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Enhancing the Prediction of Shot Suc...

Varadappa, Sebastian Mani,

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Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - Feedforward Neural Network /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - Feedforward Neural Network // Sebastian Mani Varadappa.
Author:	Varadappa, Sebastian Mani,
Description:	1 electronic resource (83 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-06.
Contained By:	Masters Abstracts International86-06.
Subject:	Athletic recruitment. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31790285
ISBN:	9798346834892

Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - Feedforward Neural Network /
Varadappa, Sebastian Mani,

Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - Feedforward Neural Network /Sebastian Mani Varadappa. - 1 electronic resource (83 pages)

Source: Masters Abstracts International, Volume: 86-06.

The advent of data-driven decision-making has sparked a transformation in the sports industry, where the precision of predictive models now serves as a pivotal factor in both team success and financial viability. This thesis examines Machine Learning and Deep Learning models for predicting NBA shot success, with team members developing Random Forest, XGBoost, Feedforward and Recurrent Neural Network models. Notably, the Recurrent Neural Network, previously unapplied in this context, emerged with superior predictive accuracy. This study's primary contribution is unveiling the RNN's potential for shot prediction, paving the way for its future integration into sports strategic planning and business analytics.

English

ISBN: 9798346834892Subjects--Topical Terms:

3556676
Athletic recruitment.

Enhancing the Prediction of Shot Success in NBA Basketball Games Using Machine Learning Techniques - Feedforward Neural Network /
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520 $a The advent of data-driven decision-making has sparked a transformation in the sports industry, where the precision of predictive models now serves as a pivotal factor in both team success and financial viability. This thesis examines Machine Learning and Deep Learning models for predicting NBA shot success, with team members developing Random Forest, XGBoost, Feedforward and Recurrent Neural Network models. Notably, the Recurrent Neural Network, previously unapplied in this context, emerged with superior predictive accuracy. This study's primary contribution is unveiling the RNN's potential for shot prediction, paving the way for its future integration into sports strategic planning and business analytics.
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