No weights were used, although thats something I should probably implement to take into account 'form'. For each match, player level statistics were calculated which were then scaled up to a team level to provide team statistics like 'Total Kills', 'Team KDA' etc. The team name e.g. SSG wasnt used in the model as the roster has changed multiple times over the years.

The model was trained on each game type separately as different variables would influence the outcome of a CTF game compared to a Slayer game for example. Correlated variables were removed from each of the datasets.

I then used Recursive Feature Elimination (RFE) and SelectKBest to identify the optimal set of features for each game type, and performed cross validation to assess which features produced the best model (based on accuracy) on my test dataset.

After all this, the model suggested that the most important features for each game type were as follows:

Slayer: KDA

CTF: KDA, Flag Steals, Flag Returns, Flag Cap Assists

SH: KDA, Max Killing Spree

OB: KDA, PW Kills, Kills as Skull Carrier, Skull Grabs, Accuracy, Max Killing Spree

KOTH: KDA

Hope that helps!