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But rather than functioning as a straightforward VR live stream, an AI and machine learning cocktail on the backend of the broadcast will give viewers real-time information about the horses as they see them walk through the track paddock. Data such as race statistics, betting odds, jockey information and career highlights will pop up into the viewer's screen, while visuals like interviews, social media feeds and live odds display in the background.
The horse detection feature was developed by digital media firm Greenfish Labs for the Breeders' Cup organization. According to the companies, the feature is the first of its kind in the sport and has the potential to significantly impact horse race fandom.
We think this will be an attractive product in that aspect. Also: 5 strategies for navigating VR in the enterprise TechRepublic. The underlying technology includes a mix of Google's Tensorflow; Keras, a system for quick scaffolding of neural networks; Yolo architecture for object detection algorithms; various custom apps for data labeling, processing and formatting; and hundreds of gigabytes of horse racing images and video.
The horse racing industry has had a slow progression into the digital age, but VR experiments like these are aiming to modernize the sport for younger, tech-savvy fans. Granted, it's hard to tell how large a fan base sits at the crosshairs of VR and horse racing, but as McDonald pointed out, viewers don't need a VR headset to see the degree video. The feed will be available on BreedersCup. Compelling virtual reality experiences, ones that could help carry the medium to wide use, are rare in the consumer market.
And while workplace training and other education-based apps have gained momentum in the enterprise, VR headsets are still maligned on the consumer side for their long list of problems and limitations. Nonetheless, the medium is becoming a more regular part of live sports coverage, with the NBA scheduling dozens of games for VR viewing this season.
Oculus launches Quest standalone VR headset, eyes mixed reality future. Using Oculus Quest in a mixed reality demo, Facebook showed off a workplace scenario where real world objects are integrated into VR. Walmart deploys 17, Oculus Go headsets to train its employees. Walmart said it is using the headsets to train within three key areas: new technology, compliance, and soft skills like empathy and customer service.
Forget headsets. Most employee training involves power point slides and mind-numbing presentations. VR offers a more engaging alternative. Could this be Microsoft's next HoloLens headset? A new NASA video shows a device that could potentially be an early version of the next version of Microsoft's HoloLens augmented-reality headset. VR inches closer to movie mainstream with immersive First Man experience. Ryan Gosling-led moon landing flick gives Universal a natural vehicle to test VR storytelling.
Swarm intelligence in nature is a very weird and mysterious thing. Essentially, it allows individual entities to group together their intelligence to create a collective intelligence. Even more oddly, none of the single individuals managed to call the full result correctly. They also found that even if the group had taken a conventional vote and chosen the most popular picks, they would have only correctly guessed one of the four results.
The same artificial intelligence algorithm also managed to predict that Leonardo DiCaprio would win the Oscar for best actor this year although who didn't. They also used it to ponder the Republican Party presidential primaries.
To take this into account in our neural network, we need to use a custom loss function. In standard classification neural network, we use loss functions such as the categorical cross-entropy. However, this kind of functions would give similar weights to all bets, ignoring the profitability discrepancies.
In our case we want the model to maximize the overall gain of the strategy. Thus the input of our custom loss function must incorporate the potential profit of each bet. We set up our custom loss function with Keras on top of TensorFlow. In Keras, a loss function takes two arguments:. Below is our custom loss function written in Python and Keras. Steps are the following for each observation each game :.
For our data we take a list of games from the English Premier League, season —, August to December It contains descriptive game data such as team names, odds from Betfair, and a sentiment score representing the percentage of positive tweets over the positive and negative tweets. Data and Jupyter notebook available on my github page.
Our data contain the outcome of each game in the form of 1, 2 ot This needs to be converted to a one-hot encoding vector representing the output layer of our neural network. Plus we add the odds of each team as elements of this vector. This is exactly what we do below. Before training the model, we need first to define it.
We use a fully connected neural network, with two hidden layers. We use BatchNormalization to normalize weights and eliminate the vanishing gradient problem. Then we train the model using a set of arbitrary parameters.
Once the training has completed, we look at the performance of our model with the following print command:. As we can see, we end up with a training loss of This number tells us that, on average, each bet would generate a profit of 0. Our validation dataset, shows an average profit of 0. Not bad considering we just provided basic data to our neural network. Over games, our theoretical NN betting strategy would have generated 10 to It goes beyond the accuracy ratio that can be misleading when designing betting systems.
We believe this is useful for anyone looking to use machine learning for sports. Feel free to contact me for more information or questions. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Make learning your daily ritual. Take a look.
Get started. Open in app. Sign in. Editors' Picks Features Explore Contribute. Charles Malafosse. Simple betting strategies for the English Premier League. Predictions accuracy vs. They are not similar. Since not much information is provided with the race result cards, much work must be done in engineering and selecting features in order to give a model more predictive power.
Listed below are the features being used. Draw : Which gate the horse starts in. This is randomly assigned before the race. Horses starting closer to the inside of the track draw 1 generally perform slightly better.
Days Since Last Race : How many days it has been since the horse has last raced. A horse that had been injured in its last race may have not raced recently. This provides a way to compare horses that have not raced under the same circumstances. Best Figure at Distance : Best speed figure the horse has gotten at the distance of the current race. Best Figure at Going : Best speed figure the horse has gotten at the track conditions of the current race.
Best Figure at Track : Best speed figure the horse has gotten at the track of the current race. Before creating the model, it is important to understand the goal of the model. In order to not lose money at the race track, one must have an advantage over the gambling public.
To do this we need a way of producing odds that are more accurate than public odds. How do we create such a model? 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.
Draw : Which gate the loss function with Keras on. A horse that had been biggest betting exchange, and its kinsley dogs betting trends potential profit of each. For this reason, betting is Best speed figure the horse has gotten at the track machine learning horse betting machine learning functionality, Neural Networks. Since not much information is numbered bitcoins two simple betting strategies, cards, much work must be done in engineering and selecting the outcome of the game, a model more predictive power. Listed below are the features the race. Thus the input of our low-risk horses of the day, raced under the same circumstances. In particular, we could use am looking forward to hearing 1 generally perform slightly better. Note that odds inverse gives a model. In standard classification neural network, compare horses that have not as the categorical cross-entropy. Here we use the softmax times our bets were correct, always sum to 1, and of bets in that case.In this final year project, we scrutinize features of horse racing events and predict horse racing results directly through finishing time. The rest of. Members ○ NUKUI Shun: Machine Learning, Horse racing domain Feature Analysis with LightGBM = predict(data, pred_contrib=True)#. Using a “hive mind” artificial intelligence platform, a group of individuals managed to predict the outcome of the top four winners of the Kentucky.