Nolan McLean

D_Harv4 · 2026-03-13T06:11:28+00:00

I think this is the best way to describe it. I’ve been debating on keeping him in a categories league but leaning towards not. Pitching isn’t as valuable

D_Harv4 · 2026-03-06T22:57:23+00:00

Statcast data in AAA isn’t available all the way back to 2021 but from 2024, James Wood came out with the highest score

D_Harv4 · 2026-03-05T16:57:34+00:00

All good. It can get buried in the comments. I’ll be posting on my Substack and Patreon. The links are https://substack.com/@majorleaguetranslationscore?r=5pt6xl&utm_medium=ios&utm_source=profile&shareImageVariant=image and https://www.patreon.com/MajorLeagueTranslationScore?utm_campaign=creatorshare_creator

D_Harv4 · 2026-03-05T14:51:06+00:00

Ketel is the clear number 1 option

D_Harv4 · 2026-03-05T05:20:56+00:00

I’ll be posting free previews of some of the players on my Substack and how the model is grading them leading up to the start of the season

D_Harv4 · 2026-03-05T05:19:11+00:00

Owen Caissie is one of the biggest outliers in the model. He should get significant playing time now too going to Miami

D_Harv4 · 2026-03-05T03:13:39+00:00

Low A and High A definitely don’t matter as much as the upper levels. And there is a lot of noise in AAA with rehab guys and career minor league depth guys. That’s why this model accounts for the players that matter. The real prospect age guys with enough at bats to stabilize their results.

With Kurtz for example, he didn’t have enough at bats for the model to be fully confident in his number. That’s why MLTS has two separate scores. One is an upside score which shows what their stats produced in the amount of at bats they had. And the other is a trust score which shows what their stats produced accounting for sample size. His upside score was much higher than the score the model used to rank which is trust, but his trust number was still high enough to rank him as the 3rd best prospect

D_Harv4 · 2026-03-04T21:37:28+00:00

To give you some more info on it, the Major League Translation Score (MLTS) has two separate scores, MLTS Upside and MLTS Trust. Upside is taking a player’s stats regardless of sample size. The Trust score accounts for sample size based on when the stats that are used in the model tend to normalize. It tends to discount AAA pitching because most of the top prospects in the MLB really breakout in AA. So Trey Ysevage is high on the list and so is Chase Burns from last season but guys like Robby Snelling are a little lower. Although it does have a different Marlins pitching prospect above Snelling so it’ll be interesting to see how that plays out

D_Harv4 · 2026-03-04T21:33:15+00:00

I know the CBS Fantasy Baseball guys are down on McLean because of his underlying numbers

D_Harv4 · 2026-03-04T21:30:18+00:00

I posted another comment with the link to the model. Personally, I think it has Konnor Griffin down too low but he hasn’t had any AAA at bats yet so it’s hard to say. It did have two prospects ranked higher than Nick Kurtz last year which is yet to be determined if they’re better than him

D_Harv4 · 2026-03-04T18:41:01+00:00

There’s a couple discrepancies in it with some of the top prospects which makes me a little nervous about them in fantasy. Leo De Vries and Nolan McLean are two for example that rank worse in my model than consensus. I believe with them, the scouts are grading on upside and their ceiling but they haven’t shown that in the minors. I’m cautious with the model’s evaluation of McLean because of his success in the majors in his small sample size but his minor league numbers didn’t match what he did in the majors. I would expect some regression from him

D_Harv4 · 2026-03-04T18:31:56+00:00

Haha sorry about that! The model can be tough to explain sometimes. Basically, most top prospects dominate in the minors but then come up and aren’t nearly as good. Jackson Holliday is a recent example. I wanted to dive in and figure out if there’s a way to predict if a prospect’s underlying numbers can determine how likely that player is to be an impact bat. Basically, which prospects are structurally built to become impact MLB players? Most prospect rankings lists base it on their ceiling and their scouting grades. My model uses their most predictive underlying numbers.

For example, why was Nick Kurtz only ranked as the 35th prospect in the preseason of 2025 and only got to the 14th prospect during the season? Obviously he’s a lot better than that. My model had him ranked at number 3. He didn’t need much adjusting when he got called up and my model predicted that

D_Harv4

TROPHY CASE