I'm having a concrete problem I'm trying to solve but I'm not sure in which direction I should go.
- Goal: Identify formation of a soccer team based on a static positional data (x,y coordinates of each player) frame
- Input: Dataframe with player positions + possible other features
- Output: Formation for the given frame
- Limited, predefined formations (5-10) like 5-3-2 (5 defenders, 3 midfield players, 3 strickers)
- Possible to manually label a few examples per formation
I already tried k-means clustering on single frames, only considering the x-axis to identify defense, midfield and offense players which works ok but fails in some situations.
Since I don't have (much) labels im looking for unsupervised neural network architectures (like self organizing maps) which might be able to solve this problem better than simple k-means clustering on single frames.
I'm looking for an architecture which could utilize the additional information I have about the problem (number and type of formations, few already labeled frames, ..).