Quantitative Football Analyst
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Job summary
Moonlizard is a technology-driven sports analytics company developing advanced systems for football performance, probabilities and betting markets. We are looking for an exceptional Quantitative Football Analyst who combines strong football knowledge with statistics, probability and data-driven decision-making.
Min Qualification:
Degree
Experience Level:
Entry level
Experience Length:
2 years
Language Requirement:
English
Working Hours:
Full Time - 8 to 5
Applicant Location:
Lagos, Nigeria
Job descriptions & requirements
Responsibilities:
- Analyse football matches using statistical and quantitative methods.
- Evaluate Asian Handicap and Over/Under markets.
- Analyse market odds and implied probabilities.
- Evaluate expected value and potential market inefficiencies.
- Analyse xG, xGA, shots, player performance and advanced team statistics.
- Evaluate injuries, suspensions, expected lineups and player availability.
- Monitor and interpret betting-market movements.
- Review signals produced by internal analytical systems.
- Approve, reject or flag opportunities for further investigation based on evidence.
- Conduct historical back testing and performance analysis.
- Track model performance and Closing Line Value (CLV).
- Identify potential weaknesses or changes in football-market behavior.
- Prepare concise analytical reports explaining recommendations.
- Work closely with the technology and trading/risk teams.
Requirements:
Candidates should have strong knowledge of:
- Football analytics
- Probability and statistics
- Asian Handicap markets
- Over/Under markets
- Decimal odds and implied probability
- Expected value (EV)
- Closing Line Value (CLV)
- Expected Goals (xG)
- Player and team performance metrics
- Statistical variance and sample size
- Football betting-market behavior
Technical skills:
Strong Excel/data-analysis skills are required. Experience with any of the following will be an advantage:
- Python
- SQL
- Statistical modelling
- Machine learning
- Data visualization
- Football data APIs
- Quantitative sports modelling
The ideal candidate is:
- Highly analytical
- Curious and evidence-driven
- Comfortable challenging assumptions
- Disciplined under winning and losing periods
- Able to separate results from decision quality
- Detail-oriented
- Comfortable working with large datasets
- Able to explain complicated analysis simply
- Honest and trustworthy
- Capable of maintaining strict confidentiality
We are NOT looking for:
- Football tipsters
- Social-media prediction sellers
- Candidates whose main evidence is winning betting slips
- People claiming unrealistic guaranteed win rates
- People who rely mainly on intuition without statistical evidence
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