I have a dataset (in
R as a
data frame) of race results for athletes.
athlete racedistance time location tracktype date coach A 100 10.0 UK typeA 2014-01-01 carlos A 200 20.0 US typeB 2014-02-01 carla A 100 9.5 AUS typeC 2014-03-01 chris B 100 11.0 UK typeA 2014-01-01 carla B 200 21.0 US typeB 2014-02-01 carlos B 400 61.0 AUS typeC 2014-03-01 carla B 100 10.5 GER typeA 2014-04-01 clive C 100 9.5 UK typeA 2014-01-01 clive C 200 21.5 US typeB 2014-02-01 chris
Is there an appropriate machine learning algorithm or method that can use the previous results of each athlete as a feature, when trying to predict the
time for an athlete in a future race?
athlete A has three races, with one month rest between them. In the third race he performs slightly better than the first race over the same distance.
Can an algorithm learn that the second race had an effect on the athlete, which meant he performed better in the third race?
From what I've read on the subject and the training examples I've completed it would appear that each 'row' of data should be independent, is this the case for all ML algorithms? Is there another prediction technique I should be considering to solve this type of problem?