There are many ways to try and predict the winner of competitions. In increasingly complex order, some options are:

1. Random selection or vibes — you can just pick who you think will win. No math involved.
2. Pick the team with the better record or winning percentage so far that season. Look up win-loss records on each team's league website or ESPN, or use AI like ChatGPT or Claude to find them for you.
3. Modify the winning percentage scheme above somehow. You could give a bonus for being the home team, you could look at only the last 10 games, you could weigh a team more heavily if they’ve had a hot win streak recently, or so on. Be creative.
4. Pick, if available, whoever has the negative moneyline (like -143) on a gambling website (DraftKings, FanDuel, and so on). This is the team most likely to win, according to “Vegas.”
5. Implement an ELO rating system and train it on old game data. Email Chris Hayes to set up an appointment and he can show you how to do this. Alternatively, try to set it up yourself. AI can be helpful, but you need to get old data.
6. Customize an ELO rating system with a different mathematical model than usual, and add in other variables and factors.
7. Decision trees, neural networks, various kinds of regression, and other advanced statistical & machine learning data science methods. You can major or minor in data science at Gallaudet to learn more about these. Check out the Gallaudet Data Science page by clicking here.