You may have noticed that many (most?) of my posts contain links to the FanGraphs database. FanGaphs is easily one of the top two sites (along with Baseball-Reference) for obtaining statistical data on players or teams. Not only that, but it's all free. One area where Fan Graphs has lacked to some extent in the past is in sabermetrics education. Newcomers to sabermetrics would sometimes get frustrated by the maze of numbers at the site and there was not always an adequate explanation of the statistics in their old glossary.
Thanks to Steve Slowinski's FanGraph's Library, you should never get lost on FanGraphs again. Steve originally created his sabermetric library about a year ago, but moved it to FanGraphs this week. There is now a clear and concise description of each statistic with an emphasis on use and interpretation rather than calculation and theory. If you want to know what wOBA is, it's defined very simply in the library. Don't understand WAR? It's there. Confused about UZR? The library will clear it up for you.
It's easy to find from the FanGraphs home page. Just click on the "glossary" tab at the top of the page and you'll get to the library. Once there, you'll see five tabs at the top of the page "offensive statistics", "defensive statistics", etc. There is even a "sabermetrics principles" tab where you can find explanations of commonly used terms such as "regression to the mean" and "sample size". And he explains all of these concepts in very simple language, so nobody should be turned off or intimidated.
One of my favorite parts of the library is the percentile charts included for each statistic. I used similar percentile charts in my book Beyond Batting Average and suggested the idea to Steve. For example, he calculated percentiles for isolated power (ISO) for players with 400 or more plate appearances in 2010. He shows that the MLB average was .145. Ryan Braun's .197 ISO was at the 75th percentile meaning that 25% of the players did better than him and 75% did worse. You can quickly see from the chart that an ISO of .200 is good and an ISO under .100 is not good.
The explanations are geared for people who are not so familiar with sabermetrics and are trying to learn it. However, if you wish to learn how to calculate a statistic or understand the theory behind it, the library includes links to more detailed articles. The library is a great addition to the site and should make it easier to use for everyone.