{"id":6697,"date":"2019-11-29T06:30:00","date_gmt":"2019-11-29T12:30:00","guid":{"rendered":"http:\/\/thesportjournal.org\/?p=6697"},"modified":"2020-10-06T08:27:28","modified_gmt":"2020-10-06T13:27:28","slug":"ability-for-tennis-specific-variables-and-agility-for-determining-the-universal-tennis-ranking-utr","status":"publish","type":"post","link":"https:\/\/thesportjournal.org\/article\/ability-for-tennis-specific-variables-and-agility-for-determining-the-universal-tennis-ranking-utr\/","title":{"rendered":"Ability for tennis specific variables and agility for determining the Universal Tennis Ranking (UTR)"},"content":{"rendered":"\n

Authors:<\/strong> Jennifer A. Kurtz* (1), Jake Grazer (2), Bradley Alban (3), Mike Martino (4)<\/p>\n\n\n\n

Corresponding Author:<\/strong>
Jennifer A. Kurtz, MS
120 Coventry Court
Fayetteville, GA 30215
Jennifer.kurtz06@gmail.com
404-509-3384<\/p>\n\n\n\n

Jennifer Kurtz is a doctoral student at The University of\nGeorgia studying exercise physiology. She is also an assistant strength and\nconditioning coach at Elite Performance Institute. <\/p>\n\n\n\n

Jake Grazer is an Assistant Professor of Exercise Science at\nGeorgia College & State University.<\/p>\n\n\n\n

Bradley Alban is an Assistant Professor of Exercise Science\nat Georgia College & State University.<\/p>\n\n\n\n

Mike Martino is an Professor of Exercise Science at Georgia College & State University.<\/p>\n\n\n\n

Ability for tennis specific variables and agility for determining the Universal Tennis Ranking (UTR): A Review and Recommendations<\/strong><\/h3>\n\n\n\n

ABSTRACT<\/strong><\/p>\n\n\n\n

Our purpose was to investigate tennis\nspecific measures to predict a player\u2019s Universal Tennis Ranking (UTR) value\nand to see what percentage of the variables most influence the ranking. Methods:\n15 male and 14 female athletes volunteered to participate in this study. Each\nvolunteer performed no more than 16 total serves or eight from the add and\ndeuce side down the \u201cT\u201d, no more than 16 total forehands and backhands down-the-line,\nthree spider tests, and two trials of footwork taps in 30 seconds. Only the top\ntwo hits were analyzed. Results: A multiple linear regression was calculated\npredicting a player\u2019s UTR based on serve, forehand, backhand, agility, and\nfootwork taps. The regression equation was significant (F (5,23) = 29.66,\np<.05) with an R squared value of 0.866. Coefficient of variation (CV) and\nintra-class correlation coefficients (ICC) were calculated to assess\nreliability between player serve (r=0.902), forehand (r=0.843) and backhand\nvelocity (r=0.858), agility (r=-0.817), and footwork (r=0.472). More noticeable\nwas the significant predictive value of serve (r=0.902) and backhand velocity\n(r=0.858) to the player\u2019s UTR. Conclusion: These results underline the important\nrelationship between the player\u2019s UTR and tennis-specific characteristics\n(serve and backhand velocity) as assessed by the player\u2019s stroke velocity. The\nability of training regimens to improve tennis-specific metrics would improve\nperformance qualities and the player\u2019s UTR. <\/p>\n\n\n\n\n\n\n\n

Key words:<\/strong> tennis,\nUTR, ranking prediction, sport-specific tests, sport performance <\/p>\n\n\n\n

INTRODUCTION<\/strong><\/p>\n\n\n\n

Tennis involves intermittent\nhigh-intensity efforts interspersed with periods of low-intensity activity in\nwhich active and passive recovery periods take place (6). Tennis matches are\ncharacterized by intermittent periods of whole-body effort, alternating short\nbouts (2-10 seconds) of high-intensity exercise, and short recovery periods\n(10- 20 seconds) interrupted by several resting periods of longer duration\n(60-90 seconds) and a typical match can last about 1.5 hours, and in some\ncases, it can last for more than five hours (29). In each point, on average, players\nrun a total of 8-15 m (with 3-4 changes of direction) and an average distance\nof 1300 to 3600m per hour during a match and hit the ball an average of 4-5\ntimes per point depending on the player\u2019s level (amateur or advanced) and court\nsurface (slow or fast) (20, 29). Knowledge of the contribution of physical and\nperformance characteristics and ranking measures could assist in determining\nthe relative importance of such variables to provide optimal training programs.\n<\/p>\n\n\n\n

The role of physical variables in\ntennis is gradually increasing due to the physical demand of the sport. The relationship\nbetween the physical capabilities and competition performance of tennis players\ncreates the possibility of forming optimal conditioning training programs (10).\nPrevious research indicated agility was the only significant fitness variable\nin prepubescent tennis players (ages 8-12) to predict competitive rankings (2,\n20, 26, 41). In preadolescence and adolescent junior tennis players (ages\n11-16), correlations were found with speed (14, 16, 23, 33, 41), agility and\nquickness (14, 23, 33, 41), explosive power of the trunk and upper body (15,\n16, 28, 47), explosive strength of the lower limbs (squat jump,\ncounter-movement jump, and drop jump, core strength of the trunk, hand-eye\ncoordination (10, 14, 16, 23), aerobic endurance (10, 14, 28, 33), flexibility (33),\nand maximal strength of the dominant arm (14, 16, 33) correlated with the\nplayer\u2019s competition performance and ranking. Furthermore, the more a tennis\nplayer matures, their results in physical characteristics showed better\nperformance levels and stronger correlations than preadolescences and\nadolescents. <\/p>\n\n\n\n

Successful tennis performance cannot\nbe defined by one predominating physical attribute; the specifics of these\nvariables have yet to be determined but correlation studies have been\nundertaken to determine which physical components have a strong relation with\nmatch results and ranking. Since it is primarily a tactical and technical sport\nthat requires open skills, competitive tennis demands a complex interaction of\nthe major physiological and physical variables (29, 47). <\/p>\n\n\n\n

Tennis is a sport with uncertainty\nand an unknown degree of transitivity with numerous variables that can affect\nthe outcome of the match (9). Tennis agility (20, 29, 47), footwork (11),\nforehand velocity (13, 22, 27, 37, 44), backhand velocity (5, 11, 13, 23, 26,\n43), and serve velocity (14, 17, 22, 25, 26, 27, 48) are predominant factors\nthat influence performance and ranking. To possess a high ranking, a player\nmust encompass strong technical skills such as the ability to produce high\namounts of force through serves and ground strokes, have efficient footwork,\nand high levels of agility (13, 29). Furthermore, stroke rating was a vital\npredictor for tournament performance and national rankings (r=0.94) (26, 39, 41).\nThe athlete has to master many aspects of their game, such as the serve, a\nmixture of strokes, footwork, ball placement, strength, endurance and strategy\nin order to exemplify high performance levels (48). Since sport specific\ntechnical skills are predominant factors in tennis, it is unknown to what\nextent these variables influence performance and ranking. There have been no studies to date analyzing the extent of\nthose variables and how they are linked to overall tennis skill and ranking (2).\nThus, much of the available research is based on our knowledge of the physical\ndemands of tennis. <\/p>\n\n\n\n

The rankings of the world\u2019s top\ntennis players provide a fast and simple method for predicting match winners\nand comparing players. The notion of an overall ranking might seem simplistic\nin a sport like tennis which features an unknown degree of transitivity.\nHowever, the plethora of variables in tennis might potentially affect the\noutcome of any individual match (9). Previous ranking systems such as the ATP\n(Association of Tennis Professionals) (40); the WTA (Women\u2019s Tennis\nProfessionals) (46); the Page Rank System (6, 9, 46); the Parametric Page Rank\nSystem (1); the Prestige Score (40); SortRank (45); Sports Ladder System (44);\nCommon Opponent Model (24, 44) and the Network-Based System (34) do not provide\na fair basis of comparison and future prediction of performance since they lack\nevaluating tennis specific variables. Official ranking systems do not precisely\nand accurately rank players according to their abilities but rather they measure\ntheir cumulative progress throughout various tournament rounds. Previous\nresearch has used rankings from a wide array of systems, but the Universal\nTennis Ranking (UTR) has yet to be investigated. <\/p>\n\n\n\n

The UTR is the most newly created\nsystem based on a 16-point scale that has been utilized to calculate a player\u2019s\nranking based on their results from their most recent 30 matches across all\ncompetitive systems in the last 12 months (19, 35, 38). The UTR is the official\nrating of The Tennis Channel, Intercollegiate Tennis Association, World Team\nTennis, Professional Tennis Registry, United States Professional\nTennis Association, International Tennis Hall of Fame, and Orange Coach (19,\n46, 38). This non-discriminant ranking system was designed to implement a new\nalgorithm to increase the accuracy and reliability of ratings to standardize\nthem to a uniform measurement for all tennis players. It categorizes every\ncompetitive player regardless of age, gender, and nationality, considers the\nopposing opponent and the score of the match and accounts for player\u2019s current\nrelative abilities and competitiveness (36-38). It calculates the player\u2019s\nranking value based off percentage of games won by the player, match outcome\nfactor for the players for their most recent matches, and opponent\u2019s player\nrating number. However, the UTR does not directly consider a tennis\nplayer\u2019s physical metrics (agility, footwork, forehand velocity, backhand\nvelocity, and serve velocity). <\/p>\n\n\n\n

Since the UTR is the highest tennis\nranking worldwide, it would be beneficial to predict a player\u2019s UTR ranking based off of sport specific movements; to date, no\nstudy has investigated collegiate tennis players and the extent of tennis\nspecific variables that influence the UTR. If coaches predict a player\u2019s\nUTR value based off tennis specific variables besides percentage of matches\nwon, they can be more accurate in programming and optimize training efficiency\nto help improve an athlete\u2019s ranking and performance. We hypothesized that\ntennis ranking performance would be enhanced by improving a player\u2019s stroke\nskills (serve, backhand, and forehand) and footwork. The purpose of this study is\nto investigate tennis specific measures (serve, backhand, forehand velocity,\nagility, and footwork) to predict a player\u2019s UTR value and to see what\npercentage of the variables most influence the ranking. <\/p>\n\n\n\n

METHODS<\/strong><\/p>\n\n\n\n

Subjects <\/em><\/p>\n\n\n\n

At the beginning of the study, 31\nmale and female tennis players agreed to participate with a mix of right and\nleft-handed hitters. Twenty-nine male (N=15) and female (N = 14) players with\nan UTR ranging from levels 5.29 to 12.99 (intermediate- advanced) (Figure 1) (35)\nparticipated in this study, which was performed in their off-season. Inclusion\ncriteria included Division II and Division III male and female tennis players\nranging from ages 18-25, a validated UTR score within the past six months, at\nleast four years of competitive tennis prior to entering college, and no\ncurrent or previous injuries in the past six months. An injury was defined by\nanything that will prevent the athlete from practices or matches. <\/em>Exclusion\ncriteria for the study included if the\nathletes did not have a validated UTR score or if they have had an injury in\nthe past six months. The UTR rankings were pulled from within a month of when testing\noccurred. The players were familiar with the tennis specific tests and were involved\nin tennis training and competitive matches for at least four years prior to\nentering college with no documented injuries that hindered performance in the\npast six months. The players were informed of the research requirements,\nprocedures, risks, and benefits before signing the informed consent form. They\nall provided a written consent for participation. This study was approved by\nthe Institutional Research Ethics Committee.<\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n

Figure 1<\/strong>: UTR 16-Level Chart (45)<\/p>\n\n\n\n

Experimental Set Up <\/em><\/p>\n\n\n\n

Testing Procedures <\/em><\/p>\n\n\n\n

On the day of testing, after a\nseven-minute warm up which consisted of two minutes of a self-selected jog\naround the court, three minutes of ground strokes hits fed by the principle investigator\n(PI) who is a proficient tennis player to the athletes incorporated forehand\nand backhand shots, and then the athlete practiced the flat serve down the \u2018T\u2019\nfor two minutes, so they were familiarized with the tests (48). Every other\nplayer received a brand-new set of Wilson tennis balls prior to warm-up. <\/p>\n\n\n\n

After warm-up, athletes were allowed\na two-minute break to drink water if needed before the assessments.\nInstructions were explained to participants which included: six flat serves\ndown the \u201cT\u201d in the add and deuce side, six forehands and backhands down the\nline in the target area, Spider test following the diagram (Figure 1), and\nperforming as many foot taps as they could in 30 seconds. All data was recorded\nfrom the fastest three trials on the serve, forehand, and backhand velocity,\nSpider drill test, and two trials for the footwork test to ensure reliability.\nThe highest of the three (serve, forehand, backhand velocity, and agility) or\ntwo (footwork test) trials were recorded. The test followed this order: serve,\nforehand, and backhand velocity, agility, and footwork taps for every athlete\nto ensure validity. All data was recorded on an individual player data sheet. <\/p>\n\n\n\n

Serve Velocity<\/em>. <\/p>\n\n\n\n

Two radar guns (Model PR1000-BC;\nStalker Professional Sports Radar; Plymouth, MN, USA) were used to measure\nserve velocity. The radar was positioned at the center of the baseline, 4 m\nbehind the server, aligned with the approximate height of ball contact pointing\ndown the center of the court (47). The serves for subjects who were\nright-handed first served to the left serve box (from the right) and the ones\nwho were left-handed served to the right serve box (from the left). The player\nwas then instructed to serve six flat serves down the \u2018T\u2019 on the add and deuce\nside. The athletes were instructed to serve into the service box, not hit the\nnet, nor commit a foot-fault, in order for the serve to count. The velocity of\nthe highest three serves that made it into the service box was recorded from\nthe average of the two radar gun measurements (41 m). Athletes were instructed\nto perform six maximal serves down the \u201cT\u201d (center line). A target area (6.40 X\n1.03m) was placed in the serve box. They were allotted no more than 16 total\nserves or eight from each side to minimize fatigue and injury. If the athlete\nonly hit one serve in the box, that score was recorded. Athletes were given a\nminimum rest period of no less than three minutes and no more than five minutes\nbefore the next test to ensure reliability. If the athlete went over the\nfive-minute time frame, their data was excluded. <\/p>\n\n\n\n

Forehand and Backhand Velocity. <\/em><\/p>\n\n\n\n

Two radar guns (Stalker Professional\nSports Radar; Radar Sales, Plymouth, MN, USA) were used to measure forehand and\nbackhand velocity. The radar guns were positioned at the service line, 4 m to\nthe right of forehand and backhand, aligned with the approximate height of ball\ncontact pointing at the of the court. A strength coach manually fed the player\nunderhand balls to the player standing in between the baseline and service\nline. The player was then instructed to hit six forehands and then six\nbackhands down the line with maximum effort. Each effort was performed\nindependently due to a maximum 30-second pause between strokes. The athletes\nwere instructed to hit the ball over the net in the opponent\u2019s part of the\ncourt, in the target area (5.50 X 2.06 m) and must not be a sliced hit for the\nstroke to count (43). The highest velocity of the top three forehand and\nbackhand strokes that made it down the line and in the coned-off region were\nrecorded. The players were allotted no more than 16 total serves or eight from\neach side to minimize fatigue and injury. If the athlete only hit one forehand\nor backhand down the line, that score was recorded. Athletes were given a minimum\nrest period of three minutes and no more than five minutes before the next test\nto ensure reliability. If the athlete went over the five-minute time frame,\ntheir data was excluded. <\/p>\n\n\n\n

Footwork. <\/em><\/p>\n\n\n\n

The footwork assessment was completed\non the athlete\u2019s respective tennis court. The GoPro (Hero5) was set up at the\nheight of 6\u201d to video all footwork taps. The assessment started off with the\nplayer standing in athletic position, greater than parallel and between 115-135\u25e6. The PI\nsupervisor measured their knee flexion using the Coaches Eye App (8) to verify\nthe athlete\u2019s knee flexion was in the appropriate range. While maintaining\nathletic position, the researcher then commanded the athlete to perform as many\nfoot taps as they could in 30 seconds. If the athlete\u2019s feet did not leave the\nground, the taps did not count. The participant was given a minimum rest period\nof one minute and maximum of three minutes before the next attempt to ensure\nreliability. If the athlete went over the three-minute time frame, their data\nwas excluded. The highest amount of footwork taps was recorded. After the\ncompletion of the footwork test, the athlete was given a minimum rest period of\nthree minutes and no more than five minutes to ensure reliability. <\/p>\n\n\n\n

Agility. <\/em><\/p>\n\n\n\n

For the agility test, certified\nstrength and conditioning coaches set up electronic timing gates using the\nBrower Timing System and placed the timing gates at an appropriate height of 1\nm for all participants and 3 m behind the baseline, to avoid any collisions\nwhen returning to the center point after each sprint (Figure 2) (20). Athletes\nstarted with a practice trial at 75% effort to ensure familiarization of the\ntest. After the trial, they were given a minimum rest period of one minute and\na maximum of five minutes before the actual test. All participants were\nrequired to complete a total of three trials to ensure reliability.\nParticipants were instructed to break the beam of the timing gates, officially\nstarting the assessment. Participants started with the sprint to the right\nfirst (number 1) and then working in a counterclockwise direction after. Sprint\nnumbers 1 and 5 represent a distance of 4.11m while numbers 2, 3, and 4 each\nmeasure 5.49 m. Each sprint required athletes to return to the center point on\nthe baseline before starting the next. Once the final sprint was completed\n(returning from sprint 5) athletes were required to turn right 90\u25e6 to complete\nthe three-meter sprint through the timing gates completing the test (Figure 1) (20).\nAthletes were given a minimum rest period of one minute and a maximum of three\nminutes before the next trial to ensure reliability. If the athlete went over the\nthree-minute time frame, their data was excluded. Total time for the Spider\ntest was recorded to the nearest hundredth of a second and the highest of the\nthree trials was recorded. If athletes breached the methodological guidelines\nfor the test (by failing to reach the line for a change of direction step), the\ntrial was voided, and an additional trial was conducted following three minutes\nof rest. Athletes were given a minimum rest period of three minutes and no more\nthan five minutes before the next test to ensure reliability. If the athlete\nwent over the five-minute time frame, their data was excluded. Previous\nresearch has shown spider test to be a valid and\nreliable measurement for change of direction movements in tennis (20). <\/p>\n\n\n\n

\"Figure<\/figure>\n\n\n\n

Figure 2:<\/strong> Schematic of the Spider Drill (20) <\/p>\n\n\n\n

Statistics <\/em><\/p>\n\n\n\n

Coefficient of variation\n(CV) and intra-class correlation coefficients (ICC) were calculated to assess\nreliability for the serve, backhand, and forehand velocity, agility, and\nfootwork (Table 5). Pearson-product moment correlations were run to determine\nthe relationships of the variables (serve, forehand and backhand velocity,\nagility, and foot taps) to a UTR ranking (Table 2). An alpha level of p\u22640.05\nwas used to determine statistically significant correlations. A multiple linear regression was calculated\npredicting a player\u2019s UTR based on serve, forehand, and backhand velocity,\nagility, and foot taps. Multiple regression analysis\nwas used to examine the amount of variance explained by the variables for UTR. The relative contribution of each variables to predict\nthe variance of UTR was used to determine contribution of each dependent\nvariable to the overall multiple regression model (32). Dependent\nvariables that did not produce a statistically significant correlation\ncoefficient (p\u22650.05) were removed from the model. The multiple regression model\nwas performed successive times with remaining variables until all dependent\nvariables produced a statistically significant correlation coefficient\n(p\u22640.05). Variables that did not produce a statistically\nsignificant prediction coefficient (P>0.05) were removed from the prediction\nmodel. Intra-class correlations and coefficient of variations were assessed for\nall variables (serve, forehand and backhand velocity, agility, and foot taps). Cohen\u2019s f2 <\/sup>effect size was calculated to assess\nthe magnitude of the model (8). The following scale was used: small\neffect f=0.02, medium effect f=0.15 and large effect f=0.35 (8).<\/p>\n\n\n\n

RESULTS<\/strong><\/p>\n\n\n\n

Descriptive\nstatistics comparing males and females for UTR and the specific assessments can\nbe found in Table 1. A significant regression equation was found (F (5,23)\n=29.66, p<.05) with an R2<\/sup>of 0.866. Model 4 produced a statistically\nsignificant prediction model (F (2,26) =79.63, p<0.01) with an R2<\/sup>of\n0.860 which included only serve and backhand velocity (Table 3). The\ncorrelations between UTR and a player\u2019s physical performance parameters are\npresented in Table 2. Foot taps showed a moderate correlation (r=.472,\nP<0.05) to UTR. The highest correlations were observed in serve velocity\n(r=.902), forehand velocity (.843), backhand velocity (r=.858) and agility\n(-.817) to UTR. Based on the results of Model 4, only serve velocity (P<0.001)\nand backhand velocity (P=0.007) were statistically significant predictors of\nUTR. <\/p>\n\n\n\nTable 1.<\/strong> Descriptive Statistics Comparing Male and Female Athletes \n\n\n \n \n \n \n
Sex<\/td>\n UTR<\/td>\n Serve Velocity (mph)<\/td>\n Forehand Velocity (mph)<\/td>\n <\/tr>\n
Male (N=15)<\/td>\n 11.35 \u00b1 1.03 (9.55-12.99)<\/td>\n 107.37 \u00b1 9.39 (86.0-122.0)<\/td>\n 88.73 \u00b1 6.34 (79.0-102.0)<\/td>\n <\/tr>\n
Female (N=14)<\/td>\n 7.97 \u00b1 1.60 (5.29-10.01)<\/td>\n 83.18 \u00b1 8.29 (69.5-94.0)<\/td>\n 70.14 \u00b1 9.22 (56.0-89.0)<\/td>\n <\/tr>\n
Note<\/em>. Values are expressed as mean \u00b1 standard deviation, (minimum-maximum value) <\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\nTable 2.<\/strong> Correlation Coefficients of tennis-specific characteristics with player performance (UTR ranking). \n\n\n \n \n \n \n \n \n \n
Variables<\/td>\n UTR<\/td>\n Serve Velocity<\/td>\n Forehand Velocity<\/td>\n Backhand Velocity<\/td>\n Spider Test<\/td>\n Foot Taps<\/td>\n <\/tr>\n
UTR<\/td>\n 1<\/td>\n 0.902<\/td>\n 0.843<\/td>\n 0.858<\/td>\n -0.817<\/td>\n 0.472<\/td>\n <\/tr>\n
Serve Velocity (mph)<\/td>\n 0.902<\/td>\n 1<\/td>\n 0.894<\/td>\n 0.813<\/td>\n -0.827<\/td>\n 0.541<\/td>\n <\/tr>\n
Forehand Velocity (mph)<\/td>\n 0.843<\/td>\n 0.894<\/td>\n 1<\/td>\n 0.805<\/td>\n -0.844<\/td>\n 0.576<\/td>\n <\/tr>\n
Backhand Velocity (mph)<\/td>\n 0.858<\/td>\n 0.813<\/td>\n 0.805<\/td>\n 1<\/td>\n -0.777<\/td>\n 0.458<\/td>\n <\/tr>\n
Agility (s)<\/td>\n -0.817<\/td>\n -0.827<\/td>\n -0.844<\/td>\n -0.777<\/td>\n 1<\/td>\n -0.597<\/td>\n <\/tr>\n
Foot Taps<\/td>\n 0.472<\/td>\n 0.541<\/td>\n 0.576<\/td>\n 0.458<\/td>\n -0.597<\/td>\n 1<\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\nTable 3.<\/strong> Multiple Regression Models \n\n\n \n \n \n \n \n \n
Model<\/td>\n R<\/td>\n R2<\/td>\n Significant F Change<\/td>\n <\/tr>\n
1<\/td>\n 0.93<\/td>\n 0.866<\/td>\n 0<\/td>\n <\/tr>\n
2<\/td>\n 0.929<\/td>\n 0.863<\/td>\n 0.534<\/td>\n <\/tr>\n
3<\/td>\n 0.929<\/td>\n 0.863<\/td>\n 0.945<\/td>\n <\/tr>\n
4<\/td>\n 0.927<\/td>\n 0.86<\/td>\n 0.417<\/td>\n <\/tr>\n
1. Predictors: (Constant), Foot Taps, Backhand Velocity, Spider Test, Serve Velocity, Forehand Velocity
\n 2. Predictors: (Constant), Backhand Velocity, Spider Test, Serve Velocity, Forehand Velocity
\n 3. Predictors: (Constant), Backhand Velocity, Spider Test, Serve Velocity
\n 4. Predictors: (Constant), Backhand Velocity, Serve Velocity<\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

As the results indicate from Model 4,\nserve velocity contributes 54.5% of the explained variance and backhand velocity\ncontributes 45.5% of the explained variance for prediction of UTR. All\nvariables showed acceptable levels of reliability within subjects (Table 5).\nCohen\u2019s f2 <\/sup>effect sizes demonstrated a very large effect for all\nvariables (f=4.0). <\/p>\n\n\n\nTable 4.<\/strong> Relative Contribution to Multiple Regression Models\n\n\n \n \n \n \n \n
Model<\/td>\n Serve Velocity (mph)<\/td>\n Forehand Velocity (mph)<\/td>\n Backhand Velocity (mph)<\/td>\n Spider Test (s)<\/td>\n Foot Taps<\/td>\n <\/tr>\n
1 (R2=0.866)<\/td>\n 29.5<\/td>\n 20.9<\/td>\n 24.7<\/td>\n 19.5<\/td>\n 5.4<\/td>\n <\/tr>\n
2 (R2=0.863)<\/td>\n 30.9<\/td>\n 22.2<\/td>\n 26.1<\/td>\n 20.8<\/td>\n –<\/td>\n <\/tr>\n
3 (R2=0.863)<\/td>\n 39.6<\/td>\n –<\/td>\n 33<\/td>\n 27.3<\/td>\n –<\/td>\n <\/tr>\n
4 (R2=0.860)<\/td>\n 54.5<\/td>\n –<\/td>\n 45.5<\/td>\n –<\/td>\n –<\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\nTable 5.<\/strong> Intraclass Correlations and Coefficient of Variations Between Variables\n\n\n \n \n \n \n \n \n
Variables<\/td>\n ICC<\/td>\n CV<\/td>\n <\/tr>\n
Serve<\/td>\n 0.95-0.99<\/td>\n 2.80-4.40<\/td>\n <\/tr>\n
Forehand<\/td>\n 0.93-0.98<\/td>\n 4.00-6.30<\/td>\n <\/tr>\n
Backhand<\/td>\n 0.97-0.99<\/td>\n 2.80-4.40<\/td>\n <\/tr>\n
Agility<\/td>\n 0.93-0.98<\/td>\n 2.10-3.20<\/td>\n <\/tr>\n
Foot Taps<\/td>\n 0.95-0.99<\/td>\n 2.10-3.30<\/td>\n <\/tr>\n<\/tbody>\n<\/table>\n\n\n\n

DISCUSSION<\/strong><\/p>\n\n\n\n

To date, this is the only study that\nhas been done to examine the effects of tennis specific measurements on\ncollegiate athletes to predict a player\u2019s UTR. The aim of the present study was\nto detect whether tennis specific characteristics (serve, forehand and backhand\nvelocity, agility, and foot taps) are related to player\u2019s performance (UTR). In\ntotal, 29 collegiate tennis players were examined in this study, including 19\nDivision II players and 10 Division III players. Thus, a player\u2019s agility,\nendurance, and stroke capabilities may be influential in performance ranking\nmeasures. A previous study demonstrated that agility was the physical ability\nthat most influenced the competitive level of young tennis players (2, 20, 26,\n29, 41). It was also suggested that skills related to tennis strokes can be\nused to maximize and predict competitive success (5, 11, 14, 22, 25, 26, 27,\n40, 43, 48). Consistent with these findings, the researchers found a\nsignificant correlation between players\u2019 ranking and serve velocity (r=0.902).\nIt is recommended, therefore, that power training to target the serve be\nincluded in the training programs of tennis players in order to improve their\nperformance (26). These findings of this study displayed significant\ncorrelations between certain tennis characteristics and tennis ranking.\nComparisons are difficult because previous studies analyzing tennis-specific\nvariables typically involve small sample sizes and non-collegiate athletes. In\nthis regard, the results of this research are contrary to previous studies of\nadvanced prepubescent and youth tennis players, which suggested that specific\nqualities such as agility (13, 20, 29), speed (14, 29), vertical jumping (11,\n15, 16, 47), and serve (13, 29) correlated most strongly with tennis\nperformance. However, findings indicated that physical performance tests do not\npredict the ability to play tennis at a competitive level (12, 41). <\/p>\n\n\n\n

In this present research assessing\ncollegiate athletes, the results regarding correlations between tennis specific\nmeasurements and performance (Table 2) showed that serve, forehand and backhand\nvelocity, and agility presented the largest correlations with the player\u2019s\nranking in all divisions, followed by tennis-specific endurance (foot taps)\nwith moderate correlation values of a player\u2019s UTR. Our hypothesis tennis\nranking performance would be enhanced by improving a player\u2019s stroke skills was\ncorrect. We were, however, somewhat surprised by the magnitude of that\ndifference between foot taps and ranking.\n <\/p>\n\n\n\n

The results with this study align\nwith previous literature explaining that tennis-specific technical measurements\nand change of direction ability have been found when comparing higher levels of\nplay to lower level players (13, 29). Based on results from the multiple\nregression, serve and backhand velocity appear to contribute the strongest\npredictors for an individual\u2019s UTR. A player\u2019s serve velocity aligns with\nprevious literature stating the serve was the strongest predictor of a player\u2019s\nranking due to relying on the multiple body segments and complex coordination\nof muscular activation to produce power to the ball (14, 25). This could be due\nto male subjects having a higher UTR and previous research has shown that males\nhave higher UTR rankings due to higher strength levels compared to female\ncounterparts (3, 18, 31). In contrast to previous research when looking at\nyouth athletes, the forehand was more strongly related to ranking (26, 41).\nThis could be due to the fact that the forehand is easier to learn since the\nbackhand is generally harder to master than the forehand stroke (26). However,\nat partly strengthening the existing research, which claims that the serve is\nthe most powerful, potentially dominant shot (12, 17, 27). Furthermore, when\ncomparing female and male athletes, we found all descriptive statistics to be\nhigher in males (Table 2). These differences could have an influence in terms\nof playing style because being taller and heavier offers an advantage when\nproducing power in the serve, forehand, or backhand. Hence, the results of the\npresent study emphasize the importance of sport-specific technical tests and\ndemonstrate their value and contribution to athlete\u2019s performance. <\/p>\n\n\n\n

Although there were aspects of this\nstudy that had never been done before, there are limitations to our study. Even\nthough there was a variety of athletes, the different levels of competitive\nplay between athletes could have affected the results of the study. We also\nonly tested tennis-specific movements and agility to ranking performance, so\nwhether similar results would be found for other intermittent tests (i.e.\n30-15) or other types of measures of performance such as lower or upper body\npower, remains to be seen. Furthermore, the PI underhand fed each ball to the\nathletes. This may have caused inconsistency in the spin and could have\naffected the velocities of the strokes. With the help of a ball machine, this\nwould have provided greater accuracy and precision with ball feeding. <\/p>\n\n\n\n

CONCLUSIONS<\/strong><\/p>\n\n\n\n

This study has shown a player\u2019s serve\nand backhand velocity can be used to determine a UTR value. Strong correlations\nwere found between the backhand and serve velocity corresponding to UTR.\nHowever, future research should aim at investigating a larger sample size of\nhigher division ranked players (Division I or professional), specifically separating\nmales and females, intermittent endurance capacity, or lower body power to\nfurther identify specific variables that may influence UTR. These results\nhighlight the importance that tennis specific stroke skills (backhand and serve\nvelocity) can be used as a practical performance test to precisely and\nindividually prescribe training regimens.  <\/p>\n\n\n\n

APPLICATIONS IN SPORT<\/strong><\/p>\n\n\n\n

Since\ntennis has progressed from a sport in which skill was the primary prerequisite\nfor successful performance, into a sport that requires the complex interaction\nof several tennis-specific components, it is vital to identify the most\ninfluential factors on performance and ranking measures. Since the UTR is the\nhighest tennis ranking worldwide, analysis shows it would be beneficial to\npredict a UTR ranking based off a tennis player\u2019s sport specific metrics\n(serve, backhand, forehand velocity, agility, and footwork). The results of\nthis present study underline the importance of tennis-specific characteristics.\nAccording to our findings, a player\u2019s power (serve and backhand velocity) seem\nto be the most important components in collegiate athletes to predict a\nplayer\u2019s UTR. Therefore, we would recommend using these tests in the framework\nof physical testing and training regimens. Additionally, the present results\ncould be useful to compare the development of players and to create individual\nfitness programs. This would enable the identification of weaknesses in\ndifferent parameters and facilitate the design of more efficient and optimize\ntraining programs. To date, no study has investigated the specific tennis\nvariables that influence the UTR and to what extent.<\/p>\n\n\n\n

ACKNOWLEDGMENTS<\/strong><\/p>\n\n\n\n

None<\/p>\n\n\n\n

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    Authors: Jennifer A. Kurtz* (1), Jake Grazer (2), Bradley Alban […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_options":[]},"categories":[994,898],"tags":[1526,1528,1527,204,1525],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p4btio-1K1","jetpack-related-posts":[{"id":318,"url":"https:\/\/thesportjournal.org\/article\/an-examination-of-preservice-routines-of-elite-tennis-players\/","url_meta":{"origin":6697,"position":0},"title":"An Examination of Preservice Routines of Elite Tennis Players","date":"October 7, 2008","format":false,"excerpt":"Submitted by: Noah B. Gentner - Ithaca College, Theodore J. McGraw - University of Tennessee, Stephen Gonzalez and Daniel R. Czech - Georgia Southern University Abstract A preperformance routine may support consistent optimal performance. Preperformance routines\u2019 benefits for closed skills are largely accepted, but effects of time and situational factors\u2026","rel":"","context":"In "Sports Exercise Science"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":8488,"url":"https:\/\/thesportjournal.org\/article\/strength-and-conditioning-practices-among-ncaa-place-kickers\/","url_meta":{"origin":6697,"position":1},"title":"Strength and Conditioning Practices among NCAA Place-Kickers","date":"March 24, 2023","format":false,"excerpt":"Authors: Dr. James A. Reid1, Todd Schaneville2, and Trey Schaneville3 1Assistant Professor of Physical Education, Tuskegee University, Tuskegee, AL, USA2Physical Educator and Coach, Brevard Public Schools, Viera, FL, USA3Graduate Student-Athlete, Appalachian State University, Boone, NC, USA Corresponding Author: James A. Reid, DA, NSCA, CSCS and CPT509 Greentree TerAuburn, Alabama 36832jreid@tuskegee.edu256-690-3581\u2026","rel":"","context":"In "Research"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/thesportjournal.org\/wp-content\/uploads\/2023\/03\/Reid-Figure-1.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":380,"url":"https:\/\/thesportjournal.org\/article\/evidence-that-support-equality\/","url_meta":{"origin":6697,"position":2},"title":"Evidence That Support Equality: Credential Characteristics of Georgia Female High School Coaches","date":"July 9, 2010","format":false,"excerpt":"Willie Burden, Trey Burdette, Drew Zwald, Daniel R. 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