Interpreting Minor League Stats (MiLB) for Fantasy Baseball Analysis (2025)

How should fantasy managers use minor league stats (MiLB) for fantasy baseball analysis? Read about predictive MiLB stats as part of our sabermetrics glossary.

Welcome RotoBallers to our overview of minor league (MiLB) statistics. This article is a deeper dive into MiLB stats and is part of our ongoing series "Using Sabermetrics for Fantasy Baseball."

In this article, we'll explore the predictive power of numbers on the farm, including how to deal with small MiLB sample sizes and when to anticipate regression. Of course, we'll stick to plain terminology that anyone can understand.

You can find our entire sabermetrics glossary, which includes links to many other sabermetric stats as part of this series. Each stat deep dive will be released over the next few days. Stay tuned!

• Fantasy baseball trade analyzer
• BvP matchups data (Batter vs. Pitcher)
• PvB matchups data (Pitcher vs. Batter)
• Who should I start? Fantasy baseball comparisons
• Daily MLB starting lineups
• Fantasy baseball closer depth charts
• Fantasy Baseball live scoreboard
• Fantasy baseball injury reports

How Can We Interpret Minor League (MiLB) Stats?

Once you've grown accustomed to having advanced tools to help make fantasy decisions, it can feel disorientating to be without them. Prospects are increasingly becoming a focal point in real and fantasy baseball, but the minors don't offer the same data as MLB. For example, Pitch Info and anything related to Statcast are all currently unavailable for most minor league campaigns.

Does this mean we go back to looking at ERA and batting average as the only indicators of future performance? Of course not! Instead, we work with what we have. The process begins by looking at the environment. Higher levels of competition result in more accurate data, so you should start by excluding anything lower than Double-A if possible.

Here's how to effectively use MiLB data to give you an edge in your fantasy baseball league.

In Leagues Of Their Own

The first point to remember is that MiLB's baseline for predictive metrics is different. Mike Podhorzer of FanGraphs.com had an excellent article detailing the specifics in 2017. For example, Double-A hitters collectively posted a .306 BABIP that year, while their Triple-A counterparts managed a .317 figure.

Both marks are significantly higher than the MLB standard, making a seemingly fluky performance league-average.

Another common sticking point is IFFB%. Double-A batters posted a ludicrous 21.6% IFFB% on their fly balls in 2017, while their Triple-A counterparts were only slightly better (20.8%). This leads many fantasy managers to conclude that every minor leaguer has a massive pop-up problem, but that's not true.

The stat is calculated differently on the farm, and you need to halve it to get something approaching an MLB projection.

Like MLB, each minor league and ballpark has unique tendencies. For example, the Pacific Coast League is notorious for inflating offensive statistics. If you want minor league ballpark factors, Baseball America posted them for 2019 here. They have also released updated 2024 ballpark factors, but you need a paid membership to access that data.

David Gerth posted the most extreme MiLB park factors for homers and overall runs in 2023:

If you want three-year factors, MiLB.com posted them for Class-A Advanced, Double-A, and Triple-A for 2017-2019. The higher levels have become increasingly hitter-friendly in recent years, especially Triple-A.

Analyzing MiLB Performance

Another common problem with minor league statistics is sample size. It's easier to run an unsustainable BABIP or ERA in a small sample than a larger one. The minor leagues compound this problem by allowing a healthy player to be called up or demoted multiple times in one season, leaving us with two or more partial-season samples instead of one full season of statistics.

Due to the small sample, metrics such as BABIP are unreliable for minor league players. In this situation, we should examine the player's plate discipline numbers and batted ball distribution (GB% vs. FB%) because they stabilize (or become predictive) more quickly.

Walk Rate (BB%)

For example, Colton Cowser reached Double-A in 2022, slashing .341/.469/.568 with 10 homers in 224 plate appearances. The corresponding .446 BABIP was a mirage. His 25.4 percent K% also suggested a strikeout problem. However, Cowser demonstrated a great eye with a 16.1 percent BB%.

Cowser earned a shot at Triple-A in 2022 and struggled, hitting .219/.339/.429 with five homers in 124 PAs. His BABIP regressed to .290 while his K% jumped to 30.6 percent, making Cowser's downside more apparent. Fortunately, he still walked at a 10.5 percent clip.

Everything clicked for Cowser when he returned to Triple-A in 2023. He slashed .300/.417/.520 with 17 HR in 399 PAs. The batting average was a fluke thanks to a .390 BABIP, but we have more advanced plate discipline data for 2023 Triple-A campaigns and beyond. You can find it on FanGraphs in the same spot as the MLB version.

Chase Rate and Strikeout Rate (K%)

An excellent 23.7 percent chase rate supported Cowser's 16 percent BB%, so his eye is truly excellent. His 11.9 percent SwStr% didn't support his 26.8 percent K%, however. His K% was caused by a 40 percent Swing% leading to called strike threes. That's a good thing since passivity is often easier to fix than swing-and-miss.

Cowser hasn't made that adjustment yet, but he still enjoyed an excellent rookie season in 2024. He hit .242/.321/.447 with 24 HR in 561 PAs. His .317 BABIP was lower than his successful MiLB seasons, but his 24.8 percent chase rate and 12.8 percent SwStr% were foreshadowed on the farm. The resulting 9.3 percent BB% was solid, with his 30.7 percent K% caused by a passive 44.3 percent Swing%.

In other words, Cowser's MiLB stats told us what he would do before he did it.

Prospect growth isn't linear, and it's possible for a player to completely transform at the MLB level or fail to replicate MiLB success in the Show. Still, a player's minor league performance tells us what to expect from rookie seasons.

There are a couple of other factors to consider. Stealing bases is easier in the minors, but strong success rates are useful when projecting fast players. If a guy is only stealing successfully half of the time on the farm, his club probably won't let him run.

Age is also a factor for minor leaguers, as a 28-year-old dominating teenagers isn't that impressive.

Conclusion

We might not know a minor leaguer's average exit velocity or BABIP on ground balls, but that doesn't prevent us from analyzing them. We have tools such as SwStr% and BB% for hitters and FIP and LOB% for pitchers. We can still gain context by examining any given league's tendencies.

Finding rookie breakouts before they happen is still challenging, but that's what makes it fun.