The new way of AI football predictions

What is AI doing to Football Predictions?

Football predictions were made even before the era of big data and the rise of AI. It started straightforwardly, with people talking in a bar or social gatherings about their predictions on who would win a football match.

Once football statistics became available, predictions shifted to statistic-based predictions instead of “gut-feeling” predictions. Traditionally, football predictions relied heavily on manual analysis, expert opinions (like retired football players) and statistical, historical, and team information. These analysts would spend countless hours behind a screen gathering and ordering the data.

They analyse football data, such as team form, player performance, historical match outcomes, and other relevant statistics. While these traditional methods provided some insight, they were often time-consuming and prone to human error and bias, leading to inconsistent and often inaccurate football predictions.

The rise of artificial intelligence, like deep learning, has revolutionised football predictions. What is deep learning? Deep learning, a subset of AI, involves training neural networks on large datasets to recognise complex patterns and make decisions. (IBM, What is deep learning?)

By analysing vast amounts of historical and real-time data, deep learning models can identify relationships and trends that we humans can’t (or will take years to see). Imagine the power of AI predicting football match outcomes with high accuracy—this is now possible with advanced technologies.

This shift to AI-driven predictions brings several advantages. The speed and efficiency of AI mean that predictions can be generated in real-time, providing up-to-the-minute insights. The accuracy of predictions is significantly enhanced as the AI processes more data than any human analyst ever could. For users of Sportmonks’ API, this evolution means access to state-of-the-art prediction capabilities without a steep learning curve. Embrace the future of football predictions with Sportmonks’ AI and stay ahead in the game.

How AI football predictions work

The mechanics of AI football predictions are fascinating and complex, but let’s break it down into manageable steps to understand how it works. Imagine you’re trying to predict the outcome of a match between two top football teams.

For example, Real Madrid vs FC Barcelona. Traditionally, you’d look at factors like team form, player injuries, head-to-head records, previous match results and match statistics. AI does the same thing, but on a much grander scale and far greater accuracy.

The core of AI football predictions

At the core of the best AI football predictions is the concept of deep learning. Deep learning involves training neural networks—systems inspired by the human brain—on massive datasets. Here’s how it works:

How we use AI for football predictions

Thanks to these new technologies, our Football Predictions API leverages advanced machine-learning techniques and historical data to accurately predict future football match outcomes. With our Football Predictions API, you’ll have access to a vast range of markets, including match-winner, double chance, total goals, and more. We are constantly improving our model and adding new features to ensure you have access to the most accurate and up-to-date predictions possible.

Our prediction API has two fundamental models:

Prediction model:
Using advanced machine learning techniques and historical data, our model accurately predicts the outcomes of future football matches, considering various factors such as team form, player injuries, head-to-head records, and more. Predictions are available 21 days before the match starts and are updated daily based on the latest data.

Value bet model:
The Value Bet model processes thousands of historical odds data and market trends to find the best value opportunities. In other words, it compares bookmakers’ odds with each other and then gives you the best value bookmaker. Using our value bet model, you can access valuable insights into the best bets, which can help you make better decisions. Read our “How to beat the bookies” blog on how you can profit from these value bets. 

Player contribution model
In addition to these comprehensive models, we incorporate a player contribution model to enhance prediction accuracy further. By analysing individual players’ performance and impact on the team, we can better predict their contribution to the match’s outcome. This model considers metrics such as the player’s recent form, position, and overall contribution to team performance. The player contribution model also considers the lineup, as illustrated on the left. 

The models and algorithms are based on five key principles:

✔ Timely and substantive
The Prediction API is updated daily with the latest data from the Sportmonks Football Database.

✔ Data controlled
No human intervention is needed. The Prediction API runs on statistical analysis results based on the entire historical Sportmonks Football Database.

✔ Precise probabilities
Thanks to our mathematical probability distribution models, the Prediction API offers the most precise probabilities possible.

✔ Predictability performance
We monitor our Prediction API’s success rate and quality, but you can also track our predictions’ performance.

✔ Machine learning
We use cross-validation machine learning models.

A Customer success story

Imagine using these powerful tools in your application. One of our clients, a famous football betting platform, integrated the Sportmonks Football Predictions API. Within weeks, they saw a significant increase in user engagement and satisfaction. The precision and reliability of the predictions led to more confident betting decisions, and the value bet model provided actionable insights that set them apart from competitors.

By embracing the Sportmonks Football Predictions API, you can revolutionise your football prediction offerings, delight your users with precise insights, and gain a competitive edge in the market. Elevate your application to new heights and read their inspiring success story now.

Wrong uses of AI in football predictions

The potential of AI for football predictions is limitless. However, we have also witnessed firsthand how this potential can lead to incorrect applications of AI.

Overreliance on Limited Data

Many companies make the mistake of using limited datasets to train their AI models. They might only consider a few seasons of match data or focus narrowly on high-profile leagues, neglecting a broader spectrum of information.

This approach leads to biased and less accurate predictions because the AI needs a comprehensive understanding of various influencing factors. In contrast, Sportmonks leverages vast amounts of historical data, including detailed statistics from 1,500+ leagues, to ensure a well-rounded and precise prediction model.

Ignoring Real-Time Updates

Another common error is failing to update predictions with real-time data. Football is dynamic, with player injuries and team form constantly changing. Some companies neglect to incorporate these real-time variables, resulting in outdated and unreliable predictions. Sportmonks, however, ensures that its AI models are updated daily, integrating the latest information to provide the most current and accurate predictions.

Neglecting Player Contributions

A significant oversight in many AI football prediction models is the failure to account for individual player contributions. These models miss critical insights into how specific players impact match outcomes by focusing solely on team-level statistics. Sportmonks addresses this gap with its Player Contribution Model, which analyses individual performances and their effects on the team, enhancing prediction accuracy.

Lack of Transparency and Validation

Companies often overlook transparency in AI model performance and continuous validation. AI predictions in football can become unreliable without regular performance monitoring and validation against actual outcomes. Sportmonks, on the other hand, is committed to transparency, providing access to prediction histories and performance metrics. This allows users to trust the accuracy and reliability of the predictions.

By avoiding these common pitfalls and adhering to best practices in AI applications, we deliver accurate, timely, and reliable football predictions, setting a benchmark in the industry.

How to implement AI predictions on your website

Integrating the best AI football predictions into your football application with Sportmonks’ APIs is straightforward. Follow these steps to enhance your football project.

Step 1: Sign Up and Get Your API Key

First, sign up on the Sportmonks website to obtain your API key:

1. Visit the MySportmonks
2. Create an account and subscribe to the Football Predictions API.
3. Retrieve your API key from the dashboard.

Step 2: Understand the API Endpoints

Familiarise yourself with the primary endpoints:

1. Predictions Endpoint: Access predictions for upcoming matches.
2. Value Bet Endpoint: Find valuable betting opportunities.

Step 3: Set Up Your Development Environment

Ensure you have the necessary setup:

1. Development Environment: Local server or development environment.
2. Programming Knowledge: Languages like Python or JavaScript.
3. Tools: API testing tools like Postman.

Step 4: Make Your First API Call

Start with a simple request to the Predictions endpoint:

import requests

api_key = 'your_api_key'
url = 'https://api.sportmonks.com/v3/football/predictions'
params = {
    'api_token': api_key,
    'include': 'fixtures.league,fixtures.teams'
}

response = requests.get(url, params=params)
print(response.json())

This snippet shows a basic GET request to the Predictions API.

Step 5: Process and Display the Data

Parse and display the response in your application:

data = response.json()

for prediction in data['data']:
    fixture = prediction['fixture']
    home_team = fixture['home_team']['name']
    away_team = fixture['away_team']['name']
    probability = prediction['probability']

    print(f"{home_team} vs {away_team} - Win Probability: {probability}%")

Step 6: Handle Real-Time Updates

Set up periodic updates to fetch the latest predictions. Use scheduling tools like cron jobs or background tasks.

Step 7: Monitor and Optimise

Continuously monitor and optimise your application based on feedback and new data. Regularly check for updates from Sportmonks.

Technical Requirements and Prerequisites

1. API Key: Obtain from Sportmonks.
2. Programming Environment: Set up with necessary tools.
3. HTTP Requests: Handle HTTP requests.
4. Data Processing: Parse and process JSON data.
5. Real-Time Updates: Schedule tasks for data fetching.

Additional Resources

For a detailed guide, refer to the How-to use the Football Predictions API  Guide.

Following these steps, you can integrate AI-driven football predictions into your application, providing users with accurate and valuable insights.