How to calculate average of dataframe column and find top 10%

I am very new to Scala and Spark and I am working on some homemade exercises using baseball stats. I use a case class, create an RDD and assign a schema to the data, and then turn it into a DataFrame, so I can use SparkSQL to select groups of players through their statistics that match certain criteria.

Once I have a subset of players I'm interested in looking further, I would like to find the average of a column; for example, medium batting or RBIs. From there, I would like to break all players into percentile groups based on their average performance compared to all players; top 10%, bottom 10%, 40-50%

I was able to use the DataFrame.describe () function to return the summary of the desired column (mean, stddev, count, min and max) all as strings. Is there a better way to get just the mean and stddev as a pair, and what is the best way to split the players into 10 percent groups?

So far, my thoughts have been to find values ​​that capture the percentile ranges and record a function that groups players through comparators, but it looks like it borders on reimagining the wheel.