2014 MLB Player Production with Runs Created

In the previous post I looked at MLB team runs created productivity.  While the model does an excellent job of explaining how runs are created, here I want to focus on the individual batters for the 2014 season and look at their individual contributions to the team's runs.

To do that, I take the estimated coefficients from the MLB team Runs Created model and multiply them by the actual player statistics for each player in 2014.  Calculating this yields an value of the players Runs Created.  (If you are interested, I have blogged previously on how to calculate MLB Player Production using MLB Team Runs Created).

So, for the past season, the most productive player in terms of Runs Created was Mike Trout.  The top 10 players for 2014 are listed in the table below.

playerID		teamID		Runs Created
troutmi01		LAA		127.23
mccutan01		PIT		116.21
bautijo02		TOR		113.96
cabremi01		DET		112.95
martivi01		DET		112.61
brantmi02		CLE		111.68
abreujo02		CHA		110.99
altuvjo01		HOU		107.00
stantmi03		MIA		105.63
cruzne02		BAL		104.04

For comparison purposes, here is the link for MLB player Runs Created in 2012 and 2013.

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.