Tailfifty desk
424,069 observationssnapshot 2026-10-15Registry

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

Will the New Orleans Saints beat the Minnesota Vikings 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

Committee45%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 45%
Market at the time45%committee minus market +1%
ResolvedNoBrierBrier 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 0.205 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 0.198 · lower is better

Judged by Kalshi KXNFLGAME-26OCT11MINNO-NO settlement at 2026-10-11: Kalshi KXNFLGAME-26OCT11MINNO-NO: resolves YES if New Orleans Saints win

0%50%100%
Sports Analyst46%
Quantitative Researcher44%
Market Positioning Analyst46%
Causal Skeptic45%
Committee45%

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

The dispute that matteredAll four analysts land within 0.02 of each other, so there is no real probability disagreement -- the substantive split is epistemic: Analysts A, B, C treat the stable Kalshi quote (~0.445) as sufficient and essentially final, while the Causal Skeptic (D) argues that none of the supporting statistical tests are robust (the one significant result is outlier-driven and fails a nonparametric check) and that the market series itself may carry data-quality problems (possible staleness/aggregation). I resolved this by treating the market price as the dominant anchor (since no analyst found a statistically significant, robust deviation signal) but widening the credible interval below and above the point estimate to reflect D's legitimate concern that the 'stable' market read could be stale or mis-specified, rather than nudging the point estimate itself.

Synthesis

Synthesis: The committee's calibration-weighted pool lands at 0.45, and my independent weighting (favoring the Sports Analyst's domain relevance and best Brier score, discounting the Positioning Analyst for duplicated evidence and worst calibration) confirms essentially the same number, 0.45, with a widened CI of [0.37, 0.52] to account for the fact that three of four analysts are restating one market-price evidence line rather than offering independent confirmation. Base rate note: a generic NFL home-team win rate (~0.57) is the naive prior; the market has already moved well below that (0.445) pricing in Minnesota's team-quality edge, and no analyst found a statistically robust independent signal to deviate further from that market-updated number in either direction. Top disagreement is epistemic, not numeric: how much to trust the 'stable' market quote given D's flagged data-quality/robustness concerns versus A/B/C's straightforward deference -- resolved by keeping the point estimate at the market-anchored 0.45 but widening uncertainty rather than shifting the mean. Tradeability: NOT TRADEABLE as a standalone edge play. The committee's own forecast (0.45) is within noise of the current market quote (0.445), expected return of taking either side is ~0, and the evidence base is heavily duplicated/non-independent rather than four confirmatory signals. I would not recommend a directional position in the Kalshi Saints-win contract at current pricing; if forced to express a view, the only marginally defensible trade would be a small long position in 'Saints win' only if available below ~0.40 (i.e., a discount to the committee's 0.45 estimate), sized small given the wide CI and weak historical calibration (accuracy 0.28-0.33) of this entire analyst panel on sports outcomes.
Observed · in the data
  • Kalshi-implied probability for a Saints home win is stable at approximately 0.445 across the most recent snapshots (n=219 series, sd=0.0115 near game week).
  • The complementary Vikings-implied win probability sits at approximately 0.555, consistent (sums to ~1.00) with the Saints quote.
  • ESPN point-spread series moved from Saints -3.5 (home favored) on 09-27 to Minnesota +1.5 (Minnesota favored on the road) by 10-11.
  • Saints season scoring-margin series has a mean of -5.625 points with sd 13.8 over the sampled games.
Modeled · a tool computed it
  • Calibration-weighted log-odds pooling of the four post-debate analyst probabilities (equal 0.25 weights) producing the deterministic committee number of 0.45.
  • Each analyst's individual calibration segment statistics (Brier score, accuracy, n) used here to justify differential weighting.
Inferred · the committee's reasoning
  • Minnesota is viewed by both the betting market and the point-spread market as a true-strength favorite despite playing on the road, implying a team-quality gap that outweighs generic home-field advantage.
  • The market quote appears liquid and converged (low variance, stable across snapshots) rather than noisy or thinly traded.
  • None of the panel's independent statistical tests (home-field regression p=0.633; Welch vs Mann-Whitney contradiction p=0.035 vs p=0.103) establish a robust, non-market-based predictive signal.
Predicted · what it expects
  • Consensus probability that the Saints beat the Vikings at home on 2026-10-11 is 0.45, with a 95% credible interval of [0.37, 0.52], reflecting near-total deference to the market price plus added uncertainty for data-quality and non-robust-signal concerns.
  • Expected return of taking a position at the current market-implied price (~0.445) is approximately 0.0, i.e., no identified edge over the market quote.
Uncertain · what it does not know
  • Whether the Kalshi/ESPN market data reflects current information (injuries, lineup changes) as of 2026-10-11 or is stale/aggregated across unrelated games, as flagged by the Causal Skeptic.
  • Whether the Saints' negative season scoring margin (-5.625) is itself driven by a small, outlier-influenced sample (sd 13.8 is large relative to the mean).
  • Whether any home-field effect exists at all in this matchup; the only direct regression test was non-significant (p=0.633, CI crossing zero).
  • General model uncertainty: all four analysts have historically low accuracy in this segment (0.28-0.33) and Brier scores clustered around 0.28-0.31, indicating the committee itself has not been well-calibrated on sports outcomes historically.
How the seats were weighed · not an average
Sports Analyst0.30Highest sports-domain relevance (1.0) and best-calibrated Brier (0.280) of the four despite low raw accuracy (0.333, n=6, small sample). Contributes one quasi-independent line (team season-margin mean -5.6, sd 13.8) beyond pure market-deference, though its own home-field regression was non-significant (p=0.633) and self-discounted.
Causal Skeptic0.25Adds genuine independent value by stress-testing the other signals: flags that the one 'significant' result (s6, p=0.035) fails nonparametric replication (p=0.103) and is outlier-driven, and raises data-quality concerns (market stickiness, possible cross-game aggregation) that others did not surface. Mid-tier calibration (Brier 0.290) and good regime applicability (cross-domain relevance 0.8).
Quantitative Researcher0.25Explicitly anchors to a 29.8% historical base rate and shows the update to 0.445 is market-driven, not signal-driven -- useful for base-rate discipline. But its core evidence (d1 Kalshi series, d2 complementary Vikings quote) is the same market price Analysts A and C also cite, so largely non-independent. Brier 0.299, n=22.
Market Positioning Analyst0.20Worst calibration of the four (Brier 0.312, accuracy 0.28) and its evidence (d1, 219 obs converging to 0.445; d1/d2 mirror correlation) is the same Kalshi series already counted via Analysts A and B -- essentially zero marginal independent information. Kept non-zero only for its note on market staleness (no intraday repricing).
Wrong if
  • If the closing Kalshi-implied probability for a Saints win diverges from 0.445 by more than 5 percentage points before kickoff on 2026-10-11, the 'stable converged market' premise underlying this synthesis is falsified.
  • If the Saints win the game on 2026-10-11, this does not falsify the probabilistic estimate per se, but if realized outcomes across a larger forward sample of similarly-priced underdog-at-home games show a win rate statistically distinguishable from ~44-45% (e.g., via a binomial test over n>=30 comparable games), the calibration of deferring to this market price is falsified.
  • If post-game review shows the ESPN spread move from -3.5 to +1.5 was driven by a specific, identifiable injury/roster event not reflected in the Kalshi quote at the time of this forecast, the data-quality assumption (market fully incorporates available information) is falsified.
Independent lines of evidence
  • Market-implied win probability (Kalshi, converged ~0.445) -- the dominant, largely non-independent-across-analysts anchor.
  • ESPN point-spread movement (-3.5 to +1.5 over two weeks) -- a second, related but distinct market (spread vs. win-prob) that corroborates the win-prob direction.
  • Saints season scoring-margin regression (mean -5.625, sd 13.8) -- a team-quality fundamental signal, independent of the betting markets.
  • Statistical robustness checks (Welch vs. Mann-Whitney contradiction; home-field regression p=0.633) -- independent methodological evidence that no tested signal is statistically robust.

Counted once: Kalshi Saints-win implied-probability series (cited as d1 by Quant Researcher and Market Positioning Analyst, and as d6 by Sports Analyst, and as d1 by Causal Skeptic) -- this is a single evidence line reported four times, not four independent confirmations.; Complementary Vikings-implied-probability / mirror-correlation check (Quant Researcher's d2 and Market Positioning Analyst's d1/d2 correlation stat) -- both are restatements of the same market-internal-consistency check and collapse to one line.

When this holds

Applicable to a single, specific NFL regular-season matchup with a short (4-day) horizon and a liquid, actively quoted prediction market already pricing the outcome. Not generalizable to illiquid markets, playoff-context games with different incentive structures, or matchups lacking a comparably converged market quote.

Model uncertainty

All four analysts effectively report the same underlying Kalshi market price through different framings (d1/d6 win-prob series), so despite nominal 4-way agreement, there is really one primary independent evidence line (the market price) plus one secondary line (Saints season margin) and one critical/skeptical overlay (D's data-quality and robustness concerns). Pooling four highly correlated views as if independent overstates confidence; the stated CI is widened accordingly. Historical calibration for this committee segment is weak (accuracy 0.28-0.33, Brier ~0.28-0.31 across all four), so confidence in the point estimate should be moderate at best regardless of market convergence.

The seats

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

Sports Analyst46%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. The market (Kalshi, d6) is pricing New Orleans at ~44.5% to beat Minnesota at home on 2026-10-11, consistent with Minnesota being a short (~1.5pt) road favorite (d7 last=1.5). Team-quality indicators (season win rate, margin) favor Minnesota by a wide margin, and my own home-field regression (s5) is too noisy (p=0.633, CI crosses zero) to justify deviating materially from the market price. I land essentially on the market number, with a very small upward nudge for home-field effects the market may be under-weighting in a small-sample regression.
Base rate

46% NFL home underdogs receiving ~1.0-2.0 points (i.e., favored to lose by a small home-field-adjusted margin) win roughly 45-48% of the time historically; raw home-field win rate league-wide is ~55-57%, but this game's market-implied line (MIN -1.5 on the road) already nets that down.

The best case against

The home-field regression (s5) point estimate of +2.5 points for is_home is positive and of plausible magnitude, but statistically indistinguishable from zero (p=0.633, 95% CI [-7.76, 12.75]) with n=24 — this is a genuinely underpowered test, not strong evidence against home-field value. If true home-field edge for the Saints at the Superdome is closer to the NFL-wide average of ~2.0-2.5 points rather than this team's noisy historical estimate, the market's 44.5% could be a few points too low, and the actual fair value might be 48-50%. I am not confident I've ruled this out; my 0.46 already partially concedes this uncertainty rather than fully defaulting to 0.445.

Strongest evidence
  • Kalshi win-prob series for this market, n=219, last 6 snapshots all 0.445 on game week — a liquid, converged market price for Saints winning.
  • ESPN spread series shows line moved from -3.5 (home favored) on 09-27 to -1.5 then to +1.5 by 10-11, i.e. Minnesota is now a 1.5-point favorite on the road — consistent with d6's sub-50% home win prob.
  • Saints season margin series (d1): mean -5.625, sd 13.8 — team has been outscored on average, supporting a below-.500 true-strength read.
  • Saints win-rate series (d2): mean 0.333 across 24 games — materially worse record than Vikings' 0.458 (d8) over comparable sample.
Wrong if

If New Orleans wins the 2026-10-11 game (Kalshi KXNFLGAME-26OCT11MINNO-NO settles YES), or if the closing Kalshi price at kickoff moves materially above 50% on injury/lineup news not visible as of this cutoff, my 0.46 estimate (anchored near the as-of market price) would be falsified as too low; if Minnesota wins comfortably (>7 points) this supports my probability being, if anything, still slightly too generous toward New Orleans.

Assumes
  • d6 kalshi_win_prob series tracks the NO team's win probability (not MIN's) and the 0.445 repeated last values represent the live/settled pre-game price as of 2026-10-08.
  • d7 espn_spread convention: positive value at 10-11 (+1.5) indicates the home team (Saints) is an underdog by 1.5, i.e., Minnesota is favored by 1.5 on the road.
  • d2 (win indicator, mean 0.333) corresponds to New Orleans' game-by-game win history and d8 (mean 0.458) corresponds to Minnesota's, based on win-rate values plausibly matching each team's known 2026 trajectory as of this cutoff.
  • d4 and d5 (injuries_out, n=17, recent daily counts ~3-4 each) represent New Orleans and Minnesota's respective current-week injury report counts; I cannot map which series belongs to which team with certainty, and injury severity/position is unknown beyond count.
  • No information after 2026-10-08 11:37 UTC (as_of) was used; kickoff is 2026-10-11, so late-week injury designations (Thu/Fri reports, inactives) are NOT reflected and could shift the true probability before kickoff.
Memo to the committee
Base rate for a ~1.5-pt road favorite: roughly 46% win probability for the home dog. The Kalshi market has New Orleans at 44.5%, consistent with the current spread (MIN -1.5 on the road) and with season-long team-strength gaps (Saints win rate 33% vs Vikings 46%, margin -5.6 vs +0.5). My home-field regression on this sample is underpowered (p=0.63) and can't justify a confident deviation from market pricing. I land at 46%, essentially agreeing with the market, nudged slightly up for unresolved home-field uncertainty and because injury-count data trending for both teams in the final days before kickoff (not yet fully reflected in d6, which predates late-week inactives) adds two-sided uncertainty I can't resolve from this data alone. This is a 'no edge' call — the interesting market-reaction question is whether Thursday/Friday inactive lists swing the Kalshi price materially before kickoff; as of now I see no basis to bet against the market.
Rebuttal
debate skipped: panel unanimous within 0.02

Would change my mind: If New Orleans wins the 2026-10-11 game (Kalshi KXNFLGAME-26OCT11MINNO-NO settles YES), or if the closing Kalshi price at kickoff moves materially above 50% on injury/lineup news not visible as of this cutoff, my 0.46 estimate (anchored near the as-of market price) would be falsified as too low; if Minnesota wins comfortably (>7 points) this supports my probability being, if anything, still slightly too generous toward New Orleans.

After the debate · 46% → 46%

debate skipped: panel unanimous within 0.02

Data and tests · 13, as of

small sample (n=24 < 40)

Quantitative Researcher44%confidence 55%

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Market Positioning Analyst46%confidence 45%

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Causal Skeptic45%confidence 35%

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Claims and evidence

Observation · seen in the data · 8
Model · a tool computed it · 3
Inference · reasoned from the above · 4
Forecast · what a seat expects · 2
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
46% · confidence 45%

The market (Kalshi, d6) is pricing New Orleans at ~44.5% to beat Minnesota at home on 2026-10-11, consistent with Minnesota being a short (~1.5pt) road favorite (d7 last=1.5). Team-quality indicators (season win rate, margin) favor Minnesota by a wide margin, and my own home-field regression (s5) is too noisy (p=0.633, CI crosses zero) to justify deviating materially from the market price. I land essentially on the market number, with a very small upward nudge for home-field effects the market may be under-weighting in a small-sample regression.

Own objection: The home-field regression (s5) point estimate of +2.5 points for is_home is positive and of plausible magnitude, but statistically indistinguishable from zero (p=0.633, 95% CI [-7.76, 12.75]) with n=24 — this is a genuinely underpowered test, not strong evidence against home-field value. If true home-field edge for the Saints at the Superdome is closer to the NFL-wide average of ~2.0-2.5 points rather than this team's noisy historical estimate, the market's 44.5% could be a few points too low, and the actual fair value might be 48-50%. I am not confident I've ruled this out; my 0.46 already partially concedes this uncertainty rather than fully defaulting to 0.445.

Quantitative Researcher
44% · confidence 55%

Base rate for a Saints win in the reference historical sample is 29.8%, but the current, stable, internally-consistent Kalshi market price implies 44.5% — a large upward revision driven by information (current rosters, injuries, home-field, recent form) not captured in the long-run base rate. None of the candidate predictive signals tested (d4 as conditional-probability trigger, regression on margin d6) showed statistically significant explanatory power, so I have no basis to diverge materially from the market-implied probability. My estimate is therefore an update from the 29.8% base rate toward the market quote of ~0.445, essentially deferring to the market absent a demonstrated edge.

Own objection: The market price (0.445) could itself be stale or thin near the 4-day horizon, and none of my tested signals (d4, d6) are strong enough to confirm or override it — my 'update' is really just adopting the market's number with no independent verification. If the market is subject to a known bias (e.g., public money overweighting the home underdog, or injury news after 2026-10-06 not yet priced in), my estimate inherits that bias blindly.

Market Positioning Analyst
46% · confidence 45%

The Kalshi market has converged to a stable, tight implied probability of ~44.5% for a Saints home win against the Vikings, down from a generic NFL home-field base rate of ~57%, reflecting a priced-in team-quality gap favoring Minnesota. The quote has been static across the most recent observations (no intraday repricing), suggesting the market is not actively digesting fresh news and crowding/momentum is low. A separate, much noisier line-movement series hints at some drift toward the home team late in the week, but the sample is far too small (n=3) to treat as a reliable signal of mispricing. Net: little evidence of exploitable edge beyond what's priced; my estimate sits close to, but modestly above, the raw market quote to account for the uncertain directional drift in d3/d4.

Own objection: The spread-movement series (d3), despite its tiny n=3 sample, shows a directional shift toward the home team across the week that is not mirrored in the flat d1/d2 probability quotes. If d3 reflects real sharp-money/injury-driven line movement (e.g., a Vikings QB downgrade) that the probability market hasn't fully incorporated due to thin liquidity on this specific contract, then the 'market is efficient, no edge' read is wrong, and the true probability of a Saints win could be understated by the flat 0.445 quote. My n=3 sample for d3 is too small to confirm this is real rather than noise, which is the central weakness of my position.

Causal Skeptic
45% · confidence 35%

None of the supplied statistical tests establish a robust causal driver of the Saints-Vikings outcome: the one 'significant' result (s6) is contradicted by its own nonparametric counterpart and is plausibly outlier-driven, the correlation tests (s3-s5) are consistently null, and the n=3 trend series (d8) is underpowered. The only informative input is the market-implied probability itself (d1≈0.445), which should anchor the estimate, but full deference to it is risky given data-quality concerns (stickiness, possible cross-game aggregation), so I keep the estimate close to but not fully equal to the raw market read, with wide uncertainty.

Own objection: If d1 genuinely is the live Kalshi market price for this exact contract, it already incorporates all public information (injuries, weather, lineup news, sharp money) more efficiently than any of the weak statistical tests supplied here, and a skeptic who distrusts the regression evidence should simply defer to that market price rather than hedge toward 0.5 — meaning my probability of 0.45 may be too conservative relative to 0.445, and the 'causal skepticism' framing adds no information the market hasn't already priced.

Revisions · Every change of mind pinned to one piece of evidence.
Sports Analyst
46% → 46%

debate skipped: panel unanimous within 0.02

Quantitative Researcher
44% → 44%

debate skipped: panel unanimous within 0.02

Market Positioning Analyst
46% → 46%

debate skipped: panel unanimous within 0.02

Causal Skeptic
45% → 45%

debate skipped: panel unanimous within 0.02

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

Synthesis: The committee's calibration-weighted pool lands at 0.45, and my independent weighting (favoring the Sports Analyst's domain relevance and best Brier score, discounting the Positioning Analyst for duplicated evidence and worst calibration) confirms essentially the same number, 0.45, with a widened CI of [0.37, 0.52] to account for the fact that three of four analysts are restating one market-price evidence line rather than offering independent confirmation. Base rate note: a generic NFL home-team win rate (~0.57) is the naive prior; the market has already moved well below that (0.445) pricing in Minnesota's team-quality edge, and no analyst found a statistically robust independent signal to deviate further from that market-updated number in either direction. Top disagreement is epistemic, not numeric: how much to trust the 'stable' market quote given D's flagged data-quality/robustness concerns versus A/B/C's straightforward deference -- resolved by keeping the point estimate at the market-anchored 0.45 but widening uncertainty rather than shifting the mean. Tradeability: NOT TRADEABLE as a standalone edge play. The committee's own forecast (0.45) is within noise of the current market quote (0.445), expected return of taking either side is ~0, and the evidence base is heavily duplicated/non-independent rather than four confirmatory signals. I would not recommend a directional position in the Kalshi Saints-win contract at current pricing; if forced to express a view, the only marginally defensible trade would be a small long position in 'Saints win' only if available below ~0.40 (i.e., a discount to the committee's 0.45 estimate), sized small given the wide CI and weak historical calibration (accuracy 0.28-0.33) of this entire analyst panel on sports outcomes.

Consensus 45% · interval 37% to 52%

Assumptions

Holding · 18
  • holding
    d6 kalshi_win_prob series tracks the NO team's win probability (not MIN's) and the 0.445 repeated last values represent the live/settled pre-game price as of 2026-10-08.
    Sports Analyst
  • holding
    d7 espn_spread convention: positive value at 10-11 (+1.5) indicates the home team (Saints) is an underdog by 1.5, i.e., Minnesota is favored by 1.5 on the road.
    Sports Analyst
  • holding
    d2 (win indicator, mean 0.333) corresponds to New Orleans' game-by-game win history and d8 (mean 0.458) corresponds to Minnesota's, based on win-rate values plausibly matching each team's known 2026 trajectory as of this cutoff.
    Sports Analyst
  • holding
    d4 and d5 (injuries_out, n=17, recent daily counts ~3-4 each) represent New Orleans and Minnesota's respective current-week injury report counts; I cannot map which series belongs to which team with certainty, and injury severity/position is unknown beyond count.
    Sports Analyst
  • holding
    No information after 2026-10-08 11:37 UTC (as_of) was used; kickoff is 2026-10-11, so late-week injury designations (Thu/Fri reports, inactives) are NOT reflected and could shift the true probability before kickoff.
    Sports Analyst
  • holding
    d1 and d2 are complementary Kalshi-implied win probabilities for NO and MIN respectively (0.445+0.555=1.00), consistent with a two-outcome market
    Quantitative Researcher
  • holding
    d3 is the historical binary outcome series 'Saints win' (n=47, mean=0.298) used as the base-rate/dependent variable in tests s3 and s8
    Quantitative Researcher
  • holding
    d4 is a candidate predictive signal (binary, mean=0.489) tested against the outcome in s3/s8/s4
    Quantitative Researcher
  • holding
    d6/d5 are point-differential or power-rating variables used as regressors in s4
    Quantitative Researcher
  • holding
    No arbitrage/mispricing adjustment is applied beyond what the regressions tested; market is treated as efficient absent a demonstrated edge
    Quantitative Researcher
  • holding
    d1 series represents the Kalshi YES (Saints win) implied probability, and d2 the complementary (Vikings win) implied probability, given they sum to ~1.0 and are strongly negatively correlated (s3).
    Market Positioning Analyst
  • holding
    d3/d4 represent point-spread or injury-adjusted line movements, with sign convention such that values moving from negative to positive indicate the line shifting toward the home team (Saints).
    Market Positioning Analyst
  • holding
    d5/d6 are secondary market/model metrics (e.g., total or volume) with no directly interpretable directional signal for win probability.
    Market Positioning Analyst
  • holding
    No new information after 2026-10-08 11:37 UTC (as_of) was used.
    Market Positioning Analyst
  • holding
    d1 is a market-implied probability series for the Saints (home team) across repeated snapshots; last observed value 0.445 as of 2026-10-08.
    Causal Skeptic
  • holding
    d2/d5 are point-differential-type metrics for one or both teams; d3/d4/d6/d7 are binary situational indicators (e.g., favorite flag, rest, streak); exact definitions are not given to the committee and are inferred from distributional shape only.
    Causal Skeptic
  • holding
    d8 (n=3) is a short recent spread-trend series with far too few points for inference.
    Causal Skeptic
  • holding
    No tool directly tested the causal mechanism between any situational indicator and the specific Saints-Vikings outcome; all 'significant' results concern a generic 'on/off' grouping, not an identified causal lever.
    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 - futures/ETF/equity/crypto) provides exposure to an NFL game outcome. The only venue for this view is a Kalshi sports-outcome contract, which is outside the simulated book's tradeable universe. Even setting that aside, the committee's own synthesis puts the estimate (0.45) within noise of the market-implied price (0.445) - expected return approximately zero after costs - with high evidence duplication (crowding 0.8) across analysts citing the same market-price line rather than independent signals, and weak historical calibration (0.28-0.33 accuracy) for this panel on sports forecasts. There is no statistical edge to express and no correlated proxy instrument (equity/commodity/crypto) that has a defensible, non-spurious link to this specific game outcome. Recommending no trade rather than forcing a mapping onto an unrelated asset.

Forecasts

SeatStagePBaseResolvesOutcomeBrier
Committeefinal45%—2026-10-11No0.205
Sports Analystprior37%46%2026-10-11No0.137
Sports Analystindependent46%46%2026-10-11No0.212
Sports Analystrevised46%46%2026-10-11No0.212
Quantitative Researcherprior33%30%2026-10-11No0.109
Quantitative Researcherindependent44%30%2026-10-11No0.194
Quantitative Researcherrevised44%30%2026-10-11No0.194
Market Positioning Analystprior48%57%2026-10-11No0.230
Market Positioning Analystindependent46%57%2026-10-11No0.212
Market Positioning Analystrevised46%57%2026-10-11No0.212
Causal Skepticprior38%57%2026-10-11No0.144
Causal Skepticindependent45%57%2026-10-11No0.203
Causal Skepticrevised45%57%2026-10-11No0.203

Reasoning graph

Every observation, test, claim, objection and forecast, as a graph
observationmemorytestassumptionclaimcounterargumentopinionforecasttrade
observationNFL-MIN.is_home [level]claim · observation · 90%The Kalshi market price for Saints …opinionSports Analysttestdescribe: mean=0.3333333333333333claim · observation · 75%Season-long team strength indicator…testdescribe: mean=0.4583333333333333testregression: beta[is_home]=2.4965034…claim · model · 60%Home-field advantage in this specif…counterargumentLeague-wide home-field edge is typi…observationNFL-MIN.win [level]claim · observation · 50%The betting line moved roughly 5 po…claim · forecast · 45%My final probability estimate (0.46…observationNFL-NO.is_home [level]claim · observation · 90%The historical base rate of Saints …testconditional_probability: P(outcome|…opinionQuantitative ResearcherobservationNFL-NO.kalshi_win_prob [level]claim · observation · 75%The Kalshi market currently prices …testbootstrap_ci: mean=0.53684873949579…observationNFL-NO.margin [level]claim · inference · 85%No tested candidate signal (d4) dem…testhypothesis_test: mean(on) − mean(of…counterargumentn=23/24 per group is small, limitin…testregression: beta[d4]=3.329710144927…claim · model · 80%The regression of margin-type varia…claim · forecast · 55%Given no demonstrated statistical e…counterargumentMarkets can mis-price home underdog…claim · observation · 75%The Kalshi market currently implies…testcorrelation: corr=-0.83678182938852…opinionMarket Positioning Analystclaim · observation · 70%The market price has been essential…claim · inference · 30%A separate line-movement series (d3…counterargumentn=3 is far too small to draw a reli…observationNFL-NO.win [level]claim · uncertainty · 90%A single observation (d4=-10 on 10/…claim · model · 55%Given the home-field base rate (~57…testbootstrap_ci: mean=0.45958904109589…claim · observation · 70%The current Kalshi-implied probabil…testdescribe: mean=0.5622128851540618counterargumentHigh autocorrelation (0.809) means …counterargumentUnclear whether this series is spec…opinionCausal Skeptictesthypothesis_test: mean(on) − mean(of…claim · inference · 65%The statistically 'significant' mea…counterargumentCohen's d of 0.63 is a moderate eff…testcorrelation: corr=0.114351999287232…claim · inference · 60%No tested correlation (d2 vs. outco…testinfluence: corr=0.11435199928723222testregime_split: full-sample corr=0.11…observationNFL-NO.espn_spread [level]claim · uncertainty · 80%The n=3 recent spread-trend series …claim · uncertainty · 50%Anchoring the forecast on the marke…counterargumentIn efficient-market terms, this is …forecastP=0.45 Will the New Orleans Saints …
Click a node. Every conclusion traces back to observations and tests.