Comparing bottom-up and top-down ratings for individual soccer players

Abstract Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.

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Making Inference across Mobilisation and Influence Research: Comparing Top-Down and Bottom-Up Mapping of Interest Systems

Political Studies ◽

10.1177/0032321717702400 ◽

2017 ◽

Vol 66 (1) ◽

pp. 43-62 ◽

Cited By ~ 33

Author(s):

Joost Berkhout ◽

Jan Beyers ◽

Caelesta Braun ◽

Marcel Hanegraaff ◽

David Lowery

Keyword(s):

Interest Group ◽

Data Sets ◽

The European Union ◽

Top Down ◽

Total Size ◽

Bottom Up ◽

Policy Influence ◽

Mapping Strategy ◽

Large N ◽

Research Questions

Scholars of mobilisation and policy influence employ two quite different approaches to mapping interest group systems. Those interested in research questions on mobilisation typically rely on a bottom-up mapping strategy in order to characterise the total size and composition of interest group communities. Researchers with an interest in policy influence usually rely on a top-down strategy in which the mapping of politically active organisations depends on samples of specific policies. But some scholars also use top-down data gathered for other research questions on mobilisation (and vice versa). However, it is currently unclear how valid such large-N data for different types of research questions are. We illustrate our argument by addressing these questions using unique data sets drawn from the INTEREURO project on lobbying in the European Union and the European Union’s Transparency Register. Our findings suggest that top-down and bottom-up mapping strategies lead to profoundly different maps of interest group communities.

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An Analysis of Reading Strategies Used by Ethiopian Higher Education Students: Evidence from Ambo University, Oromia, Ethiopia

IJOHMN ◽

10.24113/ijohmn.v5i5.142 ◽

2019 ◽

Vol 5 (5) ◽

pp. 25-53

Author(s):

Motuma Hirpassa Minda ◽

Mikire Dase Boka ◽

S. Nakkiran

Keyword(s):

Reading Strategies ◽

Qualitative Data ◽

Structured Interview ◽

Education Students ◽

Top Down ◽

Interactive Approach ◽

Bottom Up ◽

English Major ◽

Main Ideas ◽

The University

Concerned by increased problems about the students’ reading quality, this study was carried out to investigate the reading approach of English major students of Ambo University. To achieve this objective, all 52(31 male and 21 female) English major students of the University were purposely selected for the study because the number of the students is small to manage. Both quantitative and qualitative data were obtained from the respondents through Reading Achievement Tests, Questionnaire and Structured Interview and analyzed accordingly. The study mainly focused on the students’ approach to reading (adapted top-down or bottom-up) and the students’ ability to identify the main ideas and details, explicitly stated and implied information, the purpose and the tone of authors in five different reading genres: dialogues, directions, article, essays, and poems. The overall result of the study showed that 89.7% of the University students were exclusively limited to bottom-up approaches to reading and frustrated to determine the main ideas and implied information in the texts. In other words, no student answered more than 78% in reading comprehension items correctly in the tests. Moreover, half of the students could not answer above 50% in the comprehension questions. Therefore, the prescriptions for the solution to the problem lies in bringing about improvement in the students’ interactive approach to reading and thereby, improve students’ ability to identify the main ideas and details, explicitly stated and implied information, the purpose and the tone of authors in different reading genres: dialogues, articles, essays, directions and poem.

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Analytics that Inform the University: Using Data You Already Have

Online Learning ◽

10.24059/olj.v16i3.276 ◽

2012 ◽

Vol 16 (3) ◽

Author(s):

Charles Dziuban ◽

Patsy Moskal ◽

Thomas B. Cavanagh ◽

Andre Watts

Keyword(s):

Student Success ◽

Distributed Learning ◽

University Of Central Florida ◽

Top Down ◽

Bottom Up ◽

Central Florida ◽

Powerful Means ◽

Using Data ◽

The University ◽

Inform Decision Making

The authors describe the University of Central Florida’s top-down / bottom-up action analytics approach to using data to inform decision-making at the University of Central Florida. The top-down approach utilizes information about programs, modalities, and college implementation of Web initiatives. The bottom-up approach continuously monitors outcomes attributable to distributed learning, including student ratings and student success. Combined, this top-down/bottom up approach becomes a powerful means for using large extant university datasets to provide significant insights that can be instrumental in strategic planning.

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Extracting the Structure of Design Information From Collaborative Tagging

Journal of Computing and Information Science in Engineering ◽

10.1115/1.3617447 ◽

2011 ◽

Vol 11 (4) ◽

Cited By ~ 6

Author(s):

Jitesh H. Panchal ◽

Matthias Messer

Keyword(s):

Collaborative Design ◽

Collaborative Tagging ◽

Top Down ◽

Bottom Up ◽

Design Information ◽

Complementary Nature ◽

Collective Innovation ◽

Using Data ◽

Innovation Platform ◽

Computational Reasoning

Information representation in engineering design is currently dominated by top–down approaches such as taxonomies and ontologies. While top–down approaches provide support for computational reasoning, they are primarily limited due to their static nature, limited scope, and developer-centric focus. Bottom–up approaches, such as folksonomies, are emerging as means to address the limitations of top–down approaches. Folksonomies refer to collaborative classification by users who freely assign tags to design information. They are dynamic in nature, broad in scope, and are user focused. However, they are limited due to the presence of ambiguities and redundancies in the tags used by different people. Considering their complementary nature, the ideal approach is to use both top–down and bottom–up approaches in a synergistic manner. To facilitate this synergy, the goal in this paper is to present techniques for using dynamic folksonomies to extract global characteristics of the structure of design information, and to create hierarchies of tags that can guide the development of structured taxonomies and ontologies. The approach presented in this paper involves using (a) tools such as degree distribution and K-neighborhood connectivity analysis to extract the global characteristics of folksonomies and (b) set-based technique and hierarchical clustering to develop a hierarchy of tags. The approach is illustrated using data from a collective innovation platform that supports collaborative tagging for design information. It is shown that despite the flat nature of the folksonomies insights about the hierarchy in information can be gained. The effects of various parameters on the tag hierarchy are discussed. The approach has potential to be used synergistically with top–down approaches such as ontologies to support the next generation collaborative design platforms.

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Electoral Competition and the Constituent-Representative Relationship

World Political Science ◽

10.1515/wpsr-2013-0013 ◽

2013 ◽

Vol 9 (1) ◽

pp. 337-355 ◽

Cited By ~ 3

Author(s):

Audrey André ◽

Sam Depauw

Keyword(s):

Political Representation ◽

Electoral Competition ◽

Electoral Institutions ◽

Local Orientation ◽

Top Down ◽

Bottom Up ◽

District Magnitude ◽

Using Data ◽

Elected Representatives ◽

Cross National

AbstractElectoral institutions shape the incentive that elected representatives have to cultivate a personal vote, a geographically concentrated personal vote in particular. But are electoral institutions able to make representatives do what they would not do otherwise and to make them not do what they otherwise would have done? Using data from the cross-national partirep MP survey, it is demonstrated that electoral institutions shape elected representatives’ local orientation. That local orientation decreases as district magnitude grows – regardless of what representatives think about political representation. But representatives’ conceptions of representation do shape their uptake in the legislative arena from their contacts with individual constituents. The effect of the electoral incentive grows stronger as elected representatives think of representation as a bottom-up rather than a top-down process.

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Top-down, bottom-up or outside-in? An examination of triadic mechanisms on firm innovation in Chinese firms

Asian Business & Management ◽

10.1057/s41291-019-00085-z ◽

2019 ◽

Author(s):

Yu Zhou ◽

Guangjian Liu ◽

Xiaoxi Chang ◽

Ying Hong

Keyword(s):

Social Networks ◽

Future Research ◽

Top Down ◽

Firm Innovation ◽

Bottom Up ◽

High Involvement ◽

Bureaucratic Structure ◽

Using Data ◽

Two Stages ◽

Practical Implications

Abstract This paper examines the influence of the interaction of three sources of innovation, namely, top-down (bureaucratic structure), bottom-up (high-involvement HRM) and outside-in (outreaching network), on two stages of firm innovation, i.e. invention and commercialization. Using data from 620 large Chinese companies, we found that there was a synergy between the bureaucratic structure and a high-involvement HRM system in influencing firm innovation. Social networks were most effective in promoting firm innovation in the presence of a high-involvement HRM system. The bureaucratic structure inhibited social networks in contributing to firm innovation. Ideas for future research and practical implications are discussed.

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Can Elite Australian Football Player’s Game Performance Be Predicted?

International Journal of Computer Science in Sport ◽

10.2478/ijcss-2021-0004 ◽

2021 ◽

Vol 20 (1) ◽

pp. 55-78

Author(s):

J. Fahey-Gilmour ◽

J. Heasman ◽

B. Rogalski ◽

B. Dawson ◽

P. Peeling

Keyword(s):

Model Performance ◽

Test Set ◽

Australian Football League ◽

Team Selection ◽

Individual Player ◽

Australian Football ◽

Team Characteristics ◽

Using Data ◽

Player Performance ◽

Game Performance

Abstract In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

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