Soccer is the most popular sport in the world[1] and now the third most popular sport in the U.S., surpassing baseball.[2] Like many sports, fans love to make predictions on how the results are going to go.
Forecasting is not limited to only sport lovers. According to Alan Mills,[3] nearly all actuaries employ some form of forecasting: Life insurance actuaries forecast population mortality rates and company assets over lifetimes; retirement actuaries forecast pension plan payouts and plan assets during retirement; and health insurance actuaries forecast medical expenditures and premium income for two or three years.
Every four years, the World Cup transforms the world into a stadium of forecasters. Fans from dozens of countries make bold predictions with absolute confidence, often in defiance of statistical reality. Traveling internationally to attend the tournament makes this phenomenon even more vivid: You hear predictions shouted in different languages, see fans waving flags with prophetic certainty, and feel the emotional momentum that makes people believe the improbable is inevitable.
This article argues that the same psychological forces that drive fans to predict match outcomes also influence actuarial forecasting. Anchoring, overconfidence, narrative bias[4][5] and the human desire for certainty shape both sports predictions and professional risk assessments. By examining the forecasting behavior surrounding the World Cup, actuaries can better understand the cognitive traps that affect assumption setting, long-term projections, and risk modeling. The World Cup becomes a lens through which we can explore the humility required to forecast uncertain futures, whether on the pitch or in the insurance markets.
A Stadium Full of Forecasters
There is nothing quite like stepping off a plane in a foreign country during the World Cup. The airport is buzzing with jerseys, chants and flags. Strangers strike up conversations instantly, united by the shared language of football. And almost immediately, the predictions begin.
“Argentina will win it all.”
“France is unstoppable this year.”
“Don’t underestimate Japan, they’re the dark horse.”
These statements aren’t tentative. They’re delivered with the confidence of someone reading from a script that has already been written. You hear them in cafés, in metro stations, in stadium lines, and in the stands themselves. The closer you get to kickoff, the more certain everyone becomes.
When you finally enter the stadium, the atmosphere is electric. Tens of thousands of people, each convinced they know what will happen next. It’s a sea of forecasters but not the kind who rely on models, data, or scenario testing. These are emotional forecasters, driven by passion, loyalty and instinct.
As actuaries, we spend our careers building frameworks to avoid this exact kind of thinking. Yet the World Cup reminds us that the human brain is naturally drawn to prediction. We want to believe we can see the future, even when the evidence is thin.
Why Humans Love Predictions
Prediction is a deeply human impulse. It gives us a sense of control in an uncertain world. Psychologists have long argued that forecasting—even inaccurate forecasting—reduces anxiety by creating the illusion of order.[6][7]
The World Cup amplifies this impulse for several reasons.
Pattern Seeking
Humans are wired to find patterns, even in randomness.[8][9] A team that wins two matches in a row suddenly “has momentum.” A striker who scores once is “in form.” These patterns feel predictive, even when they’re statistically weak. Similarly for actuaries, forecasting is usually based on identifying historical trends and correlations. The need to find patterns usually causes humans to sometimes force the patterns even in the absence of credible information.[10]
Emotional Investment
Fans are not neutral observers. Their identity is tied to their team. Prediction becomes a form of loyalty, not analysis.
Actuaries can also become emotionally invested in assumptions, models or even business lines they work with. A model they built, a modelling software they have used frequently or even an assumption set they have used often are all things that actuaries can be attached to and cause them to drift towards loyalty rather than analysis.
Social Reinforcement
When thousands of people around you believe the same thing, it feels true. The stadium becomes an echo chamber of confidence.[11]
When everyone in a department, leadership team, or product group leans toward the same assumption, it becomes harder for an actuary to challenge it. The environment itself starts to function like an echo chamber.
A stadium full of fans can make a bad prediction feel true.
A conference room full of colleagues can do the same.
Actuaries must be the ones who step outside the noise and ask, “Is this actually supported by the data?”
Narrative Thinking
Humans love stories. The idea that “it’s finally their year” is more compelling than a probability distribution.
These psychological tendencies don’t disappear when actuaries walk into the office. They follow us into assumption-setting meetings, model reviews, and long-term forecasting discussions. The World Cup simply makes them easier to see.
The Actuarial Parallels
The forecasting behavior of World Cup fans mirrors several well-documented cognitive biases that actuaries must guard against. The parallels are striking and instructive.
Anchoring
Fans anchor on the most recent match:
“They beat a top team last week, so they’ll win again.”
Actuaries anchor too:
- Last year’s mortality improvement
- A recent claims spike
- A single economic scenario
- A familiar assumption that “has always worked”
Anchoring narrows our field of vision. It makes us overweigh recent or salient information at the expense of the full distribution of outcomes.[12]
Overconfidence
Fans often assign confidence well above what underlying odds support. They mistake desire for probability.
Actuaries face similar risks:
- Overconfidence in long-term mortality trends
- Overconfidence in lapse assumptions
- Overconfidence in economic forecasts
- Overconfidence in expert judgment
Overconfidence is especially dangerous because it compresses the tails of the distribution precisely where insurers are most exposed.[13]
Narrative Bias
Every World Cup has a storyline. Narratives feel predictive, even when they’re not.
Actuaries encounter narrative bias when:
- A product line “has always been stable”
- A demographic “never behaves that way”
- A market “is due for a correction”
- A trend “must revert to the mean”
Narratives simplify complexity, but they distort risk.
Confirmation Bias
Fans selectively remember predictions that came true and forget the ones that didn’t.
Actuaries can fall into the same trap:
- Favoring data that supports existing assumptions
- Downplaying contradictory evidence
- Overvaluing familiar models
Confirmation bias is subtle, but it can shape entire forecasting frameworks.[14]
Why Actuarial Forecasting Is Harder Than Predicting a Match
Predicting a soccer match is difficult. Predicting insurance risk is exponentially harder.
A match lasts 90 minutes.
Insurance liabilities can last 30 years or more.[15][16]
A match has 22 players.
Insurance risk involves millions of policyholders, thousands of variables, and complex interactions.
A match has a fixed set of rules.
Insurance operates within evolving regulatory, economic, demographic, and technological environments.
A match ends.
Insurance risk continues to unfold long after assumptions are set.
Actuaries must forecast:
- Mortality and morbidity
- Lapse behavior
- Catastrophe exposure
- Policyholder behavior
- Economic conditions
- Regulatory changes
- Long-term demographic shifts
The time horizon alone multiplies uncertainty. The World Cup reminds us how hard it is to predict even a short, well-defined event—let alone a multi-decade liability.
The Role of Models: What Sports Analytics can Teach Us
Sports analytics has grown dramatically in the past decade. Expected goals, possession value models, and machine-learning-based player ratings have transformed how analysts understand the game.[17][18]
Yet even the best models struggle to predict match outcomes with high accuracy. Upsets happen. Red cards happen. Weather changes. A single deflection can rewrite the script.
This is a powerful lesson for actuaries: Even the most sophisticated models cannot eliminate uncertainty.
Models are tools, not oracles. They help us understand the distribution of outcomes, not the outcome itself. The humility embedded in sports analytics—the recognition that randomness plays a larger role than we’d like to admit—is a mindset actuaries can embrace.[19]
Building Better Forecasting Habits
The goal is not to eliminate human judgment. Judgment is essential to actuarial work. The goal is to structure judgment so that it is informed, transparent and resilient.
Use Ensemble Models
Just as sports analysts combine multiple models to improve prediction accuracy, actuaries can:
- Blend statistical models with expert judgment
- Use multiple economic scenarios
- Combine deterministic and stochastic approaches
Ensembles reduce reliance on any single viewpoint, echoing large-scale forecasting competitions that find combined methods outperform any individual model.[20]
Stress-Test Assumptions
World Cup analysts simulate thousands of match outcomes. Actuaries can:
- Stress-test mortality assumptions
- Explore extreme lapse scenarios
- Model tail economic events
- Evaluate capital adequacy under adverse conditions
Stress testing reveals vulnerabilities that point estimates hide.[21]
Document the Rationale
Assumptions should not be black boxes. There should be good documentation.[22] Clear documentation:
- Reduces anchoring,
- encourages challenge,
- supports governance, and
- improves transparency.
Encourage Dissent
In a stadium, dissent is rare; everyone wants their team to win. In actuarial teams, dissent is essential. Diverse perspectives reduce blind spots.
Revisit Assumptions Regularly
Assumptions should evolve with new data, not remain static out of convenience.[23]
The Joy and Humility of Uncertainty
Traveling to the World Cup is a reminder that uncertainty is part of what makes life exciting. Fans predict because it’s fun. Actuaries forecast because it’s necessary. But both groups face the same truth: Uncertainty is humbling.
The difference is that actuaries must embrace that humility—not fight it—to build models that withstand the real world.
The World Cup teaches us that even when billions of people believe they know the outcome, the match can still surprise everyone. Insurance risk behaves the same way. The future is not a puzzle to be solved but a landscape to be navigated.
Professional actuarial practice is not about eliminating uncertainty but about understanding, communicating and managing it responsibly. Models, assumptions and expert judgment are most valuable when paired with intellectual humility and a disciplined appreciation of uncertainty.
This article is provided for informational and educational purposes only. Neither the Society of Actuaries nor the respective authors’ employers make any endorsement, representation or guarantee with regard to any content, and disclaim any liability in connection with the use or misuse of any information provided herein. This article should not be construed as professional or financial advice. Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.
Emmanuel Awotwe, ASA, MAAA, is an actuarial associate. Emmanuel can be contacted at awotwemanuel@gmail.com.
Endnotes
[1] Broadwell, K. (2024). DID YOU KNOW: Soccer is the Most Popular Sport in the World? https://www.nationalsoccernetwork.com/post/did-you-know-soccer-is-the-most-popular-sport-in-the-world
[2] Castillo, J. C. (2026). In the U.S., soccer is now more popular than baseball. These are the studies. https://sports.yahoo.com/articles/u-soccer-now-more-popular-173303727.html
[3] Mills, A. (2009). Introduction to Forecasting Methods for Actuaries. https://www.soa.org/globalassets/assets/library/newsletters/forecasting-futurism/2009/september/ffn-2009-iss1-mills-forecasting-act.pdf
[4] Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
[5] Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
[6] Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. Crown Publishers.
[7] Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
[8] Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House.
[9] Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
[10] Stevenson, R. (2020). Predictably Irrational- Applying behavioral economics to actuarial science. https://actuary.org/article/predictably-irrational/
[11] Sunstein, C. R. (2006). Infotopia: How many minds produce knowledge. Oxford University Press.
[12] Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
[13] Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. Crown Publishers.
[14] Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175
[15] Actuarial Standards Board. (2020b). Actuarial Standard of Practice No. 56: Modeling. https://www.actuarialstandardsboard.org/asops/modeling-3/
[16] International Accounting Standards Board. (2023). IFRS 17 insurance contracts. IFRS Foundation. https://www.ifrs.org
[17] Anderson, C., & Sally, D. (2013). The numbers game: Why everything you know about soccer is wrong. Penguin Books.
[18] FBref. (n.d.). xG Explained. https://fbref.com/en/expected-goals-model-explained/
[19] Silver, N. (2012). The signal and the noise: Why so many predictions fail—but some don't. Penguin Press.
[20] Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 forecasting competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54–74. https://doi.org/10.1016/j.ijforecast.2019.04.014
[21] Actuarial Standards Board. (2020b). Actuarial Standard of Practice No. 56: Modeling. https://www.actuarialstandardsboard.org/asops/modeling-3/
[22] Actuarial Standards Board. (2020a). Actuarial Standard of Practice No. 41: Actuarial communications. https://www.actuarialstandardsboard.org/asops/actuarial-communications/
[23] Actuarial Standards Board. (2020b). Actuarial Standard of Practice No. 56: Modeling. https://www.actuarialstandardsboard.org/asops/modeling-3/