My 50,000 Monte Carlo Simulation Results for the NCAA Basketball Tournament

With March Madness upon us, I have been in a solid state of sleep-deprivation. It all started with a class project assigned in late February that suggested we enter the Kaggle competition of our choice or create a similar type project.

I was immediately drawn to the March Machine Learning Mania being hosted by Kaggle and Intel. For the past three weeks, in any spare time, I have been trying to find and clean data to run models. I thought things were slowing down last week until I decided to try some new data I had found.

That being said, I was running and testing models and way overthinking this whole competition down to the very last few hours, right up to the deadline yesterday afternoon. The competition requires you to submit a probability for all 2278 (68 teams) potential matchups. Only the 63 games are actually played (they do not count the play-in games) are scored.

Since I had all of these probabilities, I decided I should write my own Monte Carlo simulation to see what would happen. I meant to post the results yesterday, but as it seems to always happen when you write code without much sleep, debugging becomes a painful mess. I had a midterm yesterday as well and may have been a bit exhausted.

But here it is – 50,000 simulated runs of the tournament, based on the last data I generated for the contest. (You were allowed to submit to sets of scores with your best score becoming your entry).

I am still a little too tired to give much insight, but will post more as the tournament goes on….