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Kris Bryant: Which players are most similar?

The Kris Bryant call-up extravaganza continues with some K Nearest Neighbor modeling. I took a look at Bryant's dominant 2014 in the Pacific Coast League and determined which players were most similar at that level.

Jerry Lai-USA TODAY Sports

Kris Bryant made his highly anticipated Major League debut last Friday against the Padres. Bryant lit the world on fire in Spring Training with a triple slash line of .435/.500/1.304 and a league leading 6 round-trippers. The Bryant hysteria was only intensified by his short stint in the minors.

Bryant’s most recent achievements are particularly exciting because he’s looked so promising in the advanced levels of minor league baseball. In his 2014 season in the Pacific Coast League, Bryant finished with a triple slash line of .295/.418/.619 while going deep in 8.6% of his at bats. Bryant’s remarkable offensive output earned him the honor of Baseball America’s number one prospect.

The numbers are unequivocally great. However, it’s hard to comprehend fully his 2014 AAA performance because the value of minor league statistics is relative. Identifying players who had similar seasons in the Pacific Coast league should add the necessary context to appreciate Bryant’s outstanding 2014 campaign. To discover these players, I built two separate K Nearest Neighbors models. The first model uses variables that are statically significant at the AAA level according to Chris Mitchell, FanGraphs writer and creator of the KATOH projection system. These explanatory variables include: age, walk rate, strikeout rate, ISO, BABIP and stolen base percentage. The model considered each position player who played in the Pacific Coast League from 1960-2014.

Below is a visualization of the 30 closest player/seasons to Kris Bryant’s 2014 AAA campaign, based off the aforementioned variables. The larger, darker bubbles indicate proximity to Kris Bryant’s 2014 season. The Euclidean distance is given below the Player/Season for reference.

This model does not serve as a Kris Bryant projection.  We cannot say with certainty that Bryant will perform like any of the players on this list because the variables used to determine nearest neighbors do not fully describe the players.

However, there is value in identifying players who had similar levels of production in the statistics that are significantly related to MLB performance. I added the chart below, which provides a more granular level of detail.

Player Season Distance Age BB% SO% SB% ISO BABIP
Kris Bryant 2014 22 14.5% 28.6% 77.8% 0.324 0.367
Gorman Thomas 1974 0.178 23 16.1% 30.3% 76.5% 0.359 0.356
Mike Schmidt 1972 0.230 22 16.4% 27.3% 75.0% 0.259 0.377
Sean Rodriguez 2009 0.249 24 11.7% 27.4% 81.8% 0.317 0.364
Carlos Pena 2001 0.250 23 15.4% 24.5% 78.6% 0.262 0.359
George Springer 2013 0.273 23 15.4% 24.4% 88.0% 0.315 0.362
Brett Jackson 2011 0.294 22 13.0% 29.8% 85.7% 0.254 0.402
Danny Tartabull 1985 0.309 22 12.3% 22.5% 81.0% 0.315 0.318
Joc Pederson 2014 0.309 22 18.1% 26.9% 69.8% 0.279 0.385
Todd Linden 2005 0.327 25 14.9% 23.4% 75.0% 0.361 0.367
Nate Rolison 2000 0.339 23 13.5% 22.6% 75.0% 0.252 0.403
Matt Tuiasosopo 2009 0.346 23 13.4% 30.9% 75.0% 0.212 0.361
Chris Carter 2011 0.368 24 12.2% 24.7% 83.3% 0.256 0.321
Adolfo Phillips 1964 0.368 22 9.9% 24.0% 80.8% 0.262 0.360
Ian Stewart 2008 0.370 23 11.4% 22.1% 77.8% 0.327 0.305
Jack Cust 2001 0.390 22 18.4% 28.9% 66.7% 0.247 0.369
Carl Everett 1993 0.391 22 12.0% 28.5% 92.3% 0.287 0.424
Rob Deer 1984 0.391 23 17.4% 31.8% 75.0% 0.259 0.290
Eddie Vargas 1982 0.393 23 10.9% 21.4% 75.0% 0.262 0.354
Kelvin Moore 1982 0.402 24 12.3% 27.2% 75.0% 0.213 0.330
George Banks 1965 0.405 26 16.6% 26.0% 71.4% 0.287 0.338
Jack Cust 2002 0.406 23 18.4% 26.9% 66.7% 0.259 0.330
Larry Hisle 1971 0.411 24 10.4% 24.1% 83.3% 0.269 0.409
Karim Garcia 1998 0.413 22 12.3% 19.7% 83.3% 0.359 0.315
Chad Hermansen 1998 0.422 20 9.7% 29.5% 84.0% 0.262 0.320
Horace Speed 1975 0.425 23 12.0% 23.8% 77.3% 0.203 0.340
Dick Simpson 1965 0.430 21 10.1% 24.5% 74.4% 0.222 0.376
Nelson Cruz 2005 0.430 24 12.2% 25.2% 69.2% 0.221 0.333
Brandon Allen 2011 0.431 25 15.8% 24.1% 63.6% 0.279 0.361
Eric Hinske 2001 0.432 23 10.7% 22.4% 74.1% 0.239 0.327
Jai Miller 2008 0.437 23 10.4% 26.7% 76.9% 0.205 0.338

The population of neighbors returned by model one is not littered with Hall of Famers. Players like Mike Schmidt and Nelson Cruz highlight Bryant’s impact-bat potential. Jack Cust, Carlos Pena, and others serve as cautionary examples of players whose propensity to strike out has persisted in the big leagues. Also, Horace Speed is on this list.

Horace Speed may be the most apt name for a pinch runner in the history of baseball. However, his inclusion in the first model’s output accentuates an issue: some of these variables are irrelevant. Bryant recorded a stolen base rate of 77.8% in 2014; however, this figure ignores his low volume of stolen bases. Because speed is a negligible part of Bryant’s game, it doesn’t make sense to use stolen base percentage as a defining variable.

The second model aims to compensate for the shortcomings of model 1.

Bryant’s 2014 season with the Iowa Cubs was defined by a sterling OPS and a high rate of both home runs and strikeouts. Model two uses home run rate, strikeout rate, walk rate, and OPS as its explanatory variables to find players most similar to Kris Bryant. The players returned by model 2 are in the graphic below.

Player Season Distance Age HR% BB% SO% OPS
Kris Bryant 2014 22 8.6% 14.5% 28.6% 1.036
George Springer 2013 0.182 23 8.2% 15.4% 24.4% 1.050
Gorman Thomas 1974 0.228 23 10.8% 16.1% 30.3% 1.069
Travis Snider 2009 0.239 21 8.0% 13.7% 23.0% 1.094
Sean Rodriguez 2009 0.242 24 8.0% 11.7% 27.4% 1.017
Joc Pederson 2014 0.245 22 7.4% 18.1% 26.9% 1.017
Melvin Nieves 1994 0.259 22 6.2% 12.2% 29.1% 0.967
Danny Tartabull 1985 0.262 22 9.1% 12.3% 22.5% 1.001
Billy Ashley 1994 0.262 23 9.5% 11.7% 25.6% 1.129
Mike Schmidt 1972 0.269 22 6.0% 16.4% 27.3% 0.960
Trayvon Robinson 2011 0.298 23 6.9% 11.2% 29.4% 0.926
Chris Carter 2010 0.305 23 6.7% 13.2% 25.0% 0.894
Brett Jackson 2011 0.308 22 5.4% 13.0% 29.8% 0.939
Todd Linden 2005 0.328 25 8.8% 14.9% 23.4% 1.120
Ian Stewart 2008 0.331 23 7.4% 11.4% 22.2% 0.979
Scott Moore 2007 0.334 23 5.9% 12.6% 26.2% 0.899
Carlos Pena 2001 0.336 23 5.3% 15.4% 24.5% 0.958
Mike Anderson 1972 0.336 21 5.5% 15.5% 25.6% 0.916
Chris Davis 2008 0.337 22 9.0% 10.2% 22.8% 1.086
J.R. Phillips 1994 0.339 24 7.5% 11.0% 23.4% 1.013
Jack Cust 2001 0.339 22 6.1% 18.4% 28.9% 0.940
Prince Fielder 2005 0.344 21 7.4% 12.2% 21.1% 0.957
Craig Wilson 2000 0.345 23 8.3% 9.3% 25.6% 0.987
Jack Cust 2002 0.347 23 6.4% 18.4% 26.9% 0.930
Karim Garcia 1998 0.353 22 9.4% 12.3% 19.7% 1.063
Jon Singleton 2014 0.354 22 7.2% 17.6% 21.8% 0.941
Kyle Blanks 2011 0.356 24 8.2% 10.5% 24.3% 1.137
Brandon Wood 2008 0.356 23 7.8% 10.0% 23.2% 0.970
Ben Petrick 2002 0.361 25 6.0% 12.9% 24.8% 1.006
Franklin Stubbs 1985 0.362 24 7.6% 16.3% 20.6% 0.984
Kyle Nichols 2004 0.365 26 8.4% 13.3% 32.8% 0.995

There are a few common threads that link most of the players on this list. A majority of these players have outstanding power and have at least demonstrated the potential to be a major power threat. We also, again, see a great deal of players who fan at an alarming clip. Also alarming: Jack Cust is on this list twice.

These collections of neighbors should be taken with a grain of salt. Like many models that rely on minor league statistics, these models do not account for any untapped potential. Additionally, there is likely a slew of similar players who could not be included because they either did not play in the PCL or had an abbreviated stay in the PCL. Bryant does, however, have a troubling strikeout rate, and these models highlight that point of concern. While strikeouts will be something to bear in mind going forward, Kris Bryant still seems like he’s really good at baseball and will remain good at baseball for many years to come.

Merry Krismas to you and yours.

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Statistics courtesy of Baseball-Reference. KATOH information obtained from Chris Mitchell's work at FanGraphs.

Cody Callahan is a Contributor for Beyond the Box Score. You can follow him on the Twitter at @codycallahan.