Run Production

Last week, I analyzed team defense by using Fielding Independent Pitching (FIP) as a measure of pitching and Defensive Efficiency Ratio (DER) as a measure of fielding. This week, I’m going to analyze team offense. How to do this is certainly under debate as each sabermetrician seems to have his own best way of doing it. The combination of statistics most commonly used by analysts is batting average (BA), on base percentage (OBP) and slugging average (SLG). These are the statistics I typically use too but the combination does have some problems.

First, BA, OBP and SLG are highly correlated. For the more statistically inclined, a look at individual seasons of players in the American League between 1991 and 2004 shows that the correlations range from .63 to .74 with the highest correlation between BA and OBP. In short, this says that the three stats are measuring similar things. Logically, consider that batting average is essentially a subset of both OBP and SLG and thus the three of them are closely related.

Another problem (and this is related to the first issue described above) is that neither OBP nor SLG measures a pure skill. OBP combines the ability to get hits with the ability to draw walks. These are two very different skills which can be loosely defined as making contact (hits) and having a good eye (walks). Similarly, slugging combines the ability to get hits with the ability to get extra base hits. Again, these are two different skills – making contact and hitting for power.

In another post last week, I explained that BA can be subtracted from SLG to give us isolated power (ISO), a purer measure of slugging ability than SLG. Similarly, BA can be “subtracted” from OBP to give us isolated walk percentage (IWP). Because BA and OBP have different denominators (at bats and plate appearances respectively), it is best not to use the equation OBP-BA. Instead, we subtract hits from times reached based in the numerator and use plate appearances as the denominator. That is, IWP=(BB+HBP-H)/PA. So we now have BA, ISO and IWP. The correlations between these three statistics range from .10 to .39 with the highest correlation between ISO and IWP. These correlations are much lower that those between BA, OBP and SLG. This shows that BA, ISO and BBP more successfully measure three different things.

Furthermore, using AL team data from 1991 to 2004, I found that BA, ISO and IWP explain 93% of the variation in team runs scored. I also found that BA, OBP and SLG explain 93% of the variation in runs scored. So the two sets of statistics have the same very strong explanatory power but the former trio is appealing because it more purely measures three separate skills.

Finally, BA in isolation, explains 65% of the variation in runs, ISO explains 57% and BBP explains 48%. So neither of the three statistics explains a great deal by itself. All three must be used together to explain runs scored.

I have not included base running in run production because the only readily available statistic is stolen bases which is not highly correlated with runs scored. I can not analyze other base running statistics at this time without a lot of data entry and potential copyright issues. So base running will have to wait for another time. Even without it though, we are explaining most of what we need to know about runs scored as that 93% figure is very high.

The first table below presents the BA, ISO and IWP for all American League teams in 2005. The second table shows the ranks for the three statistics. From the tables, we can see a pretty clear illustration of the biggest problem with the Tiger’s offense last year. They were 4^th in the league in BA and 7^th in the league in ISO but they were 11^th in runs scored. Why is that? Because they were dead last in drawing walks.

TEAM	Runs	BA	ISO	IWP
Boston	910	.281	.173	.115
New York	886	.276	.174	.117
Texas	865	.267	.201	.089
Cleveland	790	.271	.182	.094
Toronto	775	.265	.142	.095
Oakland	772	.262	.145	.097
Los Angels	761	.270	.139	.085
Tampa Bay	750	.274	.151	.083
Chicago	741	.262	.163	.088
Baltimore	729	.269	.165	.087
Detroit	723	.272	.156	.075
Kansas City	701	.263	.133	.084
Seattle	699	.256	.135	.093
Minnesota	688	.259	.132	.096
Average	771	.268	.157	.093

TEAM	Runs Rank	BA Rank	ISO Rank	IWP Rank
Boston	1	1	4	2
New York	2	2	3	1
Texas	3	8	1	8
Cleveland	4	5	2	6
Toronto	5	9	10	5
Oakland	6	12	9	3
Los Angels	7	6	11	11
Tampa Bay	8	3	8	13
Chicago	9	11	6	9
Baltimore	10	7	5	10
Detroit	11	4	7	14
Kansas City	12	10	13	12
Seattle	13	14	12	7
Minnesota	14	13	14	4