scholarly journals A comprehensive review of plus-minus ratings for evaluating individual players in team sports

2019 ◽  
Vol 18 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Lars Magnus Hvattum
Keyword(s):  
Decision Making ◽  
Team Sports ◽  
Sports Analytics ◽  
Comprehensive Review ◽  
Sports Events ◽  
Open Research ◽  
New Directions ◽  
Individual Player ◽  
Research Questions ◽  
The Many

AbstractThe increasing availability of data from sports events has led to many new directions of research, and sports analytics can play a role in making better decisions both within a club and at the level of an individual player. The ability to objectively evaluate individual players in team sports is one aspect that may enable better decision making, but such evaluations are not straightforward to obtain. One class of ratings for individual players in team sports, known as plus-minus ratings, attempt to distribute credit for the performance of a team onto the players of that team. Such ratings have a long history, going back at least to the 1950s, but in recent years research on advanced versions of plus-minus ratings has increased noticeably. This paper presents a comprehensive review of contributions to plus-minus ratings in later years, pointing out some key developments and showing the richness of the mathematical models developed. One conclusion is that the literature on plus-minus ratings is quite fragmented, but that awareness of past contributions to the field should allow researchers to focus on some of the many open research questions related to the evaluation of individual players in team sports.

scholarly journals Artificial intelligence for team sports: a survey

2019 ◽  
Vol 34 ◽  
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Outcome Prediction ◽  
Team Sports ◽  
Fantasy Sports ◽  
Sports Teams ◽  
Injury Prediction ◽  
Tactical Decision ◽  
Research Questions

Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports.

scholarly journals When Computational Representation Meets Neuroscience: A Survey on Brain Encoding and Decoding

2021 ◽  
Author(s):  
Lu Cao ◽  
Dandan Huang ◽  
Yue Zhang
Keyword(s):  
Language Processing ◽  
Research Field ◽  
Language Models ◽  
Research Progress ◽  
Artificial Intelligent ◽  
Comprehensive Review ◽  
Open Research ◽  
Research Questions ◽  
The Relationship ◽  
Computational Representation

Real human language mechanisms and the artificial intelligent language processing methods are two independent systems. Exploring the relationship between the two can help develop human-like language models and is also beneficial to reveal the neuroscience of the reading brain. The flourishing research in this interdisciplinal research field calls for surveys to systemically study and analyze the recent successes. However, such a comprehensive review still cannot be found, which motivates our work. This article first briefly introduces the interdisciplinal research progress, then systematically discusses the task of brain decoding from the perspective of simple concepts and complete sentences, and also describes main limitations in this field and put forward with possible solutions. Finally, we conclude this survey with certain open research questions that will stimulate further studies.

scholarly journals Dialogue Games in Multi-Agent Systems

2001 ◽  
Vol 22 (3) ◽  
Author(s):  
Peter McBurney ◽  
Simon Parsons
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Multi Agent Systems ◽  
Dialogue Games ◽  
Agent Systems ◽  
Open Research ◽  
Multi Agent ◽  
Research Questions ◽  
The Many ◽  
Formal Dialogue

Formal dialogue games have been studied in philosophy since at least the time of Aristotle. Recently they have been applied in various contexts in computer science and artificial intelligence, particularly as the basis for interaction between autonomous software agents. We review these applications and discuss the many open research questions and challenges at this exciting interface between philosophy and computer science.

Decision Making in Team Sports: Data, model and applied perspective

2008 ◽  
Author(s):  
Pedro J. M. Passos ◽  
Duarte Araujo ◽  
Keith Davids ◽  
Ana Diniz ◽  
Luis Gouveia ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Model ◽  
Team Sports ◽  
Sports Data

Evidence-based Assessment

2010 ◽  
pp. 76-98
Author(s):  
John Hunsley ◽  
Eric J. Mash
Keyword(s):  
Decision Making ◽  
Clinical Assessment ◽  
Assessment Process ◽  
Evidence Based ◽  
The Past ◽  
Specific Assessment ◽  
The Many ◽  
Selection Of ◽  
Made In

Evidence-based assessment relies on research and theory to inform the selection of constructs to be assessed for a specific assessment purpose, the methods and measures to be used in the assessment, and the manner in which the assessment process unfolds. An evidence-based approach to clinical assessment necessitates the recognition that, even when evidence-based instruments are used, the assessment process is a decision-making task in which hypotheses must be iteratively formulated and tested. In this chapter, we review (a) the progress that has been made in developing an evidence-based approach to clinical assessment in the past decade and (b) the many challenges that lie ahead if clinical assessment is to be truly evidence-based.

scholarly journals Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey

2021 ◽  
Author(s):  
Akrati Saxena ◽  
Harita Reddy
Keyword(s):  
Network Analysis ◽  
Knowledge Sharing ◽  
Informal Learning ◽  
Social Roles ◽  
Online Discussion ◽  
State Of The Art ◽  
Temporal Analysis ◽  
The State ◽  
Open Research ◽  
Research Questions

AbstractOnline informal learning and knowledge-sharing platforms, such as Stack Exchange, Reddit, and Wikipedia have been a great source of learning. Millions of people access these websites to ask questions, answer the questions, view answers, or check facts. However, one interesting question that has always attracted the researchers is if all the users share equally on these portals, and if not then how the contribution varies across users, and how it is distributed? Do different users focus on different kinds of activities and play specific roles? In this work, we present a survey of users’ social roles that have been identified on online discussion and Q&A platforms including Usenet newsgroups, Reddit, Stack Exchange, and MOOC forums, as well as on crowdsourced encyclopedias, such as Wikipedia, and Baidu Baike, where users interact with each other through talk pages. We discuss the state of the art on capturing the variety of users roles through different methods including the construction of user network, analysis of content posted by users, temporal analysis of user activity, posting frequency, and so on. We also discuss the available datasets and APIs to collect the data from these platforms for further research. The survey is concluded with open research questions.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Analyzing Project Management Methods in Organizing Sports Events

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402097094
Author(s):  
Martin Schnitzer ◽  
Kathrin Kronberger ◽  
Filippo Bazzanella ◽  
Sebastian Wenger
Keyword(s):  
Project Management ◽  
Focus Group ◽  
Knowledge Transfer ◽  
Focus Group Discussion ◽  
Planning Process ◽  
Group Discussion ◽  
Quantitative Survey ◽  
Progress Control ◽  
Sports Events ◽  
Research Questions

The purpose of this study was to investigate the use and importance of project management (PM) methods in organizing sports events (SEs). Furthermore, the study analyzed differences in usage and importance of PM methods in relation to the type of SE. Finally, reasons for and obstacles to the implementation of PM methods in organizing SEs were identified. To assess the research questions, a quantitative survey ( n = 78) and a focus group discussion ( n = 5) were carried out. The results showed that PM methods were employed for SEs with higher usage and importance rates in large compared with small SEs. Requirements by event stakeholders, knowledge transfer, confidence building, progress control, and justification as well as opportunities to save money by introducing an improved planning process were identified as the main reasons for using PM in the organization of SEs. This study is the first work to provide an overview of the usage of specific PM methods in organizing SEs.

A Bayesian adjusted plus-minus analysis for the esport Dota 2

2020 ◽  
Vol 16 (4) ◽  
pp. 325-341
Author(s):  
Nicholas Clark ◽  
Brian Macdonald ◽  
Ian Kloo
Keyword(s):  
Professional Sports ◽  
Hierarchical Regression ◽  
Sports Analytics ◽  
Bayesian Hierarchical ◽  
The Past ◽  
Score Statistics ◽  
Level Statistics ◽  
Traditional Sports ◽  
Level Information ◽  
Individual Player

AbstractAnalytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

Sign in / Sign up

Export Citation Format

Share Document