  Percentile-based approaches have been proposed as a non-parametric
alternative to parametric central-tendency statistics to normalize observed
citation counts. Percentiles are based on an ordered set of citation counts in
a reference set, whereby the fraction of papers at or below the citation counts
of a focal paper is used as an indicator for its relative citation impact in
the set. In this study, we pursue two related objectives: (1) although
different percentile-based approaches have been developed, an approach is
hitherto missing that satisfies a number of criteria such as scaling of the
percentile ranks from zero (all other papers perform better) to 100 (all other
papers perform worse), and solving the problem with tied citation ranks
unambiguously. We introduce a new citation-rank approach having these
properties, namely P100. (2) We compare the reliability of P100 empirically
with other percentile-based approaches, such as the approaches developed by the
SCImago group, the Centre for Science and Technology Studies (CWTS), and
Thomson Reuters (InCites), using all papers published in 1980 in Thomson
Reuters Web of Science (WoS). How accurately can the different approaches
predict the long-term citation impact in 2010 (in year 31) using citation
impact measured in previous time windows (years 1 to 30)? The comparison of the
approaches shows that the method used by InCites overestimates citation impact
(because of using the highest percentile rank when papers are assigned to more
than a single subject category) whereas the SCImago indicator shows higher
power in predicting the long-term citation impact on the basis of citation
rates in early years. Since the results show a disadvantage in this predictive
ability for P100 against the other approaches, there is still room for further
improvements.
