  A series of recent works studying a database of chronologically sorted chess
games --containing 1.4 million games played by humans between 1998 and 2007--
have shown that the popularity distribution of chess game-lines follows a
Zipf's law, and that time series inferred from the sequences of those
game-lines exhibit long-range memory effects. The presence of Zipf's law
together with long-range memory effects was observed in several systems,
however, the simultaneous emergence of these two phenomena were always studied
separately up to now. In this work, by making use of a variant of the
Yule-Simon preferential growth model, introduced by Cattuto et al., we provide
an explanation for the simultaneous emergence of Zipf's law and long-range
correlations memory effects in a chess database. We find that Cattuto's Model
(CM) is able to reproduce both, Zipf's law and the long-range correlations,
including size-dependent scaling of the Hurst exponent for the corresponding
time series. CM allows an explanation for the simultaneous emergence of these
two phenomena via a preferential growth dynamics, including a memory kernel, in
the popularity distribution of chess game-lines. This mechanism results in an
aging process in the chess game-line choice as the database grows. Moreover, we
find burstiness in the activity of subsets of the most active players, although
the aggregated activity of the pool of players displays inter-event times
without burstiness. We show that CM is not able to produce time series with
bursty behavior providing evidence that burstiness is not required for the
explanation of the long-range correlation effects in the chess database.
