Lula’s unexpected Brazilian election victory challenged prediction market forecasts. However, experts say limited liquidity, trading behaviour, and changing voter sentiment explain why markets sometimes misjudge political outcomes.
Prediction markets have gained attention for turning public expectations into real-time probabilities.
Platforms such as Polymarket and Kalshi let users trade contracts tied to political events, elections, sports, and economic developments.
However, their forecasts do not always match the final results.
October 2026’s presidential election, followed by Brazil’s betting ban, gave another example.
Incumbent Luiz Inácio Lula da Silva beat Flávio Bolsonaro. He entered the final round as an underdog on prediction markets.
The result quickly raised questions about the reliability of these platforms.
Some observers viewed it as evidence of poor forecasting, while others argued that traders simply misjudged the available information.
Experts believe the Brazilian election should not be treated as proof of wider prediction market failures. Instead, it highlights how market conditions, voter behaviour, and changing probabilities influence election forecasts.
What Happened in Brazil’s Presidential Election?
Brazil’s presidential election produced an unexpected result in October 2026.
Flávio Bolsonaro, the Liberal Party candidate, finished ahead of Lula in the first round on October 4, securing approximately 47% of the vote. However, he failed to secure the majority required to win outright.
The election therefore moved to a second round, where Lula defeated Bolsonaro and secured another presidential term.
The result surprised prediction market traders. Following Bolsonaro’s first-round performance, Polymarket reduced Lula’s estimated winning probability to around 13.5%. Before the final vote, his odds recovered to approximately 20%.
This meant traders still assigned Lula a chance of winning, but most market activity favoured Bolsonaro.
According to iGaming Business, one trader earned approximately $47,595 by backing Lula, while another trader reportedly lost around $27,100.
The result exposed a gap between market expectations and the final outcome. However, it did not establish that prediction markets consistently produce inaccurate forecasts.
Why Did Prediction Markets Misjudge Lula’s Chances?
Experts have identified several factors that may explain the unexpected result.
Limited Market Participation
Brazil has more than 200 million residents, but its prediction market activity remained relatively limited.
According to Decrypt, the Brazilian election prediction market attracted only around 4.5% of the trading volume generated by the 2024 US presidential election.
Lower participation can make it harder for markets to collect different opinions and information. It can also allow a smaller group of traders to influence prices.
Luke Nolan, senior researcher at CoinShares, argued that having more retail traders is not automatically a problem. Different participants can contribute useful information, including local knowledge.
The real issue is whether the market attracts enough informed participants who can respond to changing developments.
Traders Relied Too Heavily on First-Round Results
Bolsonaro’s first-round performance influenced expectations before the final vote.
Some traders may have assumed that voters supporting eliminated candidates would move towards Bolsonaro. Others underestimated the possibility of higher voter turnout for Lula.
This shows how political assumptions can influence market prices.
Traders may also react strongly to polling headlines without considering regional voting patterns or changes in voter sentiment.
Limited Local Knowledge
Another concern involved the lack of local expertise in Brazil’s prediction markets.
Einar Knobel, CEO of TxODDS, told Decrypt that much of the trading came from participants outside Brazil.
These traders may have relied heavily on first-round results, polling reports, and international coverage rather than detailed local information.
This can create gaps between market expectations and actual voter behaviour.
Does One Unexpected Result Prove Prediction Markets are Flawed?
Not necessarily.
Prediction markets work with probabilities, not guaranteed outcomes. A candidate with a 20% winning probability still has a real chance of winning.
If an event has a 20% probability, it should happen approximately 20 times across 100 comparable situations, assuming the forecasts are well calibrated.
Therefore, an unexpected result does not automatically indicate a forecasting failure.
A 2025 Vanderbilt University study examined nearly 2,500 prediction markets linked to the 2024 US election. Its findings suggested that prediction markets were generally well calibrated across the markets studied.
However, overall calibration does not guarantee accuracy in every individual election. Markets can perform differently in close contests, especially when information is limited, or trading activity is uneven.
Experts believe researchers should examine performance across many events rather than judge an entire forecasting system through one surprising result.
Prediction Markets Face More Scrutiny Ahead
Brazil’s election comes at an important time for the prediction market industry.
Platforms such as Polymarket and Kalshi have expanded their presence in political forecasting. Their role in the 2024 US presidential election attracted wider media attention and increased interest in event-based trading.
According to Pew Research Center figures cited by iGaming Business, trading activity across major platforms exceeded $4.2 billion during the 2024 US presidential election period.
However, regulatory concerns remain. Brazil introduced regulatory restrictions on prediction market platforms in April 2026, citing investor protection and gambling-related concerns.
The industry also faces questions about market manipulation, insider information, and the influence of large traders.
As trading volumes grow, platforms will need stronger monitoring systems, transparent market rules, and clear explanations of how probabilities work.
What Does This Mean for the Future?
Lula’s victory highlights an important distinction between market expectations and actual election results.
Prediction markets can provide useful information about public expectations, but their prices should not be treated as certain forecasts. Their reliability depends on participation, liquidity, information quality, and how quickly traders respond to new developments.
The legality of prediction markets in the near future also remains a major concern.
The Brazilian result may encourage platforms and researchers to examine how they handle markets with limited participation and local information gaps.
Ultimately, one unexpected election result does not establish a wider prediction market failure. It shows why users must understand probabilities, market conditions, and the limits of forecasting before treating trading prices as reliable predictions.
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