Sports Analytics

Scalable Infrastructure for Efficient Real-Time Sports Analytics

Companion Publication of the 2020 International Conference on Multimodal Interaction ◽

10.1145/3395035.3425300 ◽

2020 ◽

Author(s):

Håvard D. Johansen ◽

Dag Johansen ◽

Tomas Kupka ◽

Michael A. Riegler ◽

Pål Halvorsen

Keyword(s):

Real Time ◽

Sports Analytics

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

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2019-0103 ◽

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.

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A Mine of Information: Can Sports Analytics Provide Wisdom From Your Data?

International Journal of Sports Physiology and Performance ◽

10.1123/ijspp.2016-0644 ◽

2017 ◽

Vol 12 (7) ◽

pp. 851-855 ◽

Cited By ~ 11

Author(s):

Louis Passfield ◽

James G. Hopker

Keyword(s):

Web Sites ◽

Large Data ◽

Objective Evaluation ◽

Large Data Sets ◽

Data Sets ◽

Competitive Strategies ◽

Data Repositories ◽

Sports Analytics ◽

Sport And Exercise ◽

New Research

This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.

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Putting Muscle Into Sports Analytics

The Journal of Strength and Conditioning Research ◽

10.1519/jsc.0000000000002211 ◽

2017 ◽

Vol 31 (12) ◽

pp. 3253-3259 ◽

Cited By ~ 3

Author(s):

Kevin M. Kniffin ◽

Thomas Howley ◽

Cole Bardreau

Keyword(s):

Sports Analytics

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German Journal of Exercise and Sport Research ◽

10.1007/s12662-017-0487-7 ◽

2017 ◽

Vol 48 (1) ◽

pp. 13-25 ◽

Cited By ~ 5

Author(s):

Daniel Link

Keyword(s):

Sports Analytics

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Sports Analytics: Visualizing Basketball Records in Graphical Form

Data Analytics ◽

10.1201/9780429446177-13 ◽

2018 ◽

pp. 317-320

Author(s):

Muye Jiang ◽

Gerry Chan ◽

Robert Biddle

Keyword(s):

Graphical Form ◽

Sports Analytics

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

International Journal of Computer Science in Sport ◽

10.2478/ijcss-2019-0001 ◽

2019 ◽

Vol 18 (1) ◽

pp. 1-23 ◽

Cited By ~ 6

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.

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