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Introducing KILA Projections: An attempt at forecasting major league hitting performance based off minor league stats

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No system of projecting a prospect’s future, whether analytical or scouting, is definite. But adding context and accuracy can help.

San Francisco Giants v San Diego Padres Photo by Denis Poroy/Getty Images

Predicting the success of prospects into the future can be fool’s gold. But in baseball, there are a few parallel attempts in trying. Throughout the years, we’ve advanced in these attempts. Major league teams now run their own projections. Player development can be more accurately measured with new forms of technology. Everything is tracked and we’ve never been more accurate.

The truth is, there is no gospel to projecting minor league performance to the major leagues. We try to find one, but there are just too many immeasurable factors that we can’t see.

With all this being said, there is good use in the few ventures used to project the performance of minor leaguers. Scouts can pick up things computers can’t when it comes to available minor league data. How much of his success was against non-major league competition? How well can he hit high-velocities? Will smarter pitchers at higher levels be able to expose his approach at the plate?

On the flip, projections can have their helpful qualities too. How have comparable players hit at the major league level? How have batters at a certain age in a certain league fared? Both types of evaluations can’t be compared against each other. They have to be taken for what they are. Understanding the strength and limitations of both is probably the best way to do this thing. With all that said, I formally come here to tie into this, announcing a project I’ve been working on for the past few months.


Quickly after becoming a baseball fan, my biggest interests were two aspects of looking at the game for some unexplained reason. The first being minor league baseball. It’s a guessing game, but there’s just something about watching players improve and model their game pre-majors that I love. The second aspect was analytical. That should be obvious, as I’ve been writing at Beyond the Box Score for almost a year now. I’ve always had an analytical mind, and with that being an excelling point for baseball, I guess that’s why it always jumped ahead in front of other sports for me.

With how my passion for baseball was modeled through analytics and prospects, seeing a projection system like KATOH pop up was a dream. And then seeing it go away wasn’t so fun. But without KATOH and some free time on my hands, I figured... why don’t I try making one? This would originally be something not-so serious and something I only kept to myself, but ideas sprung upon ideas, and I just kept making it bigger and more detailed. A few days ago is when I finally finished this projection system, coming after months and months of sorting through hundreds of Excel pages, moving data around, coming up with formulas, and then a whole lot of refining. Thus, I bring to you the KILA Projection System (yes, it’s named after former Quad-A great Kila Ka’aihue).


The Method

The blueprint to KILA is honestly quite easy to understand, yet very detailed at the same time. The main goal of this projection system is to project a career wRC+ for any prospect in baseball (sample size pending). Correlations for minor league wRC+ to major league wRC+ are somewhat helpful, but not very great (r=~0.3). My thinking was to improve this correlation by developing a score (KILA Score) that weighted a player’s performance in certain statistical areas.

It wasn’t as simple as grabbing every minor league player season since 2007 and performing this all together. If you did that, there would be huge disparities between players in things such as league environment and competition. That’s where the biggest part of this KILA comes into play. Each player is compared to a group that contains their league and age peers. I took six statistics that I deemed noteworthy for minor league hitters through some side research (wRC+, K%, BB%, ISO, BABIP, GB%).

I originally went in with the plan that I would weigh each statistic the same at every level. Deeper research showed that wasn’t wise, the level of usefulness between stats like K-rate and ISO in the lower minors and upper minors is basically night and day. This table below will show I had to weigh each set differently.

Correlations (r=) between minor league metrics by level and future major league wRC+

Level wRC+ K% BB% ISO BABIP GB% Pull% Oppo%
Level wRC+ K% BB% ISO BABIP GB% Pull% Oppo%
Rookie 0.255 0.195 0.138 0.063 0.032 0.045 0.078 0.000
Low-A 0.327 0.110 0.084 0.148 0.045 0.100 0.032 0.078
Single-A 0.341 0.123 0.210 0.372 0.212 0.190 0.000 0.000
High-A 0.352 0.063 0.195 0.332 0.192 0.173 0.000 0.000
Double-A 0.400 0.055 0.203 0.385 0.105 0.228 0.032 0.105
Triple-A 0.354 0.078 0.215 0.297 0.134 0.118 0.000 0.071

As you can see, a lot of the metrics in short-season ball just aren’t predictable. Strikeout and walk-rate are the most important, while with the subpar fielding down there, ISO and BABIP have almost no predictability. This flips on full-season ball, as contradicting an old narrative, strikeout-rate is almost useless in predicting the success of a major league hitter. At the higher levels, power and walks are more important, while BABIP and GB-rate can have their use. I also scrapped all directional batted ball stats after initially exporting them, as their correlations are inconsistent and pretty useless.

Each player was given a z-score relative their age and league counterparts. These z-scores set an even scale for these weights to be distributed.

KILA Z-Score Weights

Linear Weights K% BB% ISO BABIP GB% Pull% Oppo%
Linear Weights K% BB% ISO BABIP GB% Pull% Oppo%
Rookie to Low-A 3.0 2.1 1.4 0.6 0.9 0.0 0.0
Single-A to Triple-A 1.4 2.1 3.0 0.6 0.9 0.0 0.0

Lastly, after all the weighted z-scores were summed for each league and age group, I added one last age adjustment in, accounting for difference in age compared to the rest of league. This was weighted and finally the KILA Scores were complete. Once I had the scores, I used simple linear regression with ‘X’ being the KILA Scores and ‘Y’ being the results of past major league hitters.

The Results

One of the main goals of this was to find minor leaguers that had somewhat fallen off the spectrum of what you would consider a “prospect,” yet were still putting up numbers that would indicate future major league hitting success. I did find a couple of those guys, such as Roberto Ramos, Jared Walker, Zach Green, and Brandon Wagner. But anyway, here were the top 50 KILA Scores from the 2018 season (sample sizes at each level eliminate a few guys, example being Juan Soto, though I’m 100 percent sure he’d be near the top if qualified).

Disclaimer: Remember this is 100% stats based. There will be obvious misses with this, while hopefully some good under-the-radar pickups. Also, there could be some sample size issues here.

Top 50 KILA hitting prospects, 2018

Name KILA Score Season Team Age 2018 wRC+ Projected wRC+
Name KILA Score Season Team Age 2018 wRC+ Projected wRC+
Wander Franco 17.930 2018 Rays (R) 17 159 122
Vladimir Guerrero Jr. 16.442 2018 Blue Jays (AA) 19 203 120
Nathaniel Lowe 16.343 2018 Rays (AA) 22 193 119
Jacob Amaya 15.221 2018 Dodgers (R) 19 160 117
Geraldo Perdomo 15.195 2018 Diamondbacks (A-) 18 149 117
Nolan Gorman 15.044 2018 Cardinals (R) 18 183 117
Daniel Vogelbach 15.035 2018 Mariners (AAA) 25 157 117
Peter Alonso 14.966 2018 Mets (AA) 23 180 117
Alex Kirilloff 14.743 2018 Twins (A) 20 176 116
Grant Lavigne 14.733 2018 Rockies (R) 18 160 116
Tyler O'Neill 14.477 2018 Cardinals (AAA) 23 170 116
Brandon Lowe 14.449 2018 Rays (AAA) 23 178 116
Yusniel Diaz 14.369 2018 Dodgers (AA) 21 152 116
Jose Fermin 14.142 2018 Indians (A-) 19 134 115
Xavier Edwards 13.901 2018 Padres (A-) 18 135 115
Antonio Cabello 13.792 2018 Yankees (R) 17 174 115
Kevin Smith 13.682 2018 Blue Jays (A) 21 190 114
Jonathan Arauz 13.681 2018 Astros (A) 19 147 114
Cavan Biggio 13.650 2018 Blue Jays (AA) 23 145 114
Alejandro Kirk 13.613 2018 Blue Jays (R) 19 160 114
Otto Lopez 13.440 2018 Blue Jays (A-) 19 134 114
Miguel Vargas 13.434 2018 Dodgers (R) 18 165 114
Franmil Reyes 13.095 2018 Padres (AAA) 22 168 113
Mark Vientos 13.032 2018 Mets (R) 18 132 113
Isaac Paredes 12.959 2018 Tigers (A+) 19 126 113
Luis Santana 12.923 2018 Mets (R) 18 150 113
Tucupita Marcano 12.359 2018 Padres (R) 18 173 112
Eloy Jimenez 12.318 2018 White Sox (AA) 21 157 112
Clint Frazier 12.250 2018 Yankees (AAA) 23 170 112
Seuly Matias 12.239 2018 Royals (A) 19 138 112
Nathaniel Lowe 12.210 2018 Rays (A+) 22 191 112
Cal Stevenson 12.182 2018 Blue Jays (R) 21 173 111
Jared Walker 12.017 2018 Dodgers (A+) 22 158 111
Blaze Alexander 11.996 2018 Diamondbacks (R) 19 190 111
Gabriel Moreno 11.968 2018 Blue Jays (R) 18 204 111
Elehuris Montero 11.955 2018 Cardinals (A) 19 157 111
Joe McCarthy 11.936 2018 Rays (AAA) 24 151 111
Roberto Ramos 11.905 2018 Rockies (A+) 23 175 111
Akil Baddoo 11.875 2018 Twins (A) 19 121 111
Nolan Jones 11.866 2018 Indians (A) 20 147 111
Jabari Blash 11.677 2018 Angels (AAA) 28 188 111
Ryan Noda 11.622 2018 Blue Jays (A) 22 160 110
Luis Garcia 11.546 2018 Phillies (R) 17 162 110
Dylan Cozens 11.507 2018 Phillies (AAA) 24 141 110
Brandon Howlett 11.504 2018 Red Sox (R) 18 159 110
Jeff McNeil 11.457 2018 Mets (AA) 26 182 110
Leonardo Jimenez 11.447 2018 Blue Jays (R) 17 96 110
Eloy Jimenez 11.427 2018 White Sox (AAA) 21 179 110
Will Benson 11.387 2018 Indians (A) 20 102 110
Gavin Lux 11.195 2018 Dodgers (A+) 20 147 110
Projected wRC+ is a career estimate translated from the KILA Score

And to set more of a guideline, here are the top 50 KILA hitting scores dating back to when extensive minor league data became available (2007-). And now you see why it’s named after Kila Ka’aihue.

Top 50 KILA Scores, 2007-2018

Name KILA Score Season Team Age Season wRC+ Projected wRC+
Name KILA Score Season Team Age Season wRC+ Projected wRC+
Fernando Tatis Jr. 20.982 2017 Padres (A) 18 154 128
Giancarlo Stanton 20.622 2010 Marlins (AA) 20 204 128
Kila Ka'aihue 20.515 2008 Royals (AA) 24 182 127
Miguel Sano 19.855 2013 Twins (A+) 20 203 126
Jared Goedert 19.622 2007 Indians (A) 22 209 126
Chris Parmelee 19.509 2012 Twins (AAA) 24 202 125
Paul Goldschmidt 19.163 2011 Diamondbacks (AA) 23 178 125
Jaff Decker 19.142 2009 Padres (A) 19 169 125
Jeff Clement 18.755 2008 Mariners (AAA) 24 184 124
Alex Bregman 18.366 2016 Astros (AA) 22 179 123
Chris Parmelee 18.247 2008 Twins (A) 20 151 123
Kris Bryant 18.040 2014 Cubs (AA) 22 220 123
Jesse Winker 18.035 2014 Reds (A+) 20 160 123
Mike Moustakas 18.021 2010 Royals (AA) 21 191 123
Akil Baddoo 17.961 2017 Twins (R) 18 183 122
Joey Gallo 17.949 2013 Rangers (A) 19 163 122
Giancarlo Stanton 17.949 2009 Marlins (A+) 19 178 122
Wander Franco 17.930 2018 Rays (R) 17 159 122
Carlos Santana 17.909 2010 Indians (AAA) 24 181 122
Matt Olson 17.870 2014 Athletics (A+) 20 145 122
Kevin Padlo 17.818 2014 Rockies (R) 17 155 122
Vladimir Guerrero Jr. 17.750 2017 Blue Jays (A+) 18 179 122
Greg Bird 17.702 2013 Yankees (A) 20 170 122
Cory Spangenberg 17.569 2011 Padres (A-) 20 205 122
Leonardo Rivas 17.467 2017 Angels (R) 19 132 122
Andrew Benintendi 17.414 2015 Red Sox (A-) 20 175 121
Bryce Harper 17.255 2011 Nationals (A) 18 164 121
Jaff Decker 17.249 2008 Padres (R) 18 192 121
Matt Skole 17.130 2012 Nationals (A) 22 174 121
Lucas Duda 17.100 2011 Mets (AAA) 25 175 121
Nick Weglarz 16.779 2007 Indians (A) 19 141 120
Oswaldo Arcia 16.749 2013 Twins (AAA) 22 186 120
Miles Head 16.695 2011 Red Sox (A) 20 175 120
Kevin Padlo 16.676 2015 Rockies (A-) 18 159 120
Bo Bichette 16.650 2017 Blue Jays (A) 19 201 120
Miguel Sano 16.523 2012 Twins (A) 19 146 120
Tyler Austin 16.502 2012 Yankees (A) 20 170 120
Derek Norris 16.500 2008 Nationals (A-) 19 163 120
Christin Stewart 16.483 2016 Tigers (A+) 22 174 120
Vladimir Guerrero Jr. 16.442 2018 Blue Jays (AA) 19 203 120
Nathaniel Lowe 16.343 2018 Rays (AA) 22 193 119
Derek Norris 16.298 2009 Nationals (A) 20 162 119
Giancarlo Stanton 16.251 2008 Marlins (A) 18 169 119
Clint Coulter 16.094 2014 Brewers (A) 20 165 119
Daniel Vogelbach 16.077 2012 Cubs (A-) 19 189 119
Matt Wieters 16.007 2008 Orioles (AA) 22 190 119
Ryan Kalish 15.958 2007 Red Sox (A-) 19 187 119
Wil Myers 15.947 2010 Royals (A) 19 152 119
Mason Martin 15.945 2017 Pirates (R) 18 198 119
Tyler Austin 15.929 2016 Yankees (AAA) 24 201 119
Projected wRC+ is a career estimate translated from the KILA Score

The Predictability

I can’t sit here and rave about KILA’s predictability as much as I want to, but what I can say is it is more predictable than any standard minor league metric you could go off of. That’s the product of added context and focused importance.

Out of a sample size that adds up to nearly 3,000 batters from since 2007, the correlation coefficient between your standard minor league wRC+ and major league wRC+ comes out to r = 0.39.

Now for the correlation between the KILA projected wRC+ and the actual major wRC+, which comes out higher, standing at r = .423.

While only a slightly better predictor, it is still better than any other minor league number you could use. I’m almost confident it could be improved in the future too, as it is a goal of mine to make these numbers ballpark adjusted, an issue often faced when analyzing minor league numbers.


Projecting how a player will perform against higher competition is a tough task. There are just too many mental and physical factors that we tend to miss. But hey, making an attempt at it is fun. If it wasn’t, then I wouldn’t put all this work into doing it. And I plan to keep working on it, as I mentioned adding in the ballpark factors above, along with adding in an age curve, making pitcher projections, and doing a whole lot more refining to the system. Expect more content on this in the future.


Patrick Brennan loves to research pitchers and minor leaguers with data. You can find additional work of his at Royals Review and Royals Farm Report. You can also find him on Twitter @paintingcorner.