Cricket team selection using evolutionary multi-objective optimization

Selection of players for a high performance cricket team within a finite budget is a complex task which can be viewed as a constrained multi-objective optimization problem. In cricket team formation, batting strength and bowling strength of a team are the major factors affecting its performance and an optimum trade-off needs to be reached in formation of a good team. We propose a multi-objective approach using NSGA-II algorithm to optimize overall batting and bowling strength of a team and find team members in it. Using the information from trade-off front, a decision making approach is also proposed for final selection of team. Case study using a set of players auctioned in Indian Premier League, 4th edition has been taken and player's current T-20 statistical data is used as performance parameter. This technique can be used by franchise owners and league managers to form a good team within budget constraints given by the organizers. The methodology is generic and can be easily extended to other sports like soccer, baseball etc

Data visualization and toss related analysis of IPL teams and batsmen performances

Sports play a very significant role in the development of the human persona. Getting involved in games like Cricket and other various sports help us to build character, discipline, confidence and physical fitness. Indian Premier League, IPL provides the most successful form of cricket as it gives opportunities to young and talented players to show case their talents on various pitch. Decision-makers are the utmost customers for all fundamentals in the sports analytics framework. Sports analytics has been a smash hit in shaping success for many players and teams in various sports. Sports analytics and data visualization can play a crucial role in selecting the best players for a team. This paper is about the Toss Related analysis and the breadth of data visualization in supporting the decision makers for identifying inherent players for their teams

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 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

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

A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.Comment: 16 page

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

Automated Feature Engineering for Time Series Data

Feature engineering for time series data, a critical task in data science, involves the transformation or encoding of raw data to create more predictive input features.This paper introduces a novel web framework designed to automate the labor-intensive and expertise-demanding process of time series feature engineering. The framework comprises advanced methods for automated feature extraction and selection, providing a wide range of application possibilities. A Bayesian Optimization strategy is also integrated to identify optimal features and model parameters for specific datasets, thereby enhancing prediction performance. The paper thoroughly explores the framework\u27s design principles and operational procedures, along with validation of its effectiveness across different domains using real-world datasets

Un Enfoque Evolutivo Multi-Objetivo al Problema de la Construcción de Grupos de Estudiantes Universitarios

The creation of working groups of students in education is a common process that is often developed by the teacher intuitively. However, such a process is actually a complex task since various students and criteria must be taken into account. In general, these criteria are often in conflict because they are a reflection of the educational interests of teachers and on the other hand, the individual preferences of students. In this sense, this paper has as general goal: to propose a mathematicalcomputational solution that efficiently automatizes, in terms of computational time and solution quality, the creation of working groups of college students. The results obtained from two real scenarios of the Universidad Tecnica Estatal de Quevedo indicate that the proposal is an effective alternative to the traditional model. &nbsp

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