2014 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 2014 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 15 seasons of data (2000-2014).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
SINGLE	0.499	0.02	24.87	0.00
DOUBLE	0.718	0.04	16.86	0.00
TRIPLE	1.137	0.12	9.32	0.00
HR	1.463	0.04	37.62	0.00
NBB	0.288	0.02	14.31	0.00
SB	0.103	0.04	2.61	0.01
GDIPCS	-0.028	0.06	-0.48	0.63
HBP	0.398	0.08	4.88	0.00
SF	0.774	0.17	4.63	0.00
OUTS	-0.102	0.02	-4.19	0.00
C	-10.644	106.03	-0.10	0.92

A few observations: first, other than grounded into double plays and caught stealing (GIDPCS) (along with the constant term) each of the variables is statistically significant at the 99% confidence level and of the correct sign. Only OUTS are negative and statistically significant. 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.

For comparison purposes, here is the blog for 2013 MLB Team Runs Created.