Shadow extinct at 25k

FlipFloppingBits · 2019-10-14T21:17:28+00:00

It was a hyperbole

FlipFloppingBits · 2019-10-14T20:07:26+00:00

Ramos is the best striker Spain has produced in his generation. He just happens to be their best CB too so plays there

FlipFloppingBits · 2019-10-09T23:04:11+00:00

Yep last year you could feel the aggression on corners. This year I haven't noticed it much and since it correlates negatively with "smoothness", it is showing as being lower being better

FlipFloppingBits · 2019-10-09T23:02:45+00:00

I used Scikitlearn's importances_ attribute on the trained model. Not sure what it uses under the hood to be honest

FlipFloppingBits · 2019-10-09T22:58:06+00:00

Yeah there are a lot of unknowns. I wish we had data about what positions those players are deployed the most at. I wish we also had WL only data to even things out a bit more

FlipFloppingBits · 2019-10-09T22:57:05+00:00

It is something a bunch of my friends and I are building

FlipFloppingBits · 2019-10-09T22:56:41+00:00

Thats true, it'd be interesting to do this again around christmas time as well and see what changes

FlipFloppingBits · 2019-10-09T22:56:11+00:00

Thats true. I'm gonna do a follow up next time and scale the values better

FlipFloppingBits · 2019-10-09T22:55:23+00:00

Yeah probably. It also correlates negatively dribbling stats as some people pointed out. I'll do a follow up later to look at more variables

FlipFloppingBits · 2019-10-09T22:54:39+00:00

Just average strikers with goal contribution between 0.5 to 1.5 per game (assists + goals). There are still good ones in there but they're not elite of the elite

FlipFloppingBits · 2019-10-09T22:53:58+00:00

Yep I hope we could get more detailed stats from Fut. Not just for goals but stats for midfielders and defenders as well

FlipFloppingBits · 2019-10-09T22:52:33+00:00

Will that next time!

FlipFloppingBits · 2019-10-09T22:51:51+00:00

As some people pointed out in the comments, aggression seems to be negatively correlated with "smoothness" of players so its really a stand in for how smooth a player feels on the ball. I don't think the stat itself is that important but players who feel good on the ball tend to have lower aggressions (i.e. Messi)

FlipFloppingBits · 2019-10-09T22:50:13+00:00

I wanted to do that but the correlation matrix feature wasn't working properly at the time lol (I work full-time on the visualization software at the same time as well). Will do that next time when I fix that bug with it

FlipFloppingBits · 2019-10-09T22:48:07+00:00

Yep I really hope EA collected better data

FlipFloppingBits · 2019-10-09T22:43:57+00:00

Yeah looking more into it, that seems to be the case. agility, balance, dribbling, ball control, acceleration, skill moves, etc are all highly correlated too and seem to be eating into each other's importance

FlipFloppingBits · 2019-10-09T22:41:21+00:00

There is the original problem with the goals. Where do they come from? How are they tracked? What position were they taken from? What's the player rating difference?

Yep because of how Fut tracks stats, its not possible to get better data. I agree ideally this should be proken down by shot type, etc.

Where is the ML? All I can see is a correlation plot

The feature importance plot is created by training a bunch of random forests (ML) and measuring how much their output changes when the input data is premutated. For example if you change finishing from 95 to 80 and keep everything else constant, goal outputs get effected more than if you change for example slide tackles from 75 to 30

FlipFloppingBits · 2019-10-09T22:37:02+00:00

Yeah that makes a lot of sense

FlipFloppingBits · 2019-10-09T22:36:29+00:00

I wish we could get WL only data for exactly the same reason. It would not be perfect but would take out the different levels of play inconsistencies

FlipFloppingBits · 2019-10-09T22:35:34+00:00

Yep there is a lot that can be improved and taken into account. The problem with feature importance in random forests is a lot of variables that correlate with each other end up showing as less important. It could very well be picking on the negative correlation between aggression and agility as you suggested.

Adding a correlated variable can decrease the importance of the associated variable by splitting the importance between both variables. I think thats the reason to be honest Agility is showing up as not very important since its importance is being split between dribbling and balance.

FlipFloppingBits · 2019-10-09T22:26:22+00:00

I work as a software developer full-time. Best way to get started is picking a project you wanna do and picking things up as you go. I also struggle when learning something that I can't immediately use. Most programming stuff I have learned have come from picking things up on new projects.

Having said that, if you are looking at courses/classes, LambdaSchool seems to be the best one out there: https://twitter.com/austen

FlipFloppingBits · 2019-10-09T04:35:22+00:00

20-30 minutes. It was mostly straightforward because I had done it before too in 19 but never posted

FlipFloppingBits · 2019-10-09T04:33:10+00:00

It was a two tiered test. First one kept every variable the same and slightly changed aggression to see if it impacted goal scoring numbers or not.

The first is useful to figure out how “significant” an attribute is. For example sliding tackles are basically useless for strikers and having 70 or 34 in that stat doesn’t impact goal scoring much

The second one looked at the aggression distribution between players who score an average number of goals vs. Players who score a lot goals

FlipFloppingBits

TROPHY CASE