Predicting Home Run Power: Unlocking the Secrets of Statcast Metrics
February 2, 2026
Let’s face it: forecasting which hitters will see a surge or decline in power is a tantalizing yet elusive pursuit. Over the years, I’ve explored countless methods—from ESPN’s Home Run Tracker to crafting xHR/FB equations and diving into Statcast’s xHR calculations. Each approach offered insights, but I always craved a more definitive edge. And now, I’ve stumbled upon something that might just change the game. But here’s where it gets controversial: what if the key to predicting power lies not just in raw metrics, but in their misalignment?
Recently, I’ve been intrigued by hitters who defy conventional wisdom. Think players with above-average maxEV (exit velocity) and HardHit% but below-average Barrel%. These aren’t your typical power hitters, yet they possess the raw power to dominate. Conversely, there are hitters with below-average maxEV and HardHit% but above-average Barrel%, somehow squeezing every ounce of power from their swings. These anomalies sparked a question: could these discrepancies predict future performance?
Group A, the hitters with raw power but subpar Barrel%, felt like untapped potential. I hypothesized they might improve their Barrel% and HR/FB rates the following year. Meanwhile, Group B, the seemingly overachieving hitters, appeared ripe for regression. But instead of jumping to conclusions, I decided to let the data speak for itself.
I analyzed every hitter with at least 300 plate appearances since 2015, leveraging Microsoft Excel’s Copilot to compare year-over-year changes in Barrel% and HR/FB rates. The results? They were nothing short of eye-opening. And this is the part most people miss: the data didn’t just confirm my hunch—it amplified it.
Here’s the breakdown:
| Group | Season Pairs | Barrel% Up | Barrel% Down | HR/FB% Up | HR/FB% Down |
|-----------|------------------|----------------|------------------|---------------|-----------------|
| A | 133 | 72.2% | 27.8% | 63.9% | 36.1% |
| B | 61 | 36.1% | 63.9% | 36.1% | 63.9% |
| Full Dataset | 1625 | 53.4% | 46.5% | 48.9% | 51.0% |
The numbers don’t lie. Group A hitters were significantly more likely to improve their Barrel% (72.2% vs. 53.4% baseline) and HR/FB rates (63.9% vs. 48.9% baseline). Conversely, Group B hitters saw declines more often than not. This isn’t just noise—it’s a pattern with actionable implications.
Here’s the takeaway:
- High maxEV and HardHit% with low Barrel%? There’s a strong chance of improvement in both Barrel% and HR/FB rates next year.
- Low maxEV and HardHit% with high Barrel%? Regression might be on the horizon.
No complex equations required. Just compare a hitter’s maxEV (league average ~111-112 MPH) and HardHit% (yearly averages on FanGraphs) to their Barrel%. If they don’t align, you’ve got a potential breakout or decline candidate.
Now, I know you’re eager for specific 2025 names in Group A and Group B, but you’ll have to wait until next week. In the meantime, here’s a thought-provoking question: Does this approach challenge how we traditionally evaluate power hitters? Could we be overlooking hidden gems or overrating certain players? Let me know your thoughts in the comments—I’m all ears.
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Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and a three-time Tout Wars champion. He’s also the author of *Projecting X 2.0: How to Forecast Baseball Player Performance. Follow him on X @MikePodhorzer or reach out via email.*
