Modeling Players’ Abilities Sample Clauses

Modeling Players’ Abilities. ‌ The focus here is to analyze the effect of Xxxxx’x withdrawal from Wimble- don 2009, especially on the expected abilities of the main competitor Xxxxxxx. It is obvious that Xxxxx’x withdrawal increases, on average, the chance of winning the tournament of all other players. However, the ability/strength of each player should not change. Thus, the winning probability for a spe- cific match, e.g., Xxxxxxx beating Xxxxxx in a potential Wimbledon 2009 final, should not be affected by Xxxxx’x withdrawal. The “true” abilities of the players are unknown, but an approximation can be derived from perfor- mance measures or winning expectancies, like the ATP rating, the seedings, or the bookmakers odds. Here, we compare all three rating strategies in a forecasting study for Wimbledon 2009. Aa above, we find that a consen- sus derived from the (prospective) bookmakers odds has higher predictive power than retrospective ratings based on historical results (in this study, the Wimbledon seeding and the ATP rankings, see Table 4.4). Subsequently, we estimate players’ abilities based on bookmakers odds using two different odds sets: one including winning expectancies for Xxxxx and one obtained after his withdrawal. The resulting expected abilities are compared to assess the effect on Xxxxx’x withdrawal. Furthermore, we use the players’ abilities in order to compare different tournament designs in a simulation study. Since the bookmakers’ expectations about Wimbledon 2009 are rather homo- geneous, we use again the very straightforward aggregation strategy comput- ing the means of the winning logits (i.e., winning log-odds) to find appropriate consensus measures of all bookmakers: lo^git(p ) = 1 Σ logit (p ) , (4.12) where B is the number of bookmakers. ^ Transforming these consensus winning logits back to the probability scale yields the bookmakers’ consensus winning probabilities pi for each player i for whom odds are available.