Sports‑based prediction markets do not treat all information equally. Some inputs move prices instantly, while others leave almost no trace. These differences follow a clear hierarchy that determines how information is weighted, when it becomes important, and how strongly it influences implied probabilities.
This vertical distribution of data is a core element of the Additional information, which examines how different tiers of access and data quality influence market movements. Understanding this hierarchy explains why prices shift at specific moments. As noted in comparisons between Related article, markets tied to real‑world events require far more complex information processing than systems driven by internal randomization.
Three Levels of Information and Their Pricing Power
Prediction markets classify information not by importance alone but by its ability to change uncertainty. The market prioritizes information that materially alters probability estimates.
Level 1: Structural & Confirmed Information
This is the highest tier. It includes verified, objective changes such as official injury confirmations, starting lineups, and significant weather shifts. These inputs directly alter probability estimates and almost always trigger immediate price adjustments because they remove a layer of unknown risk.
Level 2: Analytical & Contextual Information
This tier includes advanced performance metrics, historical matchup data, and tactical insights. These factors shape the initial modeling that produces opening prices. However, because they are often already incorporated into early estimates, they rarely cause major price movement close to the event unless a new analytical model gains sudden widespread adoption.
Level 3: Narrative & Perceptual Information
This includes media narratives, public sentiment, and “momentum” interpretations. These inputs have low statistical accuracy but can strongly influence collective behavior. Their impact on prices is indirect, driven by shifts in participation rather than changes in underlying probability.
How Liquidity Filters Information
Liquidity determines how strongly each information tier affects prices. In low‑liquidity markets, narrative‑driven information can dominate, and even minor news can cause large, volatile swings. Conversely, high‑liquidity markets resist sentiment‑driven movement and react primarily to Level 1 structural information, producing more stable and refined probability estimates. Liquidity acts as a filter that separates meaningful signals from noise.
Extending the Concept to Casino‑Style Systems
While sports‑based markets reveal the value of information through price movement, casino‑style systems embed probability logic directly into their design.
Embedded Probability: Casino games use fixed outcome sets and known probability distributions to create predictable long‑term expectations.
Pricing Structure: Payout ratios function as prices with built‑in margins, even though they are not displayed as market odds.
Bias and Liquidity: Player behavior cannot change the mathematical expectation, but higher participation (liquidity) causes results to converge more quickly toward theoretical averages.
Summary
The information hierarchy is the invisible framework that determines how probability becomes price. Despite their differences, both systems rely on the same underlying mathematics: they are not about predicting outcomes but about managing uncertainty. For more on the economic theory of how information is incorporated into prices, a foundational resource is the Efficient-market hypothesis on Wikipedia.