How Significant is Max Exit Velocity to Isolated Power?

Maybe max exit velocity isn't the best way to search for power.

With the Statcast integration into daily broadcasts, exit velocity has become similar to home run distance, in that fans can ooh and ahh over the ridiculously high numbers that are produced. There’s a lot more excitement over a Shohei Ohtani double when the broadcast shows it was 119 MPH off the bat. With the exit velocity numbers the community now has access to, it is used to help project players and their power capabilities. One of the main ways that exit velocity can be used is through max exit velocity (max EV), as it helps show a player’s strength ceiling.

While it makes sense that better results come from hitting the ball harder, I wanted to know how strong the correlation between max exit velocity and a traditional power stat is. Isolated power (ISO), while not a “traditional” stat per se, is very closely related to slugging percentage. ISO is slugging percentage minus batting average, essentially taking out singles and only looking at extra-base hits. Per Fangraphs, ISO is “a measure of a hitter’s raw power and tells you how often a player hits for extra bases.” The stat does not account for park factors or any given year’s league environment and simply, as the name isolated power implies, states how frequently a player is hitting for extra bases. Is it really a worthwhile statistic to use when trying to understand a player’s power capability? Are there better statistics to use when trying to assess a player’s power tool?




First, it is important to identify what statistics are being used to answer this question. The power statistic that will be the dependent variable is isolated power (ISO). The reason for ISO being the dependent variable is that this analysis may help continue to bridge the gap between Statcast stats and traditional stats. Taking Statcast stats and making use of them with traditional stats can help identify trends that are functional for player evaluation, especially in regards to fantasy baseball.

As for independent variables, there are three statistics that are being looked at in regard to ISO: max exit velocity, hard-hit percentage (HardHit%), and barrels per plate appearance (Brls/PA%). Obviously, max EV is the main focus of this research, but there are other factors that can affect a player’s ISO. HardHit% is the frequency of batted-ball events that have an exit velocity higher than 95 MPH. This is connected to ISO because a player who hits the ball hard regularly will likely do more damage (see Miguel Sanó) than a player who rarely hits the ball hard (see David Fletcher). Finally, Brls/PA% tell us how frequently a player is barreling the ball between plate appearances. A barrel is a batted-ball event that yields a minimum .500 batting average and 1.500 slugging percentage based on comparable exit velocities and launch angles. Brls/PA% informs us how often a player is barrelling the ball per plate appearance, instead of just by batted-ball event. Together, these three stats connect the three big facets to hitting with power: exit velocity capabilities, quality of contact, and frequency of quality contact.

For this study, the last five years of qualified hitters (approximately 130 hitters each) were examined using the statistics listed above. By using three different years of data, it can show potential variance from year to year or identify an outlier. For each year of data, a correlation coefficient and linear regression will be tested between each independent variable and ISO.




Below are the results of the correlation coefficients:

Correlation Coefficients to ISO by Year


Over the course of the last five years, the stat that is most correlated with ISO is Brls/PA%. Brls/PA% is a stat that shows how consistently elite contact occurs, so it makes sense that it is most correlated with ISO. All three stats show a decently strong relationship with ISO, but Brls/PA% is by far and away the strongest indicator. Max EV did show a decently strong correlation with ISO but is not as strong as the rate stats.

The most variable stat was max EV, which had a range of .171; HardHit% had a range of .083 and Brls/PA% had a range of .123 (all excluding an outlier year in 2018). Max EV is a singular measurement and is not representative of a player’s overall season, so the rate stats are likely to be more stable from year to year.

The 2018 season was an outlier in the dataset: the max EV correlation coefficient was not statistically significant and the HardHit% and Brls/PA% both were significantly lower than any other year. The league offensive totals for 2018 are not outliers compared to other years, but the 2018 league-wide ISO of .161 was the lowest of the five years.

For the linear regression, the only stat that produced a statistically significant result is Brls/PA%. The results are as follows:

Linear Regression of Brls/Pa% by Year


When max EV and Hardhit% are taken into account, Brls/Pa% has a strong positive relationship with ISO on its own. Throughout the four years that returned a statistically significant result, the average slope of the linear regression was .021. For every percent increase in Brls/PA%, a player’s ISO increases by 20 points. That is a decently big number as a 20 point difference could potentially bump a player’s ISO up a tier of hitters. For example, Trevor Story had a .221 ISO in 2021, good for 48th among qualified hitters. If Story were to add 20 points to his ISO, he would rank 24th and be just ahead of former teammate Nolan Arenado. A jump like that may cause the baseball community to reevaluate a player’s ability to hit for extra bases.




Max EV may be more practical for the actual scouting of raw power and not something that can be directly translated to one’s overall ability to hit for meaningful extra bases. There are many other factors that go into a player’s ability to hit for extra bases, such as their speed or their hit tendencies (i.e. pull hitters). This may seem obvious on the surface level, but I wanted to investigate the true relationship between max EV and isolated power, and doing the research confirms the claim.

The more telling stat would be Brls/PA%, as it looks at how frequently a player is hitting the ball in the perfect spot. Maybe a player isn’t hitting the ball 110+ MPH every time, but they can square the ball up enough times to hit for power. An example would be Tyler O’Neill: his 113.1 MPH max EV is good for 46th among qualified hitters and his 10.6% Brls/PA% is good for 8th among qualified hitters. It makes sense that those who barrel the ball the most have the best ISO, but undergoing this research essentially confirmed it. It potentially exposes a better way to search in evaluating power. If you are trying to evaluate a hitter’s power capabilities for fantasy purposes, consider using Brls/Pa%, as it paints a much better picture of how frequent high-quality contact occurs. Max EV might be useful for projecting young players or prospects, but it is not as useful for developed major leaguers.


Photos by Julian Avram & Mark Goldman/Icon Sportswire | Design by Michael Packard (@designsbypack on Twitter @ IG)

Nate Schwartz

Nate is currently a senior at George Washington University and is writing for the Going Deep team at Pitcher List. He is a lifelong St. Louis Cardinals fan and devil magic supporter despite being from the Chicago area. You can follow him on Twitter @_nateschwartz where he may or may not be tweeting.

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