Made In Africa Sport Launches AI Supercomputer For 2026/27 NPFL Season

Made In Africa Sport (MIAS), a sports technology and media company, has launched an artificial intelligence-powered supercomputer designed to analyse and predict all 380 matches of the 2026/27 Nigeria Premier Football League (NPFL) season.

The NPFL season which began on Friday, August 28, with 20 teams set to compete across 38 matchdays. Ahead of the opening round, the MIAS Supercomputer analysed every fixture and generated predictions covering expected scores, goals and match outcomes.

According to a statement signed by the Co-founder and Team Lead of MIAS, Olamide Abe, the system is built on a database containing more than 8,000 historical NPFL matches, providing extensive historical data on the competition.

For its season projections, the supercomputer runs 100,000 Monte Carlo simulations for each of the 380 fixtures, generating 38 million simulations in total.

Each simulation uses calculated probabilities to generate possible match outcomes, allowing the system to assess different scenarios that could influence the final league standings.

The predictions will be updated after each matchday throughout the season to reflect new results and changes in the performance of the participating clubs.

Beyond predicting winners, draws and defeats, the system estimates the probability of each club finishing in every position on the final table, including their chances of winning the league, securing continental qualification or suffering relegation.

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The full 2026/27 NPFL predictions, including match outcomes, expected goals, team ratings and projected league table, are available on the MIAS Supercomputer.

Speaking on the launch, MIAS Executive Director, Enitan Obadina, said the product was developed to address what he described as a gap in the way the NPFL is analysed.

“For a league with as much history as the NPFL, we still do not use enough of the data we have,” Obadina said.

“Every week, there are conversations about which team is in form, who will win a match or who is likely to challenge for the title, but a lot of those conversations are based on opinion and eye-test.

“What we are trying to do is put a proper data layer behind those conversations. We have gone through thousands of historical matches and built a system that can turn that information into something people can actually use to understand the league.”

The model combines historical and recent performances with data on head-to-head records, home advantage, goalscoring and transfer activity.

For each fixture, the factors are assessed to determine the probability of a home victory, draw or away win.

Recent form carries the greatest weight and is based on a team’s last five matches, while away victories receive additional weighting because of the difficulty of winning on the road in the NPFL.

The system also considers previous meetings between teams and their respective home and away performances, taking into account the historically significant home advantage in Nigerian domestic football.

Goalscoring form is measured through the number of goals teams have scored and conceded in recent matches, while transfer activity is used to account for changes in squad quality that may not yet be reflected in previous results.

The final prediction displays the most probable outcome, alongside expected goals and other factors that contributed to the projection.

Obadina stressed that the system was not intended to replace football knowledge or human judgement.

“We are not saying the computer knows what will happen. Football does not work like that. What it can do is show the probability based on the evidence available to it, and that gives you a much better starting point than simply saying a team will win because it looks stronger on paper.

“The important thing for us is that people can see how a prediction was reached. If we say a team has a 60 per cent chance of winning, there is data behind that number and the user can see the factors that contributed to it.”

The MIAS Supercomputer also projects the number of goals each team is expected to score while measuring their attacking, defensive and transfer strength.

The ratings are calculated relative to the average NPFL team, with 1.00 representing the league average.

For instance, an attacking rating of 1.07 indicates that the model considers a team seven per cent better than the average NPFL side in attack. Defensive ratings are assessed differently, with a figure below 1.00 indicating a stronger defence because it represents fewer goals conceded.

Transfer strength is calculated separately based on players’ performances during the previous season, enabling the model to account for changes that historical match results alone may not capture.

Home advantage is another major component of the system, with MIAS developing the model specifically around the characteristics of Nigerian domestic football rather than applying a generic international football prediction system.

Historical data cited by the company shows that away teams have won only 601 of the 7,966 NPFL matches played since 2003, representing 7.5 per cent of all games.

MIAS said this approach ensures that its predictions reflect the realities of the NPFL rather than patterns derived from European or other international leagues.

After calculating individual match probabilities, the system simulates the entire NPFL season millions of times.

By repeating the process 38 million times, the supercomputer calculates how frequently each team finishes in every possible league position.

This allows it to provide clubs with projected finishing positions as well as their chances of winning the title, qualifying for continental competitions or being relegated.

MIAS said the supercomputer was built specifically for the NPFL using historical league data rather than a generic international football model.

The system also gives greater weight to recent seasons, recognising changes in clubs, coaches and playing squads. Where limited data exists for a particular team, its ratings are gradually adjusted towards the league average instead of allowing a small sample size to generate extreme predictions.

Obadina said the 2026/27 launch represents the beginning of a broader data-driven project around African football.

“This is the first major version of what we want to build. We want to keep improving it as more NPFL data becomes available, so that the model becomes more useful from one season to the next.

“The bigger ambition is to build a reliable data infrastructure around African football. We want to get to a point where journalists, clubs, analysts, supporters and other stakeholders can ask meaningful questions about our leagues and have credible data to work with,” he concluded.

Made In Africa Sport is a sports technology and media company that uses data analytics, artificial intelligence, machine learning and digital innovation to develop solutions for African sport.

The company also produces journalism and digital content focused on African athletes, competitions and sporting communities, with the long-term ambition of becoming a leading sports technology company on the continent.