11 Accuracy Metrics for Senate Forecasts to Evaluate in 2026

Score 2026 senate forecasts with Brier/log loss, RPS, CRPS, interval/coverage checks, plus a snapshot protocol and calibration diagnostics.

Forecasts vs truth

Accuracy work starts before you compute a single metric. You need a clean set of forecast–outcome pairs, and that means being explicit about (1) what your “Senate forecast” object is and (2) what counts as the “truth” it resolves to.

For 2026, you’re ultimately anchoring everything to realized election outcomes from a cycle with 35 Senate seats up (33 regular elections plus 2 specials in Ohio and Florida) on November 3, 2026. But different products slice that reality into different “questions,” and you can’t score them as if they were the same question.

Forecast objects

• Race win probability (one seat): A number like “Dem wins Ohio: 0.62.” Your truth is a single 0/1 outcome for that race (who won that seat).
• Chamber-control binary (one contract): A yes/no proposition like “Party X controls the Senate.” Your truth is whatever the market/contract says “control” means at settlement—not your preferred political definition.
• Rating bucket (ordered categories): Labels like Safe/Likely/Lean are categorical forecasts. For example, one widely used map defines Lean as 60%+, Likely as 75%+, and Safe as 95%+, and it refreshes on a daily 6:00 PM Eastern schedule. Your truth is still the race winner, but your dataset must preserve the bucket (not pretend it was a probability).
• Seat-count distribution (whole chamber): Simulation-driven outputs (e.g., 50,000 daily simulations) produce a distribution over seat counts. Your truth is the realized seat count after the election is decided for the seats being modeled.

Resolution semantics

A resolution rule is the contract/product’s definition of what event occurred, and it decides what you’re allowed to treat as ground truth.

That difference is not theoretical. Kalshi’s Congressional Control Contracts are cash-settled binaries whose settlement is determined by the party affiliation of the chamber leader on the expiration date; for the Senate, that leader is the President Pro Tempore. On the “Which party will win the U.S. Senate?” market page, the contract can open Nov 3 (8:00am EST) yet reference a later date (e.g., Feb 1, 2027) for settlement context.

If you score “control” using election-night seat majority while the contract settles on leadership at expiration, you’re not measuring forecast accuracy—you’re measuring how often you overrode the rules. Decide your resolution semantics first, then every forecast snapshot can be paired to a single, unambiguous truth value.

Snapshot and archive (2026 protocol)

A “snapshot” is one frozen copy of a forecast as it existed at one timestamp. If you can’t reproduce snapshots, you can’t build clean forecast–outcome pairs without cherry-picking.

Treat each snapshot as an auditable record with (a) an unambiguous time, (b) an unambiguous forecast object, and © an unambiguous resolution target. The goal is that someone else can rerun your collection rules and get the same set of rows—then your accuracy metrics are just arithmetic.

1. Define the snapshot clock before you look at any numbers. Write down one rule for when a snapshot is taken and stick to it for the entire cycle.

• Use a single time standard: store times in UTC.
• Use one trigger: either a fixed schedule (e.g., “daily at 00:00 UTC”) or the publisher’s displayed update moment. Don’t mix them.
• Record two timestamps every time:
• observed_at_utc: when your collector fetched the page/API.
• as_of_utc: the forecast’s own “Last updated” timestamp when it’s visible on the page (MarketsPrediction displays “Live Data” with a visible “Last updated” time).
• If the page doesn’t expose an “as of” time: keep as_of_utc blank and rely on observed_at_utc—but don’t backfill or infer it later.

2. Freeze the forecast object exactly as displayed. Save enough that a third party can verify what you saw without trusting your parsing.

• Raw capture: store the exact payload you ingested (HTML and/or JSON), plus request URL and query parameters.
• Rendered capture: store a screenshot or print-to-PDF of the rendered view that shows the numbers you extracted.
• Parsing trace: store the selectors/JSON paths (or equivalent) you used to extract the probability/bucket/distribution so a reviewer can reproduce your extraction from the raw payload.

3. Store market/contract metadata alongside the odds. Odds aren’t interpretable without the instrument definition that constrains them.

• For Kalshi Congressional Control Contracts, archive the contract mechanics (e.g., the $0.01 tick size, the $0.01–$0.99 listing band, and the $1 notional) because these define what “the price” can be and how rounding should be handled in your dataset. (See Kalshi’s CONTROL product certification: https://kalshi-public-docs.s3.amazonaws.com/regulatory/product-certifications/CONTROL.pdf.)
• Persist the market identifier and the exact contract/outcome label text as displayed, not just a human nickname you make up later.

4. Capture context fields that drift. Archive any on-page fields that can change over time or disappear later, so you can audit what information was available at the moment of the snapshot.

• Examples: displayed volume, liquidity/open interest fields, end date/expiration, and any visible “rules” or settlement snippets on the page.
• Store these as separate columns (don’t bury them only in screenshots) so you can filter/QA later (e.g., detect venue changes, contract relistings, or rule-text edits).

5. Log the resolution pointer you intend to score against. A forecast is only scoreable relative to a precisely identified “truth.” Store that truth target up front and link it to every snapshot.

• Resolution ID: create a stable identifier like venue:event_slug:contract_id:resolution_version.
• Resolution source: store the exact resolution rule text you are using (copy/paste it into your archive) and the URL it came from.
• Resolution date reference: store the specific expiration/settlement date reference used by the contract. For example, Kalshi’s Congressional Control Contracts settle based on the party affiliation of the chamber leader on the expiration date; for the Senate this leader is the President Pro Tempore (https://www.cftc.gov/sites/default/files/filings/documents/2023/orgkexkalshiordersig230922.pdf).

Once this is in place, scoring becomes a bookkeeping exercise, not an argument about timing.

Formats-to-metrics map

Pick metrics that match the output type of the senate forecasts you’re scoring. Otherwise you’ll reward (or punish) a forecaster for something they never claimed to predict.

Log loss is unbounded: if a forecaster ever publishes p=0 for an outcome that happens, the score becomes infinitely bad, so you need to audit for literal zeros before you compute it.

The 11 metrics

Most evaluation should start with a strictly proper scoring rule—a scoring rule where a forecaster maximizes expected score only by reporting their true probability distribution—because it rewards honest probabilities, not just confident ones.
For binary race or chamber-control odds, the workhorses are the Brier score (mean squared error between predicted probability and the 0/1 outcome) and log loss (based on log(p) assigned to what happens; it punishes near‑zero probabilities on realized outcomes very heavily). Log loss is also unbounded (it becomes infinitely bad at p=0), which is why some evaluators prefer the bounded, more interpretable Brier score even if it’s less brutal on extreme misses.
For distributions and buckets, use CRPS (a proper score for full predictive distributions/CDFs), interval score (rewards narrow prediction intervals but penalizes outcomes outside), and RPS (compares cumulative probabilities across ordered categories).

Diagnostics that matter

Proper scores will rank your senate forecasts, but they won’t always tell you why—and with a single Senate cycle you’re working with a small set of resolved events, so a clean-looking ranking can be fragile.

Start by separating calibration—whether events you assign probability p happen about p fraction of the time, over many forecasts—from sharpness, which is how concentrated (confident) your predictive distributions are even before you check if they’re right. In the probabilistic forecasting framework of Gneiting & Raftery, the goal is blunt: maximize sharpness subject to calibration.

To see calibration failures, use a reliability diagram (a binned plot that compares predicted confidence to empirical accuracy). It bins predictions by stated probability, then compares each bin’s average confidence to what actually happened; perfectly calibrated forecasts sit on the identity line. If you need a single summary number, Expected Calibration Error (ECE)—a weighted average of bin-by-bin accuracy–confidence gaps—compresses the diagram into one scalar:
ECE = Σm (|Bm|/n)·|acc(Bm) − conf(Bm)|.

Then sanity-check discrimination separately. ROC/AUC is built from a ROC curve that plots hit rate against false alarm rate across probability thresholds; the AUC summary score runs from 0.5 (no skill) to 1 (perfect). It can look strong while staying almost silent about whether your 0.70s behave like 70%, which is how a forecaster can rank outcomes well while still being miscalibrated.

When metrics disagree, treat that as signal: you’re looking at ranking skill, probability quality, and confidence tightness pulling in different directions.

Make the call

1. Pick one “decision metric” per forecast object. Use a strictly proper score (Brier/log loss for binary odds; RPS for rating buckets; CRPS for seat distributions; interval score for ranges) and rank sources within the same object type.

2. Gate the ranking with calibration, not vibes. Treat calibration (consistency between forecasts and outcomes) as a pass/fail check, and treat sharpness (how concentrated the forecasts are) as a preference only after calibration clears. (That’s the separation Gneiting & Raftery emphasize.)

3. Classify each source into one of three actions.

• Follow: top-ranked proper score and calibration looks clean.
• Fade: good-looking headline rank, but repeated overconfidence signals (e.g., reliability curve off the identity line).
• Signal, not probability: strong discrimination but weak calibration—e.g., good AUC (ranking) even though the probabilities aren’t trustworthy; AUC runs from 0.5 (no skill) to 1 (perfect) and doesn’t “fix” biased probabilities.

4. Publish a minimal performance row you can rerun weekly. Use one row per (source × object): Source | Object | Primary score | Calibration check | Sharpness/coverage | AUC (if used) | Notes (resolution rule, snapshot clock)

5. Lock the rule, then just re-score. If your decision flips often, you’re seeing small-sample fragility—not a stable edge.

Score forecasts the way they resolve

If you want a Senate forecast you can actually trust (or trade against), stop treating every source as if it answered the same question. Lock the resolution semantics and a non-negotiable snapshot clock, then grade each forecast object with the strictly proper score it earns (Brier/log loss for binaries, RPS for buckets, CRPS for seat distributions, interval score for ranges). Use calibration as the gate—reliability diagram/ECE first, sharpness only after—and let discrimination (AUC) tell you “signal” without pretending it’s probability. Once you can rerun one minimal performance row weekly, “follow,” “fade,” and “signal-not-probability” stop being vibes and become a rule.