In Nate Silver’s new book, The Signal and the Noise, he had a chapter on climate projection. The chapter showed projections had a sweet spot at sometime in the future where they were the most accurate. Three uncertainty factors were at work affecting the projection and the sweet spot.
- The first is is initial variability. With climate, a location may experience a very cold winter in the first year of the model. The extra cold and hot winters will hopefully even out at some point. This error starts out high and eventually goes to zero.
- The second error is long term unknowns. With climate, maybe a new scientific invention is created which removes CO2 from the air or a couple of volcanoes go off at once cooling the earth. This value starts at 0 and grows steadily over time.
- The third is an underlying unpredictability. This value is the most steady of the three. Say the climatologist want create a 40 year prediction. From the beginning they will have a certain level of uncertainty. The further they want to predict into the future, the higher level of underlying uncertainty exists.
I have always wanted to create an defensive metric that doesn’t adjust to the league average. Using Retrosheet data, I have made my first stab at it. I plan on making some small improvements and eventually having data available on all players. Here is an exert from my first article at Royals Review explaining in more detail the new metric:
For years, my one issue with the current defensive metrics is they attempt to show how a player is doing compared to the league average at a position. This method helps to give run values to the defense for inclusion into complete value metrics like WAR. The problem is the pool of full seasons of playing time at each position is limited to 30. If a couple of excellent defensive players get hurt or a player is moved to another position, a player’s value may change quite a bit even though they may be creating the same number of outs (example from a few years ago when I looked at this issue with some shortstops).
In a Fanshot at RoyalsReview, some asked to have explained what is wrong with his swing. I created a few .gifs of a typical swing of his and here are the problems:
Full swing for reference
I was planning on compiling the Retrosheet data over the Christmas holidays, but I found myself with time. So for the baseball fan in your life get him more mysql data then he has time to analyze. I have imported the data and giving you a few more options in your downloads.
Full MySQL Retrosheet
Last 10 Years MySQL Retrosheet
2012 CSV Retrosheet
2012 MySQL Database
2010s MySQL Database
ESPN Insider and ESPN are interlinked, if a person gets one, they get the other. Soooo.
Amazon right now has ESPN the Mag for $5 a year. Once you get the subscription, the code on the front of the magazine can be used to unlock ESPN Insider.
Or go to your local library and see if someone is using their code for the magazine yet. Just saying.
The concept and use of WAR comparison graphs started a few years ago when I was still writing at BeyondtheBoxscore.com. The graphs are ranked according to the player’s best to their worst season according to fWAR. I have decided to graph the players on the Hall of Fame ballot for a couple reasons:
- I like the graphs vice totals because they minimize negative WAR seasons. I can’t fault a player for taking a club’s money and playing baseball even if they are well past their prime.
- I like to add in the HOF Zone. The bottom of the zone is basically the lowest WAR value a player has had in the past and still got into the HOF. The top of the zone is the average WAR values for all Hall of Famers. A different HOF Zone exists for hitters and pitchers.
Graphs with some comments. (The player’s career total fWAR is in the parenthesis next to their name.)
Bonds and Bagwell are the only 2 players who had their career WAR values over the average value. Both may not get into the HOF for years because of steroid accusations.
For the the recent The Hardball Times Annual, Brian Cartwright and I wrote a piece on Tommy John surgery. An aging curve on was cut from the article. I feel it provides significant insight so I will provide it now.
Pitchers generally get worse as they age with the one exception being that pitchers see an improvement in their walk rate until age 29. TJS pitchers show more year-to-year volatility, but over the long run the values stay the same, unlike the general pitcher population, which sees its values degrade. It is almost like the pitchers are getting a new throwing arm or at least a new ligament in the arm. Some survivor bias is present, as only pitchers that were able to return to pitch at a competitive level are shown. The bias exists with the overall aging curves, but not as much as those that have had Tommy John surgery.
Oakland decided to pick up the option for RP Grant Balfour ($4.5M) and not pick up the one for SS Stephen Drew ($10M). Balfour is about a 1 WAR pitcher and Drew is about a 2 WAR shortstop. This puts Oakland’s value for free agents to be near the $5M dollar mark.
The Yankees picked up Cano’s ($15M) and Granderson’s ($13M) extensions, but both of their $/WAR work out to be under $4M/WAR.
It was a a close race, but there was a group of hitters that hit worse than the Yankees in the ALCS.
NL Pitchers in the playoffs: .150/.203/.200
Yankees in ALCS: .157/.224/.264