Crowdsourced Scores as a Leading Indicator

I listened to a couple of podcasts this week and, combined with my own experience, my limited consensus says that Week 3 was a rough week for most people. The Sports Gambling Podcast hypothesized that it was because our conclusions are formed but are based on iffy data. You have 2 weeks of data, and in reality you have no idea how to read it, but because you have 2 weeks of data, you think it’s locked.

For example, consider the Arizona at Buffalo game. The Cardinals had just whipped the Bucs. The Bucs beat the Falcons, the Falcons beat the Raiders, the Raiders beat the Saints. Meanwhile, the Bills lost to the Ravens and to the Jets, neither of whom are highly regarded this season outside their core fan bases. Additionally, all of the money was on the Cardinals. All signs pointed to the continuing trend.

So of course, the Bills smacked the Cardinals around and won outright. But this wasn’t the only one; There were a lot of surprising results in Week 3.

So what happened?

Well, it’s hard to say in any kind of precise way, but what is really interesting is whether crowdsourcing the scores would have indicated something more granular afoot. For example, when people base decisions off assumptions, it gets harder to put numbers on their predictions. In other words, in the Cardinals-Bills game, you assume that Arizona would score 40 points again and the Bills would lay down, but if you put actual numbers against it, maybe it doesn’t seem so likely. You then start thinking through it a bit more, and maybe the crowdsourced results get closer to what actually happened than what all the signs pointed to happening.

At the end of the week, maybe the crowdsourced scores don’t say much different from the actuals, but it would be fascinating to see if maybe there was some sort of indicator that it was going to be a weird week.

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