Team Efficiency Update – Where Are They Now?

Back in June, I introduced a new statistic in an attempt to evaluate teams in a less conventional way. I wanted to try to find a way to equate how efficient a team is both offensively and as a pitching staff. Thus, I coined the phrase ‘team efficiency’, which is comprised of ‘batting efficiency’ and ‘pitching efficiency’. At a high level, you can think of batting efficiency as the rate in which a team’s offense cashed in on scoring opportunities. On the other hand, pitching efficiency is built the same way, but you are looking at it reversed: it is the rate at which opposing teams cash in on scoring opportunities. A simple combination of these two metrics form team efficiency.

The MLB standings have sure changed a lot since I wrote the previous article, so I explore how the efficiency metrics may have changed as well. Below is the plot from June:

Visualization created in R.

Let’s compare this to a plot with current data:

Visualization created in R.

Let’s dive in to these two plots. (It’s worth noting that the dimensions of these two plots are not identical, although the ‘league average’ for each efficiency is very close to the same at each date in the season.) As a brief recap, the top left quadrant is the ideal place for a team to fall as that would mean they are more efficient than the league average from both an offensive and pitching standpoint. (Recall, you want to have a low pitching efficiency metric because that means your team is allowing the opposing team to score in opportunities at a low rate.)

In June, at the time of that writing, nine of the ten would-be playoff teams were in that ideal top left quadrant (Tampa Bay is the one exception, although they were by far the most efficient pitching staff). Today, however, only six of the would-be playoff teams are ideally efficient, with Cleveland and St. Louis joining Tampa Bay in the lower left quadrant and New York sliding over to the top right quadrant). It is worth pointing out here is that Cleveland and Oakland didn’t suddenly turn into a hyper efficient pitching staff over the past two months to bring them to the same (technically better) efficiency level as Tampa Bay. If you look in the first plot, you can see the Rays were pitching astronomically efficient through the time of the June article and regression was bound to bring them back to earth, which it did, as shown in the August plot.

Matt Olson (28) and the Athletics have been one of the most efficient teams in the MLB but find themselves barely hanging on to a playoff spot.
Photo Credit: Dan Shirey/Getty Images

Speaking of Oakland, I talked about them in the previous article saying that their efficiency could lead to more success, which it has in the last couple months. Today, Oakland has the 7th best win percentage in the MLB, compared to 17th as of the June article. This increase in success has gone hand in hand with the fact that they have become one of the most efficient teams in all of baseball. Oakland, now is tied with the Tampa Bay Rays for the first spot in the AL Wild Card race. Also worth noting, is this image is that the red teams don’t perfectly correspond to playoff teams, as six of the ten teams are AL teams, and there are only five playoff spots in each league. So technically, the teams highlighted in red are the top ten teams in the MLB, not “would-be playoff” teams, as was the case in June.

Offensive Efficiency by team as of 8/21/19.
Visualization created in R.

Above and below are the offensive and pitching efficiencies, respectively, for each team as of 8/21/19. Note, the blue horizontal line is the league average. What’s interesting is that while New York and Minnesota are the two most efficient offenses in across the majors, they have the two least efficient pitching staffs when compared to the other teams in the top ten.

Pitching Efficiency by team as of 8/21/19.
Visualization created in R.

Now, if you remember from the previous article, I ran a linear regression using batting and pitching efficiency as predictor variables and current ranks as response variables. If you would like more information about how I ran this regression, feel free to refer back to the previous article. Below are the predicted results from June (actual rankings in June are in parenthesis):

  1. MIN (3)
  2. NYY (5)
  3. TBR (4)
  4. CHC (7)
  5. ARI (12)
  6. CIN (23)
  7. TEX (10)
  8. HOU (2)
  9. LAD (1)
  10. COL (11)
  11. OAK (17)
  12. ATL (8)
  13. PHI (9)
  14. WSN (21)
  15. MIL (6)
  16. BOS (14)
  17. NYM (18)
  18. SDP (16)
  19. LAA (19)
  20. CLE (13)
  21. STL (15)
  22. TOR (28)
  23. KCR (30)
  24. SEA (25)
  25. CHW (20)
  26. MIA (27)
  27. SFG (24)
  28. PIT (22)
  29. BAL (29)
  30. DET (26)

Two takeaways I had from these results was that Arizona and Cincinnati were far more efficient than their record indicated, and Milwaukee was potentially outperforming based on their efficiency metrics. Well looking at the standings today, Arizona and Cincinnati definitely have not turned into playoff teams, but I still think they are happily outperforming people’s expectations from the beginning of the season. Milwaukee, on the other hand, has regressed to where their efficiency metrics predicted them to be, as they are struggling to stay above .500 at this point in the season. Let’s now take a look at how their current efficiency data predicts their rank in the standings.

So, I ran a regression for August 22nd’s data in the same way as I did in June. From this regression, an equation was built that could be used to predict the current rank in the standings. Below are the predicted results (current standings rank is in parenthesis):

  1. OAK (7)
  2. NYY (2)
  3. MIN (4)
  4. LAD (1)
  5. ATL (5)
  6. CHC (11)
  7. CLE (6)
  8. WSN (9)
  9. HOU (3)
  10. ARI (16)
  11. CIN (20)
  12. STL (10)
  13. BOS (12)
  14. TOR (26)
  15. TBR (8)
  16. NYM (13)
  17. TEX (19)
  18. SDP (21)
  19. PHI (14)
  20. SFG (17)
  21. KCR (28)
  22. LAA (18)
  23. MIL (15)
  24. COL (22)
  25. PIT (25)
  26. CHW (23)
  27. SEA (24)
  28. MIA (27)
  29. DET (30)
  30. BAL (29)

Well, unfortunately my Twins have slipped out of the top spot, but I’m excited to see they are still in the top three. Including the Twins, eight of the top ten spots are in fact playoff teams, which is about as accurate as I would expect the model to predict. The two teams that are top ten teams but were not predicted to be there are Chicago (NL), who is actually holds the 2nd Wild Card spot in the NL but has the 11th highest winning percentage, and one of the surprise teams from the previous article, Arizona. Now, this does not mean I expect the Diamondbacks to make a late playoff push this year (although, who really knows how that Wild Card race is going to shape up), but instead I think this prediction more leans on the fact that Arizona has had less opportunities than other teams to score. This idea of mine, which I will explain using a similar example, can also help answer the question that many of you are thinking, “Why are the Dodgers and Yankees not the top two teams when they are clearly the best two in the MLB?”

I think most people would argue that these two teams are the best in all of baseball, and I don’t think these predictions reject that idea. I think that because the Yankees and Dodgers are such good teams on both sides of the ball, they get a lot of offensive opportunities and give opposing teams few offensive opportunities. With more opportunities to score, they don’t have to be the most efficient team to score more runs than their opponent. For example, let’s say the Dodgers are playing the Brewers. If the Dodgers cash in on 40% of offensive opportunities and get 15 such opportunities in the game, that would lead to six runs. Meanwhile, if the Brewers cash in on 50% of offensive opportunities and get only 10 such opportunities in the game, they would score only 5 runs, and thus lose to the Dodgers. This is a much simpler idea than how the efficiency metric works, but it illustrates a good picture.

Likewise, the Dodgers and Yankees probably do a very good job of limiting the number of opportunities the opposing team gets. By doing this, even if they aren’t the most efficient at preventing runs, they still give other teams less chances to score runs, which will lead to less total runs in a game. (the example above would illustrate this as well when thought of in terms of pitching efficiency rather than offensive efficiency). So now this idea can be applied back to Arizona but in the opposite way. Arizona likely doesn’t have nearly as many opportunities to score runs, and thus don’t score as many in total. But, when they do get the opportunity, they do a great job at driving the runs in (or vice versa as a pitching staff).

Charlie Morton and the Rays pitching staff have been one of the most efficient in the MLB all throughout the year. Unfortunately their offense have been well below average.
Photo Credit: Frank Gunn/The Canadian Press via AP

I think it is important to make clear that efficiency data is meant to be more of an ancillary metric rather than an all-encompassing stat. Pair this team statistic with other ones and you can truly begin to evaluate a team, but this alone probably won’t quite do the job. In prior years, at the time of the writing of this article, there would still be another trade deadline across the MLB. Unfortunately the one and only trade deadline has come and passed (on July 31), so teams no longer have an external way to address any issues they may see on their team. They do still have over a month to make up any ground in divisional or wild card races. Those teams that continue to be most efficient will likely put themselves in best position to get to the playoffs.

Make sure to check back at the end of September for a final efficiency update before the Postseason begins for insight on who might make an efficient October run!

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