Dynasty Prospect RankingsScouting the Statline

Scouting the Stat Line: Top 75 Stats-Only Starting Pitching Prospects for 2020 and Beyond

This article features a list of the top 75 starting pitching prospects by peak true-talent ERA (an average of xFIP and kwERA). It leverages Clay Davenport’s excellent minor-league translations work; translations can be found for every minor leaguer at claydavenport.com. Seriously, if you don’t have it bookmarked, please stop what you’re doing and make amends.

Minor league translations translate minor league statistics to their major league equivalent statistics, capturing how a player’s performance changes from one level to the next. “Peak” minor league translations add in aging curves, capturing aging changes from one year to the next until a player’s peak. Most (not all) major projection systems use minor-league translations to project prospect performance. You may have seen my hitting prospect projections leveraging peak translations.

On a related note, Six Man Rotation has recently released “Sparkman” starting pitching prospect projections. If you haven’t seen them, I encourage you to check them out. It’s awesome work, leveraging logistic regression, a different methodology than what I use here. I think it’s worthwhile to compare the two lists. Spoiler: prepare to feel even more emboldened about Simeon Woods-Richardson.

My projections only capture minor league performance in the full-season leagues. They ignore everything else, e.g., scouting, velocity, frame, delivery, height, pitch mix, pitch usage, etc. I do not suggest you ignore these things and draft Bailey Ober as your SP1 (though you should draft him eventually at his current market rate!). ‘Peak true-talent ERA’ is a function of peak projections for strikeouts per nine, walks per nine, and ground ball percentage—the same components captured in xFIP (yes, I know K% and BB% are better metrics but the data was not readily available). The projections weight the three most recent years of data, a 3/1.5/1 weighting for 2019/2018/2017. They also add 50 innings of regression to the mean for strikeouts and ground balls, and 150 innings for walks—in each case to increase predictive accuracy. The projections are built from a sample of 735 rookie pitchers who had their MLB debut sometime in 2010-2019. Within this time frame, every pitcher with at least 50 innings in his MLB debut and at least 50 minor league innings in his projection was included. Predictive accuracy, or mean absolute error, for each projection is shown below. For example, the mean absolute error for the K/9 projection is 15% (or 1.33 K/9 in the 2019 MLB context, when league average K/9 was 8.88). Predictive accuracy was substantively the same when projecting MLB K/9, BB/9, and ground balls with MLB data in the prior year rather than with minor league data. This suggests there is no meaningful difference in predictive validity between minor league data and major league data (Clay Davenport has found similar in his own work from awhile back at Baseball Prospectus).

Note GBO stands for ground ball out percentage, taken from claydavenport.com; for pitchers with at least 10 innings pitched, GBO has a .96 correlation with the more pervasive ground ball percentage

Before considering the top 75 prospects for 2020, it’s enlightening to first consider history. The best pitching prospects from 2010 to 2019 are displayed below, following the same methodology used for the 2020 list. They’re ranked by true-talent ERA an average of peak kwERA and xFIP–detailed more in the next paragraph. All statistics displayed are peak projections scaled to the 2019 MLB context (4.50 league average ERA), e.g. in 2011 Matt Moore had a peak strikeout projection of 13.34 per nine innings. Note: to be ranked a player needed a minimum of 90 innings pitched in their projection.


Onwards, the 2020 list is below, from #1 to #75. Anyone with less than 90 innings from 2017-2019 is excluded. Relievers are also excluded. Every column displays a peak projection except for the (peak) MLE (major league equivalency) columns. MLE columns show peak major-league equivalent statistics for 2019 minor league performance (scaled to the 2019 MLB context, league-average 4.5 ERA, 8.88 K/9 3.29 BB/9). These MLEs can be found for every minor leaguer at claydavenport.com. kwERA is an ERA estimator that only captures strikeouts and walks. GBkwERA is similar to SIERA (and also highly predictive), an ERA estimator that captures strikeouts, walks, and a nonlinear (quadratic) function of ground ball percentage. Read about these two estimators in Jeff Zimmerman’s piece here. You already know what xFIP is. True-talent ERA is simply an average of xFIP and kwERA. It’s reasonable to rank based on any of these estimators; I simply opted for my personal preference in averaging xFIP and kwERA. In the future, I could validate the different ERA estimators on MLB data and see which does best. For now, I’m content with relying on the validated K/9, BB/9, and ground ball rate projections to derive the various estimators.

Name#'19 MLE K/9'19 MLE BB/9'17-'19 IPK/9BB/9GB%xFIPGB-kw-ERAkw-ERATrue-Talent ERA
Brendan McKay112.092.40139.311.212.6341 %3.583.573.463.52
A.J. Puk212.924.16152.712.083.6741 %3.673.663.553.61
Joe Ryan312.022.42119.311.632.6832 %3.933.813.343.64
Tarik Skubal411.923.14125.711.583.1538 %3.813.763.533.67
Jordan Balazovic511.202.72154.710.652.9343 %3.803.803.763.78
Jesus Luzardo69.831.87151.79.822.6947 %3.733.763.953.84
Deivi Garcia712.054.27187.511.693.8038 %4.033.983.733.88
Bailey Ober810.611.32137.39.852.0235 %
Jose Urquidy910.441.911509.592.3038 %
Spencer Howard1011.432.39163.410.683.3639 %
MacKenzie Gore1111.042.79153.610.462.9736 %
Simeon Woods-Richardson129.212.19107.09.172.5445 %
Lewis Thorpe1310.062.30307.69.832.8140 %
Joe Palumbo1411.084.25126.610.653.6841 %
Forrest Whitley1510.775.50176.611.324.1939 %
Matt Manning169.322.85261.79.833.3846 %
Kris Bubic179.872.99139.49.763.0741 %
Tyler Ivey1810.563.55159.09.703.1342 %
Brett Conine198.932.62109.48.932.8347 %
Grayson Rodriguez2010.663.8491.010.383.6439 %4.334.314.114.22
Jhoan Duran219.873.66211.79.373.5950 %
Matt Hall229.473.07344.39.553.5447 %
Joey Cantillo239.802.92110.79.693.1038 %4.414.374.144.27
Cristian Javier2412.715.03255.911.744.4630 %4.654.493.954.30
Bryan Abreu2511.015.69126.311.214.8141 %4.364.354.254.30
Nate Pearson269.952.7996.69.712.9435 %4.534.454.074.30
Shawn Dubin2710.104.10109.69.943.8544 %4.304.314.324.31
Bryse Wilson287.841.86380.78.452.4944 %4.304.324.334.31
Raynel Espinal298.582.74141.39.623.1739 %4.444.414.194.31
Mitch Keller309.772.96351.79.073.2346 %
Logan Gilbert319.412.58127.39.352.7836 %4.544.484.134.34
Braxton Garrett329.083.92119.49.053.7352 %
Luis Garcia3312.334.74169.010.854.3838 %4.514.474.224.36
Shane McClanahan349.633.68119.39.533.5743 %4.404.404.364.38
Enoli Paredes3511.114.46193.310.244.0339 %4.514.484.294.40
Michael King367.182.12196.17.722.3948 %4.284.324.534.41
Penn Murfee378.242.68112.48.332.8747 %4.314.344.504.41
Seth Corry3810.514.72119.710.314.3042 %4.464.454.364.41
Cole Sands398.712.1193.08.742.5237 %4.584.534.244.41
Nabil Crismatt409.222.36425.38.612.8842 %4.464.474.424.44
Parker Mushinski418.582.96142.38.803.7051 %
Sixto Sanchez427.181.86250.37.532.2647 %4.354.384.554.45
Drew Rom439.323.4096.09.263.3640 %4.534.524.374.45
Dylan Cease448.143.95282.49.653.9543 %4.454.464.454.45
Corbin Martin459.924.17158.48.993.3844 %4.444.464.474.46
Kyle McGowin469.032.67333.38.522.8742 %4.484.494.444.46
Luis Patino479.623.61176.39.543.3937 %4.644.594.294.46
Jeffrey Passantino488.842.01116.48.712.3933 %4.734.634.214.47
Peter Solomon4913.915.15105.79.213.4241 %4.534.524.414.47
Damon Jones5011.295.16214.410.014.8550 %4.314.344.664.49
Dean Kremer518.523.26320.39.373.4438 %4.624.594.374.50
Ian Anderson5210.254.47338.010.064.2541 %4.574.564.434.50
Shumpei Yoshikawa537.992.19101.78.122.5542 %4.544.554.464.50
Taylor Hearn5410.804.50231.49.783.7437 %4.674.624.344.50
Paolo Espino558.142.19240.98.382.7241 %4.574.574.444.51
Jon Duplantier569.446.00233.49.344.1648 %4.404.434.634.52
Dustin May577.802.53381.07.422.5049 %4.364.404.674.52
Brusdar Graterol588.564.03155.38.543.3647 %4.434.454.614.52
Patrick Sandoval598.654.14256.68.833.4044 %4.514.524.534.52
Kevin Smith609.233.50113.09.183.4439 %4.634.614.434.53
Jonathan Bowlan617.551.87138.07.702.2542 %4.584.584.494.53
Devin Smeltzer628.332.05342.88.112.3938 %4.674.644.404.54
John King636.581.6491.36.932.2350 %4.384.414.734.55
Cody Morris648.593.0890.08.633.1641 %4.614.614.514.56
Drew Gagnon656.361.75341.27.822.6845 %4.514.544.614.56
Jean Carlos Mejia667.702.74127.77.732.8148 %4.444.474.684.56
Trevor Rogers678.772.53207.78.693.0038 %4.694.664.434.56
Joey Murray689.793.72133.09.693.6034 %4.824.734.324.57
Trevor Stephan698.613.64201.99.003.3940 %4.674.654.474.57
Rogelio Armenteros707.843.29322.99.233.4837 %4.764.724.434.59
Francisco Morales719.774.6196.39.644.1641 %4.664.654.534.60
Casey Mize727.752.24115.37.902.5640 %4.684.684.534.61
Miguel Yajure737.662.36198.07.452.6547 %4.504.534.714.61
Adrian Morejon749.323.73126.08.713.6045 %4.574.594.644.61
Kyle Wright758.292.74261.97.953.1047 %4.524.554.714.61

The Author

Jordan Rosenblum

Jordan Rosenblum

Jordan is an American living in Finland. In addition to writing for The Dynasty Guru, he's a doctoral candidate at Åbo Akademi researching explanations of income inequality, and a Workforce Strategist at OnWork Oy. His favorite baseball area is quantitative analysis of prospects.

Fun fact about Finland: they play pesäpallo here, which is like a soft-toss version of American baseball, except home runs are somehow outs.

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