scholarly journals Roster-Based Optimisation for Limited Overs Cricket

2021 ◽  
Author(s):  
◽  
Ankit Patel
Keyword(s):  
Random Forest ◽  
Performance Metrics ◽  
Positive Impact ◽  
Performance Outcomes ◽  
Rating System ◽  
World Cup ◽  
Research Hypothesis ◽  
Premier League ◽  
Individual Player ◽  
The Caribbean

<p>The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances.  A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning.   To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating.  The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj.  The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection.</p>

scholarly journals Roster-Based Optimisation for Limited Overs Cricket

2021 ◽  
Author(s):  
◽  
Ankit Patel
Keyword(s):  
Random Forest ◽  
Performance Metrics ◽  
Positive Impact ◽  
Performance Outcomes ◽  
Rating System ◽  
World Cup ◽  
Research Hypothesis ◽  
Premier League ◽  
Individual Player ◽  
The Caribbean

<p>The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances.  A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning.   To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating.  The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj.  The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection.</p>

scholarly journals Accreditation and Certification: Do They Improve Hospital Financial and Quality Performance?

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 887
Author(s):  
Matthew Brooks ◽  
Brad M. Beauvais ◽  
Clemens Scott Kruse ◽  
Lawrence Fulton ◽  
Michael Mileski ◽  
...  
Keyword(s):  
Higher Education ◽  
Positive Impact ◽  
Performance Outcomes ◽  
Healthcare Management ◽  
Academic Program ◽  
Future Research ◽  
Graduate Degree ◽  
Healthcare Administration ◽  
Professional Certification ◽  
The Impact

The relationship between healthcare organizational accreditation and their leaders’ professional certification in healthcare management is of specific interest to institutions of higher education and individuals in the healthcare management field. Since academic program accreditation is one piece of evidence of high-quality education, and since professional certification is an attestation to the knowledge, skills, and abilities of those who are certified, we expect alumni who graduated from accredited programs and obtained professional certification to have a positive impact on the organizations that they lead, compared with alumni who did not graduate from accredited programs and who did not obtain professional certification. The authors’ analysis examined the impact of hiring graduates from higher education programs that held external accreditation from the Commission on Accreditation of Healthcare Management Education (CAHME). Graduates’ affiliation with the American College of Healthcare Executives (ACHE) professional healthcare leadership organization was also assessed as an independent variable. Study outcomes focused on these graduates’ respective healthcare organization’s performance measures (cost, quality, and access) to assess the researchers’ inquiry into the perceived value of a CAHME-accredited graduate degree in healthcare administration and a professional ACHE affiliation. The results from this study found no effect of CAHME accreditation or ACHE affiliation on healthcare organization performance outcomes. The study findings support the need for future research surrounding healthcare administration professional graduate degree program characteristics and leader development affiliations, as perceived by various industry stakeholders.

Diversity in Alliance Portfolios and Performance Outcomes: A Meta-Analysis

2014 ◽  
Vol 43 (5) ◽  
pp. 1472-1497 ◽  
Author(s):  
Donghun (Don) Lee ◽  
Katie Kirkpatrick-Husk ◽  
Ravi Madhavan
Keyword(s):  
Positive Impact ◽  
Theoretical Orientation ◽  
Meta Analysis ◽  
Performance Outcomes ◽  
Alliance Portfolio ◽  
Quantitative Synthesis ◽  
And Performance ◽  
The Relationship ◽  
Level Of Analysis ◽  
Alliance Portfolios

Given the increasing interest in alliance portfolios, alliance portfolio diversity (APD) has been the focus of many recent studies. Yet, the performance consequences of APD—or of diversity in general—are neither theoretically clear nor empirically consistent. With meta-analytic analyses, we assess extant research on the APD–performance link. Across studies, APD has a positive impact on performance, although the level of analysis and how performance is measured influence the relationship. Going beyond conventional quantitative synthesis, however, we also systematically uncover patterns in how theoretical orientation and the operationalization of diversity moderate the APD–performance relationship. Our study serves as an invitation for future APD studies to employ more sophisticated theoretical and operationalization approaches as they expand our knowledge of diversity in alliance portfolios.

scholarly journals Financial Control Systems and Financial Systems Theory: Free Cash Flow and Profitability Nexus: a Comparative Study of Manufacturing Firms in Nigeria and Ghana

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Cash Flow ◽  
Net Present Value ◽  
Performance Metrics ◽  
Positive Impact ◽  
Research Work ◽  
Manufacturing Firms ◽  
Free Cash Flow ◽  
Estimation Model ◽  
Financial Systems ◽  
The Impact

In Financial Systems, the impact of Free Cash Flow (FCF) on the performance of a company has been in the center of academic discourse in recent years. Several studies have tried to ascertain the nature and magnitude of the relationship between free cash flow and firm profitability with conflicting results coming from different scholars. The main objective of this research work was to examine the impact of FCF on the profitability of quoted manufacturing firms in the Nigerian and Ghana stock exchanges. Data were pooled from twenty (20) different companies (ten each from Nigeria and Ghana) for a period of six years (2012 – 2017). A panel data estimation model was used to measure the impact of FCF and other performance metrics on the Return on Assets (ROA), which is our chosen profitability measure. The results show a positive but insignificant relationship between FCF and ROA both for Ghana and Nigerian manufacturing firms. Also, sales growth showed a positive impact on profitability of both countries while leverage negatively impacted on profitability. with Ghana being significant at 5%. The implication of the findings of the study is that it makes no business sense for companies to keep piling up excess funds beyond that which is needed for transactional purposes. The similarity between the results from Ghana and Nigeria in most of the variables shows that the findings of this study can be generalized to other countries. Based on the findings of the study, we recommend that the management of companies should strive to keep only the minimum needed free cash flow while the rest should be invested in other projects with positive net present value

scholarly journals Modele przemian politycznych w regionie Europy Środkowej i Wschodniej

2021 ◽  
Vol 30 ◽  
pp. 79-82
Author(s):  
Jacek Wojnicki
Keyword(s):  
Eastern Europe ◽  
Political Institutions ◽  
Central And Eastern Europe ◽  
Positive Impact ◽  
Transition To Democracy ◽  
Research Hypothesis ◽  
Political Theories ◽  
Democratic System ◽  
Transformation Processes

Models of Political Changes in the Region of Central and Eastern Europe The article discusses the issues of transformation processes in Central and Eastern Europe. The analysis took many factors into account: geographical, historical, political, political, social and economic. Internal and external premises decided about the course of political and political changes initiated at the turn of the 1980s and 1990s. Classical political theories about the Transition to democracy were included. A research hypothesis was put forward that the traditions of democratic political institutions have a positive impact on the pace and extent of consolidation of the democratic system.

Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools

2022 ◽  
pp. 1-21
Author(s):  
Gurkan Tuna ◽  
Ayşe Tuna
Keyword(s):  
Autism Spectrum Disorder ◽  
Random Forest ◽  
Performance Metrics ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
K Nearest Neighbors ◽  
Medical Test ◽  
And Behavior

Autism spectrum disorder (ASD) is a challenging developmental condition that involves restricted and/or repetitive behaviors and persistent challenges in social interaction and speech and nonverbal communication. There is not a standard medical test used to diagnose ASD; therefore, diagnosis is made by looking at the child's developmental history and behavior. In recent years, due to the increase in diagnosed cases of ASD, researchers proposed software-based tools to aid in and expedite the diagnosis. Considering the fact that most of these tools rely on the use of classifiers, in study, random forest, decision tree, k-nearest neighbors, and zero rule algorithms are used as classifiers, and their performances are compared using well-known performance metrics. As proven in the study, random forest algorithm can provide higher accuracy than the others in the classification of ASD and can be integrated into a computer- or humanoid-robot-based system for automated prescreening and diagnosis of ASD in preschool children groups.

Using a Predictive Rating System for Computer Programmers to Optimise Recruitment

2017 ◽  
Vol 19 (3) ◽  
pp. 1-14
Author(s):  
Paul J. Bracewell ◽  
Ankit K. Patel ◽  
Evan J. Blackie ◽  
Chris Boys
Keyword(s):  
Performance Metrics ◽  
Strong Relationship ◽  
Individual Performance ◽  
Assessment System ◽  
Rating System ◽  
Performance Score ◽  
Job Advertisement ◽  
Successful Recruitment ◽  
Linear Optimisation ◽  
Measure Of Performance

Using a quantitative assessment system, the number of resumes reviewed to identify a suitable developer was reduced to 3.5% with a successful recruitment decision made in 10 working days of posting the job advertisement. This paper summarises the methodology for developing that rating system. The depth and quality of an available talent pool is a function of demand, which is demonstrated by comparing globally-scaled individual performance metrics. Public code repositories are accessed and the code quality assessed algorithmically. The performance score combines accuracy, timeliness and difficulty from a series of challenges. These three attributes form a meaningful predictive measure of performance by using a non-linear optimisation routine. Bootstrapping is used to validate the approach. This process randomly omitted a scored performance observation per coder in order to calculate the performance score from the retained scores. There was a strong relationship (r = 0.70) between the predicted 1-omitted-performance score with the actual omitted score highlighting the predictive power.

scholarly journals Measuring Mental Effort for Creating Mobile Data Collection Applications

2020 ◽  
Vol 17 (5) ◽  
pp. 1649
Author(s):  
Johannes Schobel ◽  
Thomas Probst ◽  
Manfred Reichert ◽  
Winfried Schlee ◽  
Marc Schickler ◽  
...  
Keyword(s):  
Data Collection ◽  
Prior Knowledge ◽  
Performance Measures ◽  
Large Scale ◽  
Performance Metrics ◽  
Positive Impact ◽  
Mental Effort ◽  
The Self ◽  
Mobile Data ◽  
Mobile Data Collection

To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N = 80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

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