 2014
Sunday, December 8, 2013
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.
Posted by Camden at 4:07 AM