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

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

SDRS: a new lossless dimensionality reduction for text corpora

In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

Isoperformance An Alternative Design Methodology for Engineering Systems

Tradeoffs between performance, cost and risk frequently arise during architecting and design of complex Engineering Systems such as aerospace vehicles. A paradigm shift is occurring from the pure performance optimization approach of the past towards satisfying of performance targets under concurrent risk and cost minimization. This paper proposes “isoperformance” as a set based approach to designing engineering systems by first identifying the acceptable performance invariant set of designs from which a final design is chosen. This is in contrast to a multiobjective cost-risk minimization under performance equality constraints. This paper identifies a number of issues associated with finding the desired performance invariant set, I, given a deterministic or empirical system model that maps design variables x to objective variables J. Isoperformance is presented as a methodology that can quantify and visualize the tradeoffs between determinants (independent design variables) of a known or desired outcome. For deterministic systems the multivariable performance invariant contours can be computed using sensitivity analysis and a contour following algorithm, provided that a mathematical system model of appropriate fidelity exists. In the case of stochastic systems the isoperformance curves can be obtained via a regression analysis, given a statistically representative data set. Once isoperformance curves have been obtained, they are useful in extracting a set of performance invariant solutions. Applying additional objectives, other than performance, can then lead to a set of pareto-optimal designs. Specific examples from opto-mechanical space systems design and human factors are presented

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

ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families

The empirical Bayes normal means (EBNM) model is important to many areas of statistics, including (but not limited to) multiple testing, wavelet denoising, multiple linear regression, and matrix factorization. There are several existing software packages that can fit EBNM models under different prior assumptions and using different algorithms; however, the differences across interfaces complicate direct comparisons. Further, a number of important prior assumptions do not yet have implementations. Motivated by these issues, we developed the R package ebnm, which provides a unified interface for efficiently fitting EBNM models using a variety of prior assumptions, including nonparametric approaches. In some cases, we incorporated existing implementations into ebnm; in others, we implemented new fitting procedures with a focus on speed and numerical stability. To demonstrate the capabilities of the unified interface, we compare results using different prior assumptions in two extended examples: the shrinkage estimation of baseball statistics; and the matrix factorization of genetics data (via the new R package flashier). In summary, ebnm is a convenient and comprehensive package for performing EBNM analyses under a wide range of prior assumptions.Comment: 43 pages, 19 figure

ANALYSIS OF FIELDER STARTS AND BENCH ABILITY ON AMERICAN PROFESSIONAL BASEBALL PLAYERS

The development of athletes or players depends on two aspects: nature and nurture. The former is the talent and qualification of the players themselves, while the latter is the training that consumes human, material and financial resources. Take professional baseball players as an example. Matching the talents of players and referring to the relevant starting rules of the professional baseball league, when the up-and-coming players are first discovered, focused training are used on them. By doing so, the value of the players would be effectively enhanced and the players are helped to seek a better way out. This can form a virtuous circle: the pellets get quality players, and the players get better results. That is to say, strengthening the training for the shortcomings of the players with the potential of the starting players can avoid unnecessary training and huge training expenses behind them, and greatly reduce the risk of career, so that the players have higher security in their short career, and get a win-win-win situation. This study is aimed at the schedule information of the American Baseball League teams. Through feature selection of data mining, this study analyzes the main relationships and key differences between starting player and bench player of second baseman and shortstop in League of Nations teams. It is found that the on base percentage and speed of the infielders is an important ability indicator for the starting position; whereas, the second baseman emphasizes on the attack and the shortstop focuses on fielding. This feature is verified by comparing the opinions of experts and commentators.  Article visualizations