Predictive markets are the current hot topic. But when you delve deeper, every time you press Yes / No, the gears of fate begin to turn.

This article attempts to analyze controversial topics in predictive markets (mainly Polymarket) by selecting manipulable options under binary games.

Case Selection

1> Who will HBO identify as Satoshi?

2> How many gifts will Santa deliver in 2025?

3> Israel strikes Gaza by…?

It also explores possible market intervention methods through psychology, crowd effects, bookmaker game theory, and mass communication.

"Who is Satoshi?" Bet: The Market Refuses to Believe the Truth

Before and after the release of HBO's "Money Electric: The Bitcoin Mystery," a contract on Polymarket was seen as a classic example of "narrative disconnected from fact": "Who will HBO identify as Satoshi?" (October 2024)

On the surface, this was a collective guess about the ultimate mystery in the crypto circle, with participants trying to bet on who the documentary would identify as the founder of Bitcoin: Len Sassaman, Hal Finney, Adam Back, or Peter Todd, who has never appeared on any long list of conspiracy theories.

The vast majority of cryptocurrency community members, KOLs, and media firmly believed that HBO would reveal the late cryptographer Len Sassaman. This is because Len's life closely matched the characteristics of Satoshi, and his tragic and legendary image fit HBO's narrative aesthetic.

As a result, Len Sassaman's odds (Yes) soared to 68% - 70%.

The key to the event lies in the timeline.

Some invited journalists and insiders who previewed the media event began leaking snippets on Twitter and dark web forums. The leaked clips and screenshots clearly showed director Cullen Hoback questioning another developer, Peter Todd, and attempting to portray him as Satoshi.

Peter Todd himself even mocked the director on Twitter, indirectly confirming that he was the documentary's main character. At the same time, several media outlets had already written teaser articles and headlines with phrases like "doc identifies Peter Todd as Satoshi."

Despite this, the most exciting part emerged. Although the screenshots were released, the price of Len Sassaman on Polymarket did not crash and remained at a high level of 40%-50%!

Because the community refused to believe it. People brainwashed each other in the comments: "This is just HBO's red herring," "Peter Todd is just a supporting character; the final twist must be Len."

At this point, an opportunity arose. The odds for the Peter Todd / Other option were extremely tempting (at one point only 10%-20%).

This was like "picking up gold bars in a pile of cheap stuff."

—— Only when facts contradict wishes does Alpha maximize.

People wanted it to be Len Sassaman too much (because he had passed away and wouldn't crash Bitcoin, and his story was tragic). This emotional preference blinded rational judgment. In predictive markets, never bet on what you "hope" will happen; only bet on facts.

And the rules themselves state: "Who will be identified by HBO as Satoshi," not "who really is Satoshi."

Media narrative + emotional resonance. As long as the market is given a sufficiently compelling story, prices will voluntarily deviate from the facts.

"Santa Claus Codegate": When Hardcoding Becomes an Option

The second incident seemed lighter: the NORAD Santa Tracker project. Every Christmas, NORAD displays the "number of gifts Santa delivers" on a dedicated website. In 2025, this fun project became a betting target on Polymarket: "How many gifts will Santa deliver in 2025?"

Then, someone opened the browser console.

Technical traders discovered a hardcoded value in the front-end JS/JSON file of noradsanta.org: 8,246,713,529. This number was close to the "gift count" logic of previous years but was clearly lower than the reasonable range derived from historical growth rates (8.4–8.5B), more like a temporary script filled in by a programmer rushing to meet a deadline.

In the eyes of the market, this hardcode was quickly interpreted as the "ultimate answer":

• The corresponding "8.2–8.3B" range contract saw its price rise from around 60% to over 90%;
• A large amount of capital viewed this as a realization of "information advantage," treating the remaining few percentage points as free arbitrage.

But the truly subtle part was: once the leak was widely exploited by traders, the hardcode itself became a variable that could be triggered.

The NORAD website is centrally maintained, and developers have full authority to overturn previously hardcoded values at the last moment; when "lazy developers" and "hardcoding fraud" became part of social discourse, the maintainers even had the motivation to change the values in real-time to prove they weren't a makeshift team.

This meant that for those who bought "8.2–8.3B=Yes" chips at the 0.93 position, what they were truly betting on was not how many gifts Santa would deliver, but whether a developer would commit to changing that string of numbers one last time before going live.

Structurally, this market allows for multiple "intervention methods" to exert significant leverage on prices.

The NORAD website is centrally maintained, and developers have full authority to overturn previously hardcoded values at the last moment. When "lazy developers" and "hardcoding fraud" became part of social discourse, the maintainers even had the motivation to change the values in real-time to prove they weren't a makeshift team.

This means that for those who bought "8.2–8.3B=Yes" chips at the 0.93 position, what they were truly betting on was not how many gifts Santa would deliver, but whether a developer would commit to changing that string of numbers one last time before going live.

Here, the predictive market is no longer about "predicting an objective random variable," but rather provides a derivative space for a small group of people who control the system switches to "bet on how their actions will be interpreted by the outside world." Those who write front-end code inherently possess the dual power of "spoiling" and "modifying at any time."

Technically savvy players who deployed code crawlers in advance could complete their positions before most people realized the hardcode existed; media or self-media could amplify the narrative of the "hardcoding scandal," indirectly influencing whether the maintainers adjusted their strategy.

Here, the predictive market is no longer about predicting an objective random variable, but rather provides a derivative space for a small group of people who control the system switches to "bet on how their actions will be interpreted by the outside world."

"Gaza Strike" Contract: A Scripted Kill in the Early Morning Market

The third incident had the most real-world impact. Thanks to @ec_unoxx for the compilation, and trader @poliedge100, the little crocodile teacher.

A contract surrounding "Will Israel strike Gaza before a specific deadline?" staged a price cleansing with a strong "scripted feel" in the late trading phase as it approached expiration.

Initially, the market widely believed that the probability of a large-scale strike occurring before the deadline was limited, with the price of "No" remaining high in the 60%–80% range for a long time. As time passed, it seemed that "nothing happening" itself was continuously reinforcing the legitimacy of "No."

Then came the familiar rhythm: early morning hours + public opinion offensive + panic sell-off.

• In the platform's comment section, the "Yes" side began posting unverified screenshots, local media links, and even old news, creating a narrative atmosphere of "the strike has already happened; it's just that the big media is slow to react."
• At the same time, large sell orders appeared on the order book, actively breaking through the support of "No," pushing the price down to the "garbage zone" of 1%–2%.

For holders who were extremely reliant on emotionally driven information, this series of actions constituted a kind of "endgame illusion":

"Since someone is panic selling, and the comment section is saying it happened, there must be news I haven't seen."

While this panic was being manufactured, a small portion of those who insisted on fact-checking arrived at a completely different conclusion from another direction:

• Before the preset deadline, there was no sufficiently clear evidence of "airstrikes" recognized unanimously by authoritative media and consistent with the contract rules;
• From the perspective of textual rules, "No" still had a high probability of being the final legitimate settlement result.

Thus, asymmetry reappeared in the structure:

• The market price treated "No" as a 1% small probability;
• Textual evidence and rule interpretation provided a reality probability far exceeding 1%.

What truly sparked controversy was the scene after the settlement:

• After the close, someone proposed settling as "Yes" and entered a limited dispute period;
• Due to procedural reasons or insufficient resources from the participants, this settlement direction was ultimately not successfully overturned;
• The contract was finally locked in as "Yes," while many who insisted on interpreting the rules text could only argue afterward about "whether this conforms to the original rule design," but could not change the flow of funds.

This incident exposed the "greenhouse effect" of predictive markets in a very stark manner:

• Public opinion can create a price collapse in a short time;
• Capital can create the illusion of "smart money retreating" through self-directed sell-offs;
• The final settlement rights are often held by a very small number of parties with resources and organizational capabilities.

This is no longer a "bias of collective wisdom," but rather a manipulation space formed by the interplay of narrative, capital, and the authority of rule interpretation.

To summarize, in the three cases mentioned above, predictive markets present a different picture:

• For news initiators and media

Each predictive market can be seen as a real-time thermometer for narrative influence.

Documentary directors, PR teams, and topic creators can adjust their output rhythm by observing the order book: which candidates to continue promoting and which plots need to be enhanced.

In some extreme cases, content creators can even "reverse" leverage the order book to rewrite market preferences back into the script.

• For project parties/platforms

The ambiguity of rules, the choice of settlement sources, and the design of dispute mechanisms will directly affect "who can profit from end-of-day events."

Ambiguous oracles and broad adjudication powers equate to retaining a "gray area" that can be utilized by organized forces.

In this space, predictive markets are no longer passive "result registries," but rather active liquidity-boosting tools.

• For participants (retail investors/KOLs/communities)

Comment sections, social media, and various second-hand interpretations form a complete set of psychological leverage that can be exploited.

By concentrating on publishing "seemingly authoritative" screenshots, links, and out-of-context news headlines, actors can push prices from rational ranges into panic or frenzy zones in a short time.

In this structure, those with stronger discourse power (KOLs, influencers, research accounts) inherently possess the ability to manipulate narratives.

• For hackers and "system players"

Monitoring front-end code, data source updates, news APIs, and even oracle mechanisms can itself become a systematic strategy.

Capturing hardcodes, configuration errors, and edge cases in rules ahead of time and building positions before the market reacts is a form of high-leverage "structured alpha."

More aggressive players will directly study how to legally or "borderline" influence the sources of settlement information, making the world "appear" to align with their positions in a short time.

Finally, quoting @LeotheHorseman, the phrase pinned by the teacher:

The truth or falsehood of information seems to have become irrelevant (in terms of understanding and practical significance); what people are willing to pay for is the reality. The most important proposition of our current era may be how the pricing of information and the information of pricing interact with each other.

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