This content originally appeared on DEV Community and was authored by Daniel Tobi Onipe (Dexter)
Introduction
At the turn of a new decade, prediction markets and forecasting platforms have emerged as one of the most innovative and intriguing applications of monetized information. By turning valuable information into tradable assets, these systems seek to crowdsource wisdom, aggregate probabilities, and - in some cases - challenge the accuracy and precision of even the most complex and sophisticated institutions. From drastic political changes to macro-economic indicators, weather events to corporate earnings, prediction and forecasting projects are steadily evolving into a new layer of financial and informational infrastructure by mastering the art of drawing parallel lines and connecting the dots between two or more seemingly unrelated events with uncanny precision and near-perfect accuracy.
Strangely, not all prediction and forecasting platforms are built the same. Behind every market lies a set of technical and mechanical design choices that determine how truth is resolved, how markets operate, and ultimately the extent of trust which participants can have in the outcomes. Some depend solely on decentralized oracles and data; others rely on human arbitration and curated feeds. Some perform fully on-chain with transparent smart contracts; others strike a strategic hybrid balance to optimize for performance, regulatory compliance and/or user experience.
These differences are not just academic - they shape real-world usability, resilience, scalability and even regulatory legitimacy. A forecasting project’s success heavily hinges on tradeoffs between speed and decentralization, accessibility and precision, simplicity and expressiveness.
This publication proposes a typology of prediction and forecasting platforms, organized around the core technical dimensions that define them.
For each category, we will:
- Identify the underlying mechanism,
- Provide real-world project examples, and
- Analyze their strengths, weaknesses and tradeoffs.
The goal is to equip builders, researchers, and users with a structured way to evaluate forecasting systems - and to highlight how emerging models such as Trepa’s accuracy-scaled payouts are redefining what prediction markets can achieve.
Key Classification Dimensions
Before we proceed to categorize forecasting and prediction projects, there is the need to establish the dimensions that meaningfully distinguish one system from another. The intersection of these axes of classification are not all arbitrary—they reflect the foundational technical and mechanical design choices that shape how scalable, reliable, trustworthy and pivotal a forecasting or prediction platform can and should be.
In analyzing prediction and forecasting projects, four technical dimensions stand out as the most useful for classification, and they are:
- Resolution Mechanism,
- Technical Infrastructure,
- Market Design, and
- Governance & Incentive Alignment.
Each provides a different lens into how projects operate, and together they offer a holistic view of the tradeoffs domiciled in prediction platforms.
2.1 Resolution Mechanism
The resolution mechanism of a platform defines and dictates how an outcome is determined, i.e., how the system actually decides what happens in the real world. This is by far the most pivotal technical layer and this is so because without trustworthy and transparent resolution, the entire forecasting market collapses and falls through.
Human-based resolution:
Heavily hinges on a trusted and vetted set of moderators, arbiters and/or community votes to determine outcomes and results.
- Pros: Easy to implement; evolves to ambiguous events and changes.
- Cons: Potential risk of biases, corruption or collusion; leads to the advent of subjectivity.
- Examples: Early Augur markets often fell behind on the votes of token-holders.
Oracle-based resolution:
Employs the use of cryptographic oracle networks and protocols, (e.g., Chainlink, Pyth, UMA etc.) to deliver real-world data on-chain and off-chain.
- Pros: Objective, scalable, and automatable.
- Cons: Limited to a finite number of outcomes (prices, scores, statistics); oracle downtime or risk of manipulation.
- Examples: Platforms like Polymarket and Zeitgeist heavily rely on oracles.
Hybrid strategies:
Synergizes human contribution with oracle feeds (e.g., oracles for quantitative data + community arbitration for ambiguous claims).
- Pros: Adaptability to handle the demands of both clear-cut and subjective markets.
- Cons: Increased system complexity.
Automated Feeds / APIs:
For extremely complex and structured data (e.g, sports scores, financial metrics, etc.), direct API calls and integrations can resolve outcomes.
- Pros: Fast, low-friction, and minimized human subjectivity.
- Cons: High risk of centralization, trust in the API source/provider is still required.
Key Takeaway:
Resolution tradeoffs are always a split-decision between objectivity and flexibility. No one single method can be used as the standard across all use cases.
2.2 Technical Infrastructure
The dimension of infrastructure covers where computation and settlement converge. This has effects on scalability, transaction costs, and user experience.
On-chain systems:
All logic, settlement, and records are stored on a decentralized blockchain network.
- Pros: Transparent, immutable, censorship-resistant technology.
- Cons: High transaction (gas) fees, much slower throughput, scalability & sustainability bottlenecks.
- Examples: Augur (Ethereum) and Zeitgeist (Polkadot).
Off-chain systems:
Market operations (e.g., matching orders, computing & calculating payouts) occur off-chain, with only settlement data that is anchored and domiciled on-chain.
- Pros: Faster and cheaper with more user-friendliness.
- Cons: Reduced transparency; trust assumptions in off-chain operators and administrators.
- Examples: Kalshi, Predict-It.
Hybrid systems:
Utilizes a layered approach — off-chain for speed, on-chain for finalization.
- Pros: Balances performance with security; flexible and upgradable architecture.
- Cons: Increased design complexity as the system grows.
- Examples: Polymarket uses off-chain matching + on-chain finalization.
Key Takeaway:
The core compromise and tradeoff in this dimension is between efficiency (off-chain) and trust minimization (on-chain).
2.3 Market Design
The dimension of market design determines how forecasting actually takes place — whether users make binary or spread bets, trade continuous shares or turn in numerical predictions.
Binary prediction markets:
“Yes-or-No” markets on specific outcomes and results.
- Pros: Simple and easy to understand.
- Cons: Low-level of details, outcomes are reduced down to black-and-white.
Scalar or continuous markets:
Users forecast numerical values and changes (prices, percentages, event magnitudes).
- Pros: Richer and holistic information; aligns closely with real-world forecasting needs.
- Cons: Complex pricing models; requires an in-depth user understanding.
- Examples: Trepa’s specialty is in scalar forecasts (e.g., GDP growth, inflation).
Parimutuel/Pool-based models:
Users and contributors fund liquidity pools; payouts depend on share of the correct and accurate side.
- Pros: Easy implementation process with scalable liquidity.
- Cons: Can experience setbacks from poor odds if liquidity is out of balance.
Orderbook models:
Markets operate and function like exchanges, with the presence of bids/asks for outcome shares.
- Pros: Much more accurate and precise price discovery, deeper liquidity pools.
- Cons: Increased complexity; high demand of market makers.
Key Takeaway:
Market design tradeoffs balance simplicity vs. expressiveness. Simpler markets attract casual/seasonal participants, while continuous models open up greater opportunities for more sophisticated forecasting.
2.4 Governance & Incentives
Prediction and forecasting projects can be made or marred by how they incentivize honest participation and align stakeholders.
Decentralized governance:
Voting by token holders or DAO frameworks decide on disputes, upgrades, and parameters.
- Pros: Community-driven, censorship-resistant.
- Cons: Voters’ apathy and risks of plutocracy.
Centralized governance:
Project/founding team makes key decisions.
- Pros: Faster and decisive actions with easier product iterations.
- Cons: Higher risk of centralization, potential increase in user distrust.
Key Takeaway:
Governance is about who controls the rules and incentives are about why users play honestly. Projects that misalign typically collapse into manipulation or low participation and then rapidly die.
Incentive mechanisms:
- Dispute bonding which requires stake to challenge outcomes.
- Reputation-based systems which reward historical accuracy.
- Token rewards disbursed for liquidity provision and/or accurate forecasting.
Quick-View Table: Key Classification Dimensions
| Dimension | Variants/Models | Pros | Cons | Examples |
|---|---|---|---|---|
| Resolution Mechanism | Human, Oracle, API Feeds, Hybrid | Flexible, objective (with oracles), scalable | Risk of bias, oracle failure, limited scope | Augur (human), Polymarket (oracle), Zeitgeist (oracle + human) |
| Technical Infrastructure | On-chain, Off-chain, Hybrid | Transparent, secure (on-chain); efficient, fast (off-chain) | On-chain: costly & slow; Off-chain: trust assumptions | Augur (on-chain), Kalshi (off-chain), Polymarket (hybrid) |
| Market Design | Binary, Scalar/Continuous, Parimutuel, Orderbook | Simple (binary), expressive (scalar), scalable (parimutuel) | Limited granularity, complexity in pricing/liquidity | Trepa (scalar), Polymarket (binary), Predict-It (parimutuel) |
| Governance & Incentives | DAOs, Centralized team, Incentive models (staking, reputation, token rewards) | Community-driven, aligned incentives | Voter apathy, centralization risks, potential of manipulations | Augur DAO (decentralized), Kalshi (centralized), Zeitgeist (bonding disputes) |
Summary:
These four dimensions — Resolution Mechanism, Technical Infrastructure, Market Design and Governance & Incentives — synergize together to create the backbone of any classification framework. They are deeply interconnected: for instance, a project’s choice of resolution mechanism poses constraints on its infrastructure (on-chain or off-chain), which in turn shapes market design and inevitably dictates governance needs. Together, they define the technological and mechanical DNA of prediction and forecasting platforms.
Typology of Prediction and Forecasting Projects
In this section, we employ the use of the four core technical dimensions from the previous section - Resolution Mechanism, Technical Infrastructure, Market Design and Governance & Incentives - which serve as the holistic lens for classifying forecasting projects. The following categories are some practical clusters you’d see more often than not in industry and research. For each category, I list the typical resolution choices, infrastructure patterns, market structure, and governance tendencies, followed by real-world examples and their main tradeoffs.
3.1 Short-Term Operational Forecasts
Definition:
Near-perfect real-time forecasts (minutes → days) that power operational systems and automated choices and decisions.
Typical choices:
Resolution Mechanism:
Autonomous data feeds, sensor streams, oracles for market prices; void of human arbitration and participation.Technical Infrastructure:
Predominantly based off-chain or hybrid (real-time processing architectures, event streams, caches); reliable pipelines with negligible latency.Market Design:
Not usually public markets, but more continuous numerical systems or private “prediction” APIs; if properly marketed using scalar and/or orderbook-style markets for extensive liquidity and instant repricing.Governance & Incentives:
Centralized ownership of products; short and closed-circuit feedback loops; incentives are often tied to meeting the operational KPI targets.
Examples:
Load balancing, short-term forecasting demands (ride-sharing, package delivery), day-to-day financial price forecasts.
Tradeoffs/Challenges:
Low latency is heavily prioritized; risk of over-crowding to noise; lapses have instant operational costs.
Comparative SWOT Analysis
Part 3
Where Trepa Fits In
Part 4
Conclusion
Closing statement and finalization
This content originally appeared on DEV Community and was authored by Daniel Tobi Onipe (Dexter)
Daniel Tobi Onipe (Dexter) | Sciencx (2025-09-17T16:59:29+00:00) Typology of Prediction & Forecasting Projects – A Technical Guide. Retrieved from https://www.scien.cx/2025/09/17/typology-of-prediction-forecasting-projects-a-technical-guide/
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