28,065 research outputs found
Reducing the Effects of Unequal Number of Games on Rankings
Ranking is an important mathematical process in a variety of contexts such as information retrieval, sports and business. Sports ranking methods can be applied both in and beyond the context of athletics. In both settings, once the concept of a game has been defined, teams (or individuals) accumulate wins, losses, and ties, which are then factored into the ranking computation. Many settings involve an unequal number of games between competitors. This paper demonstrates how to adapt two sports rankings methods, the Colley and Massey ranking methods, to settings where an unequal number of games are played between the teams. In such settings, the standard derivations of the methods can produce nonsensical rankings. This paper introduces the idea of including a super-user into the rankings and considers the effect of this fictitious player on the ratings. We apply such techniques to rank batters and pitchers in Major League baseball, professional tennis players, and participants in a free online social game. The ideas introduced in this paper can further the scope that such methods are applied and the depth of insight they offer
Universal temporal features of rankings in competitive sports and games
Many complex phenomena, from the selection of traits in biological systems to
hierarchy formation in social and economic entities, show signs of competition
and heterogeneous performance in the temporal evolution of their components,
which may eventually lead to stratified structures such as the wealth
distribution worldwide. However, it is still unclear whether the road to
hierarchical complexity is determined by the particularities of each phenomena,
or if there are universal mechanisms of stratification common to many systems.
Human sports and games, with their (varied but simplified) rules of competition
and measures of performance, serve as an ideal test bed to look for universal
features of hierarchy formation. With this goal in mind, we analyse here the
behaviour of players and team rankings over time for several sports and games.
Even though, for a given time, the distribution of performance ranks varies
across activities, we find statistical regularities in the dynamics of ranks.
Specifically the rank diversity, a measure of the number of elements occupying
a given rank over a length of time, has the same functional form in sports and
games as in languages, another system where competition is determined by the
use or disuse of grammatical structures. Our results support the notion that
hierarchical phenomena may be driven by the same underlying mechanisms of rank
formation, regardless of the nature of their components. Moreover, such
regularities can in principle be used to predict lifetimes of rank occupancy,
thus increasing our ability to forecast stratification in the presence of
competition
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
Universal scaling in sports ranking
Ranking is a ubiquitous phenomenon in the human society. By clicking the web
pages of Forbes, you may find all kinds of rankings, such as world's most
powerful people, world's richest people, top-paid tennis stars, and so on and
so forth. Herewith, we study a specific kind, sports ranking systems in which
players' scores and prize money are calculated based on their performances in
attending various tournaments. A typical example is tennis. It is found that
the distributions of both scores and prize money follow universal power laws,
with exponents nearly identical for most sports fields. In order to understand
the origin of this universal scaling we focus on the tennis ranking systems. By
checking the data we find that, for any pair of players, the probability that
the higher-ranked player will top the lower-ranked opponent is proportional to
the rank difference between the pair. Such a dependence can be well fitted to a
sigmoidal function. By using this feature, we propose a simple toy model which
can simulate the competition of players in different tournaments. The
simulations yield results consistent with the empirical findings. Extensive
studies indicate the model is robust with respect to the modifications of the
minor parts.Comment: 8 pages, 7 figure
A network-based dynamical ranking system for competitive sports
From the viewpoint of networks, a ranking system for players or teams in
sports is equivalent to a centrality measure for sports networks, whereby a
directed link represents the result of a single game. Previously proposed
network-based ranking systems are derived from static networks, i.e.,
aggregation of the results of games over time. However, the score of a player
(or team) fluctuates over time. Defeating a renowned player in the peak
performance is intuitively more rewarding than defeating the same player in
other periods. To account for this factor, we propose a dynamic variant of such
a network-based ranking system and apply it to professional men's tennis data.
We derive a set of linear online update equations for the score of each player.
The proposed ranking system predicts the outcome of the future games with a
higher accuracy than the static counterparts.Comment: 6 figure
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