Predicting elite success: Evidence comparing the career pathways of top 10 to 300 professional tennis players

Li, Pingwei; Weissensteiner, Juanita R.; Pion, Johan; Bosscher, Veerle De

doi:10.1177/1747954120935828

Cited by 17 publications

(22 citation statements)

References 19 publications

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“…Although future 51-100 and 101-175 ranked players did not have distinctive ranking signatures at these ages (Li et al 2020;Reid et al 2014), the present study showed T100 players did engage earlier in more professional competitions compared to future T250 players in late adolescence. This suggests that current selection or LTAD guidelines, anchored alone in adolescent ranking data, may oversimplify the current performance and future ranking trajectory of players.…”

Section: ***Table 2 Near Here*** Discussioncontrasting

confidence: 72%

Competition scheduling patterns of emerging elite players in professional men’s tennis

Perri

Duffield

Murphy

et al. 2021

Journal of Sports Sciences

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This study analysed the competition scheduling of future top 100 and 250 ranked tennis players from international tournament profiles at ages 13-18y. Retrospective tournament data was analysed for 165 future top 100 (T100) and top 250 (T250) males during their junior international tournament eligibility. Tournament/match volumes, days between tournaments and consecutive tournaments (<8 days between) were quantified for junior and professional events. A two-way (age x ranking) analysis of variance determined the effects of age and ranking group on tournament profiles. Significant interactions were observed for tournament volumes across junior and professional categories, with T100 players competing in professional tournaments earlier (p<0.05). No significant interactions were observed for volumes of junior or professional matches played (p>0.05). No significant interactions were observed for days between tournaments or consecutive tournaments played (p>0.05).Significant main effects were observed for age on tournament volume, with junior and professional volume increasing at age 15 and 17, respectively (p<0.05). Higher match volumes were observed for T100 players compared to T100-S players (p<0.05). Competition schedules intensify at age 15 compared with ages 13-14y through increased tournament and match volumes. Future T100 players transition to professional tournaments earlier, alongside greater engagement in higher quality junior tournaments.

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Section: ***Table 2 Near Here*** Discussioncontrasting

confidence: 72%

Competition scheduling patterns of emerging elite players in professional men’s tennis

Perri

Duffield

Murphy

et al. 2021

Journal of Sports Sciences

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“…After dividing the top 100 according to singles rankings into 4 intervals, the authors found RAE existence in the 76–100 positions, but they observed no RAE in other three intervals (positions 1–25, 26–50, and 51–75). In a study of top 300 ranked professional tennis players from 2010 to 2018 (193 females and 180 males) at different ranking levels, Li, Weissensteiner, Pion and De Bosscher [ 36 ] were founding out at what age the athletes had started playing tennis and when they had reached the milestones of their career ratings. It was found that 75% of the top 300 players started playing tennis between the age of 3 to 7 years and also that professional rankings between 14 and 18 years were not reliable in predicting a player’s future ranking.…”

Section: Introductionmentioning

confidence: 99%

The relative age effect in top 100 elite female tennis players in 2007–2016

et al. 2022

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The Relative Age Effect (RAE) has been the subject of many studies, but few have focused on professional athletes in individual sports. The aim of this study was to verify the existence of the RAE among elite senior female tennis players (top 100 players) in the WTA Rankings (n = 1000) in the years 2007–2016. The analysis was performed among top 100, resp. top 10 female tennis players, among players in 4 age subgroups and among left-handed (LH) and right-handed (RH) players. The existence of the RAE was assessed with the use of chi-square test (goodness of fit). More than half of top 100 players were born in first semester: both in individual years (53.0–63.0%) and in the whole observed period (58.4%). Significant RAE (ES medium) was observed in top 100 female players only in 2012 and 2016; significant RAE (ES small) was detected in the period of 2007–2016. Among the top 10 players, significant RAE (ES medium) was demonstrated during the whole period. No significant RAE (ES medium) was detected in the 17–18y subgroups, significant in 19–24y and 25–30y (ES small) as well as in 31–36y (ES medium). Although significant RAE was observed in the subgroups of LH and RH female players, ES was large only in the LH. The results contribute to the expansion of the knowledge about the reduction of the RAE existence in adulthood among coaches, athletes and tennis officials.

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“…The results showed that the age of the players could predict the success of football clubs in the ACL. This result is not consistent with Li et al (2020). One of the possible causes of consensus is the difference in sports.…”

Section: Discussionmentioning

confidence: 63%

To Win or Not to Win: How to Success in the Asian Champions League

2022

CCD

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In professional sports, predicting success factors is very vital and important and is always questionable. The failure of teams in the leagues has been a constant issue and progress and gaining status has been a challenge for all clubs; and managers, officials, sports federations and clubs, and even researchers, are trying to shed light on the various dimensions of this issue. The purpose of this study was providing a model of predicting the success of football clubs in the Asian Champions League. The present study is descriptive-correlational. Seven Asian countries that won the Asian Champions League (ACL) were analyzed between 2014 and 2019. At the level of descriptive statistics, mean and standard deviation, and at the level of inferential statistics, because the response variable (success) was a qualitative variable, the logistic regression model was used. Data analysis was performed using SPSS software. Findings showed that 80% of the success of clubs in the ACL is correctly identified using the research model and foreign players, the average value of each player, the total value of the club and the number of players in a team have a positive impact on the success of football clubs. The age of the players has a negative effect, in other words, the lower average age can contribute to the success of football clubs. Therefore, these factors are considered as a comparative advantage for the success of clubs and managers and stakeholders are suggested to put the necessary planning and investment on their agenda to achieve these things.

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Predicting elite success: Evidence comparing the career pathways of top 10 to 300 professional tennis players

Cited by 17 publications

References 19 publications

Competition scheduling patterns of emerging elite players in professional men’s tennis

Competition scheduling patterns of emerging elite players in professional men’s tennis

The relative age effect in top 100 elite female tennis players in 2007–2016

To Win or Not to Win: How to Success in the Asian Champions League

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