A birth process model for association football matches

Dixon, Mark; Robinson, Michael G.

doi:10.1111/1467-9884.00152

Cited by 102 publications

(74 citation statements)

References 12 publications

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“…In particular, the functional form of the thus extracted scoring probability p(n) could be compared to the linear or exponential forms implied by equations (7) and (8) for models "A" and "B". Some data of this type have been analyzed in [29], showing a clear increase of scoring frequency as the match progresses, thus supporting the presence of feedback as discussed here.…”

Section: Scoring Modelssupporting

confidence: 74%

Football fever: self-affirmation model for goal distributions

Janke¹,

Bittner²,

Nubaumer³

et al. 2009

Condens. Matter Phys.

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The outcome of football games, as well as matches of most other popular team sports, depends on a combination of the skills of players and coaches and a number of external factors which, due to their complex nature, are presumably best viewed as random. Such parameters include the unpredictabilities of playing the ball, the player's condition of the day or environmental influences such as the weather and the behavior of the audience. Under such circumstances, it appears worthwhile to analyze football score data with the toolbox of mathematical statistics in order to separate deterministic from stochastic effects and see what impact the cooperative and social nature of the "agents" of the system has on the resulting stochastic observables. Considering the probability distributions of scored goals for the home and away teams, it turns out that especially the tails of the distributions are not well described by the Poissonian or binomial model resulting from the assumption of uncorrelated random events. On the contrary, some more specific probability densities such as those discussed in the context of extreme-value statistics or the so-called negative binomial distribution fit these data rather well. There seemed to be no good argument to date, however, why the simplest Poissonian model fails and, instead, the latter distributions should be observed. To fill this gap, we introduced a number of microscopic models for the scoring behavior, resulting in a Bernoulli random process with a simple component of self-affirmation. These models allow us to represent the observed probability distributions surprisingly well, and the phenomenological distributions used earlier can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments, including data from all past tournaments of the "FIFA World Cup" series, and found the proposed models to be applicable in all cases. To complete the picture, we conducted a field study with visitors of a science showcase to collect additional data from matches of tabletop football. As it turns out, also the latter data are represented well with our feedback models, underscoring their apparently rather universal applicability.

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Section: Scoring Modelssupporting

confidence: 74%

Football fever: self-affirmation model for goal distributions

Janke¹,

Bittner²,

Nubaumer³

et al. 2009

Condens. Matter Phys.

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“…He estimated his model by using methods of moments using only the ®nal ranking at the end of the season. An alternative approach could be based on Poisson-like models for the exact score in a game; see for instance Maher (1982), Dixon and Coles (1997) and Dixon and Robinson (1997). The reason that we prefer the ordered probit model is the simplicity of model (2): the quality of each individual team is captured by a single parameter.…”

Section: The Statistical Modelmentioning

confidence: 99%

Balance in Competition in Dutch Soccer

Koning

2000

J Royal Statistical Soc D

144

104

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Summary. We estimate an ordered probit model for soccer results in the Netherlands. The result of a game is assumed to be determined by home ground advantage and differences in quality between the opposing teams. The parameters of the model are used to assess whether the balance in competition in Dutch professional soccer has changed over time. Contrary to popular belief, we ®nd that the balance has not changed much since the mid-1970s.

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“…Clarke and Norman (1995) use a range of non-parametric techniques to identify the effect of home advantage on match results. Dixon and Robinson (1998) investigate variations in the scoring rates of the home and away teams during the course of a match. The scoring rates at any time depend partly upon the number of minutes elapsed, but also upon which (if either) team is leading at the time.…”

Section: Introductionmentioning

confidence: 99%

Forecasting football results and the efficiency of fixed‐odds betting

Goddard

Asimakopoulos

2004

Journal of Forecasting

168

104

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An ordered probit regression model estimated using 10 years' data is used to forecast English league football match results. As well as past match results data, the significance of the match for end-of-season league outcomes, the involvement of the teams in cup competition and the geographical distance between the two teams' home towns all contribute to the forecasting model's performance. The model is used to test the weak-form efficiency of prices in the fixed-odds betting market. A strategy of selecting end-of-season bets with a favourable expected return according to the model appears capable of generating a positive return. Copyright © 2004 John Wiley & Sons, Ltd.

show abstract

A birth process model for association football matches

Cited by 102 publications

References 12 publications

Football fever: self-affirmation model for goal distributions

Football fever: self-affirmation model for goal distributions

Balance in Competition in Dutch Soccer

Forecasting football results and the efficiency of fixed‐odds betting

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