This article continues an annual series providing peak projections for prospects. The projections are derived from peak major league equivalencies that use my (Jordan’s) aging curves and league translations to convert every player’s statistics to the same peak MLB (American League) baseline, making it easier to compare players at different ages in different leagues. Peak major league equivalencies take a player’s minor league performance and adjust it upward for aging growth and downward for league difficulty (for prospects who have made their MLB debut, peak major league equivalencies also account for MLB performance, adjusted upward for aging growth) . They also adjust for league scoring environment (note that they do not capture park factors, as FanGraphs wRC+ only accounts for park factors at the major league level). A leaderboard of peak MLB equivalencies, the inputs for the projections, is available for every minor league player (not just the top 1,000) and major league player at scoutthestatline.com, a sabermetrics website run by fellow Dynasty Guru author Ross Jensen and I. The leaderboard makes it easy to compare minor leaguers to major leaguers. Statlinescouting.com offers a bunch of other leaderboards to help you analyze minor and major leaguers as well–do bookmark it!

The table below offers a group of peak projections to help you evaluate prospect talent. The projection method is simple: each metric is a weighted average of the previous four years of performance in said metric (plate appearances weighted 5/4/3/2 from most recent season to least recent season). There are two versions of each projection: the no regression metrics do not add regression to the mean; the heavy regression metrics add in 1,200 plate appearances of regression to the mean. All metrics, K%, BB%, BABIP, etc., are regressed the same amount to keep things simple.  A more sophisticated projection would regress higher variance statistics like BABIP more heavily, and lower variance statistics like K% and BB% less heavily. Keep in mind that my simple projection approach thus underrates low BABIP guys (e.g., Bobby Witt Jr.) and underrates strong BB%-K% guys. To address this gap somewhat, I’ve also included “BABIP-Neutral” projections for peak MLB wRC+. The ‘Dynasty Z-Score’ metrics each place 80% weight on wRC+ and 20% on SB/600, a (very) rough approximation of dynasty value in leagues with 5 offensive categories, one of which is dedicated to stolen bases. The projections are scaled to the 2021 MLB American League scoring environment.

The first group of columns offer projections without regression. When evaluating prospects, quickness is key; analysts do not have time to wait for 2,000 plate appearances to make up their mind on a player. Adding regression can make it difficult to differentiate between minor leaguers because they often do not have many plate appearances and regression groups them more closely together. The regression-free projections should thus be considered highly volatile estimates of projected peak performance that need to be considered together with scouting reports. If the stats and scouting reports are both bullish on a player, buy more into his regression-free projection. If they diverge, take the regression-free projection more cautiously. If used wisely, the regression-free projections can help you pinpoint the game’s next superstars. However, while they may help you get in early on elite talent, putting too much weight on them can also lead to a bias toward overrating prospects more generally. That’s where the next batch of columns comes in, the heavy regression metrics.

The heavy regression metrics are more conservative projections that can be expected to be more accurate in general, with fewer big misses and perplexing outputs. The trade-off is that, by grouping all players more closely together, the heavily-regressed projections will unduly punish exciting first and second year players with few plate appearances (e.g., Corbin Carroll, Jordan Walker, Robert Hassell III), as well as generational talents (e.g. Soto, Acuña Jr., Tatís Jr.). If you ignore the regression-free projections, you’ll underrate these sorts of players and you’ll be slow on them in comparison to your leaguemates.

The table is sorted by the heavily regressed version of ‘Dynasty Z-Score,’ but I encourage you to sort it however you want. While everyone loves an omniscient and straightforward prospect ranking list, I wanted to resist that urge for this article–you won’t find an explicit “rankings” column. I think the best use of these metrics–how I use them personally– is to subjectively take into account both types of projections for each statistic, regression-free and heavily regressed, and scouting reports. Additionally, while the projections ignore other sources of information, e.g., college statistics, fielding ability (which impacts playing time), positional adjustments, Baseball America’s park factors, and other projections, you shouldn’t ignore them.

Table note: old minor league eligibility thresholds used (maximum 130 MLB at bats).