[Key metrics (KPI) for each position]

2 hours ago, milenec11 said:

i have nothing else in my view from your filters

i delete all from the list and insert again the new that you send me what else to do?!?!?! 

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.

[Key metrics (KPI) for each position]

8 hours ago, milenec11 said:

ok the same 

i delete all the files i redownload the files i reimported to the game and once more error

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.

[Key metrics (KPI) for each position]

25 minutes ago, milenec11 said:

PANDA - Scouting - In Range - Stats v24.0.fmf 16.93 kB · 0 downloads

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. 

[Key metrics (KPI) for each position]

20 minutes ago, milenec11 said:

and is the in game view

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

[Key metrics (KPI) for each position]

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

[Key metrics (KPI) for each position]

3 hours ago, milenec11 said:

What was the limit of the players ?

300 kB per file. Around 150 players

The error is about filesize or Server Error like as picture higher?

[Key metrics (KPI) for each position]

12 hours ago, milenec11 said:

bohuzel still error

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

[Key metrics (KPI) for each position]

@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

[Key metrics (KPI) for each position]

12 hours ago, milenec11 said:

when i try to run it under the insrtuction this is what happen...............

Send me your HTML file

[Key metrics (KPI) for each position]

11 hours ago, milenec11 said:

i did find the tool? where is the tool? is released?

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

[Key metrics (KPI) for each position]

On 06/03/2024 at 16:02, milenec11 said:

so to proceed with that if a keeper have played 10 games as starting eleven and with the add time has 950 actual played minutes and at this period has held parried or tipped 90 shots then simple 950/90 equals to 10,5 so at every 10 minutes he faced at least one shot if a second keeper in 10 starting games has played 880 minutes and faced 78 shots the he faced a shot at every 11 approx minutes so by the result we see the effect of the defense bigger duration faced a shot better defense .

now my second thought is to combine this ratio with the result of last goal conceded ratio plus Sv % , I didn’t find yet how to combine those but as I said I am at the start of an idea . Any thoughts on that ?

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 😂😂😂

[Key metrics (KPI) for each position]

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.

[Key metrics (KPI) for each position]

7 minutes ago, st4lz said:

I wanted to do it on my website, but if you want to do the app yourself, I won't spoil the fun for you. I'll figure out my own. Just don't forget to pin me like this time when you are done 

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.

[Key metrics (KPI) for each position]

55 minutes ago, milenec11 said:

And to finish this monologue and the KPI or moneyball or cover attributes or only stats scouting type of game that skins or excels or anything else can provide to play is a big in game number that in big rate covering all …..AVERAGE RATING.

an average rating is created by the SI using its one type to calculate the performance of a player within the game using the game facts . For example a goals counts as 0,9 and an assist 0,4 etc etc ….

So search for a player within the bunget you have the role you need the league that can be compared with your’s team actually playing and took the biggest AVR . This player about 90% is the gay you need 

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.

[Key metrics (KPI) for each position]

9 hours ago, st4lz said:

I speak Polish, so for me Czech is not only beautiful, but also funny

To moze spotkamy sie w Biedronce 😉😂😂😂

9 hours ago, st4lz said:

Isn't Save Ratio the same as Shots per Conceded, but inverted?

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.

9 hours ago, st4lz said:

Do you think it is possible to create a ranking for each stat per position and combine all the results together using weights for [P, S, O] category? We could end up having a list of best performers for each position per league. Would the results make sense and you trust the order?

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

[Key metrics (KPI) for each position]

2 hours ago, milenec11 said:

You have absolutely right on that but I believe that we need a little bit to narrow them down and to make them comparable.

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).

[Key metrics (KPI) for each position]

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.

[Key metrics (KPI) for each position]

52 minutes ago, st4lz said:

But I doubt possession won/lost is a reliable stat now, I think it may be broken.

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.

[Key metrics (KPI) for each position]

3 hours ago, milenec11 said:

do you believe that it will possible to share with us the excel sheet script that mansion above ?

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.

[Key metrics (KPI) for each position]

17 hours ago, st4lz said:

All of the above lead me to conclusion, that recruitment KPIs and performance KPIs should be different.

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.

17 hours ago, st4lz said:

It would be awesome if you put a v4 update, I would gladly take a look at what changed.

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.).

[Key metrics (KPI) for each position]

20 hours ago, st4lz said:

You can try it by yourself: https://moneyballfm.com/Strikers

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

[Key metrics (KPI) for each position]

12 hours ago, Andros said:

https://theathletic.com/3473297/2022/08/10/player-roles-the-athletic/ 

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.

12 hours ago, Andros said:

https://themastermindsite.com/2022/04/13/evaluating-players-based-on-role-continuity/  

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?

[Key metrics (KPI) for each position]

17 hours ago, janrzm said:

I was so excited when the data module was added but if you scratch below the surface it's not really doing the job you'd want it to. I want my staff to proactively present me with analysis in ways that relate to my tactic, player roles, opposition etc. No manager in any club with a data analyst is left to pour over raw data, make their own visualisations etc. The Data Hub, whilst necessary to the game is like a few other recent additions, a nice idea that hasn't been implemented anywhere near its potential.  

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.

[Key metrics (KPI) for each position]

20 hours ago, Rodrigogc said:

I haven't read the whole thread, so ignore if this has been adressed already: Do you guys take into consideration that players might be being used in an underperforming role by the AI ?  For instance, this:

 

• Dribbles Made - a good indicator for Advanced Playmaker;

Indeed that is correct, but what if a player who's got good dribbling is being used in a role where he won't dribble at all ? How do you know he is good at it ? The AI often misuses players, playing St Pauli I've seen bayern using Wirtz as a winger, Marcos Leonardo in my save was being used as a CM even though he is a striker.. 

 

I've played attributeless for a while and adored it, but turned on attributes again because I didn't know how to evaluate my own newgens. Would like to play attributeless again though. 

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.

[Key metrics (KPI) for each position]

On 29/01/2024 at 14:06, st4lz said:

Main issues:

* Some of the stats are favoring players heavily in teams that have an advantage - it is almost impossible to beat the average xG or Pas % from the underdog team

* Opposite, some of the stats are much better in the underdogs, especially the defensive ones - all of the Tackles Completed, Key Tackles, Interceptions, Clearances, Blocks, Headers, etc are higher for teams that defend often

* Lower efficiency doesn't always mean worse performance - ex. Headers Won Ratio for two CB, one is the air superiority corner target, and the other stays back during Attacking Set Pieces. The first one may have 60% HWR with 12 Aerial Attempts per 90, the other may have 80% with 3 Aerial Attempts per 90. HWR is as useless as Conversion Rate or Shots on Target % (vs. xG), you can't just aggregate them and treat all situations equally (you discriminate low probability scorers, like players who score a lot of headers or long shots will have lower).

* Some of the stats are dependent on team tactics or playing roles, and can't be compared objectively - it is hard to compare a No-Nonsense CB with a CB when it comes to Pass %, the first one is encouraged to make safe clearances. Or to compare a Pressing Forward with a Trequartista when it comes to Pressures Attempted.

* Some of the stats are negatively correlated - there is often a low Pas % that goes along a high Key Passes. Creative players do take more risks, so their mainline Pas % is lower, which doesn't mean their passing is weaker. You need to identify those relations, and not try to put too much weight on Pas % when looking for a playmaker.

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.

On 29/01/2024 at 14:06, st4lz said:

I generally like to create indicators that are constructed based on available statistics, I like the Tackle Quality (Tackles Attempted / Fouls Made) that you created.

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.

 

On 29/01/2024 at 14:06, st4lz said:

I have quite a success in finding great overperforming strikers with this model.

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.