Using Machine Learning to Pick March Madness

There isn’t a foolproof method to win your pool, but you can come close by employing machine learning to give you an advantage. There are a variety of techniques, including choosing upsets, favorites, and following so-called “expert” choices. None of these strategies helped win bracket pools. Below we will go through some tips to use when picking your bracket using March Madness betting odds.

Machine Learning to Pick March Madness

What is March Madness and How to Pick the Tournament

The men’s and women’s NCAA basketball tournaments, which take place in March for a few weeks, are known as March Madness. Sixty-eight teams qualify for the single-elimination tournament; one bad game and your title aspirations are gone. Teams are seeded based on their regular-season performance, but shocks are common, earning the tournament the nickname March Madness and making it frustratingly impossible to predict. That does not deter folks from attempting. Thousands of individuals fill out their own brackets each year, aiming to predict all 63 games, and put their picks into bracket pools for a chance to win ultimate bragging rights.

What is Machine Learning?

Machine learning is a type of data analysis that automates the creation of analytical models. It’s a field of artificial intelligence based on the premise that computers can learn from data, recognize patterns, and make judgments with little or no human input. The most popular sort of machine learning is supervised learning, which simply involves predicting a target based on labeled data. Classification and regression are the two forms of machine learning.

How Do You Predict the NCAA Tournament Using Machine Learning?

When starting a machine learning project, the first step is to choose the data that will be used. For both the winning and losing teams, the information includes conventional statistics seen in a box score. There are alternative datasets that might be used for this project, but utilizing game-by-game data is the most straightforward while still producing a highly accurate machine learning model.

How Do You Create a March Madness Machine Learning Model?

Again, because the aim is to predict which side will win each game—Team A or Team B—you’ll develop a machine learning model using a classification model.

You might be asking why games from 2003 are used to forecast games from 2022. This is when your model’s training comes into play. You provide as much data as possible into your machine learning model to instruct the computer on how to utilize your features to forecast who will win. This is possible since you already know the outcome of each game in this situation.

Consider a 2003 matchup between Duke and North Carolina. Duke triumphed, but why did they triumph? Perhaps they had superior numbers and a higher team rating going into that game. After thousands of repetitions, you’ll have a very good notion of which team traits contribute the most to a team’s ability to win a game against another team.

After you’ve trained your model, you may test it with a portion of your data. This will let you test many models, feature sets, and tiny modifications of each model to discover which works best. Each variant may be assessed using a suitable evaluation metric until the best-performing model is found.

Putting Your Machine Learning Model to the Test in March Madness

After you’ve developed your model, you’ll need to generate a dataset to run it through to gather the outcomes of each potential matchup for all 68 tournament teams. This dataset, of course, differs from the training and test datasets in that it does not include a target (win/loss) column.

One row of the data, for example, might have both Oklahoma and Gonzaga, along with their respective team metrics and ratings. Given both teams’ statistics, the model will then forecast which team is most likely to win that game. In this situation, a machine learning model predicted a 78 percent chance that Gonzaga would win. Gonzaga won by 16 points in the real game, so not terrible.

One of the lessons learned is that you don’t have to get every upset correct to win your pool — a flawless bracket is unnecessary and practically impossible to achieve. What matters most is picking the right teams to advance far in the competition. Identifying those teams isn’t straightforward, but as we’ve shown, relying on data and machine learning may help you achieve your objective far more quickly.

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