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Assessment of Physical and Technical Indicators in the Bundesliga to Predict Team Standings.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Assessment of Physical and Technical Indicators in the Bundesliga to Predict Team Standings./
作者:	Snyder, Ryan.
面頁冊數:	1 online resource (46 pages)
附註:	Source: Masters Abstracts International, Volume: 82-03.
Contained By:	Masters Abstracts International82-03.
標題:	Sports management. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28093460click for full text (PQDT)
ISBN:	9798672110080

Assessment of Physical and Technical Indicators in the Bundesliga to Predict Team Standings.
Snyder, Ryan.

Assessment of Physical and Technical Indicators in the Bundesliga to Predict Team Standings. - 1 online resource (46 pages)

Source: Masters Abstracts International, Volume: 82-03.

Thesis (M.S.)--Utica College, 2020.

Includes bibliographical references

Professional soccer players' performance is often evaluated in measures of physical and technical accomplishments. Factors outside of a player's performance impacting results of matches are match location, current score, and the quality of the opponent. This leads to the endless discussion and evaluation of home-field advantage. Sports literature demonstrated that physical indicators had minimal differences between teams who finish in the bottom or top half of league standings. Technical indicators offer a different narrative and suggest successful teams consistently outpace their competitors via accurate passes, total touches, entries beyond the middle third of the field, and shots on target. Several contributions in research have reviewed statistics and their relationship with winning but there is an inadequate amount that predicts wins, draws, and losses in an upcoming season. The purpose of this study was to predict the 2020-2021 standings for the professional soccer league in Germany (Bundesliga) by examining the physical and technical performance of teams and players. Twenty-five years' worth of data was reviewed to determine if home-field advantage existed in the league. Datasets consisting of physical and technical statistics were merged so a comparison between predictive models (linear regression, random forest, and artificial neural network) could be assessed. Findings of the linear regression model for wins revealed physical indicators only contributed to 30.5% of the variance. While technical indicators affect a match outcome, home-field advantage was prevalent from 1993-2018, with the home team securing at least one point 73% of the time. The findings of the study will hopefully lead to more public research in predicting soccer results using machine learning algorithms in professional leagues across the world. Keywords: Data Science, Dr. Michael McCarthy, Bayern Munich, UEFA, ANN, stepwise, quantitative.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798672110080Subjects--Topical Terms:

3423935
Sports management.
Subjects--Index Terms:

ANNIndex Terms--Genre/Form:

542853
Electronic books.

Assessment of Physical and Technical Indicators in the Bundesliga to Predict Team Standings.
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