Conclusion | Soccer Analytics

Machine learning models have become increasingly popular in soccer analytics, providing a data-driven approach to understanding the game. These models can be used to predict outcomes of matches, player performance, team strategies, and more. By leveraging large datasets of historical data and sophisticated algorithms, ML models can uncover patterns and insights that may not be immediately apparent to human observers.

Moreover, the use of ML models in soccer analytics can lead to better decision making by coaches and team managers, and can also lead to improved performance on the field. The models can help identify areas of weakness that need improvement and highlight the strengths that can be leveraged to achieve success.

We implemented clustering methods on our data to analyze data and uncover trends that help teams understand what makes them successful. By clustering teams based on different types of data, such as match statistics, shooting statistics, defensive data, and nationalities data, patterns and relationships were successfully identified that were not obvious using other techniques.
Our clustering results group similar performing teams based on various attributes like goal difference, shots attempted, number of second balls won, clean sheets etc. Interestingly, teams that perform well throughout a season were clustered to the left, while teams that do not perform as well are clustered to the right.
Clustering methods can offer valuable insights into soccer data, but they should be used in conjunction with other analytical techniques, and a clear understanding of the underlying statistical and mathematical principles is necessary.

Association Rule Mining (ARM) is a method that can be used in soccer analytics to find interesting patterns and insights in the data. For example, ARM can be used to identify groups of player attributes or team statistics that are commonly found together in matches or tournaments. ARM can also help in creating rules that explain the relationship between player attributes or team statistics and match outcomes, such as "If a team has a high possession rate, then they are more likely to win the match".
Using ARM on our soccer dataset, we have mainly focused on the Nationalities of players, the positions they play in, and the clubs they belong to. We discovered some unexpected trends and insights, such as the English club Brentford being dominated by Danish players. We have also found out that England as a nation produces a lot of Defenders and Midfield players, and Everton's squad has many English defenders.
These insights can be used effectively in scouting for young talent, based on their nation or the position they play. ARM is a useful technique for finding patterns and relationships in soccer data, which can provide insights into what factors contribute to team success and player performance.

Naive Bayes is a simple and efficient algorithm that is useful for analyzing large soccer datasets with many features. This is helpful in soccer analytics because there are many factors that can impact match outcomes, such as player positions and playing styles. Naive Bayes is also good at handling missing data and noisy features, which can be an issue in real-world datasets.
In our soccer dataset, Naive Bayes performed well and accurately predicted match outcomes. Naive Bayes allowed us to analyze the importance of different features in predicting match outcomes. We found that the venue of a match (Home/Away), the number of rest days between games, what competition was the match played in, did effect the outcome of a game. Overall, Naive Bayes proved to be a useful algorithm for soccer analysts looking to uncover patterns and relationships in soccer data.

Next, we used Support Vector Machines (SVM) to predict the results of soccer matches based on different match statistics. We tried three different ways to use the program, called kernels, which helped us make predictions. We found that using the linear kernel was the most accurate because it made almost perfect predictions every time. The other two kernels were also good, but not as accurate.
We also learned which match statistics were most important in predicting match outcomes. Goals Scored, Goals Conceded, Venue, and Possession were useful, but other statistics like expected goals or expected goals conceded were not reliable in predicting match outcomes.
Overall, we found that SVM is a helpful tool for predicting soccer match outcomes based on different statistics. The linear kernel was the most accurate, but more research can be done to learn about the impact of different match statistics on prediction accuracy and to explore other computer programs for analyzing soccer matches.

Lastly, we used Neural Networks to, again, predict the outcome of soccer matches based on a dataset containing various match statistics. The model was not perfect and had some misclassifications. We were also able to determine the importance of the features used in our dataset.
The results of the neural network model showed that it is decently accurate, with an 85% accuracy rate.
This suggest that Neural Networks can be a of particular use for predicting soccer match outcomes based on various statistics. However, it is important to note that Neural Networks work better when the data is relatively large. More research can also be done to investigate the impact of different features on prediction accuracy, and more complex networks can be implemented for a more in-depth analysis.

Overall, ML models have the potential to revolutionize the way soccer is played and analyzed, and can provide a competitive advantage to teams that embrace this technology!