Balance in competition in Dutch soccer

By Ruud H. Koning. 2000

208 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Gender Studies, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f32b5803f132d1087d592a59473a0d49237f5ea5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Philip A. Scarf
2. Ian G. McHale
3. Rutger Lit
4. S. J. Koopman
5. Muhammad Mat Yusof
6. John Goddard
7. Ioannis G. Asimakopoulos
8. Richard Pollard

In Gender Studies

1. Philip A. Scarf
2. Ian G. McHale
3. S. J. Koopman
4. Rutger Lit
5. Ioannis G. Asimakopoulos
6. John Goddard
7. Richard Pollard
8. Muhammad Mat Yusof

In Nature and Landscape Conservation

1. Philip A. Scarf
2. Ian G. McHale
3. S. J. Koopman
4. Rutger Lit
5. Muhammad Mat Yusof
6. John Goddard
7. Richard Pollard
8. Ioannis G. Asimakopoulos

Citations in the corpus

• A numerical study of tournament structure and seeding policy for the soccer World Cup Finals (2011, background): “We consider the competitive balance to be the relative strength of the competitors (Koning, 2000).”
• Home Advantage in Football: A Current Review of an Unsolved Puzzle (2008, background): “In addition, the general recent decline in home advantage and the significant long-term fluctuations need to be incorporated into the analysis when basing a study on more than a few years of data [5,8,39,41,42,48].”
• Forecasting football match results in national league competitions using score-driven time series models (2019, methodology): “For a description of the static models we refer to Karlis and Ntzoufras (2003), Koning (2000) and Maher (1982) for example, and for a description of the semi-dynamic models we refer to Dixon and Coles (1997).”
• An econometric evaluation of the effect of firing a coach on team performance (2003, methodology): “1 Home advantage is strong in soccer: approximately 50% of all games are won by the home team, 25% are won by the away team, and 25% end in a draw, see Koning (2000).”
• Forecasting football results and the efficiency of fixed‐odds betting (2004, background): “Koning (2000) estimates a model to describe a set of match results ex post, as part of a broader analysis of changes in competitive balance in Dutch football.”
• Top Clubs’ Performance and the Competitive Situation in European Domestic Football Competitions (2010, background): “…Maennig, 2005: German first division; Goossens, 2006: German, French, and Spanish first divisions; Groot, 2008: French and Spanish first divisions; Koning, 2000: Dutch first division; Michie & Oughton, 2004: French first division; Szymanski, 2001: English first division), others contain evidence…”
• Modelling the dependence of goals scored by opposing teams in international soccer matches (2011, methodology): “Maher (1982) proposed two independent Poisson distributions for the scores Y1 and Y2 between two particular teams in a soccer match.”