- FanGraphs Baseball - http://www.fangraphs.com/blogs -

2011 Disabled List Spreadsheet and Team Information

I have gone through all of the 2011 MLB transactions and compiled the disabled list (DL) data for the 2011 season. I have put all the information in a Google Doc for people to use

Here is an explanation of the columns

Last – Last name
First – First name
Transaction Date – Date transaction was reported to MLB
Official On DL Date – Official date the player went on DL.
On DL – In Season – Players can be placed on DL before the season starts. This date is the first day the player was on the DL during the season.
Official Off DL Date – Players can be kept on DL after the season ends. This date is the last day the player was on the DL during the season.
Official Off DL Date – Official date the player was taken off the DL
Days – Total days on the DL. In Season On and Off DL dates were used to determine this value
Des – Description of the transaction
Team – Player’s team
Position – Player’s position
DL – Type of DL: 7, 15 or 60 day DL
Location – Location on body
Type – Type of injury
Side – Side of the body that the injury occurred
Body type – What on body was injured
Extent – Extent of injury
Surgery – Did the player have surgery
Surgery date – When did the surgery happen
On 60 DL – When the player get transferred from the 15 day DL to the 60 day DL

I am making the data free to the public, so anyone can use it in any way they see fit. Also, if anyone find any errors let me know and I will correct the data.

Team Data

Here is a quick look at the team data for 2011. First, here is the number of days and trips to the DL:

The one interesting fact, besides the White Sox having almost the least amount of DL again, that sticks out to me is that younger teams, like the Rays and Royals, have fewer trips and days lost. I went ahead and plotted the average age of the team (data taken from baseball-reference.com) vs. the total number of days lost to see if there was any data behind the hunch.

A small trend does exist between the average age and days lost, but there is a bunch of other factors at work as well.

I will look some more into the data (ex. salary lost, DL projections) once some people have looked over the complete list of transactions to make sure it is as error free as possible.