• Om
• Akshay Singh
• Arihaant Arora
• Pranav Singhal

Prediction model for Football results using ML Approach: In numerous methods intended to predict the outcomes of professional football matches, the number of goals scored by each club has traditionally been used as a basic factor for evaluating a team's performance and projecting future results. The count of goals scored in a game is a significant random variable, though, and it frequently causes significant discrepancies between a team's play and the amount of goals they score or allow.The main goal of this paper is to investigate various Machine Learning methods for predicting the result of football games using in-game match events rather than the total number of goals scored by each team. We will investigate several model design hypotheses and compare the effectiveness of our models to industry standards. Instead of utilising the actual number of goals scored, we established a "anticipated goals" measure in this project to assess a team's performance We paired this metric with a computations of a team's offensive and defensive ratings that are updated after each game in order to develop a classification model that forecasts the outcomes of future matches and a regression model that forecasts the ultimate score of those matches. In relation to currently employed traditional methodologies, our models perform brilliantly and achieve a precision on par with betting exchange models.

Football Prediction , Machine Learning, Machine learning models, Expected goals, Conventional techniques, Football matches, goals scored, Results prediction

"Smart Football Predictor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.f272-f277, March-2023, Available :http://www.jetir.org/papers/JETIR2303532.pdf

"Smart Football Predictor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppf272-f277, March-2023, Available at : http://www.jetir.org/papers/JETIR2303532.pdf