RoboScout’s Top 100 Fantasy Baseball Prospects For 2025

What Is RoboScout?

The premise of projections boils down to one simple truism: Past performance does correlate to future performance. By looking at the average paired-year-over-year performances of hitters and pitchers historically, weighting by sample size and adjusting for survivor bias, one can generate expected age curves with reasonable accuracy for various statistics, such as walk rate, strikeout rate, ground ball rate or home runs per plate appearance and OPS. Given a hitter’s OPS, walk percentage and strikeout percentage, one can reasonably infer what their batting average is, and so forth. 

We can apply this same approach to the minor leagues. Taking historical paired-”level-to-level” performances of hitters and pitchers, one can estimate what a pitcher’s strikeout rate would be in Double-A given that he had, say, a 12% strikeout percentage in High-A. By understanding the expected equivalent performance at a higher level—including major league equivalence—we can thus generate an “expected” major league performance based on a minor leaguer’s performance (after additionally adjusting the statistical performance to the league’s run environment and park factors). Now add in the “age curve” calculations from the previous paragraph to this expected major league projection, and you can estimate what the hitter’s projection “should” be in his prime performance years.

Depending on how deep you wanted to go—for example, incorporating platoon splits, quality-of-competition or deriving independent age curves for different “clusters” of similar hitter archetypes—more granular adjustments can be made.

The final piece to the recipe is minor league Statcast data such as barrel rate, exit velocity, contact percentage and other metrics that are shown to be correlated to future wRC+ in order to supplement the projections. For the pitchers, RoboScout folds in the pitch-level metrics (movement, velocity, etc.) that are inputs into traditional Stuff+ models.

How To Use RoboScout To Your Advantage

To make RoboScout as simple as possible, it is solely results-based. It does not know if a hitter is an unathletic designated hitter or an 80-grade defender in center field. Nor does it know if the player is blocked at the position on his parent club by an all-star major leaguer. RoboScout merely takes the player’s performance and sorts them—with separate rankings at each minor league level—based on their (1) projected major league performance, (2) projected peak major league performance and (3) expected long-term fantasy value (generalized because of the various league formats). 

Although it is true that a player’s “true talent” is more accurately inferred by their career performance than from their most recent three months, RoboScout was specifically designed to quickly estimate future performance and only uses current season data. Think of it as a tool for high-speed “change detection” of a prospect’s value in order to grab prospects off the wire or to trade for them in dynasty leagues before industry outlets publish their updated lists (or podcasts discuss them). 

In order to do so, it is a necessary fact that we will need to make decisions with imperfect information. If you waited until the offseason to pick up or trade for Jesus Made when industry outlets began to understand how his performance measured up to previous DSL hitters, you might have missed the opportunity. Those reading the RoboScout weekly articles, however, would have already picked him up. On June 23 last year, we wrote this:

The in-season, weekly articles attempt to highlight some of the interesting prospect names—based on seasonal performance or based on recent movement in the rankings—with a deeper dive to help frame the performance into a dynasty league context that are beyond the mere “output” of RoboScout.

What the weekly articles do not do though—by design—is aggregate all of the players into one “master” list. For example, if Franklin Arias (Red Sox) is a top Complex League hitter per RoboScout, is he a better target than Kumar Rocker (Rangers) who is a top name on the Double-A pitching list? The main reason why I keep each level separated is that if one were to project fantasy value in the major leagues for the next three years, few of the DSL hitters would even get a single plate appearance in the major leagues. But of course, they have value in a dynasty league. So, the disparate lists allow you to sculpt the information to suit your team’s needs, including, for example, how one wants to allocate minor league roster slots amongst hitters and pitchers. This type of flexibility might be lost by having a single list of “Top 100 Prospects per RoboScout”.

That being said, however, I created exactly that list at the beginning of July to attempt to contextualize the relative values of the prospects and then updated it every month or so. I’ve taken the estimated likelihood of making it to the majors, projected performance at peak and fantasy value projected to be earned at peak leagues (generically to accommodate different league formats) and then ranked the hitters and pitchers accordingly (while still also not considering defense or the depth chart of the parent club to minimize human bias and my personal intervention).

A Few Notes On The List

From all of the above, the following RoboScout Top 100 list has been created from the full-season performance. As a reminder, because it’s based on 2024 performance, some names—for example, those who were just drafted in 2024 but did not debut, such as Chase Burns or Hagen Smith—will be missing. I also naturally removed any prospects who are no longer prospect eligible. Therefore, James Wood (Nationals), Paul Skenes (Pirates) and Jackson Holliday (Orioles) have been removed even though they would be the top three names, respectively, from their minor league performance in 2024.

Of course, this list will be controversial and is not meant to be the end of the conversation or be the primary input for one’s waiver wire claims. For example, you will see that Christian Scott (Mets), Luis Perales (Red Sox) and Owen Murphy (Braves) appear on this list because RoboScout does not know that they are currently recovering from Tommy John surgery and is only looking at their 2024 performance and projecting out. In other words, please apply the appropriate mental adjustments to the output knowing that these are just the “agnostic” results of RoboScout. As always, use this information as an input into your process to help inform your decisions.

Another thing to note is that the hitters’ home runs and stolen bases are scaled to 600 plate appearances. For catchers, this is likely too high, as catchers typically only get to around 450 plate appearances. In other words, Adrian Del Castillo (Diamondbacks) probably won’t hit 26 home runs in his peak years, because he possibly won’t get the opportunity as the backup to Gabriel Moreno. However, some hitters like Moises Ballesteros (Cubs) or Agustin Ramirez (Marlins), although currently catchers, may get to 600 plate appearances as their MLB teams may play them at alternate positions to get their bats in the lineup.

You might also notice is that there is no Jesus Made on the list. In fact, there are no players from the Dominican Summer League at all in the Top 100. This is purely an artifact that it is historically unlikely that a 17-year-old in the DSL makes it to the major leagues, let alone reaches his peak projection. If this list had human intervention, I think we would push Made higher as his underlying statcast data is special.

Despite this being purely data-driven from hitting (and pitching) performance, there is still naturally a lot of overlap with our curated lists that had the benefit of human intervention and scouting input. Roman Anthony (Red Sox) is at the top of the list and Noah Schultz (White Sox) and Bubba Chandler (Pirates) are all in the top 10. Sprinkled throughout the list, however, are a few “interesting” (read: “odd”) names. Some are hitters who possess an outlier ability to make contact, such as Javier Sanoja (Marlins) who RoboScout thinks is capable of hitting .280 with an above average wRC+ and chipping double digit home runs and stolen bases, and some are burly sluggers such as Johnathan Rodriguez (Guardians) and Eric Bitonti (Brewers), whom RoboScout believes can hit 25 to 30 home runs in the major leagues.

Last year, some of the odd names that were on the list—Xavier Edwards at No. 3, for example, or Wilyer Abreu (68) or Trey Sweeney (77)—don’t seem so odd in retrospect. But of course, Samuel Zavala (16), Justice Bigbie (80), Carlos Jorge (94) definitely qualify as misses.

With all that being said, here is the full list after 2024, with estimated peak projections listed:

RoboScout Top 100 Fantasy Prospects

rank	player	org	age	lvl	pitcher?	Peak k%/BA	Peak bb%/OBP	peak GB%/wRC+	peak ERA/HR	Peak whip/SB	Peak nstf+/HIt+
1	Roman Anthony	BOS	20	AAA		0.278	0.359	116	25	11	117
2	Michael Arroyo	SEA	19	A+		0.264	0.339	114	28	7	111
3	Leodalis De Vries	SDP	17	A		0.271	0.350	115	30	9	104
4	Noah Schultz	CHW	20	AA	P	24%	6%	53%	3.42	1.15	117
5	Coby Mayo	BAL	22	AAA		0.256	0.331	117	28	4	110
6	Colt Emerson	SEA	18	A+		0.272	0.349	110	18	11	118
7	Christian Scott	NYM	25	AAA	P	27%	7%	47%	3.59	1.12	115
8	Bubba Chandler	PIT	21	AAA	P	25%	6%	46%	3.66	1.14	117
9	Sebastian Walcott	TEX	18	AA		0.273	0.351	126	20	13	100
10	Adrian Del Castillo	ARI	24	AAA		0.263	0.335	114	26	1	115
11	Emmanuel Rodriguez	MIN	21	AAA		0.254	0.354	115	24	12	112
12	Zebby Matthews	MIN	24	AAA	P	22%	5%	47%	3.66	1.13	111
13	Kevin McGonigle	DET	19	A+		0.284	0.356	114	16	14	126
14	Alejandro Rosario	TEX	22	A+	P	23%	6%	49%	3.48	1.18	111
15	Cole Young	SEA	20	AA		0.277	0.353	116	17	12	111
16	Bryce Eldridge	SFG	19	AAA		0.268	0.344	112	25	4	107
17	Luke Keaschall	MIN	21	AA		0.268	0.344	114	19	12	120
18	Jarlin Susana	WSN	20	A+	P	25%	8%	57%	3.50	1.23	113
19	Cam Collier	CIN	19	A+		0.257	0.334	107	26	2	110
20	Walker Jenkins	MIN	19	AA		0.276	0.353	118	17	10	114
21	Lazaro Montes	SEA	19	A+		0.260	0.339	112	29	2	104
22	Ryan Clifford	NYM	20	AA		0.252	0.341	113	26	3	106
23	Tink Hence	STL	21	AA	P	25%	7%	46%	3.57	1.18	105
24	Samuel Basallo	BAL	19	AAA		0.283	0.353	113	25	5	102
25	Franklin Arias	BOS	18	A		0.273	0.346	116	22	18	115
26	Jacob Wilson	OAK	22	AAA		0.284	0.344	112	18	4	113
27	Josue De Paula	LAD	19	A+		0.262	0.344	105	18	12	119
28	Moises Chace	PHI	21	AA	P	26%	9%	45%	3.91	1.23	122
29	Kumar Rocker	TEX	24	AA	P	25%	7%	47%	3.71	1.19	107
30	Johnathan Rodriguez	CLE	24	AAA		0.246	0.324	112	26	6	110
31	Jasson Dominguez	NYY	21	AAA		0.263	0.331	106	22	21	110
32	Eric Bitonti	MIL	18	A		0.258	0.339	117	29	6	112
33	Moises Ballesteros	CHC	20	AAA		0.272	0.341	110	24	1	110
34	Kristian Campbell	BOS	22	AAA		0.266	0.343	110	21	12	117
35	Chase Meidroth	BOS	22	AAA		0.273	0.358	112	11	8	118
36	Shay Whitcomb	HOU	25	AAA		0.251	0.322	109	27	18	104
37	Matt Wilkinson	CLE	21	A+	P	26%	8%	44%	3.65	1.17	104
38	Luke Adams	MIL	20	A+		0.258	0.343	105	20	16	115
39	Agustin Ramirez	MIA	22	AAA		0.261	0.333	104	26	12	112
40	Luis Perales	BOS	21	A+	P	24%	8%	47%	3.83	1.25	121
41	Jack Leiter	TEX	24	AAA	P	26%	7%	44%	3.90	1.17	114
42	Will Warren	NYY	25	AAA	P	22%	6%	49%	3.76	1.20	113
43	Hao-Yu Lee	DET	21	AA		0.268	0.337	114	21	12	108
44	Deyvison De Los Santos	ARI	21	AA		0.266	0.336	121	29	3	97
45	Carter Jensen	KCR	20	AA		0.260	0.338	113	22	8	112
46	Javier Sanoja	MIA	21	AAA		0.287	0.352	111	13	10	111
47	Dillon Dingler	DET	25	AAA		0.248	0.317	106	26	6	114
48	Eduardo Tait	PHI	17	A		0.272	0.337	115	27	4	105
49	Arjun Nimmala	TOR	18	A		0.247	0.318	106	32	6	106
50	Orelvis Martinez	TOR	22	AAA		0.251	0.321	108	27	1	101
51	Heston Kjerstad	BAL	25	AAA		0.242	0.318	110	25	4	112
52	AJ Smith-Shawver	ATL	21	AAA	P	24%	6%	37%	4.00	1.17	114
53	Aidan Smith	SEA	19	A		0.261	0.338	112	26	20	108
54	Carson Williams	TBR	21	AA		0.250	0.328	114	24	18	101
55	Robert Calaz	COL	18	A		0.270	0.347	126	30	10	107
56	Nacho Alvarez Jr.	ATL	21	AAA		0.266	0.342	105	17	15	111
57	K.C. Hunt	MIL	23	AA	P	24%	7%	44%	3.80	1.21	97
58	Sal Stewart	CIN	20	A+		0.264	0.340	103	17	7	120
59	Chase Dollander	COL	22	AA	P	24%	8%	41%	4.12	1.24	117
60	Matthew Lugo	LAA	23	AAA		0.252	0.321	102	25	14	114
61	Xavier Isaac	TBR	20	AA		0.248	0.331	118	25	9	99
62	Braxton Ashcraft	PIT	24	AA	P	21%	6%	45%	3.93	1.23	106
63	Travis Sykora	WSN	20	A	P	26%	7%	44%	3.51	1.17	105
64	Yilber Diaz	ARI	23	AAA	P	26%	8%	44%	3.91	1.19	101
65	Alejandro Osuna	TEX	21	AA		0.256	0.325	111	23	10	110
66	Logan Henderson	MIL	22	AAA	P	24%	5%	36%	3.89	1.11	118
67	Juan Brito	CLE	22	AAA		0.263	0.339	107	19	7	107
68	Chandler Champlain	KCR	24	AA	P	24%	7%	43%	3.83	1.20	98
69	Dalton Rushing	LAD	23	AAA		0.261	0.334	104	27	2	119
70	Payton Eeles	MIN	24	AAA		0.258	0.330	104	15	25	113
71	Edgar Quero	CHW	21	AAA		0.270	0.338	104	25	1	109
72	Max Clark	DET	19	A+		0.262	0.334	105	18	13	112
73	Chad Patrick	MIL	25	AAA	P	22%	6%	39%	4.15	1.18	107
74	Joey Loperfido	HOU	25	AAA		0.232	0.305	102	29	15	106
75	Quinn Priester	PIT	23	AAA	P	22%	5%	52%	3.56	1.18	100
76	Jaison Chourio	CLE	19	A		0.272	0.355	110	14	22	115
77	Cade Povich	BAL	24	AAA	P	23%	6%	44%	3.93	1.19	102
78	Thomas Harrington	PIT	22	AAA	P	20%	4%	40%	4.10	1.17	107
79	Zyhir Hope	LAD	19	A		0.255	0.332	106	24	7	121
80	Brandon Sproat	NYM	23	AA	P	22%	8%	52%	3.85	1.28	110
81	Jesus Baez	NYM	19	A+		0.264	0.329	103	25	6	112
82	Kohl Drake	TEX	23	A+	P	24%	7%	43%	3.86	1.22	103
83	Alex Freeland	LAD	22	AA		0.256	0.335	107	17	17	120
84	Chayce McDermott	BAL	25	AAA	P	27%	9%	41%	3.98	1.21	110
85	Matt Shaw	CHC	22	AA		0.264	0.338	111	21	18	109
86	Max Acosta	TEX	21	AA		0.270	0.335	106	16	17	117
87	Marcelo Mayer	BOS	21	AA		0.263	0.333	111	17	11	107
88	Jimmy Crooks	STL	22	AA		0.260	0.334	113	17	4	113
89	William Bergolla	PHI	19	A+		0.276	0.338	100	11	18	105
90	Ben Shields	NYY	25	AA	P	22%	8%	49%	3.95	1.28	105
91	Welbyn Francisca	CLE	18	A		0.272	0.341	112	20	11	110
92	Angel Genao	CLE	20	A+		0.264	0.327	103	15	11	114
93	Demetrio Crisantes	ARI	19	A		0.267	0.336	107	16	13	118
94	Tre’ Morgan	TBR	21	AA		0.273	0.344	115	15	10	112
95	Blake Mitchell	KCR	19	A		0.242	0.325	104	28	12	110
96	Jedixson Paez	BOS	20	A+	P	19%	5%	48%	3.74	1.21	97
97	Sean Sullivan	COL	21	AA	P	20%	5%	38%	4.15	1.19	104
98	Yeremi Cabrera	TEX	18	A		0.260	0.337	112	25	15	105
99	Jhostynxon Garcia	BOS	21	AA		0.257	0.322	109	24	8	105
100	Jonah Tong	NYM	21	A+	P	23%	8%	47%	3.94	1.27	106