This June one of the biggest and most popular sports tournaments will take place in Austria and Switzerland, the European soccer championship 2008 (UEFA EURO 2008). Therefore millions of soccer fans in Europe and throughout the world are asking themselves: "Who is going to win the EURO 2008?" Many people, including sports experts and former players, give their guesses and expectations in the media, but there is also a group with financial incentives, like some economists who expect economical increases for the country of the winning team and bookmakers and their customers who directly make money with their beliefs. Some predictions are only guesses, but other predictions are based on quantitative methods, such as the studies of UBS Wealth Management Research Switzerland and the Raiffeisen Zentralbank. In this report we will introduce a new method for predicting the winner. Whereas other prediction methods are based on historical data, e.g., the Elo rating, or the FIFA/Coca Cola World rating, our method is based on current expectations, the bookmakers odds for winning the championship. In particular we use the odds for winning the championship for each of the 16 teams of 45 international bookmakers. By interpreting these odds as rating of the expected strength of the teams by the bookmakers, we derive a consensus rating by modelling the log-odds using a random-effects model with a team-specific random effect and a bookmaker-specific fixed effect. The consensus rating of a team can be used as an estimator for the unknown "true" strength of a team. Our method predicts team Germany with a probability of about 18.7% as the EURO 2008 winner. We predict also that the teams playing the final will be Germany and Spain with a probability of 13.9%, where Germany will win with a probability of 55%. In our study, Italy, the favorite according to the current FIFA/Coca Cola World ranking and Elo ranking, has a much lower probability than these teams to win the tournament: only 10.6%. The defending champion Greece has low chances to win the title again: about 3.4%. Furthermore, the expected performance of the host countries, Austria and Switzerland, is much better in the bookmakers consensus than in the retrospective Elo and FIFA/Coca Cola World ratings, i.e., indicating an (expected) home court advantage. Despite the associated increase in the winning probabilities, both teams have rather poor chances to win the tournament with probabilities of 1.3% and 4.0%, respectively. In a group effect study we investigate how much the classification into the four groups (A-D) affects the chance for a team to win the championship. / Series: Research Report Series / Department of Statistics and Mathematics

<p>Uppsatsen skapar en statistisk modell för beräkning av sannolikheter för utfall vid Allsvenska fotbollsmatcher. Modellens skattade sannolikheter för hemmavinst, oavgjort och bortavinst jämförs med utvalt spelbolag och visar på likvärdiga estimat. Utifrån de skattade sannolikheterna skapas odds som genererar lägre förluster än spelbolaget vid test utifrån ett spelbolags synvinkel. Sannolikheterna används även från en spelares perspektiv till att skapa ett spelsystem som genererar en positiv avkastning.</p>

Uppsatsen skapar en statistisk modell för beräkning av sannolikheter för utfall vid Allsvenska fotbollsmatcher. Modellens skattade sannolikheter för hemmavinst, oavgjort och bortavinst jämförs med utvalt spelbolag och visar på likvärdiga estimat. Utifrån de skattade sannolikheterna skapas odds som genererar lägre förluster än spelbolaget vid test utifrån ett spelbolags synvinkel. Sannolikheterna används även från en spelares perspektiv till att skapa ett spelsystem som genererar en positiv avkastning.

Recent, post-1980, immigration patterns have had a dramatic effect on U.S. labor markets, leading to considerable debate about the impact of immigration on native-born black workers. This research examines immigrant and black labor markets, across metropolitan areas, using Public Use Microdata and Summary File data from Census 2000 to generate low, mid, and high classifications of immigrant and black occupations based on socio-economic index (SEI). Multivariate findings indicate that the effect of recent immigration on black labor market outcomes differs by occupational level. Competition for low-skilled jobs is identified for native-born blacks in low-level jobs while a “bump-up” effect is identified for blacks in mid-level jobs. For example, production occupations with low language and skill requirements are shown to be contested among the groups. On the other hand, service and administrative functions emerge as bump-up mechanisms that create opportunity for black workers who amass the human capital required of these occupations. Thus, the ramifications of immigration for native-born blacks are shown to be quite different for low- and mid-SEI jobs.

The Swedish Association of Local Authorities and Regions (SALAR) and the Swedish National Board of Health and Welfare (NBHW) have published a yearly report called “Open Comparisons of Quality and Efficiency in Swedish Health Care –Regional Comparisons” since the year 2006 to compare medical outcomes, patient experience, availability and costs among hospitals and counties. In this paper, inspired by the region profiles attached to the report, we describe several possible measurement methods for comparing dichotomous data: Risk Difference (RD), Relative Risk (RR), Relative Risk Reduction (RRR) and Odds Ratio (OR), mainly by using a geometric approach as a basis for further discussion. A standard scores method is also presented as a means for more efficient comparisons.

Bachelor-Arbeit Univ. St. Gallen, 2007.

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This dissertation introduces a general framework modeling common rating processes in order to aggregate rating information stemming from a variety of raters or rating sources. Ratings play an increasingly important role in our life. They are used to evaluate a variety of objects and activities all over the world. Here we apply our model framework to two different ratings, the credit ratings and the bookmakers odds. Whereas credit ratings represent the evaluation of credit customers or firms by banks or external rating agencies, bookmakers odds are prospective ratings of the performance of the participating players or teams in a sports competition. Despite the fact that these ratings are used in different kind of areas, both rating systems have a very similar underlying rating process. In both rating processes each rater estimates an underlying numerical variable which represent a probability or is directly related to a probability. In the case of credit ratings this probability is the probability of default (PD) of a credit customer or a firm and in the case of bookmakers odds this probability is the probability of winning a specifc sports competition. The proposed model framework is then used to solve the aggregation problem of the two rating processes for different applications yielding different model specifcations. Finally, the model results are used to validate the different underlying rating systems as well as for forecasting. (author's abstract)