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27 "Frankly, my dear, I don't give a damn"

About StatSav

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  1. Definitely. I think they're also useful to compare potential targets at a glance quickly that you've picked out as being really good in one area i.e goals per 90 and see what other aspects they have to their game.
  2. 27 years old, shoots, scores, presses very well, makes a tonne of aerial challenges, dribbles well, creates chances and plays key passes... starts 10 games for a struggling Lyon side? Very interesting A very low assist count probably points towards his key passes and chance creation flying under the radar. Seems as though he must lose possession quite a lot with his pass accuracy amongst the very worst in terms of top 5 European league strikers - but maybe that's a by product of him being used as a substitute so often? Used to chase games too often that he HAS to play very risky passes? An
  3. How strange that Lyon chose to only start Toko Ekambi in 10 league matches... he still managed to rack up over 2000 minutes played, so it makes you wonder how much of an impact the sample size could have even had on his numbers here!
  4. If you click the "FM" button at the top right of the screen, you can "print screen" the page. I printed out the pages as html and then converted them to csv.
  5. Thought I'd leave a WIP radar plot using some accurate data from the 2019/2020 simulation and explain it a little. Who better to use as an example than Messi and Ronaldo? I chose these 2 as quite frankly they're 1) the most easily recognisable, 2) seem to perform quite averagely on FM compared to real life and 3) have completely different playstyles too. Will the data show this? Well to an extent, yes. First off, I'll explain a few things about how the plot works (or is supposed to work). The radar operates on a linear scale from 0-100 with 1 being the bottom 1% of the dat
  6. If you click the "FM" button at the top right of the screen, you can "print screen" the page. I printed out the pages as html and then converted them to csv. I typically use it on the player search screen where I can filter players, add the stats I want to look at and so on. I think the vast majority of top sides now have strong analytics departments. Pretty much a career goal of mine to be involved in something like that I'm looking to do more long form content like this to really apply context to the numbers. I think alongside creating radar plots, watching highlights of the
  7. The deeper look on Letizia will have to wait - I decided to start learning to code radar plots and it is certainly difficult for an R novice. Here's a REALLY early, ugly, but working radar. I hope that in a day or two I can get these looking as good as I want to, right now it's genuinely horrible but I thought they were interesting enough to post anyway. The data isn't 100% accurate, I boosted some of Salah's numbers just for more contrast. Full post at some point explaining the radars once I can make good looking ones efficiently. All variables are calculated as per 90 minutes (other
  8. I'm going to post a deeper look at him later on today or in the early hours of tomorrow morning - I've had a look at a few of his games and it's interesting how the AI chose to use him!
  9. They do. I can't really think of any practical way around this, however when applying context it isn't the biggest issue. I can manually enter matches to see which passes were considered "key" and find out pretty soon if a large amount of them are from set pieces. I think in the case of Letizia it actually makes his stats more impressive as he was not likely to be taking free kicks for Benevento due to his attribute being 9 whilst they have players like Oliver Kragl and Gianluca Caprari with 16 and 13 respectively.
  10. I'll reply to all comments later - thank you for some good ideas, I was not expecting any interest if I am honest! In the meanwhile, I've been playing around with different colours/layouts for the scatterplot. Excluding goalkeepers, here is every player that played 2000+ minutes in all competitions in Europe's top 5 leagues in my simulation to demonstrate a new layout idea. Better or worse than those in the OP?
  11. I'm considering starting a save that has somewhat "masked" attributes. I believe there are skins that instead of showing direct attribute numbers, they simply show a colour for that attribute range. So instead of 11, 12, 13, 14, 15 being shown outright, you'd just get a singular colour to go off of for attributes within the 11-15 range. This would allow you to see where someone is roughly at attributes wise, and then make decisions tactically based upon that without making it too tempting to just avoid the data side of things and sign players with high attributes.
  12. Hello, As FM20 heads towards the end of it's life cycle and I find myself living life as an unemployed graduate (thanks covid!) I've decided to start applying my novice coding knowledge to Football Manager. Whilst "plug and play" systems, 4-2-3-1 gegenpressing and so on can be fun, I feel that using the plethora of data that FM provides us can potentially provide an efficient and effective strategic advantage over the AI and other players. Whilst I will be using this thread to post some of my own progression and ideas, I'd like to strongly encourage other likeminded community members to con
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