Once the model has been trained, we are now ready to validate how well it is working. I decided to use linear regression to predict a horses finishing time given a number of input features. If nothing happens, download the GitHub extension for Visual Studio and try again. To improve the algorithm I would need to modify it to pick a different type of horse. If you are in a trotting race the driver always needs to keep his horse in a trot. The features included horse and race data as follows: One hot encoding was used for the categorical features e.g. race track, horse jockey and horse trainer.Using the features above we want to predict the horses time in seconds – this is our output or label value y. I then proceeded to train based on all the horse data stored in the database created above. 68 horse met had a standard deviation of 1.4 and above. As you can see, there was only 61 races that we bet on but the results are dramatically improved. download the GitHub extension for Visual Studio, Unique Identifier for the row. When I analyze race programs I only bet on races which I believe I can win, otherwise I move on to the next race. The reason for this type of result is that each expert has a weighted algorithm for picking horses. To select the horse I think will win, we sort the predictions and pick the lowest value. North American horse racing uses a parimutuel betting system. Obviously if this is too high the horse will tire out sooner. Harness racing runs throughout the year and the weather affects the outcome of the race. For example, a prediction value can be something like 1.45. ‎The best in horse racing algorithm prediction, featuring two sets of algorithm picks and realtime scoreboard of hit percentages per exotic wager and track. I’ve been gathering harness racing entries and results from across North America over the past 5 years. There were a few “special cases” that needed to be parsed differently. horse draw – this is the lane the horse … The challenge is to do this for every race each day is impossible! Using these features, you teach the algorithm the types of attributes a winning horse needs to have. The method has been extensively trialled, monitored and refined for even people completely new to betting. Williams and Li [5] studied horse racing in Jamaica to the prediction of winners. AI Race Predictor employs advanced AI techniques to predict the outcome of flat races in the UK and Ireland. They used Arti cial Neural Network methodology primarily. The training array are the features described above and the target results is the finish position of the horse in that race. The idea is that if there's a lot of variance between the prediction, the horse with the lowest prediction may have a better chance of winning. approaches assume independence of each horse’s chance of winning; the other horses in a race are not involved in any probability calculations. See who is a fan of Algorithms. The above machinery was encapsulated in a function called parse_xml.py that gets the data for a specific race meeting. (I wasn’t sure if this occurred in reality so I didn’t cater for it for now). Maybe another analysis on a different day! race stakes – the winnings at stake for a particular race. And thats it! It is a method for picking the strongest bets of the day for UK & Irish Horse racing and sports betting across the board. Pandas makes it easy to write data frames to databases. The horses with the lowest prediction won 27 times (40%) and came in 1st, 2nd or 3rd 59 times (86%). The easy answer is that you can’t. To get the data I wrote a script to parse the xml data from the website using the Python library called BeautifulSoup. Search the database for the particular horse and race data we want to train on. Instead of picking the best horse, the feature set would need to define horses which are the best but different than the public would choose. To do this, I took the standard deviation of all the predictions of a race. Actually this was the first thing I thought I wanted to try after studying deep learning for a while. A race with horses which have even characteristics will be harder to predict. Use various machine learning algorithms to predict horse racing results including 4 classification algorithms : logistic regression, Naïve Bayes, SVM Classifier, Random Forest, and 2 Regression methods: SVR and Gradient Boosting Regression Tree Model (GBRT). 61 races met the above criteria. This modification to the algorithm should pick races that have a horse which is much more superior than the rest. Instead of a different of 1 between the lowest and second lowest horse, I used a difference of 2. The next attempts uses a simplistic but affectinve approach to find an edge.