Predicting wins, losses and attributes' sensitivities in the soccer World Cup 2018 using neural network analysis

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes' sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model's output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15-20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

A KD framework in football data analytics: a value co-creation framework for the use of knowledge discovery technologies in the football industry

Investment in sport technologies are expected to grow by 40.1% during 2016-2022 reaching approximately $3.97 billion by 2022. As well the recent changes in technology regulations by The Federation Internationale de Football Association (FIFA) since the 2018 World Cup created promising football technologies. This research questions addressing the issue of what is the value of such technologies for professional football teams? and what are the benefits of these technologies? This is achieved by developing a framework for understanding the value co-creation process from the knowledge discovery systems in the football industry. The framework aids in mapping the resources, pinpointing the outputs, identifying the competencies leading into capabilities, and finally in realisation of the value of the final outcomes in that journey. On another words, different teams have different resources that allow them to achieve certain outputs. These outputs enable the coaching team to achieve and maintain certain abilities. By changes in practice the will improve the team ability and enhance their analytical capabilities. Therefore, that will allow and aid the coaching team to gain new outcomes such as improving training strategies, transferring players, and informative match strategies. Additionally, improved understanding of the value co-creation process from the knowledge discovery systems in the football industry answering, why are some teams better able to gain value from investment in knowledge discovery technologies than other teams in the football industry. The framework has been developed in three phases in which semi-structured interviews where used in the first and second phases for developing and validating the framework respectively. The third and final phases is verifying the framework by developing a knowledge discovery maturity model as an online assessment s tool in operationalising the research findings. The main contributions of this research are the adaptation and customisation of Melville et al. (2004) to develop a value co-creation process form knowledge discovery resources. Moreover, applying Agile (APM, 2015) artefacts and techniques and tools in improving the value co-creation process between coaches and data analysts. That s aided in developing the value co-creation knowledge discovery framework in football analytics. Additionally, the development of a key performance indicators balanced scorecard and its adaptation as a in understanding the relationships between the key performance indicators (i.e. physical, psychological, technical and tactical performance indicators). Finally, the development of the knowledge discovery maturity model in football analytics which was used in understanding and pinpointing areas of strength and weakness in the utilisation of the various football resources used in football analytics (human resources, technological resources, value co-creation resources and analytical models used)

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Data-driven evaluation of on-field player performance in football using sensor and video technologies

Data has become increasingly relevant and used in football over the years. Technological development has made it possible to gather data from various aspects of the game. However, despite the growing popularity of sports analytics, relatively little research, especially qualitative, has been done on the topic. The purpose of this thesis is to create understanding and practices for taking advance of data for evaluation of on-field player performance in football using sensor and video technologies. This is done by identifying and combining technological possibilities with sports knowledge and suggesting an approach for data-driven evaluation of the on-field player performance. Review of previous literature and semi-structured theme interviews have been used as a method to achieve the purpose of the thesis. The findings of the thesis show that data can be used in the evaluation of on-field player performance in football by assessing players’ physical, technical, tactical, and mental attributes. These attributes have several different metrics, the value of which depends on several factors such as the team's objectives. Furthermore, an approach is presented in the thesis which suggests that the selection of team-specific attributes and metrics guides the user to consider which data is needed to be able to evaluate the desired metrics, which then can be linked to certain technologies and analytical solutions presented in the thesis