Adjusted NCAA Statistics: Introduction and Top 100s
The first presentation of adjusted NCAA Division I hitting and pitching statistics that I will make is of the top 100 players. I’ll use this opportunity to explain my methods for adjusting these statistics.
In layman’s terms (and if you’re not particularly interested in methods, just read this paragraph and then skip ahead to the numbers) what my method does is calculate each player’s gross offensive production (defensive production in the case of pitchers) and then adjusts that production for the two primary factors that cause college stats to vary widely; the team’s run environment (including park factors home and away and whether they play in a hitter’s or pitcher’s conference, for example) and the team’s actual strength of schedule.
I use these adjustments to calculate the “baseline” against which the player is compared; that is what the average Division I hitter or pitcher would have compiled against the same competition in the same parks on the same days. I then look at how many runs that player’s production was over or under that average.
Now for the details …
Run Estimation Methods
First, a note on my choice of methods. I’ve been using Jim Furtado’s xRuns (Extrapolated Runs) method to calculate offensive production for hitters (and also for pitchers). The xR coefficients are for MLB over a 50-year time span, and unfortunately do not translate ideally to college numbers. They do mirror, quite closely, the BaseRuns coefficients for MLB over the same period, and for contemporary minor-league ball as well. But they don’t mirror quite as closely the BaseRuns coefficients for the NCAA environment, which is a very high-run environment due to the aluminum bats (NCAA Division I pitchers have allowed 6.52 runs per 9 innings up to May 16).
This means that power matters relatively less, and on-base percentage, particularly singles hitting, matters more. Once the NCAA season is done, I am hoping to redo these calculations with the proper BaseRuns coefficients (and Tangotiger, if you’re reading, can you send me the spreadsheet to calculate these again? I seem to have lost it) since BaseRuns will be a more accurate method. For now, though, 95% of you are reading this article for info on current and future draft prospects, so measuring how their offensive production would “translate” into what is roughly a pro context is not necessarily a bad thing.
I used xRuns Allowed for pitchers as well, though not every player has the data necessary, and using regular runs allowed is also done. The reason for not using ERA is that scoring varies widely at the NCAA level; the reason for using xR is that it can pick out pitchers who are particularly “run lucky”, which can make big differences in samples as small as a single college season.
The “Park Factor”
The “Park Factor” is really a measure of total team offensive context, and I owe it to fellow THT analyst Robert Dudek. Essentially, Robert looked at each team’s home and away games in 2004 and calculated a park factor based on runs scored/runs allowed data for the period 2000-2004. These factors should be quite accurate (especially considering the relatively small number of games played in NCAA competition) and take into account the player’s road environments as well, a big edge over traditional “park factor.” Thanks Robert.
The Park Factor is used to adjust not the player’s own statistics, but instead the average to which he is compared. (In practice this doesn’t matter, as the numbers would be the same.)
The “SOSA” — Strength of Schedule Adjustment
Division I teams play vastly different schedules, with the result that players find themselves facing vastly different levels of competition. The SOSA (or Strength of Schedule Adjustment) takes this into account by again adjusting the average baseline to which a player is compared. The reason for this is that for a team like Arizona State, which plays a very difficult schedule, the average Division I hitter wouldn’t create 6.5 runs a game. That figure would be closer to 5.2 runs a game, because ASU is facing much better pitchers day in and day out. Likewise, a team like Jackson State plays a schedule that isn’t very tough. The average Division I hitter would create about 7.6 runs per game against their opposition.
The SOSA is calculated based on a team’s opponents’ winning percentage, and a team’s opponents’ opponents’ winning percentage. The opponents’ opponents’ winning percentage is used to calculate (via the log5 method) a “true winning percentage” for the team’s opponents. In other words, how well that team’s opponents would have done against a neutral schedule.
How do we get from there, to run factors that will help us look at runs created and runs allowed statistics? Fellow THT analyst Vinay Kumar helped me with that one. I had had the idea to run the winning percentage data through the Pythagorean method, only backwards, to get from a winning percentage (versus the league average) to a ratio of runs scored or (allowed to the league average). It was Vinay who ran the numbers for me, and showed that the SOSA is equal to the fourth root of the ratio of the opponents’ won-lost ratio. That is, (win% / (1-win%)) ^ 0.25. Just trust me, it works.
The Top 100 NCAA Performers
Now to the data. This is just a list of the Top 100 pitchers and hitters in NCAA Division I, by adjusted xRuns Above Average for hitters and adjusted xRuns Saved Above Average for pitchers. I have also listed those pitchers whose adjusted Runs Saved Above Average would have put them in the Top 100, but didn’t have the data needed to caluculate xR.
This list is not limited to draft-eligible prospects; freshmen, sophomores under 21 and fifth-year draft-and-follow seniors are also listed.
All data is complete to May 16. Starting next week, the data I release should be complete to May 30. I will be releasing the whole database next week.
Pitchers
RA+ measures the pitcher versus the NCAA average; 100 is average. RSAA measures how many runs the pitcher has saved versus the average pitcher. I also in included IP, K/9 and K/BB data for comparison purposes.
Rank Name School IP K/BB K/9 xRSAA RSAA RA+ xRA+ 1 Jered Weaver Long Beach St 113.1 12.2 13.6 78.7 69.1 425 867 2 Jason Windsor Fullerton 108.2 6.1 7.1 66.1 56.4 271 383 3 Justin Hoyman Florida 110.0 2.7 6.4 65.1 55.2 238 275 4 J. P Howell Texas 100.2 3.7 11.3 61.8 59.8 324 359 5 Wade Leblanc Alabama 106.2 4.0 7.7 57.8 56.6 286 302 6 Michael Rogers NC State 96.1 5.3 9.0 56.5 49.7 267 344 7 Sam Lecure Texas 92.0 3.8 8.2 55.7 50.3 275 348 8 Wade Townsend Rice 98.2 3.3 11.0 54.4 55.8 339 340 9 Vern Sterry NC State 92.2 6.2 9.0 50.7 48.6 275 295 10 Philip Humber Rice 92.0 4.5 11.8 50.5 50.4 316 336 11 Mike Pelfrey Wichita State 91.1 5.1 9.6 50.3 42.4 261 378 12 David Purcey Oklahoma 98.0 2.5 10.0 50.1 41.9 207 249 13 Stacen Gant George Mason 96.0 2.8 6.7 49.1 44.9 279 336 14 Cesar Ramos Long Beach St 104.1 3.3 6.5 48.3 49.6 248 267 15 Ricky Romero Fullerton 115.1 3.1 7.6 47.4 46.9 198 200 16 C Falkenbach Florida 92.1 7.8 6.0 46.9 41.6 209 211 17 Rhett James Florida State 80.1 3.1 8.3 46.1 39.6 229 288 18 Matt Campbell South Carolina 88.0 5.0 10.2 46.1 36.0 192 273 19 Spencer Grogan Oklahoma State 116.2 4.6 5.3 45.9 54.3 228 179 20 Shawn Phillips Delaware State 115.2 10.1 8.6 45.4 37.3 193 242 21 Casey Janssen UCLA 92.2 3.2 8.1 45.3 33.7 181 249 22 Jeremy Sowers Vanderbilt 94.2 4.9 8.5 44.7 37.7 193 236 23 Zach Jackson Texas A&M 92.1 5.2 10.1 44.7 39.2 209 253 24 Kyle Bono Central Florida 86.2 4.5 10.2 43.4 36.7 228 315 25 Matt Fox Central Florida 89.0 3.7 10.1 42.8 38.5 234 292 26 Tom Robbins Texas State 100.1 2.0 6.6 42.7 36.3 182 219 27 Joe Koshansky Virginia 90.2 2.7 7.3 41.2 36.4 202 231 28 Michael Gardner Texas-Arlington 93.2 2.3 6.7 40.7 39.6 222 249 29 Jason Meyer Texas A&M 79.1 2.9 10.1 40.5 48.2 393 275 30 Thomas Diamond New Orleans 98.0 3.3 11.5 40.3 39.6 209 199 31 Mark Roberts Oklahoma 97.0 5.7 9.5 40.2 34.4 175 191 32 Cesar Carrillo Miami, Florida 75.1 1.9 6.7 40.2 38.2 238 237 33 Brett Smith UC Irvine 93.2 4.1 8.6 39.8 43.1 238 232 34 Mark Holliman Mississippi 88.2 3.6 8.7 39.7 45.5 249 228 35 Zach Kroenke Nebraska 89.1 2.9 6.6 39.6 41.1 143 136 36 Eddie Cannon Florida State 86.0 4.9 7.7 39.6 27.7 158 209 37 Andrew Dobies Virginia 94.1 3.6 9.3 39.2 37.4 199 208 38 Jordan Thomson Northeastern 74.2 6.6 8.8 37.9 33.1 273 410 39 Micah Owings Georgia Tech 83.2 2.5 9.0 37.5 35.7 198 194 40 Ryan Zink Ill-Chicago 89.1 6.4 9.0 37.2 31.8 218 283 41 Ian Kennedy Southern Cal 79.2 4.0 12.1 36.4 41.4 250 209 42 Ryan Mullins Vanderbilt 82.2 4.5 7.4 36.2 35.9 211 216 43 Jarrett Grube Memphis 88.2 3.8 9.2 36.1 33.3 194 213 44 Carlos Torres Kansas State 107.1 1.9 5.9 35.8 30.1 154 165 45 Justin Orenduff VA Commonwealth 89.0 4.2 11.5 35.8 33.3 207 226 46 Jeff Mousser Arizona State 75.2 1.1 4.2 35.8 32.4 197 203 47 Donnie Smith Old Dominion 77.2 4.0 10.5 35.7 36.2 269 256 48 M Prendergast VA Commonwealth 89.2 2.2 5.7 35.7 32.7 202 223 49 Jason Vargas Long Beach St 88.1 3.4 7.5 35.6 29.0 170 226 50 Andrew Kown Georgia Tech 88.1 2.7 7.6 35.4 39.8 209 175 51 Kevin Ardoin LA-Lafayette 94.1 4.3 7.7 35.1 35.1 198 202 52 Trey Taylor Baylor 78.2 2.5 5.9 35.1 22.6 150 217 53 Jason Urquidez Arizona State 82.2 2.1 8.7 34.9 44.0 258 180 54 Kevin Slowey Winthrop 108.0 9.7 7.3 34.8 30.5 161 179 55 Steve Grasley Creighton 102.0 4.6 8.1 34.6 40.2 216 179 56 Glen Perkins Minnesota 84.2 3.9 8.7 34.1 29.6 187 222 57 Derek Tharpe Tennessee 74.0 4.4 9.7 34.0 38.5 274 240 58 Tim Lincecum Washington 90.2 2.0 13.3 33.9 26.4 156 182 59 Clay Dirks Louisiana State 77.1 4.3 7.4 33.8 28.3 175 203 60 R. J. Swindle Charleston So 101.0 4.4 9.9 33.6 33.8 177 168 61 Jon Wilson Winthrop 59.2 4.9 11.0 33.5 31.1 336 417 62 T Tankersley Alabama 65.2 2.7 9.6 33.4 39.4 377 270 63 Brian Akin Davidson 86.0 3.6 8.7 33.2 23.9 163 210 64 Greg Bunn East Carolina 78.2 3.6 10.2 33.0 28.4 189 236 65 Daniel Bard North Carolina 81.1 2.9 6.6 33.0 29.5 186 203 66 Koley Kolberg Arizona 107.2 2.0 7.9 33.0 27.7 144 141 67 John Martinez UC Riverside 103.1 3.6 5.0 32.3 28.2 157 163 68 Joe Piekarz No Illinois 100.1 2.4 7.6 32.0 32.8 183 168 69 P. J Walters South Alabama 93.1 2.8 9.3 32.0 30.4 168 165 70 Aaron Wilson San Diego 108.1 2.3 5.5 32.0 29.3 158 162 71 Dennis Robinson Jacksonville 113.0 2.1 5.3 31.9 24.6 140 158 72 G Broshuis Missouri 83.1 3.6 7.8 31.8 33.5 215 197 73 Matt Scherer LeMoyne 79.1 9.0 8.2 31.4 29.2 233 259 74 R Rohrbaugh Clemson 60.0 2.1 5.0 31.1 27.8 212 233 75 William Delage Lamar 59.1 2.3 8.2 31.0 29.4 297 355 76 Zach Zuercher Rhode Island 83.2 2.9 12.8 30.9 22.8 165 214 77 Paul Lubrano Georgia 81.0 1.8 6.1 30.8 26.3 161 189 78 Ryan Heacox Stetson 77.1 2.3 4.3 30.6 29.4 215 240 79 Justin Pekarek Nebraska 64.2 4.7 9.2 30.5 29.9 143 147 80 Aaron Rawl South Carolina 90.2 6.0 7.1 30.4 25.6 149 174 81 Derek Drage SW Missouri St 98.1 3.1 7.6 30.2 25.0 154 178 82 Justin Simmons Texas 75.1 1.4 4.3 29.8 29.8 186 190 83 Jeff Samardzija Notre Dame 57.1 2.2 5.5 29.8 26.9 271 348 84 Zach Kimball NC-Wilmington 83.1 2.5 6.5 29.5 27.5 176 199 85 Arnold Hughey Auburn 91.2 3.7 7.6 29.4 23.1 142 171 86 J. D. Cockroft Miami, Florida 73.2 1.7 6.0 29.4 34.0 211 170 87 John Williams Middle Tenn St 77.2 4.2 9.7 29.4 26.3 185 205 88 Stephen Head Mississippi 59.1 3.7 7.3 29.3 33.4 292 257 89 Ryan Falcon NC-Greensboro 78.0 6.0 9.0 29.3 20.3 157 197 90 Nick Hill Army 80.1 3.7 8.7 29.1 28.5 219 224 91 Jordan Topal Campbell 75.0 2.6 5.5 29.1 19.3 148 197 92 J. Brent Cox Texas 47.0 4.2 10.3 29.0 30.1 394 363 93 Zac Cline West Virginia 71.0 2.1 5.3 28.8 27.3 200 222 94 Dallas Braden Texas Tech 79.0 4.2 8.7 28.7 28.1 177 160 95 Jeff Niemann Rice 59.1 3.5 10.6 28.7 27.4 236 267 96 Chris Bova Ohio 88.0 2.2 7.3 28.6 19.1 147 177 97 Steven Cook NC-Asheville 113.2 2.4 6.6 28.5 23.7 138 149 98 Randy Beam FLA Atlantic 102.2 2.5 5.9 28.5 28.7 158 159 99 Danny Hill Missouri 90.1 2.8 7.8 28.3 24.2 156 166 100 Clayton Turner NW State 86.2 2.5 9.7 28.2 18.5 141 188
Hitters
For hitters, for comparison purposes, I included batting average, on-base percentage and slugging percentage. This xR data includes stolen base data, so it’s not strictly “hitting.” Some players move up several spots due to being excellent base thieves. OWP is Offensive Winning Percentage.
Rank Name School AVG OBP SLG xR OWP xRAA 1 Jed Lowrie Stanford .412 .512 .794 194 .887 45.2 2 Ryan Jones East Carolina .406 .507 .891 175 .889 45.0 3 Kurt Suzuki Fullerton .439 .537 .709 189 .877 41.4 4 Alex Gordon Nebraska .383 .502 .782 193 .869 40.9 5 Chip Cannon The Citadel .362 .529 .686 188 .850 39.0 6 Chris Rahl William & Mary .391 .463 .797 197 .839 37.1 7 Mike Ferris Miami, Ohio .389 .538 .833 180 .844 36.3 8 M Vanderbosch Oral Roberts .390 .502 .533 195 .835 35.7 9 Mike Costanzo Coast Carolina .359 .470 .769 195 .840 33.8 10 PJ Hiser Pittsburgh .365 .444 .785 181 .832 33.3 11 Jon Zeringue Louisiana State .417 .475 .686 204 .846 33.2 12 Brad Corley Miss State .374 .429 .680 222 .824 33.0 13 Brendan Winn South Carolina .342 .437 .705 193 .842 32.9 14 Brian Bixler East Michigan .450 .520 .627 209 .819 32.3 15 Danny Putnam Stanford .392 .469 .660 209 .827 32.2 16 Matt Macri Notre Dame .363 .473 .663 193 .820 31.8 17 Josh Morris Georgia .341 .450 .692 182 .835 31.6 18 Richie Robnett Fresno State .382 .469 .711 204 .812 31.5 19 Dustin Pedroia Arizona State .411 .514 .657 207 .831 31.4 20 Patrick Perry N Colorado .469 .538 .808 177 .831 31.3 21 Warner Jones Vanderbilt .423 .460 .667 213 .824 31.1 22 Nick Shimer George Mason .339 .476 .734 177 .818 30.6 23 Dan Batz Rhode Island .405 .500 .746 173 .823 30.5 24 E Martinez-Este Florida State .377 .447 .679 212 .823 30.3 25 Jamie Rusco NC-Charlotte .377 .493 .697 175 .827 30.2 26 Caleb Moore East Tenn St .478 .535 .783 180 .843 30.0 27 Matt Barket Tulane .400 .481 .595 200 .805 29.6 28 Landon Powell South Carolina .354 .430 .651 209 .813 29.2 29 Jeff Frazier Rutgers .385 .459 .662 195 .809 29.2 30 Graig Badger Rutgers .368 .510 .497 185 .812 29.0 31 Steve Sherman NC-Asheville .363 .475 .678 171 .815 29.0 32 Bryant Lange Jr Jackson State .439 164 .825 28.9 33 Scott Rich Rider .381 .457 .730 189 .790 28.4 34 Brad Hayes Arkansas State .424 .473 .750 144 .855 28.3 35 Chad Huffman Texas Christian .397 .490 .618 204 .810 28.3 36 C Westervelt Stetson .378 .485 .643 185 .800 28.2 37 Brad McCann Clemson .388 .462 .660 206 .818 28.0 38 Drew Moffitt Wichita State .286 .461 .671 161 .804 27.9 39 Josh Brady Texas Tech .355 .434 .687 217 .785 27.4 40 Brett Anderson Coll Charleston .367 .507 .698 169 .788 27.4 41 Jim Geldhof C Michigan .434 .494 .654 205 .772 27.4 42 Ben Zobrist Dallas Baptist .389 .460 .597 211 .776 27.2 43 Stephen Drew Florida State .338 .472 .669 157 .849 27.2 44 Tim Grogan W Kentucky .362 .494 .606 188 .779 26.9 45 Logan Sorensen Wichita State .371 .457 .589 202 .782 26.9 46 Anthony Granato VA Commonwealth .377 .459 .560 191 .782 26.7 47 Mike Hughes Ill-Chicago .372 .457 .683 199 .766 26.5 48 Jeff Fiorentino FLA Atlantic .324 .449 .603 179 .787 26.5 49 Steve Pickerell Cincinnati .398 .498 .722 176 .809 26.5 50 Stephen Head Mississippi .360 .432 .591 203 .801 26.4 51 Jim Burt Miami, Florida .370 .469 .672 189 .812 26.3 52 Carl Lipsey Jackson State .364 151 .794 26.3 53 J. C. Holt Louisiana State .393 .466 .557 219 .791 26.2 54 Mike Butia James Madison .373 .463 .802 177 .795 26.2 55 Matt Gunning W Kentucky .400 .445 .695 220 .766 26.2 56 Brandon Morgan NW State .391 .493 .529 174 .806 26.1 57 Rob Hosgood Central Conn St .383 .487 .596 193 .770 26.1 58 C Anderson Detroit Mercy .383 .490 .713 167 .788 26.0 59 Mark Jurich Louisville .361 .431 .708 216 .762 25.8 60 Chad Kinyon Kent State .375 .475 .655 200 .770 25.3 61 Matt Miller Texas State .385 .441 .587 218 .776 25.3 62 Jim Negrych Pittsburgh .388 .475 .617 183 .782 25.3 63 Chris Looze George Mason .355 .435 .695 197 .771 25.2 64 Clete Thomas Auburn .332 .429 .522 205 .780 25.0 65 Tim Burgess Georgia State .423 .518 .673 156 .826 24.8 66 Nick Blasi Wichita State .358 .445 .590 212 .764 24.8 67 Nathan Purvis Miss Valley St .378 127 .824 24.8 68 Kiel Thibault Gonzaga .433 .511 .562 201 .782 24.8 69 Ryan Griffith Birmingham-So .371 .456 .576 205 .770 24.7 70 Ryan Norwood East Carolina .360 .420 .680 203 .766 24.6 71 Ben Harrison Florida .363 .471 .632 193 .794 24.5 72 Seth Bynum Indiana .397 .443 .655 194 .766 24.5 73 Eric Nielsen UNLV .405 .500 .726 215 .756 24.4 74 Zach Clem Washington .358 .460 .620 179 .797 23.9 75 Steve Pearce South Carolina .335 .421 .630 200 .775 23.9 76 Brett Gardner Coll Charleston .403 .485 .618 191 .751 23.8 77 Jordan Foster Lamar .369 .504 .500 176 .780 23.7 78 Hunter Pence Texas-Arlington .418 .462 .618 165 .814 23.6 79 Shelby Ford Texas Christian .313 .429 .626 198 .769 23.4 80 Eric Patterson Georgia Tech .317 .431 .519 208 .769 23.4 81 M Hubbard North Carolina .355 .427 .690 200 .767 23.3 82 Clay Timpner Central Florida .363 .419 .529 204 .758 23.3 83 Steve Caravati Ohio State .394 .449 .616 198 .761 23.3 84 Trevor Lawhorn East Carolina .311 .382 .665 212 .745 23.2 85 Jeff Justice LeMoyne .405 .460 .716 190 .751 23.0 86 Jim Fasano Richmond .313 .437 .637 179 .761 22.9 87 Nolan Reimold Bowling Green .400 .484 .719 160 .786 22.9 88 Jared Greenwood W Carolina .332 .453 .766 184 .758 22.6 89 John Mayberry Stanford .345 .427 .627 177 .780 22.6 90 A Toussaint LA-Lafayette .373 .434 .740 150 .796 22.5 92 Anthony Raglani Geo Washington .330 .456 .594 197 .733 22.5 93 Brian Hall Stanford .364 .429 .605 195 .760 22.2 94 Blake Adkison Centenary .392 .471 .731 171 .760 22.1 95 Kyle Larsen Washington .346 .453 .585 188 .770 22.1 96 Keith Stegbauer Central Conn St .436 .479 .555 218 .736 22.0 97 Travis Buck Arizona State .358 .481 .567 187 .777 22.0 98 Juan Figueroa Bethune-Cookman .383 .474 .611 175 .758 22.0 99 Cameron Blair Texas Tech .368 .440 .609 220 .749 21.9 100 J Merendino LA-Lafayette .365 .446 .597 181 .758 21.9
References & Resources
I’d like to thank Boyd Nation, Vinay Kumar, Robert Dudek, and Tangotiger for all their help on this project and the other material you’ll see out of me before the draft. Without them, I am nothting.