sports prediction

How AI in Sports Prediction Works

VWB Blog 2 years ago 7

Sports betting. It is an industry that continues to get bigger and bigger every year.

In 2021, the sports betting market was worth around $76 billion. By the end of the decade, it is projected to be worth around $167 billion.

With more people betting on sports, these people are trying to find a way to get an edge on sportsbooks. Some resort to developing formulas and systems to pick games.

However, some professional handicappers are starting to use artificial intelligence (AI) to make sports predictions. They are running data through AI technology and are using that as a source to make their bets.

So, what does this AI technology do? How can it predict the results of sports games?

This is your guide.

What Is Artificial Intelligence?

Before we can talk about using AI technology to make sports predictions, you need to understand what AI technology is. Artificial intelligence is intelligence that is gathered by a machine rather than a human.

Essentially, it is the ability for a machine to learn and adapt its approach on its own. To do this, a machine processes the information that it is given, and then it goes from there.

That is where making sports predictions comes in.

Using Analytics

When it comes to AI technology, one of the biggest factors in sports tends to be the data it has access to. This data can be anything from rosters to offensive trends, defensive trends, injuries, what patterns players and teams develop in games, statistics, and more.

A perfect example is baseball. This sport arguably uses analytics more than any other sport. Part of the reason why is because the sport is more statistically driven than the other sports.

In baseball, it has gotten to the point where teams use analytics to have defensive shifts on individual players. That means they position themselves in the field differently for each hitter that comes to the plate.

How does that get determined? AI technology can measure the success of a hitter and where they like to hit the ball.

For example, say Mike Trout has a tendency to hit the ball between the shortstop and the third baseman. Then, the infield would shift more to the left side of the field to try and limit his chances of success.

The same can be applied for pitchers that are trying to adapt to hitters. AI technology can allow them to figure out what pitches hitters are less likely to hit and where in the strike zone hitters have less success.

Let’s take a look at a player like Aaron Judge. He may be the type of hitter that can crush a fastball and do better with pitches on the inside of the strike zone.

So, what can a pitcher do with this analysis? Throw a slider low and to the outside part of the strike zone.

AI can have an impact on the entire game of baseball, from how a pitcher pitches to a hitter to how a field shifts to a hitter.

Adapting to Injuries

AI technology can assess what a player’s true impact on a game is and what could potentially change if that player was taken out of a game. The reason is that they have access to that player’s statistics, team trends while they play, and sometimes, access to statistics about their replacement’s performance.

For example, let’s go back to last year when Aaron Rodgers tested positive for COVID-19 and missed a big game for the Green Bay Packers. They were going up against the Kansas City Chiefs on the road, with their backup quarterback being Jordan Love.

How does this impact an AI’s prediction for this game? Well, the information that they have now is that Jordan Love would be playing as quarterback instead of Aaron Rodgers.

That means they can see what plays the Packers tend to run when both are playing, what percentage of passes they complete, how many quarterback pressures they can escape, what their turnover rates are, how accurate their passes are, and more.

For a position like quarterback, it is impactful enough that the prediction for that game can significantly change. In this example, lines shifted drastically once the news about Rodgers got out.

Here, the Packers went from underdogs by about two and a half points to underdogs by seven and a half points.

This was not just because of the perception that Rodgers’ loss would be massive. It statistically gave the Packers much less of a chance to win the game because Rodgers is more accurate, turns the ball over less, and the Packers are more daring with Rodgers when it comes to passing the football.

Trends and Statistics

Another thing that AI technology can use to predict the outcomes of games are what trends a team tends to have and what statistics they have in certain matchup environments.

Let’s use the Tampa Bay Bucs and the New Orleans Saints as an example. Ever since Tom Brady has gotten to the Bucs, the team has struggled in the regular season against the Saints.

AI technology can use this information to make its prediction on that game. It can take past matchups with the Saints into consideration, along with what players on each roster are still on the team for this matchup.

Another example can be trends that teams have playing in bad weather. In Week 1 of the 2022 NFL season, the San Francisco 49ers and the Chicago Bears played a game in the pouring rain.

The weather forecast did predict rain for the entire game. What AI technology can do here is take weather into consideration and see how these teams have fared in past games in the rain. Then, it can make a new prediction for the game based on that information.

What Predictions Does it Make?

If you are new to the sports betting world, you may be curious about what a machine can predict when it comes to sports betting. The truth is, it can predict almost every type of bet out there.

See an example of the main bets that AI technology predicts here: 

You will notice that the AI prediction does not match the lines by the sportsbook. That is because sportsbooks tend to like to place lines where they feel like they will get around 50% of the money on each side of the possible outcome.

Here are some of the main types of bets that AI tries to predict:

Points Spread

The first thing you are likely to look at is the points spread. AI predicts an exact score for the game, so using that scoring prediction can make you see if AI thinks a team is going to cover or not.

So, if the Kansas City Chiefs are favorites by four points against the Los Angeles Chargers, you can look at how many points AI has them winning by. In this case, it was 11 points, so AI thinks that the Chiefs will cover the spread.


Since AI tends to predict an exact score for every game, you can use this as a baseline to discover what a good moneyline bet might be for you. If you treat AI as gospel, you can see which team with the highest moneyline that AI believes will win.

An example here can be the Dallas Cowboys. They lost their quarterback Dak Prescott to an injury after Week 1 and became big underdogs after that news broke for their next game.

Yet, AI predicted them to upset the Cincinnati Bengals. Since the Cowboys have a +265 moneyline, that would be the best moneyline bet for you, according to AI.


Finally, you have the over/under point total. Since this is essentially predicting if you think the teams combined will score more or less than the line, you can just add up the score predictions from AI.

In the Cowboys’ game, AI predicted the teams to combine for 49 points. Since the over/under line was 41.5 points, you would go over there.

Use AI Technology to Make a Sports Prediction

These are some of the ways that AI technology can be useful when it comes to making a sports prediction. These machines are smart, and if it has access to the right information, it can adapt and make a prediction for the game based on the new information.

So, if you want to take trends, injuries, and statistics into consideration when it comes to betting, AI is the way to go.

For more relevant information, see our Gaming section.

Written By