東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Data Mining for Esports.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Mining for Esports./
作者:	Xenopoulos, Peter.
面頁冊數:	1 online resource (117 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-07, Section: A.
Contained By:	Dissertations Abstracts International84-07A.
標題:	Esports. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30242324click for full text (PQDT)
ISBN:	9798368451381

Data Mining for Esports.
Xenopoulos, Peter.

Data Mining for Esports. - 1 online resource (117 pages)

Source: Dissertations Abstracts International, Volume: 84-07, Section: A.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2023.

Includes bibliographical references

Over the past decade, sophisticated sports data acquisition systems have proliferated detailed player event and tracking data across many conventional sports, such as baseball, basketball or soccer. These data power machine-learning models that drive applications such as win probability prediction, high-leverage play detection or tactic discovery. At the same time, esports -- video game competitions organized like conventional sports -- has grown considerably and is now comparable in viewership and reach to many popular sports. Esports is a uniquely positioned domain for computer science as it touches aspects of human-computer interaction, machine learning and visualization. While sports analytics techniques have gained significant traction in conventional sports, esports has yet to meaningfully adopt analytics-inspired methods. This lack of cross-over is in part due to the intricacies of esports and its data that require specialized data capture and analysis. Analytical advances in esports have wide impact on real-world decision-making, such as for teams acquiring players, media companies creating content, sports bettors assessing bets, or game designers developing new experiences. In this thesis, we introduce an array of work on predicting outcomes in esports. We detail and compare various data representations, models and evaluation methods. Furthermore, we show how to use these methods to facilitate player valuation and game understanding. Finally, we outline promising research directions in the nascent esports research community.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798368451381Subjects--Topical Terms:

3702228
Esports.
Subjects--Index Terms:

EsportsIndex Terms--Genre/Form:

542853
Electronic books.

Data Mining for Esports.
LDR:02799nmm a2200361K 4500 001 2361549
005 20231019095652.5
006 m o d
007 cr mn ---uuuuu
008 241011s2023 xx obm 000 0 eng d
020 $a 9798368451381
035 $a (MiAaPQ)AAI30242324
035 $a AAI30242324
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Xenopoulos, Peter. $3 3702227
245 1 0 $a Data Mining for Esports.
264 0 $c 2023
300 $a 1 online resource (117 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-07, Section: A.
500 $a Advisor: Silva, Claudio.
502 $a Thesis (Ph.D.)--New York University Tandon School of Engineering, 2023.
504 $a Includes bibliographical references
520 $a Over the past decade, sophisticated sports data acquisition systems have proliferated detailed player event and tracking data across many conventional sports, such as baseball, basketball or soccer. These data power machine-learning models that drive applications such as win probability prediction, high-leverage play detection or tactic discovery. At the same time, esports -- video game competitions organized like conventional sports -- has grown considerably and is now comparable in viewership and reach to many popular sports. Esports is a uniquely positioned domain for computer science as it touches aspects of human-computer interaction, machine learning and visualization. While sports analytics techniques have gained significant traction in conventional sports, esports has yet to meaningfully adopt analytics-inspired methods. This lack of cross-over is in part due to the intricacies of esports and its data that require specialized data capture and analysis. Analytical advances in esports have wide impact on real-world decision-making, such as for teams acquiring players, media companies creating content, sports bettors assessing bets, or game designers developing new experiences. In this thesis, we introduce an array of work on predicting outcomes in esports. We detail and compare various data representations, models and evaluation methods. Furthermore, we show how to use these methods to facilitate player valuation and game understanding. Finally, we outline promising research directions in the nascent esports research community.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Esports. $3 3702228
650 4 $a Data science. $3 3689306
650 4 $a Computer science. $3 523869
650 4 $a Sports management. $3 3423935
653 $a Esports
653 $a Sports analytics
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0984
690 $a 0800
690 $a 0430
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a New York University Tandon School of Engineering. $b Computer Science & Engineering. $3 3698754
773 0 $t Dissertations Abstracts International $g 84-07A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30242324 $z click for full text (PQDT)