34,303 research outputs found
Tabletop prototyping of serious games for ‘soft skills’ training
Serious games offer a relatively low cost, highly
engaging alternative to traditional forms of soft skills
training. The current paper describes an approach taken to
designing a serious game for the training of soft skills. A
tabletop prototype of the game was created and evaluated
with a group of 24 participants. Initial findings suggest that the game successfully created an environment in which it was advantageous to engage in appropriate collaborative
decision making behaviors, as well as providing built-in
opportunities for a tutor to guide under-performing groups
Scoring dynamics across professional team sports: tempo, balance and predictability
Despite growing interest in quantifying and modeling the scoring dynamics
within professional sports games, relative little is known about what patterns
or principles, if any, cut across different sports. Using a comprehensive data
set of scoring events in nearly a dozen consecutive seasons of college and
professional (American) football, professional hockey, and professional
basketball, we identify several common patterns in scoring dynamics. Across
these sports, scoring tempo---when scoring events occur---closely follows a
common Poisson process, with a sport-specific rate. Similarly, scoring
balance---how often a team wins an event---follows a common Bernoulli process,
with a parameter that effectively varies with the size of the lead. Combining
these processes within a generative model of gameplay, we find they both
reproduce the observed dynamics in all four sports and accurately predict game
outcomes. These results demonstrate common dynamical patterns underlying
within-game scoring dynamics across professional team sports, and suggest
specific mechanisms for driving them. We close with a brief discussion of the
implications of our results for several popular hypotheses about sports
dynamics.Comment: 18 pages, 8 figures, 4 tables, 2 appendice
Actions Speak Louder Than Goals: Valuing Player Actions in Soccer
Assessing the impact of the individual actions performed by soccer players
during games is a crucial aspect of the player recruitment process.
Unfortunately, most traditional metrics fall short in addressing this task as
they either focus on rare actions like shots and goals alone or fail to account
for the context in which the actions occurred. This paper introduces (1) a new
language for describing individual player actions on the pitch and (2) a
framework for valuing any type of player action based on its impact on the game
outcome while accounting for the context in which the action happened. By
aggregating soccer players' action values, their total offensive and defensive
contributions to their team can be quantified. We show how our approach
considers relevant contextual information that traditional player evaluation
metrics ignore and present a number of use cases related to scouting and
playing style characterization in the 2016/2017 and 2017/2018 seasons in
Europe's top competitions.Comment: Significant update of the paper. The same core idea, but with a
clearer methodology, applied on a different data set, and more extensive
experiments. 9 pages + 2 pages appendix. To be published at SIGKDD 201
Measuring multivariate redundant information with pointwise common change in surprisal
The problem of how to properly quantify redundant information is an open question that has been the subject of much recent research. Redundant information refers to information about a target variable S that is common to two or more predictor variables Xi . It can be thought of as quantifying overlapping information content or similarities in the representation of S between the Xi . We present a new measure of redundancy which measures the common change in surprisal shared between variables at the local or pointwise level. We provide a game-theoretic operational definition of unique information, and use this to derive constraints which are used to obtain a maximum entropy distribution. Redundancy is then calculated from this maximum entropy distribution by counting only those local co-information terms which admit an unambiguous interpretation as redundant information. We show how this redundancy measure can be used within the framework of the Partial Information Decomposition (PID) to give an intuitive decomposition of the multivariate mutual information into redundant, unique and synergistic contributions. We compare our new measure to existing approaches over a range of example systems, including continuous Gaussian variables. Matlab code for the measure is provided, including all considered examples
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