How Predictive Analytics Impacts March Madness

With spring comes the season of the sporting spectacle March Madness. Held by National Collegiate Athletic Association (NCAA), this annual college basketball competition generates revenue of around $1 Billion each year. Indeed it’s a season of madness! Millions of fans gear up to fill the basketball tournament brackets. Usually, anyone can participate in the pool competition. However, pro bettors now leverage Machine Learning (ML) models and big data to improve their chances of winning.

Let’s dig deep into this new-age result evaluation approach during NCAA annual championship.

Significance of Predictive Analytics in the March Madness Bracket

Nowadays, the competition is beyond knowing the basketball game. Publicly available datasets and open-source Machine Learning tools revolutionized the NCAA tournament bracket predictions. An ML model takes the human bias out of the prediction game. It allows you to pick the correct teams based on various parameters.

If you’re wondering how to get started with march madness betting using data analytics, fret not! Using publicly available algorithm resources, competitors can plan more accurate moves while filling the brackets. For instance, Kaggle is a major player in “big data” March Madness prediction systems. It boasts twenty years of historical game-by-game basketball data, including statistics about teams, seasons, game results, and seeds.

Undoubtedly, expecting perfect brackets is nearly impossible. However, data analytics and ML can help make educated decisions about better picks. It shows the precise probability of the game’s result, such as team A upsetting team B by 55%. Thus, college basketball betting experts already benefit from predictive analytics before wagering on online sportsbooks.

Predictive Variables

Predicting the outcomes for all 63 games is a complex process. To pick the potential winner, you must comb through massive college basketball data and stats using predictive analytics. The dataset includes game-by-game simple box score statistics for the winning and the losing team.

Typically, the college basketball data covers variables from three categories. It includes team-, player-, and game-specific metrics. You can analyze a plethora of critical metrics before picking the match’s outcome, such as:

• attempted field goals
• field goals made
• rebound percentage
• free throws
• points per game
• turnovers
• tempo
• offensive-defensive rating
• personal fouls
• adjusted offensive efficiency
• assist to turnover ratio

These advanced variables play a significant role in boosting the odds for the March Madness pool competitors. However, evaluating a team’s performance odds isn’t merely choosing relevant variables. Weighing each variable relative to the others is also vital.

Dataset Testing and Building Machine Learning Model

You can also build your own 2023 March Madness Bracket ML model to predict the winners and losers. A low-code, open-source Machine Learning library, like PyCaret or Caret in R, is ideal for beginners.

Generally, you divide your data into test and training sets. Under the training set, you can build and evaluate the model’s performance on the test dataset.

After training your ML model, use a subset of the data to test it. You can experiment with feature sets and make minor variations at this stage to find the best-performing model.

Feeding a good amount of data enables the ML model to predict better future odds. The model learns how the data influence the game’s result and by how much. Its scoring review depends on the match’s outcome and confidence in its prediction.

So a winning team with 99% certainty gets more points than a prediction with only a 97% probability of winning. On the contrary, the confident model with an incorrect match result will lose more points. This ML prediction approach makes it harder to win the brackets with random chance.


1. What are the odds of predicting the March Madness bracket?

The odds of predicting winning outcomes for all 63 games is one in more than nine quintillions.

2. Has anyone ever predicted March Madness?

To date, nobody could successfully pick a perfect March Madness basketball bracket till the end of the tournament.

3. Has anyone had a perfect bracket?

Greg Nigl from Columbus, Ohio, is the first person to predict the perfect bracket. He set the NCAA March Madness bracket milestone by correctly predicting the winner of the first 49 games.

Final Thoughts

March Madness bracket is no more about hunch or casual bettors. Applying Machine Learning to predict the annual college basketball championship is the new normal. A hot streak doesn’t exist in this sports league, but you can still trust your luck and skills.

If you can analyze data like the back of your hand, the March Madness bracket ML model is your go-to betting tool. Beat the odds more often by combining your love for college basketball with statistical aptitude.

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