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However, the original datafiles contain over columns, which are not all needed for our analysis. In addition, some of the columns headers contain characters which may cause issue in some databases. Therefore, we will create a virtual dataset containing only the information that is really required with some manipulation of the header names and columns.
To create this virtual dataset in Dremio is very easy and all that is needed is a simple SQL query. The first step towards any quantitative trading strategy is to clean and process the raw, recorded data so that there is a timeseries of prices or odds with which we are comfortable to work. In this article we will be working with soccer matches betting data. Thus, our goal in this section is to create a timeseries of decimal odds for the betting market of interest, uniformly sampled at a period of one second.
A soccer game lasts for ninety minutes plus any extra time say 5 minutes in total. Assuming a fictional value of 2. The index of the Pandas series is an integer which indicates the number of seconds since the beginning of the match, while the values correspond to the numerical value of the bet odds during that second.
The name of the series will be some generic indicator of the match this timeseries corresponds to. At this point, let us remind ourselves what decimal odds are. Assuming the odds are fair, this means that if the player could place the exact same bet a very very large number of times, the player should have no profit nor loss.
If W is the number of winning bets and L is the number of losing bets, then the total profit and loss PnL of the player will be. A final note that we will be making use of in what follows is the representation of certain and impossible events. If we know that a bet has already won, then the probability of it winning is obviously This corresponds to odds of l. Odds of l imply that there will be no profit, which is to be expected since the bet has already a known outcome.
In a computer simulation infinity is not really an option, but a very, very big value for the odds will do the trick. The raw data consist of rows which contain the timestamp when the observation was taken, information about the status of the match at that timestamp and, of course, the odds for various bets.
Therefore, the columns of interest to us are the following. Initially we need to transform the date and time information to number of seconds since the kick-off of the match. There are some subtleties in this procedure which need to be taken into account. As this is not the focus of this article, the details are given in the comments in the implementation given below. The next step of the cleaning process is to make sure that the odds are consistent with the known number of goals scored so far.
As there can be delays in capturing or processing the data, sometimes we seem to be able to bet on this market, although already more than two goals have been scored. For this reason, we replace all odds after a third goals is scored with appropriate values. All of these entries are replaced with NaNs. If this is the case, only th best possible odds are kept for the analysis. Since these are time-stamped with the same second, filtering the best odds is straighforward.
Note that since the original recording of the data was done at irregular intervals, it is possible that our timeseries so far is not complete, in the sense that there are no observations for some seconds. For example, in the dataframe shown above, there are no observations for seconds 1 to 7.
For this reason the dataframe will be reindexed and NaNs will be inserted for the missing observations. The aforementioned procedure has been applied to a single datafile stored in Dremio, which contains the raw recorded data for a single game. The exact same procedure can be applied to all of the files so that a clean timeseries of the odds of interest is created for each game. In what follows, we will use these clean timeseries for different games to try and explore the various possibilities of developing a quantitative betting strategy for soccer matches.
Based on the procedure described in the previous section, three new tables have been created and stored in Dremio. To load these datasets from Dremio is again straightforward. The missing values for different periods in each game are prohibitive for our analysis.
For this reason, the missing values will be filled with the latest recorded value. Using a previous recorded value ensures we are not using potential information from the future. However, it is very important to remember that there have been seconds in each game for which no actual information was available. When backtesting a potential trading strategy, extreme care should be taken not to assume that one could have traded during those seconds.
Remember that the implied probability of a bet being successful is equal to the inverse of the odds offered. Why are implied probabilities useful? Therefore it is not easy to see how these can be averaged to see if there is any large scale inefficiency in our data. Implied probabilities, however are real numbers between 0 and 1 inclusive. Therefore, these can be easily averaged to give a meaningful number.
How can we interpret this figure? To answer this question, first we need to go through some basic bet trading concepts. Our goal is simple: make profit. How do we accomplish this? Traditionally, one way to make a profit through betting was to be right. What this really is, is accepting a bet from someone for the same outcome. Let us give an example. Let us consider all possible outcomes. If more than 2. The second bet we accepted was also successful, so we have to pay.
What happens if less than two goals are scored? This example can easily be generalised to show that if the odds change, then our profit or loss per dollar of the original bet is equal to. However, remember that the implied probability P to be successful is the inverse of the odds.
Note that a higher probability means that it is now more probable that the bet will be successful. So, in light of all of this, how can we interpret the figure. Remember that what has been plotted is the average implied probability for both the Over and Under 2.
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. If anything, this article is a toy example of what you could potentially do.
But the bookmakers have made it extremely difficult for anyone to gain sustainable profits. If there are still a lot of people placing a bet at 4. Chances are that by the time the code infers the most optimal odds, it has been changed. Furthermore, if you do start to make a regular profit, bookmakers can simply thank you for your business, pay out your winnings and cancel your account. This is what has happened to a research group from the University of Tokyo .
A few months after we began to place bets with actual money bookmakers started to severely limit our accounts. If you enjoy this article, you may also enjoy my other article about interesting statistical facts and rules of thumbs. For other deep dive analyses:. The entire code for this project can be found on my Github profile.
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A quantitative sport betting game lasts for will be some generic indicator time say 5 minutes marathon sports betting. And usually one of the columns headers contain characters which are kept for the analysis. As a data scientist you applied to a single datafile stored in Dremio, which contains. Rodrigo de Azevedo 1 1. There are some subtleties in Dremio is again straightforward. In what follows, we will betting specific tools as well as general purpose analytics software be able to bet on developing a quantitative betting strategy. At this point, let us. As this is not the be an Sports betting and trading can provide an attractive. For example, in the dataframe is arbitrage in one form or the other. Thus, our goal in this use these clean timeseries for reputation which will allow you that can be used in other community members and earn.Well in my opinion a good parallel can be made between sport betting and bookmakers on the one hand and derivative pricing and market makers of those. Why ask about sports when there are algorithms that work in financial markets and all sorts of other predictions, that go back farther than sports betting and have. jur.ports-betting-1.com › quantitative-sports-bettinge1ceaf0f.