The Predictive Power of AFL Batting Stats: A Partial Study

Despite the fact that they are generally cited as probably providing little in the way of predictive power, the batting lines of prospects in the Arizona Fall League are also frequently cited by baseball writers in discussions of those same prospects. Nor is this entirely surprising: one wants to make some sort of comment about Kris Bryant, for example, who’s just finished his own AFL season with six home runs and a .727 slugging percentage. Even after noting that he recorded those figures in just 92 plate appearances, one is compelled to suggest that Bryant’s performance was impressive. And it was, certainly, within the context of the 2013 season of the Arizona Fall League.

The present author, attempting to behave somewhat responsibly, has produced statistical reports for the AFL this fall which utilize an offensive metric (called SCOUT+) that combines regressed home-run, walk, and strikeout rates in a FIP-like equation to produce a result not unlike wRC+. By isolating and regressing those metrics (i.e. not BABIP) which become reliable in smaller samples, one reasons, it’s possible to reduce the noise otherwise present in slash lines — and perhaps to better identify how performances from the AFL might inform future major-league production.

“How successful is this (theoretically) more responsible and (definitely) more nerdy attempt to measure AFL production, to the extent that it might hold within it some manner of predictive power?” one might, perhaps already has, wondered. “Not very,” appears to be the answer.

To test how well recent AFL performances might correlate with major-league performance, I began by isolating player-seasons from the top and bottom 25% of the SCOUT leaderboards from each of the last three AFL seasons (2010-12, that is). With slightly more than 100 batters in each season, that gives us about 26 “good” hitters and 26 “bad” hitters from each of the past three years — or 79, precisely, of each.

To give the reader a sense of the difference in performance between the best and worst hitters by this methodology, here’s a table featuring the respective performances of each:

Type Age PA HR% BB% K% AVG OBP SLG BABIP SCOUT+
Best 22.5 82 3.2% 14.0% 14.9% .306 .408 .496 .340 122
Worst 22.0 76 1.3% 6.8% 25.6% .252 .310 .370 .335 79

The 79 hitters from the Best group homered about 2.5 times more frequently than, walked twice as often as, and struck out about half the rate of their counterparts in the Worst group. The strengths and weaknesses of each group are exhibited in the average slash lines, as well. The Best group posted a higher batting average (by about 50 points), higher on-base percentage (by about 100 points), and a higher slugging percentage (by nearly 130 points). Conveniently, one finds that the BABIPs for each group are almost identical as well (.340 and .335, respectively), so one needn’t make any other sort of allowances for that.

That the hitters from the Best group have been about half a year older than those in the Worst isn’t entirely suprising. All things being equal, batters who are both (a) young but also (b) closer to their peak age (i.e. about 27) are likely to perform more ably than those who are less close to their peak age.

To get a sense of how these AFL performances might be predictive of major-league performance, what I did was to identify all the players from both groups who’d graduated to the majors. Of the 79 hitters from the Best group, 41* have recorded at least one major-league plate appearance. Of the 79 hitters from the Worst group, only 31 (i.e. 10 fewer) have recorded at least one major-league plate appearance.

*Or, 42 if you count Derek Norris, who qualified as one of the best hitters in two separate AFL season.

That the former group has graduated more of its constituents to the majors probably merits some investigation. Among the possible explanations, it would seem the most likely ones are either (a) the players from the Best group are actually better, (b) the players from the Best group, being older on average, were closer to the majors anyway, or (c) the groups are equally talented, and chance is the cause for the uneven distribution.

Whatever the explanation, it’s worth noting that a higher percentage of the Best group have recorded fewer than 100 major-league appearances. Using that figure (i.e. 100 PA) as the threshold for consideration, one is left with 28 hitters from the Best group and 23 hitters from the Worst group — i.e. a difference of only five, which is less significant.

The 100-plate-appearance threshold is also convenient, insofar as it represents a sample size at which metrics like walk and strikeout rate are beginning, at least, to reflect true talent. Below are the major-league figures thus far for both of the groups in question.

First, from the Best group:

Name Team PA HR% BB% K% BABIP AVG OBP SLG wRC+
Wil Myers Rays 373 3.5% 8.8% 24.4% .362 .293 .354 .478 131
Darin Ruf Phillies 330 5.2% 10.6% 31.2% .333 .257 .348 .489 130
Bryce Harper Nationals 1094 3.8% 10.7% 19.6% .308 .272 .353 .481 128
Jason Kipnis Indians 1480 2.6% 10.4% 19.3% .316 .270 .349 .424 117
Jedd Gyorko Padres 525 4.4% 6.3% 23.4% .287 .249 .301 .444 110
Anthony Rendon Nationals 394 1.8% 7.9% 17.5% .307 .265 .329 .396 100
Jerry Sands Dodgers 251 1.6% 10.4% 23.9% .316 .244 .325 .376 100
Robbie Grossman Astros 288 1.4% 8.0% 24.3% .353 .268 .332 .370 97
J.B. Shuck 570 0.4% 6.7% 10.7% .320 .290 .335 .359 96
Derek Norris Athletics 540 3.0% 10.7% 25.4% .282 .226 .315 .383 96
Didi Gregorius 425 1.6% 8.7% 16.5% .296 .255 .330 .369 90
Nick Franklin Mariners 412 2.9% 10.2% 27.4% .290 .225 .303 .382 90
Dustin Ackley Mariners 1471 1.5% 9.2% 18.7% .294 .245 .315 .354 89
Charlie Blackmon Rockies 481 1.9% 2.9% 15.4% .332 .291 .321 .416 88
Conor Gillaspie 500 2.8% 8.2% 16.4% .262 .241 .302 .381 83
Nolan Arenado Rockies 514 1.9% 4.5% 14.0% .296 .267 .301 .405 79
Marc Krauss Astros 146 2.7% 6.8% 30.8% .279 .209 .267 .366 74
Logan Schafer Brewers 367 1.1% 7.4% 17.4% .261 .219 .285 .336 70
Johnny Giavotella Royals 424 0.7% 4.5% 16.7% .284 .240 .278 .335 65
Tony Cruz Cardinals 332 0.6% 3.9% 17.2% .280 .236 .271 .331 64
Brock Holt 144 0.0% 7.6% 12.5% .282 .250 .302 .298 64
Aaron Hicks Twins 313 2.6% 7.7% 26.8% .241 .192 .259 .338 63
Eduardo Escobar 332 0.9% 6.6% 19.9% .280 .228 .280 .307 61
Chris Herrmann Twins 197 2.0% 9.6% 27.4% .248 .189 .268 .297 58
Ryan Lavarnway Red Sox 291 1.7% 5.8% 23.4% .256 .208 .258 .327 55
David Adams Yankees 152 1.3% 5.9% 28.3% .263 .193 .252 .286 45
Cord Phelps Indians 123 1.6% 7.3% 23.6% .195 .159 .221 .248 32
Josh Vitters Cubs 109 1.8% 6.4% 30.3% .154 .121 .193 .202 3
Average 449 2.0% 7.6% 21.5% .285 .236 .298 .364 81

And next, from the Worst group:

Name Team PA HR% BB% K% BABIP AVG OBP SLG wRC+
Mike Trout Angels 1490 4.2% 12.5% 20.5% .366 .314 .404 .544 163
Brandon Belt Giants 1252 2.6% 10.1% 23.0% .339 .273 .351 .447 125
Matt Adams Cardinals 410 4.6% 6.8% 25.4% .332 .275 .324 .476 124
Christian Yelich Marlins 273 1.5% 11.4% 24.2% .380 .288 .370 .396 116
Jean Segura 789 1.5% 4.8% 13.6% .321 .287 .326 .403 99
Corey Dickerson Rockies 213 2.3% 7.5% 19.2% .307 .263 .316 .459 98
Nick Franklin Mariners 412 2.9% 10.2% 27.4% .290 .225 .303 .382 90
Kirk Nieuwenhuis Mets 422 2.4% 8.8% 30.8% .329 .236 .305 .366 88
Brandon Crawford Giants 1246 1.3% 7.9% 17.8% .285 .241 .304 .346 83
Grant Green 153 0.7% 6.5% 28.8% .351 .250 .301 .343 83
Derrick Robinson Reds 216 0.0% 8.3% 20.4% .331 .255 .322 .323 81
Anthony Gose Blue Jays 342 0.9% 6.4% 28.1% .336 .240 .294 .361 77
Xavier Avery Orioles 107 0.9% 10.3% 21.5% .286 .223 .305 .340 76
Dave Sappelt 274 0.7% 6.2% 14.6% .292 .251 .301 .343 74
Devin Mesoraco Reds 589 2.7% 7.5% 17.7% .248 .225 .282 .359 70
Pete Kozma Cardinals 552 0.5% 8.2% 20.7% .292 .232 .293 .315 66
Charlie Culberson 127 1.6% 3.1% 23.6% .333 .264 .286 .355 62
Adeiny Hechavarria 715 0.7% 4.8% 17.9% .279 .232 .269 .311 57
Ryan Wheeler 161 0.6% 6.2% 19.9% .288 .233 .280 .320 56
Andy Parrino 229 0.4% 12.2% 27.9% .267 .186 .295 .242 51
Adam Moore 271 2.2% 3.7% 28.4% .259 .200 .237 .310 48
Austin Romine Yankees 168 0.6% 5.4% 25.0% .268 .201 .248 .279 41
Jake Marisnick Marlins 118 0.8% 5.1% 22.9% .232 .183 .231 .248 29
Average 458 1.6% 7.6% 22.6% .305 .242 .302 .359 81

What one notices — besides the fact that Mike Trout was somehow the worst at something one time* — is that the groups are more or less pretty similar in terms of major-league performance.

*His line in the 2011 edition of the AFL: 111 PA, 1 HR, 4.5% BB, 29.7% K, .245/.279/.321 (.347 BABIP).

For reference, here’s each group’s average major-league line, one next to the other:

Type PA HR% BB% K% AVG OBP SLG BABIP wRC+
Best 449 2.0% 7.6% 21.5% .236 .298 .364 .285 81
Worst 458 1.6% 7.6% 22.6% .242 .302 .359 .305 81

Whereas, previously, one noted rather large differences between the two groups so far as their AFL home-run and walk and strikeout rates were concerned, here — at the major-league level — those differences have disappeared almost entirely. The Best group has homered slightly more often and — for reasons that are probably interesting, but which I’ll ignore presently — have recorded a kinda much lower BABIP. Otherwise, the offensive output has been rather similar — and, in the case of park-adjusted hitting relative to league average, precisely the same (as denoted by the 81 wRC+).

Is the Arizona Fall League good for something? Almost certainly. Are the stats produced by the players there — even those stats which become reliable in smaller samples — predictive of future major-league performance? It’s possible, yes, but if that is the case, it’s difficult to detect by this methodology.




Print This Post



Carson Cistulli occasionally publishes spirited ejaculations at The New Enthusiast.


9 Responses to “The Predictive Power of AFL Batting Stats: A Partial Study”

You can follow any responses to this entry through the RSS 2.0 feed.
  1. Joey Gallo says:

    Only 6 home runs in the AFL? Ho hum.

    Vote -1 Vote +1

  2. Baltar says:

    I told you so, Carson, more than once.
    You have greatly increased my respect for you by ‘fessing up.

    Vote -1 Vote +1

  3. Brandon Firstname says:

    Great article Carson.

    It should be noted that the AFL is a very selective sample, however, since pretty much everyone sent to the AFL is expected to have the ability to one day make a major league roster. Nobody ends up in the AFL on the sole condition of being worse than their counterparts (at least when it comes to hitters), and so low numbers in the AFL are likely to be diversions from their actual ability.

    Vote -1 Vote +1

    • steve-o says:

      Good point. It would be interesting to see the success rate of non-AFL prospects vs. AFL prospects.

      Vote -1 Vote +1

      • Wil says:

        I though in the previous article they stated the success rate of AFL and non-AFL and found they were almost exactly the same? Something like 30% or so.

        Vote -1 Vote +1

  4. Pirates Hurdles says:

    Ooh, ooh now do the Winter Leagues! I’d love to stop hearing about player X killing it in the DWL as a fanboy argument. Give me some ammo.

    Vote -1 Vote +1

  5. ReuschelCakes says:

    interesting the only 2 reasonable counterpoints to your conclusion would be:

    1. that the “best” cohort logged +35% more PAs thus far (based on medians, which should be a better measure here). I’d reject this based on the circularity – i.e., the fact that they raked in the AFL has at least some bearing on promotion and vice versa.

    and more interestingly…

    2. that it is pretty clear that the “best” group is much better at hitting dingers. the average (+40bps) and median (1.8% versus 1.3% or +50bps) support this. I suspect this explains much of the difference between the median wRC+ (best group at 85.5 and worst at 77)

    Vote -1 Vote +1

  6. Brian Cartwright says:

    The moral of the story is – you can’t predict from just one data point in isolation, but it’s a piece of the puzzle that can be adjusted for context and added to all the other available pieces.

    Vote -1 Vote +1

  7. Wolf359 says:

    Once again, just averaging numbers leads to misleading conclusions.

    Just look at the names in both lists. Nick Franklin appears in both. In the good list, he’s 12th and in the bad, he’s 7th. Then you have the human outlier Mike trout making the bad list seem much better than it actually is. Finally, which group of players would you rather have on your favorite team?

    Myers, Harper, Kipnis, Rendon, Franklin, Gyorko, Ackley and Arenado
    Or
    Trout, Belt, Yelich, Segura & Franklin?

    Vote -1 Vote +1

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>