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Regular version of the site

Social Networks and Economics in Sports — Plenary Speakers

Social Networks and Economics in Sports

Plenary Speakers

Celso Carneiro Ribeiro on SNES 2013
Celso Carneiro Ribeiro
The Fluminense Federal University, Brazil

Scheduling the Brazilian Soccer Tournament: Solution Approach and Practice

Sports, with their massive investments in players and structures, have become a big business. Professional and amateur leagues face challenging problems, including logistics, revenue maximization, broadcast rights, fairness issues, game attractiveness, and security. The annual Brazilian soccer tournament is a compact, mirrored double round-robin tournament played by 20 teams in each of its two main divisions; it is possibly the world's most attractive soccer tournament because of the quality of the teams and players in the competition. With substantial revenue and community pride on the line, devising optimal schedules is crucial to players, teams, fans, sponsors, cities, and for security issues. Fair and balanced schedules for all teams are a major issue for ensuring attractiveness and confidence in the tournament outcome. The organizers seek schedules that satisfy a number of constraints. As often as possible, the most important games should be played in weekend rounds so that the open TV channels can broadcast many attractive games. We describe the integer programming formulation of the scheduling problem and the three-phase decomposition approach we proposed for solving it. We also report on the practical experience we observed after two years of running the system and the main results achieved during its successful history.

Arnold Baca on SNES 2013
Arnold Baca
University of Vienna, Austria

Adaptive Systems in Sports

Technological systems are getting increasingly important for physical activity monitoring and assessment in general and for supervising load and performance in mass and elite sport in particular. Miniature sensors and computing devices are attached to the athletes or integrated into the sports equipment in order to acquire and process performance or load related data. Ubiquitous computing technologies are thus applied to implement systems, which provide athletes with feedback information on the quality of the motion just performed. Due to the rapid progress in hardware capabilities and the potential of data processing methods, it can be expected that “the emphasis in the future developments will shift to development of intelligent systems that could not only analyze the data but suggests strategies and interventions” [Baca, A., Dabnichki, P., Heller, M., and Kornfeind, P. (2009). Ubiquitous computing in sports: A review and analysis. Journal of Sports Sciences, 27 (12) (2009), 1335-1346]. Moreover, sports equipment will be able to sense new conditions in the environment and adapt accordingly. Essential bases for almost any such system are the successful recognition of patterns underlying the sports movement just performed and/or the prediction of future states. These analyses do not only consider kinematic parameters, but, moreover, also kinetic and physiological data. Different methods and models have proven to be useful. In the presentation, a survey of hard- and software approaches is given. Pros and cons are discussed with regard to their applicability for intelligent devices supporting athletes. Practical applications are presented and experiences reported.

Sergiy Butenko on SNES 2013
Sergiy Butenko
Texas A&M University, USA

Network-based Techniques in Sports Analytics

This presentation discusses several applications of network analysis techniques to sports analytics. The considered examples include (1) mining touch-by-touch soccer game data; (2) ranking American college football recruiting classes using publicly available scholarship offer/acceptance data, and ranking American college football teams based on their win/loss record. The availability of touch-by-touch soccer game data (collected by StatDNA) provides an opportunity to analyze the team interactions from a network perspective. In particular, the information on passes between pairs of players can be summarized in the form of a network, where players are represented by nodes and passing interactions between pairs of players are represented by weighted directed arcs, with the weight of the arc from player A node to player B node being the number of successful passes completed by A to B. Then social network analysis (SNA) techniques can be utilized to study the structural properties of the network representing the team play, such as centrality, connectivity, cohesiveness, and robustness. While opportunities for exploiting the SNA and, more generally, network-based data mining techniques in soccer analytics are abundant, in this report we restrict ourselves to illustrating some of the basic techniques along these lines. With over 120 football teams in Football Bowl Subdivision (FBS) ranking the recruiting classes and the teams based on their win/loss record (as required by the rules) are challenging problems of high importance for the parties involved. Traditional approaches to rankings in college football heavily rely on subjective expert opinions. We propose unbiased network-based analytical methods for ranking recruiting classes and teams that are based only on publicly available information. The performance of the proposed approaches is illustrated using data from recent years.


  1. V. Boginski, S. Butenko, and P. M. Pardalos. Matrix-based methods for college football rankings. In: Economics, Management and Optimization in Sports. Ed. by S. Butenko, J. Gil-Lafuente, and P. M. Pardalos. Springer, 2004, pp.1–13.
  2. S. Butenko, A. L. Johnson, E. Moreno-Centeno, J. Yates. Analytical Methods for College Football Recruiting Rankings. Submitted for publication.
  3. J. Yates and S. Butenko. Soccer Analytics via Geospatial and Network-based Data Mining Techniques. Working paper.
Jaime Gil Lafuente on SNES 2013
Jaime Gil Lafuente
University of Barcelona, Spain

Decision Making in The Management of Sports Organizations

In the last years, the management of sports activities, the ones included in the called “sports spectacle”, have turned radically as consequence of the increasing interest in mass media (television principally), including in their programming events that could reach unimaginable audience levels. In addition, the world crisis that most of the countries are suffering, boost that the economic limitations of most of the citizens of these countries can be partially alleviated by the success of either a sport club or certain sportsmen The fortunes that are being paid, for example, for signing a football, basketball or handball player or trainer are millionaire. Thinking about the amounts paid can be scary because in some cases these players or trainers do not will deliver the expected performance to compensate the investment realized. The fuzzy logic technique is a mathematic method adapted to the consumers behavior ambiguity, that we used to find or chose the best nearest candidates, the ones nearest to the ideal ones, avoiding with this mechanism, time consuming, illusions and resources mistakes that is unnecessary to continue having. With this methodology we tried to reduce the uncertainty or the risk, at the moment of signing sportsmen, trainers or sport teams.


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