How to Build a Probability Calibration Chart Using Kalshi Weather Markets

Prediction markets often look like speculation machines.

But when you reconstruct them using historical data, something more structured appears:

They behave like probabilistic forecasting systems.

This guide shows you how to turn raw Kalshi weather market data into a probability calibration chart that helps you understand how accurate market pricing actually is.

You will build a system that reveals:

• how closely market probabilities match real outcomes
• where markets systematically overestimate probability
• where markets underestimate probability
• whether prediction market pricing is actually calibrated

About the dataset

This analysis is built on the full historical dataset of Kalshi weather markets since inception.

That means we are not sampling a subset, cherry-picking examples, or working with simulated data.

We are analyzing:

every weather prediction market ever listed, traded, and resolved on Kalshi

This includes:

• all historical weather contracts
• complete market resolution outcomes
• final market-implied probabilities
• historical liquidity and volume metrics
• resolved YES / NO outcomes across the full dataset

We also exclude extremely small and illiquid markets in order to improve signal quality and reduce statistical noise.

You can read more about the underlying dataset here:

• Kalshi Historical Data Guide

And interact with the data directly here:

• https://lycheedata.com

This is effectively one of the largest structured prediction market datasets available publicly.

you are looking at the complete historical record of a real-world probabilistic forecasting system

This is what makes calibration analysis possible in the first place:

without large-scale historical resolution data, probability accuracy cannot be meaningfully measured

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What you are building

You will construct a probability calibration chart that evaluates how well Kalshi weather market prices align with real-world outcomes.

This is done by grouping all historical Kalshi weather markets into probability buckets based on their final traded price.

Specifically, you will compute:

• All Kalshi weather markets (filtered by category and volume)
• Final market-implied probability (last_price)
• Bucketed probability ranges (0–10, 11–20, 21–30, etc.)
• Number of markets in each probability bucket
• Number of YES outcomes within each bucket

From this, you construct a calibration curve by plotting:

• X-axis → predicted probability bucket
• Y-axis → empirical YES outcome frequency

This produces a direct comparison between:

what the market predicted vs what actually happened

If the market is well calibrated, the resulting line will closely follow:

y = x (the identity line)

This allows you to visually answer:

Do Kalshi weather markets actually predict outcomes well?

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Core idea: What is probability calibration?

A calibrated market means:

• If a market says 70% chance of rain
• Then it should rain ~70% of the time across similar situations

So calibration is not about whether an individual market prediction was correct.

It is about:

whether probabilities match long-run reality

This is one of the most important concepts in forecasting systems, prediction markets, and probabilistic modeling.

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Why Kalshi weather markets?

Weather markets are ideal because:

• outcomes are binary and objective
• resolution is deterministic
• high liquidity exists in major cities
• strong historical dataset coverage exists
• weather outcomes avoid many subjective interpretation problems

This makes them perfect for:

• probability calibration testing
• forecasting validation
• market efficiency analysis
• prediction market research

This guide is part of a structured deep dive series into Kalshi weather markets using historical data from Lychee.

Together, these guides break down different layers of market behavior:

• Overview: Understanding Kalshi weather market structure and data composition
• Guide 1: Building probability convergence charts using VWAP-style aggregation
• Guide 2 (this page): Probability calibration analysis using bucketed market outcomes

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Step 1 — Connect to Kalshi Historical Data

You first connect to the historical dataset inside Lychee.

This dataset contains:

• historical Kalshi markets
• market metadata
• volume statistics
• final market pricing
• market resolution outcomes

Open:

• Kalshi Historical Integration

Then select:

• Markets dataset

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Step 2 — Filter Weather Markets With Sufficient Volume

From column selection select:

• category

  • allows us to isolate Weather markets specifically

• volume

  • used to filter out low-liquidity markets with weak informational quality

• last_price

  • the final market-implied probability before settlement
  • in simple terms:
    • 0 = market believed outcome would almost certainly resolve NO
    • 100 = market believed outcome would almost certainly resolve YES

• result

  • the actual resolved market outcome

Now refine your query:

• WHERE category = Weather

  • isolates weather prediction markets only

• WHERE volume > 100

  • filters out insignificant markets with weak participation

This improves:

• signal quality
• calibration reliability
• statistical consistency

Run request to begin your data pull.

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Step 3 — Handling NaN Values

Once the data pull completes, you may see:

Null or NaN values detected

For this workflow, select:

Keep all rows

This preserves the full historical dataset and avoids unintentionally removing valid market records.

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Step 4 — Review Your Dataset

Your dataset will now load into the Data Sheet.

You have now successfully pulled:

every Kalshi weather market with meaningful trading activity

Each row represents:

• one prediction market
• one implied probability
• one real-world outcome

This is now a structured probabilistic forecasting dataset.

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Step 5 — Open Mathematics Operations

Now begin transforming the raw market data into structured probability buckets.

Open:

• Mathematics Operations

This is where we begin constructing the calibration system.

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Step 6 — Bucket Markets Into Probability Ranges

Select:

• Bucket

Now configure the following:

• Bucket style → Numeric ranges
• Range size → 10
• Bucket column → last_price
• New sheet name → <your choice>

This groups all markets into probability buckets:

• 0–10
• 11–20
• 21–30
• etc.

Next add aggregations.

Aggregation 1 — Total Markets Per Bucket

Set:

• Count
• Rows in bucket

This calculates:

how many markets existed inside each probability bucket

For example:

• how many markets ended with implied probabilities between 70–80%

Aggregation 2 — YES Outcomes Per Bucket

Set:

• Count
• result
• WHERE result = Yes

This calculates:

how many markets inside each probability bucket actually resolved YES

For example:

• if 72 out of 100 markets in the 70–80 bucket resolved YES
• then empirical outcome frequency ≈ 72%

After execution:

each row now represents a probability bucket with both predicted probability and realized outcome frequency

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Step 7 — Preview Your Bucketed Dataset

Review the transformed dataset before continuing.

You should now see:

• probability buckets
• total market count per bucket
• YES outcomes per bucket

This is now the foundation of your calibration curve.

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Step 8 — Compute Prediction Accuracy

Now calculate the actual realized accuracy inside each bucket.

Open:

• Mathematics Operations

Inside the Basics tab select:

• Operation → Divide (A/B)
• Column A → yes_correctly_predicted
• Column B → markets

This constructs:

yes_correctly_predicted / markets

In practical terms:

YES outcomes / total markets in bucket

For example:

• 73 YES outcomes
• 100 total markets

Produces:

0.73 = 73% empirical probability

Select:

• Current Sheet

Then:

• Click Apply to sheet

This creates your final probability calibration metric.

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Step 9 — Visualize Probability Calibration

Now visualize the calibration system.

Open:

• Charts

Configure:

• Chart → Line Chart
• X-axis → probability_calibration_bucket
• Y-axis → prediction_accuracy

This chart now compares:

predicted probability vs realized outcome frequency

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Step 10 — Design Your Chart

Use the chart customization panel to style your visualization.

You may customize:

• labels
• smoothing
• axis titles
• colors
• interpolation
• line thickness
• chart dimensions

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What the chart shows

The calibration chart reveals whether market pricing aligns with reality.

If the curve follows y = x

The market is well calibrated.

Meaning:

• 70% markets resolve YES roughly 70% of the time
• 30% markets resolve YES roughly 30% of the time

This suggests:

market probabilities are statistically meaningful

If the curve falls below the identity line

Markets are overconfident.

Example:

• markets predict 80%
• outcomes occur only 65% of the time

If the curve rises above the identity line

Markets are underconfident.

Example:

• markets predict 40%
• outcomes occur 55% of the time

This transforms prediction market analysis from:

subjective opinions

into:

measurable probabilistic forecasting evaluation

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Key Insight: What “accuracy” actually means here

Prediction market accuracy does not mean:

“Did a single prediction come true?”

Instead, accuracy means:

“Do probabilities match reality across large numbers of events?”

This distinction is critical.

Because probabilistic systems are evaluated through:

• calibration
• consistency
• long-run statistical alignment

Not individual guesses.

A perfectly calibrated market can still be wrong frequently.

What matters is:

whether its probabilities are honest representations of uncertainty

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What this enables next

Once you can construct calibration curves, you can extend this analysis into:

• Brier score analysis
• calibration scoring systems
• cross-category market comparison
• liquidity vs calibration studies
• volatility vs accuracy analysis
• trader behavior analysis
• market efficiency research
• probabilistic forecasting systems
• prediction market ranking systems

You are no longer analyzing isolated markets.

You are analyzing:

the statistical behavior of an entire forecasting ecosystem

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Related guides

• Kalshi Historical Data Guide
• Kalshi Weather Market Analysis

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Final takeaway

Prediction markets are not just betting platforms.

At scale, they become:

large probabilistic forecasting systems powered by financial incentives

Probability calibration is one of the clearest ways to measure whether those systems actually work.

And Kalshi weather markets provide one of the cleanest real-world environments available to study that behavior empirically.