2013 MLB Team Runs Created Productivity

In my Sports Economics course we look at how to estimate the productivity of MLB players.  In order to do that, we first look at estimating the productivity of MLB teams using a model created by Asher Blass that was published in 1992.  Almost every year I update his model using MLB data from Sean Lahman's database.  So I did the MLB Team Runs Created analysis (step-by-step procedure to estimate MLB Team Runs Created) for the 2013 season, and here are the results.  In step 8, I talk about the statistical analysis called a linear regression adjusting for heteroskedasticity, which is what I am reporting below.

Here are the estimated results for these 14 seasons of data (2000-2013).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
SINGLE	0.523	0.020	26.460	0.000
DOUBLE	0.723	0.041	17.710	0.000
TRIPLE	1.116	0.127	8.785	0.000
HR	1.423	0.039	36.291	0.000
HBP	0.425	0.080	5.303	0.000
SB	0.112	0.038	2.931	0.004
SF	0.621	0.161	3.863	0.000
NBB	0.330	0.019	17.252	0.000
GIDPCS	-0.167	0.063	-2.671	0.008
OUTS2	-0.144	0.025	-5.857	0.000
Constant	152.521	107.078	1.424	0.155

A few observations: first, that other than the constant term, each of the variables is statistically significant at the 99% confidence level and of the correct sign. Only GIDPCS and OUTS are negative, which both are using up the finite and scarce resource in baseball: outs. Second,the coefficient on HR's is greater than the coefficient on Singles, which means that a HR will on average generate more runs than a single. All the other coefficients seem to make sense as well. Finally, note that the coefficient on grounded into a double play plus caught stealing (GIDPCS) is greater in absolute value than a stolen base (SB), which means that if a player made two stolen base attempts, and was successful on one attempt but not on the other, that player on average would have cost his team more than he benefited his team.

Up next is the best hitters for the 2012 and 2013 seasons.