The EA Sports Player Performance Index is a rating system for soccer players used in the top two tiers of soccer in England—the Premier League and the Championship. Its development was a collaboration among professional soccer leagues, a news media association, and academia. In this paper, we describe the index and its construction. The novelty of the index lies in its attempts to rate all players using a single score, regardless of their playing specialty, based on player contributions to winning performances. As one might expect, players from leading teams lead the index, although surprises happen.

International evidence suggests that problem gambling tends to be 2-4 times higher among adolescents as among adults and this proves to be true of Great Britain according to the latest adolescent prevalence survey. 8,958 British children (11-15) were surveyed in 201 schools during late 2008 and 2009. The questionnaire included a standard screen, DSM-IV-MR-J, to test for problem gambling. Our regression models explore influences of demographic, home and school characteristics on probabilities (both unconditional and conditional on being a gambler) of a child testing positive for problem gambling. More than 20% of children participated in gambling and, of these, nearly 8% tested positive. Age-group prevalence of problem gambling was 1.9%, compared with 0.6-0.9% in the most recent official adult surveys. Boys were much more likely than girls to gamble and to exhibit symptoms of problem gambling if they did. Generally, home characteristics, particularly parental attitude and example, dominated school characteristics in accounting for risks. Unanticipated findings included significantly elevated probabilities of problem gambling among Asian children and among children who live in a home without siblings. Child income was also a potent predictor of gambling and problem gambling.

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The paper presents a forecasting model for association football scores. The model uses a Weibullinter-arrival times based count process and a copula to produce a bivariate distribution for the number of goals scored by the home and away teams in a match. We test it against a variety of alternatives, including the simpler Poisson distribution-based model and an independent version of our model. The out-of-sample performance of our methodology is illustrated first using calibration curves and then in a Kelly-type betting strategy that is applied to the pre-match win/draw/loss market and to the over-under 2.5 goals market. The new model provides an improved fit to data compared to previous models and results in positive returns to betting.

This paper investigates the relationship between playing success and commercial success in team sports. Utilizing a data set relating to the English Premier League that combines both financial measures and indicators of playing skills and performances, our empirical analysis is based on three behavioural equations. Our analysis indicates that on-field success can be directly related to players' skills and abilities and that revenue is positively related to on-field success. Wage expenditure is also shown to systematically reflect player skills and performances. One interpretation of this evidence is that investment in players' skills and ability buys on-field success, with richer teams becoming ever richer and able to maintain or even build upon success by spending more on players than less successful clubs. To the extent that richer clubs are successful in their objective there is a causal link between revenue earned and competitive imbalance via investments in players. The implications of this tendency within a league are discussed in our conclusion, which also considers the potentially MAINTAINING MARKET POSITION IN THE PREMIER LEAGUE 465 wider implications of our study as they relate to the evolution of firm size and issues of market share.

Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006-2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players-a fact that is ignored if the raw completion rate is used to evaluate player skill.

By modeling minute‐by‐minute television audience figures from English Premier League soccer matches, with close to 50,000 minute‐observations, we show that demand is partly driven by suspense and surprise. We also identify an additional relevant factor of appeal to audiences, namely shock, which refers to the difference between pre‐match and current game outcome probabilities. Suspense, surprise, and shock remain significant in the presence of a traditional measure of outcome uncertainty. (JEL C23, D12, L82, L83, Z20)

Research question: Several studies report modelling relating countries' medal shares at the Olympics to population and per capita income (host status and political system are typically included as controls). This paper uses a similar model but disaggregates to the level of the individual sport to ask questions such as whether some sports have a less steep relationship with income levels than others and whether hosting effects are more pronounced in some sports than others. Research methods: Employing a random effects tobit model, data on medal shares are modelled across fifteen sports at six editions of the Games (1992-2012). Marginal effects, calculated for the case of cycling, illustrate how far many poor countries are from reasonable expectation of achieving medals. Results and findings: Income is influential on outcomes in all sports, its effects most pronounced in sports with substantial requirements for specific capital equipment; the distribution of medals is less unequal in sports practised in multi-sports venues. Gains from hosting vary in magnitude, performance tending to be elevated most in sports with outcomes strongly influenced by judges. Implications: For poorer countries, the paper identifies a small group of sports on which it would be most realistic to focus resources. For Games organisers, who must decide which sports to include, it provides information relevant to the goal of spreading success more evenly across countries. For example, proposals to exclude wrestling are shown to have been potentially harmful to medal prospects of poorer countries.