If you study statistics for any length of time, eventually you’ll run across the oft-repeated aphorism of noted statistician George Box. While similar sentiments pre-dated Box, he succinctly noted in a 1978 paper…

All models are wrong but some are useful.

That statement is ingrained in statisticians worldwide. However, the saying is just the section title to Box’s larger point. Box would go on to elaborate:

Now it would be very remarkable if any system existing in the real world could be exactly represented by any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful approximations. For example, the law PV=RT relating pressure P, volume V and temperature T of an “ideal” gas via a constant R is not exactly true for any real gas, but it frequently provides a useful approximation and furthermore its structure is informative since it springs from a physical view of the behavior of gas molecules. For such a model there is no need to ask the question “Is the model true?”. If “truth” is to be the “whole truth” the answer must be “No”. The only question of interest is “Is the model illuminating and useful?”

So what does this mean? For me, it means that models need to always be evaluated, always need to be changed to reflect better data and new insights. The Rule 4 Amateur Draft is a very complicated, occasionally bewildering problem. With this in mind, I always hope that my model is at minimum interesting and at most, yes, illuminating and useful.

So what changed between the start of June and now? Well, yes, Baseball America updated their rankings to include 500 players. The College World Series has nearly been completed, with Vanderbilt and Mississippi State beginning the best-of-three final tonight. But from a DRAFT Model perspective, there are a few key changes:

  • The college statistics models for strikeouts, walks, and isolated power were updated and rerun.
  • The handling of plate appearances and innings pitched for collegiate players, essentially how we provide some regression to the mean, was adjusted.
  • The effect of certain biographical details, particularly BMI and height, was a little more constrained.
  • The effect of external rankings was changed.
  • The model looking at the level a player reached in years 1 through 3 was reworked.
  • The effect of collegiate statistics for lower-ranked players was overhauled.
  • The model for the variability of players was overhauled.
  • The pitcher usage modeled was completely overhauled.

Needless to say, it’s a lot of changes. I’m not going to comment on a player’s score changes too much because the models have undergone so much change. But that said, here are a few notes on what’s happened recently over the past month.

  • Its finally happened: Jack Leiter’s score reflects where we thought he should be. He’s finally pitched enough innings to have confidence in his stats, and even though he’s fallen in Baseball America’s rankings, he’s now #2 overall with a 33.5 DRAFT Score.
  • Prep shortstops are one of the best demographics, and the rankings both by Baseball America and the model. Marcelo Mayer, Jordan Lawlar, Khalil Watson, and Brady House are four of the top six players according to DRAFT Score.
  • Jackson Jobe is getting top-5 buzz, but his score is dragged down by the uncertainty of prep pitchers. He’s ranked 17th overall, with a score of 11.99.
  • Lots of college performer pitchers getting a boost in the latest rankings: Will Bednar (38th in BA, 12th overall DRAFT Score of 17.53), Steven Hajjar (61st in BA, 15th overall DRAFT Score of 14.45), and Dylan Smith (56th in BA, 19th overall DRAFT Score of 10.82).

With all that in mind, here’s the latest top-100 scores from the DRAFT Model. As always, you can see the full rankings (Right now up to nearly 500 players) at my blog Sabermetric Sandlot. In the next few days I’ll finally be adding player pages where you can look at the individual players in the draft and more detailed components of their scores.

1Marcelo MayerHitter34.4218.480.905
2Jack LeiterPitcher33.514.020.989
3Jordan LawlarHitter32.3516.90.899
4Kahlil WatsonHitter28.0114.010.881
5Kumar RockerPitcher26.9710.690.965
6Brady HouseHitter24.1212.180.877
7Jordan WicksPitcher23.919.170.944
8Michael McGreevyPitcher23.89.320.952
9Henry DavisHitter20.387.650.943
10Gunnar HoglundPitcher19.496.870.912
11Sam BachmanPitcher18.476.60.931
12Will BednarPitcher17.536.870.927
13Colton CowserHitter16.726.80.882
14Ty MaddenPitcher15.275.260.893
15Steven HajjarPitcher14.454.490.84
16Harry FordHitter13.385.950.77
17Jackson JobePitcher11.994.10.775
18Andrew PainterPitcher11.472.730.734
19Dylan SmithPitcher10.824.240.814
20Joe MackHitter10.644.910.723
21Matt McLainHitter9.944.120.803
22Bubba ChandlerPitcher9.083.060.683
23Jonathan CannonPitcher8.763.260.686
24Will TaylorHitter8.7350.683
25Lonnie White Jr.Hitter8.645.480.733
26Ky BushPitcher8.553.350.79
27Sal FrelickHitter8.533.220.812
27Chase PettyPitcher8.532.380.624
29Chad DallasPitcher8.453.270.645
30Peyton StovallHitter8.44.670.642
31Benny MontgomeryHitter8.334.710.692
32Carson WilliamsHitter8.074.750.614
33Joshua BaezHitter7.885.520.68
34Anthony SolometoPitcher7.772.460.62
35Ryan CusickPitcher7.593.090.725
36Joshua HartlePitcher7.522.380.581
37Izaac PachecoHitter7.244.380.607
38Frank MozzicatoPitcher7.192.170.552
39Chase SilsethPitcher6.832.930.606
40Joe RockPitcher6.83.070.665
41Jay AllenHitter6.614.450.592
42Jaden HillPitcher6.52.840.702
43Matt MikulskiPitcher6.492.920.793
44Sean BurkePitcher6.462.660.714
45Adrian Del CastilloHitter6.192.330.799
46Ben KudrnaPitcher5.882.40.504
47Thatcher HurdPitcher5.742.310.516
48Edwin ArroyoHitter5.634.740.445
49Chase BurnsPitcher5.582.360.493
50Ethan WilsonHitter5.573.010.769
51James WoodHitter5.324.250.6
52Gage JumpPitcher5.222.320.46
53Christian MacLeodPitcher5.182.830.569
54James TriantosHitter5.024.030.461
55Landon MarceauxPitcher4.762.860.53
56Troy MeltonPitcher4.692.160.478
57Davis DiazHitter4.654.090.417
58Gavin WilliamsPitcher4.612.570.829
59Maxwell MuncyHitter4.563.490.463
59Carter JensenHitter4.564.110.385
61Cooper KinneyHitter4.374.230.405
62Robert GasserPitcher4.362.530.656
63Wes KathHitter4.253.560.446
64Trey SweeneyHitter4.242.430.48
65Maddux BrunsPitcher4.172.570.478
66Colson MontgomeryHitter4.072.930.477
67Sean SullivanPitcher4.012.750.51
68CJ RodriguezHitter3.932.420.416
69Braylon BishopHitter3.874.670.35
70Tyler BlackHitter3.771.750.557
71Cameron CauleyHitter3.764.040.344
72Tyler WhitakerHitter3.753.190.412
72Daylen LileHitter3.754.040.43
74Noah MillerHitter3.713.620.369
75Braden OlthoffPitcher3.662.470.506
76Doug NikhazyPitcher3.622.720.536
77Jud FabianHitter3.481.710.492
78Braden MontgomeryHitter3.454.540.358
79Malakhi KnightHitter3.443.850.361
79Jackson MerrillHitter3.444.270.305
81Mike VasilPitcher3.432.990.38
82Gordon GraceffoPitcher3.42.870.452
83Mason BlackPitcher3.32.660.488
84Alex MooneyHitter3.2830.376
85Andrew HoffmannPitcher3.232.380.498
86Grant HolmanPitcher3.072.470.398
87Nick McLainHitter3.043.990.325
88Tommy MacePitcher2.972.470.5
88James Peyton SmithPitcher2.972.650.3
90Brandon NeelyPitcher2.962.50.298
91Pierce CoppolaPitcher2.952.460.351
92Brendan BeckPitcher2.92.550.487
93Cody SchrierHitter2.894.020.267
94Cade PovichPitcher2.872.560.47
95Connor NorbyHitter2.841.040.637
96Eric HammondPitcher2.832.360.334
97Lorenzo CarrierHitter2.824.640.285
98Jacob SteinmetzPitcher2.812.570.29
99Spencer SchwellenbachPitcher2.742.130.658
99Hagen SmithPitcher2.742.860.298
Stephen Loftus
Stephen Loftus

Orioles Analyst

Dr. Stephen Loftus received his Ph.D. in Statistics from Virginia Tech in 2015 and is an Assistant Professor of Mathematical Sciences at Randolph-Macon College. Prior to that, he worked as an Analyst in Baseball Research and Development for the Tampa Bay Rays, focusing on the Amateur Draft. He formerly wrote at FanGraphs and Beyond the Box Score. As a lifelong fan of the Orioles, he fondly remembers the playoff teams of 1996-97 and prefers to forget constantly impending doom of Jorge Julio, Albert Belle’s contract, and most years between 1998 and 2011.