April 14, 2026 | @TrewarthaShawn

Hitting a baseball is the hardest thing to do in all of sports. As pitching mechanics continue to evolve, resulting in increased velocity and sharper movement, the challenge of hitting only grows. The 2025 Colorado Rockies serve as a clear example of a team who struggled mightily at the plate, leading to season-long offensive struggles, and a final record that will be begrudgingly remembered in the history books.

Early on, while digging into the surface level batting stats, I noticed something I had more or less assumed to be true, but that still surprised me when it was laid out in the data. The Rockies didn’t just struggle to make contact – they were the worst team in the MLB in contact rate (73.0%) and had the second-highest strikeout rate (25.9%), behind only the Angels – but they were also the most aggressive swinging team in the league, leading all of baseball in swing rate and becoming the only team in 2025 to swing at more than half of the pitches they saw (50.7%).

With those numbers, the equation is pretty straightforward: they swung too often and didn’t make enough contact, which led directly to their strikeout problem. In this breakdown, I’ll take a deeper look at what led to this problem and what can realistically be adjusted throughout the 2026 campaign.

Aggressiveness at the plate

I constructed a machine learning model to predict when a player would swing based on pitch features — such as pitch type, location, and movement — as well as situational context, including the count, inning, and runners on base. Using the model I was able to identify which features were more predictive of a swing for a Rockies hitter than hitters from other clubs. 

The results indicated that pitch movement, both vertically and horizontally, was the feature with the largest difference in predictive weight between Rockies hitters and MLB hitters. In other words, for pitches with more movement, the model was more likely to predict a swing for a Rockies hitter. This is even more pronounced at Coors field, where the model is most likely to predict swings on pitches with movement.

With this in mind, each pitch was binned into 4 equally sized groups based on total movement. The difference in swing percentage between Rockies hitters and other MLB hitters for each bin was charted to visualize which types of pitches the Rockies were swinging at disproportionately more than the league average. For this, and the rest of the analysis, knuckle curves and curveballs, as well as sliders and sweepers, were combined due to the small sample sizes of knuckle curves and sweepers, which made interpretability difficult.

The figure shows that Rockies hitters, for the most part, are swinging at every pitch type at a higher rate than the MLB average. The slider shows the largest discrepancy. With a swing rate of 53%, the Rockies were swinging at sliders 5.4% more than the average MLB hitter. This issue is two-fold: a general lack of discipline at the plate, along with the impact of altitude, which causes sliders to behave differently in home versus road environments.

Unfortunately, many of these breaking pitches being offered at were not even strikes. In 2025 the Rockies had a 33.4% swing rate at pitchers outside of the strike zone to MLB’s 29.9%. While not the primary focus of this study, this likely contributes to the Rockies’ 6.7% walk rate in 2025, which was the lowest in MLB.

Beyond pitch type, similar trends appear when examining other situations. With runners in scoring position, the Rockies swung 52.9% of the time compared to 48.8% for MLB hitters. When behind in the count, their swing rate rose to 55.2%, compared to 49.9% league-wide. Even on first pitches, the Rockies remained more aggressive, swinging 35.5% of the time versus 31.5% for the rest of MLB.

Swing and Miss

Obviously, high swing rates in and of themselves are not a problem, several players possess the bat control to have high swing rates and a low strikeout rate, thanks to their ability to consistently make contact. The Rockies in 2025 were not chalked full of these types of hitters, unfortunately. 

To better understand the whiff rate problem, a similar machine learning model was created using the same pitch type and game scenarios features. This time, however, the model was trained only on pitches that resulted in a swing, in order to predict which ones resulted in a swing and miss.

While the swing model disproportionately relied on pitch movement to predict a Rockies’ swing, the swing and miss model relies more heavily on location. The features plate_x and plate_z which represent the coordinate plane of where the ball crossed the plate, and an ‘in the zone’ indicator showed the largest difference in predictive power between Rockies and MLB players. While not fully descriptive on its own, this suggests that Rockies whiff rates are closely tied to pitch location and warranted further investigation.

First, I directly compared swing rates to whiff rates for pitch types binned by absolute movement, as done earlier. The whiff rate figure closely mirrors the swing rate figure, with most of the bars shifted to the right, indicating that the Rockies are making less contact across most pitch types and movement profiles. Most notably, on curveballs, the Rockies swung and missed 42.0% of the time compared to 31.9% for MLB hitters, further illustrating their struggles against breaking pitches.

When I break the curveball Whiff percentage down by zone location, a pattern begins to emerge that helps explain why pitch location was so predictive in the model.

In the figure above, green represents zone locations where the Rockies had a lower whiff rate than the other MLB hitters, while purple represents zones where Rockies performed worse. Looking at pitches low in the zone, where the majority of Curveballs end up, the Rockies actually have a better contact rate inside the zone, bucking the trend seen across the rest of their hitting profile. The greatest struggles came on curveballs outside the zone. On either side of the plate, the Rockies’ whiff percentage was over 10% higher than the MLB average, a trend that most Rockies fans could likely recognize from watching the team throughout the season.

When I construct the same figure using all pitch types rather than just curveballs, a similar yet less dramatic pattern emerges.

Across all pitch types, when comparing Rockies and MLB’s whiff rate, in-the-zone pitches slightly skewed in favor of the MLB, but the Rockies hitters remained relatively comparable, with many locations within 1 or 2 percent of the MLB average. However, outside the zone, the Rockies are failing to make contact 48.1% of the time, compared to 42.8% for MLB hitters. This suggests that the issue is not an inability to handle strikes, but rather an inability to consistently make contact on pitches outside of the zone.

Looking Ahead

These study findings are bleak, and they accurately portray a team that had season-long difficulties at the plate which resulted in a historically bad record. Unfortunately, the early 2026 numbers aren’t showing a marked improvement. Through the first four series, the Rockies’ swing rate outside of the zone has increased from 33.4% in 2025 to 37.5% – 6% higher than other MLB hitters. Their contact rate outside of the zone and inside the zone has increased marginally, but the total whiff rate is 0.1% worse so far compared to 2025. 

This may seem paradoxical, but the overall whiff rate has increased because Rockies hitters are expanding the zone more so far in 2026. By swinging at a higher share of pitches outside the strike zone, where whiff rates are disproportionately high, they are taking more swings in situations that are more likely to result in a miss. As a result, the overall whiff rate can rise even though both in-zone and out-of-zone whiff rates have declined.

Luckily, the season is still young, and there is time to make adjustments. But unless plate discipline and contact rates improve soon, another tough summer at the plate could be in store at 20th and Blake.