Généré avec des idées et des aperçus de 11 sources

img6

img7

img8

img9

img10

img11

Introduction

  • AI in Football Predictions: There are various platforms and projects utilizing AI to predict football scores by analyzing historical data, real-time events, and other relevant statistics.

  • DataRobot's Project: Analyzes 10,000 past games to predict scoring probabilities based on in-game events, using tech like Feature Impact and feature effects.

  • FootyGPT: A platform designed to make data-driven predictions for football matches, providing insights on match sentiments and team performances.

  • Kickoff.ai: Uses machine learning to predict matches based on team strength dynamics and Bayesian Inference, accounting for player changes and historical data.

  • Zoltar's NFL Predictions: Uses reinforcement learning and neural networks to predict match outcomes against the betting spread.

AI Tools for Football Prediction [1]

  • DataRobot: A platform that predicts scoring opportunities based on data from over 900,000 match events.

  • FootyGPT: This AI assistant offers advanced data scraping, analysis, and prediction options tailored for football fans.

  • Kickoff.ai: Predicts match outcomes by dynamically modeling team strengths over time and using Bayesian inference.

  • Zoltar: Focuses on American football, employing neural networks and reinforcement learning to make predictions against betting spreads.

img6

img7

How AI Analyzes Football Data [1]

  • DataRobot Modeling: Evaluates impactful match features such as shot placement and event situations to predict scoring likelihood.

  • Feature Impact: Determines which on-field events are most critical for predicting match outcomes.

  • Dynamic Analyses: Leveraging both structured and unstructured data, including text and past match data.

  • Technological Integration: Utilizes multimodal modeling techniques to provide comprehensive predictions.

img6

img7

FootyGPT Features [2]

  • Predictions: Users can obtain predictions for specific football fixtures.

  • Data Scraping: FootyGPT can extract and summarize information from various statistics websites.

  • Sentiment Analysis: It reviews social media and other sources for sentiment data on football matches.

  • Streak and Trend Identification: Analyzes data to find team streaks, trends, and player statistics.

Kickoff.ai Techniques [3]

  • Modeling Team Strength: Uses historical data to model evolving team strengths over time.

  • Bayesian Inference: Provides confidence levels for each prediction, accounting for data dynamics.

  • Kickscore: A team's performance metric over time reflective in the predictions.

  • Probabilistic Prediction: Offers odds such as '78% chance team A will win a match'.

img6

Zoltar Prediction System [4]

  • System Details: Zoltar uses reinforcement learning and deep neural networks for prediction.

  • Point Spread Prediction: Assesses expected victory margins against the betting spreads.

  • Accuracy Rate: Achieved 65% accuracy against the betting spread in NFL predictions.

  • Example Scenario: Suggests strategic wagers when prediction significantly differs from betting spread.

img6

img7

img8

Vidéos associées

<br><br>

<div class="-md-ext-youtube-widget"> { "title": "I used AI to predict the football scores", "link": "https://www.youtube.com/watch?v=uctARYvsdZA&pp=0gcJCdgAo7VqN5tD", "channel": { "name": ""}, "published_date": "Oct 29, 2024", "length": "11:33" }</div>

<div class="-md-ext-youtube-widget"> { "title": "I Trained AI to Predict Sports", "link": "https://www.youtube.com/watch?v=LkJpNLIaeVk", "channel": { "name": ""}, "published_date": "Mar 10, 2025", "length": "11:20" }</div>

<div class="-md-ext-youtube-widget"> { "title": "Predicting Football Results and Beating the Bookies with ...", "link": "https://www.youtube.com/watch?v=00YPWyB5aR4", "channel": { "name": ""}, "published_date": "Mar 22, 2022", "length": "26:05" }</div>