This article features peak projections for all players under 29, including minor and major leaguers, as well as current projections for all players 29 and over. These projections leverage my own major league equivalencies and aging curves. See this off-season’s counterpart peak projections article for more detailed information.

These projections leverage peak major league equivalencies to convert statistics from players in different leagues and at different ages to the same 2021 MLB American League baseline. To take a purely hypothetical example: a Double-A 112 wRC+ for a 19 year-old might be “equivalent” to a 108 MLB wRC+ at the player’s peak, after adjusting for league difficulty and aging growth. For players over 28, the projections are current rather than peak, as these players are already at or past their “peak,” as defined by my aging curves. This is a solid approximation of dynasty league value: young players are valued for their peak, while seasoned veterans are valued for current production. The table is sorted by ‘Dynasty Z-Score,’ which shows how many standard deviations above MLB AL average each player is projected to be at peak (or projected to be now for players over 28). ‘Dynasty Z,’ is weighted 80% toward pure hitting ability (wRC+) and 20% toward stolen bases (SB/600) to approximate a majority of league settings that dedicate 4/5 categories to pure hitting and 1/5 categories to stolen bases. ‘Dynasty Z’ serves as a rough baseline for my dynasty hitter ranks, a single all-encompassing metric that ranks MILB hitters side by side with MLB veterans all according to their dynasty value. The projections for each metric are a simple 5/4/3/2/1 weighting of historical performance for said metric dating back to 2017, with more recent years weighted more heavily (e.g. the K% projection is a weighting of a player’s peak major league equivalent K% in the minors and majors dating back to 2017). A modest 600 plate appearances of regression to the MLB mean are added in for each metric for simplicity, however, readers should note that certain metrics have lower variance (e.g., K% and BB%) and certain metrics have higher variance (BABIP). A better model would add in a lot more regression in for more luck-based metrics like BABIP, and a lot less regression for more skill-based metrics like K% and BB%. Nonetheless, I opted to keep things simple and let readers make their own subjective adjustments–if a player has an elite BB% minus K% and a crappy BABIP, you should bump him significantly above where he is in these rankings. Scouting reports, college statistics, fielding (inasmuch as it impacts playing time), position (give catchers a subjective bump when reading through) and MILB park factors are not captured here. Neither is xwOBA or predictive wOBA (or Dynamic Hard Hit %) for the MLB statistics. The reader should bear these caveats in mind and account for them in their own analyses. For instance, Juan Soto would probably rank 1st if his 2021 xwOBA (.420) or predictive wOBA (.395) were used to formulate his peak wRC+ projection instead of his wOBA (.368). All projections are scaled to the 2021 MLB AL environment. Projections based on a larger sample of MLB PAs are more reliable: players with less than 600 PA appearances are listed at the bottom of the table (check out Jordan Walker and Francisco Alvarez!). The projections capture performances through the games of July 5th.  I have also updated a google sheet with annual peak major league equivalencies for all players since 2017, if you’d rather look at the raw equivalencies instead of a projection. In the future, Ross Jensen and I will update the full leaderboards at statlinescouting.com. Ross has already been keeping them updated at statlinescouting.com for prospects!