Introducing Team Efficiency — A New Way to Evaluate MLB Teams

In today’s game of baseball, there is a statistic used to measure every and anything imaginable. You want to evaluate how well a pitcher has pitched so far this year? Check his FIP. Not sure how to interpret OPS for your favorite batter? Use wOBA. Heck, you can even compare actual outcome stats with expected ones to see if a player is getting really “lucky” or not, although almost no part of baseball is actually “luck”, so I use that term very loosely. And not all of these statistics are individual-based either. Most can be extended and applied to entire teams to evaluate as a whole.

When you look at just team stats, it can be hard to really determine which indicate team success. If you were to look at, say, home runs, you would see the top five teams are the Twins (.681 winning percentage), the Mariners (.405), the Astros (.676), the Brewers (.557), and the Cubs (.557). Now four of these teams are either tied for or leading their division outright, while the Mariners dwell in the bottom of the American League West division. This year home runs may be more predictive than ever, with the home run becoming ever prevalent in today’s game and the league on the brink of having numerous home run records shattered, but I still wouldn’t consider it the best statistic to use when predicting team success.

The Twins (aka Bomba Squad) are on pace to hit 312 home runs in 2019, shattering the previous record of 267 set by the Yankees in 2018.
Photo Credit: Dustin Morse, Twins Communications

You could look at a team’s pitching staff’s strikeout rate in an effort to find the best teams as well. With the increase in home runs comes the increase in strikeouts, so the teams that strike out opposing batters at the highest rates must be best, right? Well, not quite. Looking at the top five teams in K/9, we have the Astros (.676 winning percentage), the Red Sox (.528), the Reds (.441), the Nationals (.457), and the Indians (.522). At this point in time, you could probably only claim that the Astros are a good team, although the Red Sox are slowly climbing out of the early hole they were in at the beginning of the season. Thus, strikeout rate is not indicative of the best teams.

Now, I specifically ignored what are probably the two best statistics to use if you want to predict a teams’ success, and those are the two mentioned in the first paragraph: wOBA and FIP. wOBA, which combines all the different aspects of hitting and proportionates each to its actual run value, and FIP, which estimates a pitcher’s ERA while eliminating the variability of defense, are probably my go-to stats for just about any hitter or pitcher, respectively.

But, when looking at teams as a whole, I was looking for something different. Don’t get me wrong, wOBA and FIP both work wonderful as a team statistic and would give you exactly what you are looking for 90% of the time, but I recently had a thought that put me into that 10%. I was curious about how efficiently a team worked, and if this was predictive of team success at all.

Thinking through the idea of a team’s efficiency, I decided to break it into two groups, batting and pitching. A team’s offense would be efficient if it cashed in on opportunities to score runs at a high rate. Very simply, I defined the total number of opportunities a team’s offense had to score runs as the total runs scored + total left on base and the batting efficiency metric as total runs scored / total opportunities. I think this definition, while simple, includes information that is vital to the game, and I’ll do my best to explain my process of thinking. 

(Editor’s note: If you enjoy baseball statistics even a little bit, continue to read Hunter’s excellent description of his new stat, Team Batting Efficiency. If you just want to see where your favorite team ranks, scroll down to the next graphic.)

When a batter comes up with runners on base, there are three general outcomes for those runners at the end of the at bat: 

  1. The batter reaches base safely with no out being recorded. One or more runners may score. This is the ideal scenario for any team at bat, and would be a successful opportunity. 
  2. The batter is retired with no runners scoring. Obviously this is not what the batter wants to do, so he would be deemed unsuccessful. 
  3. The batter or one of the runners are retired but one or more runs score. This outcome, which seems to be more complicated, should be accounted for in the above equation.

I’m sure you’ve all seen the incredible opportunity a team has when they load the bases with no outs, only to watch their chance of scoring dwindle as they can’t push across any runs that inning. Well, that team is not very efficient, as each batter that comes to the plate would have walked back to the dugout leaving three men on base, totaling nine in the inning. Going back to the idea of offensive efficiency, if you look at the total number of runs scored (0) divided by the total number of opportunities the team had to score (nine), you would simply get 0. Obviously that is poor offensive performance. Let’s say, however, that the first batter to bat with the bases loaded hit a single that moves each runner up one base. Thus, the bases are loaded again, but with one run scored so far. Then, if the next three men were to all strike out and no other runs score, the team would have scored once in ten opportunities, giving a batting efficiency rate of 0.1. Still not great considering how many runs the team could have scored. 

Now that you understand how this idea of batting efficiency works for a single inning, it can be extrapolated to the entire season, using the sums of a teams left on base (LOB) and total runs scored (R). This way, I can find the offensive efficiency for a team throughout the whole season in terms of a percentage. Now, if I were to hypothesize, I would assume that a team that cashed in on its scoring opportunities at a high rate would likely be a successful team. Looking into the data (provided by baseball-reference), I found that, in general, this proved to be the case. 

Red indicates teams that made the playoffs.
Visualization created in R.

Quick reminder that this does not display a team’s offensive success. While Boston, New York, and Houston did have arguably the three top offenses in the league last year, one thing you should know is that Cincinnati and St. Louis were 14th and 15th in terms of wOBA as a team last year, respectively. What is interesting, though, is that they show up on complete opposite sides of the graph above. While the Reds had a very average offense last year, they were not very efficient with the run scoring when compared to the rest of the league. On the other hand, the Cardinals were very efficient with the opportunities they were given which could have helped their team to greater success. It makes me wonder though, if the Reds did have a more efficient offense in 2018, could they have actually been a decent team? Well, probably not. That Reds pitching rotation was quite dreadful last year. 

Speaking of pitching, I also looked at the efficiency of teams from a pitching standpoint. Now this pitching efficiency metric is complimentary to that of the metric for batting efficiency. For batting, I wanted to look at the percentage of times teams cashed in on opportunities to score, but for pitching, I want to know how often teams prevented the opposition from cashing in on these scoring opportunities. Thus, the metric for pitching efficiency can be defined as: 1 – total runs scored (by opposing teams) / total number of opportunities (by opposing teams), where opportunities are defined again as total runs scored + total left on base. 

Red indicates teams that made the playoffs.
Visualization created in R.

Looking at this graph, you can clearly see that there is a level of correlation to a teams pitching efficiency and its success, as eight of the first eleven teams were playoff teams. What jumped out to me though was how this compared to each team’s pitching success. The team that first caught my eye was the Chicago Cubs. This team had the second most efficient pitching staff in the league, meaning when guys got on base, they stranded them. What I found odd though was that the Cubs only had the 18th best overall FIP in 2018. So this pitching staff was very efficient and yet pretty mediocre in terms of advanced analytics. How could this be? Well, FIP doesn’t rely only on runs scored, like my efficiency metric, so Chicago pitchers could have gotten into bad situations a lot, but escaped with limited damaged more frequently than other pitching staffs. This idea makes sense when looking at the visual below.

Teams in darker, larger boxes represent pitching staffs that faced more run scoring opportunities by the opposing offense.
Visualization made in Tableau.

If you see the Cubs (third column, third row), you notice their staff gave opposing offenses a lot of opportunities to score, especially when compared to teams like Houston and Tampa Bay. This is part of the reason why their team FIP was likely so high. They ultimately had capable pitching staff, though, due to their ability to limit the damage caused by opposing teams. Unlike the Cubs, when you have a great pitching staff to begin with, and are hyper efficient, you get the Astros, widely regarded as the best rotation in the league last year and one of the best ever (in 2018, the Astros pitching staff had the highest strikeout rate in the history of the MLB and had the second lowest FIP of the 21st century). They gave opposing teams such a small number of opportunities to score that they were nearly 100 less than the next closest team! I think it’s safe to say that it’s good to be efficient.

Of course, you can’t be efficient just offensively or from the pitching rubber, you need both. So, let’s take a look at how these two efficiency metrics go hand in hand.

Batting and Pitching Efficiency for each team in 2018.
Visualization made in R.

As a whole, the most efficient teams will be in the top left quadrant. Recall that in order to have an efficient pitching staff, they will limit the opposing offenses to cash in on opportunities at a low rate, so left of the vertical line means that your pitching staff is more efficient than league average. Meanwhile, you want your offense to cash in on opportunities at a high rate, so above the horizontal line means your offense is more efficient than league average. What is interesting to see here is that 7 of the 10 playoff teams are clearly in the top left quadrant. Oakland is barely to the right of the vertical line, and the Cubs and Rockies had definitive inefficiencies one way or the other. Also worth noting is that St. Louis is firmly entrenched in the ideal quadrant of efficiencies and yet they did not even make the playoffs. This could stem from a couple of things, but the most likely being that they played in such a difficult division (the NL Central, where both the Brewers and Cubs made the playoffs), that they just couldn’t capitalize on their efficiencies and play October baseball.

So, if you are one of the ten most efficient teams, you are likely to make the playoffs right? Well, I decided to test the predictive power of these efficiency metrics. I won’t go too in depth of the statistical process, but for those of you who are interested, I ran a linear regression in R using the batting efficiency and pitching efficiency as predictor variables and end-of-season rank in the standings (i.e. the team with the best record was number one and the team with the worst record for number 30) as the response variable. After running this regression, it was found that batting efficiency and pitching efficiency were both highly significant in predicting the end-of-season rank (for those interested, both variables had a p-value of less than 0.001).

For those of you without much of a statistical background, this is a very encouraging sign pointing towards predictive power in the efficiency metrics. The linear regression can give us an equation that can be used to then predict the end-of-season rank based solely on the efficiency metrics. The predicted results for 2018 were as follows (actual rankings are in parenthesis):

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

While the model did not predict perfectly, as no model ever does, it did not fail by any meaning of the word. Of the 10 teams that made the playoffs last year, the model predicted 9 of those teams to make the playoffs, with only St. Louis predicted to play in October instead of Colorado. This makes sense, as St. Louis was right in the middle of the top left quadrant of the previous image, meaning they were efficient on both sides of the ball. I still wonder if, because they played in such a difficult division (with MIL, CHC, and a respectable 82-79 PIT team), that despite their efficiencies, they just couldn’t match the talent for the full course of the season. Two other teams that stick out to me are Tampa Bay and Seattle, predicted to be 17th and 21st overall, respectively. Both of these teams nearly made the playoffs last year, yet from an efficiency standpoint, the model did not believe they would have faired that well. I wonder if these two teams were able to capitalize on the fact that five teams in the American League had less than 68 wins last year, giving them a chance to win games while still being inefficient on either ends of the ball.

One thing to note here is that this model does not predict the end result of the playoffs. The playoffs are a whole different animal once the league is shaved down to the top 33% of teams, and this model is only looking at end-of-season ranks. So while I would like to say that since Houston was at the top of these predictions, they should have won the World Series, but I think that discredits Boston immensely and the true nature of the postseason.

The Red Sox captured the 2018 MLB crown as one of the most efficient teams in the MLB. (Photo by Kevork Djansezian/Getty Images)

Now last year is definitely interesting, but let’s get into the present and see how teams currently are performing. Note that all data from here on out will be as of June 12th. Using the same definition of batting and pitching efficiency from before, here is how each team fairs in regards to both metrics.

Batting and Pitching Efficiency for each team in 2019.
Visualization made in R.

In this case, the red represents the ten teams that would make the playoff if the season were to end after June 10th. Nine of the ten would be playoff teams fall in that top left quadrant, with only Tampa Bay falling outside of it. Their pitching has been far and away more efficient than any other team in the majors, so I can see how that would offset that lack of efficiency on the offensive side. Worth noting is Arizona and Oakland have both been efficient on both sides of the ball which could lead to more success in the future.

Using this efficiency data, I ran a linear regression in the same fashion as before, with batting efficiency and pitching efficiency as predictor variables and current ranks as response variables. From this regression (which again found both metrics to be highly significant in predicting current ranks), an equation was built to predict current ranks. The predicted results for 2019 were as follows (actual rankings 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)

Yes, you read that right, your Minnesota Twins are predicted to have the best record in baseball at this point in time. And to be quite frank, I’m not terribly surprised. The offense has been nothing short of prolific and the pitching has been an incredible surprise to many. This is considerably less surprising than seeing the two teams predicted to be 5th and 6th respectively: Arizona and Cincinnati. This is a huge jump for both teams when compared to their current ranks at this point in the season, but this could mean these two teams could be performing below their potential, and could surprise us all and make a run for the playoffs later this summer. On the contrary, our friends to the East might have something to be slightly worried about, as the Brewers are predicted to be in the very middle of the pack based on their efficiency metrics. If they are to keep it up, there’s a chance we could see a decline in winning percentage and a different team (likely the Cubs) could run away with the division.

Now, when you think about it, baseball has a long season. At this point in time, the year is not even half of the way over. Teams still have plenty of time to make adjustments to become more efficient on either side of the ball and improve overall play. Milwaukee likely won’t slide all the way down to be an average team, and Cincinnati likely won’t rocket all the way up to the top of the division, but I think it’s worth noting which teams are really making the most of the opportunities they are given, and which teams have some areas of improvements.

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