Some algorithms to solve a bi-objectives problem for team selection

In real life, many problems are instances of combinatorial optimization. Cross-functional team selection is one of the typical issues. The decision-maker has to select solutions among (kh) solutions in the decision space, where k is the number of all candidates, and h is the number of members in the selected team. This paper is our continuing work since 2018; here, we introduce the completed version of the Min Distance to the Boundary model (MDSB) that allows access to both the "deep" and "wide" aspects of the selected team. The compromise programming approach enables decision-makers to ignore the parameters in the decision-making process. Instead, they point to the one scenario they expect. The aim of model construction focuses on finding the solution that matched the most to the expectation. We develop two algorithms: one is the genetic algorithm and another based on the philosophy of DC programming (DC) and its algorithm (DCA) to find the optimal solution. We also compared the introduced algorithms with the MIQP-CPLEX search algorithm to show their effectiveness

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Sports Analytics: Predicting Athletic Performance with a Genetic Algorithm

Existing predictive modeling in sports analytics often hinges on atheoretical assumptions winnowed from a large and diverse pool of game metrics. Feature subset selection by way of a genetic algorithm to identify and assess the combinatorial advantage for a group of metrics is a viable option to otherwise arbitrary model construction. However, this approach concedes similar arbitrariness as there is no general strategy or common practice design among the tightly coupled nucleus of genetic operators. The resulting dizzying ecosystem of choice is especially difficult to overcome and leaves a residual uncertainty regarding true strength of output, specifically for practical implementations. This study transposes ideas from extreme environmental change into a quasi-deterministic extension of standard GA functionality that seeks to punctuate converged populations with individuals from auxiliary metas. This strategy has the effect of challenging what might otherwise be considered shallow fitness, thereby promoting greater trust in output against innumerable alternatives

A Survey on the application of Data Science And Analytics in the field of Organised Sports

The application of Data Science and Analytics to optimize or predict outcomes is Ubiquitous in the Modern World. Data Science and Analytics have optimized almost every domain that exists in the market. In our survey, we focus on how the field of Analytics has been adopted in the field of sports, and how it has contributed to the transformation of the game right from the assessment of on-field players and their selection to the prediction of winning team and to the marketing of tickets and business aspects of big sports tournaments. We will present the analytical tools, algorithms, and methodologies adopted in the field of Sports Analytics for different sports and also present our views on the same and we will also compare and contrast these existing approaches. By doing so, we will also present the best tools, algorithms, and analytical methodologies to be considered by anyone who is looking to experiment with sports data and analyze various aspects of the game.Comment: arXiv admin note: substantial text overlap with arXiv:2209.0699

A balanced squad for Indian premier league using modified NSGA-II

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL squad as a multi-objective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player's performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers, and all-rounders. Bounds are also considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial optimization algorithms, namely, BNSGA-II and INSGA-II, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE, and MOPSO-CD, on the basis of standard performance metrics: hypervolume, inverted generational distance, and number of Pareto optimal solutions. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players' data validated the applicability of the proposed model and algorithms. The trade-off squads contained players of each expertise in appropriate proportions. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.Web of Science1010047710046

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams in Health Care System

This chapter presents a support approach for the building and the selection of multidisciplinary teams. In the first part, we propose a new general model for multidisciplinary team building. The proposed model takes professional’s preferences into account when a team building process is required for any type of project. In the second part, we develop hybridization between a multicriterion decision method and a cognitive method for selection teams. The developed methodology is based on the experiences of the past operations in order to select the adequate team for a new operation. We test the effectiveness of the model using health care domains of different complexities and describe some practical experiences of using the model in the surgical team building process

Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data

Devising player-specific strategies in cricket necessitates a meticulous understanding of each player's unique strengths and weaknesses. Nevertheless, the absence of a definitive computational approach to extract such insights from cricket players poses a significant challenge. This paper seeks to address this gap by establishing computational models designed to extract the rules governing player strengths and weaknesses, thereby facilitating the development of tailored strategies for individual players. The complexity of this endeavor lies in several key areas: the selection of a suitable dataset, the precise definition of strength and weakness rules, the identification of an appropriate learning algorithm, and the validation of the derived rules. To tackle these challenges, we propose the utilization of unstructured data, specifically cricket text commentary, as a valuable resource for constructing comprehensive strength and weakness rules for cricket players. We also introduce computationally feasible definitions for the construction of these rules, and present a dimensionality reduction technique for the rule-building process. In order to showcase the practicality of this approach, we conduct an in-depth analysis of cricket player strengths and weaknesses using a vast corpus of more than one million text commentaries. Furthermore, we validate the constructed rules through two distinct methodologies: intrinsic and extrinsic. The outcomes of this research are made openly accessible, including the collected data, source code, and results for over 250 cricket players, which can be accessed at https://bit.ly/2PKuzx8.Comment: The initial work was published in the ICMLA 2019 conferenc

Artificial intelligence-driven approach to identify and recommend the winner in a tied event in sports surveillance

The proliferation of fractal artificial intelligence (AI)-based decision-making has propelled advances in intelligent computing techniques. Fractal AI-driven decision-making approaches are used to solve a variety of real-world complex problems, especially in uncertain sports surveillance situations. To this end, we present a framework for deciding the winner in a tied sporting event. As a case study, a tied cricket match was investigated, and the issue was addressed with a systematic state-of-the-art approach by considering the team strength in terms of the player score, team score at different intervals, and total team scores (TTSs). The TTSs of teams were compared to recommend the winner. We believe that the proposed idea will help to identify the winner in a tied match, supporting intelligent surveillance systems. In addition, this approach can potentially address many existing issues and future challenges regarding critical decision-making processes in sports. Furthermore, we posit that this work will open new avenues for researchers in fractal AI

Predicting players’ performance in the game of cricket using machine learning

Player selection is one of the most important tasks for any sport and cricket is no exception. The performance of the players depends on various factors such as the opposition team, the venue, his current form etc. The team management, the coach and the captain select eleven players for each match from a squad of 15 to 20 players. They analyze different characteristics and the statistics of the players to select the best playing 11 for each match. Each batsman contributes by scoring maximum runs possible and each bowler contributes by taking maximum wickets and conceding minimum runs. This thesis attempts to predict the performance of players as how many runs each batsman will score and how many wickets each bowler will take for both teams in one-day international cricket matches. Both the problems are targeted as classification problems where number of runs and number of wickets are classified in different ranges. We used Naïve Bayes, Random Forest, multiclass SVM and Decision Tree classifiers to generate the prediction models for both the problems. Random Forest classifier was found to be the most accurate for both problems.Master of Science (MSc) in Computational Science