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A look at offensive efficiency

We know about a team's defensive efficiency (turning balls in play into outs) but what about a measure of offensive efficiency (turning baserunners into runs)? Here is an exploration of the idea.

By Chris Teeter
Greg M. Cooper-USA TODAY Sports

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:

Rank Team Actual Runs wRC Difference Efficiency
1 STL 783 715 68 109.5
2 NYY 650 603 47 107.8
3 HOU 610 583 27 104.6
4 NYM 619 594 25 104.2
5 MIA 513 493 20 104.1
6 KCR 648 624 24 103.8
7 BAL 745 719 26 103.6
8 OAK 767 743 24 103.2
9 CLE 745 725 20 102.8
10 WAS 656 646 10 101.5
11 TOR 712 704 8 101.1
12 TEX 730 722 8 101.1
13 PHI 610 604 6 101.0
14. SDP 618 612 6 101.0
15 CIN 698 692 6 100.9
16 CHW 598 597 1 100.2
17 ATL 688 689 -1 99.9
18 ARI 685 690 -5 99.3
19 LAA 733 741 -8 98.9
20 MIL 640 647 -7 98.9
21 BOS 853 863 -10 98.8
22 COL 706 719 -13 98.2
23 SFG 629 645 -16 97.5
24 CHC 602 618 -16 97.4
25 SEA 624 642 -18 97.2
26 PIT 634 658 -24 96.4
27 TBR 700 733 -33 95.5
28 MIN 614 645 -31 95.2
29 DET 796 838 -42 95.0
30 LAD 649 685 -36 94.7

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:

Rank Team PA OBP Baserunners Runs Efficiency
1 BAL 6144 0.313 1923 745 38.7
2 BOS 6382 0.349 2227 853 38.3
3 STL 6202 0.332 2059 783 38.0
4 OAK 6209 0.327 2030 767 37.8
5 CLE 6165 0.327 2016 745 37.0
6 TEX 6196 0.323 2001 730 36.5
7 TOR 6152 0.318 1956 712 36.4
8 DET 6388 0.346 2210 796 36.0
9 LAA 6260 0.329 2060 733 35.6
10 COL 6152 0.323 1987 706 35.5
11 NYY 6044 0.307 1856 650 35.0
12 ATL 6133 0.321 1969 688 34.9
13 WAS 6047 0.313 1893 656 34.7
14 TBR 6242 0.329 2054 700 34.1
15 CIN 6293 0.327 2058 698 33.9
16 HOU 6020 0.299 1800 610 33.9
17 MIL 6064 0.311 1886 640 33.9
18 KCR 6093 0.315 1919 648 33.8
19 ARI 6334 0.323 2046 685 33.5
20 PHI 6014 0.306 1840 610 33.2
21 CHC 6079 0.300 1824 602 33.0
22 PIT 6135 0.313 1920 634 33.0
23 SEA 6172 0.306 1889 624 33.0
24 SDP 6122 0.308 1886 618 32.8
25 CHW 6077 0.302 1835 598 32.6
26 NYM 6207 0.306 1899 619 32.6
27 LAD 6145 0.326 2003 649 32.4
28 SFG 6168 0.320 1974 629 31.9
29 MIN 6212 0.312 1938 614 31.7
30 MIA 6021 0.293 1764 513 29.1

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:

Rank Team Year R BaseRunners Efficiency (%) Win%
1 CLE 1994 679 1577 43.1 0.584
2 CHW 2000 978 2282 42.9 0.586
3 COL 1996 961 2248 42.7 0.512
4 MIL 1982 891 2123 42.0 0.583
5 CHW 2004 865 2064 41.9 0.512
6 BAL 1996 949 2273 41.8 0.540
7 TEX 2004 860 2058 41.8 0.549
8 TEX 2005 865 2073 41.7 0.488
9 SEA 1996 993 2386 41.6 0.528
10 COL 2000 968 2336 41.4 0.506

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:

Rank Team Year R BaseRunners Efficiency (%) Win%
1 SEA 2010 513 1785 28.7 0.377
2 CIN 1982 545 1891 28.8 0.377
3 SDP 1975 552 1908 28.9 0.438
4 LAD 1992 548 1890 29.0 0.389
5 NYM 1981 348 1197 29.1 0.390
6 SDP 1981 382 1311 29.1 0.373
7 MIA 2013 513 1764 29.1 0.383
8 SDP 1980 591 2027 29.2 0.448
9 SDP 1974 541 1848 29.3 0.370
10 CAL 1976 550 1868 29.4 0.469

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.

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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.

Conclusion

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.

. . .

All statistics courtesy of FanGraphs, Baseball Prospectus and Baseball-Reference.

Chris Teeter is a contributor to Beyond the Box Score. You can follow him on Twitter at @c_mcgeets.

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