23,319 research outputs found
Sports, Inc. Volume 3, Issue 1
The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1003/thumbnail.jp
Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm
A lot of research effort has been put into community detection from all
corners of academic interest such as physics, mathematics and computer science.
In this paper I have proposed a Bi-Objective Genetic Algorithm for community
detection which maximizes modularity and community score. Then the results
obtained for both benchmark and real life data sets are compared with other
algorithms using the modularity and MNI performance metrics. The results show
that the BOCD algorithm is capable of successfully detecting community
structure in both real life and synthetic datasets, as well as improving upon
the performance of previous techniques.Comment: 11 pages, 3 Figures, 3 Tables. arXiv admin note: substantial text
overlap with arXiv:0906.061
Sports, Inc. Volume 4, Issue 1
The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1005/thumbnail.jp
Sports, Inc. Volume 9, Issue 1
The ILR Cornell Sports Business Society magazine is a semester publication titled Sports, Inc. This publication serves as a space for our membership to publish and feature in-depth research and well-thought out ideas to advance the world of sport. The magazine can be found in the Office of Student Services and is distributed to alumni who come visit us on campus. Issues are reproduced here with permission of the ILR Cornell Sports Business Society.https://digitalcommons.ilr.cornell.edu/sportsinc/1011/thumbnail.jp
Community detection in networks: Structural communities versus ground truth
Algorithms to find communities in networks rely just on structural
information and search for cohesive subsets of nodes. On the other hand, most
scholars implicitly or explicitly assume that structural communities represent
groups of nodes with similar (non-topological) properties or functions. This
hypothesis could not be verified, so far, because of the lack of network
datasets with information on the classification of the nodes. We show that
traditional community detection methods fail to find the metadata groups in
many large networks. Our results show that there is a marked separation between
structural communities and metadata groups, in line with recent findings. That
means that either our current modeling of community structure has to be
substantially modified, or that metadata groups may not be recoverable from
topology alone.Comment: 21 pages, 19 figure
A Cheap Ticket to the Dance: Systematic Bias in College Basketball's Ratings Percentage Index
A contest model is constructed to examine the existence of conference bias in college basketball's Ratings Percentage Index (RPI). Though a general RPI bias has been identified in previous literature, this is the first study to address whether the bias is random or systematic in nature. Within the theoretical model, the RPI is shown to be systematically biased against teams in high ability conferences, even when all teams play to expectation and can be transitively compared. Further, the bias can prevent the RPI from producing an ordinal mapping from revealed team ability level to the real number line. Given the longevity of the controversial RPI as the NCAA''s primary measure of team ability, these results may indicate that the NCAA is serving a demand for team heterogeneity in selecting for the NCAA Men''s Basketball Tournament.bias
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
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
Spartan Daily, April 17, 2019
Volume 152, Issue 33https://scholarworks.sjsu.edu/spartan_daily_2019/1032/thumbnail.jp
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