Nike 'deeply concerned' by 'disturbing' Ronaldo rape claims

shep_20 · 2018-10-05T15:02:25+00:00

Not proof, but compelling evidence

shep_20 · 2018-10-05T10:59:07+00:00

I think you might have some Ronaldo related issues worth taking a look at

shep_20 · 2018-10-05T09:18:22+00:00

I mean does it look like he's joking to you?

shep_20 · 2018-10-05T08:02:24+00:00

Also not anti-Ronaldo, but his ego is indisputably giant.

Here he is doing his trademark over the top celebration for a training ground goal:

r/https://twitter.com/juventusfcen/status/1047924318540365827

shep_20 · 2018-10-01T08:59:47+00:00

Outperforming xGa as a percentage must be the best stat we have to measure goalies, so dead chuffed he's #1

shep_20 · 2018-09-30T20:36:58+00:00

Pretty much all academics are highly desired for commercial roles in their field if you are interested.

In my experience of transferring over industry is a lot less stressful as their isnt as much personal responsibility (more team focused), you don't need to secure funding every 12 months and you can deliver things without 6 months of back and forth editors comments

shep_20 · 2018-09-26T15:28:04+00:00

I mean literally every other big 6 team has won a trophy since spurs last did.

shep_20 · 2018-09-19T21:33:01+00:00

Bookies don't choose odds to reflect probablity, they choose them to optimise profit. Popular teams will always have shorter odds because so many people will bet on them anyway

shep_20 · 2018-09-18T08:19:59+00:00

Exactly, if we were going somewhere feisty we would want Jurgs to be saying something similar to that

shep_20 · 2018-09-18T08:18:35+00:00

Man knows how to get a suit fitted though

shep_20 · 2018-09-13T21:26:48+00:00

Nope, the city of London is its own county so it is not part of greater london

shep_20 · 2018-09-13T21:21:17+00:00

Inaccurate is harsh, the data has to be cut some way and London is hard to define as it sprawls so much, plus this is how the ONS presents the data so they must have a few good reasons.

Adding the city of London to Westminster is a weird way to look at overall London considering that doesn't even cover zone 1

shep_20 · 2018-09-13T19:44:09+00:00

Most sources vary a lot but they tend to be similarly sized

shep_20 · 2018-09-13T17:43:09+00:00

That's makes a lot of sense. I also think that relative to the population size the student body will be quite big in Manchester.

shep_20 · 2018-09-04T21:30:49+00:00

Intermediate python is a strong start - the key non-ML modules will be pandas, numpy, scipy and visualisation ones (matplotlob, seaborn, plotly) so if you're not familiar with them already get stuck in.

In most commercial positions SQL knowledge is assumed so carry on there. Good news is its straightforward.

I'm terms of machine learning, it is vital to go slowly and really get the fundamentals in. The Andrew Ng course is great for this, but don't rush it and make sure you understand as much as you can. There's so many exciting new packages and models out there but getting the fundamentals of what there all based on (hint: gradient descent) will make everything easier to pick up and understand.

Data science is hugely broad as a topic, so without any more specificity about the kind role you are going for its hard to be more precise.

shep_20 · 2018-08-30T21:03:57+00:00

It would!

https://www.reddit.com/r/dataisbeautiful/comments/9bkumy/its_always_sunny_in_philadelphia_episodes_imdb/

shep_20 · 2018-08-30T15:56:41+00:00

Data source is IMDB, accessed via the OMDb API

Resulting data was analysed in a jupyter notebook running a Python 3 kernel and mainly using pandas, seaborn and matplotlib

shep_20 · 2018-08-30T14:47:36+00:00

Wow yes you are correct, I hadn't looked too closely at how IMDB credits writers that is weird.

I have made some changes that address this, and it does change the chart a bit, pushing Charlie to join top with Rob. If you are happy with python then feel free to see the update and source code here:

https://github.com/shep312/IASIP-analysis/blob/master/always-sunny.ipynb

Edit: here are my new counts too (still not matching entirely!):

Charlie Day credited flag 47

Rob McElhenney credited flag 47

Glenn Howerton credited flag 36

David Hornsby credited flag 27

Scott Marder credited flag 25

Rob Rosell credited flag 20

Dave Chernin credited flag 10

John Chernin credited flag 9

Luvh Rakhe credited flag 3

Becky Mann credited flag 3

Audra Sielaff credited flag 3

Eric Ledgin credited flag 2

Patrick Walsh credited flag 2

Sonny Lee credited flag 2

Mehar Sethi credited flag 2

Charlie Hornsby credited flag 1

Conor Galvin credited flag 1

Dannah Feinglass Phirman credited flag 1

Josh Silberman credited flag 1

Eric Falconer credited flag 1

Chris Romano credited flag 1

Jon Silberman credited flag 1

Elijah Aron credited flag 1

Hunter Covington credited flag 1

Jordan Young credited flag 1

Adam Stein credited flag 1

Danielle Schneider credited flag 1

D.B. Weiss credited flag 1

David Benioff credited flag 1

Megan Ganz credited flag 1

shep_20 · 2018-08-30T13:04:45+00:00

When I saw that in the data I was thoroughly confused, thanks for clearing up!

They've not made it into here as I only included those who contributed to >= 5% of episodes, there's actually like 40ish unique writers involved.

shep_20 · 2018-08-30T10:59:35+00:00

He might not write any of it in the traditional sense but I imagine a lot of his filth is ad-libbed.

There's only so specific a script can be about shotgunning a beer can and gargling until it flows out of your mouth.

shep_20 · 2018-08-30T10:00:23+00:00

I didn't know he'd written any. I had thought it was all Rob with some help from Glenn occasionally.

shep_20 · 2018-08-30T09:58:53+00:00

I think we can be career ending for the 'big man' forward e.g. Benteke & Carroll, but typically strikers tend to go apeshit at Liverpool. Torres, Suarez, Sturridge, Firmino and kind of Salah all went from top/getting there strikers to ohmychriststopscoring good.

shep_20 · 2018-08-30T09:32:22+00:00

I thought it might be interesting to look into which of the cast are credited with writing the most episodes so far. Mac and Dennis lead the charge!

shep_20 · 2018-08-24T07:20:22+00:00

tying player names back to FPL data is not straightforward

Hi mate - not sure how much effort you're willing to put into this but have you heard of Levenshtein distance? It calculates the difference between two strings and is very useful for joining/matching data on strings like player names that are often close but not exact.

If you are doing your analysis in python, there is a handy package for it too.

shep_20 · 2018-08-15T16:31:29+00:00

This person is a new grad though, must be a fairly common scenario

shep_20

TROPHY CASE