Understanding Polymarket And Prediction Markets

Traders use Polymarket to predict outcomes, but the platform itself doesn’t predict future events. It measures how the crowd currently estimates probability based on available information, similar to how a thermometer measures current temperature rather than predicting it.

“A prediction is an estimate of the likelihood of a future event based on what we know – what information is available. That estimate is attempting to quantify some of the uncertainty,” explained David Tyler, CEO of Outlier Technology Limited in an email interview.

Polymarket’s high liquidity helps drive more accurate predictions. The wisdom of crowds works better with more participants and larger trading volumes.

Measuring Polymarket Prediction Accuracy

One of the key questions about prediction markets is whether we can evaluate how accurate their probability estimates are.

Before An Event

Measuring prediction accuracy before an event occurs is fundamentally impossible. “If we were somehow able to generate accurate metrics to measure the accuracy of the crowd, we wouldn’t need the crowd – because to measure accuracy before the event, we’d need an accurate prediction, which is why we went to the crowd in the first place,” explained Tyler. The market exists precisely because we don’t have a perfect benchmark against which to compare its predictions.

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After An Event

Measuring prediction accuracy after an event is technically possible in some cases, but raises fundamental issues that make it impractical or meaningless.

For recurring events like elections or sports matches, measuring accuracy might seem possible at first glance. “When predictions involve events that recur, patterns can emerge and that allows retrospective evaluation of accuracy,” explained Tyler.

But if we can develop accurate measurement systems for recurring events, we wouldn’t need prediction markets at all. As Tyler pointed out: “If we have the same event repeating, we’ll be able to identify core rules and metrics that point to the accuracy of the prediction, but again, if we’re doing that, what’s the value of the crowd?”

The situation is even more complicated with unique events like unprecedented geopolitical developments or technological breakthroughs. “When there’s no clear historical data to rely on,” Tyler explained, “measuring accuracy after the fact has little value since the event will never repeat in quite the same way.”

Despite these measurement challenges, prediction markets remain valuable tools. Their strength lies not in provable accuracy, but in their ability to combine diverse perspectives and quickly adapt to new information, expressing this collective knowledge in a single, clear metric – the probability of an event.

How Prediction Markets Differ From Polling

While polls ask direct preference questions, prediction markets measure probability by aggregating many other factors like insider information, historical patterns or breaking news. That’s why a candidate with 60% support in polls might have different odds of winning on Polymarket. Traders constantly update their bets based on any new information that could affect the outcome.

The Impact Of Large Traders On Polymarket

The lack of betting caps on Polymarket lets large traders move the market. For example, a $10M bet for an outcome versus $500 spread across multiple small bets could reflect one wealthy trader’s position rather than true crowd sentiment. But this effect is temporary because if the crowd disagrees with this position, other traders will see a profit opportunity and move the market back to equilibrium.

Trading Strategies Against Personal Beliefs

Traders often bet against their initial views if they spot profit opportunities.

A trader may buy positions despite doubting the final outcome, planning to profit from predictable price movements. For example, buying before scheduled debates, key announcements or milestones, then selling during the temporary price spike – regardless of their view on the ultimate result.

When crowd sentiment drives prices too high, traders may bet against outcomes they personally support. If they believe a 70% market probability is inflated compared to true chances, they’ll bet against it expecting eventual correction, even if they want that outcome to occur.

Trading On Temporary Market Shifts

Consider a strong hockey team that wins 80% of their matches over the season. During a game, events like letting in a goal or getting a penalty might temporarily drop their win probability. Experienced traders might regularly bet at these moments, knowing that the team’s fundamental strength remains unchanged over the long run. They profit not from predicting the final outcome, but from market overreaction to temporary setbacks.

The Limitation Of Prediction Markets

Prediction Markets can show better probability estimates because people are financially invested, but there are other factors that can influence their decisions.

“What makes prediction markets challenging is that participants observe other traders and incorporate that into their decision-making model. Like all markets, herding behavior can develop its own momentum and behave irrationally,” Tyler explained. Beyond herding behavior and financial incentives, various psychological and social factors can affect how traders assess probabilities on these platforms.

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