Distribution Markets

#Definition

A distribution market is a prediction market structure where traders bet on numeric ranges or exact values, collectively generating a probability distribution for continuous outcomes. Rather than asking "Will X happen?" (binary) or "Which option wins?" (categorical), distribution markets ask "What value will X take?"

This structure excels at forecasting quantities that fall along a spectrum: economic indicators, vote percentages, temperature readings, or cryptocurrency prices. Similar to range options in traditional finance, distribution markets let traders predict numeric ranges (e.g., "50-60") rather than simple yes/no outcomes. By pricing many numeric buckets simultaneously, distribution markets produce richer information than single-point forecasts, revealing not just expected values but the market's uncertainty around those estimates.

Taxonomy Note: Distribution markets can be thought of as a subset of scalar markets that generate probabilistic prices across a range of outcomes, rather than a single point estimate.

Platforms like Trepa have pioneered this approach as part of the foundational and aggregation mechanisms category of prediction market design.

#Why It Matters in Prediction Markets

Distribution markets capture nuance that other market structures cannot express.

Full probability curves

A binary market asking "Will GDP growth exceed 3%?" produces a single probability. A distribution market asking "What will GDP growth be?" produces probabilities for every value range, revealing whether the market sees 2.9% and 3.5% as equally likely or skewed toward one side. This granularity improves decision-making for anyone depending on the forecast.

Tail risk visibility

Distribution markets explicitly price extreme outcomes. When buckets for "less than -2%" or "greater than 8%" show non-trivial probabilities, the market signals meaningful tail risk that point estimates would obscure. Traders and analysts can see the full shape of uncertainty.

Precision in forecasting

Platforms like Trepa and FunctionSpace have pioneered distribution markets to extract more detailed forecasts from participants. By requiring traders to stake across multiple buckets, these markets incentivize careful reasoning about ranges rather than overconfident point predictions.

Price discovery for continuous variables

Traditional prediction markets discretize continuous outcomes into binary or categorical formats. Distribution markets preserve the continuous nature of the underlying variable, producing smoother probability curves that better match the actual uncertainty.

#How It Works

#Market Structure

A distribution market divides a numeric range into discrete buckets, each representing a possible value range:

All bucket prices must sum to $1.00 (representing 100% probability that the outcome falls somewhere).

#Trading Mechanics

1. Market creation: A question is defined with a measurable outcome and specified bucket ranges
2. Share minting: The platform creates shares for each bucket; buying one complete set costs $1.00
3. Trading: Participants buy shares in buckets they believe are underpriced and sell shares in buckets they believe are overpriced
4. Price adjustment: Buying pressure increases bucket prices; selling decreases them
5. Resolution: The actual outcome is observed, and the winning bucket pays $1.00 per share; all other buckets pay$0.00

#Numerical Example

A distribution market forecasts the unemployment rate announcement:

Setup: Six buckets covering 3.0% to 5.5% in 0.5% increments

Trader's belief: The rate will be between 4.0% and 4.5%

Current prices:

• Bucket 4.0%–4.5%: $0.20 (20% implied probability)
• All other buckets: combined $0.80

Trade execution:

Purchase: 50 shares of "4.0%–4.5%" × $0.20 = $10.00 cost
If bucket wins: 50 shares × $1.00 = $50.00 payout → $40.00 profit
If bucket loses: 50 shares × $0.00 = $0.00 payout → $10.00 loss

Expected value calculation:

If the trader believes the true probability is 35%:

EV = (0.35 × $40.00) - (0.65 × $10.00) = $14.00 - $6.50 = +$7.50

The positive expected value suggests buying, assuming the 35% estimate is accurate.

/**
 * Calculates Expected Value (EV) for a distribution market position.
 *
 * @param {number} myProbEstimate - Your estimated probability (0-1)
 * @param {number} sharePrice - Current market price of the bucket
 * @param {number} payout - Payout if winning (usually $1.00)
 * @returns {number} Expected Value per share
 */
function calculateBucketEV(myProbEstimate, sharePrice, payout = 1.0) {
  const winScenario = myProbEstimate * (payout - sharePrice);
  const loseScenario = (1 - myProbEstimate) * -sharePrice;
  return winScenario + loseScenario;
}

// Example
const ev = calculateBucketEV(0.35, 0.2);
// Result: (0.35 * 0.80) + (0.65 * -0.20) = 0.28 - 0.13 = $0.15
console.log(`Expected Value: $${ev.toFixed(2)}`);

#Distribution Reconstruction

From individual bucket prices, the market produces a complete probability distribution:

Analysts can extract:

• Mean estimate: Probability-weighted average of bucket midpoints
• Median estimate: Value where cumulative probability reaches 50%
• Confidence intervals: Ranges containing specified probability mass
• Variance: Spread of the distribution indicating uncertainty

#Examples

#Example 1: Economic Indicator Forecast

A distribution market asks what annual inflation will be. Buckets span from 0% to 10%+ in 0.5% increments.

• Conservative traders concentrate positions in 2.0%–3.5% buckets
• Tail-risk speculators buy cheap shares in 6%+ buckets
• The resulting distribution shows a central estimate of 2.8% with a long right tail

The market reveals both the expected value and the asymmetric risk of higher-than-expected inflation.

#Example 2: Election Vote Share

Rather than asking "Who will win?" a distribution market asks "What percentage of the vote will Candidate A receive?"

• Buckets: 40%–42%, 42%–44%, 44%–46%, 46%–48%, 48%–50%, 50%–52%, etc.
• If 48%–50% and 50%–52% have similar prices, the market sees a very close race
• Heavy concentration in 52%–56% buckets would indicate expected comfortable victory

This structure produces more actionable forecasts than binary win/lose markets.

#Example 3: Cryptocurrency Price Range

A distribution market forecasts where Bitcoin will trade at month-end:

• Buckets cover $50,000 to$120,000 in $5,000 increments
• Current prices cluster around $75,000–$85,000 buckets
• Notable probability mass in $100,000+ buckets indicates bullish tail sentiment
• The distribution's width reveals implied volatility expectations

Traders use this information for options pricing, portfolio hedging, or directional speculation.

#Example 4: Climate and Weather

A distribution market asks what the average global temperature anomaly will be:

• Scientific observers buy shares based on climate models
• The distribution aggregates multiple forecasting methodologies
• Wide distributions indicate scientific uncertainty; narrow distributions suggest consensus

Researchers and policymakers gain probabilistic forecasts more nuanced than single-number predictions.

#Risks and Common Mistakes

Bucket boundary effects

Outcomes near bucket edges create binary risk for adjacent positions. If buckets are 3.0%–3.5% and 3.5%–4.0%, an outcome of 3.49% versus 3.51% produces completely different winners despite trivial real difference. Traders often underweight positions near boundaries where this edge risk is highest.

Liquidity fragmentation

With many buckets, trading activity spreads thin across the distribution. Central buckets typically have deeper order books than tail buckets. A trader wanting to bet on extreme outcomes may face severe slippage or inability to execute at displayed prices.

Overconfidence in narrow ranges

Traders often concentrate positions in a few buckets reflecting overconfident point estimates. Proper probability calibration requires acknowledging uncertainty by spreading positions more broadly. Consistently losing on "near misses" suggests overly narrow betting.

Resolution source ambiguity

Distribution markets require precise numeric outcomes, making resolution criteria critical. Which data source? What measurement timing? How are revisions handled? Unclear rules create disputes and potentially unfair resolutions.

Complexity in position management

Holding positions across multiple buckets requires tracking several exposures simultaneously. Traders may struggle to calculate aggregate risk, correlations between bucket movements, or optimal rebalancing strategies as new information arrives.

Ignoring bucket width effects

Buckets of different widths should have proportionally different base probabilities. A bucket spanning 2% width should, all else equal, have higher base probability than one spanning 0.5%. Mispricing often occurs when traders overlook this geometric factor.

#Practical Tips for Traders

• Map your probability distribution before trading. Write down your estimated probability for each bucket, ensuring they sum to 100%. Compare to market prices to identify mispricings rather than trading on vague intuition.

• Check liquidity at each bucket you plan to trade. Central buckets typically have deeper books. For tail bucket positions, use limit orders and accept that execution may require patience.

• Consider spread trades across adjacent buckets. If you believe the outcome will be "around 4%" but are uncertain whether it will be 3.8% or 4.2%, buying both adjacent buckets reduces boundary risk while still expressing your view.

• Calculate your break-even probability for each position. A bucket priced at $0.15 requires the outcome to fall in that range more than 15% of the time to profit. Ensure your estimate exceeds this threshold by enough to cover fees.

• Monitor how the distribution shape changes. Shifts in the mean, widening or narrowing of the distribution, and movement in tail probabilities all convey information. Track these over time for trading signals.

• Read resolution rules with extra care. Distribution markets depend on exact numeric outcomes. Verify the data source, rounding conventions, and timing. Small differences in methodology can shift outcomes between buckets.

• Use distribution markets for hedging continuous exposures. If your portfolio or business depends on a specific economic variable, distribution market positions can hedge ranges of outcomes more precisely than binary markets.

#Related Terms

• Scalar Market
• Binary Market
• Categorical Market
• Liquidity
• Price Discovery
• Expected Value
• Implied Probability
• Resolution Criteria
• Trepa
• FunctionSpace

#FAQ

#What is a distribution market?

A distribution market is a prediction market where participants trade on numeric ranges or exact values, collectively producing a probability distribution over possible outcomes. Rather than binary yes/no questions, distribution markets ask quantitative questions like "What will the value be?" and divide the answer space into tradeable buckets. The winning bucket (where the actual outcome falls) pays $1.00 per share at resolution.

#How does a distribution market differ from a scalar market?

Both handle continuous numeric outcomes, but they structure payouts differently. A scalar market typically pays proportionally based on where the outcome falls within a range; shares might pay $0.70 if the result is 70$1.00 while others pay $0.00. Scalar markets reward directional accuracy; distribution markets reward precise range prediction.

#Are distribution markets suitable for beginners?

Distribution markets present moderate complexity that may challenge complete beginners. The multi-bucket structure requires probability thinking beyond simple binary bets. However, the concept is intuitive: betting on ranges is familiar from contexts like sports score predictions. Beginners should start by trading central buckets with better liquidity, carefully read resolution rules, and consider that spreading bets across multiple buckets reduces the risk of narrow misses. Starting with small positions builds experience before committing significant capital.

#How do I calculate expected value in a distribution market?

For each bucket position, expected value equals the probability the outcome lands in that bucket times your profit if it does, minus the probability it lands elsewhere times your loss if it does. If you pay $0.25 for a bucket and believe the true probability is 40%:

EV = (0.40 × $0.75) - (0.60 × $0.25) = $0.30 - $0.15 = +$0.15 per share

The $0.75 profit comes from the$1.00 payout minus your $0.25 cost. Positive expected value suggests buying that bucket, assuming your probability estimate is well-calibrated.

#What platforms offer distribution markets?

Several platforms have developed distribution market mechanisms. Trepa and FunctionSpace specifically focus on distribution-style markets where traders bet on numeric ranges or exact values. These platforms emphasize extracting detailed probabilistic forecasts from participants. Implementation details, bucket structures, and fee models vary by platform, so traders should review each platform's specific documentation before participating.