Annual draft combine test of National Basketball Association (NBA) is a key player testing process where prospective players with extraordinary athletic abilities are evaluated and the assessment results would further inform the determination of prospective players for the league during draft day. Nonetheless, key attributes from the combine test that distinguished successful players in the draft from those unselected has yet to be investigated. The study was aimed to: (i) compare the difference between NBA drafted and undrafted players from five playing positions, considering anthropometric characteristics and physical fitness ability during draft combine test; and (ii) determine the key combine test factors that most effectively discriminate between draft groups. A total of 3,610 players participating in the 2000–2018 NBA draft combine test were included. Independent t-test was applied to compare difference between drafted and non-drafted players in variables related to anthropometrics, and strength and agility test. A descriptive discriminant analysis was subsequently used to identify which variables could best discriminate between two draft groups in each playing position. The significance level was set at p < 0.05. The drafted players from five positions outperformed the undrafted in height, wingspan, vertical jump height and reach, line agility and three-quarter sprint test (p < 0.01, ES = 0.26–0.87). The discriminant functions for each position (p < 0.001, Λ = 0.81–0.83) were emphasized by specific variables that discriminated both draft groups. The findings revealed that in addition to height and wingspan, leg power served as key determinants for being drafted as guards, as did agility and speed for power forwards and centers.
IntroductionThe aim of the study was to (i) analyze the match performance of professional female tennis players in different Grand Slams; (ii) model the relationships between match performance variables and relative quality; and (iii) build typical performance profiles for those players in Grand Slams.MethodData of a total of 1369 matches were collected within 2014–2017 four Grand Slams (Australian Open: n = 499; Roland Garros: n = 249; Wimbledon: n = 249 and US Open, n = 372). Correlations between 37 performance variables and relative quality (difference of expected rounds between two competing players of given ranking) were determined and automatically classified into two groups of magnitudes via two-step cluster analysis. Higher-correlated variables were used to build players’ typical performance profiles via regression-based technique to give percentage evaluation scores (%ES), which means the percentage of matches where a performance variable value would be expected to be lower than the observed value considering the RQ of two competing players.ResultsPlayers had more service winners, double faults, return winners and return unforced errors in the Australian Open and US Open, implying a “fast-fast” serve strategy, and higher dominance ratio and better serving performance in Wimbledon. While receiving players had better chances to break opponents’ service game in Roland Garros. Distance covered became similar in all Grand Slams. All studied variables showed obvious correlation with RQ expect for those of physical performance.ConclusionsThe findings (i) indicate that female game in Grand Slams remained to be a contest over baseline, although players had good efficiency at net; (ii) demonstrate the influence of relative quality on serve and return, break point, net point and efficiency performance; and (iii) evidence the usefulness of applying %ES to evaluate performance of individual player.
The aim of the present study was to identify the game-related statistics which discriminate between winning, drawing and losing teams in Chinese Soccer Association Super League. The sample included 1056 balance games from the 2012-2017 Chinese Soccer Association Super League. Physical and technical game-related statistics were gathered. A one-way analysis of variance and discriminant analysis of data was done. The results showed that winning teams were significantly higher for the following game statistics: shots, shots on target, 50-50 challenge won, offsides, sprinting distance, sprinting effort, sprinting distance in ball possession and high-speed-running distance in ball possession. Losing teams had significantly higher averages in the variable crosses, passes, forward passes, sprinting distance out of ball possession and high-speed-running distance out of ball possession. Discriminant analysis concluded the following: the variables that discriminate between winning, drawing and losing teams were the shots on target, sprinting distance in ball possession, quality of opposition, passes and forward passes. Coaches and players should be aware of these different profiles in order to design and evaluate practices and competitions for their teams.
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