Tampa Bay Rays' Bunting Strategy: wOBA Analysis

You can't talk about the Tampa Bay Rays' performance this season without mentioning the bunt.

This season, the Rays' offense is characterized by three things:

  • Avoiding strikeouts
  • Hitting to the opposite field
  • Flexible use of the bunt

Watching Rays games, you'll notice they aggressively attempt bunts even with a runner on third. Using 2026 season data, I took a quick look at this strategy through the lens of wOBA.

High wOBA

The most striking feature of bunts is their high wOBA. There have been 449 total bunts in the majors this season (one at-bat ended in a strikeout on a bunt attempt), and the weighted average wOBA across teams — what Baseball Savant displays as league wOBA — sits at 0.505. This is naturally elevated because sacrifice bunts (SH) are excluded from the wOBA denominator.

Even when calculating wOBA from the full league-wide dataset, the figure remains high at 0.498. If we add cases where at least one out was recorded on a sac bunt back into the denominator, wOBA drops to 0.299 — but since the whole point of a sacrifice bunt is to advance runners, that adjustment isn't particularly meaningful.



What's interesting is the number of bunts with a runner on third. Out of roughly 200,000 pitches, 47 have already resulted in a bunt with a runner on third base. At this pace, the season is on track for approximately 165 such situations — nearly double the figure from four years ago in 2022.


And the team leading that trend is, of course, the Rays. Tampa Bay has created about 9 bunt situations with a runner on third. Their ratio of third-base-runner bunts to total bunts ranks third in the league, behind Atlanta and Toronto. It's worth noting, though, that Atlanta and Toronto have attempted just 7 and 14 total bunts respectively, while the Rays have attempted 38.

Bunting with a Runner on Third — A Closer Look

Let's examine the runner-on-third bunt situation more rigorously. I filtered to exclude two-out situations and bases-loaded situations — in other words, removing cases where a force play at home is possible or where the batter being retired would end the inning.

The excluded cases are as follows. All occurred with two outs. Following are cases where the conditions were met and no outs were recorded.



Among the remaining situations — where the conditions are met and no outs were recorded — the results break down as follows: 8 singles, 10 sacrifice bunts, and 1 fielder's choice, for a total of 19 plays. By team, the Rays lead with 6.


Looking at where outs occurred across all 41 qualifying situations: bunts that did not result in a run numbered 13, giving a scoring probability of approximately 0.682.

In the cases where the batter was thrown out at first with no run scoring, closer review shows that batter error was the dominant factor — in all four such plays, the runner at third didn't even attempt to score.


When limiting the sample to the 37 plays where the bunt was executed properly, only 9 failed to produce a run. That yields a scoring probability of 0.757.

When these successful plays with no outs recorded are reclassified as singles and wOBA is recalculated, the already-high wOBA rises — slightly, but meaningfully — both for runner-on-third situations specifically and for bunts overall.

Bunt wOBA Changing - Runner on 3rd base


Bunt wOBA Changing - Every bunt



Comparison

How does this stack up against non-bunt situations? With fewer than two outs, bases not loaded, and a runner on third, the scoring probability on non-bunt batted balls is 0.280 — far lower than the bunt's 0.682.

Furthermore, the scoring probability for bunts in this exact scenario (runner on third, 0 or 1 out, not bases loaded) has been trending upward over time.



Takeaway

Bunting with a runner on third isn't easy. There's real risk — a failed attempt can leave the third-base runner vulnerable to being picked off. For that reason, I don't expect every team to adopt this strategy wholesale.

At the same time, teams that can execute it efficiently will find it increasingly natural to attempt in runner-on-third situations. Compared to a standard swing, the success rate remains overwhelmingly higher.

The rising bunt count and success rate this season — up significantly from prior years — back this up. And if it becomes a genuine league-wide trend, we can expect even more teams to give it a serious try next year.

댓글

댓글 쓰기

이 블로그의 인기 게시물

Arm angle and Pitcher type

이미지
Danny Young New York Mets pitcher Danny Young’s Baseball Savant movement profile is as follows. He throws a sinker and a sweeper with significant horizontal movement from a low arm angle. He’s a pitcher with an interesting pitch mix. It’s clear that he confuses hitters more with differences in horizontal movement between pitches rather than vertical movement. Horizontal vs. Vertical Movement Differences This gave me an idea. I looked into which pitchers have the greatest differences in horizontal movement and which in vertical movement when pairing their pitches two by two. As previously seen, Danny Young falls into the former category. The image above shows the movement profile of Cubs pitcher Caleb Thielbar in 2020. Unlike Danny Young mentioned earlier, he features a four-seam fastball and curveball combination from a high arm angle. He falls into the latter category, characterized by a large difference in vertical movement. Based on data from 2020 to 2025 for pitchers who faced at l...
자세한 내용 보기

The Relationship Between Pitch Location Scatter Plots and Arm Slot

이미지
During the last offseason, I was looking at a scatter plot of Cleveland pitcher Tanner Bibee’s pitch locations, and it reminded me of a regression line with a negative slope. I had a similar thought after watching Aaron Nola’s knuckle curve location. However, the slope of Nola’s regression line is more gradual than Bibee’s. While this may be the result of various factors—such as the trajectory and usage of breaking balls, as well as command—I focused on something a little different: arm slot. Nola’s average arm slot is 20 degrees, whereas Bibee’s is 51 degrees. And arm slot does have some correlation with pitch movement. I looked into the relationship between pitch location scatter plots and arm slots. Here’s how: •   I plotted pitch location scatter plots for pitchers who threw more than 100 pitches of each pitch type. •   From these scatter plots, I drew regression lines and calculated the location correlation coefficients for each pitcher. •   Then, I compared the corr...
자세한 내용 보기