Football Manager includes what is effectively a parallel universe, so they modelled the effects of Brexit on the UK Premier League: ‘In my own current “save”, Brexit kicked in at the end of season three. Unfortunately I got one of the hard options, where all non-homegrown players are now going through a work permit system, albeit one that’s slightly relaxed. It means I can no longer bring in that 19-year-old Italian keeper I’d been eyeing up as one for the future. Instead I have to wait for him to break into the Italian squad, and play 30% of their fixtures over the next two years. Then he’ll be mine. Meanwhile, my TV revenue has just dropped by a few million. Let’s hope that doesn’t continue, or I won’t even be able to afford him.’
It was recently discovered that some surprising operations on Rust’s standard hash table types could go quadratic.Quite a nice unexpected accidental detour into O(n^2)
This is intriguing — using Jupyter notebooks to embody data analysis work, and ensure it’s reproducible, which brings better rigour similarly to how unit tests improve coding. I must try this.
Reproducibility makes data science at Stripe feel like working on GitHub, where anyone can obtain and extend others’ work. Instead of islands of analysis, we share our research in a central repository of knowledge. This makes it dramatically easier for anyone on our team to work with our data science research, encouraging independent exploration. We approach our analyses with the same rigor we apply to production code: our reports feel more like finished products, research is fleshed out and easy to understand, and there are clear programmatic steps from start to finish for every analysis.
neat — aggregation of histograms for Datadog statsd