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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 unless you're in an MLB front office.
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 MLB, each minor league and ballpark also 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 2022 ballpark factors, but you need a paid membership to access that data.
If you want three-year factors, MiLB.com posted them for Class-A Advanced, Double-A, and Triple-A for 2017-2019. Note that 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. The Texas League (Double-A) posted an ERA over 5.00 last year while the International League (Triple-A) saw the average team score five runs per game. Both used to be pitcher-friendly leagues in the 2010s.
Forecasting 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 can 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, Steven Kwan had an interesting season in the upper minors in 2021. He hit .337/.411/.539 with 7 HR in 221 PAs at Double-A and .311/.398/.505 with five homers in 120 PAs at Triple-A. The raw HR total doesn't move the needle in fantasy, but posting a slugging percentage over .500 at each stop suggested he wasn't a pure slap hitter. The secret was a line drive swing that produced a 25.3 LD% at Double-A and a 26.7% mark at Triple-A.
His average and OBP were also outstanding at both stops, supported by superlative plate discipline. Kwan had a 10 BB% against a 10.4 K% at Double-A and an 11.7 BB% against a 6.7 K% at Triple-A. Furthermore, he seldom swung and missed with a 2.6 SwStr% in Double-A and 2.7 SwStr% at Triple-A. He posted a .354 BABIP at Double-A, though it fell to .300 in the smaller Triple-A sample.
Kwan didn't look like a power threat, but he had enough oomph to work walks. Combine that with a refusal to strike out and you're looking at a guy who would probably post a strong average as an MLB rookie. Kwan did precisely that, hitting .298/.373/.400 for the Guardians in 2022. His 3.1 SwStr% was microscopic just as his MiLB resume suggested it would be. He walked (9.7%) more often than he struck out (9.4%), posted a great 23.3% chase rate, and produced a BABIP of .323 with an above-average 22.6 LD%.
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 really 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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