Enitan Obadina Reveals How Made In Africa Sport Will Address NPFL Data Deficiency
By Saheed Afolabi
The Executive Director (ED) of Made In Africa Sport (MIAS), Enitan Obadina has disclosed how the tech platform will solve the data deficiency in the Nigeria Premier Football League (NPFL).
MIAS is a technology and media company that has launched a new supercomputer model npfl.madeinafricasport.com designed to analyze and predict all 380 matches in the 2026/27 Nigeria Premier Football League (NPFL) season.
The NPFL season begins on 28 August, with 20 teams set to compete across 38 matchdays.
Ahead of the opening round, the MIAS model has analysed every fixture and produced predictions covering expected scores, goals and match outcomes.
The system is built around more than 8,000 historical NPFL matches in the MIAS database, giving it access to one of the most extensive collections of historical league data available on the competition.
To generate its season projections, the supercomputer runs 100,000 Monte Carlo simulations for each of the 380 fixtures, producing 38 million simulations. Each simulation uses the model’s calculated probabilities to generate possible match outcomes, allowing the system to assess the range of scenarios that could shape the final league table.
The predictions will be updated as the season progresses, each matchday, allowing the model to respond to new results and changes in clubs’ performance.
Rather than simply predicting which team will win, the model also estimates how likely each club is to finish in every position on the final table, including its chances of winning the title, qualifying for continental competition or being relegated.
The full NPFL 2026/27 predictions, including match outcomes, expected goals, team ratings and the projected league table, are available on the MIAS Supercomputer.
Explore the MIAS Supercomputer here: npfl.madeinafricasport.com
Speaking further, Obadina, revealed the product was created to address a gap in how the NPFL is analysed and discussed.
“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.”
How does the MIAS supercomputer work?
The model combines historical and recent performance data with information on head-to-head records, venue strength, goalscoring and transfer activity.
For each fixture, these factors are assessed to produce the likelihood of a home win, draw or away win.
Recent form is the most important factor and looks at a team’s last five matches. Away victories are given additional weight because winning away from home is particularly difficult in the NPFL.
The model also considers previous meetings between the two teams and how they have performed at home and away. This helps account for the strong home advantage that has historically been a feature of the NPFL.
Goalscoring form is assessed using the number of goals a team has scored and conceded in its recent matches, while transfer activity allows the model to account for changes in squad quality that may not yet be reflected in previous results.
The final prediction shows which outcome has the highest probability, alongside the expected goals and other factors that contributed to the result.
For example, the model could produce a prediction of 58 per cent for a home win, 24 per cent for a draw and 18 per cent for an away win.
Obadina said the intention was not to present the model as a replacement for football knowledge or judgement, but as another tool for understanding the competition.
“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.”
Beyond win, Draw Or Loss
The MIAS Supercomputer system also estimates the number of goals each team is expected to score, alongside its attacking, defensive and transfer strength.
The ratings are relative to the average NPFL team, with 1.00 representing the league average.
An attacking rating of 1.07, for example, means the model considers a team to be seven per cent better than the average team in that area. Defensive ratings work differently: a figure below 1.00 indicates a stronger defence because it represents fewer goals conceded.
Transfer strength is scored on a separate scale, based on a player’s previous season performance, allowing the model to account for changes that historical match data cannot capture, particularly as NPFL clubs always overhaul their squads during the transfer window.
Why Home Advantage Matters
One of the model’s defining characteristics is how it treats home advantage.
MIAS has built the system specifically around the characteristics of the NPFL rather than applying a generic football prediction model.
Historical data shows just how significant home advantage is in the league. Since 2003, away teams have won only 601 of the 7,966 NPFL matches played, meaning just 7.5 per cent of all games have ended in an away victory.
The approach is intended to ensure that the predictions reflect the realities of Nigerian domestic football rather than patterns from European or other international leagues.
How the season table is projected
After calculating individual match probabilities, the system simulates the entire NPFL season millions of times.
By repeating the process 38 million times, MIAS Super Computer can calculate how frequently each team finishes in every league position.
This means a team can be given not only a predicted finishing position but also an indication of its chances of winning the title, finishing in continental places or being relegated.
A Model Built For The NPFL
The MIAS Supercomputer has been designed specifically for the Nigerian league and is based on historical NPFL data rather than a generic international football model.
The model also gives greater weight to recent seasons, recognising that teams, coaches and playing squads change over time. Where there is limited data on a team, its ratings are gradually pulled towards the league average rather than allowing a small sample of results to produce an extreme prediction.
Obadina said the 2026/27 launch was intended to be the starting point for a broader data product rather than a one-season project.
“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.
About Made In Africa Sport
Made In Africa Sport (MIAS) is a sports technology and media company using data analytics, artificial intelligence, machine learning and digital innovation to build solutions for African sport.
Alongside its technology products, MIAS uses journalism and content creation to tell the stories of African athletes, competitions and sporting communities across the continent.
Its long-term ambition is to become the continent’s leading sports technology company, creating products that can be used by clubs, athletes, media organisations, federations and fans while helping African sport become more exposed and commercially valuable.
