RESEARCH: Sample size studies for batters' skills


In baseball forecasting, it is widely understood that more data is better when trying to model future performance. Last month we examined that assumption for pitchers, and found that occasionally a smaller data set is actually better. We also found the point at which the recent data becomes as important as the historical data. This month we’ll repeat the exercise for hitters. 


We again use data from 2010-2017, since 2010 is the season that Baseball Info Solutions began using an algorithm to classify quality of contact. For annual data, we’ll use batters with = 350 PA.  For monthly data, we use only data from batters with = 75 PA in that month.

Throughout the article we will use R2 as a measure of...


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