ProSoccerPredictor
Football Prediction & Analysis
As one of the most popular sports on the planet, football has always been followed very closely by a large number of people. In recent years, new types of data have been collected for many games in various countries, such as play-by-play data including information on each shot or pass made in a match. In addition to this, the performance of each player is also closely monitored. Each player is judged on the basis of a number of attributes. These factors play a major role in understanding the overall ability and potential of the player. The strengths and weaknesses of all the players can be analyzed with the help of a Machine Learning framework. This thorough study gives the team management a better insight and allows them to explore different ways through which they can improve the performance of the players. Many techniques to predict the outcome of professional football matches have traditionally used the number of goals scored by each team as a base measure for evaluating a team’s performance and estimating future results. The number of goals scored during a match possesses an important random element which leads to large inconsistencies in many games between a team’s performance and number of goals scored or conceded. Therefore, the main objective of our project is to explore different Machine Learning techniques to predict the score and outcome of football matches, using in-game match events and various attributes along with the number of goals scored by each team. We aim to explore different model design hypotheses and assess our model’s performance against benchmark techniques. We plan to train and test various machine learning models like SVM, Random Forest, Linear Regression, etc. and compute the accuracy for each one of them. On the basis of accuracy achieved, models will be selected for prediction. We have considered two areas of research that are especially important considering the objective of our project. Firstly, we find out how various attributes related to different teams can influence the prediction process. Secondly, we develop efficient models that predict the number of goals and eventually the final results.
A particularly important element of Machine Learning in football is the ability to evaluate a team’s performance in games and use that information to make an attempt in predicting the result of future games based on the available data. Outcomes from sports matches can be difficult to predict, with surprises often popping up. Football in particular is an interesting example as matches have fixed length as opposed to racket sports such as tennis, where the game is played until a player wins. It also possesses a single type of scoring event: goals as opposed to a sport like rugby where different events score a different number of points that can happen an infinite amount of times during a match, and which are all worth 1 point. The possible outcomes for a team taking part in a football match are win, loss or draw. It can therefore seem quite straightforward to predict the outcome of a game. Traditional predictive methods have simply used match results to evaluate team performance and build statistical models to predict the results of future games. However, due to the low-scoring nature of games which is less than 3 goals per game on average in the English Premier League in the past 15 years, there is a random element linked to the number of goals scored during a match. For instance, a team with many scoring opportunities could be unlucky and convert none of their opportunities into goals, whereas a team with a single scoring opportunity could score a goal. This makes match results an imperfect measure of a team’s performance and therefore an incomplete metric on which to predict future results. It is very important to assess and use various attributes along with the number of goals scored by the teams to make a near perfect prediction. A comprehensive player analysis in addition to determine player performance.