## Statistic Percentile Charts

How awesome is this chart? It’s simple, easy to understand, and imparts a swath of information all at once. I had no idea what league-average Isolate Power (ISO) was until now, but bam, there it is. This, my friends, is a thing of beauty. Until I saw this chart, I had no idea I needed all this information, but now that I’ve seen it, I want more.

I suppose I shouldn’t be surprised: this work of genius was created by Lee Panas, the writer at Tiger Tales and the author of “Beyond Batting Average”, a concise, well written book geared toward introducing everyday baseball fans to sabermetric statistics and analysis. It’s a great book and I recommend it thoroughly (although in the spirit of full disclosure, I must admit that I have a soft spot for Lee: not only am I a fellow saber-ed nut, but like Lee, I’m forced to root for my favorite ball team from afar, stuck in the wintery hellscapes of New England).

But I’m not writing this article as a book review (you can find those elsewhere); I’m writing because of that beautiful graphic up above. One of the complaints I hear most frequently from saber-newbies is that while they want to use these new statistics, they have no idea if the numbers they’re seeing are good or bad. Is a .320 wOBA good? Exactly how bad is a -5 UZR? I know it’s bad, but is it only mildly bad or tear-your-eyes-out bad? And what, pray tell, does a 4.00 tERA mean? It’s one thing to understand the theory behind the statistic, but sometimes understanding its scale can be just as challenging.

And so, I’ve taken Lee’s lead and included similar charts on each of the statistic pages here in the Library. The league-average rates are all accurate, and I’ve estimated percentiles based on the scores of all batters with more than 400 PA and pitchers with more than 90 IP. These percentiles may not be 100% accurate in all instances, but they are close enough to work as estimates in order to provide context.

Thanks again to Lee for the inspiration. If you like the charts, go check out some of his work.

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Steve is the editor-in-chief of DRaysBay and the keeper of the FanGraphs Library. You can follow him on Twitter at @steveslow.

15 Comments on "Statistic Percentile Charts"

Guest
Nick
5 years 5 months ago

This is great. Can it be broken down by position, as well?

Guest
Barkey Walker
5 years 5 months ago

Is this chart based on the 3.1 PA/game standard?

Guest
Barkey Walker
5 years 5 months ago

Okay, I see it now ” more than 400 PA and pitchers with more than 90 IP.” It might make more sense to weight by PAs and batters faced. There are many PAs by players who do not get to 400, like utility infielders, Relievers can be great and never hit 90 IP. Just a thought.

Member
5 years 5 months ago

This is going to be very useful, Steve. Thanks!

Guest
Erik
5 years 5 months ago

Suggestions: Typical “number of events” for stats to be significant/have predictive power. E.g, how many PAs does it take for OBP, BA, BABIP, etc to become significant? How many IP/BF for HR/9?

The main reason I ask this is because lots of different stats are used to make a point about how a player performed or will perform, with little context to indicate whether those stats are over a significant sample. While I assume we can trust FG writers to avoid articles that rely on too-small sample sizes, it would be help in evaluating articles from outside FG, our own ideas, etc.

Guest
Erik
5 years 5 months ago

and of course you beat me to it: http://www.fangraphs.com/library/index.php/principles/sample-size/

Maybe put this info on each of the stat primers, where appropriate?

Guest
5 years 5 months ago

Steve, your library is outstanding. It’s a really nice guide for people new to sabermetrics. I’m glad I was able to make a small contribution with the percentile chart idea. Your addition of player names along with percentiles is a great idea.

Guest
Randy
5 years 5 months ago

Kudos, Lee.

Guest
Mikkel
5 years 5 months ago

I’ve been clamoring for these percentile breakdowns for years! Fantastic.

Not to be pedantic, but it’s confusing that the middle row in the charts both claims to be the 50th percentile (i.e. the median) and the average. It can’t be both, since the stat distributions generally aren’t symmetric.

Also what do you mean by “estimated the percentiles?” Does it simply mean that you’re calculating the sample percentile of end-season stats for players with more than 400 PA/90 IP? If so, I think that the word “estimated” is misplaced.

I would suggest listing the percentiles as you currently do, except substituting the median for the average. The average should then be included as a separate row, as it is providing different information (it is independent of playing time definitions).

Guest
4 years 4 months ago

How can you make a similar chart for you’re own statistics over the course of this upcoming season?