Dynamic Bayesian forecasting of AFL match results using the Skellam distribution

Manderson, Andrew A.; Murray, Kevin; Turlach, Berwin A.

doi:10.1111/anzs.12225

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…Definitions (a) and (b) characterize the goal-based modeling approach and can be found, for instance, in Maher (1982), Dixon and Coles (1997), Rue and Salvesen (2000), and Karlis and Ntzoufras (2003). On the other hand, definition (c) refers to the difference-based approach and appears in Karlis and Ntzoufras (2009) or Manderson et al (2018), among others.…”

Section: Football Outcomesmentioning

confidence: 99%

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The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models

2021

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Passes are undoubtedly the more frequent events in football and other team sports. Passing networks and their structural features can be useful to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The present paper aims to show how information retrieved from passing networks can have a relevant impact on predicting the match outcome. In particular, we focus on modeling both the scored goals by two competing teams and the goal difference between them. With this purpose, we fit these outcomes using Bayesian hierarchical models, including both in-match and network-based covariates to cover many aspects of the offensive actions on the pitch. Furthermore, we review and compare different approaches to include covariates in modeling football outcomes. The presented methodology is applied to a real dataset containing information on 125 matches of the 2016–2017 UEFA Champions League, involving 32 among the best European teams. From our results, shots on target, corners, and such passing network indicators are the main determinants of the considered football outcomes.

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Section: Football Outcomesmentioning

confidence: 99%

“…Many of these fundamental contributions belong to the Bayesian framework, the inferential approach considered in this paper. Among the others, it is worth citing Baio and Blangiardo (2010), Egidi andTorelli (2020), andManderson et al (2018) as empirical applications based on Bayesian inference.…”

Section: Introductionmentioning

confidence: 99%

The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models

2021

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“…The autoregression parameter 0 < β t ≤ 1 models the decrease of the skills in time (in absence of game outcomes). While β t = 1 is used in most of the previous works we cite, β t < 1 was also used, e.g., in [24], [14], [25], and to take into account the time we may define it as…”

Section: Skills' Dynamicsmentioning

confidence: 99%

“…The very meaning of the skills may be also redefined and instead of a scalar, the player may be assigned two values corresponding to offensive and defensive skills [11], [14], [15]; further, considering the homefield advantage (HFA), three or four distinct parameters per player may be defined [11], [16], although recent results indicate that this may lead to over-fitting [17], [18].…”

Section: Introductionmentioning

confidence: 99%

“…Each of these skills-outcome models can be combined with the models describing how the skills evolve in time. With that regard, the most popular is modelling of the skills via first-order Markov Gaussian processes, e.g., [19], [20], [9], [21], [22], [23], [1], [24], [14], [25]. This formulation is then exploited to derive the on-line rating algorithms in two recursive steps: first, at a time t, the posterior distribution of the skills is found using all observations up to time t; more precisely, to simplify the problem, the Gaussian approximation of the latter is obtained.…”

Section: Introductionmentioning

confidence: 99%

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Simplified Kalman filter for online rating: one-fits-all approach

Szczecinski,

Tihon

2021

Preprint

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In this work, we deal with the problem of rating in sports, where the skills of the players/teams are inferred from the observed outcomes of the games. Our focus is on the online rating algorithms which estimate the skills after each new game by exploiting the probabilistic models of the relationship between the skills and the game outcome. We propose a Bayesian approach which may be seen as an approximate Kalman filter and which is generic in the sense that it can be used with any skills-outcome model and can be applied in the individual-as well as in the group-sports. We show how the well-know algorithms (such as the Elo, the Glicko, and the TrueSkill algorithms) may be seen as instances of the one-fits-all approach we propose. In order to clarify the conditions under which the gains of the Bayesian approach over the simpler solutions can actually materialize, we critically compare the known and the new algorithms by means of numerical examples using the synthetic as well as the empirical data.

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G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory

Szczeciński

2022

Journal of Quantitative Analysis in Sports

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In this work we develop a new algorithm for rating of teams (or players) in one-on-one games by exploiting the observed difference of the game-points (such as goals), also known as a margin of victory (MOV). Our objective is to obtain the Elo-style algorithm whose operation is simple to implement and to understand intuitively. This is done in three steps: first, we define the probabilistic model between the teams’ skills and the discretized MOV variable: this generalizes the model underpinning the Elo algorithm, where the MOV variable is discretized into three categories (win/loss/draw). Second, with the formal probabilistic model at hand, the optimization required by the maximum likelihood rule is implemented via stochastic gradient; this yields simple online equations for the rating updates which are identical in their general form to those characteristic of the Elo algorithm: the main difference lies in the way the scores and the expected scores are defined. Third, we propose a simple method to estimate the coefficients of the model, and thus define the operation of the algorithm; it is done in a closed form using the historical data so the algorithm is tailored to the sport of interest and the coefficients defining its operation are determined in entirely transparent manner. The alternative, optimization-based strategy to find the coefficients is also presented. We show numerical examples based on the results of the association football of the English Premier League and the American football of the National Football League.

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Dynamic Bayesian forecasting of AFL match results using the Skellam distribution

Cited by 5 publications

References 17 publications

The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models

The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models

Simplified Kalman filter for online rating: one-fits-all approach

G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory

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