Answers.Body: <p>I would strongly recommend that you check the book "The Perfect Bet" by Adam Kucharski. It does not mention technical methods such as neural networks but it gives a good history (and very nice stories) on what people had done on that field. It gives you the notion that in order to get achieve a better payoff, your goal is not actually making a better prediction but choosing the better options by considering what other players are doing. If you ask why it is not just the better prediction, the answer is that there is always a balance of risk and payoff and since you cannot find a 100% guaranteed way of prediction, you will have to balance risk and payoff in order to gain in the long run. In addition, although theory suggests that you can make a good prediction by gathering all variables, this is not really possible in practice. Thus, human assistance and observing what others are doing is used as a way to improve mathematical predictions and possible payoffs.</p>
  
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Answers.Body: <blockquote>
    <p>Practically, no. </p>
  </blockquote>
  
  <p>In greyhound racing (or horse racing), there is no definite underlying pattern that can be associated with the outcome of the race. There are far too many variables to record and code as features. Most of which cannot be accessed by the public. This includes eating, sleeping, and training patterns. Furthermore, there are variables that cannot be readily quantified, such as the trainer's techniques, training effort, health history, and genes. A mere history of racing results and age won't be that helpful. </p>
  
  <p>A neural network can only be as good as the features that are used to represent the instances. If the features don't capture the necessary characteristics of the instances that are associated with the problem, then the learner cannot generalize to predict the real world outcome. </p>
  
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Answers.Text: Yes I can vouch that machine learning can be used to predict horse racing at least to a level of a curacy that creates profits. However I'd rather not cite the service as it's commercially operating.
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Answers.Text: I think it's also fair to say that processing the huge amounts of information that should be computed to make the best predictions is something that only a computer can do.
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Answers.Text: I like the fact that the answer shows the limits of neural networks. However, the answer would have been improved if there was a source where someone actually used neural networks and compared its prediction accuracy to that of humans, to *prove* that there is no definite underlying pattern. Otherwise, it's just mere guessing on your end.
Answers.UserDisplayName: Left SE On 10_6_19
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Answers.Text: Given enough data yes. Given the biological stats (height, weight, etc) of the dogs and their currents health status at the time of the race, the NN maybe able to predict the winner. Also, like you said, training techniques would also contribute and other things like wind resistance etc.
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Body: <p>Were there any studies which checked the accuracy of neural network predictions of greyhound racing results, compared to a human expert? Would it achieve a better payoff?</p>
  
Tags: <neural-networks><research>
Title: Can neural networks be better than human experts at prediction of greyhound racing results?
UserDisplayName: kenorb
ThreadText: # Can neural networks be better than human experts at prediction of greyhound racing results?
  
  (Asked by: username_0 on 2016-08-15)
  
  Were there any studies which checked the accuracy of neural network predictions of greyhound racing results, compared to a human expert? Would it achieve a better payoff?
  
  **Tags:** neural-networks, research
  
  
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  ## 2 answers
  
  ### Answer by username_2 on 2016-09-13. Score: 2 | Accepted answer
  
  > Practically, no.
  
  In greyhound racing (or horse racing), there is no definite underlying pattern that can be associated with the outcome of the race. There are far too many variables to record and code as features. Most of which cannot be accessed by the public. This includes eating, sleeping, and training patterns. Furthermore, there are variables that cannot be readily quantified, such as the trainer's techniques, training effort, health history, and genes. A mere history of racing results and age won't be that helpful.
  
  A neural network can only be as good as the features that are used to represent the instances. If the features don't capture the necessary characteristics of the instances that are associated with the problem, then the learner cannot generalize to predict the real world outcome.
  
  > **Comments:**
  > **username_4**: I like the fact that the answer shows the limits of neural networks. However, the answer would have been improved if there was a source where someone actually used neural networks and compared its prediction accuracy to that of humans, to *prove* that there is no definite underlying pattern. Otherwise, it's just mere guessing on your end.  
  > **username_5**: Given enough data yes. Given the biological stats (height, weight, etc) of the dogs and their currents health status at the time of the race, the NN maybe able to predict the winner. Also, like you said, training techniques would also contribute and other things like wind resistance etc.  
  > **username_3**: Yes I can vouch that machine learning can be used to predict horse racing at least to a level of a curacy that creates profits. However I'd rather not cite the service as it's commercially operating.  
  > **username_3**: I think it's also fair to say that processing the huge amounts of information that should be computed to make the best predictions is something that only a computer can do.  
  
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  ### Answer by username_1 on 2016-09-28. Score: 3
  
  I would strongly recommend that you check the book "The Perfect Bet" by Adam Kucharski. It does not mention technical methods such as neural networks but it gives a good history (and very nice stories) on what people had done on that field. It gives you the notion that in order to get achieve a better payoff, your goal is not actually making a better prediction but choosing the better options by considering what other players are doing. If you ask why it is not just the better prediction, the answer is that there is always a balance of risk and payoff and since you cannot find a 100% guaranteed way of prediction, you will have to balance risk and payoff in order to gain in the long run. In addition, although theory suggests that you can make a good prediction by gathering all variables, this is not really possible in practice. Thus, human assistance and observing what others are doing is used as a way to improve mathematical predictions and possible payoffs.
  
  
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