Football is by far one of the most popular sports globally, and millions of people have always been closely following the competitive scene. Most football fans wish they could predict the results of the game and become profitable from sports betting.
Until now, predicting the results of sports was nearly impossible, especially for amateur bettors. Despite having access to all sorts of data, understanding the statistical models and how to use them for profit requires a brief understanding of data science and mathematics. The paradigm shifted with the release of new machine learning algorithms that can process the information and give better results.
Recent technological advancements enabled scientists to collect new data types from various competitions worldwide, including play-by-play information that shows data from each shot or pass made during a football match.
In the last decade, the sports betting market has seen exponential growth, thanks to the increased media coverage of live football games and ease of access to betting platforms thanks to the development of smartphones and tablets. A rough estimate shows that in 2021, the betting market is worth around C$203 billion.
How is data science used in football?
Data science is widely used in lots of areas of our life to make more accurate predictions. Here are the main applications of using advanced machine learning in football to collect data:
- Match analytics, strategies, and tactics;
- Player acquisition and valuation;
- Calculating betting odds;
- League table and match outcome predictions;
- Identify the best training regimens and focus;
- Determine the best playing styles of players;
- Tournament scheduling;
The ability to accurately evaluate a team’s performance in-game based on historical data is what makes machine learning algorithms so important in predictive analysis. In general, predicting a football match’s result can be tricky since many factors outside of our control could influence the outcome.
Based on our analysis, football is the hardest to predict, mainly because matches have fixed lengths. It only has one type of scoring (goals), which can occur an infinite amount of times, and are all worth 1 point.
Traditional predictive statistics procedures simply use match results to determine the team performance and use the data to create statistical models that predict the outcomes of future games. However, it has been proven that these predictions are inaccurate because of the low-scoring nature of games (on average, less than three goals have been scored in the EPL in the last 15 years).
For example, a team can have many scoring opportunities but miss all the shots. In contrast, another team could have only one opportunity and could score the goal. Because of these factors, the traditional measurements are imperfect and can’t be used as a metric to determine future results.
Experts have recently discovered a solution to the problem – analyzing in-game statistics, which allows them to develop expected goals predictions that can better estimate the number of points a team should expect to score in a game.
To remove the random element of scoring goals, scientists use machine learning frameworks, which allow them to get better predictions in various regression and classification issues. By experimenting with modern algorithms, statisticians have developed improved models that can more accurately predict the outcome of a match and the exact score.
The objectives of machine learning frameworks in football match predictions
Data scientists have recently developed a highly functional football match prediction framework, which combines deep neural networks and artificial neural networks. Their aim for this project is to maximize the predictive performance of the model.
To generate accurate predictions, statisticians need high-quality data sanitized and used in the machine learning model. Once they have the datasets, they are divided into three sections: training, validation, and testing.
Scientists using this framework architecture has successfully increased their prediction accuracy for the 2018 World Cup and guessed the result of 63.3% of the matches. However, experts believe that the accuracy can be increased to nearly 100% with the proper datasets and accurate information regarding the team’s performance.
Recently, machine learning specialists started gathering data from online bookmakers and casinos listed in Canada’s leading online gaming platform directory. According to their hypothesis, by comparing usage statistics and odds from different online platforms, they will find out the win rate of casual gamblers and discover new methods to improve it.
So far, they were able to find out that casinos that offer a no deposit promotion have better win rates since these deals entice users to make more informed decisions and carefully analyze all possible outcomes, as they have a chance of winning a bet without actually depositing real money.
The analysis of online gambling usage statistics is still in the early stages, and we will soon find out how efficient this data is for the machine learning algorithms. The main concern of specialists is that, in general, an amateur bettor will make a prediction based on a set of factors, including:
- Recent performance;
- The game’s location (home or away);
- Player transfers;
- Coach or staffing changes.
However, humans’ ability to predict the outcome accurately is deeply influenced by player preferences, emotions, and the perception regarding different players. Thus, the machine learning framework will always make better predictions. It has no preferences and takes decisions based on forecasts and statistical models.
The bottom line
Few years from now, data scientists will reach a new technological milestone –creating a high accuracy machine learning-based football prediction framework. For now, the accuracy is around 63.3%, but that’s just because the data hasn’t been refined entirely.
Soon enough, we will be seeing statistics companies selling betting predictions that are 99% accurate, and that will mark the revolution of the sports industry. We shall see if bookmakers will go bankrupt or if they will find new ways to prevent using machine learning algorithms for your bets.