World Cup Final 2018

Many people believe the World Cup should be a good test of the hypothesis that great events focusing attention from millions around the world will correlate with departures from expectation in the data from the GCP network. My friend Stephan Schwartz asks, for example, "Am I correct in assuming the GCP eggs [will] reflect the mass focused intension awareness, with high emotion to boot around the World Cup? 3.4 Billion are reported to have watched at least part of the games. Half the human race."

We have looked at the world cup several times in the past, with interested people selecting specially good tests of the notion. Certainly the 2018 Final between Croatia and France qualified as a highly interesting and emotionally engaging example.

The match was contested by France and Croatia and held at the Luzhniki Stadium in Moscow, Russia, on 15 July 2018. It began at 18:00 local time and the last goal was at 69 minutes. France won the match 4–2, having taken a 2–1 lead during the first half on an own goal and penalty awarded by the video assistant referee in the system's first use at a World Cup final. It was the first time that an own goal had been scored in a World Cup final. (Extracts from Wikipedia.)

Specific Hypothesis and Results

The GCP exploration was set for 18:00 to 20:00 local time (15:00 - 17:00 UTC). As in previous assessments of World Cup effects on the GCP data, we looked at 2 hours of data beginning at match time. The result is very interesting, and though single events like this cannot be reliably interpreted, it appears the data do reflect the mass engagement. The graph below shows an unusually strong trend in the positive (predicted) direction for the first hour and a quarter, then the trend reverses and nearly cancels the earlier deviation. It appears that the inflection occurs near the end of the match.

Interpretation

The following graph is a visual display of the statistical result. It shows the second-by-second accumulation of small deviations of the data from what’s expected. Our prediction is that deviations will tend to be positive, and if this is so, the jagged line will tend to go upward. If the endpoint is positive, this is evidence for the general hypothesis and adds to the bottom line. If the endpoint is outside the smooth curve showing 0.05 probability, the deviation is nominally significant. If the trend of the cumulative deviation is downward, this is evidence against the hypothesis, and is subtracted from the bottom line. For more detail on how to interpret the results, see The Science and related pages, as well as the standard caveat below.

Standard caveat

It is important to keep in mind that we have only a tiny statistical effect, so that it is always hard to distinguish signal from noise. This means that every success might be largely driven by chance, and every null might include a real signal overwhelmed by noise. In the long run, a real effect can be identified only by patiently accumulating replications of similar analyses.