3,264 research outputs found
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
Quantitative and empirical demonstration of the Matthew effect in a study of career longevity
The Matthew effect refers to the adage written some two-thousand years ago in
the Gospel of St. Matthew: "For to all those who have, more will be given."
Even two millennia later, this idiom is used by sociologists to qualitatively
describe the dynamics of individual progress and the interplay between status
and reward. Quantitative studies of professional careers are traditionally
limited by the difficulty in measuring progress and the lack of data on
individual careers. However, in some professions, there are well-defined
metrics that quantify career longevity, success, and prowess, which together
contribute to the overall success rating for an individual employee. Here we
demonstrate testable evidence of the age-old Matthew "rich get richer" effect,
wherein the longevity and past success of an individual lead to a cumulative
advantage in further developing his/her career. We develop an exactly solvable
stochastic career progress model that quantitatively incorporates the Matthew
effect, and validate our model predictions for several competitive professions.
We test our model on the careers of 400,000 scientists using data from six
high-impact journals, and further confirm our findings by testing the model on
the careers of more than 20,000 athletes in four sports leagues. Our model
highlights the importance of early career development, showing that many
careers are stunted by the relative disadvantage associated with inexperience.Comment: 13 pages, 7 figures, 4 Tables; Revisions in response to critique and
suggestions of referee
Bayesian estimation of in-game home team win probability for National Basketball Association games
Maddox, et al. (2022) establish a new win probability estimation for college
basketball and compared the results with previous methods of Stern (1994),
Desphande and Jensen (2016) and Benz (2019). This paper proposes modifications
to the approach of Maddox, et al. (2022) for the NBA game and investigates the
performance of the model. Enhancements to the model are developed, and the
resulting adjusted model is compared with existing methods and to the ESPN
counterpart. To illustrate utility, all methods are applied to the November 23,
2019 game between the Chicago Bulls and Charlotte Hornets.Comment: 15 pages, 7 figures, 5 tables. arXiv admin note: text overlap with
arXiv:2204.1177
Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution
This study presents a novel deep learning method, called GATv2-GCN, for
predicting player performance in sports. To construct a dynamic player
interaction graph, we leverage player statistics and their interactions during
gameplay. We use a graph attention network to capture the attention that each
player pays to each other, allowing for more accurate modeling of the dynamic
player interactions. To handle the multivariate player statistics time series,
we incorporate a temporal convolution layer, which provides the model with
temporal predictive power. We evaluate the performance of our model using
real-world sports data, demonstrating its effectiveness in predicting player
performance. Furthermore, we explore the potential use of our model in a sports
betting context, providing insights into profitable strategies that leverage
our predictive power. The proposed method has the potential to advance the
state-of-the-art in player performance prediction and to provide valuable
insights for sports analytics and betting industries
Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry
Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas
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