Hello!
My friend and I developed 3 models for predicting a win in a Tetr.io match based on playstyle and gameplay. We used this dataset: https://www.kaggle.com/datasets/n3koasakura/tetr-io-top-players-replays, and we had 7 million rows to work with.
Some interesting findings for someone who is about only a month into playing Tetr.io (i copypasted from my notebook):
• The amount of garbage received in a match is the most dominant contributor to losing. Receiving a large amount of garbage tends to lead to losses. This suggests that the model is very sensitive to a player's inability to clear garbage. If a player fails to clear garbage despite a high attack_per_piece, then they are likely to lose.
• High attack moves, such as t-spins and back-to-back moves turn out to be negative contributors. This does not mean that such moves are considered negative, but rather that prioritizing flashy setups can be very risky for a player. It may remove their defensive timing and leave them open to incoming_garbage.
I wonder how much of our findings are actually true or are just base knowledge for any Tetr.io player.
You guys can also check it out here: https://github.com/Solenad/tetrio-win-prediction
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