Minor League Hitter Projections: 8/17 Update

Rank	Player	xWAR
1	Wander Franco	3.7
2	Luis Robert	3.4
3	Dylan Carlson	3.3
4	Gavin Lux	3.3
5	Drew Waters	2.8
6	Trent Grisham	2.6
7	Jo Adell	2.5
8	Bo Bichette	2.4
9	Carter Kieboom	2.4
10	Daulton Varsho	2.4
11	Isaac Paredes	2.4
12	Gabriel Moreno	2.3
13	Cristian Pache	2.1
14	Alejandro Kirk	2.1
15	Jarred Kelenic	2.0
16	Kyle Tucker	1.9
17	Luis Campusano	1.9
18	Abraham Toro	1.8
19	Xavier Edwards	1.8
20	Lucius Fox	1.7
21	Vidal Brujan	1.7
22	Nick Madrigal	1.7
23	Andres Gimenez	1.6
24	Jeter Downs	1.6
25	Kevin Padlo	1.6
26	Ty France	1.6
27	Willi Castro	1.6
28	Nate Lowe	1.6
29	Alek Thomas	1.6
30	Ryan Vilade	1.5
31	Mickey Moniak	1.5
32	Geraldo Perdomo	1.5
33	Brendan Rodgers	1.5
34	Miguel Vargas	1.5
35	Tyler Freeman	1.5
36	Brennen Davis	1.4
37	Khalil Lee	1.4
38	Leody Taveras	1.4
39	Luis Garcia	1.4
40	Yusniel Diaz	1.4
41	Alex Kirilloff	1.3
42	Heliot Ramos	1.3
43	Keibert Ruiz	1.3
44	Brice Turang	1.3
45	Josh Lowe	1.3
46	Oneil Cruz	1.3
47	Josh Rojas	1.3
48	Nico Hoerner	1.3
49	Nolan Jones	1.3
50	Ke’Bryan Hayes	1.2
51	Cole Tucker	1.2
52	Brandon Marsh	1.2
53	Julio Rodriguez	1.2
54	Jazz Chisholm	1.2
55	Jose Devers	1.2
56	Isan Diaz	1.2
57	Luis Santana	1.2
58	Mason Martin	1.1
59	Randy Arozarena	1.1
60	Josh Stephen	1.1
61	Alec Bohm	1.1
62	Taylor Trammell	1.1
63	Nick Allen	1.1
64	Akil Baddoo	1.1
65	Yainer Diaz	1.0
66	Daniel Johnson	1.0
67	Malcom Nunez	1.0
68	Yonny Hernandez	1.0
69	Eguy Rosario	1.0
70	Lolo Sanchez	1.0
71	Nick Gordon	1.0
72	Daz Cameron	1.0
73	Seth Beer	1.0
74	Myles Straw	1.0
75	Taylor Walls	1.0
76	Ryan Mountcastle	1.0
77	Miguel Amaya	1.0
78	Simon Muzziotti	0.9
79	Royce Lewis	0.9
80	Luis Guillorme	0.9
81	Triston Casas	0.9
82	Elehuris Montero	0.9
83	Jake Cronenworth	0.9
84	Jarren Duran	0.9
85	Gabriel Arias	0.9
86	Domingo Leyba	0.9
87	Gabriel Maciel	0.9
88	Samad Taylor	0.9
89	Braden Shewmake	0.9
90	Sean Murphy	0.8
91	Ji-Hwan Bae	0.8
92	Jose Fermin	0.8
93	Jesus Sanchez	0.8
94	Yorman Rodriguez	0.8
95	Jason Martin	0.8
96	Andrew Velazquez	0.8
97	Luis Barrera	0.8
98	Canaan Smith	0.8
99	Adam Haseley	0.8
100	Jeisson Rosario	0.8

 

One of last week’s blurb highlights, Josh Rojas (No. 47), has gotten the call to the majors. It’s been a good week for the Diamondbacks’ farm system, with Daulton Varsho (No. 10) also vaulting into the top 10. There’s plenty of other movements in the rankings, especially towards the bottom of the list where the projections are a virtual tie that is often broken by recent performance. The methodology of the system for projecting peak WAR can be found here, and more detailed projections with percentile outcomes beyond the top 100 can be found in the link here.

This week’s update will be a little different in that I’d like to discuss the historical accuracy of these projections. Some healthy skepticism is absolutely warranted in condensing the future performance of minor league players to one number, so these results should give a better idea of how to treat the projections. I took a four-year test sample of years from 2008-2011 as the most recent timespan where the early careers of prospects have more or less played out. Output are the results of the correlations between xWAR and actual average 3-year peak WAR for every hitter projected, just the top 100 projected hitters, and just the top 50 respectively.

 

Year	Total r2	Top 100 r2	Top 50 r2
2008	.2432	.2329	.4057
2009	.2085	.1316	.2134
2010	.2326	.2469	.1658
2011	.2616	.1979	.1726

 

The high correlation for top-50 prospects in 2008 is likely an outlier, but the model is fairly stable any way the groups are sliced—with some leaning towards higher accuracy in separating higher-level prospects from non-prospects. These numbers aren’t perfect, but they indicate some value can be found in a field with high variance. And of course, a balanced approach that considers the scouting side will likely yield better results than just the numbers alone.

An additional measure of fit to consider is the RMSE (Root Mean Square Error). This tells us the spread of residual error from the projections. The table below shows the results for the same years as above with comparison to the standard deviation from each sample as a minimum baseline that the model should achieve to provide value.

 

Year	Total RMSE	Total STDEV	Top 100 RMSE	Top 100 STDEV	Top 50 RMSE	Top 50 STDEV
2008	.6360	.7309	1.5629	1.7415	1.7522	1.9963
2009	.7458	.8378	1.7505	1.8717	1.7635	1.9317
2010	.7550	.8593	1.7381	1.9520	2.2068	2.3770
2011	.7337	.8493	1.7972	1.9734	2.2320	2.4048

 

The RMSE beats the standard deviation for each category, implying that the model achieves at a minimum less error than using the mean in each sample. These results more distinctly show that error increases in separating higher-level prospects from each other and that players in the same tier should be treated as roughly the same.

Hopefully, this update is illuminating to the strengths and weaknesses of these projections and how to best use them. Next week we’ll be back to celebrating the unheralded Gabriel Morenos and Kevin Padlos of the minor league baseball world.

(Photo by Mark LoMoglio/Icon Sportswire)