I know ML is used in real horse-racing and other sports, where team/player history matters and can be used as a predictor for future games.
What about for "simulated" games, where the outcome is decided by probability?
Here's an example of what I mean:
Suppose there are 3 tracks T1 to T3, and 12 horses H1 to H12. Every week, all 12 horses are randomly assigned to the 3 tracks (4 horses per track) and assigned betting odds, ranging from 2-to-1 to 15-to-1. The horses in a track are also given a random type of feed (e.g. the horses in T1 might be given hay, while the horses in T2 might be given barley. The feed is random per track.)
As the player, you can place up to 8 bets on all 3 tracks. As the week goes on and other players place bets, the horse odds may increase or lower. At the end of the week, the horses race and you win back some money if your bet(s) hit.
In terms of available data, we know:
- The starting and current odds of each horse.
- The win history of each horse (for example, H1 might win 55-65% of the time).
- The overall strength of each horse.
- The feed given at each track.
- The above data for the past 1000 races, including the winners.
Only the odds are known to influence the outcome of the race for sure. It is unknown if the other variables (history, strength, feed) affect the outcome, or by how much.
I ask because, even though past history is given, there's a good chance the winners are simply decided by the current race's odds rather than by history. Since this is a simulation, the outcome is likely decided by randomly generating a number for each track and seeing what range it falls into.
Given the above information, would this something a machine learning algorithm or neural network could predict reasonably accurately? And if so, what would be a good starting point to look at?