Much of the available baseball related data and statistical analysis is very useful for evaluating individual performance. But trying to evaluate teams is an inherently more complex endeavor. Nevertheless, understanding how team performance on offense and defense contributes to winning is critical. On defense a simple measure of efficiency (Defensive Efficiency) was developed that tells us the rate at which a team converts balls put in play into outs. A defensive efficiency of 71% is around average, over 73% is considered excellent, and below 69% is terrible. While this efficiency metric does not necessarily capture the nuance of defense, it can be used to broadly assess a team's defense and has a relationship* with winning. What about a measure of offensive efficiency?
The basic idea of an offensive efficiency measure would be to examine the rate at which teams turn baserunners into runs and there have been some strong efforts made to pin down a measure of offensive efficiency. The basic idea is to examine if a team has scored as many runs as would be expected given their number of singles, doubles, triples, home runs, etc. We know that getting on-base is the most critical aspect of a plate appearance and having more baserunners should lead to more runs. But perhaps there are teams that are doing more with less.
One way to approach to this question is to compare a team's actual runs scored with their weighted runs created (wRC). wRC gives us a measure of how many runs a team should have scored given their offensive output (i.e., singles, doubles, etc). The ratio of these numbers will give us an indication of the extent to which a team is outperforming their expectation, which can be taken as an index of efficiency. Below are the ‘efficiency' rankings according to this analysis for the 2013 season:
Cardinals and Yankees on top? This looks like an interesting measure of efficiency. Well not really if we consider how it relates to winning games. There are only two playoff teams in the top 10, and look at some of those teams in the top 10: Astros, Mets, Marlins, Orioles. At the bottom, on the ‘inefficient' side of things, we have a bunch of playoff teams: Red Sox, Dodgers, Rays, Pirates and Tigers. The correlation between this efficiency and the team's winning percentage is -0.002. Thus, while this measure is interesting it does not seem to be capturing something about winning, but perhaps is indexing a more random component of performance like luck.
Let's return to the initial idea of developing a defensive efficiency-like measure and look at rates for converting baserunners to runs as a way to define a measure of offensive efficiency. For any team we can approximate their number of baserunners with PA*OBP. We can then look at the rate at which baserunners score by taking the ratio of runs and baserunners. Here is how this looked in 2013:
This looks a little better than our previous measure. Five of the top 10 teams are playoff teams, while the bottom 10 only has two playoff teams (Dodgers are down there again). The correlation between this measure and the team's winning percentage is 0.586, which is a pretty solid relationship and shows that efficiency is accounting for a reasonable proportion of the variance in winning. Thus it seems as though there are efficient teams and being efficient relates to winning.
We can examine if this simple measure of efficiency's relation to winning is evident in a larger sample. To do so I accumulated team data back to 1974. With this larger sample the correlation drops slightly to 0.463, which is again a pretty solid relationship. Out of interest here are the 10 most efficient teams from this sample:
These teams were bringing home over 40% of their baserunners. That is very impressive. One thing that the teams on this list likely make you think of is power. Most of these teams played during the ‘steroid era'. These 10 teams hit a lot of home runs. The average HR total per season for this group is 221.2, which is well above the per season average for the entire sample (146.9).
Here are the 10 least efficient teams:
These teams were converting fewer than 30% of their baserunners to runs and losing a lot of games. They did not hit HRs with an average season total for this group coming in at 74.6. Note that almost all of these inefficient teams played during the late 70s - early 80s; an era that did not have the same run-scoring as the late 90s and 2000s, which means some sort of era-adjustment would be useful going forward.
The best correlates of this offensive efficiency measure are SLG (r = 0.887), ISO (0.836), wOBA (0.812), HR (0.767), wRC (0.740), OBP (0.666), RE24 (0.584), and GUILLEN# (0.581). These relationships should not be particularly surprising, as the metrics involve getting runners on base and/or moving them around. The contribution of this offensive efficiency measure is the demonstration that there is important variance in a team's ability to take advantage of baserunners in order to score runs and win games. In fact, this offensive efficiency correlates better with winning than does runs scored (RS) and all of the above metrics with the exception of OBP, wOBA, and RE24. Teams that combine high rates of OBP with high rates of efficiency will likely find success, but some of that is also contingent on their ability to prevent runs as well.
While the measure of efficiency given here is certainly not perfect, the idea of offensive efficiency is an interesting aspect of team performance that could be fruitful for continued investigation. Examining park-adjusted, or era-specific efficiencies could add to the options for assessing team performance.
* r = 0.341 for the sample (1974-2013) discussed here.
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Chris Teeter is a contributor to Beyond the Box Score. You can follow him on Twitter at @c_mcgeets.