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424,069 observationssnapshot 2026-10-15Registry

Sports · 4d · asked 2026-10-08 · scored 2026-10-11

Will the Green Bay Packers beat the Chicago Bears at home on October 11, 2026?

4 seats worked this question alone; the panel agreed within a few points, so no debate was run. Code ran every test and computed the number. How the desk works

Committee56%logodds poolLog-odds pool How the committee number is made: each seat's probability is turned into log-odds, averaged with calibration weights, and turned back. Deterministic. The portfolio manager explains it; it does not pick it. glossary · PM stated 56%
Market at the time45%committee minus market +11%
ResolvedYesBrierBrier score How wrong a probability was, squared. Say 70% and it happens: (0.7 − 1)² = 0.09. Say 70% and it doesn't: 0.49. Lower is better; always saying 50% scores 0.25, so anything above 0.25 is worse than a coin flip. glossary 0.196 vs marketMarket Brier The same squared error, but for the Kalshi price at the moment the question was asked. It is the bar: beating it means knowing something the price did not. glossary 0.308 · lower is better

Judged by Kalshi KXNFLGAME-26OCT11CHIGB-GB settlement at 2026-10-11: Kalshi KXNFLGAME-26OCT11CHIGB-GB: resolves YES if Green Bay Packers win

0%50%100%
Sports Analyst56%
Quantitative Researcher54%
Market Positioning Analyst54%
Causal Skeptic59%
Committee56%

Hollow: before the debate. Gold: after. Blue rule: the market. Green rule: what happened. Debate skipped: the seats agreed within a few points. Sat out: Historical Analog Analyst.

The dispute that matteredD nudges above the market/base-rate consensus (0.59) using an explicit, admittedly untested domain prior about historical Packers dominance over the Bears at home, while A/B/C anchor almost entirely to the converged Kalshi market price (~0.549-0.555) and treat any deviation as unsupported. Resolved by downweighting D's idiosyncratic adjustment (it is explicitly flagged by D as unverified against a placebo test) but not discarding it, since it is the one piece of evidence not duplicative of the shared market series -- this pulls the pooled estimate slightly above the raw market print (0.55) toward 0.56, matching the deterministic log-odds pool.

Synthesis

Consensus settles at 0.56, matching the deterministic calibration-weighted log-odds pool, because the four analysts' post-debate views (0.54-0.59) cluster tightly and three of the four are effectively re-reading the same converged market-price series rather than contributing independent evidence. The committee's job here was less about reconciling genuinely different information and more about recognizing that 'four analysts' really means 'two lines of evidence': (1) a stable, low-volatility market price around 0.53-0.56, and (2) a generic home-field base rate nudged upward by the Causal Skeptic's explicitly unverified claim about Packers dominance over the Bears. I weighted the market-price line heavily (it is observed, liquid, and autocorrelated in a way consistent with genuine convergence, not noise) and gave D's independent-but-speculative adjustment enough weight to justify sitting slightly above the raw 0.549-0.555 market print, landing at 0.56. No analyst's secondary statistical tests (lift ratios, correlations) reached significance, so none of them should be treated as an edge -- this is reflected in the low statistical_quality score (0.4). Tradeability verdict: NOT tradeable as a standalone edge against the current market price. The pooled 0.56 is within noise of the observed market consensus (~0.55), and expected return from taking either side at the current implied price is near zero (~1-2 cents). If an expression is wanted, the only defensible one is a conditional limit order: buy GB only if the market-implied probability drops below ~0.50 (a 5-6 point discount to the pooled consensus) or sell/fade GB only if it rises above ~0.62 (richer than the CI upper bound) -- i.e., trade the tails of the stated credible interval, not the midpoint. Absent such a dislocation, the recommendation is to pass and treat this as a priced, efficient market.
Observed · in the data
  • A Kalshi (or similar) GB-win contract series, n=101, mean 0.549, sd~0.0215, last observed 0.535, highly autocorrelated (0.76).
  • A second market-implied series, n=213, mean 0.501, with recent values rising to ~0.555 near game date.
  • Conditional-probability/lift tests (p=0.563-0.688) on small samples (n=10) that do not reach statistical significance and have CIs crossing zero.
  • A negative correlation (-0.886) between two price-like series, flagged as autocorrelation-inflated.
Modeled · a tool computed it
  • Calibration-weighted log-odds pooling of the four analysts' post-debate probabilities, producing the deterministic committee number of 0.56.
  • Each analyst's influence_weight and router weight (relevance x calibration) as computed by the system from historical segment performance.
Inferred · the committee's reasoning
  • The market has converged on GB as a modest favorite (~54-56% implied) with low recent volatility -- i.e., the price is a stable consensus, not noise.
  • None of the secondary statistical signals (lift tests, correlation tests) constitute a validated edge over the market price.
  • Evidence cited by A, B, and C substantially overlaps (same underlying market series), reducing the number of truly independent evidence lines to roughly two: (1) the market price series, (2) generic home-field-advantage base rate plus D's unverified rivalry-history prior.
Predicted · what it expects
  • GB wins the October 11, 2026 home game vs. Chicago with probability ~0.56, within credible interval [0.50, 0.62].
  • No statistically defensible edge exists over the prevailing market price (~0.55); expected return from trading this view is near zero (~1-2 cents per dollar) absent a price dislocation.
Uncertain · what it does not know
  • Whether the underlying data handles (d1, d2, s1-s4) are truly specific to the GB-Bears matchup or are generic/mislabeled series, as explicitly flagged by the Causal Skeptic.
  • Whether D's 'historical Packers dominance over Bears at home' adjustment reflects a real, persistent effect or is an untested narrative prior.
  • Whether the small-n conditional-probability tests (n=10) reflect any real signal versus pure sampling noise.
  • Injury reports, weather, and lineup news between now (2026-10-07) and kickoff (2026-10-11) that could shift the true probability outside the stated CI.
How the seats were weighed · not an average
Sports Analyst (A)0.27Highest domain relevance to sports (1.0) and highest router weight (0.98), but calibration sample is small (n=6, accuracy 0.33) and its core evidence (Kalshi series s3, s4) is the same market-price series B and C also cite -- not independent. Weighted up for relevance, down for small-n calibration and evidence overlap.
Quantitative Researcher (B)0.26Larger calibration sample (n=22) and sound statistical hygiene (flags non-significance explicitly), but domain relevance to sports is only moderate (0.7) and its two series (d1, d2) are the same underlying Kalshi/market price line already used by A and C -- duplicated evidence, not a second independent line.
Market Positioning Analyst (C)0.22Good relevance (0.9) and reasonable calibration (n=25), but lowest accuracy (0.28) in-segment and its entire case rests on the same d1 Kalshi series plus a correlation (s3) explicitly flagged as autocorrelation-inflated. Treated as the third voice on one evidence line, so weight is capped.
Causal Skeptic (D)0.25Only analyst offering a genuinely independent evidence line -- the NFL home-favorite base rate plus an explicit, labeled 'untested' domain adjustment for Packers-over-Bears history -- rather than re-reading the same market series. Lower stated confidence (0.35) and lower accuracy (0.28) pull weight down, but independence and honesty about the adjustment's untested status pull it back up.
Wrong if
  • If the closing Kalshi/market price for GB at kickoff on 2026-10-11 is outside [0.50, 0.62], the committee's CI was miscalibrated and should be revisited before relying on this process again.
  • If Chicago wins outright, this specific forecast resolves false (expected under the stated ~44% probability of a Bears win, not alone sufficient to indict the model).
  • If a post-hoc check of NFL divisional home-favorite games in the 2020-2025 seasons shows a base rate materially different from ~55% (e.g., outside 50-60%), the base-rate anchor used by all analysts should be revised.
  • If D's claimed 'historical Packers dominance over Bears at home' is checked against actual GB-Chicago home records since the rivalry's modern era and the home win rate for GB specifically is not meaningfully above the generic home-field rate (~55%), D's upward adjustment should be discounted to zero in future similar matchups.
Independent lines of evidence
  • Market price consensus: a converged, low-volatility Kalshi-style contract price for GB clustering at 0.53-0.56 in the days before kickoff (cited in overlapping form by A, B, C).
  • Generic NFL home-field base rate (~55-57%) plus an explicitly unverified domain prior about Packers' historical record over the Bears at home (D only).

Counted once: Kalshi/market-implied GB-win probability series (A's s3/d5, B's d1/d2, C's s1/s3) -- all reference the same or highly correlated market-price data (n~101-213, mean 0.50-0.55, high autocorrelation 0.76) and are collapsed into a single 'market price' evidence line.; Conditional-probability lift tests (A's s4, D's s1) both report small-n (n=10) lift estimates (1.11-1.15x base rate) with p-values far from significance (0.56-0.69) -- collapsed into one 'non-significant signal test' line.

When this holds

Applies to a single NFL regular-season divisional matchup with a short (4-day) pre-game horizon where market pricing has already converged; does not generalize to playoff games, early-season games with sparse market history, or matchups lacking a liquid pre-game contract. Home-field-advantage base rates used here reflect pooled historical NFL data, not a Packers-Bears-specific causal estimate validated in this analysis.

Model uncertainty

The pooled estimate rests on calibration statistics (accuracy 0.28-0.33, Brier ~0.28-0.31) that are themselves mediocre -- none of the four analysts has strong in-segment accuracy, so the weighting scheme is distinguishing between four similarly weak track records rather than selecting a clearly superior one. Additionally, three of four analysts draw on the same underlying market-price series, so the 'four independent views' framing overstates true evidence diversity; effectively this is a two-line-of-evidence problem (market price + base-rate/domain prior), which should temper confidence in the tightness of the stated CI.

The seats

Each analyst's own memo, evidence and tests. Open a seat to read it.

Sports Analyst56%confidence 45%

Objective: translate sporting information into win probabilities and compare them to betting-market prices. Injuries (who, position, replacement quality), rest and schedule, home advantage, form vs. noise, and how fast lines move after news. You care about the *market reaction function*: which news the line absorbs in minutes and which it underreacts to. A 60% team is only interesting if the market says 52%. Discipline: state the prediction cutoff (kickoff) and use nothing after it. Team series live under NFL-<ABBR> (margin, win, is_home, points_for/against, espn_spread, espn_total, kalshi_win_prob, injuries_out, injuries_questionable). Estimate team effects with partial pooling and report uncertainty, not point claims. Model dispersion as well as the mean. Beat a simple baseline (home field + the market line) out of sample before believing any feature. Injury counts are a proxy for who is missing; say so.

Thesis. Market-implied probability for Green Bay (home, divisional favorite) has stabilized around 54-56% over the final week of pricing, and nothing in the point-differential or injury-signal data provides a statistically significant edge against that price, so the independent estimate should sit close to the market rather than materially diverge from it.
Base rate

57% NFL home team base win rate across the league, unconditional

The best case against

GB's trailing 7-game point-differential (d1, mean -3.86, sd 15.4) is negative while CHI's trailing differential (d2, mean +8.86) is strongly positive over the same window; if this reflects real current form (e.g., Love injury or offensive line issues) rather than noise, the market's 55% GB price is underreacting to a Bears team playing better football right now, and the true line should be closer to a toss-up or even favor Chicago.

Strongest evidence
  • Kalshi win-prob series (n=101) for this contract has mean 0.549, median 0.55, 95th pct 0.59, with strong autocorrelation (0.76) indicating a converged, sticky market price around 54-55% for GB.
  • Most recent observed values cluster at 0.535 before rising, consistent with the market drifting slightly toward GB as kickoff approaches.
  • Conditional probability test of a signal vs GB win outcome: P=0.6 vs base rate 0.52, lift 1.15, but p=0.688 and CI [-0.22,0.33] crosses zero on n=10 -- not statistically distinguishable from noise.
Wrong if

If the Kalshi KXNFLGAME-26OCT11CHIGB-GB price moves below 0.50 at any point before kickoff on 2026-10-11 (e.g., following a confirmed Jordan Love or key starter injury designation), this estimate is falsified and should be revised toward Chicago.

Assumes
  • d5/s3 series represents the Kalshi-implied win probability specifically for GB in this matchup, converging toward settlement.
  • d1/d2 are GB and CHI margin-of-victory series over their last 7 games respectively (not matchup-specific), used only as noisy form indicators.
  • d3 (n=25, mean 0.52) approximates a longer-run GB win-rate baseline; d4 (mean 0.40) is a secondary covariate of uncertain direct relevance.
  • No injury report beyond what's encoded in d6/d7 (out/questionable counts, both teams in the 3-11 range) is available as of 2026-10-08 02:30 UTC.
  • Nothing published after the as_of cutoff (game is Oct 11; cutoff is Oct 8 02:30 UTC) is used.
Memo to the committee
Base rate for NFL home teams is ~57%. The specific Kalshi market for this GB-CHI game has converged near 54-56% (s3), with rising recent values, and I see no statistically significant signal (s4, p=0.688, CI crosses zero) to justify deviating materially from that price. The main internal tension is GB's negative trailing point differential vs CHI's strongly positive one (s1 vs s2), but both series show negative autocorrelation (mean-reversion), making them weak evidence of true current form versus noise. I set probability at 0.56, essentially tracking the market median with a small haircut for unresolved injury-count ambiguity (d6/d7) and the possibility the market is underpricing Bears momentum. Confidence is moderate (0.45) given small samples throughout and unclear series-to-team mapping for several handles (d3, d4, d6, d7).
Rebuttal
debate skipped: panel unanimous within 0.05

Would change my mind: If the Kalshi KXNFLGAME-26OCT11CHIGB-GB price moves below 0.50 at any point before kickoff on 2026-10-11 (e.g., following a confirmed Jordan Love or key starter injury designation), this estimate is falsified and should be revised toward Chicago.

After the debate · 56% → 56%

debate skipped: panel unanimous within 0.05

Data and tests · 15, as of

small sample (n=10 < 20)

Quantitative Researcher54%confidence 45%

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Market Positioning Analyst54%confidence 45%

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Causal Skeptic59%confidence 35%

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Claims and evidence

Observation · seen in the data · 7
Model · a tool computed it · 3
Inference · reasoned from the above · 5
Forecast · what a seat expects · 1
Uncertainty · what a seat does not know · 3

The debate

Who challenged whom · severity 0 to 1
SportsQuantiMarketCausal
Sports
Quantitative
Market Posit
Causal Skept
Sealed views · Each seat wrote alone. Nobody had seen another view.
Sports Analyst
56% · confidence 45%

Market-implied probability for Green Bay (home, divisional favorite) has stabilized around 54-56% over the final week of pricing, and nothing in the point-differential or injury-signal data provides a statistically significant edge against that price, so the independent estimate should sit close to the market rather than materially diverge from it.

Own objection: GB's trailing 7-game point-differential (d1, mean -3.86, sd 15.4) is negative while CHI's trailing differential (d2, mean +8.86) is strongly positive over the same window; if this reflects real current form (e.g., Love injury or offensive line issues) rather than noise, the market's 55% GB price is underreacting to a Bears team playing better football right now, and the true line should be closer to a toss-up or even favor Chicago.

Quantitative Researcher
54% · confidence 45%

Available market-implied probability series for a GB win are mixed (0.50-0.56 range) and all secondary statistical signals (conditional-probability lifts, mean-difference tests, bootstrap CIs on point differentials) fail to reach significance, so the best estimate is close to the historical home-team base rate, modestly discounted for signal ambiguity, landing near 0.54.

Own objection: The two primary market series (d1, d2) disagree directionally over time and are strongly negatively correlated (s7: corr=-0.8863, p=7e-35, though flagged as inflated by autocorrelation). This suggests they may not be measuring the same underlying quantity cleanly, or one is stale/mispriced relative to the other, making it hard to pin a single consensus probability. If d2's more recent uptick to 0.555 is the better predictor near game time, true YES probability could be meaningfully higher than my point estimate; conversely if d1's declining trend (mean 0.549 to last 0.535) is the more reliable signal, probability could be lower. My estimate essentially splits this disagreement, which is a weak resolution of real uncertainty rather than a sharp inference.

Market Positioning Analyst
54% · confidence 45%

The Kalshi GB-win contract (d1) is a tight, highly autocorrelated series sitting at ~0.535-0.55 with very low recent volatility, indicating the market has already converged on a near-coin-flip-with-slight-home-edge price. There is little evidence of a mispriced crowd trade (no sustained one-sided drift, no open blow-off move that didn't mean-revert except one isolated spike in a less-trusted series). Edge over the current market print is limited; the fundamental 'Packers should win at home' case is already substantially priced in.

Own objection: d7 shows a sharp spike to 0.995 on 2026-10-04 (far outside its own 95th percentile of 0.735), which could reflect a real information shock (e.g., injury news to a Bears starter) that the d1 series (sd=0.0215, essentially flat) has NOT yet fully incorporated - if true, d1 at 0.535 could be stale and the true market-clearing probability is materially higher, meaning there IS an edge to buying GB now before the price catches up.

Causal Skeptic
59% · confidence 35%

The base rate for an NFL home team win is approximately 55%. None of the provided statistical tests (s1, s2, s3, s4, s8) achieve significance, and several carry explicit reliability warnings (small n, strong autocorrelation, optimistic p-values). The data handles are not clearly labeled as Packers/Bears-specific, raising selection and reverse-causality concerns (especially for d7, which behaves like a market price series with high autocorrelation). I find no statistically validated causal signal beyond generic home-field advantage. I nudge the probability modestly above the base rate (0.55 to 0.59) using unverified domain knowledge of historical Packers dominance over the Bears at home, but this adjustment is itself untested against a placebo comparison and should be treated with low-to-moderate confidence.

Own objection: If the unlabeled series d1-d8 actually do encode Packers/Bears-specific recent form, injury trends, or market movement, then dismissing them as noise because individual tests are underpowered (n=12-50) risks a false negative — real but modest effects (Cohen's d=0.44 in s3) are plausible but simply not detectable at these sample sizes. Historical Packers dominance over the Bears at Lambeau is a real, well-documented pattern independent of any of these noisy proxies, and that alone could justify a probability above the generic home-field base rate even without statistically significant confirmation here.

Revisions · Every change of mind pinned to one piece of evidence.
Sports Analyst
56% → 56%

debate skipped: panel unanimous within 0.05

Quantitative Researcher
54% → 54%

debate skipped: panel unanimous within 0.05

Market Positioning Analyst
54% → 54%

debate skipped: panel unanimous within 0.05

Causal Skeptic
59% → 59%

debate skipped: panel unanimous within 0.05

Synthesis · The portfolio manager weighs the seats. Not an average.
Portfolio Manager

Consensus settles at 0.56, matching the deterministic calibration-weighted log-odds pool, because the four analysts' post-debate views (0.54-0.59) cluster tightly and three of the four are effectively re-reading the same converged market-price series rather than contributing independent evidence. The committee's job here was less about reconciling genuinely different information and more about recognizing that 'four analysts' really means 'two lines of evidence': (1) a stable, low-volatility market price around 0.53-0.56, and (2) a generic home-field base rate nudged upward by the Causal Skeptic's explicitly unverified claim about Packers dominance over the Bears. I weighted the market-price line heavily (it is observed, liquid, and autocorrelated in a way consistent with genuine convergence, not noise) and gave D's independent-but-speculative adjustment enough weight to justify sitting slightly above the raw 0.549-0.555 market print, landing at 0.56. No analyst's secondary statistical tests (lift ratios, correlations) reached significance, so none of them should be treated as an edge -- this is reflected in the low statistical_quality score (0.4). Tradeability verdict: NOT tradeable as a standalone edge against the current market price. The pooled 0.56 is within noise of the observed market consensus (~0.55), and expected return from taking either side at the current implied price is near zero (~1-2 cents). If an expression is wanted, the only defensible one is a conditional limit order: buy GB only if the market-implied probability drops below ~0.50 (a 5-6 point discount to the pooled consensus) or sell/fade GB only if it rises above ~0.62 (richer than the CI upper bound) -- i.e., trade the tails of the stated credible interval, not the midpoint. Absent such a dislocation, the recommendation is to pass and treat this as a priced, efficient market.

Consensus 56% · interval 50% to 62%

Assumptions

Holding · 16
  • holding
    d5/s3 series represents the Kalshi-implied win probability specifically for GB in this matchup, converging toward settlement.
    Sports Analyst
  • holding
    d1/d2 are GB and CHI margin-of-victory series over their last 7 games respectively (not matchup-specific), used only as noisy form indicators.
    Sports Analyst
  • holding
    d3 (n=25, mean 0.52) approximates a longer-run GB win-rate baseline; d4 (mean 0.40) is a secondary covariate of uncertain direct relevance.
    Sports Analyst
  • holding
    No injury report beyond what's encoded in d6/d7 (out/questionable counts, both teams in the 3-11 range) is available as of 2026-10-08 02:30 UTC.
    Sports Analyst
  • holding
    Nothing published after the as_of cutoff (game is Oct 11; cutoff is Oct 8 02:30 UTC) is used.
    Sports Analyst
  • holding
    d1 and d2 are point-in-time market-implied probability series for GB winning (or closely related proxies), updated through 2026-10-07/08.
    Quantitative Researcher
  • holding
    d3-d6 are binary outcome/signal indicators from a reference sample of n=49-50 prior games (possibly GB or similar matchup history), used for conditional-probability and mean-difference tests.
    Quantitative Researcher
  • holding
    d7/d8 are point-differential series (e.g., margin or a performance metric) for the same reference sample.
    Quantitative Researcher
  • holding
    No injury report, weather, or line-movement data beyond what's in d1/d2 was provided, so I cannot adjust for those directly.
    Quantitative Researcher
  • holding
    d1 is the closest available proxy to the actual Kalshi KXNFLGAME-26OCT11CHIGB-GB settlement price series.
    Market Positioning Analyst
  • holding
    d7's 0.995 print is either noise/a different instrument or a transient overreaction that reverted, since d1/d8 show no corresponding jump as of their latest timestamps.
    Market Positioning Analyst
  • holding
    No injury or lineup news post 2026-10-08 02:30 UTC is available to me.
    Market Positioning Analyst
  • holding
    d1-d4 are binary outcome/signal indicators pooled across many NFL games, not Packers/Bears-specific history, given n=49-50 which exceeds any single team's season game count
    Causal Skeptic
  • holding
    d7's narrow band (0.47-0.605) and high autocorrelation (0.76) suggest it is a market-implied probability series, not a fundamental driver
    Causal Skeptic
  • holding
    d8 (n=16, integer range 3-9) is of unknown semantic meaning (possibly weather, rest days, or injury count) and cannot be causally linked to this specific game without a label
    Causal Skeptic
  • holding
    No data handle explicitly isolates Packers or Bears team-level performance, QB status, or injuries for this specific matchup
    Causal Skeptic

Simulated trade

The portfolio manager proposed no paper trade. No instrument in the available catalog (HENRY_HUB, UNG, XLE, VLO, MPC, PSX, CORN, BTC, ETH, WTI) has any defensible causal or statistical linkage to an NFL game outcome -- there is no sports-outcome market in this book, so the pooled view cannot be expressed as a trade at all. Independently, even if such a market existed, the pooled P=0.56 CI=[0.50,0.62] is within noise of the observed market consensus (~0.53-0.56) and offers near-zero expected return (~1-2 cents) after costs and crowding (0.8). OBSERVED: four analyst estimates cluster 0.54-0.59. INFERRED: three of four are restating the same market-price series, so independent evidence is really two lines (market price + one unverified historical-dominance adjustment), which is why statistical_quality is scored low (0.4) despite tight clustering. MODELED: calibration-weighted log-odds pool = 0.56. PREDICTED: E[r]~0.01 at the midpoint, inside transaction-cost/noise band. UNCERTAIN: true live market-implied price, since no odds-feed tool was queried this session; also uncertain whether any late-week injury/weather news before 2026-10-11 shifts the line materially. Decision: decline to propose a trade -- pass and treat as a priced, efficient market with no available instrument to express it anyway.

Forecasts

SeatStagePBaseResolvesOutcomeBrier
Committeefinal56%—2026-10-11Yes0.196
Sports Analystprior62%57%2026-10-11Yes0.144
Sports Analystindependent56%57%2026-10-11Yes0.194
Sports Analystrevised56%57%2026-10-11Yes0.194
Quantitative Researcherprior62%57%2026-10-11Yes0.144
Quantitative Researcherindependent54%57%2026-10-11Yes0.212
Quantitative Researcherrevised54%57%2026-10-11Yes0.212
Market Positioning Analystprior62%58%2026-10-11Yes0.144
Market Positioning Analystindependent54%58%2026-10-11Yes0.212
Market Positioning Analystrevised54%58%2026-10-11Yes0.212
Causal Skepticprior62%55%2026-10-11Yes0.144
Causal Skepticindependent59%55%2026-10-11Yes0.168
Causal Skepticrevised59%55%2026-10-11Yes0.168

Reasoning graph

Every observation, test, claim, objection and forecast, as a graph
observationmemorytestassumptionclaimcounterargumentopinionforecasttrade
testhypothesis_test: mean(on) − mean(of…claim · observation · 75%The market-implied probability of a…observationNFL-GB.margin [level]opinionSports Analysttestconditional_probability: P(outcome|…claim · observation · 60%GB's recent (7-game) point differen…testconditional_probability: P(outcome|…counterargumentNegative autocorrelation suggests m…testinfluence: corr=0.11844484241978356claim · inference · 40%An injury/rest-based signal tested …testdescribe: mean=0.5490099009900987claim · observation · 50%Injury counts (out/questionable) fo…testdescribe: mean=5.0625counterargumentWithout knowing which series maps t…claim · forecast · 55%The question resolves via Kalshi se…observationNFL-GB.win [level]claim · observation · 65%The market-implied probability of a…observationNFL-GB.is_home [level]opinionQuantitative Researcherclaim · inference · 70%Binary conditional-probability test…counterargumentWide confidence intervals crossing …testdescribe: mean=8.857142857142858claim · uncertainty · 75%Point-differential means (d7, d8) i…testcorrelation: corr=-0.43231520519815…claim · inference · 55%A mean-difference test (s8) splitti…counterargumentModerate effect size (d=0.44) in un…claim · model · 80%No single test in the provided evid…claim · observation · 75%The Kalshi-proxy price d1 has been …counterargumentd1 sample may be a weighted/smoothe…opinionMarket Positioning Analystclaim · observation · 55%A distinct series (d7) shows an out…counterargumentCould be a different, possibly unre…claim · inference · 50%d2 and d8 (likely the complementary…observationNFL-GB.injuries_out [level]counterargumentIf d2/d8 are literally the same und…claim · model · 50%Given the tight, autocorrelated pri…counterargumentd7's anomalous spike and divergence…claim · observation · 85%None of the statistical tests provi…counterargumentAll point estimates are directional…opinionCausal Skepticclaim · uncertainty · 60%The data handles (d1-d8) are not de…observationNFL-CHI.win [level]counterargumentThese could be rolling/aggregated f…claim · inference · 55%d7's high autocorrelation (0.76) an…counterargumentIf d7 is an independent fundamental…claim · uncertainty · 80%s8's correlation (-0.4323) is flagg…claim · model · 40%Historical Packers home dominance o…counterargumentNo placebo test (e.g., Packers vs. …forecastP=0.56 Will the Green Bay Packers b…
Click a node. Every conclusion traces back to observations and tests.