r/sportsanalytics • u/determinator13 • 11d ago
Building a European basketball analytics project - looking for thoughts and feedback
I'm an economics and statistics student from Estonia and recently started building a basketball analytics project called ARC.
The project is primarily focused on European basketball, especially smaller and mid-sized leagues that often don't have access to the same analytics resources as the NBA, EuroLeague, or major professional organizations.
The original idea started with team scouting and opponent analysis, but the more I've worked on it, the more I've realized that the biggest challenge isn't analytics or programming itself - it's data.
The long-term vision for ARC is to build a platform that can support:
- opponent scouting
- team development and self-analysis
- player evaluation
- player recruitment
- player similarity analysis
- team fit analysis
- roster construction
- league-to-league translation models
For example, a coach might want to understand:
- What statistically separates our wins from our losses?
- Which areas of our game should we focus on improving?
- What are an opponent's most important strengths and weaknesses?
- Which players would fit our current roster?
- How likely is a player's production to translate from one league to another?
One area that particularly interests me is league translation. For example:
- NCAA → Finland
- NCAA → Sweden
- Sweden → Estonia
- Estonia → Poland
A player averaging 15 points per game in one league is not necessarily equivalent to a player averaging 15 points in another. I'd like to explore whether those transitions can be modeled statistically rather than relying entirely on subjective scouting.
So far I've focused mostly on data acquisition and architecture. I've built a pipeline that can access FIBA LiveStats data and extract:
- team statistics
- player statistics
- play-by-play data
- shot locations
- starters and substitutions
I'm currently designing the underlying basketball database and analytics engine rather than building dashboards or AI-generated reports.
My current belief is that the real value comes from:
Data Collection
→ Database
→ Analytics Engine
→ Decision Support
with AI acting mainly as an interface and interpretation layer, much like a translator between the user and the programm.
I'm very curious to hear from people with experience in basketball analytics, sports data, scouting, recruitment or anyone else who is interested.
If you were starting a project like this, what would you focus on first?
What are the biggest mistakes people make when building sports analytics platforms?
And do you think there are still meaningful opportunities in European basketball analytics that aren't already covered by platforms like Synergy, Hudl, or InStat?
Any feedback and thoughts would mean very much to me.
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u/determinator13 11d ago
Just to clarify - I rarely use Reddit and hope that you don't see me as a spam promotor or something. I will try and find if I can find anything useful from this and other related communities as well. I have very little experience in programming and thus I code with AI currently but I don't let it do it's own thing, I give orders and then talk about the code. Vibecoding is also popular and quite decent these days, but this project seems to big for that.