I have an issue with selection bias in my independent variable and I am uncertain of the best approach to correct for it.

I'm looking at soccer data and trying to make some forecasts on players from different amateur level clubs to what they might be expected to do at the professional level. Therefore, the categorical variable is the league that the player's club is in (low level or high level).

The issue is that there are a few players from lower level leagues who actually make it to the professional ranks and perform well. The regression model suggests that being form a lower level has a high predicted future performance than playing at a higher level. This obviously is not correct. The problem here is selection bias as the few lower level players are not representative of larger population from that level. Similarly, there are a larger number of players at the higher level leagues who make it to pro status, however some of them do not pan out as good players.

As such, is there a way to correct for the selection bias of the lower level players and the substantial amount of sample size differences in the classes of the categorical predictor in my model?


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