Can AI predict the outcome of the 2023 Womens World Cup?
This years women’s world cup in Australia has shown to be an event filled with thrill, surprises and top tier performances. Who would’ve thought that Spain, a rising star in women’s football, would make they’re way to the finals, leaving some of the usual powerhouses USA, and Germany in their wake? Or that England would seize the chance to make history following last years Euro’s victory, edging closer to their first ever women’s world cup win? With the final between England and Spain just days away, speculations, and predictions are taking over the football community. Will it be coming home, or will Spain’s impressive run culminate in ultimate glory? In this article, we unveil the mechanisms and insights of our logistic model, aiming to predict the final outcome of the tournament.
Our initial model, that we built around the Men’s world cup, laid the foundation for our current prediction, relying on decades worth of data, including FIFA rankings, player statistics and team performance metrics. Using machine learning libraries like Pandas, Numpy and Sckit-learn, we engaged the logistic Regression algorithm, an excellent tool for binary classification problems when it comes to predicting win/loss scenarios.
Looking into the nature of women’s football, we fine tuned the model for the women’s world cup. We took into account unique features specific to the women’s game, such as goals, defensive prowess and individual brilliance from the group stages right up to the semi-finals. The logistic regression allowed us to create a boundary known as a hyperplane dividing the outcome space into two categories of interest being victory for England or victory for Spain.
Logistic regression which is a fundamental supervised learning technique, leverages the sigmoid function to model probabilities. Given this context, the model evaluates the log odds of England’s win over Spain, producing a probability value that signifies England’s chance of making history. Of course, the sport is one of great uncertainty, whereby the data and statistics can only do so much and go so far. However our model paved through this unpredictability, learning from the distinctive vigour of women’s football.
Through rigorous testing, iterations and validations, our model achieved an accuracy rate of 71% and an F1 Score of 65% offered valuable insights. Although these metrics are not perfect they definitely provide a robust prediction within the complex industry of sports forecasting. The disparities between the accuracy and FI score show valuable insights, which reveal the potential for further refinement and improvement.
Our model’s verdict? England stands at ease to win over Spain in the final, claiming their first women’s world cup. The model while sensitive to the stochastic nature of football, does resonate with England’s outstanding performance over the last couple of weeks, their tactical game play and balanced squad.
As the excitement builds towards the final whistle in Australia, we at NWT are thrilled to witness whether our data driven insights align with the real world performance on the pitch. Whether you cheer for the Lionesses or La Roja, the final promises to be a clash for ages enshrined in the historical records of football.