Performance evaluation of Tour de France cycling teams using Data Envelopment Analysis

This paper uses a robust (order-m) DEA approach to evaluate the efficiency of Tour de France cycling teams. Since there are multiple ways this event can be successful for a cycling team, we take it that managers face strategic input decisions regarding team and rider characteristics. Specifically, we distinguish between ranking teams, sprint teams, and mixed teams, and compute for each team an efficiency score as due to the team’s performance relative to similarly classified teams and an efficiency score that is the consequence of the team type. We find that ranking teams are generally more efficient than other types of cycling teams.

[1]  Léopold Simar,et al.  Advanced Robust and Nonparametric Methods in Efficiency Analysis: Methodology and Applications , 2007 .

[2]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[3]  Emmanuel Thanassoulis,et al.  Assessing pupil and school performance by non-parametric and parametric techniques , 2010, J. Oper. Res. Soc..

[4]  Dieter J. Haas.  Technical Efficiency in the Major League Soccer , 2003 .

[5]  Benno Torgler,et al.  “La Grande Boucle” , 2007 .

[6]  William W. Cooper,et al.  Handbook on data envelopment analysis , 2011 .

[7]  Carlos Pestana Barros,et al.  Analyzing the Performance of the English F.A. Premier League With an Econometric Frontier Model , 2006 .

[8]  School Outcomes: Sharing the Responsibility Between Pupil and School1 , 2002 .

[9]  Thomas R. Sexton,et al.  Player Salaries, Organizational Efficiency, and Competitiveness in Major League Baseball , 2007 .

[10]  Desheng Dash Wu,et al.  Competition strategy and efficiency evaluation for decision making units with fixed-sum outputs , 2011, Eur. J. Oper. Res..

[11]  John Ruggiero,et al.  Performance evaluation of National Football League teams , 2000 .

[12]  L. Cherchye,et al.  Robust Rankings of Multidimensional Performances , 2006 .

[13]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[14]  Joe Zhu,et al.  Quantitative models for performance evaluation and benchmarking , 2003 .

[15]  William W. Cooper,et al.  Selecting non-zero weights to evaluate effectiveness of basketball players with DEA , 2009, Eur. J. Oper. Res..

[16]  Stephen Dobson,et al.  Stochastic Frontiers and the Temporal Structure of Managerial Efficiency in English Soccer , 2000 .

[17]  Karl W. Einolf.  Is Winning Everything? , 2004 .

[18]  R. Ward,et al.  Production and Efficiency in Association Football , 2001 .

[19]  Manuel Espitia-Escuer,et al.  Measuring the Efficiency of Spanish First-Division Soccer Teams , 2004 .

[20]  John Ruggiero,et al.  Measuring Technical Efficiency in Sports , 2011 .

[21]  Seongho Song,et al.  Estimating Production Efficiency in Men’s NCAA College Basketball: A Bayesian Approach , 2010 .

[22]  J. Florens,et al.  Nonparametric frontier estimation: a robust approach , 2002 .