All the statements that seem to make sense. But how accurate are they, is there anything genuine for them?Well, there are measures. Sports economists have long implemented festival measures in sports league industries to quantify this. Below is the ‘concentration index’, a market percentage index of a subset of corporations in one sector, implemented in england’s first department since 1888, i. e. the proportion of issues obtained across the 3 most sensitive groups compared to the rest:
Everything is fine, but it depends on the number of groups in the league: in the early years there were only 12 or 14 groups. Besides, maybe we’ll worry about the distance between groups in the division. Perhaps we are concerned about the battles of descent as well as the most sensible battles. With other personal tastes so we’re interested in, there’s a pletle of other metrics.
Dimitris Karlis, of the Athens University of Economics and Commerce, recently presented an article on this subject and together with Ioannis Ntzoufras wonder: what is the uncertainty related to these measures?
In other words, if we look, say, 0. 25 on the chart above for the Premier League in 2016, is it different to practice 0. 18 in the mid-1990s?
Many measure the festival hole in a league compared to a perfectly balanced ideal: all groups have the same strength. Such concepts simply assume that in a scenario where the groups are of equivalent strength, all groups automatically win an equivalent number of games. , the maximum asymmetric league has the team of maximum productivity automatically outperdating all those below them, and the team below them to everyone else, unless the groups above them.
But sport, and football in particular, is not so, football has a random detail. That detail we love. When the loser succeeds, when Villa hits Liverpool 7-2. So what can we do about it? Well, we can assume that the results, and indeed the objectives, happen according to a probability distribution, that is, they are generated randomly.
Not completely random. Pretty random, say, there’s a 30% chance that Liverpool will score once against Man United.
If we are a little more accurate and assume that the objectives of each team are distributed independently of Poisson (this is not the most productive speculation, but does not matter too much for our needs), then if Team A gets 2 and Team B 1 Array, there is only a 10% chance that the adjustment will end 2-1 as opposed to Team A. There is still a 10% chance that it will end 2-2, or 1-1, or 1-0 as opposed to Team AA 1 The draw -1 or 2-2 leads to a different end result to 2-1 or 1-0 and therefore to another final table and another competitive balance measure.
Good groups on paper can lose many games, the most productive team does not win.
A broader approach would be to think about quantifying the uncertainty surrounding each competitive balance measure in the same way we do for statistical or hypothetical testing. Is this competitive equilibrium measure statistically significant?
Karlis and Ntzoufras created the following graphic:
This is the concentration rate discussed above, but standardized to be between 0 and 1 (0 better, 1 worse), and plotted for England and Germany. The problems and undeniable lines that are inscribed in them are the bars for any of the leagues. England has been less competitive recently, but once vertical lines, confidence periods (or confidence bands) are added, we see them overlap each season.
There is too much uncertainty to say with some degree of certainty that England was more or less competitive as a league than Germany between 2000 and 2016.
In addition, Karlis and Ntzoufras calculated the diversity of values that would be consistent with a perfectly balanced league. These are the horizontal dotted lines. Therefore, if a trusted band for a league is on the horizontal dotted lines, it suggests that for that specific season, that specific league should be distinguished, statistically, from a league in which all groups were perfectly equivalent in talent.
At this point, England and Germany have moved away from this in recent years, however many observations on the pattern are such that there is no difference. These leagues may have been leagues with equally balanced teams, statistically speaking.
But what does that really mean? Well, that forces us to question our perceptions a little more. It’s hard to think that a Premier League with Manchester United, Chelsea, Arsenal and Man City dominating as they did in this period can actually be felt just as balanced. recommends that, as a control of balanced leagues, they are not strict measures: they cannot distinguish between an equivalent league and an uns selected league.
But things happen in a very, very unforeseen way, consistent with a sufficiently high point of uncertainty: Leicester City won the Premier League in 2016, not if they can do it again, but how do we really know how strong the groups are??
I am professor of economics at the University of Reading, teach sports economics and do research. I have a lot of magazine articles, e-book chapters and others.
I’m a professor of economics at the University of Reading, teach game economics and research. I have published many articles in journals, e-book chapters and other online articles on how we can be more informed about the economics of games and how the economy can be used to make in-game decisions.