Random Forest-Based Fantasy Football Team Selection

Fantasy football is a popular pastime that involves selecting a team of players and competing against others based on the real-life performance of those players. The success of a fantasy team is largely determined by the ability to accurately predict player performance and select a strong lineup. A team's selection can be influenced by several factors, including the players' current form, injuries, tactical considerations, and opposition analysis. The concept of "form" in soccer refers to a player or team's current level of performance. A player or team can be described as "in form" if they are currently performing well and consistently winning matches, while they can be described as "out of form" if their performance is poor and they are losing matches. We have developed a machine-learning model using a random forest algorithm to assist in this process. The model is capable of predicting the performance of individual players and generating a predicted optimal team lineup. By training multiple decision trees on different subsets of the data and averaging their predictions, the model is able to reduce overfitting and improve the generalization performance. The model also could analyze the key attributes that are needed for a particular position, opposition analysis, and tactical considerations. The model was back tested on historical data to generate predicted lineups for each match of the past three seasons. These predicted lineups were then used to simulate fantasy football leagues, with the model's team competing against other teams selected by human managers. We compared the model's predicted outcomes to the actual outcomes of the matches. The model had a Mean Square Error (MSE) of 4.921 and a Root Mean Square Error (RMSE) of 2.2275. This means that almost 70% of the time the team created by the model could help win the match for the person betting on fantasy football. This means that over the long run, the model could generate 20% of profitability on average.