SPORTS DATA ANALYTICS
NPFL Data Analytics
A six-season football analytics project examining the relationship between geography, population and club performance in the Nigerian Premier Football League, while also exploring league competitiveness, home advantage, player contribution, stadium environments and match-outcome prediction.
THE CENTRAL QUESTION
The analysis suggests that population size alone does not explain club performance. Instead, the strongest patterns emerge around sustained club performance, home advantage, competitive balance, player contribution and the broader sporting environment.
PROJECT OBJECTIVE
This project analyses six seasons of NPFL data from 2020/21 to 2025/26 to investigate the relationship between geography, population and football performance.
The analysis also evaluates league competitiveness, team and player performance, home advantage, stadium environments and the potential for match-outcome prediction.
The objective was not simply to describe what happened in the league, but to investigate the patterns behind performance and identify what the findings could imply for the wider NPFL ecosystem.
ANALYTICAL QUESTIONS
DATASET
The analysis covers six NPFL seasons from 2020/21 to 2025/26. The 2022/23 season is treated separately where appropriate because it was an abridged season.
DATA ARCHITECTURE
Source datasets obtained through a paid FootyStats subscription.
Data-quality assessment, cleaning and feature preparation.
Structured relational warehouse using staging, dimensions and facts.
Exploration, correlations, competitiveness, player analysis and modelling.
Interactive reporting, ranking logic and season-aware measures.
Insights, recommendations and strategic implications.
SQL SERVER
SQL Server was used to transform the source datasets into a structured analytical warehouse.
The warehouse follows a dimensional modelling approach with three main schemas:
Dimension tables include states, seasons, players, dates, stadiums and teams.
Fact tables include matches, player statistics and team statistics.
PYTHON ANALYSIS
Python served as the main statistical and analytical layer, covering team performance, player analysis, geographic relationships, competitiveness and predictive modelling.
Matches, wins, draws, losses, goals, goal difference, points, PPG, home PPG, away PPG and league position.
Goals, appearances, minutes, goals per 90, position, contribution and expected goals where available.
State population, geopolitical zones and the relationship between geography and performance.
PPG variation, coefficient of variation and the changing gap between stronger and weaker clubs.
Actual goals were compared with modelled expected goals to provide additional performance context.
A Random Forest classification model was used to investigate relationships between match features and match outcomes.
STATISTICAL TESTING
Population vs average club PPG
p = 0.5548Population vs total points
p = 0.7954These results suggest that larger population alone was not a reliable explanation for stronger club performance in the project sample.
POWER BI REPORT
The Power BI report translates the analytical work into an interactive reporting environment. Each page was designed around a specific analytical question rather than simply displaying as many metrics as possible.
High-level view of the NPFL dataset and key league performance indicators.
Examining league balance, performance distribution and changes across seasons.
Comparing club performance, rankings, consistency and sustained success.
Exploring geographic differences and the relationship between population and football performance.
Analysing goals, appearances, scoring efficiency and player contribution by position.
Exploring stadium usage, capacity, goals and stadium-associated home advantage.
KEY FINDINGS
The analysis did not find statistically significant evidence that clubs from more populated states consistently achieve higher performance.
Population may provide a larger potential talent and supporter base, but the analysis suggests that population by itself does not determine sporting success.
Club performance varied across Nigeria's geopolitical zones, with the strongest average club PPG observed in the southern zones.
Outside the abridged 2022/23 season, later seasons showed a more compressed competitive distribution.
Several clubs repeatedly appeared among the strongest performers across the six-season period.
Rivers United · Enyimba · Enugu Rangers · Plateau United · Remo Stars · Kano Pillars · Abia Warriors
Rivers United recorded the highest overall points total in the project's six-season club analysis.
Home teams averaged almost four times as many points per game as away teams.
In the 2025/26 player analysis, forwards accounted for almost two-thirds of recorded goal contribution.
Some clubs recorded substantially more goals than their modelled expected goals, while others recorded less.
The highest actual-goals-to-xG ratios included El-Kanemi Warriors, Kano Pillars, Katsina United, Enugu Rangers and Kun Khalifat.
Actual goals versus xG is treated as an efficiency indicator rather than definitive proof of finishing skill.
Stadium analysis examined match usage, capacity, average goals and home/away performance.
Among the most-used venues were Enyimba International Stadium, Godswill Akpabio International Stadium, Pantami Stadium, New Jos Stadium and Nnamdi Azikiwe Stadium.
The Random Forest model demonstrated useful predictive information in historical team-performance variables.
However, substantial class imbalance meant that the model strongly favoured home wins and performed poorly when identifying draws and away wins.
STRATEGIC IMPLICATIONS
Population alone does not appear to determine club performance. Investment should therefore focus on systems capable of converting Nigeria's large football talent pool into sustained sporting performance.
Improving pitch quality, stadium facilities, supporter experience, accessibility and broadcast infrastructure could strengthen both sporting and commercial outcomes.
A stronger NPFL data ecosystem could support performance analysis, recruitment, scouting, broadcast storytelling, fan engagement and commercial analytics.
Better broadcast coverage, digital content and consistent data collection can increase the league's visibility among supporters, sponsors, broadcasters, scouts and clubs.
A stronger domestic football ecosystem can connect sporting performance with fan engagement, digital reach, commercial partnerships and long-term investment.
DATA QUALITY & LIMITATIONS
The 2022/23 season contains fewer matches, making direct comparison of some season-level totals inappropriate without considering season structure.
Duplicate player records were identified during preparation and handled where appropriate.
Some match records contain missing or unknown stadium information.
Geographic analysis depends on the quality and consistency of state and club mapping.
Some advanced player statistics are incomplete across the full analysis period.
Expected-goals metrics depend on the underlying model and should be interpreted as modelled estimates.
Overall accuracy alone is not an adequate measure of model quality because the outcome classes were substantially imbalanced.
The project is primarily observational. Identified relationships should not automatically be interpreted as causal relationships.
TECHNICAL SKILLS
OVERALL CONCLUSION
The analysis produces a more nuanced picture of the NPFL than a simple population-versus-performance comparison.
Nigeria has a large population, deep football culture and a strong history of football achievement. However, the analysis does not show population size alone translating directly into club performance.
Instead, the strongest patterns are found in sustained club performance, home advantage, competitive balance, player contribution, geographic differences, stadium environments, data availability and sporting systems.
The reduction in PPG variation across the six-season period suggests that the league became more competitively balanced toward 2025/26, while repeated high performance among certain clubs shows that sustained success remains achievable for clubs capable of maintaining strong systems.
Nigeria has the football talent and culture; the data suggests population isn't the limiting factor — the bigger opportunity is building the sporting, infrastructure, data, broadcast and commercial systems that convert that potential into sustained domestic value.
DATA SOURCE & LICENSING
The football datasets used in this project were obtained through a paid FootyStats subscription.
The underlying source datasets are not included in the public repository. Original CSV files, cleaned versions that substantially reproduce the licensed source data, database backups and full database dumps are intentionally excluded.
The project instead focuses on demonstrating analytical methodology, SQL engineering, statistical analysis, data modelling, Power BI development, DAX calculations, visualisation, analytical storytelling, findings and recommendations.
This project is an independent data-analysis portfolio project. The analysis is intended for educational, analytical and portfolio purposes and does not represent an official publication by the Nigerian Premier Football League, any NPFL club, FootyStats or any other data provider.
Statistical relationships and model outputs should be interpreted within the limitations described in this project.