rbi risp and slg off and on over the last several months threads about rbis and related topics have gotten me to thinking about how well we can predict a player s rbis using information about his overall performance and the number of runners in scoring position risp that he bats with in the brock model bill james calculated predicted rbis as rbi total bases home runs this completely ignores the context which was all that brock could do since context was unknown to it so i thought i d take that idea as a starting point and look how good a fit to the data you get by comparing rbi home runs to slg risp i ve started with team data using data from the elias s that i ve picked up over the years when a i could afford them and b i could stomach the thought of increasing elias s profits that gave me the years and i don t have team rbis for or i could add that year if you run a simple least squares fit to the data you get rbi home runs slg risp the correlation between the lhs and the rhs is which is significant at a ridiculously high level so i feel like the fit is good at the team level i ve no started to move on to the player level and have looked at players will clark ozzie smith joe carter and don mattingly i hope to add quite a few more during my copious free time this year it doesn t do too badly except the equation underpredicts the low hr hitter smith which may be a fault of the model or it could just be ozzie the results rbi hr years actual predicted carter clark matt ly smith i think we can make a case and i hope to make it stronger that rbis can be predicted simply from knowing how a player slugs overall and how many men are in scoring position when he comes up more later harold harold brooks hbrooks uiatma atmos uiuc edu national severe storms laboratory norman ok i used to work for a brewery too but i didn t drink on the job p bavasi on dal maxvill s view that florida can win the nl east in
