Rankings in professional men's tennis: a rich but underutilized source of information.

Success in professional tennis is measured, at least in part, by rankings. However, there is little quantitative evidence to inform stakeholders regarding what represents the typical ranking progress of top-ranked players. The objective of this study was therefore to compare the ranking trajectories of male players whom achieved peak professional rankings in the Top 250, 175, 100, 50, 20 and 10. The 11,396 birthdates and weekly professional rankings of all players between 27 August 1973 and 31 October 2011 were collated. The peak ranks for each athlete according to their both chronological age and number of years on tour were identified and athletes were categorised into one of six career-peak ranking bands. One-way analysis of variance tests confirmed distinctive ranking trajectories, which were most pronounced among Top 10 players. The rankings of these players were statistically distinguishable following players' second year on tour or by 17 years of age. The ranking signature of all Top 100 players emerged as significantly different to players that failed to enter the Top 100 by their fourth year on the tour. Indeed, the representation of ranking as a function of years on tour should be considered for use by tennis policy-makers in the future.

Applying decision tree induction for identification of important attributes in one-versus-one player interactions: a hockey exemplar.

Decision tree induction is a novel approach to exploring attacker-defender interactions in many sports. In this study hockey was chosen as an example to illustrate the potential use of decision tree inductions for the purpose of identifying and communicating characteristics that drive the outcome. Elite female players performed one-versus-one contests (n = 75) over two sessions. Each contest outcome was classified as either a win or loss. Position data were acquired using radio-tracking devices, and movement-based derivatives were calculated for two time epochs (5 to 2.5 seconds, and 2.5 to zero seconds before the outcome occurred). A decision tree model was trained using these attributes from the first session data, which predicted that when the attacker was moving at ≥ 0.5 m · s(-1) faster than the defender during the early epoch, the probability of an attacker's win was 1.00. Conversely, when the speed difference at that time was below this threshold the probability of a loss was 0.78. Secondary attributes included defender speed in the lateral direction during the early epoch, and angle of attack (i.e., angle between the respective velocity vectors of the attacker and defender) during the late epoch. The model was then used to predict outcomes of one-versus-one contests from the second session (accuracy = 0.643; area under the receiver operating characteristic (ROC) curve = 0.712). Moreover, decision trees provide an intuitive framework for relating spatial-temporal concepts to coaches, and the suitability of decision trees for analysing the features of one-versus-one exchanges are discussed.