Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

International audienceWhole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions

Score-Based Bayesian Skill Learning

We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes. TrueSkill has proven to be a very effective algorithm for matchmaking - the process of pairing competitors based on similar skill-level - in competitive online gaming. However, for the case of two teams/players, TrueSkill only learns from win, lose, or draw outcomes and cannot use additional match outcome information such as scores. To address this deficiency, we propose novel Bayesian graphical models as extensions of TrueSkill that (1) model player's offence and defence skills separately and (2) model how these offence and defence skills interact to generate score-based match outcomes. We derive efficient (approximate) Bayesian inference methods for inferring latent skills in these new models and evaluate them on three real data sets including Halo 2 XBox Live matches. Empirical evaluations demonstrate that the new score-based models (a) provide more accurate win/loss probability estimates than TrueSkill when training data is limited, (b) provide competitive and often better win/loss classification performance than TrueSkill, and (c) provide reasonable score outcome predictions with an appropriate choice of likelihood - prediction for which TrueSkill was not designed, but which can be useful in many applications. © 2012 Springer-Verlag

How does a vacation from work affect employee health and well-being?

Item does not contain fulltextHealth and well-being (H&W) improve during vacation. However, it is unclear whether this general development applies to all employees, while also little is known about the underlying processes causing such an improvement. Our research questions were: (1) Does every worker experience a positive effect of vacation on H&W? and (2) Can vacation activities and experiences explain changes in H&W during vacation? In a 7-week longitudinal field study, 96 workers reported their H&W 2 weeks before, during, 1 week, 2 and 4 weeks after a winter sports vacation on 6 indicators (health status, mood, fatigue, tension, energy level and satisfaction). Sixty percent of the sample experienced substantial improvement of H&W during and after vacation. Yet, a small group experienced no (23%) or a negative effect of vacation (17%). Spending limited time on passive activities, pleasure derived from vacation activities, and the absence of negative incidents during vacation explained 38% of the variance in the vacation effect. Although vacation has a positive, longer lasting effect for many, it is not invariably positive for all employees. Choosing especially pleasant vacation activities and avoiding negative incidents as well as passive activities during active vacations apparently contributes to the positive effect of vacation on H&W.17 p