This year’s Ballon d’Or had football fans everywhere talking. When Rodri took home the trophy, many believed Vinícius Júnior missed out because of something called “vote-splitting.” The idea is simple: when two players from the same team—like Vinícius and Jude Bellingham—are nominated, their votes get divided, making it easier for someone else to win.

At first, this explanation sounds reasonable. But the more I thought about it, the more it felt… off. Is “vote-splitting” really to blame, or are we committing “False Cause Fallacy”? Let’s dig in.

What is the Vote-Splitting Effect?

The vote-splitting effect happens when two strong candidates from the same group—say, teammates—end up dividing the support of a shared pool of voters. This can weaken both candidates’ chances by spreading their votes too thin, allowing a third-party contender (like Rodri) to emerge as the favorite. Vote-splitting is common in political elections or competitive awards when:

• Voters have limited choices or points, causing them to split votes across similar candidates.
• There’s a perception of equal merit within a group, making it difficult for one candidate to dominate.

But here’s the problem: Ballon d’Or voting doesn’t work like that. Each voter submits a ranking based on each player’s individual performance over the season. The idea of “splitting” assumes there’s a fixed pool of Real Madrid votes up for grabs, which doesn’t reflect the way voters actually think. Each voter has the freedom to assign points based on merit, not based on team alliances.

The Two Fallacies at Play: False Cause and Zero Sum Fallacy

Two key cognitive fallacies drive the vote-splitting argument:

1. False Cause Fallacy: This fallacy arises when we assume that one thing caused another simply because they’re related. Here, some fans attributed Vinícius’s loss to Bellingham’s presence rather than Rodri’s individual performance.
2. Zero-Sum Fallacy: The vote-splitting concept also implies a zero-sum approach—thinking that a fixed pool of “Real Madrid votes” had to be divided between Vinícius and Bellingham. In reality, each voter ranks players individually based on their own merit, not on shared team alliances.

Using Bayesian Thinking to Analyze Ballon d’Or Results

To further examine this, let’s imagine how Bayesian thinking might assess the Ballon d’Or race before the voting took place. Bayesian reasoning involves setting prior probabilities based on known data and updating those probabilities as new information comes in. Here’s how that might look for the Ballon d’Or:

1. Setting Prior Probabilities

Before voting, we might start with these probabilities based on each player’s season:

• Rodri: 35% chance (standout season with critical contributions)
• Vinícius Júnior: 35% chance (also had an exceptional season, though with strong competition)
• Jude Bellingham: 20% chance (impressive season, but slightly overshadowed by teammates)
• Other Players: 10% combined

These represent initial beliefs based on individual performance.

2. Gathering New Information

As more data emerges, such as early reactions or analyst commentary, Bayesian reasoning would adjust these probabilities. For example, if Rodri’s reputation as a game-changer strengthened, we could raise his probability while slightly lowering those of Vinícius and Bellingham.

3. Post-Outcome Reflection with Bayesian Thinking

Now that Ballon has won, we can use Bayesian thinking to evaluate the outcome:

• Hypothesis A: Hypothesis A: Rodri Was More Likely the Favorite Among Journalists, Leading to His Win
  Rodri’s standout season, along with his reputation as a favorite among journalists and football analysts, made him a more probable candidate to win.
• Hypothesis B: Vote-Splitting Played a Role
  Since Vinícius didn’t lose purely due to Bellingham’s presence, Bayesian thinking would reduce the likelihood of vote-splitting as a factor in this context. This result lowers our tendency to view award voting through a zero-sum lens when two teammates are in contention.

The Takeaway: Applying Bayesian Thinking to Combat Biases

Applying Bayesian reasoning post-results doesn’t predict the outcome, but it helps refine our understanding and avoid cognitive biases. This analysis suggests that individual performance matters more than team dynamics in Ballon d’Or voting, which could shape how we view similar award processes in the future.

✍🏼Quote of the Week

It is the mark of a rational mind to entertain an idea without accepting it.” – Aristotle

🧠Food for Thought

How often do you find yourself accepting explanations that feel logical but might not be fully accurate? What assumptions have you challenged recently?