Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "sports analytics" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Entrepreneurship in Sport: Sport in Business, Using Professional Football as an Example
Autorzy:
Kowalski, Wojciech Szymon
Powiązania:
https://bibliotekanauki.pl/articles/2234032.pdf
Data publikacji:
2022-12-30
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
entrepreneurship
football
sports analytics
transdisciplinarity
Opis:
The issue of entrepreneurship in sports joins the more general trend of catching up with the long Renaissance period of “reflecting” on the character of the professional sportsman (athlete), so peculiarly overlooked, and one of the main protagonists of the culture of antiquity, alongside the artist or philosopher. The author of the article adopts the convention of the ‘corporate athlete’, for which he sees a contemporary exemplification in football, the most popular sport. The examples cited from the economic history of football, preceded by an outline of the basic categories of entrepreneurship, are an attempt to show the essence of an economic, two-way view of these issues. The description of the institutionalisation of analytics and football’s ‘information bank’, highlights the effectiveness of an interdisciplinary approach to entrepreneurship in sport. In contrast, the characterisation of the re-engineering carried out at FC Barcelona is a case of an approach that treats sport as a natural economic environment. Providing a wholesome, inspirational building block, grounding some elements of management and entrepreneurial.
Źródło:
Studia Historiae Oeconomicae; 2022, 40, 2; 21-52
0081-6485
Pojawia się w:
Studia Historiae Oeconomicae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
One-match-ahead forecasting in two-team sports with stacked Bayesian regressions
Autorzy:
Lam, M. W. Y.
Powiązania:
https://bibliotekanauki.pl/articles/91870.pdf
Data publikacji:
2018
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
sports analytics
one-match-ahead forecasting
winning probability
Gaussian process regression
Opis:
There is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two-team sports for the sake of one-match-ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high standard of performance, Sparse Spectrum Gaussian Process Regression (SSGPR) – an improved algorithm for the standard Gaussian Process Regression (GPR), was used to solve Bayesian regression tasks, resulting in a novel predictive model called TLGProb. For evaluation, TLGProb was applied to a popular sports event – National Basketball Association (NBA). Finally, 85.28% of the matches in NBA 2014/2015 regular season were correctly predicted by TLGProb, surpassing the existing predictive models for NBA.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2018, 8, 3; 159-172
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies