Crowdsourcing Dataset

From a company for sports statistics, we obtained data and profile photos from all soccer players (N = 2053) playing in the first male divisions of England, Germany, France and Spain in the 2012-2013 season and all referees (N = 3147) that these players played under in their professional career. We created a dataset of player'96referee dyads including the number of matches players and referees encountered each other and our dependent variable, the number of red cards given to a player by a particular referee throughout all matches the two encountered each other.

Multiple independent analysts are recruited to investigate the same hypothesis or hypotheses on the same data set in whatever manner they see as best. The independent analysis strategies produce two datasets of interest:
(1) the variation in analysis strategies, and
(2) the variation in estimated effects.

These two can be partially independent. Different analysis strategies may converge to a very similar estimated effect - indicating robustness despite variation in analysis strategies. Alternatively, the estimated effect may be highly contingent on analysis strategy. In the latter case, there are at least two methods of resolution:
(1) consider the central tendency of the estimated effects to be the most accurate, or
(2) critically evaluate the analysis strategies to determine whether one or more should be elevated as the preferred analysis.

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