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

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            Abstract

            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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            Author and article information

            Journal
            ScienceOpen Preprints
            ScienceOpen
            24 May 2021
            Affiliations
            [1 ] Budapest University of Technology and Economics
            Article
            10.14293/S2199-1006.1.SOR-.PPTAUKV.v1
            bead5f26-16a4-40cc-a9ca-47c8d831e324

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .


            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Risk management,Computational finance
            Betting, ,Dawson model, ,Football, , xG, , Pitch partitioning, , possession sequences, , expected goal model,player evaluation

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