This article discusses creating AI sports betting models to identify value bets and monetize AI predictions. It covers AI’s role in sports betting, creating prediction models, implementing AI sports betting predictions, and considerations for success.
Introduction
In this article we will first start with creating / leveraging AI’s ability to make sports betting predictions and we will focus on identifying value bets, creating your value betting model & potentially offering this as a service to clients which you can sell your AI predictions on a subscription basis. Whilst this won’t be comprehensive, it will hopefully get you thinking about how you could start monetising AI Sports Betting.
By building an AI prediction bot, leveraging sophisticated algorithms and real-time data, can significantly enhance the betting experience by providing accurate betting tips and predictions.
Understanding AI in Sports Betting
AI in sports betting involves using machine learning models to analyze vast amounts of data, identify patterns, and predict future outcomes with remarkable accuracy. These AI models can process information far beyond human capability, considering numerous variables including team performance, player statistics, weather conditions, and more to generate reliable betting tips and predictions.
How Do I Create an AI Sports Prediction?
The good news is that there are already models available on the market to help you better predict an outcome of a sporting event. Whilst these models may have inaccuracies, you will want a company that will provide you with transparent view of their model so you can finetune any correct any inaccurate predictions. We recommend something like SportMonks or feel free to check out our article on building a GPT model where we feature some other sources of data.
What is a Value Bet?
A value bet is when there is a higher likelihood of an event taking place than the odds suggest, leading to a discrepancy (or value) in taking a bet as it is offered below it’s ‘true’ value.
In this context, a value bet is where an AI prediction has identified that there is a discrepancy in the likelihood of a certain probability happening and the bookmakers implied outcome of that probability. For example, on Manchester United v Arsenal, a bookie has implied odds that suggest Arsenal winning by a wide margin, therefore giving lower odds to Manchester United.
Your AI prediction model however, has noticed that in certain conditions (weather, home vs away matches etc.) Manchester United are the favourites or in fact Manchester United might not lose as badly and in fact could score at least 1 goal, this data will inform what’s known as a value bet where you have identified that the odds being offered are different to the odds you found.
How to Implement AI Sports Betting Predictions
OK, now you have established that AI Sports Betting predictions may give you an edge over the bookies by allowing you to determine your own odds / probabilities for success. How do you actually implement a prediction system to find value bets and take advantage of these opportunities.
Step 1: Plan Your AI Prediction Bot
Before diving into development, clearly define the scope of your AI prediction bot. Determine which sports or leagues you’ll focus on, the type of predictions you want to offer (e.g., match outcomes, over/under scores), and how you plan to present these betting tips to your users. This planning phase is crucial for setting a clear direction for your project.
Step 2: Integrate SportsMonks API
To start, sign up for SportsMonks API and obtain your API token. Explore the documentation to understand how to fetch the data relevant to your chosen sports or leagues. Here’s a simple Python snippet to demonstrate how to make an API request:
import requests
API_TOKEN = 'your_api_token'
BASE_URL = 'https://soccer.sportmonks.com/api/v2.0/'
def fetch_data(endpoint, params={}):
params['api_token'] = API_TOKEN
response = requests.get(f"{BASE_URL}{endpoint}", params=params)
return response.json()
Step 3: Collect and Prepare Your Data
Use the SportsMonks API to collect historical data and current odds for the matches you’re interested in. This data forms the foundation of your AI model, enabling it to learn from past outcomes and understand the dynamics of the sport. Data preparation involves cleaning the data, handling missing values, and selecting features that are likely to influence match outcomes.
Step 4: Develop Your AI Model
With your data ready, it’s time to build the AI model. Start with simpler models like logistic regression or decision trees to establish a baseline. As you progress, experiment with more complex algorithms like Random Forest or Gradient Boosting Machines for improved accuracy. Use libraries like Scikit-learn for model development, and consider TensorFlow or PyTorch if you venture into deep learning models.
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
# Example: Preparing your data
X = data.drop('outcome', axis=1)
y = data['outcome']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
Step 5: Validate and Refine Your Model
Model validation is critical to ensure your AI prediction bot is reliable and accurate. Use techniques like cross-validation to assess your model’s performance, focusing on metrics such as accuracy, precision, and recall. Refine your model by tuning hyperparameters, adding new features, or trying different algorithms based on validation results.
Step 6: Deploy Your AI Prediction Bot
Once satisfied with your AI model’s performance, integrate it into a user-friendly application. Streamlit is an excellent choice for quickly deploying Python applications, allowing users to interact with your AI prediction bot and access real-time betting tips. Ensure your application is secure, scalable, and complies with legal requirements related to sports betting.
Step 7: Monetize Your AI Prediction Bot
Consider various monetization strategies for your AI prediction bot. A subscription model, offering tiered access to betting tips, is a popular choice. You could provide basic predictions for free while charging for premium tips or personalised betting strategies. Affiliate marketing, partnering with betting platforms, can also generate revenue by referring users to their services.
Key Considerations
- User Experience: Focus on creating an intuitive and engaging user interface. Your application should make it easy for users to access and understand the betting tips provided by your AI prediction bot.
- Continuous Improvement: The sports world is dynamic, with constant changes that could impact your model’s accuracy. Regularly update your model with new data and refine it based on performance and user feedback.
- Ethical Betting Practices: Promote responsible betting practices among your users. Ensure your platform includes resources on gambling awareness and adheres to ethical standards.
Conclusion
Creating an AI prediction bot for sports betting is a challenging yet rewarding endeavor. Remember, the success of your AI sports betting bot lies in its accuracy, user experience, and ethical considerations. With continuous improvement and a focus on delivering real value, your AI prediction bot can become an indispensable tool for sports bettors worldwide.
As AI technology continues to advance, the potential for AI in sports betting is vast. Whether you’re a seasoned developer or just starting, the journey to building an AI prediction bot is filled with opportunities for innovation and growth. Embrace the challenge, and you may just transform the sports betting landscape.
Author Profile
James is the founder and CEO of Free Bet and a former FTSE100 AI Director. He has years of experience in building and deploying complex AI models for products like the advanced AI sports betting algorithm used in Free Bet and is an experienced bettor since 2008.
