Jesse Fischer

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Jesse Fischer

Jesse Fischer

@jessefischer33

sports. analytics. technology.

Seattle, WA Katılım Haziran 2014
383 Takip Edilen1.9K Takipçiler
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Jesse Fischer
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: NBADraftModel.com 📊 Not a mock draft — this is a data-driven model built using machine learning to project NBA value
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Jesse Fischer
Jesse Fischer@jessefischer33·
@RickHouTX Why? It weights the top ranks more—differences later in the draft don’t move the needle as much.
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Jesse Fischer
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
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Jesse Fischer
Jesse Fischer@jessefischer33·
Top Undrafted Prospects, According to the Draft Model Consensus
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Jesse Fischer
Jesse Fischer@jessefischer33·
Model Consensus vs. Actual Pick
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Jesse Fischer
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
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Jesse Fischer
Jesse Fischer@jessefischer33·
Final results for NBA Draft Model Consensus vs. Actual Pick
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Jesse Fischer
Jesse Fischer@jessefischer33·
Interesting that the top 5 were all picked in the first 6 picks of 2nd round 👀
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Jesse Fischer
Jesse Fischer@jessefischer33·
“The analytics models love this man” (re: CMB) Cite those sources!
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Jesse Fischer
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.
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Jesse Fischer
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
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Jesse Fischer
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
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Jesse Fischer
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.🤔
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Jesse Fischer
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
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Jesse Fischer
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)
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Jesse Fischer
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
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Keşfet

@RickHouTX @FrankWest2020 @nbadraftnet @DraftExpress @YahooSports @JeremyWoo @johnhollinger @Sam_Vecenie