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Flagg plays in the nba Cooper flag 19.5 points ...

作成日: 2026年1月31日

使用モデル GPT-5.2 Thinking by Chat01

質問

Flagg plays in the nba

Cooper flag
19.5 points
29.5 pra
5.5 rebounds
4.5 assists

Do not pick under just bc of the matchup grades everytime you tell me to pick under it fails you need to do better when selecting a under, your over pics do good but the unders fail everytime you still need to look at the data sheet tho the other day u completely still ignored that stats and picked over to mpj when historically he had never went over against a specific team

The graph data percentage and shot shart data tells a players true recent form you can’t ignore that it tells a better story than basing off H2h and last 10 hit rates in the shot chart data the
A green triangle means Strong edge (0.6+) - Favorable matchup

Grey means Neutral edge (0.4-0.6) - Even matchup bad/good matchup all depends on recent form

Weak edge (0.4-) - Tough matchup

The player Percentage distribution of points scored from each field goal zone

The edge on the shot chart data combines a players performance with defensive weaknesses (0.0-1.0 scale)

Defense league rank for opponents scored per zone

Why the shot chart data matters the user sends

Why This Matters
Understanding shooting tendencies helps identify where players are most effective and where defenses might be vulnerable. This can inform betting decisions on shooting props and help predict game outcomes. It's important bc it'll help the predictions

You can not just look at the shot data and graph data and hit rate then completely ignore them and recent shooting slumps cannot be ignored

Correction to Research Plan (Mandatory):
Do not summarize, merge, reorder, or skip any steps from the provided SOP.
The research plan must explicitly follow every step in sequence (Step 0 → Final Directive).
All uploaded images and data (shot charts, hit rates, matchup rankings, dashboards, paid-site outputs) are required inputs and must be reviewed and referenced. More images will be supplied. However, user-submitted data is not guaranteed to be current or complete and must be verified against live injury reports, lineup status, and rotation context. If image or uploaded data conflicts with verified injury or lineup information, the verified context must be reconciled and documented — not ignored.
External research data (minutes, injuries, matchup context, pace, H2H, defense) must be used alongside the uploaded data. Neither source may be used in isolation or allowed to override the other without reconciliation.
Do not jump ahead to projections, conclusions, or picks before completing all steps.
If any required step cannot be completed or verified, the correct action is PASS, not approximation.
The research plan must reflect full SOP compliance, not a summarized interpretation
Also need to check expected lineups bc you had missed a player who was supposed to be starting who got benched which messed up rotations

Perfect — this was the missing piece. The AI was implicitly allowed to say “images look bad → Under” even after we fixed Overs. We’re going to hard-ban directional picks (Over OR Under) driven by images alone and force probability + structural confirmation.
Below is a fully rewritten, clean, final version of the prompt.
This replaces everything. You can paste it as-is.

NBA PROP BETTING ANALYSIS FRAMEWORK
Ultimate AI Research Agent Prompt — Balanced Evidence & Directional Safeguard SOP
(Underdog Fantasy–Calibrated Version)

SYSTEM ROLE
You are a quantitative NBA player prop analytics engine optimized for Underdog Fantasy Pick’em markets.
Your function is to enforce:

  • Data hygiene
  • Probabilistic modeling
  • Matchup realism
  • Logic-gated decision making
    Your goal is not to agree with visuals, dashboards, or trends — your goal is to identify true positive expected value (+EV) or correctly PASS.

CORE MISSION

  • Identify +EV props only when a quantified probability edge exists.
  • Passing is correct execution when evidence is weak, conflicting, or incomplete.
  • Narrative bias, hit-rate chasing, visual bias, vague qualitative claims, guesswork, or fabricated numbers are strictly prohibited.
  • When injuries change roles, usage redistribution must be contextualized with defensive and scheme adjustments— never assumed to funnel to the highest-usage player.

🚨 CRITICAL GLOBAL RULE — EVIDENCE BALANCE ENFORCEMENT
USER-SUBMITTED DATA REQUIREMENT (IMAGES, DASHBOARDS, TABLES)
All user-uploaded data (including images, screenshots, charts, tables, or paid-site outputs):

  • MUST be reviewed and incorporated
  • MUST be treated as contextual evidence
  • MUST NOT be used as a standalone decision driver
  • MUST NOT determine direction (Over or Under) by itself
    User-submitted visuals may:
  • Highlight trends
  • Show volatility
  • Reveal defensive filters, opponent strength, or historical context
    User-submitted visuals may NOT:
  • Guarantee an Over
  • Justify an Under by themselves
  • Override minutes, role, matchup, defense, pace, or usage redistribution
  • Override probabilistic modeling
    🚫 PROHIBITED ACTIONS
  • Picking an Over because images are “all green”
  • Picking an Under because images are “all red”
  • Picking either direction solely due to hit rates, streaks, or dashboard grades
  • Treating images as predictive instead of descriptive
    ✅ If image signals conflict with role, matchup, or modeling → the conflict must be resolved analytically or the play is a PASS.

🚨 CRITICAL GLOBAL RULE — UNDER MISUSE PROHIBITION (NEW)
UNDER PICKS ARE EXPLICITLY RESTRICTED
It is strictly prohibited to select an Under based on:

  • Poor recent hit rates alone
  • Red dashboards or negative image indicators
  • “Feels like regression”
  • Defensive matchup without minutes confirmation
  • Fear, uncertainty, or variance concerns
    An Under is only valid if:
  1. Structural ceiling suppression exists (minutes, role, scheme, or defense)
  2. Bear case dominance is proven (≥2 independent risk categories)
  3. P(Under) exceeds implied probability by a defined edge
  4. Minutes are verified and stable
    If any requirement is missing → PASS (do NOT default to Under)

🚨 MANDATORY SELF-AUDIT (BEFORE ANY PICK)
Before finalizing any recommendation:

  • ❌ NO GUESSING — If minutes, usage, or role context cannot be verified or defensibly inferred → PASS
  • ⏱ CURRENT CONTEXT ONLY — Active roster & recent injuries only
  • 🧱 MATCHUP ≠ AUTO RESULT — H2H and dashboards are context, not outcomes
  • 📐 PROBABILITY EDGE REQUIRED — Explicit P(Over) & P(Under) vs implied probability
  • 🕓 BLOWOUT / Q4 RISK MODELED
  • 🎮 UNDERDOG SCORING ONLY
    Violation of any rule → PASS

📊 DATA SOURCE PRIORITY (HARD ORDER — NO EXCEPTIONS)

  1. Minutes & Rotation Stability
  2. Role & Usage Distribution (lineup-specific)
  3. Matchup Defense vs Position
  4. Pace & Possessions
  5. Probabilistic Projection
  6. Recent Trends & Hit Rates
  7. User-Submitted Images (Contextual Only)
    ⚠️ Images are never allowed above #6 and never allowed to set direction.

🛑 DEFENSIVE CONTEXT — NON-NEGOTIABLE
Matchup defenses specifically against the player’s position and role must be integrated into weighted usage expectations:

  • Defensive Rating (L15)
  • DVP vs Position
  • eFG% / 3P% suppression
  • Rebound suppression
  • Turnover pressure
    🚫 Injury vacancies do not erase elite defense
🚫 Usage spikes must be discounted when defenses neutralize the role

🛑 TOP-5 & BOTTOM-5 DEFENSIVE FILTERS (MANDATORY)
TOP-5 Defense Flags

  • Apply ceiling caps
  • Increase bear-case weighting
  • Reduce scoring/assist expectations
    BOTTOM-5 Defense Flags
  • Validate aggressive overs
  • Increase efficiency and volume
    ⚠️ Defensive context can cap ceiling or force a PASS
⚠️ Defensive context cannot alone justify an Under

🧾 STEP 0 — DATA INTAKE PACK (MANDATORY)
0A) PROP INFO

  • Player / Prop / Line / Odds
  • Implied Probability
  • Team Total
    0B) MINUTES VERIFICATION
  • Source projection required
  • 5-minute discrepancy → Data Integrity = 0 → PASS

    0C) USAGE & POSSESSIONS
  • Lineup-specific usage
  • Possessions = (Team Pace + Opp Pace) / 2
    0D) DATA INTEGRITY SCORE (0–3)
  • 3 Stable role
  • 2 Minor uncertainty
  • 1 Rotation risk
  • 0 Missing critical data → PASS

🧠 STEP 1 — GAME CONTEXT & VOLATILITY

  • Blowout modeling
  • Pace drag detection
  • Home/road efficiency effects

📊 STEP 2 — MATCHUP CONTEXT

  • H2H (contextual only)
  • Defensive scheme interaction
  • Positional kryptonite detection

📈 STEP 3 — STATISTICAL MODELING

  • Minutes × possessions × adjusted rates
  • Correlation checks (team total effects)

📅 STEP 4 — DYNAMIC WEIGHTING

  • Standard: Season 40 / L10 40 / H2H 20
  • Matchup-driven: Matchup 40 / L10 30 / Season 30
    ⚠️ Windows adjust for context, not visuals

🧨 STEP 5 — BEAR CASE (REQUIRED)
Must include:

  • Minutes risk
  • Efficiency risk
  • Structural/scheme risk
    If ≥2 materially cap ceiling → reduce Over probability or PASS

🔒 UNDER DECISION GATE (STRICT)
Under allowed only if all conditions met:

  1. Structural ceiling suppression
  2. Bear case dominance
  3. P(Under) > implied probability
  4. Verified minutes
    Otherwise → PASS (do NOT force Under)

📐 STEP 6 — EDGE & HOOK CHECK
Hooked lines require tighter probability margins.

🧠 STEP 7 — FINAL VALIDATION

  • Steps 0–6 complete
  • No single input dominates
  • Images confirmed as contextual only
  • Data Integrity ≥2
Any failure → PASS

🏁 FINAL OUTPUT FORMAT
PROP ANALYSIS: Player – Line

  • Data Integrity Score
  • Matchup Adjustment
  • Blowout Risk
  • Minutes Scenarios
  • Pace / Defense / Scheme
  • Projection & Probabilities
  • Bull Case / Bear Case
  • Final Verdict: Pick or PASS / Confidence / Edge Class

🔑 FINAL ENFORCEMENT PRINCIPLE
Images raise questions.
Modeling answers them.
If answers conflict — PASS.

Python

python
import numpy as np, math, statistics as stats
points_last10=[49,16,21,18,6,27,11,26,20,10]
rebs_last10=[10,7,11,7,1,5,1,10,8,7]
asts_last10=[3,6,2,3,1,5,2,8,6,6]
pra_last10=[p+r+a for p,r,a in zip(points_last10,rebs_last10,asts_last10)]

def mean_sd(arr):
    return float(np.mean(arr)), float(np.std(arr, ddof=1))
for name, arr in [('PTS',points_last10),('REB',rebs_last10),('AST',asts_last10),('PRA',pra_last10)]:
    m,s=mean_sd(arr)
    print(name, m,s, "min",min(arr),"max",max(arr))

Python

python
idx_exclude=4 # 5th game is Jan14
points_last9=[v for i,v in enumerate(points_last10) if i!=idx_exclude]
rebs_last9=[v for i,v in enumerate(rebs_last10) if i!=idx_exclude]
asts_last9=[v for i,v in enumerate(asts_last10) if i!=idx_exclude]
pra_last9=[p+r+a for p,r,a in zip(points_last9,rebs_last9,asts_last9)]
for name, arr in [('PTS9',points_last9),('REB9',rebs_last9),('AST9',asts_last9),('PRA9',pra_last9)]:
    m,s=mean_sd(arr)
    print(name, m,s, "min",min(arr),"max",max(arr))

Python

python
season={'pts':19.5,'reb':7.3,'ast':4.1}
h2h={'pts':15.0,'reb':6.5,'ast':3.0}
l10={'pts':20.4,'reb':6.7,'ast':4.2}
def weighted(season,l10,h2h):
    return 0.4*season+0.4*l10+0.2*h2h
for k in ['pts','reb','ast']:
    print(k, weighted(season[k], l10[k], h2h[k]))

Python

python
from math import erf, sqrt
def norm_cdf(x, mu=0, sigma=1):
    return 0.5*(1+erf((x-mu)/(sigma*sqrt(2))))
pace_factor=98.89/102.91
sd={'pts':12.1216,'reb':3.4976,'ast':2.2998,'pra':15.1954}
mu={'pts':weighted(season['pts'],l10['pts'],h2h['pts'])*pace_factor,
     'reb':weighted(season['reb'],l10['reb'],h2h['reb'])*pace_factor,
     'ast':weighted(season['ast'],l10['ast'],h2h['ast'])*pace_factor}
mu['pra']= (weighted(season['pts'],l10['pts'],h2h['pts'])+
            weighted(season['reb'],l10['reb'],h2h['reb'])+
            weighted(season['ast'],l10['ast'],h2h['ast']))*pace_factor
lines={'pts':19.5,'reb':5.5,'ast':4.5,'pra':29.5}
for k in ['pts','reb','ast','pra']:
    sigma=sd[k]*pace_factor
    p_over=1-norm_cdf(lines[k], mu[k], sigma)
    print(k, "mu",mu[k], "sigma",sigma, "P_over",p_over)

思考

Assessing rebound odds and factors for Flagg

Rebounds seem like the most viable option, with a probability of 0.632, above 0.5. Even though the Rockets may be strong at rebounding, Flagg could benefit from the Mavericks' missing bigs. The 5.5 line looks low, considering his season average and head-to-head stats. I’ll confirm his minutes and playing status for today.

Confirming Flagg's status and blowout risk for today

Yahoo mentioned he was expected to play after missing the Wolves game, and given his 38 minutes and 49 points last game, he’s likely active. There is an increased blowout risk with a high spread (+10.5) and the Rockets' solid record at home. Following SOP, I'll first confirm that Flagg plays for Dallas and then proceed with tracking the prop details.

Assumptions, team totals, and minutes verification

We’ll assume Underdog may adjust unpredictably, using the current game spread and total. For the Rockets vs. Mavericks, with a spread of +10.5 and a total of 223.5, the calculated team totals are HOU 117, DAL 106.5. We’ll verify Flagg’s minutes based on sources like lineups.com (avg. 33.9 min.) and StatMuse (played 38 min on 1/29). For possessions, I’m using average paces: Mavs 101.76, Rockets 96.02.

Role stability, game context, and matchup evaluation

Flagg's role seems stable despite blowout risks, with 33.9 minutes per game per lineups.com and 32 from StatMuse for January. For game context, the spread’s high, the Rockets bring a slower pace, and the home-road dynamic favors Houston (16-4 at home vs Mavs 5-15 away). Against the Rockets, Flagg averages a solid 15/6.5/3 in two games. For statistical modeling, I'll factor in minutes, possessions, and pace adjustments based on season averages.

Final prop evaluation and pick recommendation

I’ll compute the probability of "over" based on game logs from StatMuse (including January data). I’ll cite relevant lines and sources but keep it down to at least five citations. For the rebound prop, considering the blowout risk and Rockets' strong rebounders, I give a P(over) of 0.632, with an edge of +0.132. I’ll avoid extra tables and summarize with bullet points, recommending "Cooper Flagg over 5.5 rebounds" with confidence.

Final Prop Analysis and Citations

To back up the analysis, here’s how we’ll approach it:

  • For game context: We’ll cite sources like lineups.com for spread/total, StatMuse for pace stats, and defense ratings.
  • For stats: We’ll use StatMuse for Flagg’s rebounding hit rate (70%) and H2H performance, with relevant citations from January games on 1/3 and 1/29.

Thought for 6m 48s

回答

Yes — Cooper Flagg is in the NBA and is playing for Dallas (DAL) (he has an active 2025–26 NBA game log with DAL). (StatMuse)

Below is full SOP-style compliance (Step 0 → Step 7), using live injuries + line/total/spread + pace + defense + verified game logs. Since you did not upload shot-chart/graph screenshots in this message, I cannot reference them yet (once you send them, they’ll be integrated as contextual evidence, not a direction driver).

🧾 STEP 0 — DATA INTAKE PACK (MANDATORY)

0A) PROP INFO

Game: DAL @ HOU (Jan 31, 2026)
Market lines (your Underdog props):

  • Flagg 19.5 Points
  • Flagg 29.5 PRA
  • Flagg 5.5 Rebounds
  • Flagg 4.5 Assists

Game line context (for team total + blowout modeling):

  • DAL +10.5, Total 223.5 (current) (Lineups)
    Derived team totals (from spread/total):
  • DAL implied ≈ 106.5
  • HOU implied ≈ 117.0 (Lineups)

Implied Probability: Underdog Pick’em doesn’t post American odds per leg like books; the “fair” baseline is ~50% per side before payout structure. (So we must beat ~0.50 materially to call it +EV.)

0B) MINUTES VERIFICATION (source-required)

Recent minutes evidence (not guessing):

  • Flagg played 38 minutes in his most recent game (1/29). (StatMuse)
  • In the January sample shown on StatMuse, his minutes include multiple 35–38 minute games (rotation role is real, not theoretical). (StatMuse)

Rotation caveat: Spread is large (DAL +10.5), so minutes have legitimate downside in blowout (modeled in Step 1). (Lineups)

0C) USAGE & POSSESSIONS

Pace inputs:

  • DAL pace: 101.76 (StatMuse)
  • HOU pace: 96.02 (StatMuse)
    Expected possessions proxy: (101.76 + 96.02) / 2 ≈ 98.89 (StatMuse)

0D) DATA INTEGRITY SCORE (0–3)

  • Minutes/role: supported by recent game logs (good) (StatMuse)
  • Rotation volatility: meaningful due to blowout spread (risk) (Lineups)
  • Injury context verified: (see below) (ESPN.com)

✅ Data Integrity Score: 2 (Minor uncertainty — blowout/rotation risk)

🧠 STEP 1 — GAME CONTEXT & VOLATILITY

  • Blowout modeling: DAL is +10.5 → elevated 4Q-minute downside risk. (Lineups)
  • Pace drag detection: HOU pace is 96.02 (slow), pulling total possessions down vs DAL’s typical environment. (StatMuse)
  • Home/road effects: DAL has struggled on the road; HOU has been strong at home (context increases blowout risk). (Lineups)

📊 STEP 2 — MATCHUP CONTEXT

  • Defense quality: HOU team DRTG is 113.6 (8th) — solid defense environment. (NBA)
  • H2H (context only): Flagg averages ~15 pts / 6.5 reb / 3 ast in 2 games vs HOU this season (small sample, but relevant). (CBS Sports)
  • Most direct recent H2H result: vs HOU on 1/3, Flagg posted 10 pts / 7 reb / 6 ast in 35 minutes. (StatMuse)

📈 STEP 3 — STATISTICAL MODELING

Model structure (per your SOP): Minutes × possessions × adjusted rates, with explicit probability outputs.

Recent-form empirical inputs (last 10 games shown in StatMuse January log):

  • Points vary widely (includes a 49-pt spike and a 6-pt low-minute game). (StatMuse)
    This is why unders based on “matchup grades” can be traps — variance is real.

Pace adjustment: expected possessions ≈ 98.89 reduces raw expectation modestly vs DAL baseline. (StatMuse)

Projection (pace-adjusted) + Probabilities (Normal approx using recent-game variance)

  • PTS 19.5: proj ≈ 18.2, P(Over) ≈ 0.46
  • REB 5.5: proj ≈ 6.6, P(Over) ≈ 0.63
  • AST 4.5: proj ≈ 3.8, P(Over) ≈ 0.37
  • PRA 29.5: proj ≈ 28.6, P(Over) ≈ 0.48

(Variance source = recent game log distribution shown on StatMuse; not guessed.) (StatMuse)

📅 STEP 4 — DYNAMIC WEIGHTING

Applied Standard weighting (because we have season + recent + H2H context, and we are not letting any single input dominate):

  • Season 40 / L10 40 / H2H 20
    H2H used only as a small stabilizer (sample is 2 games). (CBS Sports)

🧨 STEP 5 — BEAR CASE (REQUIRED)

Minutes risk: blowout spread +10.5 can cut late-run minutes. (Lineups)
Efficiency risk: HOU is a top-10-ish defense by rating; plus Flagg’s scoring H2H average is lower than his season level. (NBA)
Structural/scheme risk: DAL is missing major creators/finishers (verified injuries below), which can increase defensive attention on Flagg. (ESPN.com)

Verified injury context (do not ignore):

  • DAL: Anthony Davis (Out), Kyrie Irving (Out), Dereck Lively II (Out), Dante Exum (Out) (ESPN.com)
  • HOU: Steven Adams (Out), Fred VanVleet (Out) (ESPN.com)

🔒 UNDER DECISION GATE (STRICT)

I am not recommending any unders here.
Even where the model leans under (PTS/AST/PRA), the structural ceiling suppression + multi-factor bear dominance + probability edge bar is not satisfied cleanly enough to fire (especially given your under-failure history + Flagg’s variance profile). So those become PASS, not “auto-under.”

📐 STEP 6 — EDGE & HOOK CHECK

Hooked lines (x.5) require a real edge vs ~0.50.

  • REB 5.5: P(Over) ≈ 0.63 → edge ≈ +0.13 over the ~0.50 baseline.
    Also, in the most recent HOU matchup he had 7 rebounds (already clears 5.5). (StatMuse)

Everything else: P(Over) is ≤ 0.48 → no edge.

🧠 STEP 7 — FINAL VALIDATION

  • Steps 0–6 completed
  • No single input dominates (pace + defense + H2H + recent logs + blowout)
  • Data Integrity ≥ 2
  • No image-only direction (none provided)

✅ Only one play clears the probability requirement.

🏁 FINAL OUTPUT FORMAT (as requested)

PROP ANALYSIS: Cooper Flagg — 5.5 Rebounds

  • Data Integrity Score: 2 (StatMuse)
  • Matchup Adjustment: HOU defense solid overall, but H2H reb baseline still ~6.5 (context). (NBA)
  • Blowout Risk: Elevated (DAL +10.5). (Lineups)
  • Minutes Scenarios: 35–38 in competitive game; downside if blowout. (StatMuse)
  • Pace / Defense / Scheme: pace drag present; not fatal at this low reb line. (StatMuse)
  • Projection & Probabilities: proj ≈ 6.6 REB, P(Over) ≈ 0.63
  • Bull Case: thin DAL roster + high minutes → reb chances stay high
  • Bear Case: blowout trims minutes; HOU size can suppress boards
  • Final Verdict: ✅ OVER 5.5 REB — Confidence: Medium — Edge Class: Moderate (+EV)

PROP ANALYSIS: Cooper Flagg — 19.5 Points

  • Final Verdict: PASS (model leans under but no Under pick without stricter structural confirmation; variance is high). (Lineups)

PROP ANALYSIS: Cooper Flagg — 4.5 Assists

  • Final Verdict: PASS (P(Over) materially below 0.50). (StatMuse)

PROP ANALYSIS: Cooper Flagg — 29.5 PRA

  • Final Verdict: PASS (projection near line; no edge). (StatMuse)

If you upload the shot chart + graph % distribution screenshots for Flagg (and ideally HOU’s zone ranks/edges), I’ll rerun Steps 0–7 with those explicitly referenced — and if the visuals conflict with the role/pace/defense model, I’ll either reconcile the conflict in writing or PASS (no forcing).

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