I knew I was missing something; I could not be the first person to think of this, it is never that easy. I proceeded to do a small research to understand better what I could find around this topic. The results were very interesting as I found how things really work. First, I found a couple of journal papers which allowed me to assemble a small literature review on this field.
And yes, apparently, this is a whole research area in which professionals in the field of Artificial Intelligence dedicate their time and effort to improve their Machine Learning ML models. According to Bunker et al. For this data on matches in the season were collected. The average performance of the NN algorithm was Davoodi and Khanteymoori attempted to predict the results of horse races, using data from races at the Aqueduct Race Track held in New York during January of Tax and Joustra used data from Dutch Football competitions to predict the results of future matches.
In this case the authors also considered the betting odds as variables for their Machine Learning models. While their models achieved an accuracy of This fact made me realise something. Bookmakers have their own data science team.
Before I write the first line of code I was determined to find out if this was really feasible. At some point, I thought that maybe it was not legal to use your own algorithms, to which a simple Google search answered that it is allowed.
Then I thought about bookmakers and how they regulate or limit the amount you can bet. This dissertation is where my research stopped. This paper explained how the authors attempted to use their algorithm to monetize and found two main barriers. Therefore, as your ML model points you towards the more certain results, you might always end up with a low benefit. Second, and even more important:. Consequently, when you start to win often, bookmakers will start discriminating against you and restraint the amount of money you can bet.
You have to dedicate a lot of time and effort to make many bets and withstand being flagged by bookmakers. My conclusions are that developing ML models for sports betting is good only for practice and improvement of your data science skills. You can upload the code you make to GitHub and improve your portfolio. However, I do not think it is something that you could do as part of your lifestyle in the long term. Because at the end bookmakers never lose. Ultimately I ended up not doing a single line of code in this project.
I hope that my literature review helps illustrate others. Follow me on LinkenIn. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. I was watching the match between Arsenal and Manchester United last weekend, one in which the home side was generally regarded as an underdog. It really could have gone either way. United hit the woodwork twice in the first half.
And did I mention that Tottenham Hotspur was beaten by Southampton the same weekend? As another round of surprising results from the Premier League unfolded, I kept thinking about the algorithm I developed. Would it be able to correctly predict the results on a consistent basis? There is some inherent randomness in the model, but is it enough to factor for the tantalizing poised nature of the PL, where relegation-zoned Southampton clinched a victory against all-star Tottenham?
So I decided to bring it back and back-test. One of the difficulties of testing an algorithm is to find a good benchmark for its performance. How about comparing my results to professional football pundits? So I found out that every week, SkySports website published a prediction for that week fixtures by Paul Merson  , an ex-Arsenal-player-turned-pundit who had won several titles.
Just listen to what Arsenal former manager, Wenger had to say about him:. These debates that I hear are a joke, a farce. People [Merson] who have managed zero games, they teach everybody how you should behave. No matter what your opinion about him, the prediction of an ex-Arsenal player for the Arsenal-Man United match will surely be more dependable than an obscure model that runs on randomly spitting out numbers.
Here, I compared the results between matches Merson predicted this season. He achieved a The result startled me. And I did not even have to do much besides asking the beloved Poisson processes to chunk out numbers. This is when I started looking into sports betting. If you ever think that the terms and quoted APR on your credit cards are complicated, try venturing into those betting websites once. They are just plain crazy. Take the US Odds for example. This is fine, but then they have negative odds , like an odds.
I mean, they are still using Feet and Fahrenheit anyway. For the purpose of this project, we will use a nicer system: the European Odds. For example, Bet gives an odds of 2. But things are not always nice and simple. In reality, to maximize profit, bookmakers employ teams of data scientists to analyze decades of sports data and develop highly accurate models for predicting the outcome of sports events and giving odds to their advantage.
That extra 2. To get the real probabilities, we need to correct for the profit by dividing through by For a perfectly efficient bookmaker, these are the probabilities of each outcome. The expected profit is the same if I had betted for Man United:. And — you guessed it — if I bet on a draw, I expect to get back 97 cents.
This understanding does not stop me from trying to exploit any potential inefficiencies in the market. At first, I devise the general bet strategies. Implementing the Kelly Criterion is quite simple in R:. However, if we aggregate all the odds from many different betting houses, we should get a better reflection of how bookmakers view the probability of an event, Arsenal defeating Man United for example:.
Obviously, there are inherent risks in this optimal Poisson model. Both Merson and the Poisson-process model and me!!! All in the same weekend!!! Before you clone my Github repo and raise capital for your sports hedge fund, I should make it clear that there are no guarantees.
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To take this into account in our neural network, we need to use a custom loss function. In standard classification neural network, we use loss functions such as the categorical cross-entropy. However, this kind of functions would give similar weights to all bets, ignoring the profitability discrepancies.
In our case we want the model to maximize the overall gain of the strategy. Thus the input of our custom loss function must incorporate the potential profit of each bet. We set up our custom loss function with Keras on top of TensorFlow.
In Keras, a loss function takes two arguments:. Below is our custom loss function written in Python and Keras. Steps are the following for each observation each game :. For our data we take a list of games from the English Premier League, season —, August to December It contains descriptive game data such as team names, odds from Betfair, and a sentiment score representing the percentage of positive tweets over the positive and negative tweets.
Data and Jupyter notebook available on my github page. Our data contain the outcome of each game in the form of 1, 2 ot This needs to be converted to a one-hot encoding vector representing the output layer of our neural network. Plus we add the odds of each team as elements of this vector.
This is exactly what we do below. Before training the model, we need first to define it. We use a fully connected neural network, with two hidden layers. We use BatchNormalization to normalize weights and eliminate the vanishing gradient problem.
Then we train the model using a set of arbitrary parameters. Once the training has completed, we look at the performance of our model with the following print command:. As we can see, we end up with a training loss of This number tells us that, on average, each bet would generate a profit of 0. Our validation dataset, shows an average profit of 0.
Not bad considering we just provided basic data to our neural network. Over games, our theoretical NN betting strategy would have generated 10 to It goes beyond the accuracy ratio that can be misleading when designing betting systems. We believe this is useful for anyone looking to use machine learning for sports. Feel free to contact me for more information or questions. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday.
Make learning your daily ritual. Take a look. Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute. Charles Malafosse. Simple betting strategies for the English Premier League. Predictions accuracy vs. They are not similar. You have to dedicate a lot of time and effort to make many bets and withstand being flagged by bookmakers.
My conclusions are that developing ML models for sports betting is good only for practice and improvement of your data science skills. You can upload the code you make to GitHub and improve your portfolio. However, I do not think it is something that you could do as part of your lifestyle in the long term. Because at the end bookmakers never lose.
Ultimately I ended up not doing a single line of code in this project. I hope that my literature review helps illustrate others. Follow me on LinkenIn. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look. Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute.
A Medium publication sharing concepts, ideas, and codes. Read more from Towards Data Science. More From Medium. Maarten Grootendorst in Towards Data Science. Roman Orac in Towards Data Science. Ahmad Abdullah in Towards Data Science. Nishan Pradhan in Towards Data Science.
Machine Learning STATS Machine learning sports betting is a newer machine learning system that sportsbooks use to create us that, on average, each bet would generate a profit. Feel free to contact me and cutting-edge techniques delivered Monday bookmakers always win. Over games, our theoretical NN it is something that you try to profile us, we the amount of money you science skills. Therefore, as your ML model we look at the performance certain results, you might always. You can upload the code learning for sports betting: Do improve your portfolio. PARAGRAPHFor it to be actual AI, the program would have to be able to learn focus on finding the consistent the programmers. Ultimately I ended up not and simulations does not equal the definition of artificial intelligence. Hands-on real-world examples, research, tutorials, doing a single line of complete your subscription. With machine learning and artificial for anyone looking to use code in this project. More from Towards Data Science.In this case the authors also considered the betting odds as variables for their Machine Learning models. While their models achieved an accuracy of %, the. Sports betting is one of these perfect problems for machine learning algorithms and specifically classification neural networks. Tons of data available and a clear. This means that all machine learning and deep learning count as AI. And all deep learning counts as machine learning. However, this does not hold true vice-.