As someone who wants to work in Baseball Operations for an MLB club, I spend a lot of time on websites like Fangraphs and Statcast. I’m mostly on Fangraphs to look at their Transaction Tracker tool on Roster Resource and read the blogs that are posted almost daily. On Statcast, I like diving into the data and what makes a player so good or why he’s struggling. A stat that has always stood out to me has been Sweet Spot%. Sweet Spot%, or SwSp%, is defined by Statcast as “a batted-ball event with a launch angle between 8 and 32 degrees.” In other words, if you hit a ball in the “sweet spot”, it’s going to be a line drive or a low fly ball. Both of those kinds of batted balls are better than a ground ball (dependent on the situation) but as you’ll see later in this post, it depends on how hard you hit the ball.
My process in researching this data started by exporting data from Statcast’s website. I took data from 2023 season qualified hitters only, the 4 other stats I exported along with SwSp% were Barrel%, BABIP, OPS, and wOBA. These stats/metrics were chosen because they each tell a different story about a hitter. Barrel% tells you how often a hitter can hit the ball hard at an optimal angle to do real damage, home runs. I chose BABIP (Batting Average on Ball in Play) because I figured the better your SwSp% was, the higher your BABIP would be because hitting line drives give the best chance to get a hit. OPS (On-Base Plus Slugging%) is a simple stat that everyone reading has probably seen before, it even paired well with wOBA (weighted On-Base Average) which is becoming increasingly popular.
Those were the stats I used, to filter through and visualize the data, I used Excel and Tableau. When I entered all the qualified hitters into Tableau, I decided to filter down my data set even further to look at just hitters with an above average SwSp% this past season. I cut down on data points because I wanted to look at just players who performed well in SwSp%. Remembering how to use Tableau was a process as the only time I used it before was the end of the spring semester my freshman year. I don’t think the way I visualized the data was perfect, but it serves its purpose for what I was trying to do in my research. The goal of this was to see if a high SwSp% correlates to success in general or just in certain areas of offensive production.
Barrel%
The top 3 qualifiers in SwSp% for 2023 were Freddie Freeman (46.6%), Luis Arraez (44.7%), and Mookie Betts (42.5%), I’ll be referring to these 3 quite often to see how they performed in the other metrics I’m breaking down. Each of their numbers for Barrel% were 11.1%, 3.5%, and 12.4%. So, while each of them hit a lot of line drives/low fly balls this past season, only Freeman and Betts capitalized on them, unlike Arraez. Betts and Freeman were in the 86th and 58th percentile in hard hit% but Arraez is in just the 3rd percentile. It’s no surprise that this led to Betts having a career high 39 home runs season, Freddie Freeman had 29 home runs and led all of baseball with 59 doubles, and Arraez had just 10 home runs. Sweet Spot% doesn’t predict Barrel% well because to get a “barrel”, you need not just an ideal launch angle, but also the ball needs to get hit hard (98+ MPH). Another example of a player with a high SwSp% but low Barrel% would be White Sox outfielder Andrew Benintendi. He had a 40.9% SwSp% but his hard-hit rate was like Arraez’s at 27.0%, just the 4th percentile. Benintendi’s 2023 SwSp% was the highest of his career, the next closest was in 2019 where he had a solid year at the plate. The reason his production was so much better than it was this year was a mix of him hitting the ball harder more often and playing half of his games in Fenway Park instead of Guaranteed Rate Field. Benintendi and Arraez are similar in that they are contact first hitters who bring their value to a lineup by getting on base, not through their power output. The data painted a picture that SwSp% doesn’t correlate with Barrel% if hitters don’t hit the ball hard often enough. Arraez and Benintendi have 1 of the 2 pieces to have a good Barrel% but they are missing the exit velocity numbers which isn’t really part of their game.
wOBA and OPS
wOBA and OPS were grouped together in this because I couldn’t differentiate the results between the two stats. It seemed for almost every player in my dataset, there was a similar trend. In other words, as their OPS got higher, so did their wOBA and vice versa. wOBA or OPS didn’t have any correlation with SwSp% because like the Barrel% chart, those who were above average in each metric had a significant power output. The extra base hits and home runs would obviously boost OPS as it considers slugging% and wOBA because extra base hits (especially home runs) are more valuable than just singles. For example, the 3 hitters that were at the bottom of the player pool in SwSp%, but with high wOBAs and OPS were Shohei Ohtani, Kyle Tucker, and Marcell Ozuna. Ohtani led all of baseball in slugging% at .654, Tucker slugged .517 in a near 30-30 season, and Ozuna had 40 home runs slugging .558. None of these 3 players are strangers to hitting for power and a near league average SwSp% didn’t stop them one bit from contributing to each of their lineups. Like Barrel%, there isn’t much to break down in the wOBA and OPS column except for hitting hard fly balls continues to be productive, even if they are outside the “sweet spot”.
BABIP
When analyzing our player pool while looking at BABIP, there was a much clearer trend when it came to consistency in the numbers. Of the top 12 qualifiers in SwSp%, only 1 had a below average BABIP, that was Nationals catcher Keibert Ruiz at .263. Players with a SwSp% of 37% or higher were much closer to league average BABIP compared to those behind them, see the graphic below.
What I took away from this was that the higher a player’s SwSp% is, the higher their floor raises for BABIP because they are hitting so many more line drives. As you could see from above, the lower your SwSp% was, the more inconsistent the player’s Barrel% was. This isn’t going to be the same for each player and BABIP is very dependent on the defense, pitcher’s style, and a bit of luck. Referring to our Top 3 SwSp% hitters again, the Top 2 in SwSp% (Freeman and Arraez) were also Top 2 in BABIP for 2023. Betts fell down as in 2023 he intently tried to hit for more power while also having his highest season in BA and BABIP since 2018, an adjustment that nearly landed him his 2nd MVP if not for a legendary season from Ronald Acuna Jr. 3rd in BABIP was Bo Bichette who is coming off his 5th MLB season where he hasn’t finished with an OPS+ below 120. Coincidently Bo Bichette’s SwSp% was 38.3% in 2023, leaving him 9th in my player pool.
Conclusion
What I took away from my research was that SwSp% can be another way of looking at a player’s hit tool. Of the 10 players in my second data pool by SwSp%, only 3 had an above average K% in 2023. 2 were rookies, Josh Jung and James Outman, understandable that they would post higher strikeout totals as they adjust to the highest level of baseball. The other was Ryan McMahon who has also had a high K% in his 7 MLB seasons. Both rookies had impressive rookie years and McMahon just posted his second straight season a mid .750s OPS, a productive season to go along with his good defense. So, even though they struck out a lot, everyone in the top 10 had a good season outside of Andrew Benintendi. However, I don’t believe SwSp% is a good way to project player performance down the road because it doesn’t consider an important factor of how hard the ball is hit. The metric is flawed but it will tell you how often a player can stay on plane with the ball and be on time.
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