A Case Study Using Neural Networks Algorithms: Horse Racing Predictions In Jamaica.

It's probably unrealistic to hope to build a network which picks the winners for all nine races each day. One of the first people I trained to use the new approach to Thorobrain, Neil Levin, took my setups and had a sustained period of amazing results at Santa Anita, Hollywood Park and Del Mar. A well trained ANN should be able to match the performance of a human tipster. Input data for such problems may include data about a race that is the same for all horses called common statistics, e. Discover the world's research

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So, computers can be used successfully in horse race handicapping and AI as well is a tool that still could have a future with the right mixture of good programming, AI and the proper knowledge of what needs to be focused on by someone who is erudite in the more subtle and seasoned understanding of the role data management plays in having a real time and correct impact insight into what is important and what is not.

As they say, junk in, junk out. It is even more true in horse racing where there are so many myths about the sport and the facts of what contributes to profitable wagering. So many simply false ideas are believed to be facts by horseplayers who have no statistical or real knowledge of Thoroughbred racing.

The sport keeps evolving, more rapidly it seems every year. Unfortunately the sport itself is rather stagnant, as far as the customer base is concerned. Racing management has not handled things in such a way as to instill growth in the industry. It is a provincial and idyllic world, full of incredible events and stories, magnificent accomplishments such as the career of American Pharoah and other great horses as well as great riders and trainers. Still, the sport itself, especially concerning the lower strata of Thoroughbreds is rather damaging to the animals themselves.

Horses are actually one species of the very few surviving animals that existed in prehistoric times. What I learned from her, is that the animals could be treated much better. The wear and tear of racing can be quite damaging for them, although it could be much better. You might be better advised to try it on the dog-races. I know people who tried both the horses and the dogs, and the dogs seemed to be a bit more predictable than the horses. This just makes it hard to scale up a winning solution, even if you find one….

There are so many factors in a race that you cannot predict. There are many software programs that will allow you to download data about the horses and then build algorithms either your own or pre-determined to pick horses to bet on. These are your competitors. I love handicapping the horses because I love numbers and also watching the horses run.

But I have a ton of fun, win or lose. This page may be out of date. Save your draft before refreshing this page. Submit any pending changes before refreshing this page. Ask New Question Sign In. Has anyone ever used AI to make a horse race predictor? Can it be done? Well, by AI if you're talking about the sensor and intelligent-agent architecture, I'm not sure if it could ever be done. However, I'll present my views on using artificial neural network ANN to predict the outcome of horse races.

To answer you in a sentence, yes, people have attempted using ANNs to predict the horse racing results and have been partially successful. For one thing, events like horse racing are environmental dependent. But some researches like this one - http: According to them, Back Propagation performs better than other algorithms.

Some other researches have employed boosting to increase the accuracy of predicting the outcome of such events. How do I beat the horse races? How use beyer numbers for horse race handicapping? Can an AI ever be considered human? What is the best website to make a one time wager on a horse race? I hope this helps answer your question.

ML workstations — fully configured. Let us save you the work. Our machine learning experts take care of the set up. Learn More at lambdal. You dismissed this ad. The feedback you provide will help us show you more relevant content in the future. It has been applied -- a simple Google search "machine learning" "horse racing " reveals that a few people have tried it -- mostly by -- it seems -- graduate students out of curiosity.

A quick survey reveals it's occasionally slightly better than the odds-on favorite, but not by much. And if anyone did apply machine learning successfully to horse racing, given how lucrative it would be, I'm not sure they'd be eager to publish. A well trained ANN should be able to match the performance of a human tipster. At the end of the day they both using past results to work out the probability of any horse beating the other runners.

I would apply caution to the claims made in some of the papers available online - these results seem a little too accurate. If these were achievable it would have already been done and all the bookies would be out of business. BAK extension so you will not lose your original file. Define Inputs and Outputs. Next, Sam needs to specify in NeuroShell 2 which of the columns are inputs and which are the actual outputs. The module displays all of the column names in the exploded file.

Sam marks the first six columns common data and statistics for two horses as Inputs, the following two columns Place 1 and Place 2 as Actual Outputs, and all the rest of the columns named C9, C10, and C11 are blank or Unused.

These last columns contain no actual data, and they appear in the exploded file because of the first lines that contain information about the initial column names in the file before explosion you may still see this file, as it has been renamed by the Race Handicapping Prenetwork module to RACE.

Please refer to Tutorial Example One for a more detailed description of this module. For this example, we shall assume that in the Design module he selects the Standard 3-Layer Backpropagation network architecture with all default settings. Sam wants to see if his network is producing good results.

He double clicks the "Apply to File" icon, and enters the Apply module. OUT file" and "Include in. OUT file actuals minus network outputs". When applying a trained network to a file created with the Race Handicapping module, do not select the check boxes that create extra columns in the file, e. OUT file or Include in. OUT file actuals minus network outputs.

The extra columns will interfere with restoring your file to its original condition in the Race Handicapping Postnetwork module. The Apply Module defaults to processing the. PAT file, which is the first set of data that Sam entered. Each pattern in the exploded pattern file is processed through the trained network and it computes the values of the two outputs. These values determine which of the two horses is the probable winner.

Statistics which measure the accuracy of the trained network are displayed on the screen. Please refer to the description of the Apply Backpropagation Network module for detailed explanations of the statistics. The trained network produces an R squared value of. Sam records the R squared statistic to compare this network with other ones he might create later. Now Sam realizes that it would be more convenient to view the network results along with input data and with the desired outputs.

This is also necessary if he wants to view the results for all horses in a race in a single file row. So he leaves the Apply module and double-clicks the Attach Output File icon. The default settings for this module join the RACE. Sam selects the Attach Files item from the Attach menu. After attachment is complete, he exits the module. Race Handicapping Postnetwork Module. Now it is time to use the Race Handicapping Postnetwork module to "implode" the file. Sam double-clicks the Custom icon in the PostNetwork column of the Advanced system, and then he double-clicks the Race Handicapping icon.

However, this module defaults to all of the correct settings taken from the Prenetwork module, so the only thing Sam needs to do is to select the Begin Implosion item from the Implode menu. The result is a file that includes all of the information for a single race on a single row in the spreadsheet. The output neuron results for each horse is combined to produce a ranking for each horse.

Sam is now ready to look at the new. OUT file, which now again contains all the information in the same form that was in the initial pattern file before exploding. He does that, selecting the View Pattern File item from the File menu. In the Viewer module that pops up, he is able to see only the first 10 rows of the pattern file.

However, if he is not satisfied with this, he can press the Transfer to the Datagrid button, thus invoking the Datagrid module. The Datagrid is not a commercial grade spreadsheet and is in fact somewhat slow loading large files. If you have a very fast computer this may be all right; otherwise use your usual spreadsheet. Please refer to Tutorial Example One for the description of how to change NeuroShell 2 so that it always calls your spreadsheet instead of the Datagrid, how to view the data graphically.

At the end of Tutorial Example One you can also find some tips on how to make your predictions better. Making predictions with a trained network can be performed in five simple steps. Check your data to comply with File Requirements. If you're using the file to make predictions, you do not need to include a finish place for each horse in the output columns. This file should have a. The Production mode is turned on in the Options menu of the main Advanced System screen.

If you are applying the network to a file in which you know the results of the race, you may place the actual race results in the actual output columns. The network's predictions will be placed in columns to the right of the actual output columns.

Other than A above, the rest of the file should match the specifications for creating a. PAT file for training purposes. Use the Race Handicapping Prenetwork module. When this module is selected for processing a. The default information was entered when the network was trained and should not be changed if it is correct when applying the network to new data.

Use the Explode Menu to begin processing the file or to interrupt processing. Use the Apply module. When you use the Apply module, the trained network is used to produce results for two horse comparisons.

When you are in the Production mode, the Apply module defaults to producing results for a. Use the Attach Output File module. After applying the trained network, you may want to view the file which includes the network's prediction for each horse in the race. You need to use the Attach module to attach network predictions to the input file. Use the Race Handicapping Postnetwork module.

Use the Race Handicapping Postnetwork module to "implode" the file. The result will be a file that includes all of the information for a single race on a single row in the spreadsheet. The neuron output values in the. OUT file will have been mathematically combined to produce rankings for the horses.

If you get an error message when imploding the file, it is probably because you have neglected to do one of the following: Apply the trained network to the exploded file you are trying to implode. Turn off the check boxes in the Apply module that add extra columns into the. OUT output file, such as actual network outputs and differences between actual outputs and network outputs. Run the Attach module to put the inputs back into the.