An application of incomplete pairwise comparison matrices for ranking top tennis players
November 02, 2016 Β· Declared Dead Β· π European Journal of Operational Research
"No code URL or promise found in abstract"
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Authors
SΓ‘ndor BozΓ³ki, LΓ‘szlΓ³ CsatΓ³, JΓ³zsef Temesi
arXiv ID
1611.00538
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.GT,
stat.AP
Citations
116
Venue
European Journal of Operational Research
Last Checked
3 months ago
Abstract
Pairwise comparison is an important tool in multi-attribute decision making. Pairwise comparison matrices (PCM) have been applied for ranking criteria and for scoring alternatives according to a given criterion. Our paper presents a special application of incomplete PCMs: ranking of professional tennis players based on their results against each other. The selected 25 players have been on the top of the ATP rankings for a shorter or longer period in the last 40 years. Some of them have never met on the court. One of the aims of the paper is to provide ranking of the selected players, however, the analysis of incomplete pairwise comparison matrices is also in the focus. The eigenvector method and the logarithmic least squares method were used to calculate weights from incomplete PCMs. In our results the top three players of four decades were Nadal, Federer and Sampras. Some questions have been raised on the properties of incomplete PCMs and remains open for further investigation.
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