  Any collection can be ranked. Sports and games are common examples of ranked
systems: players and teams are constantly ranked using different methods. The
statistical properties of rankings have been studied for almost a century in a
variety of fields. More recently, data availability has allowed us to study
rank dynamics: how elements of a ranking change in time. Here, we study the
rank distributions and rank dynamics of twelve datasets from different sports
and games. To study rank dynamics, we consider measures we have defined
previously: rank diversity, change probability, rank entropy, and rank
complexity. We also introduce a new measure that we call ``system closure''
that reflects how many elements enter or leave the rankings in time. We use a
random walk model to reproduce the observed rank dynamics, showing that a
simple mechanism can generate similar statistical properties as the ones
observed in the datasets. Our results show that, while rank distributions vary
considerably for different rankings, rank dynamics have similar behaviors,
independently of the nature and competitiveness of the sport or game and its
ranking method. Our results also suggest that our measures of rank dynamics are
general and applicable for complex systems of different natures.
