This is a short piece on a project I’m developing at NHS England, using Python to analyse patients’ inpatient care and determining where a Clinical Commission Group’s (CCG) patients are being treated significantly differently to those patients in its peer group CCGs (similar 10 CCGs as determined by NHS Rightcare Methodologies).
For the past 2 weeks I’ve been working on developing a game in my spare time: it’s tentatively titled ‘Adventurer Guild Simulator’, as it’s about running an adventurer guild in a fantasy setting (think Dungeons and Dragons). In the game you’ll be hiring adventurers to go on quests to raise the reputation of the guild and to thwart the plans of villains within the area. Adventurers will be fighting monsters, solving the secrets of locations, gaining experience and collecting riches on your behalf. The game is being developed in Unity, which I can highly recommend to any budding game developers!
In this final Splatfest Analysis, we will be taking a closer look at the distributions of the 9 games I had with the Splattershot to determine whether its consistent place in the top 3 was due to me consistently playing well with the weapon, or whether the averages were skewed by particularly high leverage outliers.
After watching a long session of ‘VSauce’ videos (great brain food videos, albeit very addictive!), I came across this video discussing ‘Zipf’s Law’. Zipf’s law states that in any corpus of natural language, the frequency of any word is inversely proportional to its rank in the frequency table. This Zipfian distribution applies to many different types of data studied across a variety of fields (the video discusses a large variety of these instances). Zipfian distributions also follow the ‘Pareto Principle’, the 80-20 rule. 80% of the words used in any corpus are only 20% of the unique words used.
After being utterly captivated by this phenomenon, I decided it would be fun to check whether my blog, being a corpus of natural language, followed Zipf’s law.
Up next in this multi-part analysis is a look at my performance with each weapon I used during the Splatfest. I’ll take a closer look at my Win to Loss ratio by each weapon and how well I did overall with them regardless of outcome of the game.
Splatoon 2 came out on the Nintendo Switch a couple weeks back, and they’ve had their first ‘Splatfest’ since the game was released. ‘Splatfests’ are worldwide events that pit two similar things against each other in an opinion poll (for this Splatfest it was Mayo vs Ketchup). After voting for your favourite, you’ll then be playing the game mode ‘Turf War’ against members of the opposite team, to determine which side is the best at Splatoon. I picked Ketchup, and I battled valiantly to prove that Mayo is bad and everyone who likes it deserves to lose at Splatoon.
During this Splatfest, I used Nintendo’s phone app to collect the data of my Splatfest matches in order to analyse my performance over the 24-hour event. I’ll be using this data for a multi-part analysis, covering a variety of sections like which weapons was I best with, what stages I was better at and more!
Spurred on by conversations with my brother and various Reddit posts making claims about immigrant populations in the UK, I decided to check the ONS website to find some of their recent analyses on the UK population (which are fantastic, by the way)1.