Scouting the Statline - Top 100 Prospects for Dynasty

For those new to this series, Scouting the Statline focuses on statistical analysis using using curves and major league equivalencies to generate peak projections. Peak projections put all minor and major leaguers on equal statistical footing for comparative purposes.

We are constantly looking to fine-tune our data models to deliver improved results, most recently adopting a new approach to the way we order our rankings. Please follow this link to view the full list of over 1,600 eligible minor leaguers! Once the regular season starts, this list is updated on a weekly basis, while our 2022 prospect leaderboard will be updated daily! Watch these lists evolve and much more at scoutthestatline.com.

What’s New

The order of our rankings is now based on z-score!

What is Z-Score?

Z-score helps determine a player’s value compared to the rest of the league. By totaling the z-scores across multiple stats we believe help indicate a player’s future fantasy value, we have created a new ranking methodology!

Why the Change?

This is really two-fold.

Our rankings were previously based on peak projected wRC+. We believe wRC+ is a useful real world “player value” data point, but fantasy players almost universally rely more heavily on stolen bases, which are not a part of the wRC+ calculation. We believe using z-scores helps give more credit to the speed demons out there. We have to strike a careful balance, however, because there are some ridiculously fast players in the low minors that will never advance due to limited bats and we don’t want them to benefit too much from the change!
Eventually, we will roll out pitching translations. In order to integrate them into our rankings, we cannot use wRC+ since it is a hitting stat. However, since z-score is a standard comparative metric, it can be used for any set of data. Voila! While pitching translations are not yet ready, we have tested it with sample datasets and it passes the eye test.

At Scouting the Statline, we always believe the numbers are more important than the rank, but our new approach better aligns with traditional views of prospect rankings for fantasy..

The New Top 100

Rank	Name	Last Team	Age	Career PA	Peak wRC+	Peak wOBA	Peak BA	Peak OBP	Peak SLG	Peak HR/600	Peak SB/600	CS	Peak BB%	Peak K%	Peak BABIP
1	Julio Rodriguez	SEA	20	962	151.2	0.387	0.297	0.377	0.507	25	12	0	9.8%	16.9%	0.327
2	Anthony Volpe	NYY	20	663	136.2	0.364	0.234	0.355	0.478	26	17	0	12.9%	19.4%	0.252
3	Jordan Walker	STL	19	366	138.2	0.367	0.287	0.353	0.490	22	12	0	8.2%	20.3%	0.333
4	CJ Abrams	SDP	20	348	124.3	0.345	0.285	0.347	0.437	11	25	0	7.6%	14.1%	0.320
5	Riley Greene	DET	20	809	135.8	0.363	0.271	0.358	0.470	24	11	0	11.1%	23.9%	0.329
6	Francisco Alvarez	NYM	19	582	137.7	0.366	0.246	0.356	0.483	32	7	1	12.8%	18.4%	0.246
7	Anthony Garcia	NYY	20	394	130.3	0.355	0.217	0.326	0.493	41	15	0	13.6%	35.2%	0.271
8	Oneil Cruz	PIT	22	1475	128.6	0.352	0.270	0.330	0.480	25	16	0	8.1%	22.0%	0.314
9	Gabriel Moreno	TOR	21	802	137.6	0.366	0.289	0.345	0.499	25	5	0	6.6%	11.8%	0.293
10	Josh H. Smith	TEX	23	492	124.8	0.346	0.254	0.367	0.409	15	19	0	11.4%	19.2%	0.301
11	Bobby Witt Jr.	KCR	21	744	121.2	0.341	0.249	0.317	0.470	28	19	0	8.3%	22.0%	0.277
12	Nick Yorke	BOS	19	442	130.7	0.355	0.279	0.364	0.441	19	8	3	10.8%	13.6%	0.300
13	Spencer Torkelson	DET	21	530	134.0	0.360	0.234	0.350	0.477	30	4	0	13.8%	20.4%	0.242
14	Andy Pages	LAD	20	1029	133.1	0.359	0.226	0.344	0.483	34	5	0	12.0%	23.1%	0.241
15	Khalil Lee	NYM	23	1885	121.0	0.340	0.241	0.371	0.390	15	18	0	13.6%	28.0%	0.341
16	Alek Thomas	ARI	21	1273	127.0	0.350	0.267	0.347	0.447	19	10	0	9.8%	18.2%	0.306
17	Brennen Davis	CHC	21	692	128.5	0.352	0.244	0.348	0.456	26	8	0	11.0%	23.9%	0.288
18	George Valera	CLE	20	599	126.4	0.349	0.217	0.355	0.436	30	10	0	16.4%	22.9%	0.230
19	Vidal Brujan	TBR	23	1720	102.3	0.311	0.249	0.325	0.376	11	35	0	9.6%	14.9%	0.279
20	Zac Veen	COL	19	479	114.7	0.331	0.240	0.344	0.404	19	19	9	12.8%	23.6%	0.293
21	Josh Lowe	TBR	23	1951	113.3	0.328	0.241	0.329	0.418	20	20	0	11.2%	25.4%	0.301
22	Jhonkensy Noel	CLE	19	759	125.6	0.347	0.264	0.327	0.484	32	6	0	6.9%	18.9%	0.276
23	Edouard Julien	MIN	22	514	116.6	0.334	0.219	0.366	0.381	17	16	0	16.8%	29.0%	0.310
24	Everson Pereira	NYY	20	478	120.3	0.339	0.230	0.312	0.476	35	11	0	10.0%	27.8%	0.257
25	Jarren Duran	BOS	24	1163	108.1	0.321	0.258	0.327	0.397	15	24	0	7.7%	23.1%	0.319
26	Richie Palacios	CLE	24	618	115.0	0.331	0.260	0.356	0.385	7	16	0	11.6%	18.8%	0.321
27	Triston Casas	BOS	21	871	122.8	0.343	0.238	0.343	0.439	25	5	0	13.0%	19.5%	0.259
28	Joey Wiemer	MIL	22	472	114.7	0.331	0.234	0.329	0.428	27	14	0	10.6%	24.5%	0.275
29	Robert Hassell III	SDP	19	516	111.6	0.326	0.249	0.344	0.389	14	17	0	12.3%	16.8%	0.281
30	Greg Jones	TBR	23	535	98.8	0.306	0.234	0.321	0.366	15	31	0	9.3%	30.8%	0.330
31	Miguel Vargas	LAD	21	1330	120.3	0.339	0.275	0.342	0.428	17	7	0	8.5%	14.6%	0.299
32	Ji-hwan Bae	PIT	21	904	106.5	0.318	0.263	0.342	0.369	8	21	0	9.8%	18.9%	0.320
33	Orelvis Martinez	TOR	19	614	121.0	0.340	0.228	0.304	0.496	36	4	0	8.3%	20.7%	0.230
34	Dustin Harris	TEX	21	704	113.6	0.329	0.267	0.340	0.403	17	12	0	8.9%	17.0%	0.301
35	Jairo Pomares	SFG	20	557	121.5	0.341	0.271	0.315	0.479	27	3	0	4.9%	23.1%	0.314
36	Josh Jung	TEX	23	540	122.0	0.342	0.268	0.337	0.444	20	2	0	7.8%	22.7%	0.326
37	Chris Gittens	NYY	27	1189	123.3	0.344	0.235	0.346	0.436	28	0	0	13.7%	31.4%	0.315
38	Samad Taylor	TOR	22	1516	100.8	0.309	0.224	0.316	0.385	18	24	0	10.5%	24.7%	0.279
39	Felix Valerio	MIL	20	992	108.8	0.322	0.248	0.340	0.383	11	15	0	10.7%	12.5%	0.271
40	Curtis Mead	TBR	20	639	116.0	0.333	0.266	0.326	0.436	17	7	0	7.1%	14.3%	0.287
41	Xavier Edwards	TBR	21	1093	104.2	0.315	0.281	0.353	0.340	3	20	0	9.9%	9.8%	0.310
42	Nick Gonzales	PIT	22	369	116.9	0.334	0.247	0.329	0.435	20	5	2	9.8%	27.6%	0.322
43	Drew Waters	ATL	22	1754	104.9	0.316	0.252	0.320	0.393	12	18	0	8.1%	26.6%	0.335
44	Tyler Freeman	CLE	22	1172	111.9	0.326	0.282	0.336	0.397	7	10	0	4.2%	9.9%	0.305
45	Leo Jimenez	TOR	20	663	117.3	0.335	0.263	0.390	0.352	7	4	0	12.5%	14.4%	0.310
46	Luis Matos	SFG	19	781	109.9	0.323	0.268	0.319	0.423	17	12	0	5.7%	11.0%	0.278
47	Nolan Jones	CLE	23	1726	115.2	0.331	0.231	0.350	0.397	19	6	0	14.5%	28.2%	0.308
48	Kevin Alcantara	CHC	18	312	112.4	0.327	0.253	0.317	0.430	22	9	0	8.0%	20.7%	0.294
49	Juan Yepez	STL	23	1537	117.5	0.335	0.246	0.323	0.445	24	2	1	8.9%	20.1%	0.272
50	Vaughn Grissom	ATL	20	564	110.9	0.325	0.260	0.349	0.378	12	9	0	10.0%	14.3%	0.289
51	Oswald Peraza	NYY	21	1221	97.3	0.304	0.251	0.314	0.372	15	24	0	7.2%	18.8%	0.291
52	Otto Lopez	TOR	22	1443	105.0	0.316	0.280	0.339	0.367	5	15	0	7.3%	15.3%	0.329
53	Heriberto Hernandez	TBR	21	793	113.8	0.329	0.216	0.330	0.414	23	5	0	12.6%	27.3%	0.269
54	Kevin Smith	TOR	24	1736	106.6	0.318	0.233	0.297	0.437	25	13	0	7.5%	26.6%	0.279
55	Eddys Leonard	LAD	20	901	112.8	0.328	0.235	0.329	0.418	23	6	0	10.2%	22.9%	0.277
56	Cooper Hummel	ARI	26	1428	115.3	0.332	0.232	0.355	0.390	16	3	0	13.8%	23.6%	0.291
57	Terrin Vavra	BAL	24	866	108.9	0.322	0.240	0.344	0.378	13	10	0	12.4%	20.9%	0.292
58	Nick Pratto	KCR	22	1784	109.4	0.322	0.215	0.311	0.429	25	9	0	11.3%	29.1%	0.267
59	Diego Cartaya	LAD	19	344	115.6	0.332	0.233	0.322	0.443	28	2	0	10.1%	21.9%	0.254
60	Seth Beer	ARI	24	1265	117.4	0.335	0.241	0.343	0.415	21	0	1	7.8%	20.7%	0.279
61	Vinnie Pasquantino	KCR	23	761	115.5	0.332	0.239	0.323	0.442	22	2	0	10.0%	16.5%	0.251
62	Adley Rutschman	BAL	23	698	114.9	0.331	0.238	0.347	0.400	19	2	1	13.2%	18.5%	0.266
63	Brayan Rocchio	CLE	20	1055	103.3	0.313	0.246	0.312	0.399	18	15	0	6.9%	16.4%	0.271
64	Bryson Stott	PHI	23	680	110.4	0.324	0.249	0.342	0.388	15	7	0	12.1%	23.7%	0.313
65	Austin Martin	TOR	22	418	105.8	0.317	0.235	0.375	0.320	6	12	0	13.3%	20.2%	0.301
66	Brett Auerbach	SFG	22	368	100.3	0.309	0.225	0.309	0.397	22	18	0	9.2%	25.4%	0.270
67	Bryan Lavastida	CLE	22	731	107.5	0.320	0.254	0.343	0.372	10	10	0	10.9%	18.6%	0.301
68	Steven Kwan	CLE	23	947	110.7	0.324	0.269	0.344	0.383	10	6	0	9.4%	10.5%	0.289
69	Luis Campusano	SDP	22	1248	114.9	0.331	0.262	0.334	0.418	18	1	0	8.7%	15.8%	0.286
70	MJ Melendez	KCR	22	1620	112.3	0.327	0.214	0.303	0.454	31	3	2	11.0%	26.8%	0.238
71	Austin Shenton	SEA	23	598	114.9	0.331	0.238	0.327	0.429	18	0	0	9.6%	24.3%	0.295
72	Jacob Robson	DET	26	1921	98.9	0.306	0.247	0.340	0.342	5	17	0	11.5%	32.6%	0.394
73	Shay Whitcomb	HOU	22	444	98.8	0.306	0.235	0.299	0.404	23	17	0	7.3%	31.1%	0.315
74	Marco Luciano	SFG	19	669	107.6	0.320	0.219	0.315	0.417	26	7	0	11.4%	22.5%	0.243
75	Canaan Smith-Njigba	PIT	22	1225	104.0	0.314	0.241	0.342	0.359	12	11	0	12.9%	23.5%	0.307
76	Luke Berryhill	HOU	23	330	111.7	0.326	0.225	0.331	0.409	21	2	1	10.1%	31.7%	0.323
77	Nolan Gorman	STL	21	1309	109.9	0.323	0.244	0.307	0.437	26	4	0	7.9%	23.0%	0.278
78	Jonathan Aranda	TBR	23	1054	111.4	0.326	0.260	0.341	0.392	12	2	0	9.1%	19.0%	0.311
79	Esteban Quiroz	TBR	29	779	111.3	0.325	0.215	0.331	0.406	20	2	0	12.3%	27.1%	0.277
80	Jordan Groshans	TOR	21	619	111.8	0.326	0.262	0.339	0.397	13	1	1	10.2%	18.2%	0.306
81	Geraldo Perdomo	ARI	21	1392	100.7	0.309	0.228	0.345	0.341	12	13	0	13.1%	17.1%	0.263
82	Jorge Caballero	MIA	21	493	110.5	0.324	0.256	0.357	0.365	12	2	2	12.5%	27.6%	0.362
83	Aldenis Sanchez	TBR	22	736	88.0	0.289	0.244	0.327	0.311	4	27	0	9.4%	19.1%	0.306
84	Michael Harris II	ATL	20	632	95.9	0.302	0.249	0.320	0.355	8	18	0	8.4%	16.6%	0.291
85	Keibert Ruiz	LAD	22	1492	110.8	0.325	0.251	0.320	0.420	23	1	0	8.4%	7.3%	0.237
86	Mark Vientos	NYM	21	1276	110.8	0.325	0.238	0.305	0.446	29	1	2	8.2%	23.4%	0.267
87	Jeremy Pena	HOU	23	790	101.7	0.311	0.244	0.318	0.383	16	11	0	7.5%	21.9%	0.295
88	Jordan Westburg	BAL	22	506	103.3	0.313	0.234	0.334	0.368	12	9	0	10.8%	25.8%	0.313
89	Curtis Terry	TEX	24	1475	110.2	0.324	0.236	0.309	0.438	26	1	1	6.6%	25.4%	0.280
90	Brett Baty	NYM	21	613	107.5	0.320	0.233	0.336	0.386	16	4	0	12.3%	25.8%	0.297
91	Jayce Easley	TEX	21	587	77.1	0.273	0.201	0.337	0.255	5	38	0	15.4%	22.2%	0.269
92	Jose Barrero	CIN	23	1349	101.2	0.310	0.249	0.308	0.397	16	11	0	6.1%	21.1%	0.295
93	Skye Bolt	OAK	27	1566	102.9	0.312	0.240	0.327	0.379	13	9	0	10.5%	28.5%	0.332
94	Jake McCarthy	ARI	23	835	87.6	0.289	0.214	0.288	0.369	14	26	0	8.5%	25.4%	0.270
95	Fidel Castro	CIN	22	677	102.8	0.312	0.213	0.306	0.409	23	9	0	10.0%	35.0%	0.312
96	Jose Miranda	MIN	23	1873	110.0	0.323	0.269	0.319	0.419	19	1	1	5.4%	12.9%	0.284
97	Peyton Burdick	MIA	24	804	106.4	0.318	0.206	0.325	0.396	23	5	1	12.6%	29.7%	0.267
98	Jorbit Vivas	LAD	20	928	104.5	0.315	0.246	0.329	0.382	14	7	0	8.0%	12.6%	0.262
99	Rangel Ravelo	LAD	29	1238	110.5	0.324	0.268	0.347	0.384	10	0	0	8.8%	18.2%	0.320
100	Austin Wells	NYY	21	469	102.2	0.311	0.216	0.325	0.378	17	9	0	11.8%	24.9%	0.273

Observations

The highest-end players remain mostly unchanged, a testament to the strong resume supporting their cases. After updating to the most current conditions, the top five prospects are Julio Rodriguez, Anthony Volpe, Jordan Walker, C.J. Abrams, and Riley Greene.
Felix Valerio, Vaughn Grissom, Bobby Witt, and Xavier Edwards all benefit under the new rankings thanks to their stolen base prowess.
Despite efforts to limit it, there are instances of low-level minor league speedsters that creep up in the ranks – beware of players that climb the rankings with wRC+ projections near 100 or lower. 100 is major league replacement level, meaning these players carry higher playing time risks!

If you enjoyed this content, follow us on Twitter and visit our website for more frequent updates.

We are always open to feedback!

The Dynasty Guru: @DynastyGuru

Ross Jensen: @rossjensen12

Jordan Rosenblum: @rosenjordanblum

Scouting the Statline: statlinescouting.com

5 Comments

Todd Hedberg

March 21, 2022 at 2:46 pm

Whao, either this math is revolutionary or really flawed. Haven’t heard of many of these players…

Loading...
- Ross Jensen
  
  March 21, 2022 at 2:52 pm
  
  Revolutionary, for sure :p
  
  Though honestly, while we want our rankings to make sense, we also don’t want to follow the general consensus to create something safe/vanilla. We hope that our data models can help reveal the players that are over-looked by conventional wisdom!
  
  Loading...
Logan

March 22, 2022 at 9:56 am

Ross, this is a very interesting list. Definitely has given me some names I haven’t heard of that I need to keep an eye on. I’d be interested to know how you are using this list for yourself in leagues? Are you averaging your findings with that of the “general consensus” during drafts? Or are you taking this as your only guide when making picks?
i.e., are you taking Nick Yorke over Bobby Witt Jr. if you have the opportunity to grab one of them?

Loading...
- Ross Jensen
  
  March 23, 2022 at 3:54 pm
  
  The name of the game is to trade for the best prospects before they become “general consensus” best prospects (ie Anthony Volpe, who I received for Christhian Vaquero early last season), and trade the prospects that are failing to measure up before they fall out of “general consensus” favor (ie Royce Lewis, who I traded for CJ Abrams two seasons ago). The best way to do this is to follow our daily leaderboard and weekly updates during the season (at statlinescouting.com) to see who is rising and who is falling. In my experience, general consensus always lags a little, so it’s important to be ahead.
  
  Regarding Witt, he was a pretty big name right out of the gate, so he would have been a tough one to get. Yorke is a different story – I traded for him everywhere I could, generally for good discounts to his current general consensus ranking (which is still too low, imo). If I had the opportunity to pick either, right now I would still grab Witt because his perceived value is higher, which means you can get more in exchange for him. However, I think the two are a lot closer than most think, people haven’t fully caught on to Yorke’s true value yet!
  
  Loading...
  - Ross Jensen
    
    March 23, 2022 at 4:39 pm
    
    In that scenario, you could snag Witt, trade him, then add Yorke. Or trade Witt for Yorke-plus.
    
    Loading...

Ryan Felix Fernandes

on Scout the Statline: Just Another Player Highlights Article

I'm still looking for that prospect who you give the Mick/Juan Soto comp!!! Good stuff as usual Ross!

on The Juan Soto Quandary

Thank you Big Dog!!! Woof!

Ehhhh Ross-inator!

Ross Jensen

Agreed. Pure talent, eh?

Big Dog Bill

Ordinarily I skip over articles like this one, but the creative writing is genius! I had to read the article to the end. Great write-up and hope to see more…

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What’s New

What is Z-Score?

Why the Change?

The New Top 100

Observations

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Scouting the Statline – Top 100 Prospects for Dynasty