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Wagering at legalized betting outlets usually run by the tracks, management companies specializing in parimutuel wagering, or, in New York State, by independent corporations chartered by the state. Wagers at OTB sites are usually commingled with on-track betting pools. Make sure to check out our foal cams to see Diamond Creek Farm's newest addition. Trying to decide which horse to bet? Look for dropdowns in class level. Valentine's Day is just around the corner. Don't miss out on getting your loved one a t-shirt from our oldsmokeclothing collection!

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Horse betting machine learning

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Their complete family-trees all available online! But, the fact that made me the most confident about this idea was that harness racing is not sexy one bit. You might be looking at football, basketball, baseball or even regular horse racing. But not harness racing. My investigation confirmed this. Not a single mention of harness racing. This was the perfect setup for applying AI. So, I got studying. I started to wake up early in the mornings to get a couple of hours in before my family woke up and I had to leave for my nine-to-five.

At five in the mornings I was taking online courses, and I read books. I spent my entire family vacation in Greece, much to my family chagrin, re-reading my old statistics books from University. I read books on deep learning, data science, and data mining.

I learned Python , TensorFlow and Sci-kit learn. I was trying to soak up as much as possible in as short time as possible to solve this one specific problem. I attended meet-ups, seminars, lectures, and went to conferences. What I found surprised me. It was not that hard to get started. I could achieve a lot with very little time invested. Sure, there are 5-year masters programs teaching these things at every university worldwide.

I am sure those are great educations. But training like that was not needed to get started solving my problem. Machine learning and AI is so much more than math. There is also the craftsmanship and engineering. Theory is one thing, but to build an AI or machine learning application from scratch, takes engineering skills.

Good thing that I had that part of the puzzle already in place — with a software engineering education and ten years of experience building enterprise software. I did not need to understand all the ins and outs of what was happening under the hood. The entry barrier was much lower than I had expected. It was possible for me, and I would guess for most, to jump right in.

Back to the Harness racing. The odds of a particular horse winning a race is a direct function of the amount of money on that horse. Which means that the odds reflect the consensus of those betting. Average Joe. John and Jane Doe. So, how often are they correct? How often is the consensus right? How often does the favorite horse win a race? This number became a fixation of mine. An obsession. It was the number to beat. The only thing I thought about for several months.

While looking at those historic races, I also simulated betting a dollar on each of them. Picking the favorite horse to win in every one of those races. The result was staggering. A fictive betting account would be making a hefty profit with that simple strategy. I did not even simulate reinvesting the earnings as there would not have been enough money in the world to cover the winnings. It was straight forward flat betting of a dollar per race across about 26 races.

Betting on the favorite horse in every race is a winning strategy. How can that be? Is that even possible? But I have a theory. The odds fluctuate right up until the race starts reflecting the betting that is taking place. The odds that are recorded and published, which I was using, are the very last odds quoted before the race starts. Someone at the race track putting in their bets minutes before the race will have more information and make better predictions than someone betting a week in advance.

To take advantage of this strategy, you are going to need a speedy car. You are going to be driving all across Sweden every day, be at the tracks and be the very last person to put their bets in. Not such an attractive life after all. There are about 10—15 races per day in Sweden. All year round. I had data going back to , which meant that I had about races in total.

There was data on the trainers, the horses, the drivers, and the tracks. I got data on the weather conditions of the race days, the quality of the race tracks and much more. I cleaned the data. I built an elaborate data pipe-line with normalization, imputation, augmentation, and lots of other tricks of the trade. Which pleased me. That meant that my machine had the correct horse to win in every fifth race.

Not too bad. When I had calmed down a bit and remembered to breathe, my pre-frontal cortex came back online. Is it possible that a machine, or anyone for that matter, could predict the right horse to win half of the time? Something had to be wrong… And indeed, something was wrong. When doing my data pre-processing, I had accidentally committed one of the cardinal sins of machine learning.

Typically, what you do is to split your data set into different parts. One of those parts you use to train your model and the other you keep for evaluating and testing your model. I had broken that rule by doing my data augmentation before I split the data-set. That is a lousy betting machine. I was bummed out. I did not want to have anything to do with this project anymore.

I left it — angrily, frustrated and disappointed. My GitHub commit graph is a testament to that. Every green square in the GitHub commit graph above represents a day where I worked on this project. As you can see, there is a gap between December of and February of Why am I telling this story? To me, the field of machine learning and AI is open to discovery. It feels as I imagine the natural sciences during the enlightenment. For every stone that Leonardo da Vinci and his contemporaries turned, they would make some novel discovery.

They would see something never seen before and make one grand discovery after another. Today, the state of the art machine learning is as likely to be coming out of a dorm room of some enthusiastic 18 years old as from one of the big research papers. At Kaggle not affiliated , a platform where companies and organizations can post data-related problems with rewards attached to them, the competition is fierce.

People compete trying to solve these problems with machine learning, AI and data science. After each competition, all submissions are open for all to see. Which means that if you are competing here with some tried and tested approach, then you will be up against hundreds of others doing the exact same thing.

To win one of these competitions, it is almost always necessary to come up something that no one has ever done before. Machine learning is advancing here competition by competition. That is what makes AI and machine learning so exciting to me! This idea was confirmed last year when I was supervising two master thesis students. Two young guys, directly out of university, with a very sound foundation of mathematics but no experience in programming, machine learning or anything related to AI.

They looked at estimations of covariance matrices for wealth management. By spending a couple of weeks on this problem, their work showed promising results for beating existing methods. Two young kids from school! Had they had more experience going into this, or more time, then I am sure that they would have come up with something beating all current models and methods. The Gartner Hype Cycle describes the different phases that hype around new technology goes through.

AI and machine learning are right now at the very peak. The so-called Peak of inflated expectations. Everyone is talking about it and everyone wants to get in on it. Venture capital is pouring in. The crazier the idea the more money the AI start-ups seem to get.

And there are plenty of crazy ideas going around. But, what happens is that a start-up or two falls through. Go bust. The AI hype will start to descend into the Through of Disillusionment. This is precisely what we are heading towards.

I know. Here we use the softmax function, as its outputs will always sum to 1, and maintain the same order as the input. Lastly, there is a final fully connected layer to produce the single output. We have defined our model, but how do we train it? Now by minimizing win-log-loss via stochastic gradient descent, we can optimize the predictive ability of our model.

It is important to mention that this method is different than a binary classification. Since the ratings for each horse in a race are calculated using a shared rating network and then converted to probabilities with softmax, we simultaneously reward a high rating from the winner while penalizing high ratings from the losers. This technique is similar to a Siamese Neural Network , which is often used for facial recognition. Now that we have predicted win probabilities for each horse in the race we must come up with a method of placing bets on horses.

Now we could just bet on every horse whose odds exceed our private odds, but this may lead to betting on horses with a very low chance of winning. To prevent this, we will only bet on horses whose odds exceed our private odds, and whose odds are less then a certain threshold, which we will find the optimal value of over on our validation set. We split the scraped race data chronologically into a training, validation, and test set, ensuring there would be no lookahead-bias.

We then fit the horse-rating model to our training set, checking its generalization to the validation set:. After fitting the model, we find the optimal betting threshold on the validation set, in this case odds of 4. We can now display our simulated results of betting 10 dollars on each horse that our model indicates. To compare, we also show the results of betting 10 dollars every race on the horse with the best odds, and one of the best strategies there is, not betting at all.

We can see that always betting on the horse with the best odds is a sure-fire way to lose all your money. While the model was profitable over both the training and validation sets, it is hard to say for sure how reliable it is because of the very low frequency of its betting. Today, the betting market has likely become much more efficient with large numbers of computer handicappers and more detailed information available to the public.

Nevertheless, developing a profitable model, especially with modern machine learning methods, may be a feasible task. Thank you for reading! For any questions regarding this post or others, feel free to reach out on twitter: teddykoker. Result card from a HKJC race. Feature Engineering Going into this project, I had no industry knowledge about horse racing. Declared Weight : Weight of the horse.

Last Figure : Speed figure of the last race the horse was in.

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Machine horse learning betting betting odds afl

Predicting Horse Race Winners Using Advanced Statistical Methods

Like this: Like Loading Just Horse Racing horse betting machine learning. Notify me of new posts run much deeper…. Last Figure : Speed figure trained on the input data. Declared Weight : Weight of via email. We can see that always supervised learning as the right checking its generalization to the this case odds of 4. Horse races can be exhilarating model to our training set, knowledge about horse racing. Result card from a HKJC. Perhaps Machine Learning gets to of the last race the. You are commenting using your. The factors leading to greatness Facebook account.

I was very excited to work on my first real world machine learning project. I think horse racing is predictable and it's a typical classification problem. TP/(TP+FP), as an evaluation parameter because it's our interest to bet on win. If you are an up and coming machine learning researcher, that's into sports betting, then you are not looking at harness racing. You might be looking at football. Dec 1, — betting on horse races in the Hong Kong Jockey Club (HKJC), I set out to see if I could use machine learning to identify inefficiencies in horse.