The Injury Report Problem: Missing Data and Multiple Imputation
Injury reports, DNPs and partial seasons leave holes in every sports dataset. Work a complete multiple imputation pipeline: name the missingness mechanism, impute with mice, and pool results the way the method actually requires.
- Free · live
- Monday September 21
- 13:30-14:45 EDT · 19:30-20:45 CEST
- 75 minutes
- Intermediate
- Live seat, no card
Three people this session was built for.
You know the stats, but sports data keeps embarrassing the textbook version
Real sports data is messy, correlated, and small where it matters. You leave having watched a working model built on it, decisions explained as they are made.
You can fit a model, but you cannot always defend one
The gap between fitting and defending is what a live room closes: you see which choices matter, which are taste, and how a practitioner argues for theirs.
You are deciding whether this field is worth your learning time
One free session, one real model, taught live by someone who builds them. You will know by the end of the hour whether this is your lane.
Why Dropping Rows Quietly Changes Your Answer
Every sports dataset has holes in it. Players miss games, tracking systems drop frames, and seasons end early. The common response is to drop the incomplete rows, which is convenient and, in most cases, quietly wrong. This session names the mechanism behind the missingness first, because the mechanism determines whether any fix is valid, then runs a full multiple imputation workflow with mice and pools the results the way the method actually requires.
You will watch this get built
- A complete multiple-imputation workflow on a dataset with missing values
You will leave knowing
- Why dropping missing rows quietly biases your results
- Mechanisms of missingness: MCAR, MAR, and MNAR
- Multiple imputation with the mice package
- Pooling results correctly across imputations
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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One free hour. Here is the whole deal.
- Date · Monday September 21
- Time · 13:30-14:45 EDT · 19:30-20:45 CEST
- Length · 75 minutes
- Cost · Free
- Format · Live, 75 minutes, questions in the room
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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?
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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.