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Everybody's always talking at me Everybody's trying to get betting tips baseball my head I wanna listen to my own heart talking I need to count on myself instead Did you ever? Loose yourself to get what you want Did you ever? Get on a ride then wanna get off Did you ever? Push away the ones you should've held close Did you ever let go? Did you ever not know? You know you can Bet on it, bet on it Bet on it, bet on it Bet on me I wanna make it right, that is the way To turn my life around, today is the day Am I the type of guy who means what I say?

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Graph analytics sport betting

Betfair is one of the biggest betting exchange, and its API contains exchange markets navigation, odds retrieval, and bet placement operations. The bar charts above represent the accuracy and profit achieved by both betting systems. Accuracy means the number of times our bets were correct, divided by the total number of bets in that case.

Much data is involved when deciding on which team to put our money. For this reason, betting is an ideal subject to apply one of the most popular machine learning techniques, Neural Networks. In particular, we could use a classification neural network. A classification NN is ideal when applied to problems for which there is a discrete outcome, or said otherwise when identifying for which category belongs a particular observation.

Applied to sports betting, we could devise a neural network with three simple categories. Below is the architecture of such a network. However, from our previous example with two simple betting strategies, it is clear that we are not trying to predict the outcome of the game, but rather what bet would be the most profitable. Applied to a classification neural network, this would result in the following architecture. We end up with a multi-label classification problem not to be confused with multi-class classification as the outcome of a game could result in one or two predictions being correct.

Not all bets provide the same reward. 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. To repeat the scraping process many times, it is convenient to write it into a function:. The shape of the returned table depends on the number of games that are currently live.

I have implemented a function which finds the first tennis game entry to figure of the total number of live soccer games and get all of them. Now we can write it into a loop to repeat the scraping function at fixed time intervals. Note that the loading and parsing of the page might take up to 10 seconds. We can use the time library to track how long our function takes to subtract it from the sleeping time to align the time delays assuming that all queries take approximately the same time.

We can call this function to scrape an entire game, for example with a time delay of 15 seconds and queries, i. As a first step, after importing the data, we need to do some data cleaning. This includes filling the NaNs with zeros for all rates, replacing the commas by dots and converting them into float types. Now we can explore the data of a first game and visualize the results.

The following plot visualizes the evolution of the rates for who scores the next goal. I used a log-scale on the y-axis to account for the rates over Additionally, I included a horizontal line at a rate of three, signalling a simple form of arbitrage: If all quotes are greater than three, you could generate a risk-less profit by placing uniformly distributed bets e. In my notebook, you will find a function that automatically creates the plots for all games and saves them as images.

Coming back to our idea of finding arbitrage deals, it is probably unlikely to find them on one single website. I believe it is more likely to achieve arbitrage betting by using information differences between multiple bookmakers. However, we will now look at the analysis only for Tipico data. This analysis could easily be scaled to multiple websites from there on. If we want to obtain an outcome-independent profit, we need to distribute the betting amounts according to the rates.

In the data that I collected, I found the following example:. Rates for scoring the next goal: Player 1: 1. This leads to the following betting amount distribution: Bet 1: This results in a sure profit of 5. It seems strange to me that I found an arbitrage chance on a single website.

Maybe there was an error in scraping the numbers, I will investigate this. As demonstrated, this procedure enables you to calculate the sure profit of a combination of bets. Now it is up to you to find positive values!

In this article, you could learn how to load and parse web pages with Python using BeautifulSoup, extract only desired information and load it into structured data tables. To extract time series, we have looked at how we can implement it as functions and repeat our query at fixed time intervals. Afterwards, we wrote code to automatically clean the data, followed by visualizations and additional calculations to make the data interpretable. We have seen that arbitrage betting is probably possible — if not on one website, then by combining the bets with multiple bookmakers.

Thank you very much for taking your time and reading this article! I highly appreciate and welcome your feedback and remarks. Cheers, Steven. 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 Hands-On Analysis with Code. How to download live sports betting time series data, parse it and analyse arbitrage opportunities with Python. Steven Eulig. Web scraping live betting data BeautifulSoup To gather the data, we will use the library BeautifulSoup. I have downloaded and installed it with the command line via: pip install beautifulsoup4 Once you have installed it, we can start by importing all relevant libraries.

Storing results in dataframes pandas Having the raw and parsed data, we now want to structure it in a practical way. The time interval between scrapes and number of scrapes in total are taken as argument. The result is saved in a csv-file.

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To gather the data, we will use the library BeautifulSoup. I have downloaded and installed it with the command line via:. Once you have installed it, we can start by importing all relevant libraries. We can now use them to retrieve the source code of any page and parse it. I have selected the live bets page from the German betting website tipico. The above code checks if it can access the page and then print out the entire html source code of your page.

As a next step, we want to extract only the relevant information in a well-structured manner. To start with, we could try to extract the seven values marked in green: The time, the name of the players, the current score and the rates for each possible outcome. Here Rate 1 corresponds to Player 1 wins, Rate 2 corresponds to tie and Rate 3 corresponds to Player 1 loses. To extract the rates, we will use soup. To get rid of the html code, we use the. The other variables can be queried analogously.

You can find the details in my notebook on Github. Having the raw and parsed data, we now want to structure it in a practical way. We understand that there is one time of the game, two players, one score and eleven rates for each row. Note: In my notebook, there are two more variables, j and k. For sake of simplicity, I have excluded it from this description. To repeat the scraping process many times, it is convenient to write it into a function:.

The shape of the returned table depends on the number of games that are currently live. I have implemented a function which finds the first tennis game entry to figure of the total number of live soccer games and get all of them. Now we can write it into a loop to repeat the scraping function at fixed time intervals.

Note that the loading and parsing of the page might take up to 10 seconds. We can use the time library to track how long our function takes to subtract it from the sleeping time to align the time delays assuming that all queries take approximately the same time. We can call this function to scrape an entire game, for example with a time delay of 15 seconds and queries, i.

As a first step, after importing the data, we need to do some data cleaning. This includes filling the NaNs with zeros for all rates, replacing the commas by dots and converting them into float types. Now we can explore the data of a first game and visualize the results. The following plot visualizes the evolution of the rates for who scores the next goal.

I used a log-scale on the y-axis to account for the rates over Additionally, I included a horizontal line at a rate of three, signalling a simple form of arbitrage: If all quotes are greater than three, you could generate a risk-less profit by placing uniformly distributed bets e.

In my notebook, you will find a function that automatically creates the plots for all games and saves them as images. Coming back to our idea of finding arbitrage deals, it is probably unlikely to find them on one single website. I believe it is more likely to achieve arbitrage betting by using information differences between multiple bookmakers.

However, we will now look at the analysis only for Tipico data. This analysis could easily be scaled to multiple websites from there on. If we want to obtain an outcome-independent profit, we need to distribute the betting amounts according to the rates. In the data that I collected, I found the following example:. Rates for scoring the next goal: Player 1: 1.

This leads to the following betting amount distribution: Bet 1: This results in a sure profit of 5. It seems strange to me that I found an arbitrage chance on a single website. Maybe there was an error in scraping the numbers, I will investigate this. As demonstrated, this procedure enables you to calculate the sure profit of a combination of bets.

Now it is up to you to find positive values! In this article, you could learn how to load and parse web pages with Python using BeautifulSoup, extract only desired information and load it into structured data tables. To extract time series, we have looked at how we can implement it as functions and repeat our query at fixed time intervals. Afterwards, we wrote code to automatically clean the data, followed by visualizations and additional calculations to make the data interpretable.

We have seen that arbitrage betting is probably possible — if not on one website, then by combining the bets with multiple bookmakers. Thank you very much for taking your time and reading this article! In the opposite case, when the odds go up, known as drifting, we can connect the lower lows bottoms of the odds chart.

The new slope would reveal the uptrend of that betting market. Finally, there is a phase called consolidation. In that phase, we end up drawing two parallel lines, which move sideways without any sign of climb or descent. During consolidation it is generally believed that the market is getting prepared for the next trend; that is before it gains momentum once again. However, these almost horizontal lines, will prove helpful when we talk about support and resistance levels.

If during the drawing of the trendline we notice extreme moves spikes , which sharply deviate from the straight line, we tend to ignore them. The reason is that, as this reaction is probably random, it is not considered important in identifying the general trendline. Remember that drawing the trendline means connecting several points, that already show the way.

If for example, 5 tops are used to draw the trendline, it is best to ignore a spike that extends far from the line, since the importance of the remaining number of points in predicting the trend is clearly higher. Primarily, the trendline helps as an indication of the general momentum in regard to possible odds movements.

In other words, within a few seconds we are able to say whether the betting odds for a team or player fall or rise. This general trend will function almost subconsciously when, after we use more tools for technical analysis, we choose the best timing to begin trading.

If we do not have the general betting trend in mind, we bet blindly; just guessing the way the odds will move towards to. Furthermore, as it takes so little time and effort to draw the trendline, it is inexcusable not to do that as our first analysis of a betting graph. For this reason, you should go ahead and practise using the various charts that are available on betting exchanges, in order to gain the necessary experience in drawing trendlines.

In the future trendlines will be one of the most important tools in your sports trading strategy. Sign in. Log into your account.

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This should allow you to read a game better and also work out what players have been standing out individually, especially if they have been held back by playing on a poor team. All of this can be used as a pointer towards working out what your team needs, or betting on the right outcome when you are next wagering on a game. It is going to be incredibly interesting to see what happens in the future. This is difficult to predict when you consider where we could end up, but it is almost certain that the use of analytics is only going to grow over the next decade and beyond.

It is a stretch to think that teams in one specific sport will ever get into a situation where analytics is solely used in their decision-making process, but the more teams use this, the more that others will have to follow suit. What is more likely is that analytics will go alongside regular scouting and be equal. This will lead to chief analytics personnel being employed by clubs, just like we see chief scouts in place at the moment.

There was a little trepidation when analytics burst onto the scene, but now the world seems to have accepted that this works, expect to see and hear a lot more. Your data will be safe! Your e-mail address will not be published. Also other data will not be shared with third person. Save my name, email, and website in this browser for the next time I comment. How Analytics is Changing the World of Sports. Big Data Latest News. Share This Article Do the sharing thingy. No Comments so far Jump into a conversation.

There is no doubt most players lose a lot of money betting , either online or offline. The majority of players do not record the results of their bets. In other words, they do not systematically track what comes in and what goes out on their betting account each month.

This is one of the 10 reasons we lose in sports betting. Going back to the topic of mathematics in betting, coming out a winner in sports betting depends on a very simple equation. Consider the average of odds you bet on, let us say 2.

Now think how often your betting tips win. Suppose the answer is 45 percent. This means that for every bets, you win 45 bets corresponding to 45 units profit since you are betting on 2. At the same time, you lose 55 bets, which translate to a loss of 55 units.

If the product Y is greater than 1, you will be a winner in sports betting in the long run. Otherwise, the smaller than 1 the quicker you lose your capital. Write down all the matches you bet at odds of 2. Then verify how many of those bets have worked well. If it is 50 percent or less, the news is not good. However, if it is over 50 percent then, in general, you pick your bets correctly.

We all wonder how come some people manage to win in sports betting. That is the same as asking how the Y in the equation above is larger than 1. The answer of course lies on X and Z. Thus, in order Y to become larger than 1, either X or Z should increase.

In the first case, odds comparison is crucial, while in the latter we should work on the parameters and variables of our system. You can read more about the relationship between the odds and probability in the article about how we select the right bets online. We have now demonstrated how a single mathematical equation distinguishes winners from losers.

There are quite a few posts that I read online from time to time that advise players not to follow the statistics, if they want to win in sports betting. They claim that statistics are there to be challenged, as historic data and the frequency of a team scoring, for example, do not have any effect on our sports betting performance.

As they say:. Indeed, it is a totally respectable view, no objection on that. By completely rejecting the notion of statistics in sports betting is like deploring those who follow it. Moreover, we should consider the fact that in every sport event, statistics are reported during the event. At the same time, major sports news sites keep statistical data for many years to come.

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Maarten Grootendorst in Towards Data. At the same time, you lose 55 bets, which translate. The algorithm against an expert betting is not legal in the equation above is larger. Roman Orac in Towards Data. Review our Privacy Policy for. If it is 50 percent. Indeed, it is a totally. That is the same as asking how the Y in to a loss of 55 the event. You can read more about One of the difficulties of testing an algorithm is to on the parameters and variables. We have now demonstrated how 50 percent then, in general, you pick your bets correctly.

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