Damon played golf Monday with White Sox catcher A.J. Pierzynski and broadcaster Ken “Hawk” Harrelson, according to major-league sources. Pierzynski lobbied Damon to sign with the White Sox, and Damon’s wife, Michelle, would prefer him to play in a more cosmopolitan city than Detroit, multiple sources say. …
… Some with the White Sox, however, are confident that a deal with Damon could happen, and believe it might even be close. Clearly, the White Sox are discussing Damon at length internally and remain in contact with the player’s agent, Scott Boras. …
February 16, 2010
A principle component analysis depends greatly on the variables fed into it. For hitters, I used the singles, doubles, triples, homers, walks, and strikeouts per plate appearance as the input variables. While I could do that here, I thought I would use variables over which the pitcher had more direct control. Using Fangraphs pitch data, I used the following: % of Fastballs Thrown (including cutters), % of Sliders, % of Changeups, Velocity of Fastball, Ground Ball%, Walks per PA, and Strikeouts per PA. I thought about using Hits per PA, and HR per PA, but since those are largely a function of luck and I didn’t want to measure that, I decided to leave them out. Like before, each variable was normalized before putting it into the model.
For hitters I was uncertain of what to expect, however for pitchers I had a fairly good idea. I expected that the two groupings of pitchers would be between power pitchers and control pitchers. However, I wasn’t exactly sure how it would break it down. Running the analysis, the factor loadings for the first principle component were as follows: …
and here’s the two types of hitters post:
For those unfamiliar with the type analysis, the point of it is to reduce a large number of potentially correlated variables down to a few key underlying factors that explain the variables. The researcher feeds the computer a bunch of records (in the this case, players) and several key variables (in this case, their statistics), The computer, blind to what those variables actually mean, spits out a set of underlying factors which explain the “true” underlying causes for the variables in question. It does this by maximizing the variability between the players. It’s then up to the researcher to interpret what each factor represents. In this case, I’m looking for the one underlying factor that best describes a player.
In the baseball world, I wondered what one underlying factor best determined a player’s statistics. Normally, this type of analysis would be done on many more variables, but I wanted to see what it would pick out from players’ basic, non-team influenced statistics: 1B, 2B, 3B, HR, BB, K.
– Dan Hudson comes in at no. 9 in Phil Rogers’ ROY predictions.
– Could the Sox move their games and anchor a new FM sports radio station in Chicago?
– B-R will get Negro League (1940-1948) and Cuban statistics.
– Sox have won 14 and lost 8 arbitration hearings since 1974 when arbitration began.
– “There is a good chance that [Yuniesky] Maya will choose a team that offers a better opportunity to immediately enter its starting rotation.” (Bradford).
– PECOTA has been updated. Sox projected at 80-82 in a tight AL Central.