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- Study of the Technical and Tactical Variables Determining Set Win or Loss in Top-Level European Men's Volleyball by Rodriguez-Ruiz, David/ Quiroga, Miriam E./ Miralles, Jose A./ Sarmiento, Samuel/ de Saá, Yves and García-Manso, Juan M.
- Does Effectiveness of Skill in Complex I Predict Win in Men's Olympic Volleyball Games? by Zetou, Eleni/ Moustakidis, Athanasios/ Tsigilis, Nikolaos and Komninakidou, Andromahi
- A Starting Point for Analyzing Basketball Statistics by Kubatko, Justin/ Oliver, Dean/ Pelton, Kevin and Rosenbaum, Dan T
- The Dreaded Middle Seeds - Are They the Worst Seeds in the NCAA Basketball Tournament? by Morris, Tracy L. and Bokhari, Faryal H.
- Comparing and Forecasting Performances in Different Events of Athletics Using a Probabilistic Model by Godsey, Brian
U-Scores for Multivariate Data in Sports
1The Rockefeller University
1The Rockefeller University
1University of California at Davis, Clinical and Translational Science Center
1Central Michigan University, Department of Mathematics
Citation Information: Journal of Quantitative Analysis in Sports. Volume 4, Issue 3, Pages –, ISSN (Online) 1559-0410, DOI: 10.2202/1559-0410.1129, July 2008
- Published Online:
In many sport competitions athletes, teams, or countries are evaluated based on several variables. The strong assumptions underlying traditional 'linear weight' scoring systems (that the relative importance, interactions and linearizing transformations of the variables are known) can often not be justified on theoretical grounds, and empirical `validation' of weights, interactions and transformations, is problematic when a `gold standard' is lacking. With µ-scores (u-scores for multivariate data) one can integrate information even if the variables have different scales and unknown interactions or if the events counted are not directly comparable, as long as the variables have an `orientation'. Using baseball as an example, we discuss how measures based on µ-scores can complement the existing measures for `performance' (which may depend on the situation) by providing the first multivariate measures for `ability' (which should be independent of the situation). Recently, µ-scores have been extended to situations where count variables are graded by importance or relevance, such as medals in the Olympics (Wittkowski 2003) or Tour-de-France jerseys (Cherchye and Vermeulen 2006, 2007). Here, we present extensions to `censored' variables (life-time achievements of active athletes), penalties (counting a win more than two ties) and hierarchically structured variables (Nordic, alpine, outdoor, and indoor Olympic events). The methods presented are not restricted to sports. Other applications of the method include medicine (adverse events), finance (risk analysis), social choice theory (voting), and economy (long-term profit).