- Projects 1
- Followers 0
kavin karthick G M
Bengaluru, Karnataka
Football Prediction Algorithm
The proposed system is a machine learning system that uses data from past matches, player and team performance metrics, and other information to generate predictions for future matches. The system will use supervised learning algorithms to identify patterns in the data and make accurate predictions. ...learn more
Project status: Under Development
Overview / Usage
Football is a sport in India that is slowly gaining popularity in India and is popular in the rest of the world. In recent years, the club variation of this game has grown in popularity. The Indian Super League (ISL), a tournament based on this structure, has risen dramatically in recent years. On the other hand, Football is known as the game of chance. Fans and followers are also concerned with predicting the victor of a tournament or match. Technology, on the other hand, is rapidly changing. After training a model, researchers always turn to machine learning algorithms to predict something. So, in this research, we use various supervised learning approaches to predict the winners of Indian Super League matches.We have the following attributes in this system: team names, goals scored, key tackles, goals on target, shots attempted, number of clean sheets, match winner, goal difference, and referee present for the match. Logistic regression, Decision trees, Random Forest, SVM, Naive Bayes, Gradient boost, and KNN are examples of supervised techniques utilized in this research.
Methodology / Approach
Football prediction models leverage Machine Learning techniques to predict events that may occur during football matches. The goal of such models is to optimize predictions based on the data provided by the users. Such data includes the teams’ strengths and weaknesses, recent match results, and statistical data.
The model is a supervised Machine Learning model that utilizes historical data to predict future events. This type of model uses the results from previous games to inform the predictions for future games. It takes into account the different characteristics of the teams and players, evaluates their strengths and weaknesses, and tunes its parameters to deliver the best results for a given prediction task. The model is trained on a large dataset which includes the teams’ past match results, as well as detailed statistics such as the total number of shots, passes, and tackles. Tand up-to-date predictions to the public.
The model is then tested against a series of test data sets to evaluate its performance. The performance of the model is monitored regularly to ensure the accuracy of the predictions.The model can be used to make predictions on any football match, such as predicting the
winner of a match, the scores of both teams, and the probability of a team winning a match.
This can be used to inform the decisions of players and teams, as well as to present accurate
Repository
https://github.com/sauriya30/Hackathaon.git