Classic statistical and modern machine learning methods for modeling and prediction of major tennis tournaments

Simple item page
dc.contributor.advisorGroll, Andreas
dc.contributor.authorBuhamra, Nourah
dc.contributor.refereePauly, Markus
dc.date.accepted2025-06-11
dc.date.accessioned2025-07-03T06:34:09Z
dc.date.available2025-07-03T06:34:09Z
dc.date.issued2025
dc.description.abstractThe cumulative dissertation proposes a comprehensive approach to predicting outcomes in Grand Slam tennis tournaments, focusing on the probability that the first-named player will win. Our study incorporates several classical regression and machine learning models, evaluated using cross-validation and external validation through performance measures such as classification rate, predictive likelihood, and Brier score. Two specific aspects are examined in greater detail: non-linear effects and the inclusion of additional player and court-specific abilities. Moreover, we analyze the predictive potential of statistically enhanced covariates and apply procedures from the field of interpretable machine learning to make complex models more understandable. Our analyses show that in predicting Grand Slam tennis matches, while there are slight differences across various statistical and machine learning approaches, the specific forecasting strategy used plays an even more critical role. Additionally, the results confirm that enhanced variables contribute positively to model performance and provide deeper insights into predictors of match outcomes in sports analytics.en
dc.identifier.urihttp://hdl.handle.net/2003/43789
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25563
dc.language.isoen
dc.subjectGrand Slam tournamentsen
dc.subjectTennis matchesen
dc.subjectPredictionen
dc.subjectCross validationen
dc.subjectExpanding windowen
dc.subjectPenalizationen
dc.subjectMachine learningen
dc.subjectStatistical enhance covariatesen
dc.subjectInterpretable machine learningen
dc.subject.ddc310
dc.subject.rswkTennisturnierde
dc.subject.rswkGrand Slamde
dc.subject.rswkPrognosede
dc.subject.rswkStatistische Analysede
dc.subject.rswkMaschinelles Lernende
dc.titleClassic statistical and modern machine learning methods for modeling and prediction of major tennis tournamentsen
dc.typeText
dc.type.publicationtypePhDThesis
dcterms.accessRightsopen access
eldorado.secondarypublicationfalse

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Dissertation_Buhamra.pdf
Size:
1.72 MB
Format:
Adobe Portable Document Format
Description:
DNB
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.82 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Statistical Methods for Big Data