The Armchair Scout: Building a Player Similarity Engine App
Build a player similarity engine from raw stats: assemble feature vectors, pick distance metrics that make sense for sport, and turn the scores into a scouting tool you can query.
- Free · live
- Monday September 28
- 09:00-10:15 EDT · 15:00-16:15 CEST
- 75 minutes
- Intermediate
- Live seat, no card
Three people this session was built for.
You love the sport, and you want the work to be rigorous
Sports analytics done seriously is data science with a scoreboard. You leave having watched a real sports question worked with professional tools.
You do adjacent work and want to see how the sports version runs
The methods transfer in both directions. You see where sports data is different and where your existing skills already apply.
You are deciding whether this field is worth your learning time
One free session, one real question, taught live by someone who does the work. You will know by the end of the hour.
Who Else Plays Like This?
Player comparison is one of the oldest questions in scouting and one of the easiest to do badly. This session builds a similarity engine from raw stats: assembling feature vectors that represent a player honestly, choosing distance metrics that behave sensibly for sport rather than defaulting to Euclidean, and wrapping the result in something you can actually query.
You will watch this get built
- A working player-similarity engine you can query for comparable players
You will leave knowing
- How player-similarity engines actually work
- Building feature vectors from player stats in Python
- Distance and similarity metrics that make sense for sport
- Turning similarity scores into a usable scouting tool
Taught live by the person who does the work.
The people on the other side of the table.
I've always been intrigued by the intersection of sports analytics and data science. AthlyticZ provides students with the perfect platform to explore this passion. The hands-on projects are particularly empowering, allowing them to apply theoretical concepts to real-world scenarios.
AthlyticZ has completely transformed the learning approach to data science through the use of sports-based problems. The instructors are the best of the best, and the practical projects have immediate impact to students.
The sports industry moves fast. Managing the pace of evolving technology, data, and AI isn't optional - it's imperative. Our Analytics team was looking for a partner to meet us where we are, to help us in the continued investment of our people and capabilities without pulling us away from the work that matters. AthlyticZ delivers exactly that: a structured, practitioner-led platform that fits how modern sports analytics groups actually operate.
We've teamed up with AthlyticZ to supercharge how we use data science and statistics to impact our sports. More clarity in the numbers means bigger results for our athletes.
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- 1,000+ lessons of production-grade curriculum
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One free hour. Here is the whole deal.
- Date · Monday September 28
- Time · 09:00-10:15 EDT · 15:00-16:15 CEST
- Length · 75 minutes
- Cost · Free
- Format · Live, 75 minutes, questions in the room
Save your seat.
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Questions people actually ask.
Is it actually free?
Yes. Attending live is free, and there is no card involved. You register so we can send you the join link.
What do I need?
A browser and the join link we send you. There is nothing to install to attend.
What if I cannot make it live?
Register anyway. Afterwards we send you our write-up of what got built, and the invitation to the next session. Attending live is the free part; everything that outlives the hour belongs to the Masterclass.
What level is this?
The session page states the level under the title. Sessions are taught as live builds, so watching the decisions get made is useful one level up and one level down from the stated one.
What happens after I register?
You get a confirmation with a calendar link now, the join link before the session, and our write-up of what got built afterwards. That is the whole of it.