Arlington69 has written yet another post here he claim to have evidence supporting the presence of a momentum feature, i.e. some kind of build in functionality triggering momentum swings if someone has a too big lead.
The alleged evidence is an observation apparently showing that it becomes harder to score additional goals if you go from 3-1 to 3-2 than from 2-2 to 3-2. According to Arlingto69, this suggests that the game invokes momentum swings if someone takes too big a lead (i.e. 3-1). According to him, it ought to be equally easy to score additional goals from 3-2 and onwards no matter how you arrived there.
Is he finally on to something or did he miss something? We decided to give his claim a service check.
Like most players, Arlington69 plays a lot of matches and in some of those matches, he at some point has a 3-2 lead. There are two paths to a 3-2 lead: From a 2-2tie to a 3-2 lead or from a relatively safe 3-1 lead to a less safe 3-2 lead.
The basis of Arlington69’s latest claim is a comparison between all those matches where he took either of these paths. Arlington69’s starting point assumption is that it ought to be equally easy to score goals after 3-2 no matter what path you took to 3-2. Arlington69 may have had the suspicion that this wasn’t quite the case. If this was the case, he was right:
Below, we see the important parts of Arlington69’s aforementioned statistic.
|n||Scenario leading to 3-2||Goals (own) scored after 3-2||Goals (opponent) scored after 3-2|
|102||2-2 –> 3-2||30||30|
|26||3-1 –> 3-2||0||11|
In the first row, we see the 102 matches where Arlington69 went from 2-2 to 3-2. In those matches, Arlington69 and his opponents scored the same number of goals after 3-2, namely 30 each.
And then comes the strange part:
In the 26 matches where he went from 3-1 to 3-2, his opponents scored another 11 goals after 3-2 while he scored none at all.
So, it would indeed seem that it becomes harder to score additional goals if you go from 3-1 to 3-2 rather than from 2-2 to 3-2. If you tap into Arlington69’s mindset, this observation indeed seems to support the idea that the game invokes momentum swings if someone takes “too big a lead”.
Based on the observations above, he infers that the game has build in logic, which creates momentum swings:
“In conclusion this is more evidence that there are momentum swings within EA’s FIFA 20. I believe this is coded into the game”
We have no reason to believe that the data are incorrect. But the question that begs for attention here is whether these observations are as abnormal as Arlington69 apparently thinks.
The momentum-mechanism that Arlington69 on former occasions has claimed to be present, is a feature which allegedly makes matches more even:
“[I]t seemed clear to me that the game would swing in favour of one player or another and usually the player losing or with the worse team.”
(– Quote from “Why I believe in momentum” on Reddit)
But there is an obvious problem between Arlington69’s data and his conclusion:
The basis of his assertions is a data set that he used to draw a “scoreline flowchart” which for another post on his blog. The flow chart illustrates how often a certain scoreline in a match developed into a certain other scoreline. According to said flowchart, 2-3 or 3-2 leads only transformed into the most even result of them all – a draw – in 16 % of the cases.
So, if we look at the full deck of data put in front of us here, we find it difficult to justify the claim that these results were caused by a build in mechanism, which favors the losing player. In reality, there are a lot more data leading us in the opposite direction.
But there is an even bigger problem with Arlington69’s conclusion.
False cause fallacy
It is a fact that Arlington69 scored more goals when he went from 2-2 to 3-2 than when he went from 3-1 to 3-2. Hence, the (a) “score-line before 3-2” and the b) “goal distribution after 3-2” definitely appear to be correlated although the statistical uncertainty (small sample sizes) prevents us from concluding this with certainty.
But a quick peek on page 1 in any science book would tell you something that Arlington69 clearly missed here:
The fact that two variables are correlated doesn’t lead to the conclusion that they also are causally connected, i.e. in this case that it became more difficult to score at 3-2 because he came from 3-1 rather than 2-2.
Before we can conclude anything about causality, we need to effectively rule out all other possible explanations. And the fact that remains is that Arlington69 made no attempt at ruling out any other explanations. Maybe he wasn’t looking, maybe he just didn’t see it. But no matter what, there is another very obvious explanation to the observations presented here: Namely the statistical phenomenon known as regression to the mean.
When things turn normal
Regression to the mean can be seen everywhere in life, but football offers so many examples.
One of the best examples is the following situation from the Spanish La Liga, 2018/19 season. You may recall that Paulinho – a new joiner – in mid January had surprised everyone by scoring an impressive 8 goals. Meanwhile, Cristiano Ronaldo was having his worst season ever and had scored only 4.
And then, Paulinho and Ronaldo both were hit smack in the face by regression to the mean:
When La Liga ended in May, Paulinho had increased his goal tally to just 9 goals, whereas Cristiano Ronaldo had increased it to an impressive 26 in total.
In that way, the season ended up as an average season for both players. They both regressed to the mean.
If you were to apply Arlington69’s logic here, you would compare the situation in January to the situation in May. And you would see what Arlington69 would call a momentum shift: The formerly trailing Ronaldo catching up with Paulinho and eventually surpassing him. However, it doesn’t take much football knowledge to realize that this so-called “momentum shift” is completely natural. We are clearly not witnessing a God-like force intervening in Spanish football but plain and simply that a player’s performance may start at an extreme high or low, but still eventually normalize.
Given that Arlington69’s experiment is based on his own matches, a natural question to ask is whether Arlington69 is Ronaldo or a Paulinho when it comes to FIFA. Or to put it differently: What is his “mean” and what is his “extreme”?
His scoreline flowchart provides a few hints about his skill level relative to his average opponent:
Arlington69’s opponents scored 2 % more goals than him, but he won 1 % more matches than he lost. None of the deviations are statistically significant. Hence, a fair conclusion is that Arlington69 is an average player relative to his opponents.
With that knowledge, let’s take another look at his experimental design.
Designed to fail
Arlington69’s experiment is a classic comparison of two samples.
Sample #1 consists of matches where he was leading 3-1, meaning that sample #1 contains matches where he on average was performing above his own norm at some point.
Sample #2 consists of matches where the scoreline at some point was 2-2, meaning that he at that point was performing to his own norm.
And when you make that comparison, you should expect to see exactly what Arlington69 sees here: Namely that the matches in sample #1 tend to regress from above average to average, whereas the matches in sample #2 in general won’t change because they already are average.
This is in other words completely as expected.
Arlington69’s latest experiment follows in the footsteps of a long list of earlier, failed attempts to prove momentum from his side. But it is also another lesson into why people tend to believe such things in the first place.
Regression to the mean is just as inevitable as night and day. But few people have heard about it, and even fewer understand the implications. The observations that Arlington69 makes are without question very real, and numerous other players have witnessed these things, although few of them have bothered recording it in a spreadsheet. But they are not only real but also completely natural.
Yet, combined with our tendency to prefer explanations, which attribute a meaning to otherwise random events, we have everything we need for yet another good conspiracy theory.