The authors assess the performance of professional tennis players from the perspective of the efficiency of their game using data envelopment analysis (DEA). This research can complement the information provided by the Association of Tennis Professionals (ATP) ranking, which is concerned with their competitive performance. The DEA provides an index of the overall performance of players by aggregating the ATP statistics regarding the different aspects of the game. The DEA benchmarking analysis also allows identifying strengths and weaknesses of the game of the players. To the ranking of players, the authors use the cross-efficiency evaluation, which assesses the players in a peer evaluation with different patterns of game.
AlpI. (2006). Performance evaluation of goalkeepers of the world cup. G.U. Journal of Science, 19, 119–125.
2.
AndersonT. R.SharpG. P. (1997). A new measure of football batters using DEA. Annals of Operations Research, 73, 141–155.
3.
BankerR. D.CharnesA.CooperW. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30, 1078–1092.
4.
BarrosC. P.AssafA.Sá-EarpF. (2010). Brazilian football league technical efficiency: A Simar and Wilson approach. Journal of Sports Economics, 11, 641–651.
5.
BoscáJ. E.LiernV.MartínezA.SalaR. (2009). Increasing offensive or defensive efficiency? An analysis of Italian and Spanish football. Omega, 37, 63–78.
6.
CharnesA.CooperW. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9, 181–186.
7.
CharnesA.CooperW. W.RhodesE. (1978). Measuring the efficiency of decision making units. European Journal of Operations Research, 2, 429–444.
8.
ChenW.-C.JohnsonA. L. (2010). The dynamics of performance space of major league baseball pitchers 1871-2006. Annals of Operations Research, 181, 287–302.
9.
CooperW.W.RamónN.RuizJ.L.SirventI. (2011). Avoiding Large Differences in Weights in Cross-Efficiency Evaluations: Application to the Ranking of Basketball Players. Journal of Centrum Cathedra, 4, 197–215.
10.
CooperW. W.RuizJ. L.SirventI. (2009). Selecting non-zero weights to evaluate effectiveness of basketball players with DEA. European Journal of Operational Research, 195, 563–574.
11.
DawsonP.DobsonS.GerrardB. (2000). Estimating coaching efficiency in professional team sports: Evidence from English association football. Scottish Journal of Political Economy, 47, 399–419.
12.
DoyleJ. R.GreenR. H. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45, 567–578.
13.
EinolfK. W. (2004). Is winning everything? A data envelopment analysis of major league baseball and the national football league. Journal of Sports Economics, 5, 127–151.
14.
Espitia-EscuerM.García-CebriánL. I. (2004). Measuring the efficiency of Spanish first-division soccer teams. Journal of Sports Economics, 5, 329–346.
15.
FernandezJ.Mendez-VillanuevaA.PluimB. M. (2006). Intensity of tennis match play. British Journal of Sports Medicine, 40, 387–391.
16.
FizelJ. L.D’ItriM. P. (1999). Firing and hiring of managers: Does efficiency matter?. Journal of Management, 25, 567–585.
17.
FriedH. O.LambrinosJ.TynerJ. (2004). Evaluating the performance of professional golfers on the PGA, LPGA and SPGA tours. European Journal of Operational Research, 154, 548–561.
18.
FriedH. O.TauerL. W. (2011). The impact of age on the ability to perform under pressure: Golfers on the PGA tour. Journal of Productivity Analysis, 35, 75–84.
19.
González-GómezF.Picazo-TadeoA. J. (2010). Can we be satisfied with our football team? Evidence from Spanish professional football. Journal of Sports Economics, 11, 418–442.
20.
HaasD. J. (2003). Technical efficiency in the major league soccer. Journal of Sports Economics, 4, 203–215.
21.
HaasD. J.KocherM. G.SutterM. (2004). Measuring efficiency of German football teams by data envelopment analysis. Central European Journal of Operations Research, 12, 251–268.
22.
KlaassenF. J. G. M.MagnusJ. R. (2009). The efficiency of top agents: An analysis through service strategy in tennis. Journal of Econometrics, 148, 72–85.
23.
LiangL.WuJ.CookW. D.ZhuJ. (2008). Alternative secondary goals in DEA cross-efficiency evaluation. International Journal of Production Economics, 113, 1025–1030.
24.
LovellC. A. K.PastorJ. T. (1995). Units invariant and translation invariant DEA models. Operations Research Letters, 18, 147–151.
25.
LovellC. A. K.PastorJ. T. (1999). Radial DEA models without inputs or without outputs. European Journal of Operational Research, 118, 46–51.
26.
LozanoS.VillaG.GuerreroF.CortésP. (2002). Measuring the performance of nations at the summer Olympics using data envelopment analysis. Journal of the Operational Research Society, 53, 501–511.
27.
O’DonoghueP.IngramB. (2001). A national analysis of elite tennis strategy. Journal of Sport Science, 19, 107–115.
28.
PastorJ. T.RuizJ. L.SirventI. (2002). A statistical test for nested radial DEA models. Operations Research, 50, 728–735.
29.
PrietoA. M.ZofioJ. L. (2001). Evaluating effectiveness in public provision of infrastructure and equipment: The case of Spanish municipalities. Journal of Productivity Analysis, 15, 41–58.
30.
RamónN.RuizJ. L.SirventI. (2010a). A multiplier bound approach to assess relative efficiency in DEA without slacks. European Journal of Operational Research, 203, 261–269.
31.
RamónN.RuizJ. L.SirventI. (2010b). On the choice of weights profiles in cross-efficiency evaluations. European Journal of Operational Research, 207, 1564–1572.
32.
RamónN.RuizJ. L.SirventI. (2011). Reducing differences between profiles of weights: A “peer-restricted” cross-efficiency evaluation. Omega, 39, 634–641.
33.
Santos-RosaF. J.GarcíaT.JiménezR.MoyaM.CervellóM (2007). Predicción de la satisfacción con el rendimiento deportivo en jugadores de tenis: Efecto de las claves situacionales. Motricidad: European Journal of Human Movement, 18, 41–60.
34.
SextonT. R.LewisH. F. (2003). Two-stage DEA: An application to major league baseball. Journal of Productivity Analysis, 19, 227–249.
35.
SextonT. R.SilkmanR. H.HoganA. J. (1986). Data envelopment analysis: Critique and extensions. In SilkmanR. H. (Ed.), Measuring efficiency: An assessment of data envelopment analysis (pp. 73–105). San Francisco, CA: Jossey-Bass.
36.
SoaresJ. C.Angulo-MezaL.Branco Da SilvaB. P. (2009). A ranking for the Olympic games with unitary input DEA models. IMA Journal Management Mathematics, 20, 201–211.
37.
SueyoshiT.OhnishiK.KinaseY. (1999). A benchmark approach for baseball evaluation. European Journal of Operational Research, 115, 429–448.
38.
ThompsonR. G.SingletonF. D.ThrallR. M.SmithB. A. (1986). Comparative site evaluations for locating a high-energy physics lab in Texas. Interfaces, 16, 35–49.
39.
UedaT.AmatatsuH. (2009). Determination of bounds in DEA assurance region method—Its application to evaluation of baseball players and chemical companies. Journal of the Operations Research Society of Japan, 4, 453–467.
40.
VolzB. (2009). Minority status and managerial survival in major league baseball. Journal of Sports Economics, 10, 522–542.
41.
WuJ.ZhouZ.LiangL. (2010). Measuring the performance of nations at Beijing Summer Olympics using integer-valued DEA model. Journal of Sports Economics, 11, 549–566.
42.
ZhangD.LiX.MengW.LiuW. (2009). Measuring the performance of nations at the Olympic games using DEA models with different preferences. Journal of the Operational Research Society, 60, 983–990.
