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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 do not have all of the data available for MLB players. For example, advanced plate discipline stats, batted ball data, HR/FB, xFIP and PITCHf/x 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.
In this article, you will learn how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.
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- Daily MLB starting lineups for fantasy baseball
- Fantasy baseball BvP matchups data (Batter vs. Pitcher)
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- Who should I start? Fantasy baseball player comparisons
- Fantasy baseball closer depth charts, bullpens, saves
- Fantasy Baseball live scoreboard, daily leaderboards
How to Interpret Minor League Stats
Like MLB, each minor league and ballpark has its own unique quirks and tendencies, skewing the results in one way or another. For example, the Pacific Coast League is a Triple-A league notorious for inflating offensive statistics. Imagine if an entire league played in Coors Field every game. That's basically the PCL.
For PCL players, a batting line may look good at first glance, but really represent only an average performance. Likewise, pitchers may put up dreadful numbers even after they are ready for the Show. For instance, three years ago a certain PCL pitcher put up a 9-7 record with a 4.60 ERA in 133 IP in 2014. His K% was a robust 24.9%, but none of his other stats screamed MLB ready.
However, some fantasy owners noticed that his BABIP against was a ludicrous .378, a number that would almost certainly regress in a different environment. The pitcher never ran a BABIP that high in any other minor league stop. His LOB% of 67.2% would likely climb as the BABIP dropped. We do have FIP for minor leaguers, and this pitcher's was 3.70--still not great, but much better than his ERA.
Despite the ugly Triple-A results in 2014, this pitcher pitched in the majors for 150 innings in 2015. His 9-7 record repeated itself, but his ERA fell to 3.24, right in line with a FIP of 3.25. The K% he flashed in the PCL translated to the majors, where he posted an elite 27.5 percent rate. His name is Noah Syndergaard, and he definitely had owners, who took his 2014 numbers as indicative of his ability, kicking themselves by the end of 2015. Nothing changed last year, as Syndergaard went 14-9 with a 2.60 ERA and 29.3 percent K%. Clearly, he was MLB-ready despite ugly PCL numbers.
If memorizing each league's tendencies is too overwhelming for you, you can look at Weighted Runs Created Plus (wRC+) as a shortcut. This metric sets 100 as the league's average offensive output, with each number higher or lower representing a one percent difference in either direction. This means that a wRC+ of 95 is five percent worse than league average, while a mark of 110 is 10 percent better. While the formula does not directly translate to fantasy value, park and league adjustments are already included in the calculation.
Another common problem with minor league statistics is sample size. It is easier to run an unsustainable BABIP or HR/FB 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. Astros shortstop Carlos Correa illustrates this, as he had a grant total of 246 PAs at Double-A and Triple-A combined before his MLB call up in 2015.
Due to the small sample, Correa's BABIP was unreliable. In this situation, I like to examine the player's plate discipline numbers because they stabilize (or become predictive) more quickly. At Double-A, Correa had an 11.3 percent BB% against an 18.8 percent K%, indicating a strong knowledge of the zone. Triple-A saw his BB% drop slightly to 10.6 percent, but a drop in K% to 12.4 percent made his overall plate discipline profile stronger.
Correa posted a 9.3 percent BB% and 18.1 percent K% en route to his Rookie of the Year award in 2015. Correa was even more willing to walk last season (11.4 percent BB%), but struck out a little more often as the league adjusted to him (21.1 percent K%). Plate discipline is harder in the majors than the minors, and we don't have the additional information provided by metrics such as O-Swing%. Still, Correa seemed to possess strong discipline in the minors and managed to take it with him as soon as he was called up to the bigs. In general, a player won't be completely over-matched in the majors if he had strong plate discipline numbers in the minors.
The minor leagues do not have scouting at anywhere near the level of MLB, so dead pull hitters that manage to hit the ball hard every time tend to have very high BABIPs on the farm. In the majors, these players are confronted with shifts that tend to reduce their BABIPs far below their minor league history. Stealing bases is also easier in the minors, but elite success rates are still something to look for when projecting fast players. Age is also a factor for minor leaguers, as a 28-year-old dominating a bunch of teenagers at Rookie ball isn't really that impressive.
Conclusion
To conclude, the fact that we do not know a minor leaguer's LD% or BABIP on ground balls does not prevent us from analyzing minor league players for fantasy purposes. We have tools such as BABIP 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 a worthy endeavor.
Our next article will look at Statcast data, breaking down abstract numbers such as exit velocity and launch angle to produce an easy to understand metric called Barrels.
