Predicting the Past (Time Series)
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  • Release Date: 2021-03-04
  • Arimax Time Series
  • Law Assessment
  • Contradictory Dilemma
  • Hooliganism
  • Public Safety
  • Violence
  • Football
  • Soccer
  • Pernambuco
  • Brazil
  • Intervention Analysis
  • Time Series Analysis
  • Crime
  • Torcidas Organizadas
Video Introduction

The discussion on the alcohol effect over the violent behavior of sports spectators besides recurrent seems controversial in many aspects. From one side, many studies suggest a positive association between alcohol intake and hooligan behavior [1][2][3]. On the other side, empirical and social-psychological research provides good arguments against this assumed relationship [4][5][6]. The formation of hooligan gangs is another concern for public safety policy makers, once they are the center of violent behavior tendencies not limited to the English culture, which extends to illegal drug abuse and commercialization, especially in Latin America countries.

One of the main reasons for this underlying controversy roots is the deficiency for performing robust statistical assessments beyond punctual inferences. Because most of the gangs or alcohol sanctions worldwide are enforced over specific matches, long-period data analysis is scarce. Pernambuco, however, had a form of public intervention concerning alcohol for over 7 years and concerning football gangs for over 2 years. This dataset includes 1363 hooligan incidents in national and international leagues, excluding few matches during 2014 Brazilian World Cup in which alcohol was legalized and non-resident fans attended most matches in the state. In regards to alcohol, Pernambuco's state law number 13748 was in force from 2009 to 2016, banning the sales of alcoholic beverages in stadiums aiming to minimize the social problem of violence among football supporters.

Nepomuceno et al. (2017)[7]  provide an interesting assessment on this public sanction in Brazil. The authors' results support the conclusion that alcohol criminalization had no significant effect on the hooligan occurrences. Under similar sanctions, three other Brazilian states have used their work and results in Legislative plenaries and forums to discuss the issue. Two of them (the states of Alagoas and Ceará) legalized alcohol sales. Brazil has the highest number of hooligan-related deaths in the world due to a culture of violence in football, which becomes more intense because of the high degree of social aggressiveness and emerged political corruption and criminality[8].

Football gangs in the state and their associates receive financial incentives to attend the games and ignite the crowd to support the home team against the adversary. This monetary incentive was removed in two different periods. During a few months along 2013 and from 2014 to 2015 fan-bases (torcidas organizadas) were prevented from attending stadiums wearing clothes that could identify the organization, aiming to reduce the hooligan incidents during official matches. The hooligan data corresponds to the following occurrences registered in the Football Supporters Court's proceedings from 2005 to 2015: Promote turmoil, practice or incite violence; break into the field or restricted area; promote turmoil, practice or incite violence by up to 5km from the sporting event location; contempt; slander; battery, assault and battery; aggravated battery;; fisticuffs; misdemeanor of pitching or placing dangerous object; threat; abuse of authority; damage to public or private property; lewd acts; unseemly conduct; false alarm; unlawful use or attack on means of transport; illegal constraint and incitement to crime.

A time scenario can be defined as a stochastic process created by time-sorted observations to predict the outcome for an expected scenario in the past from an observed reality. In other words, how some past observed distribution for a particular incident should have been if one (or more) of the variables were changed, and everything else kept the same. The proposed methodology aims to evaluate the results of a treatment imposition, in particular, the creation of laws, regulations, or legal sanctions applied to an entire population, making infeasible the possibility to discriminate the population into different control groups – with and without the influence of the inputted treatment (law). The statistical methods are applied in three steps to assess the impact of the intervention. 

Part of the analysis in this entry refers to the paper:

  1. Carol A. Bormann; Michael H. Stone; The Effects of Eliminating Alcohol in a College Stadium: The Folsom Field Beer Ban. Journal of American College Health 2001, 50, 81-88, 10.1080/07448480109596011.
  2. Daniel I. Rees; Kevin T. Schnepel; College Football Games and Crime. Journal of Sports Economics 2009, 10, 68-87, 10.1177/1527002508327389.
  3. Sage, G. H., & Eitzen, D. S. Sociology of North American sport (9th ed.); Oxford University Press: New York, 2013; pp. xx.
  4. Richard Giulianotti; Scotland's Tartan Army in Italy: The Case for the Carnivalesque. The Sociological Review 1991, 39, 503-527, 10.1111/j.1467-954x.1991.tb00865.x.
  5. Matthew Guschwan; Riot in the Curve: Soccer Fans in Twenty‐first Century Italy. Soccer & Society 2007, 8, 250-266, 10.1080/14660970701224467.
  6. Roberto Maniglio; The Hooligan's Mind. Journal of Forensic Sciences 2006, 52, 204-208, 10.1111/j.1556-4029.2006.00315.x.
  7. Thyago Celso C. Nepomuceno; Jadielson Alves De Moura; Lúcio Câmara E Silva; Ana Paula Cabral Seixas Costa; Alcohol and violent behavior among football spectators: An empirical assessment of Brazilian's criminalization. International Journal of Law, Crime and Justice 2017, 51, 34-44, 10.1016/j.ijlcj.2017.05.001.
  8. Thyago Celso Cavalcante Nepomuceno; Ana Paula Cabral Seixas Costa; Invalid Votes, Deliberate Abstentions, and the Brazilian Crisis of Representation. Politics & Policy 2019, 47, 381-406, 10.1111/polp.12298.
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Nepomuceno, T.; Silva, W. Predicting the Past (Time Series). Encyclopedia. Available online: (accessed on 22 April 2024).
Nepomuceno T, Silva W. Predicting the Past (Time Series). Encyclopedia. Available at: Accessed April 22, 2024.
Nepomuceno, Thyago, Wilka Silva. "Predicting the Past (Time Series)" Encyclopedia, (accessed April 22, 2024).
Nepomuceno, T., & Silva, W. (2021, March 04). Predicting the Past (Time Series). In Encyclopedia.
Nepomuceno, Thyago and Wilka Silva. "Predicting the Past (Time Series)." Encyclopedia. Web. 04 March, 2021.