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.

Gambling among college students can start as a pastime activity. However, this pastime can lead to problem gambling and pathological gambling. This cross-sectional study aimed to identify and explain gambling behavior among university students using a novel fourth-generation multi-theory model (MTM) of health behavior change. Data were collected from a sample of 1,474 university students at a large southwestern university in the U.S. between January 2023 and February 2023, utilizing a validated 39-item survey. The statistical analyses employed in this study encompassed descriptive statistics, independent samples t-tests, and hierarchical regression modeling. Among students who engaged in gambling in the past month, the constructs of participatory dialogue (β = 0.052; p < 0.05), behavioral confidence (β = 0.073; p < 0.0001), and changes in the physical environment (β = 0.040; p = 0.0137) accounted for 27.7% of the variance in the likelihood of initiating the behavior change. Furthermore, the constructs of emotional transformation (β = 0.104; p = 0.0003) and practice for change (β = 0.060; p = 0.0368) accounted for 22.6% of the variance in the likelihood of sustaining quitting gambling behavior. The Multi-Theory Model (MTM) can be employed to design interventions aimed at reducing problem gambling among college students.