football prediction

Tencent Cloud Developer

Jul 4, 2024 · Artificial Intelligence

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

The study combines team statistics and bookmaker odds with machine‑learning models—including Poisson, regression, Bayesian, SVM, Random Forest, DNN, and LSTM—to predict football match outcomes, identify confidence‑based betting intervals that yield profit, and suggests extensions to broader data, features, and financial trading.

Data MiningMachine LearningSVM

0 likes · 23 min read

Football Match Outcome Prediction and Betting Strategy Using Machine Learning

AntTech

Jun 20, 2024 · Artificial Intelligence

Predicting Football Match Outcomes with Graph Neural Networks and Large Language Models: The “Smart Guess Football” Project

During the 2024 European Championship, TuGraph engineers built an interactive system called “Smart Guess Football” that combines graph computing, graph neural networks, transformers and large language models to model player relationships and predict match outcomes, achieving up to 71% accuracy on limited test matches.

AISports Analyticsfootball prediction

0 likes · 7 min read

Predicting Football Match Outcomes with Graph Neural Networks and Large Language Models: The “Smart Guess Football” Project

Model Perspective

Feb 24, 2024 · Fundamentals

Can You Predict Soccer Match Outcomes with a Simple Poisson Model?

This article presents a statistical approach to forecasting football match results by calculating league-wide average goals, deriving offensive and defensive indices for each of the 20 teams, adjusting for home‑field advantage, and applying the Poisson distribution to estimate score probabilities.

Poisson distributionSports Analyticsfootball prediction

0 likes · 9 min read

Can You Predict Soccer Match Outcomes with a Simple Poisson Model?

Tencent Cloud Developer

Dec 2, 2022 · Artificial Intelligence

Football Match Prediction Using Machine Learning and Betting Strategy Analysis

The study applies machine‑learning models—including logistic regression, SVM, random forest, deep neural networks and a DNN‑SVM ensemble—to 17‑dimensional team features and 51‑dimensional bookmaker odds, achieving up to 54.5% match‑outcome accuracy, proposing a profit‑condition betting strategy and extending the approach to stock‑price forecasting.

Betting StrategyMachine LearningSVM

0 likes · 21 min read

Football Match Prediction Using Machine Learning and Betting Strategy Analysis