Expanding ARAM Predictions
I recently managed to achieve a 67% success rate in predicting the outcomes of ARAM matches. This was done on a dataset from North American players of League of Legends. Since then, I upgraded my data storage and crawling mechanisms which has allowed me to collect data from other regions. Since Riot’s API limits are on a per-region basis, I was able to collect this data in parallel. I now have data on tens of thousands of matches from each new region: Korea, EU West, and EU North.
My original 67% model was trained on matches from patch 7.11. Since then, patch 7.12 was released and almost all of the new ARAM matches I collected where on that patch. Out of curiosity, I wanted to see how well my previously learned model would perform on the new data. Surprisingly, it worked better than expected, achieving the same 67% accuracy.
One hypothesis in my last post was that Korean players would produce better training data since they’re known as being more consistent. To my surprise, this Korean-trained, Korean-tested model performed about the same as its North American counterpart. I also attained about a 67% accuracy on my EU North and EU West datasets. I’m still collecting more data for training (because it’s so easy to do now), but I don’t expect it to move the models accuracy much.
I am still working on gaining a deeper understanding of champions and getting humans to review the mistakes the model made to learn new insights that could lead to better accuracy.