Ask three people what a prediction market is and expect three answers: a sophisticated gambling platform, an opinion platform, or a crowd-intelligence engine—depending on how much vested interest they have in the industry. After months of conversations with users, founders, investors, and sceptics, I’ve heard all three.
To the uninitiated, prediction markets are platforms where users back a “yes” or “no” position on whether an event will happen, with the market price reflecting how likely participants think that outcome is. In effect, people with domain knowledge can put a price on their conviction.
My strongest case for prediction markets starts with what they were originally built to do: turn dispersed knowledge into a signal. The Iowa Electronic Markets, a University of Iowa research project launched for the 1988 US elections and one of the earliest examples of prediction markets, tested whether people putting money behind their beliefs could forecast better than traditional polling. It worked. A 2008 study from the university’s researchers compared the market with 964 national polls across five presidential elections and found it closer to the final result 74% of the time.
However, modern prediction markets have since undergone several iterations, evolving into platforms like Polymarket and Kalshi that trade contracts on everything from elections to inflation to sports. In recent years, sports contracts have come to dominate trading volume on Kalshi and Polymarket, while politics accounts for a smaller but still significant share, according to the Pew Research Centre.
Across several conversations, users and operators told me that prediction markets let “informed” participants back their beliefs with money because they think they have an edge.
Micheal, a user on the Nigerian prediction market platform Bayse Markets, told me he earns about ₦100,000 trading social media markets, such as how many likes a post by a popular person will receive. He said he uses maths and probability and is wrong about 30% of the time.
“For example, it’s like I’m putting ₦500,000 in Bayse [Markets] this month and taking out ₦600,000 in the same month. My PnL [profit and loss] is ₦100,000. I haven’t had a [loss] since I started in April; I treat it as an investment, which takes my time,” Micheal told me.
One thing that struck me is how closely he watches the numbers. He forms his own estimate of how likely a social media post is to reach a particular number of likes, then compares it with the probability implied by the market price. If he thinks the market has got it wrong, he backs the outcome he believes is more likely.
While Micheal’s story is compelling, it’s worth noting that consistent profits in any market—especially a nascent, illiquid one—are the exception, not the rule.
Diran Otegbade, a finance professional and early investor in Bayse Markets, told me prediction markets keep him informed about finance—his sphere of influence—and let him bring a belief, backed by deep analysis, to bear on a price.
The kicker with prediction markets is that most people will still come for the money, particularly in markets with low disposable incomes, where they believe they can turn in quick, unrealistic gains. Nigeria’s experience with CBEX, which promised to double deposits in 30 days before collapsing in April 2025, showed how powerful a promise of returns can become. Traditional sports wagering thrives on the same incentive. Prediction markets risk being lumped in with these schemes in the public mind, even when their mechanics are different.
To be clear, some form of betting happens on prediction markets. The difference is that each bet is supposed to be backed by some rigour—whether that is information, intelligence, or just domain knowledge that gives traders an advantage—not just hope.
But ultimately, prediction markets are a victim of their own complexity. Their original promise requires users to understand information, probabilities, prices, liquidity, and resolution. When participants back opinions that aren’t grounded in data or skin in the game, the entire promise of crowd- or market-intelligence is defeated.
I have a hard time with sports prediction markets in particular. They undermine the logic on which prediction markets were built. The proposition weakens considerably when mere guesses replace strong, often informed opinions. You cannot reliably say a team will lose a game or a player will be carded without information that the wider market does not have.
Sports consistently account for the most-traded markets on global prediction platforms, often open to ordinary fans with no domain knowledge. Yet, prediction markets have leaned into sports contracts in their marketing because it lets them pitch a niche product to a mass audience using sports’ global appeal. That’s when prediction markets risk being lumped in with traditional sports betting, even though the comparison isn’t entirely accurate.
As a result, other, more sophisticated markets that require domain knowledge are, as expected, thinly traded. When more experts with domain knowledge predict and back their opinions with money, prices become a truer reflection of reality—and closer to the actual outcome.
For example, a CNBC analysis in September found that during the 2026 FIFA Men’s World Cup, traders on Polymarket and Kalshi backed Egypt with $158 million to win the tournament even though the country’s probability never crossed 0.5%. Spain, the eventual winner, traded at $152 million, underscoring how high volumes can reflect fandom rather than informed probability, a pattern very evident in sports, the most-traded market.
Africa is building its own version
In January, Oluwaleke Fakorede, chief technology officer of Bayse Markets, told me that his prediction for Africa’s prediction market sector (no pun intended) was that it would find product-market fit and that a $100 million prediction market startup would emerge from the continent.
At the time, he said Bayse Markets had processed over $13 million in trading volume with more than 200,000 users. There may be a glimmer of hope with his first prediction, but ten months into the year, the latter remains an open question.
But the playing field has since widened. In March, Luno, the UK-headquartered crypto firm, launched a prediction market focused on crypto prices. Busha, a Nigerian crypto trading startup, launched Signal in August, licenced by the Lagos State Lottery and Gaming Authority (LSLGA) and timed for the English Premier League season, which makes sense as sports is one of the most-traded markets.
But it also raises a question about what happens when a market isn’t liquid enough. Andy Tudhope, chief technology officer at LAVA, a Web3-focused venture capital firm, where he leads technical due diligence, noted that in traditional finance, professionals who supply liquidity for trades protect themselves by taking an opposite position elsewhere.
A prediction market offers nothing to offset against, because no second market exists for a president not saying a particular word, for example.
Few professionals take that risk, so the pool of money remains shallow, and a single large purchase can swing the price. Tudhope told me the goal is never to end manipulation, which he considers impossible. It is to make honesty pay: when a price is pushed the wrong way, an informed trader can buy the other side and profit when the market resolves.
The mechanism only works when everyone can see the trades and the money in the pool, which is where the blockchain is useful.
Despite their sophistication, prediction markets risk being reduced to gambling because of how they are used, the financial incentives, and how they somewhat model the risk-reward idea—not the method—behind traditional betting apps.
The regulatory treatment complicates the sector’s attempt to distance itself from gambling. In Lagos, the state’s gaming regulator has issued gaming facilitation permits to Busha and Bayse Markets.
In a conversation, Fakorede rejected the gambling label. He noted that the fact that traders can lose money for being wrong is precisely what makes their positions useful. People are putting money behind what they believe will happen, and when they are wrong, someone else can take the other side.
In the traditional betting model, users are always up against the house, which controls the incentives and rewards. The argument for prediction markets, then, is that they aggregate conviction rather than simply take bets.
The promise needs to be clearer
Tudhope said prediction markets suit people who care about the signal enough to accept a smaller return for it, but the problem is that most platforms do not pitch themselves to them—instead, they seek a general audience that doesn’t fit the bill for the kind of market intelligence that’s valuable on prediction markets.
Prediction markets will always be different things to different people. But if the distinction between intelligence and gambling must be made clear, platforms need to be deliberate about what they are selling. The money may draw people in, but the information is what gives the market staying power.
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I am the CEO of 9JAGISTLAND.My name is Adedeji Timothy and my co-owner is Oyekola Damilare.This blog is born out of our passion to educating to people in all aspect of internet life.We are enthusiastic about writing posts and articles online.
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