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Forecasting the FIFA Women's World Cup Winner: A Decision Matrix Approach

The competition's unpredictability makes it an exciting event, where anything can happen on the field. But what if we could make informed predictions about the potential winner without using complex data models?
Forecasting the FIFA Women's World Cup Winner: A Decision Matrix Approach
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As the FIFA Women's World CupTM final approaches, soccer fans worldwide eagerly anticipate cheering for their favorite teams and witnessing thrilling matches. The competition's unpredictability makes it an exciting event, where anything can happen on the field. But what if we could make informed predictions about the potential winner without diving into complex mathematical models?

In this blog post, we'll showcase how a straightforward decision matrix can be a powerful tool to forecast the FIFA Women's World Cup winner. By considering three key criteria, we'll demonstrate how this method can help you make educated guesses without the need for advanced data models.

A decision matrix is a practical and intuitive technique that allows us to evaluate multiple options against specific criteria. In our case, the options are the participating teams in the FIFA Women's World Cup, and the criteria are team rankings, recent form, and head-to-head records.

Gathering Data

We started our forecast just after the Group stage, during the Round of 16. To ensure accurate predictions, we analyzed multiple data points, each offering valuable insights into team performance.

Team Ratings: In the FIFA Women's World Cup, team ratings are vital indicators of their performance and potential. Higher-rated teams are seen as strong contenders, reflecting their past successes and current form.

Recent form: We used two different metrics from the Group stage to inform each team’s recent performance:

  1. Points: During the Group stage,  teams are awarded points based on their match results, with three points for a win, one point for a draw, and zero points for a loss. Accumulating points is crucial for securing a spot in the round of 16. 
  2. GD (Goals Difference): It represents the difference between the number of goals a team has scored and the number of goals they have conceded in all their matches. A positive GD indicates that a team has scored more goals than they have conceded, giving them an advantage over their opponents when teams are level on points in the standings.

Head-to-head records: We also used two different metrics to evaluate head-to-head records. Data was extracted from a dataset including matches from several different tournaments, as well as some friendly matches. We crunched the numbers manually analyzing the spreadsheet, with some AI help from ChatGPT.

  1. Wins: the number of times each team has won against their direct opponent during all historical head-to-head matches. 
  2. GD (Goals Difference): same as described above, except this time we are evaluating historical GD against their direct opponent.

Note that Jamaica & Colombia and Morocco & France are facing each other for the first time in a major tournament during the 2023 World Cup, so we don’t have historical data for these matches.

With these powerful data-driven approaches, we embarked on an exciting journey to identify the potential Women's World Cup champion. But how can we decide who would win each match based on all this information? That’s where a decision matrix will come in handy. 


Scoring plays a crucial role in the decision matrix, ensuring all factors are comparable. Various scoring systems, like the 1-10 scale, allow us to assess performance from "poor" to "excellent." In our case, each match acts as its own decision matrix, focusing on two teams. We opted for a simple binary system, where 1 denotes a higher score and 0 a lower score. Summing up the scores, we arrive at the results below. To validate our scoring, we've included real match results for comparison, enabling us to assess the accuracy of our predictions against real-life outcomes.

Note that there are two matches where the table above didn’t predict the results accurately.

One is match 6 (United States vs. Sweden) and the other is match 7 (Australia and Denmark). For the former, the total score is exactly the same (2 points) and for the latter, it predicted a win for the US. That’s because we are missing an important element in a decision matrix: weights.

Assigning Weights

Weights assign varying degrees of importance to each criterion, ensuring that certain factors hold more influence over the final outcome. By applying weights, we can better reflect the relative significance of different aspects when making predictions. For example, goals scored or recent form may carry more weight in determining the winner, while head-to-head records might have a lower impact. As we continue to refine our approach, the predictive power of our analysis will be further enhanced, elevating the precision of our predictions in future matches.

Weights are usually represented as percentages or fractions. The sum of all weights should equal 100% or 1. The weighted scores for each option are calculated by multiplying the scores for each criterion by its corresponding weight, and then summing up the results.

Choosing the correct weight takes a bit of logic and a bit of art. In this particular case, we decided to use higher weights for the GD criteria, as we believe that the number of goals a team can score vs. concede is a strong predictor of team performance.

We also compared our weighted score with the real-life results of the round of 16, to make sure that our results are accurate.

With the weights appropriately applied, our results align closely with the real-life outcomes of each match in the round of 16. This gives us confidence in our decision matrix and its ability to make accurate predictions. Armed with this validated approach, we are now prepared to forecast the results of upcoming matches with greater precision and confidence. 

Predicting the results of the Quarter-final matches

As we enter the quarter-final matches, we have updated our head-to-head records for the new team pairings.

Using the same criteria and weights described earlier, we have created a new decision matrix to predict the outcomes of these exciting matchups.

So here’s our prediction for the quarter-final matches:

  • Spain vs Netherlands - Winner: Netherlands
  • Japan vs. Sweden - Winner: Japan
  • England vs. Colombia - Winner: England
  • Australia vs. France - Winner: France

Predicting the results of the Semi-finals

As we reach this pivotal stage, we now have two intense semi-final matches on the horizon. We now gather the head-to-head records for the upcoming encounters: Netherlands vs. Japan and England vs. France.

Armed with this crucial data, we will update our decision matrix to deliver precise forecasts for these highly anticipated showdowns.

This is a compelling demonstration of the significance of assigning weights in our analysis. Despite Japan and Netherlands having an equal number of historical wins, Japan's superior performance during the 2023 World Cup group stage tips the scales in their favor. As a result, our prediction leans towards a victory for Japan in this thrilling match.

Here’s our prediction for the semi-final matches:

  • Netherlands vs. Japan - Winner: Japan
  • England vs. France - Winner: England

Predicting the results of the Final match

This is it! The moment we’ve all been waiting for. The final match of the 2023 FIFA Women’s World Cup will happen on August 20th, and we believe it will be played by Japan vs. England.

Let’s check the historical records of matches between these two teams.

In the past, Japan and England have met on 7 occasions, with 4 of these encounters taking place during later World Cups (2007, 2011, 2015, and 2019). Among these matchups, England emerged victorious in 5, while Japan secured 1 win, and the final encounter ended in a draw. Additionally, England holds an advantage in terms of goals scored versus conceded. 

However, during the group stage, Japan showcased their power by netting more goals. Now, let's turn to our conclusive decision matrix to unveil our prediction for this highly anticipated final clash. The stage is set, and we are ready to reveal our forecast for this thrilling showdown.

Our final prediction is that the big winner of FIFA Women’s World Cup 2023 will be: ENGLAND!

While this may seem like a bold prediction, considering England has never claimed the Women's World Cup title before, we have compelling reasons to back our call. Notably, with previous champions Norway and the United States eliminated during the Round of 16, and Germany's departure even earlier, the competition field has opened up for a potential new champion.

Although Japan, a previous World Cup winner in 2011, is a formidable opponent, we firmly believe that England's stellar performances throughout the tournament, coupled with its strong historical record against Japan, make them a promising contender for the coveted trophy. The stage is set, the stakes are high, and we eagerly await the crowning of the FIFA Women's World Cup 2023 champion – may the best team take the honor!


So, as you gather with family and friends to enjoy the FIFA Women's World Cup, take a moment to use your newfound knowledge from this blog post to make your predictions. Remember, while the beauty of soccer lies in its unpredictability, the decision matrix can undoubtedly add to the excitement and fun of the tournament!

Let's cheer for our favorite teams and celebrate the joy of soccer, the world's most beloved sport!

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About the author:

As the Head of Editorial at Learner, Mathias combines a deep passion for education with creative flair. With a diverse background and a brief stint in full-time parenting, he focuses on delivering inspiring, educational content. When not at work, he can be found knitting, writing books for children and spending time with his family.

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