100,000+ Football Matches Dataset With Betting Odds: A Powerful Resource for Sports Betting Research and Data Science
A massive, structured football dataset combining match results and betting market data — built for sports betting research, predictive modeling, machine learning, and serious football analytics.
If you work with football analytics, sports betting research, or machine learning, access to a large, clean, and structured dataset is often the difference between theoretical ideas and real, testable models.
That’s why I’m excited to announce the release of the Football Matches Dataset (100,000+ Games with Betting Odds) — a structured historical dataset designed specifically for quantitative football analysis, betting market research, and predictive modeling.
This dataset brings together match results, bookmaker odds, implied probabilities, and market margins across more than 100,000 professional football matches, covering multiple leagues and seasons.
Whether you’re a data scientist, bettor, researcher, or football analyst, this dataset provides a solid historical foundation for building and validating football prediction models.
Why Historical Football Data Matters
Most football analytics projects eventually hit the same bottleneck:
Data availability and data quality.
Public football datasets often have one of two problems:
They only contain match results
They lack structured betting market information
Without odds data, you can’t properly study market efficiency, bookmaker margins, or betting value strategies.
This dataset was designed specifically to solve that problem.
It combines match results and betting market data in a clean structure ready for analysis.
What Makes This Dataset Valuable
The Football Matches Dataset (100,000+ Games with Betting Odds) includes key variables used in sports analytics and betting research.
Core Match Data
Each row represents a professional football match and includes:
League
Country
Season
Round
Match date (UTC)
Home team
Away team
Final score
Match result (home win / draw / away win)
This structure allows you to easily build models based on historical match outcomes and league-level dynamics.
Betting Market Data
Where this dataset becomes particularly powerful is the inclusion of bookmaker market data.
Each match contains:
Home odds
Draw odds
Away odds
Implied probabilities
Bookmaker margin (overround)
Favorite team according to the market
Indicator showing whether the favorite actually won
This makes it possible to study:
Market efficiency
Favorite-longshot bias
Betting value strategies
Closing-line value proxies
Market pricing behavior
These are essential ingredients for serious sports betting research.
Dataset Structure
The dataset contains the following columns:
match_id
league
country
season
season_status
round
date_utc
home_team
away_team
home_goals
away_goals
result
odd_home
odd_draw
odd_away
implied_prob_home
implied_prob_draw
implied_prob_away
bookmaker_margin
favorite_side
favorite_won
The structure follows a flat tabular format, which makes it extremely easy to load and analyze.
For example, loading the dataset in Python is straightforward:
import pandas as pd
df = pd.read_csv("football_matches_dataset.csv")
From there, you can immediately begin building models, visualizations, and research pipelines.
Get the Full Dataset
If you’re serious about football analytics, sports betting research, or predictive modeling, having access to a large historical dataset is essential.
This 100,000+ football matches dataset with betting odds gives you the raw material needed to build models, run backtests, and explore betting market behavior at scale.
Instead of spending weeks collecting and cleaning data, you can start analyzing immediately.
👉 Get instant access to the dataset here:
What You Can Build With This Dataset
This dataset opens the door to a wide range of football analytics and betting research projects.
Here are some of the most powerful applications.
1. Poisson-Based Football Prediction Models
One of the most common approaches in football modeling is the Poisson distribution, which estimates the probability of goals scored by each team.
With over 100,000 matches, you can build robust estimates of:
Attack strength
Defensive strength
Expected goals distributions
Match outcome probabilities
These can then be compared against bookmaker odds to identify potential value bets.
2. Betting Strategy Backtesting
If you want to test betting strategies, historical data is essential.
This dataset allows you to backtest ideas such as:
Betting on market underdogs
Betting on home favorites
Value betting based on model probabilities
Strategies based on bookmaker margin
Filters by league or season
Instead of guessing whether a strategy works, you can simulate thousands of historical bets.
3. Machine Learning Models
Because the dataset is large and structured, it can easily be used for machine learning experiments.
Examples include:
Logistic regression models
Gradient boosting classifiers
Neural network predictors
Outcome probability estimators
Potential target variables include:
Match result
Favorite wins
Probability calibration
You can also generate additional features such as:
Rolling team performance
League-level goal averages
Market-implied strengths
4. Betting Market Efficiency Research
Another fascinating application is studying how efficient football betting markets actually are.
Using this dataset, you can analyze:
Whether favorites win as often as the market predicts
How bookmaker margins vary across leagues
Whether implied probabilities are well calibrated
Market bias patterns across seasons
These insights are particularly valuable for academic research and professional betting analysis.
5. Football Data Visualization Projects
For those who enjoy building dashboards or analytics tools, this dataset is also perfect for visualization.
You can build charts such as:
League goal distributions
Favorite win rates
Odds vs result comparisons
Market margin analysis
Season-level trends
If you want inspiration for what this kind of analysis can look like, you can explore the Football Hacking analytics app:
The app demonstrates the type of data-driven football insights that can be built from structured datasets like this one.
File Format and Compatibility
You will receive the dataset in a clean CSV file:
football_matches_dataset.csv
It works seamlessly with:
Python (Pandas, NumPy, Scikit-Learn)
R
SQL databases
Excel
Google Sheets
Data visualization tools
This makes it suitable for both advanced data scientists and analysts working with spreadsheets.
Built for Data Science Workflows
The dataset was intentionally structured to integrate easily with data science pipelines.
Because it follows a simple tabular format, you can:
Import it directly into Python or R
Store it in SQL databases
Use it in machine learning workflows
Create dashboards and analytics tools
In other words, it is designed to get you analyzing immediately instead of spending hours cleaning raw data.
Important Note About Current Seasons
Some leagues in the dataset may still be in progress.
These matches are clearly labeled using the:
season_status
column.
This allows you to easily filter between completed seasons and ongoing competitions when performing analysis or backtests.
Created by Football Hacking
This dataset is part of the Football Hacking ecosystem, a project dedicated to:
Data-driven football analysis
Probabilistic modeling of football matches
Sports betting research
Quantitative football analytics
The goal is simple:
Use data to understand football outcomes and betting markets more objectively.
If you’re interested in seeing the type of models and analytics that can be built from structured football data, explore the Football Hacking app:
Final Thoughts
Football analytics and sports betting research both depend heavily on high-quality historical data.
The Football Matches Dataset (100,000+ Games with Betting Odds) provides exactly that: a large, structured dataset combining match results and betting market information.
With over 100,000 matches, it becomes possible to:
Build robust predictive models
Backtest betting strategies
Study betting market efficiency
Train machine learning algorithms
Create football analytics dashboards
For anyone serious about football data science or betting research, this dataset provides a powerful starting point.
If you want to explore what can be built from structured football data, take a look at the Football Hacking app:
And if you want to run your own analyses, models, and backtests, the 100,000+ match dataset gives you the raw material to do exactly that.
Turn Football Data Into Real Insights
Behind every serious football model, betting strategy, or analytics project is one thing: reliable historical data.
This dataset gives you 100,000+ structured football matches with betting market information, ready for immediate use in Python, R, SQL, Excel, or any analytics workflow.
Whether you want to build prediction models, analyze betting markets, or backtest strategies, this dataset gives you the data infrastructure to do it.