Introduction

I currently work as a supervisor in live data production for primarily South/Latin American professional football leagues including the Copa Libertadores, Brazil Série A-B, Argentine Primera División, Uruguayan Primera División, Chile Primera División, Colombia Primera A, Ecuador Liga Pro, Peru Liga 1, Liga MX, MLS, and the Canadian Premier League. We produce live match details for media clients including lineups, goals, assists, cards, substitutions, and play-by-play details with the league detail level dependent on the data package requested by our clients. As I seek to turn a cool job into a viable career in the sport that I love, I have decided to begin taking statistics and data analytics courses in my free time while practicing practical applications using the extensive and easily exportable data produced in Football Manager. This story will document my journey from data monkey to data expert (hopefully, probably not). I will begin as an unemployed, unlicensed, former Sunday league footballer (as is the case in real life) applying for jobs in the lowest available division of the USA league system (USL League Two). My database will be limited to the countries I currently cover at work and I will use a data-driven approach in my recruitment and team selection. Let the game begin!

The First Step

After applying for every job in the USL2 Southeast Division, where I currently reside, I have agreed to become FC Miami City‘s manager. Nearly half of the clubs in the division turned me away for demanding they fund my first coaching course. I feared I would strike out in every interview in the division before needing to accept a role without the promise of a coaching license on the horizon. Luckily, FC Miami City agreed on the proposal after I promised to guide them to a top-half finish, exceeding their initial proposed expectations. 

The Club

FC Miami City was founded in January, 2014 as a member of the United Soccer League. The club has strong ties to Paris Saint-Germain, playing a role in the development of Paris Saint-Germain Academy USA players by offering them pathways to the USL. Our home ground, Central Broward Park, is a 20,000-capacity dedicated cricket stadium in Lauderhill, Florida, one of just two dedicated cricket stadiums in the country.

Expectations

With the fourth strongest squad in the league (according to the bookmakers), we should be able to easily achieve the agreed upon expectations of finishing in the top half of the table. I have yet to perform an in-depth analysis of our squad and it's strengths/weaknesses, but I will do so and provide a breakdown in my next update. Hala Miami City!

This sounds pretty cool. Looking forward to seeing how you bring the data element in at that level 

 The Squad

Right from the jump, it became clear I would need to be creative in my tactical setup to accommodate our strengths and cover our abundant weaknesses, the most obvious of which being the near complete lack of options at center back. At the moment, our only natural central defender is Jesus Tinajero and the transfer window is closed for another month. We have strength in depth at wing-back and far too many strikers. Two solutions became clear, convert the wing-backs into wide center backs and utilize a two-striker partnership. With relatively high aggression, work-rates, and pace spread around the squad, a gegenpress style of play was also the clear tactical choice. Defending deep was not an option considering the lack of height and strength available in the wide center back positions. 

The System

I settled on a 3-1-3-1-2 formation, which I have honestly never used in any previous save. I normally prefer a somewhat standard 4-3-3 formation with the roles dependent upon the players available. However, I'm relishing the opportunity to build a new system and style to get the most out of my eccentric Floridian squad. The idea is to play a wide game with plenty of crossing, either from deep with our very competent crossers at wide center back or from the byline with our touchline wingers. Garcia will float around the edge of the box to pick up second balls and thread passes into the box. Smith & Jackson both possess strong tackling ability and work-rates, hopefully preventing counter attacks through the middle. As I mentioned before, this system is far outside my comfort zone so it is very much a work-in-progress. 

The Table

The season is already in full swing, with the club currently underperforming in 7th. Hopefully we can begin the climb the table as we work out the kinks in the new system and I will likely make further changes following a dive into our performance data to this point. I will likely post another update looking over our performance metrics and areas for improvement in the following update, stay tuned!

Love a good Moneyball save. Def following.

Team Performance Data

It has been a slow process but I have finally extracted and organized the team performance data in several ways to better understand my team's playing style, strengths, and weaknesses prior to my arrival. As you can see in the screenshot above, my performance analyst was of very little help in this process. He did point out that we have been playing in a 4-2-3-1 setup for the most part, so at least he's aware of formations I suppose.

The first step was extracting the data provided in the Competitions>Stats>Team Detailed page within the game, which is not easily exportable like the player data in the squad overview or the recruitment hub. Being the data monkey I am, I manually entered each value into a Google Sheet to lay the foundation for my analysis. The goal was to identify areas in which we were the among the weaker sides in the league and those where we were strongest, first seeking correlations and possible causations and then digging into each area of the data which could be improved upon or further exploited.

Using a Python script, I derived correlation values between each of the team performance metrics to identify any surprising or interesting correlations that may not be expected. Unfortunately, there were no real surprises and pretty much all of the data points aligned as you would expect. Feel free to peruse the correlation matrix above for yourself but you likely won't find anything out of the ordinary. For reference, dark red indicates strong correlations, dark blue indicates strong negative correlations, and the lighter areas indicate weaker correlations.

FC Miami City Performance Data

The next step was to dig into our performance data and create visualizations that would help us better understand our standing within the league. I used Google Sheets to generate percentile values for each column to ease the creation of radar charts that would help display each area of our performance in relation to the other teams in the league. In the chart above, you can see how our shooting frequency and efficiency compares to the other teams based on percentile values of each metric. While we are taking very few shots per game, we are relatively efficient in our finishing ability. We are slightly underperforming our xG by -0.05 per game, however our shot on target rate (SoT %) is the strongest in the league at 43.33%. Our conversion rate is also very strong, while the rest of the shooting metrics leave us somewhere around the middle of the pack. Essentially, it seems we are creating few chances per game yet we are creating high quality chances in each game, and our finishing ability is strong enough in comparison to some of the other teams. To better understand our chance creation, I then created a radar chart to demonstrate our creativity metrics in relation to the league.

It quickly became clear that we are not a possession-based team and have been relying on our strong crossing ability to generate chances for our lone striker in our 4-2-3-1 formation. As I suspected, we are among the lowest in the league in terms of chances created per game, passes completed per game, final third passes per game, and fouls won per game. However we rank very high in the league in terms of cross completion rate, volume of crosses completed per game, and successful dribbles per game. Surprisingly, despite this direct play-style, our pass completion rate is also quite high in relation to the other teams. Hopefully with the new 3-1-3-1-2 system we can extract more value from these strengths by placing even more emphasis on wide play while also benefitting from an extra striker in the frontline. The final step of my analysis was to better understand our defensive tendencies.

In this visualization, I reversed many of the percentiles to better represent our strengths & weaknesses, meaning the lower percentile in areas such as “Goals Allowed Per Game” (GA/Game) indicates a higher number of goals allowed per game. As you can see above, while we rank very highly in expected goals allowed per game (meaning we are allowing very few expected goals per game), we are allowing far more goals per game than most of the other teams in the league. Considering that we are allowing very few shots per game and very few shots on goal per game, it's clear that our goalkeeping is an area of weakness. In addition, we are very weak aerially, which was already made abundantly clear in my initial squad overview when I realized we only have one natural center back. We rank fairly highly in the league in terms of the number of times we're winning possession of the ball in each game, which correlates strongly with points per game. However, we are allowing a relatively high pass completion rate for our opponents and a relatively high number of passes per defensive action, indicating a less engaged defensive style. Hopefully with our emphasis on a high pressing style of play, we can begin to disrupt our opponents' play more effectively and further limit the number of chances our goalkeeper has to face before we are able to find a replacement for the keeper to shore up that weakness. Over the next week, I will dig into individual performance metrics and potentially create another post here about those findings before finally beginning to play some actual football. Bear with me, please, haha. 

Player Performance Data - Goalkeeper

It took quite a bit of trial and error to clean and arrange the data across numerous spreadsheets to arrive at the graphic above, but we're here. I wanted to create a visual in a similar style to the lovely FBRef.com to easily compare the percentile values for all of my players' performance metrics in relation to the other players in their position across the wider USL League Two. The blue text labels to the right of each percentile bar state the true value of each statistic, rather than the number of the percentile itself. In the visual above, you can see how my starting goalkeeper, Adam Noble, compares to the other 87 goalkeepers who have played a minimum of 450 minutes in the USL2. The goalkeepers are missing quite a lot of statistics, mostly because they are not involved in the phases of play in which many of these actions occur but also because several key goalkeeping statistics seem to be bugged in FM24. However, we can still glean some insights from this graph particularly when paired with the “Defending” graph in my previous “Team Performance Data” post. Here is another copy of that graph below, for ease of reference:

He is around the middle of the pack in terms of his Average Rating, Player of the Match awards, and Points Per Game, however his Distance Covered (km) suggests he has been playing as a very active sweeper keeper. This surprised me, considering the relatively high number of Passes Per Defensive Action we allow our opponents. I had assumed we were not pressing high enough to disrupt opposition build-up but perhaps we have an inefficient or disorganized high press. This would also explain the high amount of goals we’re allowing despite conceding a low number of shots. Noble is ranked very low in the league in terms of Pass Completion, Saves, and Save Percentage, yet he is in the top 29% for Expected Goals Prevented and the top 33% for Expected Goals Prevented/Goals Conceded Ratio. This suggests he is facing primarily one-on-one situations when conceding goals, and doing quite well in that regard. I initially believed our defensive underperformance was the result of poor goalkeeping, but now it appears an ineffective high pressing style is causing our goalkeeper to face too many one-on-one chances. Noble may not be a top tier keeper in this division but his underlying numbers are better than I had expected. In the next few posts, I will continue looking at our individual performance metrics in each position of our team to find exactly where our main issues are arising, and where our main strengths could potentially be further exploited. 

Player Performance Data - Defense

Center Backs

Our primary starting partnership in central defense to this point has been Jesus Tinajero and Mike Kaplan. While Kaplan prefers playing as a right back, he is actually a fairly competent and natural center back. Combine this with his very strong crossing ability and he could be the perfect wide center back in the new system. Kaplan seems to be the more progressive passer of the two, while Tinajero is the more aerially dominant, traditional center back. Kaplan tends to cover more ground and press higher than Tinajero, while Tinajero registers more blocks and interceptions. They are both strong in the tackle and unafraid of an aerial duel, although Kaplan's aerial success rate is not quite as impressive as Tinajero's. I will likely try to maintain this partnership in these early stages to ease the transition into the new system, though with a left-footed wide center back to the left of Tinajero.

Full Backs

Andrew Herold, Francis Ankrah, and Max Snitko have operated as our full backs in the back 4 system thus far. Our backup left back, Mike Nolly, only featured in one starting appearance so I have not created a percentile chart for him due to the lack of minutes to justify a relevant comparison. Andrew Herold and Max Snitko appear to be the most creative among our defenders, with Snitko possessing the strongest crossing ability and Herold seemingly creating more chances from cutback passes into the box. Herold will be starting along with Tinajero and Kaplan in central defense, but he will need to rely more on crosses from deep rather than overlapping for the cutback pass. Hopefully that doesn't stifle his creativity too much. Snitko has no experience playing as a central defender but he could be an ideal candidate for the right wide center back role based on his very impressive crossing numbers and relatively strong aerial ability in relation to the other full backs in the league. I was surprised to see Ankrah with more appearances than Snitko given how Snitko appears to be performing better in most areas, so I will give Snitko a run in the team before trying Ankrah in central defense, particularly given Ankrah's weak aerial presence. 

As it stands, Herold-Tinajero-Kaplan will begin as my starting 3 in central defense with Snitko rotating in fairly often. Kaplan will be my preferred replacement for Tinajero when a rest is needed. I will have to rotate strategically to maintain fitness levels and avoid needing to use one of the full-backs in the central role of the back 3. Kaplan is also comfortable playing as a left back, so he could be a versatile option to rotate around all 3 roles when necessary. The league schedule goes through periods of quick turnarounds between matches and longer breaks, so I will try to use the Tinajero-Kaplan partnership sparingly during those periods when fixtures are coming thick and fast. This is when Snitko will see more frequent game time. Nolly and Ankrah will serve as last resort backup in the wide center back roles, as neither possess experience as central defenders nor the height/jumping reach to be effective in the air.  

Player Performance Data - Midfield

Defensive Midfielders

David Jackson and Harry Smith have operated as our double pivot in all 6 matches so far this season. They have similar profiles, both suited to the ball-winning midfielder role with limited technique but great tenacity and bravery. This made it difficult to decide who should occupy the deeper defensive role or the more advanced, balanced central midfield role. Several factors came into consideration, ultimately making it clear that Smith is more suited to both positions but will be my choice for the central midfield position. Smith possesses a more expansive passing range and a creative presence that would be useful in the more advanced role. Jackson on the other hand tends to play more progressive passes but fewer risky passes, hence the lower number of key passes played but exceptional pass completion rate. The way I see it, Jackson can safely progress the ball from deeper in midfield to either the wingers or to Smith ahead of him, who can then play the key pass that kicks the attacking move into another gear. While Smith possesses the greater aerial presence which could be valuable in the deeper role, I feel that he can be an asset in the central midfield role whereas Jackson could be more of a liability there. They are both somewhat inefficient in their tackling and fairly efficient in their pressing. Given Smith's slightly superior pressing efficiency and interception ability, as well as Jackson's propensity for blocking, I think Smith could be more suited to defending from the front and preventing the out-ball while Jackson disrupts opposition chances from behind. The final major factor in the decision was the players' relative pace. The data above is actually misleading in that regard, as Smith covers more ground and registers more high intensity sprints than Jackson, suggesting he relies on his pace more than Jackson. However, after looking at their actual attributes, I discovered that Jackson actually possesses far greater pace than Smith. Considering that the ball-winning midfielder will need to cover more ground than the more stable central midfield role, this pace difference was the confirmation I needed to make the final call.

Attacking Midfielders

Jose Antonio Dominguez and John Garcia have been vying for the starting attacking midfield role throughout the season, with Dominguez generally being favored by the previous coach. In my initial tactical setup post I selected both players in my ideal starting lineup, with Dominguez playing as the left-sided striker and Garcia playing behind the front two as the preferred attacking midfielder. This selection makes a lot of sense based on the data above. Dominguez tends to look for goal and make runs in behind more often than most attacking midfielders. He is ranked in the top 2% of USL2 attacking midfielders for goals per 90 and is overperforming his expected goals. However, the fact remains that we are very light in terms of depth in midfield but have too many options at striker. We currently have 5 strikers, not including Dominguez, and it simply does not make sense to stretch our midfield depth further to push Dominguez into a less familiar role despite being suited to it. As such, the two players will have to continue fighting for a starting role and occasionally rotate into the deeper central midfield role occupied by Harry Smith when necessary. Dominguez appears to be the more creative risk-taker of the two while Garcia seems to be better at occupying the half-space to safely circulate the ball and maintain pressure in the opponents' final third. Both players have their merits and will be useful for different opposition. Garcia will be used mainly against the more defensive teams to maintain pressure and concede fewer counter-attacks with Dominguez serving as a super sub. Dominguez will be used more against the sides who like possession and leave space in behind the defense. Garcia will be my preferred choice in the deeper central midfield when Smith needs a rest, so both players will receive frequent minutes. In the next post, I will analyze my strikers and wingers and then finally start playing some actual football.

Player Performance Data - Attack

Wingers

For starters, this segment immediately made me realize an error that I missed during the data cleaning process. The Expected Goals Overperformance metric was malfunctioning within Football Manager so I had to adjust it using functions within my spreadsheets. I'll have to go back to my previous posts and adjust them for each of the other players, but at least I caught it here when it matters most in my attacking unit. 

Rhys Hughes-Mason and Rafael Galindo have started all 6 matches this season. Due to the relative lack of game time for the backup wingers Chris Wheeler and Tom Turner, it's not really fair to judge them on those few performances alone. However, it is interesting to see their impact off the bench to this point. Hughes-Mason and Galindo are both fairly creative, though I would like to see an improvement in their chance creation and assist numbers now that we'll have a two-striker partnership. It seems we tend to focus the build-up on Galindo's side as he registers more passes, dribbles, and shots than Hughes-Mason, although Hughes-Mason has nearly twice as many cross attempts per 90. They're creating a similar number of expected assists so it seems like a healthy balance overall. However, Galindo seems to be far more effective in terms of pressing and winning the ball. A similar trend can be seen when comparing Chris Wheeler and Tom Turner, with Turner being more involved defensively and in the final third. I'll likely give Wheeler and Turner some more minutes once the lads are more used to the new system, paying special attention to the training ratings leading up to matches. Galindo is our standout winger in terms of his overall performance and his attribute ratings, so there will be less rotation on his side despite Turner's impressive numbers off the bench.

Strikers

Gerardo Torres and Hugo Acosta have been sharing the starting striker spot, with Daryl Bradley proving an effective super-sub. Bradley has scored twice while Torres and Acosta have only registered one goal each. They all seem to have unique abilities that will allow me to create different partnerships depending on the opposition. Torres will play a prominent role as an effective supporting target man. Acosta and Bradley will share the other role, with Acosta acting as an advanced forward and Bradley as more of a poacher. Bradley will function as a poacher in attack but I'd like to see more defensive output from him as he doesn't seem to be involved in the press at all, so I'll need to find a role that suits him without necessarily using him as an actual poacher to improve his pressing involvement. Acosta is an efficient presser but at 34 years old his legs will start to go soon. We have two other backup strikers waiting in the wings to get their debut, Ashley Parrado and Aidan Martin. Parrado will serve as backup for Torres in the supporting striker role, though he is more of a deep-lying creative forward rather than a target man. Martin is a talented advanced forward so he will compete with Bradley as backup to Acosta. We have a good balance of strikers with differing abilities so I'm excited to see how the partnerships develop over the remainder of the season as we seek to increase our pressing efficiency from the front. 

Now that we've taken an admittedly drawn out look at all of our players' strengths and weaknesses, it's time to play some football! I'll provide an update on our performances once we're about halfway through the season, and for my next data-focused project within the save I will create a correlation matrix of the performance data and player attributes to get a better idea of how the actual players' abilities relate to each performance data point. I'll wait to do so until everyone has more minutes under their belt to get a more reliable data sample, and post my findings here. Thank you for your patience, if you're still here haha. Until next time!

2024 Season Review

My inaugural season has finally come to a close on a somewhat bittersweet note. We only managed to move up two places in the table from our initial standing when I took charge, with our points-per-game average increasing by a meager 0.07. While it may seem like a small change on the surface, our underlying numbers tell a different story. We grew significantly throughout the season, becoming arguably the most front-footed team in the division. Ultimately we failed to capitalize on the strengths of our performances in key moments which could have seen us rise further up the table. As you can see from the table above, it is a league of very fine margins with just 6 points separating 1st and 8th. Perhaps in a longer season we could have pushed onto another level as the team grew in confidence but unfortunately our strange league only allowed us to play 14 matches despite the presence of 9 other teams. To be honest, I'm unsure whether that's an issue with the database or a unique feature of the league. Regardless, we achieved our board's expectations of achieving a top-half finish and I'm proud of the progress we made. Let's take a look at our underlying numbers in comparison to our earlier metrics prior to my arrival.

Performance Data

Defending

In the radar charts above you can see how our defensive metrics changed from the beginning of the season (left) to the end of the season (right). As previously explained in my earlier posts, some of the percentile metrics are reversed to provide a clearer picture of how we're performing across the board. For instance, prior to my arrival at the club we were around the middle of the pack in terms of the number of passes completed by our opposition (Passes Comp. Ag./Game) and the number of final third passes allowed per game (Final 3rd Passes Ag./Game). However, by the end of the season we were allowing fewer passes per game and fewer final third passes per game than any other team in the division due to our possessional dominance and pressing intensity. Our opponents' pass completion percentage (Pass Comp. Ag %), passes per defensive action (Opp. Passes/Def. Action),  expected goals (xGA/Game), and goals (GA/game) also decreased, demonstrating how we limited our opposition to fewer chances by engaging them higher up the pitch with our press. While the relatively low number of shots we allowed per game (Shots Ag./Game) remained stable, we allowed more shots on target (SoT Ag./Game) than we previously were and continued to allow our opposition to over-perform their expected goals due to the type of chances we tended to concede. This was to be expected as with any high line but it's definitely an area to improve in the coming season with the right reinforcements and potential adjustments to the formation. Lastly, our aerial success rate (Headers Won %) improved significantly from being among the worst in the division to among the best. Although we ended the season with the same relatively short group of players we started with, we managed to improve in this aspect of our game by containing our opposition in their half for the majority of our matches while utilizing our target man to exploit their weaker center backs. To summarize, we were able to successfully implement a high pressing style of play which marginally improved our defensive record but significantly benefited our attacking play as you will see in the following graphics.

Creativity

Here you can see how our style of play with the ball evolved throughout the season. While we were still focused on getting crosses into the box, we often utilized a more patient style of play against the more aerially dominant sides and as a game management technique which allowed us to dominate the possession in nearly every match. As you can see in the chart on the right, we led the league in the number of passes completed per game, average possession, pass completion percentage, and possession lost per game (meaning we lost the ball fewer times per game than any other team). Our chance creation skyrocketed and our non-penalty expected goals (NPxG/90) increased as well. We slightly increased the number of final third passes per game although much of our possession occurred in the middle third where we probed and attempted to manufacture crossing situations and through balls from deeper pockets of space. Our cross completion percentage decreased due to the higher frequency of crosses against low blocks compared to the more transitional crossing situations we were likely exploiting in the earlier part of the season before my arrival. Similarly our dribbles per game decreased due to the less transitional style of play. Lastly, we won more fouls per game due to the higher amount of possession we maintained throughout the latter half of the season. Overall we managed to improve our dominance over the ball and our goal threat significantly by creating overloads with our strange 3-1-3-1-2 formation and intense style of play.

Shot Frequency & Efficiency

As you can see in the graphics above, we improved in almost every shooting metric, the most obvious of which being our shots per game. Before I arrived the team were playing a flawed, rigid 4-2-3-1 which limited the number of final third entries and shots they managed each game. However by the end of the season we were regularly registering 20-30 shots per game after an initial adaptation period. Our non-penalty expected goals increased along with our goals per game, conversion rate, shots on target per game, and our expected goals over-performance (NPG-NPxG/90). Now this is all great information for me to have in terms of tracking our progress, but I'd like to now show you how I'm actually implementing a data-driven approach to improve the team's performance.

Opposition Analysis

During the first few matches, I took an admittedly flawed approach to my opposition analysis which proved largely ineffective and unnecessarily time-consuming. At the time I was exporting player performance data for USL2 players in each position before every match. Each positional sheet then had a second formula sheet which converted the metrics into per 90 stats as well as a few other performance indicators that I created, such as the number of progressive passes per completed pass or the number of key passes per completed pass. Each positional per 90 sheet had a third sheet (as shown above) which calculated the percentile ranking of the player in each of the 67 statistics. These sheets fed into yet another sheet which displayed graphical representations of my opponents' percentile rankings compared to other players in their positions within the wider USL2, the same visualizations I shared in my previous Player Performance Data posts. Now this is a great start to creating a recruitment strategy, however it was not telling me how each player functioned within the context of the team they were playing for. As such, I adjusted my approach and began simply exporting and analyzing the performance data of only my next opposition before each encounter with them. That sheet was then converted into per 90 statistics in the same way as my earlier sheets, as shown below.

This sheet was created for Brevard FC just before our final encounter with them. It calculates the averages or sums of each category depending on the kind of metric each column contains. For instance, the entire squads' average rating (Av Rat) is averaged below the squad list, whereas goals per 90 (Gls) is a summation. Similarly, their pressure success ratio (Pres R) is averaged, whereas their pressure attempts per 90 is a summation. Essentially, any kind of ratio metric column would create an average and any kind of frequency metric column would create a total. I did this for all 67 statistics, most of which are not shown in screenshots above. This sheet would feed into one final sheet which would ultimately be used to analyze the opposition.

This final sheet essentially calculates either the percentage a player is above or below the average in any ratio metric column, or the players' percentage of the teams' total in any frequency metric column. Prior to every match, I would first analyze the overall teams' metrics (in the same way I have displayed our team performance data in the radar charts above) in comparison to the rest of the league to identify a broad playing style. In the case of Brevard FC, they utilized a highly structured defensive approach (4-4-2) with a counter attack focused on getting crosses into the box. This could have been a potential banana skin for my 3-1-3-1-2 pressing system, however I was able to identify some key areas to exploit using the sheet shown above. First of all, I needed to differentiate between their more regular starters and their lesser used squad players, the regulars are highlighted in yellow. I then looked at their primary sources of creativity and goal threat. As you can see in the “Gls” column, their strikers (Vyacheslav Lobanov and Rhys Romero) were unsurprisingly their greatest goal threats, while their wingers (Jonathan Zusi and Olly Barajas) were their primary sources for assists (Ast) along with their playmaker, Kyle Pamplona. To get a better idea of how these chances were occurring, I looked at their greatest sources of progressive passes, key passes, crosses, and aerial duels. From this I determined that their game plan boiled down to winning the ball deep, playing it out wide to the full-backs who would progress the ball to Kyle Pamplona to spray through balls to the wingers who would then cross the ball into either striker, with Romero being the primary aerial threat. My first solution was to use the full-backs as pressing triggers to shut down the passing lanes to Pamplona. I also wanted to man-mark Romero with my strongest defender in terms of aerial duels, mark Zusi with my more mobile left wide center back, and mark Barajas with my right defensive winger. I then set the team instructions to trap them inside and engage the wide players to prevent crosses into the box. You can see the resulting effect from the match stats shown below:

We successfully suffocated them by winning the ball back high and preventing Pamplona from getting the ball early during transitional phases. The few times he did, he was stranded in the middle with very few forward passing options. Zusi and Barajas were nullified so the strikers received very little service. Brevard FC were one of the weaker sides in the division, finishing the season with just 9 points. To show you show I adapted this approach to the better sides, let's take a look at our match against the champions of the division, The Villages.

The Villages presented a difficult proposition as they led the league in most overall team performance metrics. They are a team that wants to keep the ball and suffocate the opposition in the same way we do. It didn't help matters that we would be traveling to their ground while attempting to beat them at their own game. However, it quickly became clear that their entire game primarily revolved around 3 key players: Andrew Bender (playmaker), Ben Eissele (right inside forward), and Eric Burt (mobile, lethal striker). They played a fluid 4-3-3 but I decided the best course of action was to focus on shutting down those 3 players to force them to adapt their game plan. I instructed my left wide center back, Mike Kaplan, to man-mark Ben Eissele tightly, show him onto his weaker foot, tackle him hard, and close him down every time he touched the ball. Jesus Tinajero was tasked with tightly man-marking Eric Burt, while Andrew Bender was the pressing trigger. My right defensive winger was also tasked with tracking back to cover Alan Vazquez to help nullify their left side. I adjusted the team instructions to force them out wide with our press due to their preference to keep possession in the middle. In attack, I instructed the team to build up down the left to avoid the higher pressing engagement and efficiency shown by the The Villages’ players on the opposite side and to take advantage of our creative strength down the left wing. In the end, we lost the match in infuriating fashion, conceding 2 late goals including a dubious penalty kick. However, our game plan worked magnificently as you can see here:

We showed up to the league leaders' ground and did not allow them to play the game on their terms. Ben Eissele, undoubtedly their best player and probably the best player in the league, was their worst performing player on the day due to the fine work of Mike Kaplan. Unfortunately, fatigue got the best of our under-stocked midfield which allowed Andrew Bender to slot a through ball into Eric Burt who slipped past Tinajero and scored the equalizer. A sloppy tackle by our right wide-center-back in the box was the nail in the coffin, with the penalty goal securing the 3 points for The Villages. However, it was incredibly satisfying watching the game plan in action for the first 75 minutes. I don't think I've ever been as invested in a single Football Manager match as I was during this performance. If we had managed to hold on, we could be looking at a very different table and FC Miami City could be gearing up for a play-offs run in the USL2. Alas, we will have to wait until 2025 to continue our journey. Until then I will look to strengthen the squad, potentially create a more balanced formation, and continue working towards my coaching qualifications. However, the club has recently rejected my proposals to fund coaching courses for my staff so if they decide to do the same for me, I may be looking at the job market fairly soon. Only time will tell. Anyway, thank you for coming to my Ted Talk.