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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 both real and fantasy baseball, but the minors simply don't have all of the data available for MLB players. For example, advanced plate discipline stats, Pitch Info, and anything Statcast-related are all currently unavailable for 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 do our best to 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 a player's track record allows it.
Since there was no minor league baseball in 2020, the year will be a blind spot for fantasy managers for the foreseeable future. That said, most current prospects have some MiLB data even if a year is missing from their ledger. Here's how to effectively use MiLB data to give you an edge in your fantasy baseball league throughout the season.
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In Leagues Of Their Own
The first point to remember is that the baseline for certain predictive metrics is different on the farm. 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 performance that looks fluky 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 league prospect has a massive pop-up problem, but this is not the case. The stat is calculated differently on the farm, and you need to halve it to get something approaching an MLB projection.
Like the MLB, each minor league and ballpark has its own unique quirks and 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 2023 ballpark factors, but you need a paid membership to access that data.
David Gerth posted the most extreme park factors for homers and overall runs last season:
If you want three-year factors, MiLB.com posted them for Class-A Advanced, Double-A, and Triple-A for 2017-2019. Note that the MLB renamed the former Class-A Advanced league to High-A for 2021 but most of the parks haven't changed. The higher levels have become increasingly hitter-friendly in recent years.
Analyzing MiLB Performance
Another common problem with minor league statistics is the sample size. It is simply 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 may be unreliable for minor league players. In this situation, it's advisable to examine the player's plate discipline numbers and batted ball distribution (GB% vs. FB%) because they stabilize (or become predictive) more quickly. We don't have the additional information provided by metrics such as O-Swing%, but these metrics are still a good way to start MiLB analysis.
For example, James Outman reached Double-A for the first time in 2021. He hit nine homers in 187 plate appearances, suggesting he had pop. He also struck out too often with a 27.3% K%, but offset that by working walks (9.5 BB%) and maintaining a .368 BABIP. Outman displayed the same characteristics at Double-A in 2022, hitting 16 HRs over 307 PAs with a 29% K%, 12.4% BB%, and .386 BABIP.
Outman was promoted to Triple-A mid-season and remained true to form, hitting 15 HRs in 252 PAs with a 25% K%, 12.7% BB%, and .343 BABIP. We expect Outman to demonstrate above-average pop, a high BABIP, and plenty of walks and strikeouts in his rookie season based on his MiLB resume.
Sure enough, Outman slashed .248/.353/.437 with 23 HRs over 567 PAs for the Dodgers in 2023. His 31.9% K% was high, but he offset it with a 12% BB% and .343 BABIP, which made his average tolerable and his on-base percentage a fantasy asset. Outman's minor league track record told us exactly what kind of player to expect.
Prospect growth isn't linear, and it's possible for a player to completely transform at the MLB level or fail to replicate success on the farm in the Show. Still, a player's minor league performance is the single best indication of 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 still something to look for when projecting fast players. If a guy is only stealing successfully 50% of the time on the farm, his big league club probably won't give him a green light. Age is also a factor for minor leaguers, as a 28-year-old dominating a bunch of teenagers isn't that impressive.
Conclusion
To conclude, the fact that we don't know a minor leaguer's average exit velocity or BABIP on ground balls doesn't prevent us from analyzing them for fantasy purposes. We have tools such as SwStr% and BB% for hitters and FIP and LOB% for pitchers. We can still place these numbers into context by examining any given league's tendencies. Finding rookie breakouts before they happen is still challenging, but that's what makes it fun.
Stay tuned to learn more about how to apply advanced stats within a fantasy context.
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