Tailfifty desk
461,287 observationssnapshot 2026-10-20Registry

Sports · 5d · asked 2026-10-19 · scores 2026-10-23

Will the Chicago Bears beat the New England Patriots at home on October 23, 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

Committee54%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 54%
Market at the time60%committee minus market −5%
Resolves2026-10-23

Judged by Kalshi KXNFLGAME-26OCT22NECHI-CHI settlement at 2026-10-23: Kalshi KXNFLGAME-26OCT22NECHI-CHI: resolves YES if Chicago Bears win

0%50%100%
Sports Analyst56%
Quantitative Researcher54%
Market Positioning Analyst53%
Causal Skeptic54%
Committee54%

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

The dispute that matteredWhether the statistically significant home-field margin effect (s4/stat_01M593A24F5PGJ608W0C43, p=0.0101, d=0.75) is real incremental signal beyond the market price (A and B's implicit view, justifying a modest upward nudge toward 0.54-0.56) or is a small-sample, likely-multiple-comparisons artifact that should be discounted entirely in favor of anchoring to the market (D's explicit critique, backed by C's finding that the market is tightly converged and showing no drift). I resolved this in favor of D/C: with n=25-26 per bucket, no stated correction for the number of candidate signals tested, and a regime split that flips sign (per D), the margin-effect test does not survive scrutiny as an independent causal driver. The Kalshi market (98 ticks, sd=0.013, tight bootstrap CI) is treated as the dominant, already-aggregated information source, and the margin test is downweighted to a minor supporting role rather than a basis for diverging from market price.

Synthesis

Base rate: generic NFL home favorites win roughly 57-58% historically; team-specific, market-adjusted base rate for a ~1-2 point home favorite is closer to 53-55%. The committee's pooled estimate of 0.54 is essentially a reaffirmation of the market price, not a meaningful update away from it -- three of four analysts (B, C, D) are substantially restating the same Kalshi series, and the only genuinely new information (A's NE injury signal) is unverified by peers. I weight D most heavily because its critique (small-sample margin test, regime sign-flip, non-significant lift test) is the only analysis that interrogates rather than restates the market price, and that critique is what keeps the consensus anchored near 0.54 rather than drifting toward A's 0.56. Net view: 0.54, CI [0.49, 0.59], consistent with the deterministic pool. Tradeability: NOT tradeable at current pricing. The de-vigged market probability (~0.535) and the committee consensus (0.54) differ by less than half a percentage point -- smaller than the ~0.6% overround and well within the noise of analysts whose segment-level accuracy (0.38-0.50) is near coin-flip. Any directional bet on Chicago at current Kalshi prices has an expected edge (~0.4pp) that does not clear transaction costs or the vig. If a position is taken, it should be sized as a minimal/no-trade signal, not a conviction bet; the only scenario that would make this tradeable is a confirmed, material, asymmetric injury/lineup update on New England between now and kickoff that the market has not yet priced -- watch for Kalshi price moving outside [0.49, 0.59] as the trigger to revisit.
Observed · in the data
  • Kalshi market price for Bears-win contract has traded in a tight 0.535-0.54 range across ~98-105 recent ticks as of 2026-10-17, with low variance (sd≈0.013).
  • The two-sided market (CHI vs NE implied probability) sums to roughly 1.006, a ~0.6% overround.
  • In the Bears' own historical game log, point margin averages +7.46 when a home/signal flag is on vs -3.08 when off (Welch t-test, p=0.0101, n≈25-26 per group).
  • A conditional-probability test on a separate predictive signal showed only a 1.12x lift over base rate (CI95 [-0.125, 0.233], p=0.404).
Modeled · a tool computed it
  • Calibration-weighted log-odds pooling of the four analysts' post-debate probabilities (0.56, 0.54, 0.53, 0.54) yields a deterministic committee estimate of 0.54.
  • De-vigged market-implied probability for Chicago computed as 0.538/(0.538+0.468) ≈ 0.535.
  • Bootstrap 95% CI on the market price series: [0.5353, 0.5405].
Inferred · the committee's reasoning
  • The market price has already incorporated most public information (injuries, rest, travel, home-field edge) given its stability over ~100 ticks and lack of late drift.
  • The margin-effect test (s4) should be heavily discounted for small-sample size and likely multiple-comparisons/selection bias since it is one of several candidate signals examined.
  • New England injury signals (d6/d7) may justify a small marginal probability nudge above the raw market price, but this is not independently verified by any other analyst.
Predicted · what it expects
  • Chicago Bears win probability over New England at home on 2026-10-23: point estimate 0.54, 90% credible interval [0.49, 0.59].
  • Expected return from taking the Bears-win side at the current market price (~0.538) is approximately +0.4 percentage points of edge before transaction costs/vig -- i.e., statistically indistinguishable from zero and likely consumed by the ~0.6% overround.
Uncertain · what it does not know
  • Whether the home-field margin effect (p=0.0101) reflects a genuine, stable causal mechanism for this specific matchup or is a non-replicable artifact of a small, cherry-picked sample.
  • Whether New England's injury situation (cited only by Analyst A) materially shifts the true win probability beyond what the market has already priced -- this evidence is not corroborated by B, C, or D.
  • Whether any late-breaking injury, weather, or lineup news between 2026-10-19 and game time on 2026-10-23 will move the market away from its current tight 0.535-0.54 cluster.
  • The true out-of-sample calibration of all four analysts in this exact segment, given n=12-42 and accuracy ranging 0.38-0.50, which is close to coin-flip and limits confidence in any analyst's edge over the market.
How the seats were weighed · not an average
Sports Analyst (A)0.30Highest domain relevance (sports=1.0) and only analyst citing an independent, game-specific signal (New England injury reports d6/d7) not referenced by others. Calibration in-segment is mediocre (accuracy 0.50, Brier 0.265, n=12, small sample) so weight is capped despite relevance. Partially duplicates the Kalshi market series already used by B/C/D.
Quantitative Researcher (B)0.15Decent calibration (n=39, Brier 0.289) but moderate sports relevance (0.7) and its primary evidence (market price d1, home-field margin stat) duplicates what C and D already use as their core signal. Adds the R2=0.13 caveat but doesn't introduce a truly independent data source.
Market Positioning Analyst (C)0.20Strong market/positioning relevance and the most rigorous treatment of the market-price series itself (bootstrap CI, de-vigged probability, overround check). This is high-quality market microstructure evidence, but it is the SAME underlying Kalshi series A, B, and D all lean on -- counted once as the dominant evidence line, not stacked four times.
Causal Skeptic (D)0.35Highest relevance score for cross-domain methodological rigor and the only analyst who independently stress-tested the statistical claims (flagged multiple-comparisons risk, regime sign-flip in s4, and non-significant lift in s2). This is genuinely independent reasoning (a critique, not a restatement of the market price) and directly resolves the committee's main tension, so it earns the largest weight despite similar raw calibration stats to B.
Wrong if
  • If the Kalshi Bears-win contract price moves outside the [0.49, 0.59] band before kickoff on 2026-10-23 without a corresponding update in this synthesis, the consensus is falsified as stale.
  • If Chicago wins by a margin inconsistent with the home-field effect described in s4 (e.g., loses outright, or wins by >20 with no home/away signal differentiation), the claimed home-field margin mechanism (s4) is undermined and should be excluded from future pooling.
  • If post-game review shows New England's injury list (cited in A's d6/d7) was materially less severe than implied, Analyst A's unique evidence line should be discounted in future calibration.
  • Resolution event: Bears win vs. lose on 2026-10-23 directly falsifies or confirms the directional call; a Bears loss despite the 0.54 consensus is within the stated uncertainty band and not itself proof of miscalibration unless it recurs systematically across similar games.
Independent lines of evidence
  • Kalshi market price series (d1/d2) -- cited by all four analysts, counted once as the dominant, already-aggregated information source.
  • Home-field point-margin statistical test (s4 / stat_01M593A24F5PGJ608W0C43) -- cited by A and B on the same underlying game-log data, counted once, and critically re-examined (not corroborated) by D.
  • Causal Skeptic's methodological critique (multiple-comparisons flag, regime sign-flip, non-significant conditional lift test s2) -- genuinely independent reasoning layer, not a new data source but a distinct analytical check.
  • New England injury signals (d6/d7) -- cited only by Analyst A, uncorroborated by any other panel member.

Counted once: Kalshi market price for Bears-win contract (d1) is cited independently by Sports Analyst (A), Quantitative Researcher (B), Market Positioning Analyst (C), and Causal Skeptic (D) -- this is one evidence line, not four.; The home-field margin t-test (s4 in A's notes, stat_01M593A24F5PGJ608W0C43 in B's notes) appears to be the same underlying statistical result cited by both A and B -- collapsed to one line.

When this holds

Applies to a single NFL regular-season home-favorite game with a liquid, converged prediction market; does not generalize to games with thin/illiquid markets, playoff stakes, or teams with recent coaching/QB changes not reflected in the historical game log used for the margin-effect test.

Model uncertainty

All four analysts' calibration stats (n=12-42, accuracy 0.38-0.50, Brier 0.265-0.300) are close to chance-level performance in this specific NFL segment, meaning the router weights themselves carry substantial estimation noise; the pooled 0.54 should not be treated as sharply more informative than the raw market price of ~0.535-0.54 it was largely derived from.

The seats

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

Sports Analyst56%confidence 35%

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. Chicago is a slight home favorite over New England; the Kalshi market (~53.5-54%) already captures most of the home-field edge, and the significant margin effect (s4) plus rising New England injury signals (d6/d7) justify only a small upward nudge, not a large divergence from market price.
Base rate

55% League-wide home-team win rate in the NFL (long-run, unconditional on team quality), roughly 0.55-0.57; I use 0.55 as the generic prior for a home favorite in a pick'em-ish matchup.

The best case against

The win-rate conditional test (s3) found only a 6.6pp lift from the same signal with p=0.404 (not significant), and a bootstrap CI on the overall win rate (s7) spans [0.41, 0.69] — the data cannot reject a coin-flip outcome. The significant result is on margin (s4), not on win probability directly, and margin dispersion is huge (sd~15 points, s2/s6), so a positive expected margin does not reliably translate into a win. My 0.56 estimate leans on a margin effect that may not cross the win/loss threshold reliably, and the market (0.537) may already be the better-calibrated number.

Strongest evidence
  • Welch t-test: margin when 'signal' (likely home-field) is on averages +7.46 vs -3.08 off, diff=10.54, p=0.0101, Cohen's d=0.75 — a statistically significant home-field margin effect in this team's own game log.
  • Kalshi market itself is pricing Chicago at ~53.5-54% win probability as of 2026-10-17, very close to my estimate, suggesting the market has already priced in most of the home-field edge.
Wrong if

If, by kickoff on 2026-10-23 (prediction cutoff), the Kalshi market price for CHI drifts below 0.50 on fresh injury/line news, or if New England's injuries_out count (d6) drops back below 4 while d7 reverts to 0 (indicating key players return), I will revise my probability down toward 0.50 or below, since the injury-driven edge would be eroded.

Assumes
  • d1 series represents the Kalshi market-implied win probability for Chicago (home team) in the CHI-NE game, with recent prints ~0.535-0.54
  • d3 is Chicago's game-level win indicator (1=win) over the last ~51 games; d4 is an is_home indicator with similar mean (~0.51), used as the 'signal' in s3/s4
  • d2/d5 are point-differential (margin) series, one for CHI games generally and one restricted to a subset (e.g., home games), used in the Welch t-test (s4)
  • d6/d7 are injury-count / injury-flag series (likely New England's injuries_out or injuries_questionable) trending up into game week, with d7 flipping to 1 on 2026-10-17/18
  • No data on specific starting QB/injury replacement quality was provided beyond aggregate counts, so I cannot assess positional severity directly
Memo to the committee
Base rate for a home favorite ≈0.55. The Kalshi market already prices Chicago at ~53.5-54% (d1), stable with low dispersion, suggesting the market considers this close to a coin flip with modest home edge. I find a statistically significant home/signal margin effect (+10.5 pts, s4, p=0.01) but the corresponding win-rate lift is not significant (s3, p=0.40), and the overall win-rate bootstrap CI (s7) spans 0.41-0.69, overlapping 0.50. New England's injury proxy (d6/d7) rose and flipped positive right before the as_of date, a modest tailwind for Chicago, but I cannot verify position-level severity. Net: I nudge slightly above the market to 0.56, reflecting the margin-effect significance and rising opponent injuries, but keep confidence low given contradictory win-rate significance tests and wide CIs. This is not a strong edge trade — the gap versus market price (0.537) is small and could easily be noise.
Rebuttal
debate skipped: panel unanimous within 0.03

Would change my mind: If, by kickoff on 2026-10-23 (prediction cutoff), the Kalshi market price for CHI drifts below 0.50 on fresh injury/line news, or if New England's injuries_out count (d6) drops back below 4 while d7 reverts to 0 (indicating key players return), I will revise my probability down toward 0.50 or below, since the injury-driven edge would be eroded.

After the debate · 56% → 56%

debate skipped: panel unanimous within 0.03

Data and tests · 16, as of

small sample (n=0 < 30) · insufficient data

signal never fires

Quantitative Researcher54%confidence 45%

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Market Positioning Analyst53%confidence 55%

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Causal Skeptic54%confidence 35%

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Claims and evidence

Observation · seen in the data · 7
Model · a tool computed it · 2
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 35%

Chicago is a slight home favorite over New England; the Kalshi market (~53.5-54%) already captures most of the home-field edge, and the significant margin effect (s4) plus rising New England injury signals (d6/d7) justify only a small upward nudge, not a large divergence from market price.

Own objection: The win-rate conditional test (s3) found only a 6.6pp lift from the same signal with p=0.404 (not significant), and a bootstrap CI on the overall win rate (s7) spans [0.41, 0.69] — the data cannot reject a coin-flip outcome. The significant result is on margin (s4), not on win probability directly, and margin dispersion is huge (sd~15 points, s2/s6), so a positive expected margin does not reliably translate into a win. My 0.56 estimate leans on a margin effect that may not cross the win/loss threshold reliably, and the market (0.537) may already be the better-calibrated number.

Quantitative Researcher
54% · confidence 45%

Market price for Bears home win is stable near 0.54; home-field effect is statistically significant but weak in explanatory power (R2=0.13), so estimate anchors close to market consensus rather than the generic home-favorite base rate.

Own objection: The entire quantitative case rests on a market price (d1) that could reflect vig/structural skew rather than true win probability, and the only causal-looking test (t5, home-field effect) explains just 13% of variance in a 42-game sample with no opponent-quality or Patriots-specific controls — the edge over a coin flip is not demonstrated out-of-sample, and the true probability could legitimately be anywhere in 0.45-0.60 with no statistical basis to prefer 0.54 over 0.50 or 0.57.

Market Positioning Analyst
53% · confidence 55%

The Kalshi market itself (d1≈CHI implied prob, d2≈NE implied prob) has been remarkably stable over the last ~98-105 observations leading into game week, pricing CHI at roughly 53.5-54% to win with very low variance (sd≈0.013, tight bootstrap CI95 [0.535,0.541]). De-vigging the two series (0.538/(0.538+0.468)) gives a true implied probability of ~0.535 for CHI. This is consistent with a modest home-field-driven favorite price and shows no sign of a late crowding shift, surprise line move, or liquidity dislocation — the market has converged and is not mispricing a well-known storyline. There is little evidence of an exploitable edge distinct from the settled market price itself.

Own objection: The market-implied probability from d1/d2 may already be stale relative to injury news or lineup changes between 10/17 and game day (10/23) that are not captured in this window — a single QB/injury headline in the final days could move the line 5-10 points well outside the historical sd=0.013 band, and the tight historical variance says nothing about tail risk from a yet-unknown news shock in the remaining 5-day horizon."

Causal Skeptic
54% · confidence 35%

The only evidence directly tied to this specific game (d1: a tight cluster at 0.535-0.54 across 98 recent ticks on 2026-10-17) reads like a market/model implied win probability for Chicago, already pricing in injuries, rest, travel and public information. The other statistical results (s2, s3, s4, s5) purporting to find a predictive relationship for a margin/outcome variable are built on a small sample (n=26 vs 25, or n=51 total), the win-probability lift is not statistically distinguishable from the base rate (s2: p=0.404, CI crosses zero), and the regime split (s4) shows the sign of the correlation flips across regimes, undermining any claim of a stable causal mechanism. The significant margin result (s3, p=0.0101) is for a continuous outcome that is not the resolution variable and was very likely one candidate among many tested signals, so its significance should be heavily discounted for multiple-comparisons/selection bias. Net: anchor near the market-implied home-field baseline (~0.535-0.54) with minimal upward adjustment from the other tests, which do not survive scrutiny as causal evidence for this specific matchup.

Own objection: If d1 is literally Kalshi's own live price for this contract, citing it as 'evidence' is circular — I'm using the market's own implied probability to forecast the market's settlement, not an independent causal test, and it assumes market efficiency rather than demonstrating it. Also, s3 (p=0.0101, Mann-Whitney p=0.0165) combined with zero sign flips under leave-one-out (s5, min_abs_corr=0.116) is a nontrivial, reasonably robust-looking signal on margin outcomes; dismissing it purely as 'likely cherry-picked' without being shown the full universe of tested signals is itself an unproven assertion, and I may be underweighting real information content in d3/d6/d7/d8 about recent form, injuries, or scoring trends that the market has not fully absorbed.

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

debate skipped: panel unanimous within 0.03

Quantitative Researcher
54% → 54%

debate skipped: panel unanimous within 0.03

Market Positioning Analyst
53% → 53%

debate skipped: panel unanimous within 0.03

Causal Skeptic
54% → 54%

debate skipped: panel unanimous within 0.03

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

Base rate: generic NFL home favorites win roughly 57-58% historically; team-specific, market-adjusted base rate for a ~1-2 point home favorite is closer to 53-55%. The committee's pooled estimate of 0.54 is essentially a reaffirmation of the market price, not a meaningful update away from it -- three of four analysts (B, C, D) are substantially restating the same Kalshi series, and the only genuinely new information (A's NE injury signal) is unverified by peers. I weight D most heavily because its critique (small-sample margin test, regime sign-flip, non-significant lift test) is the only analysis that interrogates rather than restates the market price, and that critique is what keeps the consensus anchored near 0.54 rather than drifting toward A's 0.56. Net view: 0.54, CI [0.49, 0.59], consistent with the deterministic pool. Tradeability: NOT tradeable at current pricing. The de-vigged market probability (~0.535) and the committee consensus (0.54) differ by less than half a percentage point -- smaller than the ~0.6% overround and well within the noise of analysts whose segment-level accuracy (0.38-0.50) is near coin-flip. Any directional bet on Chicago at current Kalshi prices has an expected edge (~0.4pp) that does not clear transaction costs or the vig. If a position is taken, it should be sized as a minimal/no-trade signal, not a conviction bet; the only scenario that would make this tradeable is a confirmed, material, asymmetric injury/lineup update on New England between now and kickoff that the market has not yet priced -- watch for Kalshi price moving outside [0.49, 0.59] as the trigger to revisit.

Consensus 54% · interval 49% to 59%

Assumptions

Untested · 16
  • untested
    d1 series represents the Kalshi market-implied win probability for Chicago (home team) in the CHI-NE game, with recent prints ~0.535-0.54
    Sports Analyst
  • untested
    d3 is Chicago's game-level win indicator (1=win) over the last ~51 games; d4 is an is_home indicator with similar mean (~0.51), used as the 'signal' in s3/s4
    Sports Analyst
  • untested
    d2/d5 are point-differential (margin) series, one for CHI games generally and one restricted to a subset (e.g., home games), used in the Welch t-test (s4)
    Sports Analyst
  • untested
    d6/d7 are injury-count / injury-flag series (likely New England's injuries_out or injuries_questionable) trending up into game week, with d7 flipping to 1 on 2026-10-17/18
    Sports Analyst
  • untested
    No data on specific starting QB/injury replacement quality was provided beyond aggregate counts, so I cannot assess positional severity directly
    Sports Analyst
  • untested
    d1 is the Kalshi market price series for this specific contract (CHI-side), not a composite index
    Quantitative Researcher
  • untested
    d2-d7 are Bears-specific game-log covariates feeding regression t5 (n~42 games)
    Quantitative Researcher
  • untested
    d8 (n=328) is pooled cross-game market data not specific to this matchup and is excluded from direct updating
    Quantitative Researcher
  • untested
    No injury, weather, or depth-chart news beyond what the market has already priced in is available or assumed
    Quantitative Researcher
  • untested
    d1 represents the Kalshi-implied probability (or a closely tracking proxy) of a CHI win and d2 the complementary NE-side price.
    Market Positioning Analyst
  • untested
    d3/d4 and d5/d6 series (unlabeled) are secondary signals (e.g., spread/volume proxies) too ambiguous in this context to override the direct market price read.
    Market Positioning Analyst
  • untested
    No material injury or lineup news has broken between the last data point (10/17) and the as_of time (10/19) that would invalidate the observed price stability.
    Market Positioning Analyst
  • untested
    d1 represents a market- or model-implied win probability for Chicago in this specific matchup, not an unrelated series.
    Causal Skeptic
  • untested
    The 'signal' tested in s2/s3/s4/s5 was defined using data available before 2026-10-19 (no leakage of game outcome), though its exact construction is not disclosed.
    Causal Skeptic
  • untested
    No major injury/roster news between 2026-10-17 (last d1 observation) and game day (2026-10-23) that would shift the market meaningfully.
    Causal Skeptic
  • untested
    d7/d8 (small integer series, possibly injury counts or line-related metrics) are not disclosed in enough detail to assign them causal weight.
    Causal Skeptic

Simulated trade

The portfolio manager proposed no paper trade. Two independent reasons bar a trade. (1) No edge after costs: the committee's pooled probability (0.54, CI [0.49,0.59]) is statistically indistinguishable from the de-vigged market-implied probability (~0.535) — the gap (~0.4-0.5pp) is smaller than the market overround (~0.6%) and far smaller than the noise implied by analyst segment accuracy of 0.38-0.50. Three of four analysts restate the same Kalshi series (not independent evidence), and the one new signal (NE injury report, analyst A) is unverified by any peer and could reverse by kickoff. Taking a position here is betting on noise, not signal. (2) No instrument match: the simulated book's available instruments (HENRY_HUB, UNG, XLE, VLO, MPC, PSX, CORN, BTC, ETH, WTI) contain no NFL outcome contract, sports-linked derivative, or plausible proxy with any defensible correlation to a Bears-Patriots game result. Forcing this view into an unrelated futures/equity/crypto ticker would introduce basis risk and correlation assumptions with zero empirical grounding — worse than not trading. Correct action: no position. Revisit only if (a) Kalshi/market price on the actual game contract moves outside [0.49, 0.59], signaling the market has priced new information (e.g., confirmed NE starter injury/scratch), or (b) a genuine sports-outcome instrument becomes available in the tradeable catalog.

Forecasts

SeatStagePBaseResolvesOutcomeBrier
Committeefinal54%—2026-10-23open—
Sports Analystprior42%55%2026-10-23open—
Sports Analystindependent56%55%2026-10-23open—
Sports Analystrevised56%55%2026-10-23open—
Quantitative Researcherprior47%57%2026-10-23open—
Quantitative Researcherindependent54%57%2026-10-23open—
Quantitative Researcherrevised54%57%2026-10-23open—
Market Positioning Analystprior42%57%2026-10-23open—
Market Positioning Analystindependent53%57%2026-10-23open—
Market Positioning Analystrevised53%57%2026-10-23open—
Causal Skepticprior60%55%2026-10-23open—
Causal Skepticindependent54%55%2026-10-23open—
Causal Skepticrevised54%55%2026-10-23open—

Reasoning graph

Every observation, test, claim, objection and forecast, as a graph
observationmemorytestassumptionclaimcounterargumentopinionforecasttrade
observationKXNFLGAME-26OCT22NECHI-CHI.implied_…claim · observation · 90%The Kalshi market is pricing Chicag…opinionSports Analysttestregime_split: full-sample corr=0.13…claim · inference · 65%A home-field (or similar binary) si…counterarguments3 shows the same signal does NOT s…observationNFL-NE.margin [level]claim · observation · 50%New England's injury burden (proxie…observationNFL-CHI.injuries_out [level]counterargumentInjury counts are a proxy only; I c…testcorrelation: =Noneclaim · observation · 80%Chicago's overall (unconditional) w…claim · model · 40%The market price (0.537) is close t…claim · observation · 75%The market-implied probability of a…testdescribe: mean=0.5380102040816327opinionQuantitative Researchertestregression: beta[is_home]=10.380952…claim · model · 50%Bears exhibit a statistically signi…counterargumentR2=0.13 means 87% of margin varianc…counterargumentn=42 is a small sample for a regres…observationNFL-NE.injuries_out [level]claim · uncertainty · 55%Dataset d8 is not matchup-specific …observationt4 (failed: AttributeError: 'dict' …claim · uncertainty · 90%A requested regression test (t4) in…claim · inference · 55%The current ~0.535-0.54 Kalshi pric…claim · observation · 80%The Kalshi market has priced Chicag…testhypothesis_test: mean(on) − mean(of…opinionMarket Positioning Analystclaim · inference · 65%De-vigged two-sided market price im…observationNFL-CHI.win [level]testdescribe: mean=0.5380102040816327claim · observation · 60%No evidence in the given data of a …testconditional_probability: P(outcome|…claim · uncertainty · 30%Secondary series d5/d6 (high sd, os…testinfluence: corr=0.13601382690174096claim · observation · 55%d1 is a market/model-implied win pr…counterargumentNo explicit label confirms d1 is ti…opinionCausal Skepticclaim · inference · 75%The proposed predictive signal (s2)…counterargumentSmall n (26 vs 25) limits power to …claim · inference · 70%The margin-outcome relationship (s3…counterargumentNo sign flips under leave-one-out (…claim · forecast · 50%Final probability should sit close …forecastP=0.54 Will the Chicago Bears beat …
Click a node. Every conclusion traces back to observations and tests.