Machine Learning and horse racing. When developing machine learning models for horse racing we quite rightly need some way to evaluate how successful they are. Horse race betting is a bit different to predicting face recognition or breast cancer diagnosis because these applications are all about accuracy of predictions. With betting, accuracy clearly has a part to play, but its not the complete picture.
A less accurate model in terms of predicting winners could be more profitable and for that reason we tend to focus on profit. The two most common profit measurements are flat stake profit and variable stake profit. Of course in both examples the stake can be whatever you want it to be. The advantage of variable stake monitoring is that it is not prone to inflation from one or two big priced winners which may give you a never to be repeated profit that sends you skipping off to remortgage your house.
The variable stake monitoring does not suffer from this and gives a more realistic impression of possible future performance. Probably not, a mixture of metrics can help give us more confidence if all of them are showing improved signs over a rival model. Horse Racing models often have a degree of inbalanced data.
One metric that is useful for inbalanced data sets is the Brier Score and what I am about to describe is its close cousin the Brier Skill Score. Looking at the above you can hopefully see that if the lower rated horses tend to lose and higher rated horses tend to win we will get a lower Brier score than if races were predicted the other way round. This measures the Brier Score against some other measure, after all stating that the score above is 0.
We just know its better than 0. The BSS is calculated by first working out some sort of measure we can compare to.
In this case we will opt for a baseline measure of simply predicting all horses with a value of 0. Why 0.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I'm very new to Python and web scraping.
I want to scrape the racing post site to get the daily races and the stats for each race. I'm trying to get all the partial links for the races to use later to extract the stats for that race.
Example: 32Red. I'm using this code, but I get all links in the page. I need to locate the extraction data, but I have no clue how to do that. I have tried several approaches but I didn't got the expected results. Unfortunately, there is no class on the column but this should be safe enough with the structure you have. Learn more. Asked 4 years, 1 month ago. Active 1 year, 4 months ago.
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Creating a horse racing Tableau dashboard from data scraping
Lubinho Lubinho 1 3 3 bronze badges. Active Oldest Votes. If I understand your question right, you want the links in the table's third column, right? Hi, thanks for your help. I m very new to python and i'm trying to learn by myself, but many times i get blocked.
The idea relative to my question is grab the relative links and use them to scrap the correspondent pages. For the main link, " betting. The new comment drew my attention back to this. It sounds like you want to build a proper web scraper which scrapes a whole site. Have you looked at Scrapy? Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.Come and enjoy a warm welcome to The Horse Race Predictor! Y our method does it all for me and offers additional information to boot!
Well done, thank you and please keep up the good work! I think you have a very informative, wide-ranging product which I am sure will generate profit for anyone who uses it. Still finding it hard to believe after following duff tipsters for to many years.
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You can actually work these methods and make money by sitting on a beach in the South of France, at home in your room, lunchtime in the office or a hotel anywhere in the world. If you are able to follow some relatively simple criteria and are willing to learn something new, you could be about to dramatically improve your earning potential. If you can develop and display a cool temperament, you can use what you learn here to build your betting bank at a steady and balanced tempo. The Horse Race Predictor contains all the strategy and tools you need for the job.
Compared to a lot of the utter rubbish that is sold and marketed via the internet or postal list circulars, the Horse Race Predictor will be like a breath of fresh air. The Horse Race Predictor can deliver the following benefits:.Released: Jul 29, View statistics for this project via Libraries.
Tags horse, racing, class, library. In most cases, an automated installation via PyPI and pip will suffice, as follows:. To gain access to bleeding edge developments, the package can be installed from a source distribution. To do so, you will need to clone the git repository and execute the setup.
If you would prefer to install the package as a symlink to the source distribution for development purposesexecute the setup. Provider class. To do so, you will need to provide a compatible web scraper and a database connection.
Scraper API, support calls such as the following:. The database connection can be any object that implements the pymongo.
Database API, supporting calls such as the following:. Scraper as the web scraper and pymongo. Database as the database connection. To set up the required dependencies in your own project using the same packages, execute the following code in your Python interpreter:. Provider class as follows:. The provider instance can now be used to scrape, store and access a range of racing data, as illustrated in the following sections…. Meets represent a collection of races occurring at a given track on a given date.
To get a list of meets occurring on a given date, execute the following code in your Python interpreter:. Races represent a collection of runners competing in a single event at a meet. To get a list of races occurring at a given meet, execute the following code in your Python interpreter where meet is an existing Meet object obtained via the provider. The meet. Runners represent a single combination of horse, jockey and trainer competing in a race.
To get a list of runners competing in a given race, execute the following code in your Python interpreter where race is an existing Race object obtained via the Meet. The race. Furthermore, Runner objects also offer the following calculated values as properties that can be accessed using dot-notation:. In addition to the properties listed above, Runner objects also offer the following performance lists see below as properties that can also be accessed using dot-notation:.
The performance list properties described above return PerformanceList objects.RaceQuant is a startup specializing in consulting for horse race betting.
RaceQuant enlisted our team to use machine learning to more accurately predict the outcome of horse races, to advise betting strategy. They provided three years worth of Hong Kong Jockey Club horse racing data from the tracks in Sha Tin and Happy Valley, including public data from the HKJC website and enhanced data which included 35 additional variables.
Our approach was to model the probability of each horse winning in a given race and compare this to the market perceived probability and then recommend bets on horses whose chances exceeded the market probability.
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We will discuss in detail below. The raw data contained over 29, observations, covering 2, HKJC races between and Before this data could be fed into models, some transformation was necessary. The data cleaning and processing was done using the Pandas library in Python.
Some features did not have data for all horses. Some of these could be reasonably imputed. In other cases, features with missing data were replaced altogether by new features. Because of the nature of horse races many discrete races with horsesit is difficult to build a model which predicts horse rank in a given race outright. Furthermore, many betting strategies rely on predicting the probability of a given horse winning a race and comparing it to the perceived market probability to determine what to bet.
Consequently, our approach was to build models to predict horse run times and use these to simulate each race many times, allowing us to extract the probability of each horse for each race. Breaking it down:.
The first measure of success that we used was to consider the number of races for which we correctly predicted the winner. Out of races in our test set, our best model correctly predicted the first place horse in It correctly predicted the second place horse in The return on investment for this strategy was Note that the payout on each race is dependent on how much the betting population bet on each horse a proxy for how much each horse is expected to win. The more bets placed on an individual horse, the lower the payout.Machine Learning and horse racing.
I have prepared some introductory sessions on machine learning for horse racing using Python and Scikit Learn. You do not need previous experience of either of these two tools but it would help if you are at least familiar with some basic programming concepts. For example it would help if you know what a FOR loop is, what an assignment statement is even if it is not in Python.
The main data file will be freely available until Tuesday 2nd February for those who showed an initial interest. A modest members fee will enable you to access it. OK to get started you will need to have downloaded and installed Anaconda Python v3. All comments, questions and feedback should be posted to this blog post, that way they can act as a FAQ source.
First of all download the following zip file, double click on it to reveal all the contained files and copy them into your horseracing folder. The next step is to download the following file into your horse racing folder. When you click the link it will probably display the contents in your web browser. Just right click the display and you will have the option to save to a file the screen data. This file is now housed in the utilities section of the smartersig. You now have the required files.
To get started first open a msdos command window the black box type. Kick start Ipython Notebook by typing in ipython notebook and pressing return. Once notebook is loaded up you will be presented with a directory screen of folders.
Double click on the horseracing folder that you created to go into that folder. Mike said:. January 30, at pm. What would the general approach be to assigning a probability of winning to a given horse based on its data? For example could you look at x number of nearest neighbors and average their win rates?At The Races "Derek and Clive * Horse Racing
IE: find the 20 nearest neighbors and if 5 of them won return a result of 0. January 31, at am. Logistical regression would give a universal zero prediction to all the fresh data simply because it predicts chances and none of its predicted chances would be above 0. Maybe we can go on to look at this method later. Steve Tilley said:. January 31, at pm. There are several ways of getting a probabilistic out put. Most classification methods can output the chances of a given case being in a particular class or classes.
February 1, at am. The probabilities do not seem very promising within this data set and method. I increased K to give a finer grain of probabilities but it produced nothing promising. Maybe if you have a play around with other values of K you might find some value. February 1, at pm. Another problem is that there are far more losers than winners. This means a model will be successful it it just says everything loses.
One way round this is to make a data set with all the winners, and say twice as many losers with the losers being selected at random from the main data set. February 2, at pm.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again.
If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The application is controlled over app. The pnl overview web server is launched over webserver. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. A fully functional automated horse racing trading system for betfair in python. Collects data, uses a neural network to analyse the data, makes trading recommendations and places the bets before races start.
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Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Betfair horse racing The code consists of the follwoing parts Collecting data from betfair and matchbook below cronjobs need to run Analyzing the pricing data of horse racing through neural networks and creating a model to make betting recommandations for backing or laying Executing bets according to the trained model Flask web interface that shows logging activity, graphical pnl overview and statistical analysis of past bets Building the venv You can create a venv with the environment.
On Linux you need to use conda env create -f environment. Usage: app. Example plots of neural network training:. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.