Performance analysis and identifying performance characteristics associated with success are of great importance to players and coaches in any sport. However, while large amounts of data are available within elite tennis, very few players employ an analyst or attempt to exploit the data to enhance their performance; this is partly attributable to the considerable time and complex techniques required to interpret these large datasets. Using data from the 2016 and 2017 French Open tournaments, we tested the agreement between the results of a simple new method for identifying important performance characteristics (the Percentage of matches in which the Winner Outscored the Loser, PWOL) and the results of two standard statistical methods to establish the validity of the simple method. Spearman’s rank-order correlations between the results of the three methods demonstrated excellent agreement, with all methods identifying the same three performance characteristics ( points won of 0–4 rally length, baseline points won and first serve points won) as strongly associated with success. Consequently, we propose that the PWOL method is valid for identifying performance characteristics associated with success in tennis, and is therefore a suitable alternative to more complex statistical methods, as it is simpler to calculate, interpret and contextualise.
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