One of the biggest problems in sabermetrics is trying to figure out how to evaluate players in the minor leagues. With so many factors involved, such as age relative to their league, experience before being drafted and the pitcher or hitter-friendly nature of parks and leagues, on top of limited data in the first place, it can be hard to get a true gauge on what a player’s future will be by using numbers alone.
However, Carson Cistulli of Fangraphs recently attempted to do just that when he created minor-league wins above replacement (mWAR). Similar to its big league counterpart, WAR, mWAR attempts to determine how many more wins a player is worth than the average player that would be called up to replace him. Cistulli limited his analysis to only hitters in this case.
To determine the hitting component of mWAR, Cistulli used wRAA (weighted runs above average). Essentially this just takes the raw number of runs above average that a player was worth relative to their league. The pro of this is that it is fairly straight forward and accounts for the actual number of runs a player was worth.
The con of using wRAA in this case, however, is that it does not adjust for park. This is a problem, as it can cause mWAR to not have a 100 percent accurate representation of a player’s offensive skills.
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An example of how this would have a negative affect would be with someone who plays for the Triple-A New Orleans Zephyrs, who play in the hitter-friendly Pacific Coast League that is filled with high-elevation parks. However, they do not play in a high-elevation, hitter-friendly park themselves. Because wRAA is relative to league, a hitter who played for New Orleans would have a lower wRAA than if it were adjusted for parks.
Traditional WAR also takes into account a player’s baserunning skills. In the minor league case, BsR (baserunning runs), which is used to calculate WAR, is not available, so Cistulli substitutes it with Spd (speed score). This is not a completely accurate account of how many runs that baserunning is worth, but, as Cistulli notes in his article, Spd is correlated well enough with BsR in the big leagues to make it usable for minor league purposes.
Because of the correlation, Cistulli uses a simple linear regression model derived from major league data in order to convert minor league Spd scores into how many runs a player was worth above or below average on the basepaths. This may not be the ideal way to calculate baserunning value, but overall it is a decent representation.
The biggest downfall of mWAR is that it has a poor representation of defense. Because advanced defensive metrics are not available for the minor leagues, at least outside of the industry, there is no way to determine how many runs above or below average a player is worth on defense.
Cistulli does have a slight representation of defense by utilizing positional adjustments. This rewards players who play a premium position regardless of their defensive skill there, which is also done in WAR.
Overall, mWAR is far from a perfect stat, but it is usable when taken in context of other players’ mWARs. It is most useful for seeing which minor league players had the biggest offensive impacts on their teams while allowing a little less offensive production for players playing a more premium position. While it may not exactly be setting the bar as the premier minor league stat, it is a start, and as more data becomes available for minor leagues, versions of it will become more and more practical.