Shrewnaldo

Fair point, well made Cheers Dan. I'm hoping I've done enough prep work to have identified the right people to bring in. With the stats being wiped by the time players are released, I've created shortlists of those who had the right stats for each role. Having played until 1 July last night, I've managed to get a number in on trial so I'm hoping we'll be able to sign enough Good question. We have Atalanta as a parent club, so that is definitely a market I'll consider. I'd also really like to bring Mattia Compagnon back on loan from Juve. He's nowhere near good enough for them and has been transfer listed, but the new "we want him to be tested in a new environment" feature (to stop the multi season loan glitch thing) means they're blocking it for now. We ended up getting a bit of money but it will need to be kept for signing on fees

Ok, so I'm an idiot - there are 4 teams relegated from Serie B - but the relegation zone only shows three on the table because there's an odd Italian rule for the fourth spot: If there is more than 4 points separating 16th and 17th, then 17th is automatically relegated. If there is 4 points or less separating them, then they enter a playout. Luckily for me, we finished 14th. 9 points ended up separately Catanzaro in 16th and Reggiana in 17th. So the bottom four are automatically relegated. With the final table looking thus, Cremonese took the title and Parma are going up in second. Playoffs will decide the third promoted side.

Lenny Nangis - what a blast from the past that is. He was a flying winger for me back in my Toulouse save on FM12. I can only imagine he's somewhat slower these days. What's the "builder" target for Nancy then? At what point can you move on?

Survival Leoni del Garda - FeralpiSalò So before the epic tome on recruitment strategy, you may recall that I'd decided to take over FeralpiSalò in as 'realistic' a scenario as I could create. Therefore, I holidayed the game to match with my IRL start date (20th October 2023) and found the Lions of Garda languishing at the foot of the table. Sacking Stefano Vecchi then seemed like a reasonable thing for the board to do - indeed the real life board caught up with my idea only three days later by terminating the actual manager's contract. In my FM world, rather than Marco Zaffaroni assuming the reins, my alter ego Naldo Toporagno took charge (Toporagno being Italian for Shrew). And no, this is not intended to look anything like me. There was barely time to peruse the squad and check out any changes for the new game before taking charge of our first match - a relegation six-pointer against AS Reggiana. And I must say that I was rather concerned when we followed up a 4-1 hammering of I Granata with regulation victories over Catanzaro and Cosenza, scoring 9 and conceding just 1. I was after a challenge and three wins from three just smacked of 'beta easy mode'. Thankfully matters soon returned to the norm and we had a couple of periods of real struggle, including two 6-game winless streaks: between the turn of the year and late February, and then March to mid-April. Regardless, after the initial burst of points, we managed to keep our head above water and, with three games to go, have confirmed our survival in Serie B for another season. Cremonese and Parma will fight it out for top spot but both go up regardless, whilst Sampdoria look like being the biggest side to miss out on the playoffs. At the other end, Lecco are confirmed down and Venezia look like following them out of the leak - whilst Cittadella and Reggiana will attempt to escape the final relegation spot. For our part, we will end up between 13th and 16th - which we have to consider a success given the starting position and quality of players at our disposal. I'd outlined my intention to play a low-block, counter-attacking game and I've absolutely committed to that as some key metrics from the Data Hub will confirm. I like the 'pitch tilt' graphic best. We sit deep and only really press within our only final third - and we rarely get into the opponent's final third. But that's fine. We don't want to hang around once we get there; we prefer to be direct and try to get off shots as early as possible with a view to creating the highest xG/shot that we can. And that has been a great success. Despite taking the fourth fewest shots in the league, we create the fifth highest xg per shot. Yet the offence is still clearly lacking. 40 goals scored (so far) is the joint-third worst in the division so whilst I like what we're doing in attack, it's clear we need to be doing a bit more of it. Meanwhile, our defensive performance has not suffered. Conceding 1.37 goals per game is definitely higher than I would prefer, but it's 0.11 less than the league average. We have the sixth best defence in Serie B. All of which provides a merry picture going forward into our first summer of recruitment. To which end, I have been immersing myself in some hardcore excel sessions this evening. I exported all my chosen offensive and defensive statistics for every player we know in the 30 loaded, playable leagues that is out of contract in June and has started at least 7 matches. I was then able to create filters which ranked the resulting 1160 players according to the various profiles that I'm needing. Unfortunately, that's just about the whole team. As I'd mentioned previously, six of the first choice team are loanees who will be returning to their parent clubs. Another five members of the first-team squad will leave when their contracts expire. In terms of regular starters that will be here come August 2024, that only leaves me with: Semuel Pizzignacco - an excellent goalkeeper who is probably our only sellable asset and may well leave if a Serie A club decides he's worth his £3.1m release clause Michele Camporese - decent centre back Loris Baccheti - strong centre back who has already told me he's leaving on a free in 2025 Federico Carraro - average central midfielder who doesn't fit our physical requirements Davide Balastrero - excellent midfielder who has already told me he's leaving on a free in 2025 Alessio da Cruz - injury prone winger Karlo Butic - very poor striker that I'd love to sell That's a big yikes and means I'll need to bring in maybe 10-12 players in the summer. And this is when I point back to the money again. Or lack thereof. An even bigger yikes. It also means that I can't commit to signing players now and I'm needing to take a chance that they'll still be available once my own players have left... and hopefully the board are a little more generous than the proposed budget for next season. I'm really not sure what's going to happen for next term. According to the game, we're due to move back to the Lina Turina in Salò, vacating the rented stadium in Piacenza - 120 miles down the road. But the reason we've been playing at the Leonardo Garilli is that the Lina Turina holds just 2364 supporters and doesn't meet Serie B's minimum requirement (5500). With no indication in the game that we're paying for the Lina Turina to be upgraded, I suspect that we'll be temporarily housed elsewhere again... and maybe that will bring some increased funding? Wishful thinking perhaps. For now, I'm having a great time doing stuff like this: I appreciate that probably isn't very easy to read, but it's essentially taking all the stats for the 1160 players then creating normalised ranks by comparing the output from each player to the average output of their peers (subtotalled averages based on the filters, i.e. only creating averages from other strikers above a certain height). It then includes some self-generated statistics such as "goal contributions as a percentage of team goals" (goals per 90 + assist per 90 / team goals per 90), net possession impact (possession lost per 90 - possession won per 90) and pressure success (successful pressures / pressures attempted). All very straightforward. Then the subtotal for these selected statistics is multiplied by the deviation that I've created to account for the quality of the league the player is in. And, having done this for 9s, 8s, 6s, fullbacks, centrebacks and wingers I'm off to do quite a bit of scouting and watching selected games from some of these players. Fun times. Forza Feralpi!

Yes, exactly that. Everything else was a manual entry by lifting the data from the game. Only that line has any formula so excel highlights it as being different.

So it looks like I've just messed up for that line. It is supposed to be the average of the three Serie C rows, but the formula has swapped out. If you unhide between rows 4 and 10, then you'll reveal the Serie C lines and then be able to correct it again. No idea how that happened but good spot

Average age in La Liga is 26.67; in the Premier League it's 26.11. So about half a year's difference, which may have some difference on the attributes. The Bundesliga, just for a comparison, is 25.64.

Not sure about the infographic... have you seen my graphical skills?! Re the age profile, that'd be fairly quick to check - could just start up a new game then add a manager to any club in the league and use the comparison page. Pretty sure it has the average age to 2 decimal points? I'll check later I'm sure they'll all have some great talents - I've certainly found some brilliant young players in Croatia in the past. But, just using this method, they're not as likely to have value as the other leagues. Poland seems the most likely of three with a slightly higher than global attribute average and a slightly higher attribute rank than reputation rank; Sweden is over-ranked and Croatia is well over-ranked. *But* this method is very broadbrush - it's talking about the leagues as averages. If you limited Croatia to just Dinamo Zagreb, then you know that you're much more likely to find a high-quality newgen or fourteen. Same with Brommapojkarna. As I'm a Serie B club with less than no money, I need to cast my net as wide as possible to pick up bargains and free transfers, so I wanted to get away from just targeting the well-known talent factories.

That's awful. Hopefully they patch this and it's compatible with existing save games

Ah that's weird. It should be the average of the three individual Serie C cells. Looks like something has gone wrong in transit there. I'll take a look later today

That sucks about the bug. Is it just the Belgian lower levels that are affected, or throughout the tiers?

The "average value" is the average transfer value in that league. As I referred to above, this just appears to be broken. I don't think it's able to deal with the value ranges that were introduced in fm22. South Korea has the highest average transfer value in the game. Because it doesn't work, I just hid the column Italy 3 is different because I've had to capture the three sub-leagues of Serie C separately. I've then averaged them into the line that I've left visible so there's only one record for the Italian thirdly tier

The raw data is attached, to be done with as you will. FM24 data export.xlsx

When does your contract run out? Just aware that leaving a contract early sometimes puts a black mark against you for other jobs.

Ah ok, well I have the second tier covered for the major nations and a few selected others; and I've also covered Serie C. So I can also adjust the rankings for any of these leagues. And if I stumble across someone in a competition that I haven't covered, then it should be easy enough to add them to the baseline retrospectively.

Thanks, much appreciated. You mean dividing the attribute splits between the positions? Thinking perhaps that certain leagues will tend to produce better defenders and therefore it's better a better market? It's possible to do that but it does add workload to creating the baseline. Even just splitting by 'keepers, defenders, midfielders and strikers quadruples the effort. Splitting by sides adds again etc etc. For me, the effort would outweigh the benefit. I'm happy to share the data export and the views, if folk think they'd be useful. But I'd encourage people to use the data in their own way rather than copy my logic. Indeed, I'm not likely to be doing much retraining. With the older squad, it's less likely anyway - but it is much more difficult to identify opportunities for re-training without the numerical attributes. Re defensive stats - it is tough. I'd really like to be able to make possession-adjusted stats but FM doesn't record the possession average when a player is on the pitch. Instead, what I can do is use the overall numbers to produce a longlist that I want the scouts to investigate. This then allows me to whittle the list down to 5-10 targets, and that's then very easy to go and find their team's average possession manually - and thereby produce possession adjusted stats. Obviously this still isn't perfect, but it's better.

Recruitment Strategies (Part 2) Leoni del Garda - FeralpiSalò It's been mentioned above, but one of the difficulties with the statistical approach has always been comparing statistics across different leagues. How comparable is a striker with 0.7 xG/90 in the Ekstraklasa to a striker with 0.5 xG/90 in Liga NOS? The latter is the stronger league but by how much? I wanted to come up with a quantifiable way of comparing the leagues so I spent the first day of FM24 creating a manager in almost every playable league in the game. Then, using the Comparison tables (under Squad Planner > Report) I manually recorded the average attributes, wage and transfer value. I was then able to produce average values for the Physical, Mental, Technical and Overall attributes in every single top-tier, plus a few lower tiers. Like this: The first thing to notice is that the 'average value' is absolutely useless. I think the value ranges that were introduced in FM22 (?) have borked this completely and no-one has noticed. South Korea has the highest average value in the game at £36m, which is clearly just wrong. So I'm going to ignore that metric completely. The next moderately interesting conclusion is the overall average: Just highlighting the top few leagues, you can see that Spain is 'the strongest' with the highest average attributes whilst Brazil is in fifth with Mexico seventh - all three benefitting from higher technical averages than any other league. So for those managing big clubs, it seems to make a lot of sense to go shopping here. Making statistics comparable However, my objective with this task was to quantify the differences between the leagues. Having these raw numbers I could then normalise the results to compare league to league, and country to country. Again, I've quantified this as 1.00 cf 100% purely because of personal aesthetic preference and have produced the figures for the recorded top-tiers below: Ok, so what? Well I hope this means that I can adjust the statistical output between countries and leagues, to provide make the metrics comparable, albeit accepting that it's never going to be perfect. The obvious conclusion I'm trying to draw here is that playing against opposition with average attributes of 10.46 in Switzerland is slightly easier than playing against opponents with average attributes of 11.56 in Italy. So when I'm producing the normalised ranking from the statistical models in Part 1, I can adjust these figures by multiplying by the corresponding Deviation figure. Making up an example to illustrate: Kaly Sene, playing for Lausanne, has a statistical output which gives him a 6.5 false 9 ranking from my chosen metrics. Adjusting by the Deviation for the Swiss league, his adjusted score would be (6.5*1.016) = 6.604 Marko Djuricin, playing for Spartak Trnava, has a 6.8 false 9 ranking. Adjusting for the Deviation from the Slovak league, his adjusted score would be (6.8*0.938) = 6.38 Is this fair? Is it accurate? Will it work? I haven't got the slightest scoob but, like I've said a few times, I just want to try something different and am willing to give it a shot. Finding markets with value The second conclusion I hope to be able to draw from this data extract is where transfer value can be found. We've already seen that the transfer value averages are broken, so we can't use that to find leagues with players who are cheap relative to their attributes. However, we can compare the actual attribute averages to the reputation of the league - reputation being one of the key factors in driving transfer value in Football Manager. To do this, I compared the 'FM Ranking' of each league (World > Competitions > Leagues) to their ranking by average attributes. I've ordered the below by the offset in their ranking, i.e. how much their attribute ranking deviates from their reputation ranking. Positive figures means their attributes are better than their reputation would suggest. This one is going to come with some heavy caveats: some of the lower rep leagues will have artificially high offsets because higher reputation leagues are unplayable and therefore I have not captured average attribute data some of the non-European leagues will be affected by the bias towards Europe in terms of reputation reputation is not the only show in town when it comes to valuation - therefore I have also included the average wage from each league which will provide some indication of the league's financial strength So what, if anything, can we draw from this? In my view, there are a couple of theories we can test from it: There may be value in shopping in the second tiers of big leagues. Spain, Germany, France and Italy all perform well whilst maintaining a solid attribute average that is higher than the global average (10.31) However, the second tiers in smaller leagues, whilst having a solid offset, have average attribute levels which make finding value unlikely Romania, Colombia and Uruguay look like fertile shopping grounds - solid average attributes above the global average, comparatively low reputation and low average wages Denmark and Mexico may also present an opportunity but likely at higher cost, given their higher starting reputation and higher average wages Despite having solid average attribute values, the likes of Norway, Portugal, Belgium, the Netherlands and Austria may not offer the best value. Of course, if you're a really big club then who cares about value. Just splash the cash and these leagues definitely offer fertile talent For my own game, then, I'll be looking at Romania primarily. Serie B clubs can't sign non-EU players from outside Italy so Uruguay and Colombia are out (for now). And I'll be looking to compare the statistical output from these players using the Deviation offset. I'd welcome any thoughts on the above. Have I missed something obvious? Am I barking up the wrong tree? Have I bored you massively? Are you a walrus? All important questions. Forza Feralpi!

I hope they never get rid of the 1-20 attributes - but I like the idea that SI provide various options. Something like the coloured stars I've been using, or the bars that others have used, would be a nice alternative to have available. Thanks all, the more the merrier Regarding how transferable statistics are... watch this space. I've got a couple of thoughts. I love those players that you have when they're output far outweighs what their attributes would suggest - think they're my favourite sort of players. I still remember Antonino La Gumina that I had in my old Samp save (funnily enough, he's since joined irl). His attributes were average but he'd always produce - no matter where I played him. But I like your point about individual versus team performances, it's often hard to see the wider picture. That's why I add "team conceded per 90", "team scored per 90" and "points per 90" to all my statistics view. This means I can check to see if the team is performing unusually well / poorly when a particular player is on the pitch. That then prompts me to go look deeper at the individual stats to see if I'm missing anything.

Yeuch, that's rough. Have they mentioned whether or not any fixes would be save game compatible? Following along, keeper, as always - although you know I'm more of a lurker round these parts

Ideal, if you've got any tips to share then I'm always happy to shamelessly steal stuff from other people's saves Cheers, I appreciate that First signing confirmed as 6'6" Norwegian striker will join on a free from Serie C club Ancona in the summer. Destined to be the back-up 9, Kristofferson scored a 7.38 on my Target Forward Rank, putting him 21st overall (of 361). Excellent aerial performance, as you'd expect for his height, and the 14th best xG/shot really stood out. He's not good enough to be first-choice, but I like him as the bench option a lot.

The holidays came first, in this instance. I love Lake Garda. Will definitely be heading back once the kids are away. I really like the third kit. So much so that I got one delivered today

Recruitment Strategies (Part 1) Leoni del Garda - FeralpiSalò FM used to be all about the tactics for me but, for various reasons, my interest in that side of the game has waned. Now I tend to focus more on the squad-building and recruitment is a huge part of that. Part of my increased interest has come with a focus on analytics, rather than just judging players by their numerical attributes. Indeed, I've taken to playing without the 1-20 attributes in recent versions. Whilst some will play entirely attributeless, it's just not for me. I don't think it's realistic that you wouldn't have any idea whatsoever about the players at your disposal - just think about the people that work for you, or with you, in real life. I'm sure you could assign some strengths and weaknesses to each person according to some basic work-related attributes. Or at least, a good manager should - and this is what the attributes represent for me. Regardless, if I use the numerical attributes then I struggle to see past them, and just tend to let the numbers dominate my thoughts. Replacing them with coloured stars (or similar) in the last few versions has really helped me focus on statistics instead. Everyone is aware that stats are becoming more prevalent, if not ubiquitous, in football and whilst FM is some way behind the curve, in both the type of stats that are collected and their accuracy, there are a number of metrics available for players to use. Whilst I wait for a graphical attributes skin, I thought I'd outline the strategy I'll be taking to analyse our own squad, in order to identify the recruitment priorities, and then how to find the players to fill those gaps. I really like the addition of this experience matrix - allowing the player to very quickly assess the age profile of the squad and any gaps. Up until my Bristol City save (Statman and Robins), I'd have been like most FMers and aimed for a squad that fills out the left-hand side of this matrix - bringing in youth players that we can develop and then sell on for profit. Indeed, this is one of the many strategies that has been erroneously claimed as a 'Moneyball' approach to FM. Indeed, 'Moneyball' would probably be the opposite, with the book making numerous references to Bill James' conclusions that "(older) college players are a better investment than high school players by a huge, huge, laughably huge margin". The reasoning behind this conclusion is that high-school baseball players do not have verifiable statistics from which franchises can draw direct conclusions about the player's likely professional future. It is this theme which I have carried through to FM - purchases will be based on evidence, and that comes from statistical output. Attributes and scout reports are not statistical outputs. So that means I'm not going to sign a raft of 18 year-old wonderkids who have played just a handful of games each. I'm looking for a track record and will be keeping saves from the end of each season so that I can go back and check detailed statistics from each campaign, before they are wiped when the game refreshes itself in June / July. In my Robins and Telstar saves, I also abandoned youth development completely - focusing solely on the senior squad. Here I'm going to have a halfway house - jettisoning the under-18 squad at the end of the season but keeping the under-20s. Primarily this is to honour the club vision and FeralpiSalò's real-life commitment to youth development. Sadly, the facilities are pretty poor and even this lip-service may be abandoned in a couple of seasons. So it's all starting to look a bit Big Sam - low blocks, direct football and a focus on physical, experienced players. And like the much-maligned Mr Allardyce, we'll be heavily into the analytics. Both the Data Hub and custom views such as the below will be thoroughly used. I've got a decent idea of the profile of players that I'm after, and a combination of these statistics and some basic logic will highlight the three to four profiles / positions that require strengthening. And then it's off shopping. I really, really don't like the Recruitment Focus system that was introduced in FM23. It takes away a huge part of what I want to do myself, how I want to find players and what I want my scouts to do. So whilst I'll set up the odd focus just to see what comes back, it's not the primary means of finding players. Instead, I typically go through the following steps: I use the Players in Range screen to extract a record of statistics from all the players within our scouting package I used to avoid this screen like the plague, but I've recently accepted that it isn't the cheat screen I'd previously thought. It does not provide access to every player within the game. Instead, it's just those that your club would realistically know about. As FM now presents it to you, I see it as your club going to one of the data providers (Wyscout, Opta, etc) and buying access to their data across a given geographical range To make it less 'gamey', I de-select the "interested" tickbox (so that all players are shown) and, whilst I always use attribute masking anyway, I never use it to search by attributes I use custom views such as that shown below to extract the data into excel and from there manipulate it into a few metrics for the profile I'm looking for For example, our 32 year-old on-loan targetman Andrea La Mantia is very likely to leave come the end of the season. Looking for a replacement 9, I've extracted the data for all strikers within our package who have started at least 5 games and are out of contract come the end of the season - all information that is readily available to clubs' recruitment teams Opening the export in excel, I then normalise a few selected statistics that I believe will help me rank the profile of player I want. Taking the targetman example again, I've normalised headers won (%), shots / 90, xG per shot, conversion rate, non-penalty xG per 90 and the net possession won versus possession lost. These are normalised by taking the average for all players in the export (removing those with zero returns as they will be in unplayable leagues), then comparing the player's output against this average. That comparison is effectively a percentage but I've set it to a numerical figure (100% = 1.00) for no other reason than I prefer the way 1.00 looks to 100%. Normalising in this way brings all the metrics into the same scale, allowing me to sum the metrics that I've chosen to be important for that profile (Column M below) A quick sense-check against the names returned lets me know if I'm on the right track. Yussuf Poulsen, Mehdi Taremi and Duván Zapata being top of this list suggests I'm bang on the money. I can then look for names that I want my scouts to find out some more about. So Poulsen et al are clearly out - not exactly being realistic targets for FeralpiSalò. But Daniel Ciofani, coming to the end of his contract at fellow Serie B side Cremonese... that's more like it Essentially, all I'm doing is conducting a statistical screen to produce targets for my scouts - rather than getting the output from a recruitment focus and then use stats to filter out that subset. Either option is entirely viable and I'm sure lots of people will think all this excel stuff is boring as, but who cares? This is how I like to play. Perhaps the analyst options in the Recruitment Foci will eventually become good enough that I can do this within the game. Perhaps not. For now, this is how I want to play my game and how I'll be looking for signing targets come the summer. It let's me combine statistics into overall ranking scores, and create new statistics by combining data that FM separates - for example, identifying players who might have low goals per 90 metrics, but who score a large proportion of their team's goals. Are these good players hidden in a poor team? Perhaps. Using statistics like this will help me identify such players and ask my scouts to find out. Specifically for my save, we've got a number of obvious targets - a 9 is key, as already mentioned. But our first choice XI features at least 6 loanees. That's not a comfortable position to be in, particularly when we don't have the money to secure any of them on permanent deals. So it could be a very busy second half of the season and an even busier summer. Forza Feralpi!

Indeed. Only Lecco below them - who are incidentally the only team Feralpi has beat this season (narrowly). And they had a big loss yesterday. I reckon it's pretty close to what I've been saying above - the board just think he's a bit out of his depth at this level.

Life imitating art here. FeralpiSalò has just relieved Stefano Vecchi of his job on the 23rd October 2023, 3 days after I created that scenario for this save.

Understood. Looking forward to the new version of the skin. Should tie-in with my game style nicely Cheers Matty, always appreciate your input to the recruitment queries I post