Summary of The Evolution Of Football Betting- a Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation, by Purnachandra Mandadapu
The Evolution of Football Betting- A Machine Learning Approach to Match Outcome Forecasting and Bookmaker Odds Estimation
by Purnachandra Mandadapu
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper delves into the intersection of professional football and the betting industry, tracing their evolution over six decades. The symbiotic relationship between these sectors has driven rapid growth and innovation. With advancements in data collection, including high-definition cameras and AI-driven analytics, this study aims to utilize Machine Learning algorithms to forecast premier league football match outcomes. By analyzing historical data and identifying key features, the study seeks to determine the most effective predictive models and their impact on match results. The findings will inform bookmaker odds, highlighting the potential for informed decision-making in sports forecasting and betting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how professional football and the betting industry have changed over time. It explores how they work together to make predictions about football matches. The study uses special computer programs (Machine Learning) to analyze data from past games and try to figure out what makes a team more likely to win. By doing this, it can help bookmakers set better odds for fans who want to bet on the outcome of a game. |
Keywords
* Artificial intelligence * Machine learning
