Bolster Your FIFA World Cup Bracket with @RISK

The 2022 FIFA World Cup is right around the corner and the tournament predictions are pouring in. While checking team rankings and relying on gut instinct might be the classic approach to choosing your champion in your FIFA World Cup bracket, risk analysis software is changing the game by helping to quantitatively predict match outcomes.

“The outcomes of many decisions we make are uncertain because of lack of information and things outside of our control,” said Steve Begg, veteran oil and gas industry analyst and football enthusiast, in Probability Outcomes in the 2018 FIFA World Cup. “Uncertainty is crucial in predicting the chance of an oil or gas field being economic. In the World Cup, it determines the many ways the whole tournament might play out – there are nearly 430 million possible outcomes of the Group Stage alone. What makes it so hard to predict is not just uncertainty in how a team will perform in general, but random factors that can occur in each match.”

Injuries, team performance, and home field advantages all contribute to the unpredictability of the matchups and could throw off even an expert’s best guess. Martin Green, SportsLine’s soccer insider, questions top-ranked Brazil’s potential to take the gold based on past late tournament performance.

“It is hard to justify Brazil's status as the +450 favorite," said Green in SportsLine’s recent article, 2022 World Cup Futures Odds, Picks. "There is a very strong spine to the team, featuring Alisson, Marquinhos, Casemiro, Fabinho and Neymar, while the likes of Vinícius Junior are dangerous. However, Brazil has not won the World Cup since 2002, and it crashed out in the quarterfinals in 2018. It may find France, England or Spain too strong in the latter stages of this tournament."

To account for the extensive unknowns when making football predictions, Begg employs Lumivero’s @RISK to create a probability model to estimate the chances of various outcomes happening in the tournament. By using Monte Carlo simulation in @RISK, Begg can account for the uncertainty in a team’s performance and map out their chance of winning by modeling thousands of possible scenarios.

“The difficulty is in knowing how to propagate uncertainty in something we can assess, the teams’ playing ability, through to an assessment of their chance of advancing to various stages of the tournament, ultimately to the final. This is what Monte Carlo simulation enables us to do,” said Begg.

For his FIFA World Cup model, Begg ran 100,000 simulations and used the PERT probability distribution function to describe uncertainty in tournament form. “The PERT distribution is easy to use because it just requires three numbers, a minimum, maximum and most likely,” Begg says.

Begg derived the “most likely” value from past FIFA rankings and his own knowledge of international soccer. The PERT minimum and maximum values were assigned based on the most likely values, with the distribution skewed upwards for lower-ranking teams based on the theory of playing better than their rankings suggest and vice versa.

“It’s important to realize that probability is subjective. It depends on what information you have. There’s this tendency for people who do this kind of work to obsess on data,” said Begg. “You might argue that these simulations are the most useful when you have no data at all. But you do need to understand your uncertain quantities well enough to assign a probability distribution that reflects your degree of belief in what the outcomes might be. What’s crucial is that neither the information nor your reasoning is biased.”

Clay Graham, Chief Analytical Architect at Action Sports, shows another approach in his recent webinar, Sports Gambling – Ahead of Tomorrow the New Paradigm.

“We (at Action Sports) have developed an aggressive approach to model soccer including: Generate a discrete density function(s), derived from the mean and variance of goals scored. Optimize the data look-back period to enhance predictability and minimize noise and incorporate Monte Carlo simulation and logistic regression to calculate a probability of winning along with integrated Bayesian statistics to extrapolate a posterior probability of winning,” said Graham.

So before you submit your FIFA predictions this year, leverage @RISK to model thousands of possible scenarios and bolster your FIFA World Cup bracket through risk analysis.

Download your 15-day free trial of @RISK today.

The 2022 FIFA World Cup is right around the corner and the tournament predictions are pouring in. While checking team rankings and relying on gut instinct might be the classic approach to choosing your champion in your FIFA World Cup bracket, risk analysis software is changing the game by helping to quantitatively predict match outcomes.

“The outcomes of many decisions we make are uncertain because of lack of information and things outside of our control,” said Steve Begg, veteran oil and gas industry analyst and football enthusiast, in Probability Outcomes in the 2018 FIFA World Cup. “Uncertainty is crucial in predicting the chance of an oil or gas field being economic. In the World Cup, it determines the many ways the whole tournament might play out – there are nearly 430 million possible outcomes of the Group Stage alone. What makes it so hard to predict is not just uncertainty in how a team will perform in general, but random factors that can occur in each match.”

Injuries, team performance, and home field advantages all contribute to the unpredictability of the matchups and could throw off even an expert’s best guess. Martin Green, SportsLine’s soccer insider, questions top-ranked Brazil’s potential to take the gold based on past late tournament performance.

“It is hard to justify Brazil's status as the +450 favorite," said Green in SportsLine’s recent article, 2022 World Cup Futures Odds, Picks. "There is a very strong spine to the team, featuring Alisson, Marquinhos, Casemiro, Fabinho and Neymar, while the likes of Vinícius Junior are dangerous. However, Brazil has not won the World Cup since 2002, and it crashed out in the quarterfinals in 2018. It may find France, England or Spain too strong in the latter stages of this tournament."

To account for the extensive unknowns when making football predictions, Begg employs Lumivero’s @RISK to create a probability model to estimate the chances of various outcomes happening in the tournament. By using Monte Carlo simulation in @RISK, Begg can account for the uncertainty in a team’s performance and map out their chance of winning by modeling thousands of possible scenarios.

“The difficulty is in knowing how to propagate uncertainty in something we can assess, the teams’ playing ability, through to an assessment of their chance of advancing to various stages of the tournament, ultimately to the final. This is what Monte Carlo simulation enables us to do,” said Begg.

For his FIFA World Cup model, Begg ran 100,000 simulations and used the PERT probability distribution function to describe uncertainty in tournament form. “The PERT distribution is easy to use because it just requires three numbers, a minimum, maximum and most likely,” Begg says.

Begg derived the “most likely” value from past FIFA rankings and his own knowledge of international soccer. The PERT minimum and maximum values were assigned based on the most likely values, with the distribution skewed upwards for lower-ranking teams based on the theory of playing better than their rankings suggest and vice versa.

“It’s important to realize that probability is subjective. It depends on what information you have. There’s this tendency for people who do this kind of work to obsess on data,” said Begg. “You might argue that these simulations are the most useful when you have no data at all. But you do need to understand your uncertain quantities well enough to assign a probability distribution that reflects your degree of belief in what the outcomes might be. What’s crucial is that neither the information nor your reasoning is biased.”

Clay Graham, Chief Analytical Architect at Action Sports, shows another approach in his recent webinar, Sports Gambling – Ahead of Tomorrow the New Paradigm.

“We (at Action Sports) have developed an aggressive approach to model soccer including: Generate a discrete density function(s), derived from the mean and variance of goals scored. Optimize the data look-back period to enhance predictability and minimize noise and incorporate Monte Carlo simulation and logistic regression to calculate a probability of winning along with integrated Bayesian statistics to extrapolate a posterior probability of winning,” said Graham.

So before you submit your FIFA predictions this year, leverage @RISK to model thousands of possible scenarios and bolster your FIFA World Cup bracket through risk analysis.

Download your 15-day free trial of @RISK today.