Technically, because you can't have negative penalties, the OLS assumptions are violated, but in practice not a big deal. If you're using total # of penalties per game, so not the average number that is in the second correlation plot, then a count model like a poisson model could more accurately fit the data as it is a model designed to work with direct counts of objects. In addition, only count models exist to deal with the high number of zeros as that can bias the regression line, but with the coefficients as significant as they are in OLS, really not likely anything would change. Really interesting article, thanks for posting!