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

15 Pages Posted: 25 May 2021

Date Written: May 22, 2021

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.

Keywords: Betting, Dawson model, Football, xG, Pitch partitioning, possession sequences, expected goal model and player evaluation

JEL Classification: C60

Suggested Citation

Kollár, Aladár, Football analytics for better betting: Pitch partitioning, possession sequences, expected goal model and player evaluation on Dawson model (May 22, 2021). Available at SSRN: https://ssrn.com/abstract=3852372 or http://dx.doi.org/10.2139/ssrn.3852372

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