I'm not sure of your ultimate goal, but it looks as if you want to find "outliers" in events based upon "normal" historical behavior. The problem with using just plain old "average" and "std deviation" is that if the "model" of the data isn't a perfect Gaussian distribution (i.e. a "bell curve") , then using avg and stdev will yield misleading results.
An alternative approach is to do what the Prelert Anomaly Detective app does, which uses dynamic modeling to "fit" the data better and you'll get more accurate results. And, it's easy to use!
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