The consensus is unanimous: the sample size is just too small to say one way or the other.
After intense deliberation, data analysts across Major League Baseball have come to a decision: we don’t know for sure, the sample size is just too small.
Data analysts across the league agree that more data would be instrumental in making the decision easier and more informed.
“You can’t throw around evaluations too rashly,” said one data scientist in the Kansas City Royals organization. “You’d really prefer to have 300-400 seasons of league data to provide a fair analysis of productivity. We’ve got to weed out the outliers in the dataset.”
Another statistician with the Baltimore Orioles explained it was too early, we’re comparing apples to oranges, no single stat tells the whole story, and the data collection methodology itself is imperfect.
“The raw numbers look great,” she explains, “but the rates are less clear and only provoke more questions. We really just need more data to work from.”