Football analytics for better betting: Pitch partitioning, possession sequences, expected goal model and player evaluation on Dawson model

10.20944/preprints202105.0619.v1 ◽

2021 ◽

Author(s):

Aladár Kollár

Keyword(s):

Evaluation Method ◽

Mathematical Methods ◽

Mathematical Concepts ◽

Goal Model ◽

Model Based

One of the most significant developments in the sports world over the last two decades has been the use of mathematical methods in conjunction with the massive amounts of data now available to analyze performances, identify trends and patterns, and forecast results. Football analytics has advanced significantly in recent years and continues to evolve as it becomes a more recognized and integral part of the game. Football analytics is also used to forecast game outcomes, allowing bettors to make educated guesses. This article describes mathematical concepts related to football analytics that enable a better betting strategies. We explain how the pitch is partitioned into different zones and we define possession sequences. Furthermore, we explain what an expected goals model is and which expected goals model we use in this research. Furthermore, we define two general characteristics of a player evaluation method, each corresponding to one of the equations of the Dawson model. Based on these characteristics, we describe the developments of several general approaches for evaluating players in the context of the Dawson model.

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Comprehensive Evaluation Method of Sensitization Model Based on Intuitionistic Fuzzy Cross-entropy

2020 13th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid51228.2020.00035 ◽

2020 ◽

Author(s):

Lingmei Li ◽

Caikun Zhang ◽

Guanyu Qiao

Keyword(s):

Evaluation Method ◽

Comprehensive Evaluation ◽

Cross Entropy ◽

Comprehensive Evaluation Method ◽

Model Based ◽

Intuitionistic Fuzzy

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Classification Model Based Automatic Static Evaluation Method for C Programs

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery - Lecture Notes on Data Engineering and Communications Technologies ◽

10.1007/978-3-030-89698-0_47 ◽

2022 ◽

pp. 449-456

Author(s):

Xinfeng Shu ◽

Fangyao Guo

Keyword(s):

Evaluation Method ◽

Classification Model ◽

C Programs ◽

Model Based ◽

Static Evaluation

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Model-Based Dependability Evaluation Method for TTP/C Based Systems

Dependable Computing EDCC-4 - Lecture Notes in Computer Science ◽

10.1007/3-540-36080-8_23 ◽

2002 ◽

pp. 271-282 ◽

Cited By ~ 3

Author(s):

Pavel Herout ◽

Stanislav Racek ◽

Jan Hlavička

Keyword(s):

Evaluation Method ◽

Model Based ◽

Dependability Evaluation

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A novel GM(1,N) model based on interval gray number and its application to research on smog pollution

Kybernetes ◽

10.1108/k-12-2018-0694 ◽

2019 ◽

Vol 49 (3) ◽

pp. 753-778

Author(s):

Pingping Xiong ◽

Zhiqing He ◽

Shiting Chen ◽

Mao Peng

Keyword(s):

Prediction Model ◽

Practical Significance ◽

Upper And Lower Bounds ◽

Gray Model ◽

Number Sequence ◽

Mathematical Methods ◽

Public Attention ◽

Content Type ◽

Model Based ◽

Definition Of

Purpose In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods. Design/methodology/approach This paper establishes a new gray model (GM) (1,N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1,N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness. Findings To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1,N) model, the new GM(1,N) prediction model established in this paper has better prediction effect and accuracy. Originality/value This paper improves the traditional GM(1,N) prediction model and establishes a new GM(1,N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.

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Tracking Error Modeling of the Theodolite Based on GRNN Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4870 ◽

2011 ◽

Vol 121-126 ◽

pp. 4870-4874

Author(s):

Miao Li ◽

Hui Bin Gao

Keyword(s):

Evaluation Method ◽

Tracking Error ◽

High Accuracy ◽

Error Model ◽

Error Modeling ◽

General Regression Neural Network ◽

Engineering Practice ◽

Tracking Accuracy ◽

Model Based ◽

General Regression

To meet the requirement of high tracking accuracy as well as develop more reasonable evaluation method, in this paper, the General Regression Neural Network (GRNN) has been applied to build the tracking error model of the theodolite. First, we analyze the nonlinear factors in the theodolite. Second, we discuss the principle of GRNN, including its structure, the function as well as its priors. Third, we build the tracking error model based on GRNN and verify the model through the different parameters. The result indicated that the network model based on GRNN has high accuracy and good generalization ability. It could instead the real system to a certain extent. The research in this paper has important value to the engineering practice.

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