Python Developer for Football Match Prediction
Budget: $10 – $30 USD
Description:
I am looking for a skilled Python developer with experience in machine learning and sports analytics to develop a football match prediction system. The system should train machine learning models using historical and current season data from CSV files and then predict match outcomes for upcoming fixtures using multiple statistical and machine learning methods.
Requirements:
✅ Train models using historical and current season data from CSV files
✅ Predict match outcomes using the following methods:
Poisson Distribution
Bivariate Poisson Model
Monte Carlo Simulation
XGBoost Regression
✅ Load historical match results & current season data from CSV files
✅ Generate predictions for upcoming fixtures from a separate CSV file
✅ Optimize the process by training each league only once and storing the trained models
Skills Required:
✔ Python
✔ Machine Learning (XGBoost, Scikit-learn)
✔ Data Processing (Pandas, NumPy)
✔ Sports Analytics
✔ Probability & Statistical Modeling (Poisson, Monte Carlo)
Deliverables:
? A Python script that reads historical match data & current season data from CSV files
? A fixture prediction script that generates match outcome probabilities
? Results saved in a structured CSV or Excel file
? Clean, efficient, and well-documented code
? Bonus: If you have experience working with football analytics, ELO ratings, or betting models, that's a plus!
? Budget: Flexible based on experience
⏳ Deadline: ASAP
If you have experience with football data modeling and machine learning, apply now! ⚽?
I am looking for a skilled Python developer with experience in machine learning and sports analytics to develop a football match prediction system. The system should train machine learning models using historical and current season data from CSV files and then predict match outcomes for upcoming fixtures using multiple statistical and machine learning methods.
Requirements:
✅ Train models using historical and current season data from CSV files
✅ Predict match outcomes using the following methods:
Poisson Distribution
Bivariate Poisson Model
Monte Carlo Simulation
XGBoost Regression
✅ Load historical match results & current season data from CSV files
✅ Generate predictions for upcoming fixtures from a separate CSV file
✅ Optimize the process by training each league only once and storing the trained models
Skills Required:
✔ Python
✔ Machine Learning (XGBoost, Scikit-learn)
✔ Data Processing (Pandas, NumPy)
✔ Sports Analytics
✔ Probability & Statistical Modeling (Poisson, Monte Carlo)
Deliverables:
? A Python script that reads historical match data & current season data from CSV files
? A fixture prediction script that generates match outcome probabilities
? Results saved in a structured CSV or Excel file
? Clean, efficient, and well-documented code
? Bonus: If you have experience working with football analytics, ELO ratings, or betting models, that's a plus!
? Budget: Flexible based on experience
⏳ Deadline: ASAP
If you have experience with football data modeling and machine learning, apply now! ⚽?