There have been a countless number of metrics used to measure a hitter’s power throughout the course of baseball history. The king of all power statistics has always been home runs, but recently many teams have used statistics such as slugging percentage and isolated power to better evaluate a player’s true power. In the past couple of years, many teams have begun to use exit velocity, a statistic now available thanks to Statcast, to determine how fast the ball travels after the player makes contact with it. This is the newest power metric used by teams, and perhaps one of the most objectively effective.
One thing none of these metrics are able to effectively measure is power efficiency. All of these metrics can determine who is the most powerful, but not who is the best at using their power. Teams should expect that the strongest players hit the ball with the most power. However, there is also value in players who might not be the strongest but can provide more power than expected. Finding players who are the most efficient with their power will be greatly beneficial to teams, especially during the draft, when many players are valued by their physical traits as much as their on-field performance.
The question remains – how can power efficiency be measured? Perhaps the most general way to measure efficiency is by using a measurement of body mass index (BMI). BMI is calculated by dividing a person’s weight in kilograms by his or her height in meters squared. Since BMI does not differentiate between fat and muscle when calculating weight, and since most baseball players have a healthy ratio of muscle to fat, it is safe to assume in this case that greater weight equates to more muscle and therefore more strength.
Using BMI with other power statistics will allow for the calculation of power efficiency. The easiest statistic to use in this scenario is home runs. Power efficiency can be calculated in this sense by determining how many home runs a player hit per kg/m^2 of BMI. Here are the top ten most efficient power hitters in 2016 according to a ratio of HR/BMI.
|Player||HR/BMI||Number of HR||Rank in Number of HR in 2016|
|Manny Machado||1.600||37||14 (tied)|
|Brian Dozier||1.584||42||3 (tied)|
|Nolan Arenado||1.556||41||6 (tied)|
|Khris Davis||1.504||42||3 (tied)|
|Edwin Encarnacion||1.421||42||3 (tied)|
|Chris Carter||1.374||41||6 (tied)|
Interestingly, nine of these ten players ranked in the top ten in the category of home runs hit in 2016. These rankings show that, when power is determined by home runs, the players who usually hit the most home runs are also the players that are the most efficient with power. The one exception to this rule is Manny Machado, who, despite only finishing tied for 14th in total home runs hit in 2016, finished in an astounding second place in power efficiency. This will become a running theme as other measurements of power efficiency are calculated later.
Next, let’s look at a measure of power efficiency using slugging percentage. In this case, the percentage of total slugging points per kg/m^2 of BMI is calculated. The top ten list of this measurement is a little different than that of HR/BMI.
|Player||SLG/BMI (calculated as a percentage)||SLG||Rank in SLG in 2016|
|Manny Machado||2.30%||.533||17 (tied)|
Right away, the top two players in this category jump out. Both Mookie Betts and Manny Machado finished outside of the top ten in slugging percentage in 2016, and both of these players had lower slugging percentages than any of the other eight in the top ten in SLG/BMI. Including Betts and Machado, four of the top ten in SLG/BMI did not finish in the top ten in slugging percentage in 2016. This indicates that SLG/BMI does a good job of showing players who are efficient with power, but not among the league leaders in slugging percentage.
Finally, let’s look at a calculation of power efficiency using isolated power. This metric will be calculated in a similar fashion as SLG/BMI, as it will calculate the percentage of total ISO points per kg/m^2 of BMI. Here are the top ten in ISO/BMI in 2016.
|Player||ISO/BMI (calculated as a percentage)||ISO||Rank in ISO in 2016|
|Mark Trumbo||1.03%||.277||3 (tied)|
|Manny Machado||1.03%||.239||20 (tied)|
|Brad Miller||1.01%||.239||20 (tied)|
|Khris Davis||0.99%||.277||3 (tied)|
Like the leaders in SLG/BMI, four of the top ten in ISO/BMI finished outside of the top ten in ISO. Both Manny Machado and Brad Miller, who finished tied for fourth and seventh respectively, finished at 20th in ISO in 2016, showing that they have incredibly efficient power when power efficiency is calculated by ISO/BMI. However, it is also worth noting that the top two leaders in ISO/BMI, David Ortiz and Brian Dozier, also finished as the respective top two in ISO in 2016.
With all of this in mind, what conclusions can be made regarding measures of power efficiency? If all three of the previously calculated metrics are evaluated together, usually the most efficient power hitters are also the ones who finish among the leaders in each of the power metric categories. This means that the power metrics are usually good predictors of power efficiency. In fact, both the correlation between HR and HR/BMI and the correlation between ISO and ISO/BMI are greater than 96%. The correlation between SLG and SLG/BMI, meanwhile, was only 83.3%, and this is likely lower due to some of the outliers in the leaders in SLG/BMI.
However, one outlier has stood out in all of three of the calculated measures of power efficiency: Manny Machado. Machado finished in the top five in all three of HR/BMI, SLG/BMI, and ISO/BMI despite finishing outside of the top ten in all three of HR, SLG, and ISO. This shows that, despite not being among the most physically strong hitters in the MLB, Machado is one of the best at utilizing his strength to its fullest potential, if not the best. Despite not being very prevalent in popular baseball metrics today, power efficiency is certainly useful in finding players like Manny Machado who excel in hitting with more power than their physical strength suggests, and this metric may become more popular as teams try to find power hitters who are undervalued.
Sports Analytics and Business, Indiana University
Statistics used for calculations courtesy of Fangraphs