Sports Data Mining Technology Used in Basketball Outcome Prediction

Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to complete a convincing result, data of 5 regular NBA seasons was collected for model training and data of 1 NBA regular season was used as scoring dataset. After processes of automated data collection and cloud techniques enabled data management, a data mart containing NBA statistics data is built. Then machine learning models mentioned above is trained and tested by consuming data in the data mart. After applying scoring dataset to evaluate the model accuracy, Simple Logistics Classifier finally yields the best result with an accuracy of 69.67%. The results obtained are compared to other methods from different source. It was found that results of this project are more persuasive since such a vast quantity of data was applied in this project. Meanwhile, it can be referenced for the future work

Tastes for True Talent: How Professional Baseball Scouts Define Talent and Decide Who Gets to Play

A growing body of research focuses on talent identification as a critical building block in the process of talent development. Professional baseball scouts’ level of expertise in identifying baseball talent directly impacts organizations’ competitive success at the Major League level, yet the performance productivity of MLB franchises’ draft selections fails to generate a positive rate of investment return. This qualitative, phenomenological study examined how 13 veteran, professional baseball scouts define player attributes and make decisions to identify or eliminate prospects. My analysis of participants’ in-depth, reflexive interviews employed the theoretical lens of reflective knowledge (Schon, 1983), talent development (Bloom, 1985; Csikszentmihalyi, Rathunde, and Whalen, 1993), and performance expertise (Ericsson, 1993; 1996; 1998; 2007, June; 2009). My findings outlined a three-stage talent identification model and uncovered three trends found within professional baseball scouts’ talent identification mindsets and prospect decision-making. First, scouts’ dispositional mindset influences comparative recall and visual knowledge. Second, the makeup traits of competitive adaptability, extra effort, instinct and intellect are highly valued. Third, guess, gut, and instinct integrated with visual knowledge and valued makeup traits direct scouts’ player selection decisions. These outcomes clarify the sources of scouts’ talent identification knowledge, their preferred prospect attributes, and their player selection tendencies. In response to these findings, I recommended six benchmarks that frame the cognitive fundamentals of effective baseball scouting. These fundamentals provide a framework directed toward increasing MLB franchises’ net yield for successful Major League player identification, selection, and development

Business analytics in sport talent acquisition: methods, experiences, and open research opportunities

Recruitment of young talented players is a critical activity for most professional teams in different sports such as football, soccer, basketball, baseball, cycling, etc. In the past, the selection of the most promising players was done just by relying on the experts' opinions but without systematic data support. Nowadays, the existence of large amounts of data and powerful analytical tools have raised the interest in making informed decisions based on data analysis and data-driven methods. Hence, most professional clubs are integrating data scientists to support managers with data-intensive methods and techniques that can identify the best candidates and predict their future evolution. This paper reviews existing work on the use of data analytics, artificial intelligence, and machine learning methods in talent acquisition. A numerical case study, based on real-life data, is also included to illustrate some of the potential applications of business analytics in sport talent acquisition. In addition, research trends, challenges, and open lines are also identified and discussed

From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition

As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research. However, approaches remain largely generic, and do not carefully consider the special properties of scenes. In this paper, inspired by the intuitive differences between scenes and objects, we propose Adi-Red, an adaptive approach to discriminative region discovery for scene recognition. Adi-Red uses a CNN classifier, which was pre-trained using only image-level scene labels, to discover discriminative image regions directly. These regions are then used as a source of features to perform scene recognition. The use of the CNN classifier makes it possible to adapt the number of discriminative regions per image using a simple, yet elegant, threshold, at relatively low computational cost. Experimental results on the scene recognition benchmark dataset SUN397 demonstrate the ability of Adi-Red to outperform the state of the art. Additional experimental analysis on the Places dataset reveals the advantages of Adi-Red, and highlight how they are specific to scenes. We attribute the effectiveness of Adi-Red to the ability of adaptive region discovery to avoid introducing noise, while also not missing out on important information.Comment: To appear at the ACM International Conference on Multimedia (ACM MM 2018). Code available at https://github.com/ZhengyuZhao/Adi-Red-Scen

Data-Driven Analytics for Decision Making in Game Sports

Performance analysis and good decision making in sports is important to maximize chances of winning. Over the last years the amount and quality of data which is available for the analysis has increased enormously due to technical developments like, e.g., of sensor technologies or computer vision technology. However, the data-driven analysis of athletes and team performances is very demanding. One reason is the so called semantic gap of sports analytics. This means that the concepts of coaches are seldomly represented in the data for the analysis. Furthermore, sports in general and game sports in particular present a huge challenge due to its dynamic characteristics and the multi-factorial influences on an athlete’s performance like, e.g., the numerous interaction processes during a match. This requires different types of analyses like, e.g., qualitative analyses and thus anecdotal descriptions of performances up to quantitative analyses with which performances can be described through statistics and indicators. Additionally, coaches and analysts have to work under an enormous time pressure and decisions have to be made very quickly. In order to facilitate the demanding task of game sports analysts and coaches we present a generic approach how to conceptualize and design a Data Analytics System (DAS) for an efficient support of the decision making processes in practice. We first introduce a theoretical model and present a way how to bridge the semantic gap of sports analytics. This ensures that DASs will provide relevant information for the decision makers. Moreover, we show that DASs need to combine qualitative and quantitative analyses as well as visualizations. Additionally, we introduce different query types which are required for a holistic retrieval of sports data. We furthermore show a model for the user-centered planning and designing of the User Experience (UX) of a DAS. Having introduced the theoretical basis we present SportSense, a DAS to support decision making in game sports. Its generic architecture allows a fast adaptation to the individual characteristics and requirements of different game sports. SportSense is novel with respect to the fact that it unites raw data, event data, and video data. Furthermore, it supports different query types including an intuitive sketch-based retrieval and seamlessly combines qualitative and quantitative analyses as well as several data visualization options. Moreover, we present the two applications SportSense Football and SportSense Ice Hockey which contain sport-specific concepts and cover (high-level) tactical analyses

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

The Efficacy of the “Big Data” Syndrome and Organizational Information Governance

This paper addresses the challenge of big data for the design of organisations in governance. Big data refers to the availability to organisations of massive amounts of heterogeneous and continuously updated information. Practitioners agree that the availability of such information creates challenges and opportunities for organiations that have never been seen before. The article presented here takes up this challenge and discusses avenues for future research and practice on organsiation design in the era of big data. The importance of digital technologies for social and economic developments and a growing focus on data collection and privacy concerns have made the internet a salient and visible issue in global politics. Surprisingly, little research has explored questions about the relations between business, governance and the internet. Government organisations are feverishly exploring ways of taking advantage of the big data phenomenon. This paper seeks to expand our knowledge of the intersections between business management, global governance and the digital domain