Jesse Fischer

sports. analytics. technology.

[Jesse Fischer @jessefischer33]

🚨 It’s here! The 2025 NBA Draft Analytics Guide is LIVE 🏀📊
100+ pages of projections, stats, comps, and AI-style scouting reports
📥 Download: https://t.co/CpGEVQmX7i
📊 Not a mock draft — this is a data-driven model built using machine learning to project NBA value

[Jesse Fischer @jessefischer33]

@RickHouTX Why? It weights the top ranks more—differences later in the draft don’t move the needle as much.

[Jesse Fischer @jessefischer33]

Top Undrafted Prospects, According to the Draft Model Consensus

[Jesse Fischer @jessefischer33]

@RickHouTX Roughly:

1. Convert each rank to an expected pick value (e.g. smoothed avg historical peak win shares by pick #)
2. Apply to each model’s ranks
3. Avg values across models for each player
4. Sort to get a consensus rank

Note: Missing rank gets imputed with last pick value

[Jesse Fischer @jessefischer33]

@FrankWest2020 And 2023 https://t.co/SyODE0qAXh

[Jesse Fischer @jessefischer33]

Here is Models vs. Actual. Let's add Cam to the value list since he turned into value and see who got all these guys...

Cam = HOU
Dick = TOR
Pod = GSW
Miller = MIN
Sensabaugh = UTAH
Nnaji = BOS
Colby = SAC
Phillips = CHI
Cissoko = SAS
Amari = CHA
Jaylen = MIN
Tubelis = PHI (FA)

[Jesse Fischer @jessefischer33]

Model Consensus vs. Actual Pick

[Jesse Fischer @jessefischer33]

@FrankWest2020 Here is last year https://t.co/0bfI48TpxH

[Jesse Fischer @jessefischer33]

📊 Draft model consensus vs. actual picks in the 1st round of the 2024 NBA Draft! 🏀✨

💎Clingan (POR) / Topic (OKC) / McCain (PHI) / Collier (UTA)
❌Risacher (ATL) / Salaun (CHA) / C. Williams (UTA) / Da Silva (ORL) / AJ Johnson (MIL)
#NBADraftModel #DraftModelGems

[Jesse Fischer @jessefischer33]

🚨 Top Undrafted Players from the 2025 NBA Draft! 🏀💎

According to Draft Model consensus, these are the best bets for UDFA steals.

Keep an eye on these names 👀📈

#NBADraftModel #UndraftedGems

[Jesse Fischer @jessefischer33]

Final results for NBA Draft Model Consensus vs. Actual Pick

[Jesse Fischer @jessefischer33]

Highest rank correlation with actual pick:
1) Mock consensus (0.95)
2) @nbadraftnet / @DraftExpress / Analyst Consensus (0.92)
3) @YahooSports / @JeremyWoo (0.91)

[Jesse Fischer @jessefischer33]

Interesting that the top 5 were all picked in the first 6 picks of 2nd round 👀

[Jesse Fischer @jessefischer33]

“The analytics models love this man” (re: CMB)

Cite those sources!

[Jesse Fischer @jessefischer33]

It’s not just rank — it’s philosophy:

👁️‍🗨️ Analysts: what they’ve seen
📈 Models: what the numbers say
🎯 Mocks: what teams might do

The contrast reveals how each group thinks — not just who they rank.

[Jesse Fischer @jessefischer33]

🔍 Analysts vs. Mocks: What's the pattern?

Analysts are lower than mocks on:
- Derik Queen — Crafty, physically imposing big with elite post skill and advanced feel for the game
- Joan Beringer — Rim-protecting big with elite length and a rapidly developing interior presence

[Jesse Fischer @jessefischer33]

📊 The full correlation map tells a story:
- Mocks sit in the middle, holding the ecosystem together
- Models split to the edges — some align with scouting, others go full stat
- Analysts cluster left-center, closely tracking mocks — except Hollinger, who hangs with the models

[Jesse Fischer @jessefischer33]

Analysts tend to track mock consensus closely (and maybe even influence it), but some subtle trends emerge:

📉 Lower on:
- Derik Queen / Joan Beringer

📈 Higher on:
- CMB / Noa / Jase / Thiero / Saraf

Interesting: Every one of those "analyst leans" matches the models.🤔

[Jesse Fischer @jessefischer33]

🤯 What happens when you put models, mocks, and analysts on the same board?

Clarity. Clusters. Chaos.

Compare some of your favorites draft analysts (@DraftExpress, @johnhollinger, @KevinOConnorNBA, @Sam_Vecenie) against the mocks/models 👇

#NBADraft #ModelConsensus

[Jesse Fischer @jessefischer33]

Why do these gaps matter?

- Models weigh production, not hype
- Scouts lean into tools & projection
- Data helps spot hidden gems early

This isn’t a vibe check — it’s predictive modeling

[Jesse Fischer @jessefischer33]

🏀 Model vs. Mock: Let’s talk disagreement

Which prospects do data-driven models love… and who might be getting overhyped?

Discover the steals and the traps 🧠

#NBADraft #ModelConsensus #HiddenGems

[Jesse Fischer @jessefischer33]

Not all models tell the same story. My model is...

📈 Higher than consensus on:
- Jeremiah Fears (#3 / #11 / #7)
- Egor Demin (#6 / #18 / #15)
- Ben Saraf (#4 / #27 / #32)
- Hansen Yang (#12 / #31 / #35)
- Bogoljub Markovic (#18 / #29 / #38)
(JF vs. Model Consensus vs. Mock)