Statistical Fibra in #FM22 — CoffeehouseFM

Welcome back,

It has been a few years since I've properly written about the concept of 'fibra', or even applied the recruitment strategy to a saved game. Since then, the FM discourse around how a lot of people recruit has evolved. Data-led findings to aid decision making is following the same path in-game as it has in real-life, the FM22 Data Hub is an example of that. We, the Managers, are more informed and better equipped to rate players individually or against peers, and it's only going to be expanded upon for future editions of the game.

What is the fibra method?

Simply put, the fibra method is the acquisition of mentally strong players to suit an aggressive and demanding style of football. The players do not necessarily have to be good with the ball at their feet, instead the players within the fibra ideology will hurry and harass by working hard for the team. The players will come together and be worth more than the sum of their parts. The exact set of attributes used is relatively subjective. For example, I've often not muddied the waters with adding Physical attributes (e.g. Stamina) to the set of parameters; but you certainly can. As mentioned a moment ago, for fibra to work it has to go hand-in-hand with a complementary style of football. It's all well and good getting these warrior-like players…but if you're not using them for what you brought them in for, then what's the point? The concept is nothing ground-breaking, and something a lot of people previously did naturally in their saves, but perhaps it was never brought into blogging consciousness until my few early posts back in FM17.

Why revisit it?

When I look back, my previous approaches to fibra are very basic. I solely looked at attributes from the Player Search screen and recruited players over time to fit a tactical system. It worked, notably with Estudiantes in FM18, but just how well? I have so many unanswered questions, and that evolution within both the game and the blogger psyche is what makes me want to revisit fibra again in 2022. Questions like…

Does a Fibra attribute weighted score correlate with player performance, from data that I consider best demonstrates 'fibra'? e.g. Tackles, Distance Covered, Duels etc.
Does fibra continue to translate into the match engine? i.e. does it make a difference in perceived pressing, duels won/loss etc.
Can we statistically recruit fibra before looking at attributes?

If any of this interests you, then please read on…

Part I: Does Fibra correlate with player performance data?

We're simulating a year in FM22 in order to take a look at the data from the top five loaded leagues around Europe (that's England, France, Germany, Italy and Spain). Let's be frank about this, a correlation should absolutely exist. If not, what's the point in attributes? Admittedly, there could be my misunderstanding about the effectiveness and/or legitimacy of certain attributes within things 'under the hood' of the match engine. There is also the chance that those warriors are being set out by the AI and held back from fighting their way to success in less aggressive tactical instructions. So, a simulation isn't a deciding factor in determining whether or not fibra correlates with player performance data…but it's a good start.

Simulating a season will give me a partial answer. From there, I'm going to look at taking on management of a team that scores highly in fibra and further instill more of it into their side via player recruitment. I'll also be playing a system which, in my view, will suit the players and I'd be hoping to see fibra 'perform' in the repeated exercise of drawing out the numbers from Football Manager after season two.

So, to recap:

Sim a season (2021/22) of European football. See if players' fibra attributes scores correlate with player performance. Pick a side to manage for Season 2.
Manage a top-level club for the 2022/23 season, recruit or utilise existing fibra and shape the team to make use of it.

Let's have a look by first defining what makes my fibra attribute score (FAS). I'm keeping it simple, and purely mentally focussed*:

Aggression - a highly aggressive player is more likely to tackle hard and leave his mark on a player.
Bravery - a brave player is more willing to put his body on the line and engage in physical duels.
Determination - a highly determined player is more likely to help the team fight back from losing positions or improve his own poor performance during matches.
Teamwork - a player with a high teamwork attribute will follow tactical instructions and complement the attacking/defensive units of the team.
Work Rate - a player with a high work rate will exert more of his physical capability during a match.

Five attributes meaning I am rating players out of 100, therefore giving attributes equal weighting…simply because I don't know the exactness of what makes the most difference in the match engine**. Oddly enough, I don't even want to know…I think I like the mystery around Football Manager and it certainly keeps me coming back each year.

With FAS I am able to see if a higher score correlates with a number of player metrics that could be crucial to my tactical approach, particularly towards the defensive side of the game (although winning headers can definitely be a route to goal for us too):

Yellow Cards per Tackle
Tackles Won Per 90
HDRS A - Headers Attempted
AER A/90 - Aerial Challenge Attempts per 90
HDRS - Headers Won
HDRS W/90 - Headers Won per 90
HDR % - Headers Won ratio
K TCK - Key Tackles
TCK - Tackles per game
TCK R - Tackle Completion Ratio
ITC - Overall number of interceptions
INT/90 - Interceptions per 90
Distance Covered per 90

*Disclaimer: Some/All of these metrics will be influenced by other technical/mental and physical attributes e.g. Interceptions affected more by Anticipation, Concentration and Positioning. But I at least want to see if there is a FAS correlation.

**Another disclaimer is that you could just as easily build a tactic/style first and then evaluate which attributes are making the most impact on certain metrics, using regression analysis. The best ever example I have seen of this in FM circles is when FM Tahiti applied it on his nine (!) years of player data when playing a 442. What I am getting at here is that my study is still very much basic/and from a top-level with pre-defined assumptions of my fibra attributes. Certainly no 9 years of player data performance here; although maybe that’s one for the future 😃

Season 2021/22

So, I loaded the leagues on save creation and went on a one-year holiday meaning the 2021/22 processed in the Full Match Engine (the engine that would have been used if a human player was playing in). As mentioned, we have the top 5 leagues in Football Manager loaded (as rated by FM). Only two of the 2021/22 league winners match reality: PSG in France and Bayern in Germany. Diverging from reality we saw titles wins from Barcelona in Spain, Liverpool in England and Juventus in Italy. A mirrored Champions League Final took place though, only for Liverpool to be crowned the winners in this alternate reality, beating Madrid 3-1 after extra-time. YNWA.

But enough of alternate timelines, how does fibra correlate to the in-game statistics mentioned a moment ago? At season's end, I exported every player from the top leagues who had played 1,000 minutes or more. This equates to circa 1,500 players. I first eliminated the hundred or so Goalkeepers that had played over 1,000 minutes (their role on the pitch is so vastly different that I did not want them to distort the analysis). That being said, fibra is probably an underrated quality in GK…especially in terms of Aggression and Bravery in terms of one vs ones or aerial battles. But let’s save that analysis for a rainy day.

With a revised count of 1,350 players I was able to total up their FAS and then compare that score alongside the different player metrics using a scatter plot. I have already mentioned the many disclaimers with this kind of high-level study, but some observations can be taken:

Minutes Played – A slightly trivial one to start with, but there was a positive correlation of FAS and minutes played throughout the season. There could be many reasons for this, and fibra could be just the tip of the iceberg in terms of AI decision making when fielding an XI…but the higher the fibra, the more minutes you could expect to play (generally).

Tackles won per 90 – there was a slight positive correlation. An aggressive and brave player is probably going to win the 50:50s against less aggressive players, this could be the explanation.

Distance per 90 – eeekkk a negative correlation. At first this surprised me, but then I thought more about it. An explanation for this is that a significant number of fibra players are central defenders (20% of players with a FAS score were natural at CB). This is an outfield position that covers less distance per game, so this would bring any positive correlation down.

What’s interesting is that a positive correlation exists when you exclude all central defenders and look at the remaining 75% of outfield players (as shown below).

Headers per 90 – positive correlation. Bravery will influence the player going for aerial duels, this makes sense to me. Also, many of the high scoring FAS players are more than likely to be used in central roles; the likelihood of engaging in aerial duels (and winning headers per 90) from those central areas of the pitch is significantly increased.

Interceptions per 90 – slight positive correlation, but not enough to certainly say FAS has a huge influence. This supports my preconceived opinion that interceptions are more likely to be influenced by the likes of Anticipation, Concentration and Positioning. Again, further study needs to be done here.

Mini-Conclusion

Overall, the one year simulation of Europe’s top 5 leagues has shown that fibra positively correlates with some player data (Headers and tackles per 90 and minutes played), but not much with some metrics (Interceptions per 90). We’ve also seen that player position is a big influence on Distance Covered per 90 than FAS, which seems logical to me.

We knew where this post was going didn’t we? We’re off to find a club, in order to see if we can utilise fibra ‘better’ than the AI. So, if you want to know which eight European clubs have the most fibra, or whether we can smash our way to domestic glory…then read on.

Part II: Can I utilise Fibra better than the AI?

We’ve seen that Fibra (or my Mental version at least) correlates with certain metrics, but not all and perhaps not as strong as I thought it would. However, we have to accept this was just a one year analysis, and with the AI in control.

Could a human manager (in this case: me), build a system and get better outputs from fibra? As previously mentioned, I’ll be taking a side which is already pretty well stocked in fibra and look to further improve on it via player recruitment; before putting it all together in a tactic.

So, I took the top 150 FAS rated players in Europe (that’s the top 10% - where players have an average of 15.6 in each of the five FAS attribute scores) and looked to see which side had the most representation of fibra orientated players. I then filtered down to a list of eight clubs, who occupied positions 1-3:

1. Lazio - 7 players

2. FC Bayern - 6 players

3. Inter - 5 players

3. Chelsea - 5 players

3. Hertha Berlin - 5 players

3. Juventus - 5 players

3. PSG - 5 players

3. Tottenham - 5 players

(Note - I previously assumed Atlético de Madrid would score highly, but they only have three players in the top 10%: Luis Suárez (82/100), Felipe (79/100) and Koke (78/100)).

There’s only one choice for me in that list, Hertha Berlin who finished 13th in the Bundesliga and who have Estudiantes alumni Santiago Ascacíbar in their ranks (who has a FAS of 86/100). It’s not just Santi embodying fibra, they also have Davie Selke (86/100), Lucas Tousart (81/100), Suat Serdar (81/100) and Vladimír Darida (80/100). The central core of this squad are warriors.

Additionally, from that list above, Hertha Berlin will be the least reputable and with less expectation to challenge for domestic honours. In fact, the season objective is to finish in the top half…I therefore think this is perfect for the siege mentality I would like to deploy on my German counterparts. “Ich bin ein Berliner, baby!”

How did the AI perform?

In 2021/22 Hertha Berlin played a 4-2-3-1 most of the time. A quick review of certain metrics against their FAS showed slightly more positive correlations between fibra and performance statistics mentioned earlier on in this piece. This could mean that the previous AI Manager used a more intense tactic compared to the average tactic used in 2021/22, and in doing so helped justify that fibra matters. Why? Taking a view of Distance per 90: central midfielders Vladimír Darida, Lucas Tousart and Santiago Ascacíbar are putting in a shift and score well alongside Hetha’s attacking left hand side (Mittelstädt/Bjørkan) and right wing back Lukas Klünter.

Another observation from 2021/22 Hertha player performance data is Davie Selke’s monstrous 15.09 Aerial Attempts per 90, and his ability to win 64% of them as a lone forward. His fibra qualities will certainly be something I look to make use of in the tactic I build.

Certain 2021/22 Hertha player metrics (1,000 or more minutes played).

My 442 Evolution v1-2

It’s fair to say that the first half of the 2022/23 Bundesliga with Hertha Berlin have been everything I love and loathe about Football Manager. There’s been good moments, but also some really bad ones. We’ve had to chop and change roles, instructions and water down what I wanted to initially achieve with fibra. The above tactic can probably be considered a ‘version 2’ of the 4-4-2. Initially, I had no playmaking element in the side, and used dual Pressing Forwards…and it was not working: three draws and a 0-4 defeat Vs Schalke at home. I also feel that I was asking the team to press a bit too much. I had asked four midfielders and two strikers to close down more. Combined with a team instruction of ‘More Often’ or ‘Much More Often’ pressing, the squad was putting in one hell of a shift…for not much of an end result!

I added an Advanced Playmaker (at MC) and a DLF (replacing the supportive PF) to the side after a conversation with GrassNGear ep60 special guest Cleon. This did initially pay off, but a horrid run over September culminating in the 6-1 defeat at Bayern saw me move to a v3 feat. Wide Playmaker (more on this tactic later). Ironically, a Wide Playmaker was initially advised by Cleon…and I told him it’s not fibra-like and duly ignored him.

In terms of Team Instructions, here was my reasoning for using various instructions over others:

Balanced Mentality - I tend to not deviate up and down the levels too much in terms of team mentality, because it changes a fair bit. I will probably be Balanced against teams I expect to beat, but Cautious against more difficult opposition.

In Possession

Pass Into Space - Initially I did not have this selected, but added passing into space during pre-season in order to break through the lines and open our 4-4-2 up a bit more.

Wide - Playing a wide formation, so let’s be wide in possession, stretch play a bit more so we can create space for attackers and complement passing into space.

Floated Crosses [optional] - Usually, I like to have Mixed Crosses selected in order to have a variety of crosses challenging opponents. However, since I have two aerially dominant forwards (Embolo and Selke),I think it makes sense to float the crosses as they stand a decent chance to win the aerial battles. This is something I will add on a game-by-game basis depending on how I rate the opposing Centre-Backs pre-match.

Play For Set Pieces [optional] - I've mentioned about having two players with good free kick taking ability, who both like to curl the ball. I also have the aforementioned forwards who are brave and tall, with brilliant jumping reaches. It makes total sense to me to play for set pieces, to provide respite to our low-mid block against superior teams, but also be a threat offensively against weaker ones.

In Transition

Counter - Initially, I set about playing both Regroup (when possession is lost) and Counter (when possession is won). However, I felt the press wasn't quite working well during pre-season…because we were telling midfielders to fall back into shape but simultaneously also press. It's something that Atletico Madrid's 442 used to do well under Simeone, but I didn't like what I was seeing in FM. So, by just being on Counter it's a bit simpler.

Distribute To Full Backs [optional] - My Goalkeeper has Kicking 11, for this reason I am choosing to send the ball shorter to the Full Backs and we’ll build from there.

Out Of Possession

Lower Defensive Line & Lower Line Of Engagement - We're not a low block, but we're certainly not a high one. I've withdrawn the lines slightly in order to offer a bit more protection against the speedier sides.

More Often/Much More Often (Closing Down) - As mentioned earlier, I initially combined the closing down PI on the midfielders with a wider team instruction in order to press more often. I since toned this down in v3, and instead press ‘Much More’ on a team level.

Opposition instructions are something I rarely use in large quantities, but I thought I would have a go at asking my side to press the central areas like men possessed. This is a semi-experiment after all, so I want them to win the midfield battleground at all costs. No pressing from the front bollocks or limiting the Goalkeepers’ distribution. If the opposition has the ball in their own half, that’s great…they can’t really do much damage there. But if their DMs or midfielders start nearing the centre circle, let’s commit some atrocities and press them, led by the forwards. The wider opposition players are typically the guys crossing from bylines, so let’s keep them on their weaker foot and try to nullify that supply too.

Recruitments

Jonathan Schmid - €3m from Frieburg

Across all 1,500 players from the top 5 leagues, Freiburg’s Jonathan Schmid had the best Interceptions per 90 stat (3.96 per 90, and ranked 12th overall in total interceptions made with 143 for the season) playing as a Wing Back in a 442 in all but one of their Bundlesiga games. He bettered current Hertha Berlin Right Back Lukas Klünter on many metrics, and has a superior FAS (76 to Klünter’s 71). To my surprise, he was transfer listed at $3m with Freiburg missing out on a European place by 1 point on the final day of the season. “One man’s trash is another man’s treasure”, our Director of Football quickly made the signature to bring Schmid to the capital.

In terms of attributes he looks decent, with strong fibra complemented with decent crossing and physical attributes to get up and down the flanks; I hope he can be a threat. Schmid also has the ‘Curls Ball’ trait coupled with good free kick taking ability (15), and will be the 2nd player in the squad to have this godlike combo (alongside Marvin Plattenhardt). We will therefore play for set pieces, knowing that there is likely always somebody on the pitch to guarantee effective deliveries.

Breel Embolo - €6.5m from Borussia Mönchengladbach.

Ex-FM Wonderkid alert! Breel Embolo joins Hetha as our flagship fibra signing. Controversially, he didn’t make it on to the 1,500 player spreadsheet…because he failed to meet the minutes played (885 minutes, instead of 1,000 over 2021/22). However, Breel was transfer listed by Mönchengladbach despite having a solid 6 goals in 6 Bundesliga starts (and 9 subs) from the right wing. His 0.87 goals per 90 and 72% shots on target ratio are better than the Bundesliga average and this attracts me. However, I’m worried I’m a bit blinded by the romance of getting a fallen starboy to perform, but I’m hopeful he can deliver if played centrally.

That’s right, centrally is where Breel Embolo belongs in my team. I personally feel he fits well in the tactic in either forward roles, and has a FAS of 78/100. I’m excited to see if €6.5m turns out to be a bargain!

Valentin Rongier - Transfer Failed

Having assessed the squad over the first few weeks of the season, I felt I could have done with one more utility player on the right side of the pitch. A Deadline Day deal was arranged with Marseille to bring Valentin Rongier in for €13m. He ticked a lot of boxes in terms of fibra metrics, particularly with his strong tackling (Rated 40 with 2.5 Key Tackles per 90) and I liked that he could play both Full Back, Central & Right Midfield. In real-life he is used heavily by Sampaoli in a variety of roles (something I wrote about here), and he naturally suits a pressing style with a FAS of 74/100. However, the nature of doing business late means deals fall through…and sadly this happened when OM could not find a replacement in time.

Hertha Berlin Playthrough (August to December 2022)

Update from 17 league games in - Position 9th - 6 wins/4 draws/ 7 defeats

I don’t want this post to be too save-update heavy, as it is already so long, but we do need to explain what’s happened after 50% of the league has been played. The double benefit of reflecting what happened will also help me identify what I need to do during the two-month break due to the 2022 World Cup. I will start with four positives first, before moving on to four negatives…

1st in conversion rate (15%), due to only four other teams taking fewer shots than us (10 shots per 90).
4th best attackers in the league with 27 goals (1.59 per 90).
4th best at gaining possession (1472 occurrences/86 per 90).
5th in terms of headers won (983 headers/11 per 90).

The negatives…

In contrast to the first positive I listed above, a negative is that we take too few shots. Averaging 10 shots per 90 is due to my instructions, simply put: our attacks need to be more common.
We allow the opposition to take too many shots on goal, and too many on target (7 opposition shots on target per 90). Whilst our lower lines allow the opposition closer to goal, the shots on target we’re given up mean our approach is now questionable.
We are the 3rd worst for conceding (1.88 goals against per 90). This hurts.
I thought this side would be effective from crossing, and whilst we’re crossing a lot (30 attempted crosses per 90), we’re only completing 22% of them (6.7 per 90). The tactic is encouraging something I initially thought we’d be decent at, but we’re not.

In a league of heavy metal pressing football, our lower lines are not really working. 9th is bang on the Board requirements (top half), but there’s an opportunity to recruit wisely in January and tweak a few parts of our game in order to aspire to be better.

I certainly feel we need a creative player, preferably a left footed playmaker that could cut inside from the right wing and play that Wide Playmaker role nicely. Creativity is not something I recruited last transfer window, and now I’ve assessed the squad further…we’re crying out for somebody who can move between the lines and create clear cut chances.

I’ll therefore be running the numbers once again on the 2022/23 season data on players who have played 500 minutes or more from the top 5 leagues in Europe. I will deviate from the fibra focus somewhat (but I’ll be keeping FAS in mind at all times), by looking for attacking metrics like Chance Created per 90, Assist per 90, Key Passes per 90 and Dribbles per Game.

I filtered on: At least 1 Chances Created per 90 and At least 0.3 Assists per 90. Then looked at those metrics alongside Key Pass per 90 and Dribbles per game. You will see from the above that all targets are from within the Bundesliga, probably because I had the filter of only interested players (selected as ‘Doubtful’).

It’s a great list, and there’s a top three for me (all coming with relatively the same fibra).

Amine Harit is on big wages, but he’s young and attainable with an active minimum fee release clause of €10m. He tops the Assists per 90 and is clocking up modest amounts of Dribbles per game.
We also see that Louis Schaub is a statistical god at the moment in the Bundesliga, with ridiculous chances created per 90 and 2nd best for Key Passes per 90.
Perhaps the most romantic signing is Ondrej Duda. He’s ex-Hertha, and although I’m not sure the circumstances of his exit, it could be a potential homecoming? He is 2nd best for chances created, behind Schaub.

Whilst I am aware of the chances being boosted by set pieces, such as corners, the inclusion of other metrics highlight other areas of playmaking…and I’m hopeful any of the three will do a decent job for me as the side’s creator. Who did we get?

The Creator

Louis Schaub is signed for €6m! After deliberating extensively, I actually went for Amine Harit first of all because I felt he had the higher ceiling being 2 years younger than the other 27-year-olds. But Amine wanted a much higher wage than his current deal. So, we get Schaub for half the price and on a wage of €2m per annum. A yearly €5m difference in salary compared to what Harit wanted! 🤯

My 442 v3 (January to May 2023)

v3 continues to use two banks of four, mainly to maximise the amount of fibra I can get in the side. A 2-month injury to Breel Embolo, and continued ankle issues for Krzysztof Piątek, encouraged me to move to a Supportive Striker-Poacher relationship up top in the end to try and get a Davie Selke and Stevan Jovetić partnership firing.

After another period of tweaking after the winter World Cup break, I feel like we finally got into our groove moving into the last quarter of the season. What’s now changed?

I pushed the lines up a bit, it should reduce the number of shots on target against us. We’ll also press a tiny bit less as a result, we’re diluting our fibra but maybe I need to balance our qualities?
Passing is now shorter, so I’ve returned to standard width. We’ll see if this can bring about better offensive chances, giving the Wide Playmaker plenty more options in attack and hopefully more one-twos/interchanges.
I have changed the Right Back to a Wing Back, making full use of the playmaker role cutting in and providing him with definite offensive support on the right hand side.
I’ve also ditched the left sided Winger (and a % of crossing threat), instead we’ll have an Inverted Winger who will be cutting inside and moving into central areas with the ball. He has a supportive Wing Back behind him.
The Central Midfield is more balanced, and as much as I loved the CM-A’s previous performances (in v1) or feeling that AP-A could offer more (v2)…there were times where they were too advanced and ahead of play, offering a gaping hole for the opposition to exploit*. Eventually I swapped the more offensive CMs around, because the CM-D covers the Wide Playmaker’s freedom to roam around and offers a lot more protection.

*This was a huge example of cognitive bias for me in Football Manager. I remembered the CM-A’s key goals and deep runs…but a lot of what made us vulnerable was our soft centre due to the CM-A’s high positioning, which I often overlooked. Since toning it back a bit, I’m now seeing a better team performance.

Overall, we came very close to securing a European spot and I felt with the momentum we had we could have made it if the league was 1-2 games longer! We also got to the DFB-Pokal Semi, losing out to eventual winners BMG. A great season? Not quite, but an improvement on 13th place and a step closer to Europe with minimal expenditure.

But what about fibra?

Data Findings

Here are some summary findings from the 2022/23 season:

I gave 1,000+ minutes to a greater range of players compare to the AI of last season, and although some were lower fibra I kept the squad’s average at last season’s score of 72 out of 100.
The side made more key tackles (9.8% increase) and interceptions (4.85% increase) in 2022/23 (compared to last year). We are also covering a greater distance in matches (9.69% increase).
We did not tackle as much (despite more being Key than last year) - a decrease of 3.34%, and we contested fewer aerial duels too.
If you look at the sub-70 FAS players, you can see they underperformed in metrics against higher FAS peers (who obviously have the more combative positions/roles in the side).

(Certain 2022/23 Hertha player metrics (1,000 or more minutes played).

Conclusion: Of course, I wanted this post to prove that fibra is all powerful and that I could use it better than the AI. In some ways I did, in other ways not so well. I had to adapt our approach somewhat during the season to get results. Our lower lines, and tailored press (as limited as it is in FM), simply were not working as well as I hoped. Perhaps it would have with better players, or more focussed recruitment over time. We have to accept this was a one season project, and also accepting that I didn’t want to achieve success with a high press gegen tactic, commonplace in the FM content creation sphere.

I guess my conclusion is that fibra worked best for Hertha Berlin when it was in moderation - place it into your team in the roles that matter. For instance, the lack of playmaker (and my refusal to use a role like Wide Playmaker simply because I felt it didn’t work hard enough) held us back until I corrected it. Creativity and unpredictability was needed and the signing of Louis Schaub gave us that for the 2nd half of the season….complementing the fight we already had in the squad.

TL;DR

What have I learnt? Well, I’ve learnt that (1) Fibra correlates positively with certain metrics and (2) that you can use those data metrics to recruit high performing fibra intensive players (e.g. Schmid). However, I didn’t quite get those performances throughout the whole squad in a one season playthrough with Hertha Berlin. One season isn’t enough…maybe as a philosophy we need to judge and reflect further down the line. 3, 5…maybe 10 years of constant recruitment and training?

This has been statistical fibra. I’m sure we will come back to it again one day, thank you for reading 👊🏻

Tony / FM Grasshopper