Closing the Gap — How Free to Play Users of EA FC24 Can Succeed on a Budget

By Daniel Adams

Introduction

EA FC24, formerly known as the FIFA series, is a soccer video game played around the world. A key game mode in FC24 is Ultimate Team. In this mode, users can compete online and build their “ultimate” squad with players from leagues around the world. While some users focus on signing players that they like or support, the majority of users seek to sign the best players so they can dominate their opponents. In order to sign players, users need to utilize the in-game transfer market, which contains cards of real life players that can be bought using the in-game currency. Player cards are broken down into three rarities, bronze, silver, and gold. The gold cards are the best of these three types, however, overall ratings of gold cards range from 75 overall to 91 overall. With that in mind, cards with higher overalls require more in-game currencies for their cost. In addition to the transfer market described above, users can claim free packs from unlocking rewards. Additionally, players can use real money to buy store packs. However, free packs and store packs do not yield the same quality of players.

Question

A critical grievance many users hold against Electronic Arts (EA), the creators of FC24, is that the game has become pay-to-win over the years. The difference in return of free packs versus store packs has caused users to claim this since getting high overall players with free packs has become exceedingly rare. This has resulted in those who do not spend real money on packs to fall behind in player quality, and has brought about great frustration to the free-to-play users in the FC24 user base. Regardless, many free-to-play players still play the game hoping to be lucky enough to get the best players from free card packs. That being said, these free-to-play users still need to field a team competitive enough to win and therefore unlock the free reward packs. This creates a cycle of these users unable to unlock the best cards since their team can not propel them to finish the requirements for free packs. Therefore, how are these users able to field a good enough team to break this cycle?

Stakeholders and Approach

The stakeholders for this analysis are the free-to-play users of FC24. This analysis seeks to answer the question of acquiring good players that do not require significant amounts of in-game currency. In order to solve the stakeholder’s problem, the analysis utilizes similarity metrics to find comparable players to the game’s best and most expensive players. These replacement players will allow free-to-play users to have a team consisting of less expensive alternatives that still maintain competitive levels of quality.

Data Cleaning

Data used for this analysis was gathered from Kaggle.com. The dataset consisted of all players in EA FC24, with a player representing one row. In the row relevant identifying data was included, such as the player name, nation, club, position, age, and overall rating. Additionally, the in-game statistics of each player were included. For the scope of this analysis, the nation, club, age, and other non statistical features were removed from the table. Other than this, the data set was ready for further analysis.

Data

Once the data set only contained the player name and all in-game stats, the dataset was ready to be analyzed. These features are listed below. Note: all features besides name and position are bound between 1–99.

Identifying features:

• Name: The player’s name
• Position: The player’s position
• Overall: The overall rating of the player

Physical statistics:

• Acceleration: The rate at which a player can obtain top speed
• Sprint: The maximum speed a player can reach
• Jumping: The height a player can jump
• Stamina: The amount of stamina a player has throughout the match
• Strength: Player strength when tackling or protecting the ball
• Composure: Ability to withstand pressure from the defense when shooting and passing
• Reactions: Time to execute a user given command

Attacking statistics:

• Positioning: The AI controlled off-ball position of a player
• Finishing: The inside the box shooting capability
• Shot: The shot power of the player
• Long: The outside the box shooting capability
• Volleys: The first touch shooting capability (shooting upon receiving a pass)
• Penalties: The capability of scoring from the penalty spot

Passing statistics:

• Passing: Short passing
• Vision: Ability to pass through open gaps between defenders
• Crossing: Passing from the outside into the opponent’s box
• Free: Free kick accuracy
• Curve: The amount of bend applied on crosses, shots, and aerial passes

Dribbling statistics:

• Agility: The player’s elusiveness on and off the ball
• Balance: The player’s ability to stay on their feet when facing pressure
• Ball: Ball control when receiving a pass and dribbling
• Dribbling: A player’s elusiveness with the ball at feet

Defending statistics

• Interceptions: Capability of reaching an opponent’s pass
• Heading: Heading accuracy when passing and shooting
• Def: The AI controlled off-ball defensive position of a player
• Standing: Stand tackling
• Sliding: Slide tackling
• Aggression: The aggression when fighting for a loose ball or aerial pass

Data Analysis

The analysis conducted sought to find cheaper alternatives to some of the game’s best players. To execute this, the cleaned dataset was broken into three separate tables, one for forwards, midfielders, and defenders. Once the three tables had been created, each grouping of positions were assigned a target player to replicate. For the forwards Kylian Mbappé was selected, as he is one of the most coveted forwards in the game. Kevin De Bruyne, the highest rated midfielder, was selected as the target midfielder. As for the defenders, Virgil van Dijk was selected since he is the highest rated center back, which is a defensive position. With these target players selected, cosine similarity was calculated on each target player’s perspective set of features.

Forwards:

Mbappe possesses high speed, agility, dribbling, and shooting. His passing is relatively average and his defending low, which is common for attacking players. That being said, great, cheap alternatives to Mbappe are Elye Wahi, Cody Gakpo, Donyell Malen, and Alexander Isak. Interestingly, Wahi is lower rated than the other three players mentioned. However, my experience using Wahi suggests that his in-game ability and similarities to Mbappé make his humble 78 overall misleading. Nonetheless, Gakpo, Isak, and Malen in addition to Wahi all possess fluid movement and lethalness in front of the net to a similar degree of Mbappé.

Midfielders:

Key attributes of Kevin De Bruyne are his attacking qualities. He has great vision, short passing, and long passing, which make him the best distributor of the ball in the game. Additionally, De Bruyne boasts relatively high shooting stats for a midfielder. While his passing and shooting are elite, De Bruyne’s defending is not the best for midfielders, as he is classified as an attacking midfielder. Therefore, similar players given are also attacking oriented midfielders, known as central attacking midfielders (CAMs). Of the recommended options, Mount, Gross, Bourigeaud, and Mac Allister are great CAM alternatives at a low cost.

Defenders:

Virgil van Dijk is regarded as the best defender in FC24, as he possesses very high defending, strength, and aggression attributes. Therefore, the recommended players are center backs who possess strong physical and defensive attributes. Two great alternatives are Rüdiger and Bastoni. While these two defenders are 85 overall, they both have a modest price in respect to their quality. For an even cheaper alternative, the stakeholder could use Aguerd, Todibo, and Mings, who are also great alternatives to van Dijk.

Limitations

A critical limitation of this analysis was the dataset’s lack of player price. This limited the analysis from showing the alternative players’ price tags, which would significantly help the stakeholder. That being said, many websites exist that can show the current market price of any player, such as Futbin.com. Additionally, the stakeholder may be looking for a different style of player apart from Mbappé, De Bruyne, and van Dijk. A future extension of this analysis could include player prices and more detailed player comparisons based on the desired play style.

GitHub Code:

https://github.com/dadams16/INST414AdamsModules/blob/main/module3.py