3,881 research outputs found
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
A Dimension Reduction Approach to Player Rankings in European Football
Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from position to position, but also it is based on competition, time played and team play-styles. Because of this, raw player statistics are not comparable across players and must be processed to facilitate a fair performance evaluation. Furthermore, teams may have different requirements and a generic player performance evaluation does not directly serve the particular expectations of different clubs. In this study, we provide a generic framework for estimating player performance and performing player-fit-to-criteria assessment, under different objectives, for left and right backs from competitions worldwide. The results show that the players who have ranked high have increased their transfer values and they have moved to suitable teams. Global nature of the proposed methodology expands the analyzed player pool, facilitating the search for outstanding players from all available competitions
Using Gap Charts to Visualize the Temporal Evolution of Ranks and Scores
To address the limitations of traditional line chart approaches, in particular rank charts (RCs) and score charts (SCs), a novel class of line charts called gap charts (GCs) show entries that are ranked over time according to a performance metric. The main advantages of GCs are that entries never overlap (only changes in rank generate limited overlap between time steps) and gaps between entries show the magnitude of their score difference. The authors evaluate the effectiveness of GCs for performing different types of tasks and find that they outperform standard time-dependent ranking visualizations for tasks that involve identifying and understanding evolutions in both ranks and scores. They also show that GCs are a generic and scalable class of line charts by applying them to a variety of different datasets
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
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
Recommended from our members
State of the Art of Sports Data Visualization
In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR’s website: https://sportsdataviz.github.io/
FootGPT : A Large Language Model Development Experiment on a Minimal Setting
With recent empirical observations, it has been argued that the most
significant aspect of developing accurate language models may be the proper
dataset content and training strategy compared to the number of neural
parameters, training duration or dataset size. Following this argument, we
opted to fine tune a one billion parameter size trained general purpose causal
language model with a dataset curated on team statistics of the Italian
football league first ten game weeks, using low rank adaptation. The limited
training dataset was compiled based on a framework where a powerful commercial
large language model provides distilled paragraphs and question answer pairs as
intended. The training duration was kept relatively short to provide a basis
for our minimal setting exploration. We share our key observations on the
process related to developing a specific purpose language model which is
intended to interpret soccer data with constrained resources in this article.Comment: 10 pages, 3 figure
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