4. Bo Davidson OF. DOB: 7/5/2002. B-L, T- R. 6' 1", 205 lbs. Rule 5 Eligible Dec. 2026.
2025(A+): .309/.412/.507, 10 HR, 12 SB, 14.6 BB%, 22.1 K%, 335 PA.
2025(AA): .234/.312/.401, 8 HR, 7 SB, 10.2 BB%, 24.1 K%, 187 PA.
Undrafted free agent from 2023 draft cycle. Davidson gained national buzz has he raked his way through the Giants system. While his BA took a step back after a late-season promotion to AA, his HR/PA went up It appears that result is due to a change in approach as his GB/FB went from 1.70 to 0.75 giving him more of a 3-true outcomes stat line. I expect him to start 2026 back in AA and he'll try to break the Richmond/EL curse on hitting prospects. I saw him play with A San Jose in 2024 and he certainly looks the part. Impressive size and athleticism and barreled up everything.
Hey all and Doc,
ReplyDeleteI just want to post something here for thought. Also Bo Davidson is maybe my favorite prospect, and is impossible not to root for as an underdog.
I asked GPT an in depth question about whether AA Eastern League stifles hitting/positional prospects, more than the AA Southern League and AA Texas League. (This is a question I have bc of all the discussions here and elsewhere throughout Giants prospecting history.)
There's no perfect answer of course, but here's a key line that I thought was really impactful, assuming true (and GPT took like 3 whole minutes to deliberate on this answer so we KNOW it's good):
..."Trend A: Eastern League suppresses results more than it suppresses skills
Even when scoring is down, some components tend to “carry” better across environments:
• BB% and K% (plate discipline / contact) are generally more portable than batting average or HR totals.
• Power output (HR/SLG) is more environment-sensitive, so EL power often “shows up later” when the same hitter reaches warmer parks/levels." ...
The point being, when we watch for performance in AA Eastern, looking for a prospect to sustain BB% and K% along with power numbers in ISO or HR/AB, this might be much more indicative of how they're really doing, versus watching for BA and for pretty looking traditional BA/OBP/SLG lines.
YMMV! Lol!!
Cheers.
SF Fan in Michigan
Hey DOC. Just in case it would be useful to you, here's the full Question and Answer I had with ChatGPT around AA Eastern stifling hitting prospects..
ReplyDeleteProbably not worthy of publishing, but maybe interesting just for the long term context of "The Giants Positional-Prospect Eastern League Curse."
Question, SF-to-Michigan guy:
"I'm a fan of baseball, specifically the San Francisco Giants, and I have always loved meticulously following the top players in the Giants minor league system.
It seems like (and many people online philosophize on this topic) Giants positional/hitting prospects run into a wall in AA, Eastern League, which is known for good pitching but also for weather that is hard to hit in.
Is there any record of success rate, or how many successful MLB hitters graduate, based on the players having gone through the Eastern League, relative to the Southern League or the Texas league?
I'm looking for interesting trends or statistics that show whether prospects are better off going through one of these AA Leagues relative to the other two."
GPT Answer:
You’re not imagining it: the Eastern League can make good hitting prospects look “stuck” in a way the Southern/Texas Leagues don’t, even when the underlying talent is similar. The tricky part is that what most people mean by “success rate” (how many EL AA hitters become good MLB hitters) isn’t commonly published as a clean, apples-to-apples statistic — but we can get at the underlying “why” with league run environments + how MLB analysts already adjust for this.
1) The AA leagues don’t play the same offensively (recent example)
Using 2023 league totals (runs scored + games) from The Baseball Cube standings pages, you can estimate the scoring environment:
• Eastern League (AA): 7,608 runs across 816 league games → 9.32 runs/game (both teams) → 4.66 runs/team/game 
• Southern League (AA): 5,584 runs across 549 league games → 10.17 runs/game → 5.09 runs/team/game 
• Texas League (AA): 7,201 runs across 690 league games → 10.44 runs/game → 5.22 runs/team/game 
So in that season, the EL was roughly ~10% lower scoring per team-game than the Texas League (4.66 vs 5.22). That alone will make AA stat lines (AVG/SLG/HR) look meaningfully worse for the same hitter.
Why this matters for the Giants: Richmond lives in that EL environment, and the “wall” you’re describing is often just context whiplash when a guy leaves a more offense-friendly league/parks and suddenly plays in colder air + tougher run scoring.
2) “Success rate” by AA league is hard to find because analysts don’t evaluate it that way
If you try to compute “% of EL hitters who become successful MLB hitters,” you immediately run into selection bias:
• Each AA league gets a different mix of org strategies (some orgs push prospects aggressively, others repeat levels, some stash older depth, etc.).
• Promotions to AAA/MLB depend on 40-man needs, injuries, org depth, not just performance.
• Raw stats are heavily shaped by park + league run environment, so comparing leagues without adjustments is misleading.
(Cont') for reference
ReplyDelete...
"That’s why modern prospect evaluation leans on translated/adjusted performance (park + league context) rather than raw lines. Classic public approaches include league/park adjustments and Major League Equivalencies concepts (e.g., Clay Davenport’s translations) , and broader public work on putting minor-league performance “in context” .
3) What is a real trend you can use?
Trend A: Eastern League suppresses results more than it suppresses skills
Even when scoring is down, some components tend to “carry” better across environments:
• BB% and K% (plate discipline / contact) are generally more portable than batting average or HR totals.
• Power output (HR/SLG) is more environment-sensitive, so EL power often “shows up later” when the same hitter reaches warmer parks/levels.
So the practical scouting takeaway is: in the EL, you should be a bit quicker to trust process stats (BB%, K%, swing decisions, quality of contact) than slash-line aesthetics.
Trend B: Park effects can dominate league effects
Even within the same league, parks can be wildly different. That’s why park-factor frameworks exist and why analysts stress park adjustment when comparing hitters . In other words, “Eastern League is tough” can be true, and the bigger effect for a given prospect might be “his home park is brutal.”
Trend C: People inside baseball explicitly call the EL “toothy”
There’s at least some team-side acknowledgement that the EL can be a higher degree-of-difficulty stop than the other AA leagues (even if it’s not a rigorous academic claim) .
4) If you want the exact “success rate” numbers, here’s the clean way to build it
If you’re down for a little sabermetric project, here’s the most defensible definition I’ve seen people use:
1. Define a cohort: all hitters with (say) 200+ PA in AA in a given year, tagged by which league (EL/SL/TL).
2. Define MLB “success” (pick one):
• Reached MLB and accrued 500+ MLB PA, and/or
• Produced MLB wRC+ ≥ 100 over their first 500 PA, and/or
• Career WAR threshold, etc.
3. Adjust for age: compare “success rates” after controlling for age vs level (a 20-year-old in AA is not the same as a 25-year-old in AA).
4. (Optional but recommended) Use context-neutral AA stats (park/league adjusted) instead of raw slash lines.
FanGraphs’ minor-league leaderboards make the “get the AA cohorts by league” part much easier than it used to be .
If you want, tell me what you’d consider a “successful MLB hitter” (PA threshold? wRC+? WAR?), and I’ll outline an exact query/schema you can run (and what biases to watch for) so you get a result you can actually trust.
5) Bottom line for a Giants prospect watcher
• Yes: the EL is often a tougher run-scoring environment than the SL/TL (2023 is a clear recent example). 
• Maybe, but hard to prove cleanly: a different “success rate” caused by the league itself. Any raw rate will be confounded by org behavior and selection effects.
• Best actionable approach: judge EL hitters more on discipline/contact indicators + context-adjusted production than on the “pretty” slash line.
If you want to make this Giants-specific, I can also suggest a “Richmond translation lens” (what to weight most heavily for EL hitters) that matches how public projection models tend to behave."