“Researchers Successfully Predict NFL Professional Football Scores” on the Pure AI Web Site

I contributed to an article titled “Researchers Successfully Predict NFL Professional Football Scores” on the April 2023 edition of the Pure AI web site. See https://pureai.com/articles/2023/04/03/zoltar.aspx.

Zoltar is my prediction system for American NFL football. For the 2022-23 NFL season, Zoltar achieved 65% accuracy against the Las Vegas point spread.

Zoltar is the result of an informal collaboration among myself and two of my machine learning colleagues. We work at three different large tech companies. Zoltar is strictly a research project and isn’t used for actual wagering.

The Zoltar program uses a combination of reinforcement learning and deep neural network technology to predict football results. The phrase “predicting football results” has several different meanings. Zoltar predicts against the point spread, which is best explained by an example.

Suppose you are interested in an upcoming game between the Chicago Bears and the Detroit Lions. Several “sports books,” mostly based in Las Vegas, publish an opening point spread such as “Lions -6.0 at Bears.” This means the Lions are favored by six points and are playing at the Bears home field. If a person wagers money on the favored Lions, the wager will win only if the Lions win by more than six points. If the Lions win but by less than six points, or if the underdog Bears win by any score, the wager on the Lions loses.

The Zoltar program computes the expected margin of victory between two teams. Zoltar then suggests a hypothetical wager when the difference between the computed margin of victory and the point spread margin of victory is greater than 4.0 points.

I provided some quotes. “Probability can trace its roots to mathematician Gerolamo Cardano who wrote a book titled ‘Games of Chance’ in the mid-1500s.”

“The Zoltar program was created to explore machine learning algorithms applied to problems where mathematics interacts with human psychology. For example, some people will bet with their hearts rather than with their heads, by wagering on the team located in the city where they live. If enough people do this, it can create mathematical imbalances between the point spread and a mathematically predicted margin of victory.”

I played Electric Football when I was a young man. The game was invented in 1948 by Tudor Metal Products Corporation and it’s still popular today. It was basically impossible to predict how the players would move on the vibrating playing field, but that was part of the fun. Left: The basic set is quite plain. Right: Some people paint their players and use a lot of detail.

This entry was posted in Machine Learning. Bookmark the permalink.

Leave a Reply

Please log in using one of these methods to post your comment:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s