A potentially useful projection system

The best hitter in the league is going to have a .423 wOBA according to Steamer. - Jesse Johnson-US PRESSWIRE

Projection systems are often (rightfully) conservative when projecting individual player seasons. What if we focused on the distribution?

The projection systems we use in baseball are notoriously conservative. That's the nature of the business. You can't project anyone to have a Barry Bonds season because those seasons are incredible rare. A good system will miss on most players, but will miss evenly. Some players will beat projections, some will fall short, and very few will miss dramatically in either direction. But the results we see are the results of a process that tries to predict individual players rather than the performance of the league, which has always left me with a burning question. What if we just tried to predict the qualifying seasons without worrying about which player was attached to which projection?

Don't get me wrong, this is not going to be better than ZiPS or Steamer; it's going to be different because it has a completely different goal and it's entirely experimental. The goal here is to project the 140 qualifying seasons for 2014 without being concerned about whether or not the projection makes sense for the player in question.

Steamer's 2014 projection has just one player with a .400 wOBA or better, but there were eight players with a wOBA that high in 2013, five in 2012, and ten in 2011. Steamer is trying to predict each player as best as it can, so the conservatism makes sense. Instead of trying to get the individual projections right, I'm going to shoot for getting the distribution right and will simply attach a player to the projection based on where they fall in the Steamer projection. Things should be clear once we get started.

To develop the system, we'll take the 2011-2013 seasons and will rank the qualifying players 1-140 by their wOBA. To make sure the numbers match, we'll drop the qualifiers in 2011 and 2012 below the 140 cutoff with the fewest number of plate appearances. Next, we'll average wOBA across the ranking, so the ninth best wOBA in each season will be added together and divided by three to get the 2014 projection for ninth in wOBA. We'll then grab the player who is ranked ninth in Steamer wOBA and compare the two.

Before we take on the players, let's check out the wOBA distribution for 2011-2013 and then the 2014 Steamer wOBA distribution for the top 140 players by plate appearances.

Projected

Steamer

You'll notice over the last few seasons, the distribution has been wider than Steamer projects. So we're going to rank the players based on their Steamer projection and fit them to the distribution from the last three seasons.

Rank Name SteamerwOBA ProjwOBA
1 Miguel Cabrera 0.423 0.438
2 Mike Trout 0.399 0.424
3 Joey Votto 0.397 0.419
4 Giancarlo Stanton 0.392 0.408
5 Prince Fielder 0.389 0.405
6 Troy Tulowitzki 0.388 0.402
7 Paul Goldschmidt 0.384 0.401
8 Jose Bautista 0.383 0.400
9 Jose Abreu 0.383 0.397
10 Andrew McCutchen 0.382 0.393
11 Carlos Gonzalez 0.380 0.391
12 Ryan Braun 0.376 0.387
13 David Ortiz 0.373 0.385
14 Edwin Encarnacion 0.372 0.384
15 Shin-Soo Choo 0.371 0.383
16 Chris Davis 0.370 0.381
17 Matt Holliday 0.370 0.381
18 Freddie Freeman 0.370 0.380
19 Buster Posey 0.368 0.379
20 Bryce Harper 0.366 0.376
21 Yasiel Puig 0.365 0.375
22 David Wright 0.364 0.374
23 Albert Pujols 0.363 0.373
24 Adrian Beltre 0.362 0.372
25 Jason Heyward 0.362 0.370
26 Joe Mauer 0.361 0.370
27 Billy Butler 0.361 0.368
28 Adrian Gonzalez 0.360 0.368
29 Anthony Rizzo 0.360 0.367
30 Evan Longoria 0.360 0.365
31 Justin Upton 0.360 0.365
32 Robinson Cano 0.359 0.364
33 Carlos Beltran 0.357 0.363
34 Allen Craig 0.356 0.362
35 Michael Cuddyer 0.355 0.361
36 Ryan Zimmerman 0.353 0.360
37 Wilin Rosario 0.353 0.359
38 Pablo Sandoval 0.351 0.358
39 Carlos Santana 0.350 0.358
40 Brandon Belt 0.350 0.356
41 Eric Hosmer 0.348 0.355
42 Matt Carpenter 0.348 0.354
43 Brian McCann 0.348 0.353
44 Hanley Ramirez 0.347 0.352
45 Jay Bruce 0.347 0.352
46 Josh Donaldson 0.346 0.351
47 Dustin Pedroia 0.346 0.350
48 Mark Teixeira 0.346 0.350
49 Kendrys Morales 0.345 0.350
50 Matt Kemp 0.345 0.349
51 Alex Gordon 0.343 0.349
52 Adam Jones 0.342 0.348
53 Adam Lind 0.341 0.347
54 Domonic Brown 0.341 0.347
55 Ben Zobrist 0.340 0.346
56 Mark Trumbo 0.340 0.346
57 Nick Swisher 0.339 0.345
58 Austin Jackson 0.338 0.345
59 Corey Hart 0.338 0.345
60 Brett Lawrie 0.338 0.344
61 Norichika Aoki 0.337 0.344
62 Torii Hunter 0.337 0.344
63 Josh Willingham 0.337 0.342
64 Victor Martinez 0.337 0.342
65 Yonder Alonso 0.337 0.341
66 Martin Prado 0.336 0.340
67 Kyle Seager 0.336 0.338
68 Yoenis Cespedes 0.336 0.338
69 Matt Adams 0.336 0.338
70 Nick Markakis 0.335 0.337
71 Hunter Pence 0.334 0.336
72 Jose Reyes 0.334 0.336
73 Kole Calhoun 0.334 0.335
74 Pedro Alvarez 0.334 0.335
75 Ian Kinsler 0.333 0.334
76 Aaron Hill 0.333 0.333
77 Wil Myers 0.333 0.333
78 Melky Cabrera 0.333 0.333
79 Nolan Arenado 0.332 0.332
80 Jacoby Ellsbury 0.331 0.331
81 Jason Kipnis 0.331 0.331
82 Justin Ruggiano 0.331 0.330
83 Brandon Moss 0.331 0.329
84 Brad Miller 0.330 0.328
85 Chase Headley 0.330 0.327
86 Cody Ross 0.330 0.326
87 Chase Utley 0.330 0.326
88 Jedd Gyorko 0.329 0.326
89 Josh Hamilton 0.329 0.325
90 Mitch Moreland 0.328 0.324
91 Adam Eaton 0.328 0.324
92 Ian Desmond 0.326 0.323
93 Gaby Sanchez 0.326 0.323
94 Anthony Rendon 0.326 0.323
95 Coco Crisp 0.325 0.322
96 Miguel Montero 0.325 0.321
97 Justin Smoak 0.325 0.321
98 Xander Bogaerts 0.325 0.321
99 Alex Rios 0.323 0.320
100 Jose Tabata 0.323 0.320
101 Angel Pagan 0.322 0.320
102 Carl Crawford 0.322 0.319
103 Carlos Gomez 0.321 0.319
104 Gerardo Parra 0.321 0.318
105 Dexter Fowler 0.320 0.317
106 Christian Yelich 0.318 0.316
107 Michael Morse 0.318 0.314
108 Howie Kendrick 0.318 0.312
109 Brett Gardner 0.317 0.311
110 Jason Castro 0.317 0.310
111 Desmond Jennings 0.316 0.309
112 Starlin Castro 0.316 0.309
113 Jean Segura 0.316 0.308
114 Dustin Ackley 0.316 0.307
115 Brandon Phillips 0.315 0.307
116 Denard Span 0.315 0.306
117 Andrelton Simmons 0.314 0.305
118 Elvis Andrus 0.313 0.304
119 Daniel Murphy 0.313 0.303
120 Jose Altuve 0.312 0.302
121 J.J. Hardy 0.312 0.302
122 Jurickson Profar 0.312 0.301
123 Curtis Granderson 0.312 0.301
124 Alfonso Soriano 0.309 0.299
125 Matt Dominguez 0.309 0.298
126 Marco Scutaro 0.308 0.298
127 Stephen Drew 0.305 0.296
128 Michael Bourn 0.304 0.296
129 Erick Aybar 0.304 0.293
130 Robbie Grossman 0.302 0.292
131 Everth Cabrera 0.301 0.290
132 Marcell Ozuna 0.301 0.289
133 Alexei Ramirez 0.297 0.286
134 Tyler Colvin 0.296 0.285
135 Jimmy Rollins 0.295 0.284
136 Zack Cozart 0.294 0.282
137 Brandon Crawford 0.292 0.281
138 Jose Iglesias 0.288 0.269
139 Alcides Escobar 0.284 0.267
140 Billy Hamilton 0.283 0.262

We're letting Steamer do the legwork as far as deciding how players are going to perform relative to one another, but we're adjusting the distribution, meaning that good players are projected to do better and below average players are projected to do worse.

This post doesn't come with a magic takeaway point. It's just an adjustment to projection system to see how it works. We won't know if this is useful until the end of the season, but the basic idea is one about which I've been thinking during projection season. The systems try to get the players right relative to their actual performance. This system takes Steamer's rankings relative to each other and imposes them onto an expected distribution. Can we do better if we break the work of projecting into two separate stages; rankings and distribution?

So bookmark this page and come back at the end of the year. If it goes well, we'll do it again. If it doesn't, the comments section are yours to remind me.

. . .

All statistics courtesy of FanGraphs.

Neil Weinberg is the Associate Managing Editor at Beyond The Box Score, contributor to Gammons Daily, and can also be found writing enthusiastically about the Detroit Tigers at New English D. You can follow and interact with him on Twitter at @NeilWeinberg44.

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