  Individual sports competitions provide a natural setting for examining the
relative importance of talent and luck/chance in achieving success. The belief
that success is primarily due to individual abilities and hard work rather than
external factors is particularly strong in this context. In this study, we test
this belief using tennis as a case study, due to its popularity and competition
structure in direct-elimination tournaments. Our dataset covers the decade
2010-2019 of main events in the ATP circuit and consists of tourney results and
annual rankings for professional male players. After a preliminary data
analysis, we introduce an agent-based model able to accurately simulate the
tennis players' dynamics along several seasons. We show that, once calibrated
on the dataset, the model is able to reproduce the main stylized facts observed
in real data, including the results of single tournaments and the development
of players' careers in the ATP community. The strength of our approach lies in
its simplicity: it requires only one free parameter a to determine the
importance of talent in scoring every single point: a = 1, if only talent
matters; a = 0, if the outcome of each point is entirely due to chance. We find
the best agreement between real data and simulation results when talent weights
substantially less than luck, i.e. when a is between 0.20 and 0.30. A further
comparison between data and simulations, based on the analysis of the direct
networks of all the matches, confirms the previous finding. A posteriori, we
notice that this surprisingly important role of chance in tennis tournaments is
not an exception. On the contrary, it can be explained by a more general
paradoxical effect that characterizes highly competitive environments,
particularly in individual sports. In other words, when the difference in
talent between top players is minimal, chance becomes determinant.
