Can a Data Scientist Replace a NBA Scout? ML App Development for Best Transfer Suggestion

Concerning the crucial set of group_1 features, they are almost balanced between left/right-skewed. However, the dominant holding factor is the great presence of outliers beyond the pertinent upper boundary. Induction #1: We have to deeply study group_1, in a way that will not only guarantee significant levels for the respective features, but also won't compromise (the greatest possible number of) the rest. With that in mind, we initiate a naive approach of sorting the dataset by a master feature (AST_PCT), taking the upper segment of it (95th Percentile) and evaluating the plays'horizontally' (across all features). By comparing the population with the 95th percentile average features, we see that by maximising along AST_PCT many of the remaining features get worse, violating that way Assumption #2.