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.

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.

Sponsoring Early Day Motions in the British House of Commons as a Response to Electoral Vulnerability

2013 ◽  
Vol 1 (2) ◽  
pp. 263-280 ◽  
Author(s):  
Michael Kellermann
Keyword(s):  
Political Science ◽  
Negative Binomial ◽  
Hurdle Model ◽  
House Of Commons ◽  
Bayesian Hierarchical ◽  
The Past ◽  
Electoral Outcomes

While the importance of individual candidates in British elections has long been minimized, this article argues that early day motions (EDMs)—formal, non-binding expressions of opinion—allow backbench MPs to cultivate reputations with constituents. First, this article demonstrates that greater sponsorship of EDMs is associated with better electoral outcomes, which suggests that EDMs could help vulnerable MPs improve their electoral prospects. Secondly, a Bayesian hierarchical negative binomial hurdle model, which accounts for specific features of EDM sponsorship and is novel in political science, shows that members from electorally competitive constituencies are more likely to introduce EDMs, and introduce them more often, than members from less competitive constituencies. Moreover, this relationship has increased over the past 20 years.

scholarly journals Estimating autoantibody signatures to detect autoimmune disease patient subsets

Biostatistics ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Zhenke Wu ◽  
Livia Casciola-Rosen ◽  
Ami A Shah ◽  
Antony Rosen ◽  
Scott L Zeger
Keyword(s):  
Immune Responses ◽  
Autoimmune Diseases ◽  
Gel Electrophoresis ◽  
Sodium Dodecyl ◽  
Disease Patient ◽  
Bayesian Hierarchical Model ◽  
Multiple Sources ◽  
Bayesian Hierarchical ◽  
The Past ◽  
Shrinkage Priors

Abstract Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors—including irrelevant intensity differences and warping of gels—have challenged automation of pattern discovery from autoradiograms. In this article, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients.

Bayesian hierarchical regression models for detecting QTLs in plant experiments

2008 ◽  
Vol 35 (7) ◽  
pp. 799-808 ◽  
Author(s):  
Edward L. Boone ◽  
Susan J. Simmons ◽  
Haikun Bao ◽  
Ann E. Stapleton
Keyword(s):  
Regression Models ◽  
Hierarchical Regression ◽  
Bayesian Hierarchical

YouTube channels, uploads and views

2018 ◽  
Vol 24 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Mathias Bärtl
Keyword(s):  
Random Sample ◽  
Video Data ◽  
Small Minority ◽  
The Past ◽  
Analytical Results ◽  
Level Statistics ◽  
High Level

To this date, it is difficult to find high-level statistics on YouTube that paint a fair picture of the platform in its entirety. This study attempts to provide an overall characterization of YouTube, based on a random sample of channel and video data, by showing how video provision and consumption evolved over the course of the past 10 years. It demonstrates stark contrasts between video genres in terms of channels, uploads and views, and that a vast majority of on average 85% of all views goes to a small minority of 3% of all channels. The analytical results give evidence that older channels have a significantly higher probability to garner a large viewership, but also show that there has always been a small chance for young channels to become successful quickly, depending on whether they choose their genre wisely.

scholarly journals Importance of language for children’s theory of mind: Comparison of verbal and nonverbal theory of mind tasks

Psihologija ◽  
2020 ◽  
pp. 23-23
Author(s):  
Sanja Simlesa ◽  
Kaja Hacin ◽  
Maja Cepanec ◽  
Jasmina Ivsac-Pavlisa
Keyword(s):  
Theory Of Mind ◽  
Language Comprehension ◽  
Cognitive Skills ◽  
Mental States ◽  
False Belief ◽  
Hierarchical Regression ◽  
Typically Developing ◽  
Typically Developing Children ◽  
The Past ◽  
Verbal Tasks

The ability to attribute mental states to oneself and others, known as the theory of mind (ToM), has been widely researched over the past 40 years, along with its relation to language comprehension. However, a majority of the research on the relation between ToM and language used only verbal tasks assessing false belief understanding as a measure of ToM. Therefore, this study aimed to analyze the relation between language and ToM, using a larger battery of ToM measures, with different language demands. A total of 203 typically developing children between 46 and 68 months of age, with average nonverbal cognitive skills, were assessed using language comprehension and ToM tasks. The language aspect was assessed using the Reynell Developmental Language Scales (Language Comprehension scale A). To assess ToM, verbal and non-verbal tasks were taken from the ToM subtest of the NEPSY-II. Results indicated a significant correlation between language comprehension and verbal and non-verbal ToM measures. Hierarchical regression showed that language comprehension was a significant predictor for children's performance on both verbal and non-verbal ToM tasks. Specifically, language comprehension affected ToM, regardless of the language demands of the ToM tasks. However, language comprehension was a stronger predictor for verbal than non-verbal ToM tasks. The results of this study contribute to the view that the relation between language and ToM is fundamental and exceeds the features of specific tasks.

scholarly journals Growth Opportunities and IPO Initial Performance. Is There an Interacting Effect of Public Issue?

2020 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Siti Suhaila Abdul Rahman ◽  
Norliza Che-Yahya
Keyword(s):  
Regression Models ◽  
Hierarchical Regression ◽  
Growth Opportunities ◽  
Significant Interaction Effect ◽  
Public Issue ◽  
The Public ◽  
The Past ◽  
Explanatory Factors ◽  
Initial Return ◽  
Main Relationship

The declining rate on initial return of Malaysian IPOs over the past years have alarmed investors to astutely choose IPO firms for a better security of their investment’s income. In an attempt to offer an aid for the danger on the loss of capital that the investors might suffer, this study is initiated to search for explanations on the variation of IPO returns particularly in the initial aftermarket. This study proposes “growth opportunities in an IPO firm” and “allocation of IPOs through public issue approach” as its main explanatory factors which the former acts as main explanatory variable while the latter serves as interacting variable. This study defines “growth opportunities of firms” as the possibility of an IPO firm to enlarge its market share over the long period. The “growth opportunities of firms” are measured by the total allocation amount received from the issuance of IPOs to activities (e.g., assets acquisition and business expansion) which possibly support growth of a firm in a future. To an extent, a higher amount of proceeds channeled to “growth activities” are expected to increase growth opportunities of firms such that will encourage higher participation on the shares of the issuing firms as well as higher returns of the shares. Nonetheless, “growth opportunities of firms” can be accurately gauged only if the amount of cash from the sale of IPOs are owned by the issuing firms. That is, the public issue approach that an IPO firm adopts when issue for its shares to public should finalize the final proceeds eligible to be allocated to any intended activities of the firm. Using a total 447 IPOs listed in Main Market and ACE Market of Bursa Malaysia from 2000 to 2018, tested using hierarchical regression models, this study found that the proceeds allocated to growth activities significantly positively influence initial return. However, this study is not able to produce a significant interaction effect of public issue on the main relationship tested earlier.

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