It is a joy to watch a great offense take their hacks.
Everyone wants their own team to have a high powered offense, a star-studded lineup from the top to bottom.
But nothing is worse than watching a team struggle to score runs, leaving countless runners on base in the process.
That is not a joy - it is torture.
When I think of the ideal offensive attack, I think of efficiency. I want to be certain that my team will make the most of every opportunity and come through when it matters.
So how can we quantify offensive efficiency? Which teams in recent history were the most effective in scoring their runs?
To calculate offensive efficiency, I thought of a number of avenues available to me. For one, I could have calculated R/PA or looked at things like moving runners over via bunt. LOB totals could have been useful for this venture, looking at the rate in which teams score in relation to their LOB totals.
Calculating efficiency through LOB could be deceiving. For one, the team with the least amount of men left on base will most likely be the teams that find trouble getting men on in the first place. Because we are quantifying efficiency, the results will be opportunity independent -- meaning that the most efficient offenses are not necessarily the ones that scored the most, but rather converted their opportunities into runs at a higher rate.
Enter run expectancy totals.
Now, run expectancies are an important part of the game. They determine the weights that go into wOBA and tell us the relative worth of each event in context of its run value. But rarely do we see run expectancies applied to a whole team to evaluate their overall offensive attack. Looking at these run expectancies, we will isolate the teams that truly had an efficient offensive attack.
To begin, I created a run expectancy matrix in Retrosheet for each season since 2002.
Then, I calculated the run expectancies for each team, in every situation -- bases empty, one out; empty, 2 outs; and so on.
Using the actual run expectancy totals for each team, we adjusted by the league average run expectancy for each situation.
NOTE: The league average run expectancy is from the year in which the team played -- so as to account for the scoring environment -- (actual/league_average)*100
Next, we take the average of those adjusted totals to represent the overall offensive efficiencies of each team. However, these totals need to be adjusted by park factors; this will take care of the run-scoring environment in a given park.
Along the way I compiled offensive efficiency into a single metric -- EFF + -- and created it as a "plus stat" for optimal presentation.
The population is of 330 teams spanning 11 seasons. The average EFF + was 100 with a standard deviation around 4.
The top 5 are as follows:
2003 Kansas City Royals -- oEFF: 113.27
Surprisingly, the 2003 Kansas City Royals top our list. With the bases loaded in 2003 the Royals had an accumulative run expectancy of nearing 2.5. Small sample size aside, the Royals converted when the bases were full. With two outs in general the 2003 Kansas City Royals had an oEFF of 130. In the end, the Royals managed to score 836 runs in 2003. Despite their effective offense, they finished a measly 83-79, good for third in the AL Central. In the end, it was the Royals' astronomical ability to perform especially well with the bases loaded and no one out (with an oEFF of 180) that catapulted them to the top of our list.
Below is a table that details situational offensive performance.
|KC||0||1B & 2B||138.54|
|KC||0||1B & 3B||116.17|
|KC||0||2B & 3B||130.46|
|KC||1||1B & 2B||132.34|
|KC||1||1B & 3B||121.62|
|KC||1||2B & 3B||140.65|
|KC||2||1B & 2B||124.36|
|KC||2||1B & 3B||168.99|
|KC||2||2B & 3B||120.81|
2011 St. Louis Cardinals -- oEFF: 110.90
The 2011 World Series champs place second in offensive efficiency. There was no doubt that this team was clutch and great in converting base-runners into runs. In addition, the Cards led the National League in Runs/G, BA, OBP, OPS+, you name it. This team was among the decade leaders in run expectancy with the bases loaded with 2 outs and 1 out, also converting at a high rate with second and third base occupied.
|STL||0||1B & 2B||102.16|
|STL||0||1B & 3B||87.24|
|STL||0||2B & 3B||124.06|
|STL||1||1B & 2B||114.02|
|STL||1||1B & 3B||86.47|
|STL||1||2B & 3B||146.02|
|STL||2||1B & 2B||110.40|
|STL||2||1B & 3B||118.64|
|STL||2||2B & 3B||141.64|
2010 Colorado Rockies -- oEFF: 110.83
The 2010 Rockies featured a devastating 3-4 combo of Cargo and Tulo, having fantastic years simultaneously. Because of Coors' high homerun rate, the Rockies had above league average rates for all two out situations due to the likelihood of a well timed home run. The Rockies were especially effective with two outs and a runner on third with a oEFF of 160.
|COL||0||1B & 2B||99.66|
|COL||0||1B & 3B||121.40|
|COL||0||2B & 3B||123.24|
|COL||1||1B & 2B||117.72|
|COL||1||1B & 3B||126.22|
|COL||1||2B & 3B||124.71|
|COL||2||1B & 2B||140.39|
|COL||2||1B & 3B||139.97|
|COL||2||2B & 3B||101.87|
2012 Texas Rangers -- oEFF: 110.71
The 2012 Texas Rangers featured a stacked lineup that was capable of hitting a ball out of the ballpark 1-9. This is evident by their 128 oEFF with 2 outs and a man on first, where they were 28% times more likely to score in that situation compared to the average team that year. With zero on and zero out, the Rangers had a 118 oEFF -- 18% more likely to score than league average run expectancy would suggest.
|TEX||0||1B & 2B||110.76|
|TEX||0||1B & 3B||98.20|
|TEX||0||2B & 3B||132.08|
|TEX||1||1B & 2B||123.59|
|TEX||1||1B & 3B||108.49|
|TEX||1||2B & 3B||149.31|
|TEX||2||1B & 2B||101.34|
|TEX||2||1B & 3B||122.81|
|TEX||2||2B & 3B||147.45|
2007 New York Yankees -- oEFF 110.55
Finally, the 2007 Yankees round out the top 5. In particular, the Yankees had an oEFF of 116 with a no one on and none out, a 141 with bases loaded and no one out, and a 129 with 2 outs and nobody on.
|NYY||0||1B & 2B||111.14|
|NYY||0||1B & 3B||125.66|
|NYY||0||2B & 3B||142.48|
|NYY||1||1B & 2B||110.60|
|NYY||1||1B & 3B||126.79|
|NYY||1||2B & 3B||115.04|
|NYY||2||1B & 2B||102.97|
|NYY||2||1B & 3B||118.46|
|NYY||2||2B & 3B||126.99|
Lastly -- here is the bottom 25 in oEFF, descending:
In future installments we will investigate if offensive efficiency correlates well year-to-year. We will also run tests to see if an efficient offense means a winning team.
Also, tune in to witness how we can find a team's clutch factor through their efficiency totals.
Max Weinstein can be contacted on twitter Follow @MaxWeinstein21