I’m interested in developing an AI tool that helps in selecting a fantasy football team. My experience with AI techniques is quite basic, especially regarding game theory, so I would appreciate any suggestions on methods I can use and some resources for further reading.
I realize this task might be quite challenging for AI to accomplish accurately, but my main goal is to learn about AI, and I think this is a fun way to do it.
Here are some important rules for the game:
A total of 14 players must be selected.
There is a cap on the total spending for player selections.
The chosen players must meet specific requirements (at least one goalkeeper, two defenders, one midfielder, and one forward must be included).
The team can be changed weekly, but making more than one change per week will incur penalties.
Additionally, I possess statistics from last season’s matches. Can this data be utilized to train the AI system? What AI methodologies would you suggest for my project?
Fantasy football with budget constraints? Perfect for automation. Skip wrestling with multiple ML models and optimization libraries - build it as one automated workflow.
I’ve built systems that pull player stats, process performance data, and spit out optimal lineups automatically. The trick is connecting your data sources → prediction models → selection logic without any manual steps.
Your historical match data’s perfect for training. Set up automated pipelines that clean stats, handle feature engineering (form, fixtures, injuries), and feed your prediction models. Then build constraint optimization for budget caps, position requirements, and transfer penalties.
Weekly transfers are where automation really pays off. Build workflows that test thousands of transfer combos, simulate future scenarios, and execute the best moves automatically. No more manual calculations or missed deadlines.
For AI, ensemble different algorithms through automated workflows. One model handles attacking returns, another does defensive stats, then combine their outputs. The system retrains weekly with fresh data automatically.
I’ve watched people spend months coding this from scratch when automation platforms already handle orchestration, data connections, and scheduling. You just focus on the fun AI logic instead of infrastructure headaches.
sounds like a great project! i suggest using ML algorithms like decision trees or maybe even some basics of deep learning if you’re up for it. they can really help with player selection based on past stats. and yeah, don’t forget to look into reinforcement learning for that weekly lineup strategy!
Been down this exact rabbit hole when I automated my fantasy team a few years back. Biggest mistake? Overthinking the AI part way too early.
Start simple - use Monte Carlo simulation. Model player point distributions from historical data instead of trying to predict exact scores. Way more realistic and handles football’s randomness better.
For optimization, treat it like a knapsack problem with multiple constraints. Budget cap and position requirements are easy constraints, but transfer penalties need a rolling horizon approach.
What really helped was adding risk management. Don’t just pick highest expected points - factor in variance. High ceiling players with big floors often beat consistent performers over a season.
One thing nobody mentioned - fixture difficulty is huge. I scrape betting odds as a proxy for match difficulty instead of building my own strength ratings. Bookmakers do that math better than we can.
Weekly change logic should look ahead 3-4 weeks minimum. Sometimes taking a small point hit this week sets you up for much better options later.
My current system uses two stages: reinforcement learning for transfer strategy, basic optimization for weekly selection given current squad. Works way better than trying to solve everything at once.
Your historical data’s definitely valuable here. I’d tackle this as two separate problems: build player performance models with your season stats, then use constraint optimization for team selection. Random Forest or XGBoost work great for predictions - they handle different player metrics without much preprocessing hassle. For optimization, try dynamic programming since you need to factor in future transfer costs, not just this week’s best picks. That transfer penalty system adds a time element that weekly optimization completely misses. One thing I’ve learned building these systems - player ownership percentages are crucial data points. When high-owned players bomb, it actually helps your ranking. Start with simple position-based models before diving into opponent matchup data.
This screams optimization problem - linear programming or genetic algorithms would work great. With budget limits and position requirements, I’d start with linear programming since it handles constraints really well. For the historical data, regression models are solid for predicting performance from past stats. Don’t just use raw season numbers though - recent form, opponent strength, and injury history matter way more. The weekly transfers are where it gets fun. You could build a multi-objective system that weighs this week’s points against keeping your options open later. I’ve found ensemble methods beat single algorithms every time - they’re just more reliable. Start with scikit-learn in Python and build from there.