How Important Are the Various FUT Stats in FIFA 15 Ultimate Team?

How much do certain FUT stats actually contribute to the efficiency of a striker in FIFA 15 Ultimate Team? It is a common belief that pace has a lot to say, but what about physicality and shooting? In this article, we will examine that by applying statistical methods.

The data we will use is goals-per-match data collected from cards currently up for sale. As described in a previous article about the performance of in-form players, there are a few statistical issues that need to be considered for comparison reasons.

To work around these issues, I decided to use EA’s recently released top scorer tables from all the top leagues. However, I removed a number of players where other factors may affect the results:

  • Wingers, midfielders and CF’s to reduce the effect of position bias
  • Players where less than 300 cards are up for sale to reduce statistical inaccuracy in the performance data

This leaves 32 players, below ordered after the recorded goals-per-match average when the sample was taken.

 M goalsGoals / matchPrice (PS)
Aguero 4.46 0.94 339,000
Costa 6.59 0.89 43,000
Tevez 5.63 0.89 20,000
Benzema 6.59 0.89 32,750
Balotelli 6.25 0.88 6,800
Sturridge 10.27 0.88 25,250
Lacazette 7.31 0.87 1,500
Ramos 10.78 0.86 1,200
Lewandowski 3.90 0.86 118,000
Uche 3.06 0.85750
Ibarbo 7.20 0.84 1,500
Cavani 4.53 0.84 4,500
Benteke 6.28 0.82 1,000
Lukaku 7.87 0.82 1,700
Gignac 1.04 0.82 400
Dos Santos 4.57 0.81 1,400
Braithwaite 1.54 0.81800
Welbeck 7.34 0.81650
Immobile 5.75 0.80900
Vargas 6.50 0.80 1,500
Palacio 3.48 0.80750
Rodrigo 5.48 0.80 1,200
Jovetic 5.05 0.79 1,100
Higuain 5.50 0.79 1,900
Mandzukic 4.90 0.79900
Rossi 3.01 0.77850
Remy 10.25 0.76 5,200
Gomez 2.35 0.76850
Torres 2.94 0.76800
Hernandez 6.30 0.74 1,200
Huntelaar 3.01 0.72900
Negredo 2.68 0.70850

Analysis of FIFA 15 Ultimate Team Stats

What we want to know here is basically how the stats affect the player’s performance. In statistical terms, we want to know how performance correlates with the stats. Correlation is a measure of whether one set of data may be statistically related to another set of data.

A correlation of 0 indicates that there isn’t a relationship, whereas a correlation of 1 means that there may be a direct, statistical relationship. In this case, we expect to see a direct relationship, meaning that the higher the correlation coefficient, the better.

Which stats should we consider as likely causes of these scoring rates?

Although the players in question are strikers, I started out by including all six aggregated stats and workrates. Following that, I removed the stats one stat at a time in order to see whether it affected the correlation coefficient. Stats that had a positive impact on the correlation coefficient were left in.

Not surprising, pace, shooting, dribbling and physicality turned out to have a positive impact, whereas defense and passing wasn’t relevant. With regards to workrates, I have assumed that high attacking and low defensive workrates are preferable.

I have assigned the value 90 to the best workrate, 60 to the second best and 30 to the worst possible workrate. This is definitely a bit arbitrary, but it’s the best I can do.

In the chart below, I have plotted each player, using the sum of his aggregated FUT stats + work rates and goals-per-match ratio as coordinates:

Unweighted Total

We end up with a correlation coefficient of .70, which indicates a strong uphill relationship.

By adding all the stats together, it is assumed that all stats contribute equally. This is not likely, and the real purpose of my analysis is to determine the individual contribution / importance of each stat.

Hence, I have assigned a weight factor to each stat based on it’s assumed importance. Following that, I have adjusted the weight factors until I wasn’t able to increase the correlation coefficient any further. In this case, adding the weight factors increases the correlation coefficient to .76.

In the diagram below, I use the weighted total FUT stats as Y-axis coordinate:

Weighted Total

Below I have inserted the weight factors, which produced the highest correlation coefficient:

  • Pace .28
  • Shooting .23
  • Physicality .12
  • Dribbling .24
  • Attacking WR .05
  • Defensive WR .08

The numbers above are interesting, because they show the relative importance of each stat. As expected, pace is the most important attribute, although shooting isn’t far behind.

The reason why it is relevant knowledge to every FUT-player is pretty obvious: Building squads involves compromises, and the numbers above provides solid knowledge regarding to what extent it makes sense to accept a reduction in one stat in order to achieve an improvement on another stat.

As an example, I would definitely go for 1 extra pace point instead of 1 point better dribbling, but I would definitely pick +2 dribbling over +1 pace.

As for chemistry styles, the consequences are obvious: Hunter is by far the preferable stat boost for a striker, closely followed by Hawk. This is no surprise, but still – now we know for sure.

Having identified the relative importance of each stat, it’s possible to calculate a weighted stat total (multiplying each stat by its weight) per player in order to identify the best strikers in the game. Below, I have inserted the top 20 among the strikers in consideration here, but obviously not including players like Messi, Zlatan, IF Ronaldo and other informs:

Weighted Stat Total Table (Strikers)

NamePriceWeighted total
Carlos Tévez 20,000 81,8
Sergio Agüero 339,000 80,4
Daniel Sturridge 25,250 79,4
Mario Balotelli 6,800 79,4
Karim Benzema 32,750 79,2
Robert Lewandowski 118,000 78,7
Diego Costa 43,000 78,5
Víctor Ibarbo 1,500 78,2
Ikechukwu Uche 750 77,9
Alexandre Lacazette 1,500 77,6
Giovani Dos Santos 1,400 77,4
Edinson Cavani 4,500 77
Christian Benteke 1,000 76,8
Romelu Lukaku 1,700 76,6
Martin Braithwaite 800 76,5
Eduardo Vargas 1,500 76,5
Danny Welbeck 650 76,4
Adrián Ramos 1,200 76,4
Ciro Immobile 900 74,8
André-Pierre Gignac 400 73,1

Based on the above calculation, of the most important stats in FIFA 15 Ultimate Team, you should try to cherry pick bargain players. By incorporating a team full of players with higher weighted FUT stats you should notice your team’s performance increase relative to the coins that you have invested.

Also, with the FIFA Team of the Year (TOTY) market crash just around the corner, now may be a great time to invest in some of the popular strikers listed above.

About David Cotton

David has been a passionate video gamer his entire life and has owned and played most gaming consoles since his early childhood. He has over nine years experience writing about video games and is a well known FIFA Ultimate Team expert and 'FUT Founder'. As well as providing insight on all the latest FIFA Ultimate Team news, David has a history of creating extremely effective in-depth guides on how to make FUT coins. He founded and created UltimateTeamUK in 2011 and has previously worked as the 'Head of FIFA' at the popular esports company, Dexerto.

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