The bar chart at 5:22 is not showing colts and fillies have a higher chance of winning. It is merely showing that there is a higher population of colts and fillies than geldings. You need to compare win rates against the population of each gender.
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1:26 - it is a production database, however its an OLAP database not an OLTP database so for fast queries that you're used to with RDBMS you won't get instant results. for inferences however with BQ ML you can use it for front end apps
Wouldn't a regression here not better suited? A classification model gets less feedback in training, because when it predicts place 2 for the winner, it is as bad, as place 6 for the winner (for a classification). But indeed, predicting place 2 for the winner is a much better prediction than place 6. And a regression, probably would then just predict something like place 1.78 instead, so you know this horse usually will be 2nd, but might as well win, too by a close similar chance and I'd bet the even if you just round the predicted results to the next integer, the output will be more precise than the classification training as for the learning algorithm there is more feedback.
Why did you not create the dataset of actual position = 1. Also on the predicted set. What is the key data to be used against the set you trained on. For example the racecard does not give you the same set of features to compare.
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How can you apply this to real data? you would need the data in the proper format, but who provides this? If you have no real data, you have no logical conclusions...
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