Yesterday, I laid out a new method of quantifying depth that we’re looking into here at FanGraphs. It’s based on the idea that while our depth charts are a point estimate of how much each player will play, the real-life way things work is different. To mimic reality more closely, we’re experimenting with removing players from the depth charts algorithmically and rebuilding teams on the fly to see how they look without their best players. Yesterday’s article covers our methodology in great detail.

There’s one thing that I wanted to add to that article but couldn’t find the space for: more tables. There was one big giant table in there that showed each team’s winning percentage as we removed more and more players from their squad. But that’s just not a great way to look at anything – 300 numbers in a giant table is more information than our brains can easily process. I don’t have a lot of new information today, but I thought I’d slice that data up into more bite-sized chunks so you can look at it without your eyes glazing over. One quick note: All the tables in this article are sortable, so you can order them by whichever category you so desire.

First, here’s how much each team in baseball would lose in winning percentage in a vacuum if they played the entire season without their best player, along with their winning percentages in both the before and after cases:

Next, here’s the same thing for teams losing their top two, and then top three, players:

And here’s the same thing for their top four and five players:

And six and seven:

And eight and nine:

And finally, their top 10 players:

That’s an easier way for me to look at things. Here’s another way: check out how the AL East relative standings change as we remove more players from each team:

And the AL Central:

And the AL West:

And the NL East:

And the NL Central:

And finally, the NL West:

This presentation of the data helps me to visualize it better. To reiterate from yesterday’s introduction, I’m not saying that this is exactly how injuries work in real life. Teams don’t only lose their best players in sequence, starting with the very best. We haven’t fully decided how we want to handle the likelihood of using each scenario in our playoff odds. We might use parts of several all blended together. It’s still absolutely a work in progress.

With all of those caveats in mind, though, I hope this data is helpful. If you’re wondering what happens to your team, or any number of teams, in some very scary injury scenarios, these tables should help explain our best guess.

Source