Prediction markets have emerged as one of the most compelling applications of blockchain technology. By enabling users to put capital behind their beliefs about future events, prediction markets aggregate dispersed information into real-time markets, creating powerful signals around everything from elections and economic indicators to sports outcomes and crypto markets.

At this year’s Convergence hackathon, developers demonstrated a shift in how prediction markets are designed, moving beyond simple binary markets to programmable markets powered by real-world data, custom computation, AI-assisted research, and automated resolution. Enabled by the Chainlink Runtime Environment (CRE), these applications securely access external systems, execute complex workflows, and settle outcomes across a wide range of event types, expanding what prediction markets can track while preserving the transparency and verifiability of blockchain-based settlement.

Expanding What Prediction Markets Can Be

Historically, prediction markets have been limited by the types of questions they can ask and the methods available to resolve them. Many markets rely on predefined data sources, manual intervention, or narrow event categories.

With CRE, developers can build prediction markets around virtually any verifiable event, combining onchain settlement with offchain data retrieval, computation, and automation.

Building Real-Time Prediction Markets

One of the projects submitted was TAPL, a real-time tap trading platform that transforms short-term price forecasting into an interactive experience.

Rather than asking users to predict outcomes days or weeks in advance, TAPL enables predicting the movement of crypto assets such as BTC, ETH, and more with a single tap. Users select from a grid of price ranges and time windows powered by live market data, with dynamically adjusted payouts based on probability and distance from the current price.

Behind the scenes, TAPL’s pricing engine performs thousands of simulations every 100 milliseconds to calculate fair odds and payouts. Once a round closes, CRE workflows batch outcomes, calculate results, and commit settlement data onchain.

The result is a highly responsive prediction market experience that would be much more difficult to implement using smart contracts alone, demonstrating how CRE enables sophisticated computation and automated settlement workflows.

Transforming Community Competition Into Onchain Markets

Prediction markets can also serve as coordination mechanisms for online communities.

MemePull Arena reimagines prediction markets through the lens of memecoin culture. Its PvP Battle feature allows communities to compete directly against one another by staking behind their preferred token. Winners are determined based on token performance, measured using time-weighted average price (TWAP), with the victorious community claiming the majority of the prize pool.

The platform also supports prediction markets around verifiable onchain milestones, such as whether a token will reach a specified market capitalization by a future date.

CRE automates the collection of market data and the resolution process, enabling trust-minimized settlement without requiring manual intervention. By combining prediction markets with community competition, MemePull Arena demonstrates how forecasting mechanisms can become social experiences rather than purely financial ones.

Bringing Prediction Markets Into the Real World

Some of the most promising prediction market applications involve real-world events that extend far beyond blockchain ecosystems.

Flight Markets explores this concept through a decentralized market focused on airline delays. Users can predict whether a specific flight will exceed a predefined delay threshold, creating a market that functions similarly to parametric insurance while preserving the open participation of prediction markets.

When settlement is requested, a CRE workflow retrieves flight status information from an external aviation data provider, computes the outcome, generates a verifiable evidence package, and submits a signed report onchain. The smart contract then finalizes the market and distributes payouts accordingly.

This architecture demonstrates how prediction markets can be connected to real-world events through automated, transparent workflows while maintaining auditability and verifiable settlement.

Unlocking Capital-Efficient Prediction Markets

As prediction markets grow, developers continue to explore ways to improve capital efficiency.

Delphic enables users to take positions on prediction markets while continuing to earn yield on their underlying assets. Instead of requiring users to hold idle stablecoins, Delphic allows them to deposit yield-generating assets such as wstETH as collateral. The protocol then uses that collateral to borrow USDC through lending markets across multiple chains and deploy it into prediction market positions.

By combining lending infrastructure, cross-chain interoperability, and automated execution workflows, Delphic creates a new model for prediction market participation that enables capital to remain productive while users express market views.

This approach highlights how prediction markets can be integrated with the broader DeFi ecosystem, rather than operating as standalone applications.

Automating Resolution Across Many Market Types

Several projects focused on expanding the types of questions prediction markets can support.

Oracle enables users to create and participate in markets covering cryptocurrency prices, stock performance, weather events, sports outcomes, and AI-resolved questions. By combining multiple data sources and automated settlement mechanisms, the platform supports a broad range of prediction categories within a unified trading interface.

PredictChain applies similar principles to sports prediction markets. Users can create and participate in markets around sporting events while CRE automates the full market lifecycle, from event monitoring to outcome determination and settlement.

MetaPredict takes this concept even further by creating prediction markets about prediction markets themselves. Users can speculate on metrics and developments related to platforms such as Polymarket, Kalshi, and Azuro. Depending on the type of question being resolved, the platform dynamically routes requests to different data sources, including APIs and AI-powered web research systems.

Together, these projects demonstrate how CRE unlocks sophisticated resolution mechanisms that adapt to the unique requirements of many different market categories.

Looking Ahead

Prediction markets have already established themselves as one of the most powerful mechanisms for aggregating information and tracking the probability of future outcomes. As the ecosystem matures, the next wave of innovation will likely come from developers expanding the scope of what prediction markets can measure, how they resolve outcomes, and how users participate.

The projects built during Chainlink’s Convergence hackathon offer a glimpse into that future. By combining real-world data, automated workflows, custom computation, and intelligent resolution systems, developers are building prediction markets that are more flexible, data-rich, and capable of addressing a far broader range of questions than ever before.

As prediction markets continue to evolve, CRE is providing the infrastructure required to support this next generation of applications. 

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