Help improving sports prediction model

  1. Tanh may make for a better activation layer than Sigmoid for intermediate layers.
  2. Conv1d or a TransformerEncoder may provide better results, as games further away in time may have less impact on the outcome. Structure the data so that input dims are something like [ batch_size, num_game_season, (win/tie/loss, score ratio)]
  3. You could encode the results of past games with Win = 1.0, Tie = 0.5, Loss = 0.0 for inputs and probability distribution for outputs.
  4. Dropout on the intermediate layers may help. TransformerEncoder can be set with the dropout argument.
  5. Simply using a score ratio of loser/winner scores could be added as a second channel, or 0.5 for tie(that will prevent divide by zero in the case of 0 / 0).
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