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2015 Fantasy Baseball Pitcher Comparison Tool

This post will feature the final tool in our suite of player comparison tools — pitchers!

If you would like to look at the first two tools, you can reference them here:

This tool, like the previous tools, allows Fantasy Baseball owners to compare players with regards to their relative strengths and weaknesses. For example, Kenley Jansen and James Shields are projected to have similar overall values, but how they arrive at that overall value is very different.

The same can be said for two pitchers that are projected to have the same overall value, but one of those pitchers is projected to have an inordinate amount of their value derived from their projected win totals; I’ll take the pitcher whose overall value isn’t ascribed from luck.

If you read the 2015 Fantasy Baseball Player Comparison Tool: Hitters article, you can skip to the Pitchers Tool, the paragraphs that follow are the same as the former article.

The Projection Systems: ZiPS, Steamer, FANS

The past couple of years, the method that I’ve used to produce my fantasy rankings has been achieved through the aggregation of a number of statistically based projection systems; the projections that the player comparison tool uses is a result of the  same process.

The projections—ZiPS, Steamer, FanGraphs crowdsourcing projections—that I combined together this year are valuable in and of themselves, but the aggregation process of these particular projection systems proves to be a valuable process for several reasons:

  1. Group projection systems are able to produce better results than individual forecasts in almost every field that it has been studied, from The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t by Nate Silver (2012).
  2. Statistically based forecast often do better than projections done by individuals because they are done on computers which are free from biases like, “he was on my team last year and I’ll never own him again”; just because a player performed poorly last year, that doesn’t mean he’ll perform poorly this year, and just because you don’t care for him that doesn’t mean you shouldn’t discard him irrespective of his inherent value (e.g. if you can get a 5th round player in the 6th round, you draft him; how you feel about him shouldn’t and doesn’t matter; the value that he provides is all that matters.)
  3. This year, and because of the recent body of work produced around the topic of crowdsourcing, I decided to add a human element in the form of the FanGraphs crowdsourcing projections.

The projections below, and the spider charts/comparison tools that use those projections are based off of 15 team rotisserie league roster formats that use two catchers, one middle infielder, 1 corner infielder, 1 1B, 1 2B, 1 3B, 1 SS, 5 outfielders, 1 utility player, 9 pitcher spots, and seven bench spots; I believe this is the format for NFBC leagues, but I could be mistaken.

The values have been translated through the average of the three forecasts—ZiPS, Steamer, FanGraphs crowdsourcing—into the zSCRs, which in the absence of league specific data is the best substitute for SGP.

The 2015 Fantasy Baseball Comparison Tool: Pitchers

[iframe src=”’Sheet1′!A1%3AB22&wdHideGridlines=True” width=”100%” height=”490″]

NOTE: To pick a new player you have to clear the cell first (i.e. 1.) CLICK INTO THE CELL 2.) PRESS SPACE 3.) THEN, PRESS ENTER)

You can also search for players, BUT THE CELL HAS TO START OUT AS A BLANK CELL (i.e. the note above) before you can search for a player: 1.) Start to type out the player’s name 2.) Click the drop down arrow 3.) Click the players name.

To use the chart you have to start from a clear/empty cell:

1. Click into the area underneath the cell that says “Pitcher A” or “Pitcher B.”

Screen Shot 2015-02-23 at 4.25.06 PM

2. Click the drop down arrow that appears after you click into the cell.

Screen Shot 2015-02-23 at 4.25.28 PM

3. Now, select the player that you wish to have displayed on the chart. If you only want to view one player, you have to make sure one of the cells where a player is listed is blank (note above).

Screen Shot 2015-02-23 at 4.25.40 PM



[iframe src=”” width=”100%” height=”620″]

Silver, N. (2012). How to Drown in Three Feet of Water. In The signal and the noise: Why so many predictions fail–but some don’t. New York: Penguin Press.

Photo Credit: Dirk Hansen

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