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Quickie: MLB Playoffs by Pitching Statistics February 23, 2010

It’s cold out today. Last night, Buffalo was covered in a thin layer of freezing rain. I’m trying to stay warm by turning up my hot stove the way only an economist can – crunching the numbers on playoffs.

I’m re-using the dataset from my Cy Young Predictor a few entries ago in the interest of parsimony. It contains dummy variables teamdivwin and teamwildcard which take value 1 if the pitcher’s team won the division or the wildcard respectively. I then created a variable playoffs which took the value of the sum of teamdivwin and teamwildcard – just a playoff dummy variable.

Using a Probit model and a standard OLS regression model, I estimated the effects of individual pitching stats on playoffs. Neither model has very strong predictive value (linear has R-squared of about .05), which is unsurprising since it doesn’t take the team’s batting into account at all. None of the coefficient values are shocking – in the American League (designated as lg = 1), teams have a higher probability of making the playoffs because there are fewer teams, and although complete games appear to have a negative effect, the positive shutout effect more than makes up for that in both models. I’m interested in whether complete game wins and complete game losses have differential effects – that will probably be my next snowy-day project.

Results are behind the cut.

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