Don’t trust surveys — use on-chain analysis of prediction markets instead.
How I use Polymarket
Whenever we want to check society’s reaction to a particular event, surveys are the first thing that comes to mind. However, there are serious issues with traditional surveys.
Why you shouldn’t trust surveys
- The survey provider could have a bias, and it can make him manipulate the results
- The survey provider, instead of manipulating results, could select the biased set of participants
- You have no way to verify the data and have to trust the source
Example of unbiased metric
Imagine that you have to evaluate a vendor that sells some goods. How would you do that?
On the one hand, you can read reviews — but the content of the review could be affected by the affiliation between the author and vendor. If you ask the vendor or his associates, they’ll try to convince you that you will never find a better deal, but you know that their opinion is biased. It’s nothing different from a paid promotion. What could be a better metric for the vendor’s quality?
If you can get access to the purchasing history over several months, this will tell you everything. If their products are continuously sold out, then demand is high, and constantly high demand is clear evidence of a good price-to-quality ratio. The volume of customer money flowing towards the vendor is a better indicator than reviews.
Now, you could tell the vendor: If your product is as good as you claim, why do sales reports tell a different story?
How to make unbiased survey
The first problem of the survey is the quality of the dataset. How does the survey provider select participants?
Some people don’t have opinions and could give a random answer just for fun, and some other people can lie for whatever reasons. But everything changes once there is a requirement to bet your money on your answer. The subset of participants becomes much cleaner — only those who have strong opinions are willing to participate.
Whom would you trust more — the one who says: “I believe in this outcome” or the one who says: “I believe in this outcome and am willing to bet $100 on it”?
The second problem is the lack of transparency — how could you protect data from manipulation?
The solution is to store data on-chain. It guarantees transparency and prevents manipulation — you can see in real-time every bet made and verify the authenticity of this data.
All these features are implemented in prediction markets running on-chain. The most famous market is Polymarket.
If you have all the data on-chain, you can see the number of people who bet their money on one option over another and the total size of bets. This metric serves as an unbiased representation of public opinion.
How the prediction market works
Let’s say some future event could have one of the following outcomes: A, B, C, or D.
You can bet on any of these outcomes by purchasing votes on-chain. If you change your opinion later, you can sell your previously purchased votes and exit the bet, or you could purchase votes for another option.
The price of the vote for a particular option is dynamic — it changes based on demand. This means that you don’t have to wait until the event is resolved — you can fix your profit or loss before the resolution.
If you bought votes for option A when it was unpopular and the price of a vote was low, and then public opinion shifted towards that option, and it became more expensive, you can sell your votes and take a profit.
This gives us a chance to determine the public’s reaction to a certain event by looking at the vote price graph. If, right after a certain event, public opinion shifts towards option B, you’ll see the price spike on the graph of vote B.
On-chain analysis for US presidential debates results
Here is the graph from Polymarket titled “Biden drops out of presidential race?” This market has only resolutions and two votes — “Yes” or “No”.
We can clearly see the spike in price for the vote “Yes” that coincided with the time debates started. This is evidence that during the debates, more and more market participants decided to bet on the option “Yes”. The spike on the graph is caused by Mr. Biden’s disastrous performance during the debates. The price dropped later cause people started taking profit by selling their “Yes” votes. But even after the drop, the new price level is much higher than before the debates.
Conclusion
From now on, Polymarket is my number one source for analyzing public reaction to global events. But if you also want to use this strategy, be careful — always check the total volume of a particular market and price graph. The lower the volume, the easier it would be for “whales” to manipulate the market. Also, if you see sudden spikes on the price graph and there were no important events at the time of the spike, this could mean that some “whale” is trying to manipulate the market.
