18,793 research outputs found
PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach
The problem of evaluating the performance of soccer players is attracting the
interest of many companies and the scientific community, thanks to the
availability of massive data capturing all the events generated during a match
(e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated
and widely accepted metric for measuring performance quality in all of its
facets. In this paper, we design and implement PlayeRank, a data-driven
framework that offers a principled multi-dimensional and role-aware evaluation
of the performance of soccer players. We build our framework by deploying a
massive dataset of soccer-logs and consisting of millions of match events
pertaining to four seasons of 18 prominent soccer competitions. By comparing
PlayeRank to known algorithms for performance evaluation in soccer, and by
exploiting a dataset of players' evaluations made by professional soccer
scouts, we show that PlayeRank significantly outperforms the competitors. We
also explore the ratings produced by {\sf PlayeRank} and discover interesting
patterns about the nature of excellent performances and what distinguishes the
top players from the others. At the end, we explore some applications of
PlayeRank -- i.e. searching players and player versatility --- showing its
flexibility and efficiency, which makes it worth to be used in the design of a
scalable platform for soccer analytics
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Collective dynamics of belief evolution under cognitive coherence and social conformity
Human history has been marked by social instability and conflict, often
driven by the irreconcilability of opposing sets of beliefs, ideologies, and
religious dogmas. The dynamics of belief systems has been studied mainly from
two distinct perspectives, namely how cognitive biases lead to individual
belief rigidity and how social influence leads to social conformity. Here we
propose a unifying framework that connects cognitive and social forces together
in order to study the dynamics of societal belief evolution. Each individual is
endowed with a network of interacting beliefs that evolves through interaction
with other individuals in a social network. The adoption of beliefs is affected
by both internal coherence and social conformity. Our framework explains how
social instabilities can arise in otherwise homogeneous populations, how small
numbers of zealots with highly coherent beliefs can overturn societal
consensus, and how belief rigidity protects fringe groups and cults against
invasion from mainstream beliefs, allowing them to persist and even thrive in
larger societies. Our results suggest that strong consensus may be insufficient
to guarantee social stability, that the cognitive coherence of belief-systems
is vital in determining their ability to spread, and that coherent
belief-systems may pose a serious problem for resolving social polarization,
due to their ability to prevent consensus even under high levels of social
exposure. We therefore argue that the inclusion of cognitive factors into a
social model is crucial in providing a more complete picture of collective
human dynamics
Finite element modelling and experimental study of oblique soccer ball bounce
In this study, we develop a finite element model to examine the oblique soccer ball bounce. A careful simulation of the interaction between the ball membrane and air pressure in the ball makes the model more realistic than analytical models, and helps us to conduct an accurate study on the effect of different parameters on a bouncing ball. This finite element model includes a surface-based fluid cavity to model the mechanical response between the ball carcass and the internal air of the ball. An experimental set-up was devised to study the bounce of the ball in game-relevant impact conditions. Ball speed, angle, and spin were measured before and after the bounce, as well as ball deformation and the forces during the impact. The finite element model has been validated with three different sets of data, and the results demonstrate that the finite element model reported here is a valuable tool for the study of ball bounce. After validation of the model, the effect of the friction coefficient on soccer ball bounce was studied numerically. Simulation results show that increasing the friction coefficient may result in reversal of the horizontal impact force
Event detection based on generic characteristics of field-sports
In this paper, we propose a generic framework for event detection in broadcast video of multiple different field-sports. Features indicating significant events are selected, and robust detectors built. These features are rooted in generic characteristics common to all genres of field-sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested across multiple genres of field-sports including soccer, rugby, hockey and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video
In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable
Having the Second Leg At Home - Advantage in the UEFA Champions League Knockout Phase?
In soccer knockout ties which are played in a two-legged format the team having the return match at home is usually seen as advantaged. For checking this common belief, we analyzed matches of the UEFA Champions League knockout phase since 1995. It is shown that the observed differences in frequencies of winning between teams first playing away and those which are first playing at home can be completely explained by their performances on the group stage and - more importantly - by the teams' general strength
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
Event detection in field sports video using audio-visual features and a support vector machine
In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable
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