A Quick Recap of the Theory

What is the Wisdom of the Crowd? How can it work with sports?

With the season just about to start, we wanted to recap how we think the Wisdom of the Crowd can work with NFL betting. (A more detailed post is here.)

Tl;Dr: Vegas’ goal is to match betting lines to public sentiment, not to predict final outcomes. We believe that the crowd can identify when public sentiment differs significantly from the predicted final outcome so we can take advantage of betting line values.

Thanks for visiting Crowdsourced Scores! Please get your Week 1 predictions in now!

What is the Wisdom of the Crowd?

The Wisdom of the Crowd is the theory that, over time, the crowd is a more effective predictor than any single individual. For the crowd wisdom to be “valid”, the crowd has to be:

  • Diverse: the crowd has to have a significant variety of opinion to ensure that biases are normalized.
  • Independence: an individual in the crowd develops their opinion on their own and without being influenced by another person.
  • Decentralization: the crowd is formed by people who have come from different backgrounds and experiences.
  • Aggregated: the individual opinions can be aggregated appropriately.

In short, the crowd needs to have a diverse group of individuals who are able to generate their opinions on their own.

How can the Wisdom of the Crowd Work for Sports Scores?

We believe that sports fans satisfy the three core components of a crowd: diversity, independence, and decentralization. will do the work of aggregating. The each individual of the crowd is their own super computer, processing all of the news and information out there and putting a prediction together based on their own calculations, and we aggregate the results.

How does this differ from what Vegas does? Doesn’t Vegas move the line based on the crowd behavior?

The main difference between us and Vegas is that Vegas is interested in matching the¬†perception of each side and we are interested identifying where the public perception may be inaccurate. Most importantly, Vegas wants to balance the money. If one side has a heavy amount of money riding on it, the book is in serious danger is that side wins. A perfect 50/50 split BEFORE THE GAME is Vegas’ goal. They are not interested in matching the final outcome; they are interested in perfectly matching public perception to keep money flowing evenly on both sides.

Additionally, Vegas moves the line based on dollar values rather than on gross volume of bets. One $10,000 bet on the favorite would require 100 $100 bets on the underdog to match it so the line may move to encourage bets on the underdog even though only one person has bet on the favorite.

What we are trying to do is identify value in the line. In the stock market, this is called the margin of safety. If the crowd predicts a significant deviation from the Vegas line, we want to see if that prediction has value (we believe that it does).

What about Who Picked Whom?

Who-picked-whom seems like it is the same as crowd-sourcing predictions. If 70% of users are picking the underdog, how is that different from what Crowdsourced Scores is doing?

The short answer is that Who-Picked-Whom is a binary question that is limited by the same lack of detail as the betting lines. Do you believe that the underdog will simply cover the spread or win outright? Do you believe that the favorite will not only cover the spread but beat the tar out of the underdog?

In this situation, all choices fall into one of two buckets, and they are not distinguished at all. Therefore, it may tell you where the public sentiment is, but it doesn’t tell you if there is any value in the line. Additionally, the public is roughly 50% season over season.

We’re aiming to top that.

By aggregating score predictions, we are adding fidelity that will identify not only how many users choose one side or the other, but also how much each side believes its pick will win by. This will help identify when the crowd sees value in a given betting line and inform bettors.

Best of luck!

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