vonTrips

Like I wrote, I don't know what you're doing wrong. (Probably import old file) And you are only one who have a problem. I'm sorry, but I can't help you.

I don't know what you're doing wrong, or what's wrong with your game. But I can still see the old view in the screenshot. After SV% must be SAVES/90, like this: But there are other changes in the view (e.g. Open Play Crosses). The errors I've seen are clearly due to the use of the old view in the game. The fault is on your side... Because I will print these 17 filtered players into HTML and insert them into the app: Thats all. But one important note! It is necessary to use the Czech or English language. The application does not convert positions from other languages.

Yes, this is right file. But in game, you use old version. I see it in your screenshot. First - delete all my views, what you are imported to the game. Second - import new version of all views.

But you still use old view! Delete all my views and than import the new ones.

@milenec11 If the error persists, send me a screenshot and HTML file.

300 kB per file. Around 150 players The error is about filesize or Server Error like as picture higher?

OK, send HTML again. But 3 other people tested it with new views pack, and no error

@milenec11 My fault, I'm so sorry. I shared old views pack. Here is right one - https://www.mediafire.com/file/6qhzc5j1ndtuv9y/FM24-views.zip/file

Send me your HTML file

Yes, link is in post... or here - https://analysis.fmseries.cz/

I don't think this is possible, with the current version of the game stats. What we need to know is expected Saves (can't get from game via views) and xG per Shot faced. Maybe even Shots Faced and how they are counted. Only then your ideas could be implemented into a formula. But even I would know how to use these metrics 😂😂😂

Hi everybody again, especially @st4lz @milenec11 Here is post about my online tool for evaluating players - https://medium.com/@FMPanda/panda-tool-veřejná-verze-3abd4c46d3ee (yes, Czech lang again 😂) Enjoy it, if you want. Or only use percentile table - https://analysis.fmseries.cz/front.analysis/ - for your tools.

What I have is an online application (PHP - Nette). It will be publicly available. I want to deploy it by the end of this week. Of course, I'll pin you And if you have an idea how to better evaluate GK, or how to replace ShotPerConc, please help me. Even the discussion itself is important. It helps to find an interesting idea (solution) that I wouldn't have come up with otherwise. I'm not omniscient. I like to read other people's thoughts.

This is absolutely not true, within Moneyball you have to look for undervalued players who will perform well and won't cost you a lot of €. And the second thing is that Average Rating is offensively minded! So a CB who scores goals (e.g. from SP) will have a better AR than a CB who defends better. But which CB is better? I've tried searching for players by AR and you often get a hit, but you pay a big price. But if you're looking for a specific player (good defender/tackler/header), AR won't help so much to you. That is why this post was created and why I am finding KPIs. As for goaltending, that's a longer discussion, but I also agree that it's a lot about luck. I still need to fine-tune the GK ratings and their KPIs.

To moze spotkamy sie w Biedronce 😉😂😂😂 Good point. The problem is that I didn't edit the GK this time, I just added Clearances into others metric. But if I remember, I wanted to differentiate that the goalie has 75% SV and it's 1 goal from 4 shots. But then there's GK who got 5 goals, but from 20 shots. The expected save ratio and xG faced metric would be a great solution for this, but unfortunately you can't get them from the game. I'm afraid I didn't get the result right. Maybe it should have been the Shots Faced Per 90 metric. I'll have to take another look at it. I would like to publish a tool that will process an HTML file and then say these numbers. And from what I've tested (so far), those numbers can be trusted. But as I've written before, the final decision is always on manager

That's your point of view. What I have posted here is my perspective. If you want to make any changes, make them 😉 That's all I can tell you And of course you're right, the numbers are not easily comparable. You can't compare 0.2 xA achieved in the Premier League with the same number achieved in the Austrian Bundesliga. But what I've listed here is just a guide. The final decision is on each manager (like as in real life).

Hi everybody! Especially @st4lz @milenec11 @Rodrigogc @dunk105 @BL1TZ_GT Here is my new version of KPI: Full post about it with an explanation is on my blog - https://medium.com/@FMPanda/moneyball-klíčové-metriky-4-8f2e4bb4e3e5 - and yes, it's in Czech language. But Czech language is beautiful, trust me 😉😂 Alternatively, use DeepL for translation.

Again all I can say is - great idea. This is exactly what I addressed in our Discord discussion. That no one really knows how Poss Won/Lost is made. That I actually only know how successful the passes are, but I can only tell which ones were unsuccessful (forward? sideways? backwards?) from the graphs. Most importantly, I have no idea if the loss is from centers, dribbles, etc. Knowing this data is important for me to be able to evaluate players correctly.

I have it in a spreadsheet just for the format. There are no formulas/scripts. I used to build some sort of tool in excel, but it proved to be unnecessarily complex/necessary to extend/maintain. So I don't see any reason to share it, if I can, I can put it here as plain text.

This is a perfect idea! 😉 I've been toying with the idea of not evaluating my own players this way for a while now. So far I'm doing it because it seems easy, but... The second important truth that emerged in your post is the influence of tactics/role and team quality on metrics values. I address this mainly with CBs, where high clearences and blocks numbers are found in a lot of material. It's just that my CBs have an AR of 7.00+ and those numbers are extremely low. That's why, for example, I don't want to use CLR and BLK specifically when looking for CBs. Because high numbers say nothing about a player's performance. For the same reason I don't address Press Att, Press Com and Press Ratio at all. Of course I will post it here, at least again as a table. A more comprehensive text will be on my blog (in Czech lang), but this time I want to publish the values that I measure myself. From the whole spectrum I take the 85th percentile as the ideal (100%), or the 15th percentile (e.g. for goals conc.).

Thanks so much, I'll definitely check it out as soon as I have time.

This is what I read and partly based on. The problem is that this is a real world condition. In game it's different unfortunately, there's the Match Engine and therefore every role needs to be mapped towards the ME. Sometimes it goes against the grain. I don't know, but I'll definitely check it out. Wouldn't there be the same problem though, that it's a description of the real world, not a description of the game?

Totally agree! DATA HUB doesn't work, just like exporting data from the game doesn't work. For example, in the Data Hub you will find that there is also Open-play xA, but there is nothing like that in the views. That's actually the reason why I solve the KPIs, why I export everything and process it through a script. I have a primary KPI for player filtering and nothing else in the game. I keep a history of the data by making a backup of the file at the end of the season, and then possibly going back to it.

You can get youth team stats out of the game. It's laborious, you have to take a special look at each competition, but it can be done. Check out my blog https://medium.com/@FMPanda/fm23-statistiky-rezervních-týmů-3bdb8b2ab7ec But as @st4lz wrote, when a player plays out of position, or on the wrong team, you just overlook him - it can happen. Then it comes down to having historical data. Because you can look at how he's played over multiple seasons, or how performance has changed over the course of a season as well.

It's certainly an interesting note worth exploring. On the other hand, I've been picking players based on these KPI's for the third time in my career (3 diff saves) and so far I've been pretty successful (but I don't play for top teams). Sure there are mistakes, but not major ones. If you look at my blog and find my career with Baník Ostrava, you will see that I bought a left-back (I guess David Schnegg) who had bad numbers this season because he played in the wrong team. But I also had historical data stored in DB, so I saw that he played the last two seasons well, but not the current one (he was on loan). So I bought him and he played great for me again. Of course, if this was his first season (which I have dates from), I certainly wouldn't have given him a second glance. But that's how it works in real life, sometimes you just miss a good player. I don't want to have a magic formula, but I want to evaluate players and make my own decisions accordingly. One more important thing - only the primary KPI is used to filter players. The secondary and other KPIs are for deciding who could play better. Thanks, there are more of those custom (computational) indicators. And I have ideas for more, but those stats would have to be separated in-game - e.g. separate defensive and offensive headers, separate open-play xA from xA, etc. I trust you on this and I like your approach, I may try it out myself accordingly. And I would be happy if you post your research somewhere here on the forum. I'm not saying that my way is the only right way, quite the opposite. I'm constantly improving it myself, I now have version 4 of the KPI for example. But I still need to validate it against the data after the game update.