Authors: Jennifer A. Kurtz* (1), Jake Grazer (2), Bradley Alban (3), Mike Martino (4)

Corresponding Author:
Jennifer A. Kurtz, MS
120 Coventry Court
Fayetteville, GA 30215
Jennifer.kurtz06@gmail.com
404-509-3384

Jennifer Kurtz is a doctoral student at The University of Georgia studying exercise physiology. She is also an assistant strength and conditioning coach at Elite Performance Institute.

Jake Grazer is an Assistant Professor of Exercise Science at Georgia College & State University.

Bradley Alban is an Assistant Professor of Exercise Science at Georgia College & State University.

Mike Martino is an Professor of Exercise Science at Georgia College & State University.

Ability for tennis specific variables and agility for determining the Universal Tennis Ranking (UTR): A Review and Recommendations

ABSTRACT

Our purpose was to investigate tennis specific measures to predict a player’s Universal Tennis Ranking (UTR) value and to see what percentage of the variables most influence the ranking. Methods: 15 male and 14 female athletes volunteered to participate in this study. Each volunteer performed no more than 16 total serves or eight from the add and deuce side down the “T”, no more than 16 total forehands and backhands down-the-line, three spider tests, and two trials of footwork taps in 30 seconds. Only the top two hits were analyzed. Results: A multiple linear regression was calculated predicting a player’s UTR based on serve, forehand, backhand, agility, and footwork taps. The regression equation was significant (F (5,23) = 29.66, p<.05) with an R squared value of 0.866. Coefficient of variation (CV) and intra-class correlation coefficients (ICC) were calculated to assess reliability between player serve (r=0.902), forehand (r=0.843) and backhand velocity (r=0.858), agility (r=-0.817), and footwork (r=0.472). More noticeable was the significant predictive value of serve (r=0.902) and backhand velocity (r=0.858) to the player’s UTR. Conclusion: These results underline the important relationship between the player’s UTR and tennis-specific characteristics (serve and backhand velocity) as assessed by the player’s stroke velocity. The ability of training regimens to improve tennis-specific metrics would improve performance qualities and the player’s UTR.

Key words: tennis, UTR, ranking prediction, sport-specific tests, sport performance

INTRODUCTION

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

The role of physical variables in tennis is gradually increasing due to the physical demand of the sport. The relationship between the physical capabilities and competition performance of tennis players creates the possibility of forming optimal conditioning training programs (10). Previous research indicated agility was the only significant fitness variable in prepubescent tennis players (ages 8-12) to predict competitive rankings (2, 20, 26, 41). In preadolescence and adolescent junior tennis players (ages 11-16), correlations were found with speed (14, 16, 23, 33, 41), agility and quickness (14, 23, 33, 41), explosive power of the trunk and upper body (15, 16, 28, 47), explosive strength of the lower limbs (squat jump, counter-movement jump, and drop jump, core strength of the trunk, hand-eye coordination (10, 14, 16, 23), aerobic endurance (10, 14, 28, 33), flexibility (33), and maximal strength of the dominant arm (14, 16, 33) correlated with the player’s competition performance and ranking. Furthermore, the more a tennis player matures, their results in physical characteristics showed better performance levels and stronger correlations than preadolescences and adolescents.

Successful tennis performance cannot be defined by one predominating physical attribute; the specifics of these variables have yet to be determined but correlation studies have been undertaken to determine which physical components have a strong relation with match results and ranking. Since it is primarily a tactical and technical sport that requires open skills, competitive tennis demands a complex interaction of the major physiological and physical variables (29, 47).

Tennis is a sport with uncertainty and an unknown degree of transitivity with numerous variables that can affect the outcome of the match (9). Tennis agility (20, 29, 47), footwork (11), forehand velocity (13, 22, 27, 37, 44), backhand velocity (5, 11, 13, 23, 26, 43), and serve velocity (14, 17, 22, 25, 26, 27, 48) are predominant factors that influence performance and ranking. To possess a high ranking, a player must encompass strong technical skills such as the ability to produce high amounts of force through serves and ground strokes, have efficient footwork, and high levels of agility (13, 29). Furthermore, stroke rating was a vital predictor for tournament performance and national rankings (r=0.94) (26, 39, 41). The athlete has to master many aspects of their game, such as the serve, a mixture of strokes, footwork, ball placement, strength, endurance and strategy in order to exemplify high performance levels (48). Since sport specific technical skills are predominant factors in tennis, it is unknown to what extent these variables influence performance and ranking. There have been no studies to date analyzing the extent of those variables and how they are linked to overall tennis skill and ranking (2). Thus, much of the available research is based on our knowledge of the physical demands of tennis.

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

The UTR is the most newly created system based on a 16-point scale that has been utilized to calculate a player’s ranking based on their results from their most recent 30 matches across all competitive systems in the last 12 months (19, 35, 38). The UTR is the official rating of The Tennis Channel, Intercollegiate Tennis Association, World Team Tennis, Professional Tennis Registry, United States Professional Tennis Association, International Tennis Hall of Fame, and Orange Coach (19, 46, 38). This non-discriminant ranking system was designed to implement a new algorithm to increase the accuracy and reliability of ratings to standardize them to a uniform measurement for all tennis players. It categorizes every competitive player regardless of age, gender, and nationality, considers the opposing opponent and the score of the match and accounts for player’s current relative abilities and competitiveness (36-38). It calculates the player’s ranking value based off percentage of games won by the player, match outcome factor for the players for their most recent matches, and opponent’s player rating number. However, the UTR does not directly consider a tennis player’s physical metrics (agility, footwork, forehand velocity, backhand velocity, and serve velocity).

Since the UTR is the highest tennis ranking worldwide, it would be beneficial to predict a player’s UTR ranking based off of sport specific movements; to date, no study has investigated collegiate tennis players and the extent of tennis specific variables that influence the UTR. If coaches predict a player’s UTR value based off tennis specific variables besides percentage of matches won, they can be more accurate in programming and optimize training efficiency to help improve an athlete’s ranking and performance. We hypothesized that tennis ranking performance would be enhanced by improving a player’s stroke skills (serve, backhand, and forehand) and footwork. The purpose of this study is to investigate tennis specific measures (serve, backhand, forehand velocity, agility, and footwork) to predict a player’s UTR value and to see what percentage of the variables most influence the ranking.

METHODS

Subjects

At the beginning of the study, 31 male and female tennis players agreed to participate with a mix of right and left-handed hitters. Twenty-nine male (N=15) and female (N = 14) players with an UTR ranging from levels 5.29 to 12.99 (intermediate- advanced) (Figure 1) (35) participated in this study, which was performed in their off-season. Inclusion criteria included Division II and Division III male and female tennis players ranging from ages 18-25, a validated UTR score within the past six months, at least four years of competitive tennis prior to entering college, and no current or previous injuries in the past six months. An injury was defined by anything that will prevent the athlete from practices or matches. Exclusion criteria for the study included if the athletes did not have a validated UTR score or if they have had an injury in the past six months. The UTR rankings were pulled from within a month of when testing occurred. The players were familiar with the tennis specific tests and were involved in tennis training and competitive matches for at least four years prior to entering college with no documented injuries that hindered performance in the past six months. The players were informed of the research requirements, procedures, risks, and benefits before signing the informed consent form. They all provided a written consent for participation. This study was approved by the Institutional Research Ethics Committee.

Figure 1

Figure 1: UTR 16-Level Chart (45)

Experimental Set Up

Testing Procedures

On the day of testing, after a seven-minute warm up which consisted of two minutes of a self-selected jog around the court, three minutes of ground strokes hits fed by the principle investigator (PI) who is a proficient tennis player to the athletes incorporated forehand and backhand shots, and then the athlete practiced the flat serve down the ‘T’ for two minutes, so they were familiarized with the tests (48). Every other player received a brand-new set of Wilson tennis balls prior to warm-up.

After warm-up, athletes were allowed a two-minute break to drink water if needed before the assessments. Instructions were explained to participants which included: six flat serves down the “T” in the add and deuce side, six forehands and backhands down the line in the target area, Spider test following the diagram (Figure 1), and performing as many foot taps as they could in 30 seconds. All data was recorded from the fastest three trials on the serve, forehand, and backhand velocity, Spider drill test, and two trials for the footwork test to ensure reliability. The highest of the three (serve, forehand, backhand velocity, and agility) or two (footwork test) trials were recorded. The test followed this order: serve, forehand, and backhand velocity, agility, and footwork taps for every athlete to ensure validity. All data was recorded on an individual player data sheet.

Serve Velocity.

Two radar guns (Model PR1000-BC; Stalker Professional Sports Radar; Plymouth, MN, USA) were used to measure serve velocity. The radar was positioned at the center of the baseline, 4 m behind the server, aligned with the approximate height of ball contact pointing down the center of the court (47). The serves for subjects who were right-handed first served to the left serve box (from the right) and the ones who were left-handed served to the right serve box (from the left). The player was then instructed to serve six flat serves down the ‘T’ on the add and deuce side. The athletes were instructed to serve into the service box, not hit the net, nor commit a foot-fault, in order for the serve to count. The velocity of the highest three serves that made it into the service box was recorded from the average of the two radar gun measurements (41 m). Athletes were instructed to perform six maximal serves down the “T” (center line). A target area (6.40 X 1.03m) was placed in the serve box. They were allotted no more than 16 total serves or eight from each side to minimize fatigue and injury. If the athlete only hit one serve in the box, that score was recorded. Athletes were given a minimum rest period of no less than three minutes and no more than five minutes before the next test to ensure reliability. If the athlete went over the five-minute time frame, their data was excluded.

Forehand and Backhand Velocity.

Two radar guns (Stalker Professional Sports Radar; Radar Sales, Plymouth, MN, USA) were used to measure forehand and backhand velocity. The radar guns were positioned at the service line, 4 m to the right of forehand and backhand, aligned with the approximate height of ball contact pointing at the of the court. A strength coach manually fed the player underhand balls to the player standing in between the baseline and service line. The player was then instructed to hit six forehands and then six backhands down the line with maximum effort. Each effort was performed independently due to a maximum 30-second pause between strokes. The athletes were instructed to hit the ball over the net in the opponent’s part of the court, in the target area (5.50 X 2.06 m) and must not be a sliced hit for the stroke to count (43). The highest velocity of the top three forehand and backhand strokes that made it down the line and in the coned-off region were recorded. The players were allotted no more than 16 total serves or eight from each side to minimize fatigue and injury. If the athlete only hit one forehand or backhand down the line, that score was recorded. Athletes were given a minimum rest period of three minutes and no more than five minutes before the next test to ensure reliability. If the athlete went over the five-minute time frame, their data was excluded.

Footwork.

The footwork assessment was completed on the athlete’s respective tennis court. The GoPro (Hero5) was set up at the height of 6” to video all footwork taps. The assessment started off with the player standing in athletic position, greater than parallel and between 115-135◦. The PI supervisor measured their knee flexion using the Coaches Eye App (8) to verify the athlete’s knee flexion was in the appropriate range. While maintaining athletic position, the researcher then commanded the athlete to perform as many foot taps as they could in 30 seconds. If the athlete’s feet did not leave the ground, the taps did not count. The participant was given a minimum rest period of one minute and maximum of three minutes before the next attempt to ensure reliability. If the athlete went over the three-minute time frame, their data was excluded. The highest amount of footwork taps was recorded. After the completion of the footwork test, the athlete was given a minimum rest period of three minutes and no more than five minutes to ensure reliability.

Agility.

For the agility test, certified strength and conditioning coaches set up electronic timing gates using the Brower Timing System and placed the timing gates at an appropriate height of 1 m for all participants and 3 m behind the baseline, to avoid any collisions when returning to the center point after each sprint (Figure 2) (20). Athletes started with a practice trial at 75% effort to ensure familiarization of the test. After the trial, they were given a minimum rest period of one minute and a maximum of five minutes before the actual test. All participants were required to complete a total of three trials to ensure reliability. Participants were instructed to break the beam of the timing gates, officially starting the assessment. Participants started with the sprint to the right first (number 1) and then working in a counterclockwise direction after. Sprint numbers 1 and 5 represent a distance of 4.11m while numbers 2, 3, and 4 each measure 5.49 m. Each sprint required athletes to return to the center point on the baseline before starting the next. Once the final sprint was completed (returning from sprint 5) athletes were required to turn right 90◦ to complete the three-meter sprint through the timing gates completing the test (Figure 1) (20). Athletes were given a minimum rest period of one minute and a maximum of three minutes before the next trial to ensure reliability. If the athlete went over the three-minute time frame, their data was excluded. Total time for the Spider test was recorded to the nearest hundredth of a second and the highest of the three trials was recorded. If athletes breached the methodological guidelines for the test (by failing to reach the line for a change of direction step), the trial was voided, and an additional trial was conducted following three minutes of rest. Athletes were given a minimum rest period of three minutes and no more than five minutes before the next test to ensure reliability. If the athlete went over the five-minute time frame, their data was excluded. Previous research has shown spider test to be a valid and reliable measurement for change of direction movements in tennis (20). 

Figure 2

Figure 2: Schematic of the Spider Drill (20)

Statistics

Coefficient of variation (CV) and intra-class correlation coefficients (ICC) were calculated to assess reliability for the serve, backhand, and forehand velocity, agility, and footwork (Table 5). Pearson-product moment correlations were run to determine the relationships of the variables (serve, forehand and backhand velocity, agility, and foot taps) to a UTR ranking (Table 2). An alpha level of p≤0.05 was used to determine statistically significant correlations. A multiple linear regression was calculated predicting a player’s UTR based on serve, forehand, and backhand velocity, agility, and foot taps. Multiple regression analysis was used to examine the amount of variance explained by the variables for UTR. The relative contribution of each variables to predict the variance of UTR was used to determine contribution of each dependent variable to the overall multiple regression model (32). Dependent variables that did not produce a statistically significant correlation coefficient (p≥0.05) were removed from the model. The multiple regression model was performed successive times with remaining variables until all dependent variables produced a statistically significant correlation coefficient (p≤0.05). Variables that did not produce a statistically significant prediction coefficient (P>0.05) were removed from the prediction model. Intra-class correlations and coefficient of variations were assessed for all variables (serve, forehand and backhand velocity, agility, and foot taps). Cohen’s f2 effect size was calculated to assess the magnitude of the model (8). The following scale was used: small effect f=0.02, medium effect f=0.15 and large effect f=0.35 (8).

RESULTS

Descriptive statistics comparing males and females for UTR and the specific assessments can be found in Table 1. A significant regression equation was found (F (5,23) =29.66, p<.05) with an R2of 0.866. Model 4 produced a statistically significant prediction model (F (2,26) =79.63, p<0.01) with an R2of 0.860 which included only serve and backhand velocity (Table 3). The correlations between UTR and a player’s physical performance parameters are presented in Table 2. Foot taps showed a moderate correlation (r=.472, P<0.05) to UTR. The highest correlations were observed in serve velocity (r=.902), forehand velocity (.843), backhand velocity (r=.858) and agility (-.817) to UTR. Based on the results of Model 4, only serve velocity (P<0.001) and backhand velocity (P=0.007) were statistically significant predictors of UTR.

Table 1. Descriptive Statistics Comparing Male and Female Athletes
Sex UTR Serve Velocity (mph) Forehand Velocity (mph)
Male (N=15) 11.35 ± 1.03 (9.55-12.99) 107.37 ± 9.39 (86.0-122.0) 88.73 ± 6.34 (79.0-102.0)
Female (N=14) 7.97 ± 1.60 (5.29-10.01) 83.18 ± 8.29 (69.5-94.0) 70.14 ± 9.22 (56.0-89.0)
Note. Values are expressed as mean ± standard deviation, (minimum-maximum value)
Table 2. Correlation Coefficients of tennis-specific characteristics with player performance (UTR ranking).
Variables UTR Serve Velocity Forehand Velocity Backhand Velocity Spider Test Foot Taps
UTR 1 0.902 0.843 0.858 -0.817 0.472
Serve Velocity (mph) 0.902 1 0.894 0.813 -0.827 0.541
Forehand Velocity (mph) 0.843 0.894 1 0.805 -0.844 0.576
Backhand Velocity (mph) 0.858 0.813 0.805 1 -0.777 0.458
Agility (s) -0.817 -0.827 -0.844 -0.777 1 -0.597
Foot Taps 0.472 0.541 0.576 0.458 -0.597 1
Table 3. Multiple Regression Models
Model R R2 Significant F Change
1 0.93 0.866 0
2 0.929 0.863 0.534
3 0.929 0.863 0.945
4 0.927 0.86 0.417
1. Predictors: (Constant), Foot Taps, Backhand Velocity, Spider Test, Serve Velocity, Forehand Velocity
2. Predictors: (Constant), Backhand Velocity, Spider Test, Serve Velocity, Forehand Velocity
3. Predictors: (Constant), Backhand Velocity, Spider Test, Serve Velocity
4. Predictors: (Constant), Backhand Velocity, Serve Velocity

As the results indicate from Model 4, serve velocity contributes 54.5% of the explained variance and backhand velocity contributes 45.5% of the explained variance for prediction of UTR. All variables showed acceptable levels of reliability within subjects (Table 5). Cohen’s f2 effect sizes demonstrated a very large effect for all variables (f=4.0).

Table 4. Relative Contribution to Multiple Regression Models
Model Serve Velocity (mph) Forehand Velocity (mph) Backhand Velocity (mph) Spider Test (s) Foot Taps
1 (R2=0.866) 29.5 20.9 24.7 19.5 5.4
2 (R2=0.863) 30.9 22.2 26.1 20.8 –
3 (R2=0.863) 39.6 – 33 27.3 –
4 (R2=0.860) 54.5 – 45.5 – –
Table 5. Intraclass Correlations and Coefficient of Variations Between Variables
Variables ICC CV
Serve 0.95-0.99 2.80-4.40
Forehand 0.93-0.98 4.00-6.30
Backhand 0.97-0.99 2.80-4.40
Agility 0.93-0.98 2.10-3.20
Foot Taps 0.95-0.99 2.10-3.30

DISCUSSION

To date, this is the only study that has been done to examine the effects of tennis specific measurements on collegiate athletes to predict a player’s UTR. The aim of the present study was to detect whether tennis specific characteristics (serve, forehand and backhand velocity, agility, and foot taps) are related to player’s performance (UTR). In total, 29 collegiate tennis players were examined in this study, including 19 Division II players and 10 Division III players. Thus, a player’s agility, endurance, and stroke capabilities may be influential in performance ranking measures. A previous study demonstrated that agility was the physical ability that most influenced the competitive level of young tennis players (2, 20, 26, 29, 41). It was also suggested that skills related to tennis strokes can be used to maximize and predict competitive success (5, 11, 14, 22, 25, 26, 27, 40, 43, 48). Consistent with these findings, the researchers found a significant correlation between players’ ranking and serve velocity (r=0.902). It is recommended, therefore, that power training to target the serve be included in the training programs of tennis players in order to improve their performance (26). These findings of this study displayed significant correlations between certain tennis characteristics and tennis ranking. Comparisons are difficult because previous studies analyzing tennis-specific variables typically involve small sample sizes and non-collegiate athletes. In this regard, the results of this research are contrary to previous studies of advanced prepubescent and youth tennis players, which suggested that specific qualities such as agility (13, 20, 29), speed (14, 29), vertical jumping (11, 15, 16, 47), and serve (13, 29) correlated most strongly with tennis performance. However, findings indicated that physical performance tests do not predict the ability to play tennis at a competitive level (12, 41).

In this present research assessing collegiate athletes, the results regarding correlations between tennis specific measurements and performance (Table 2) showed that serve, forehand and backhand velocity, and agility presented the largest correlations with the player’s ranking in all divisions, followed by tennis-specific endurance (foot taps) with moderate correlation values of a player’s UTR. Our hypothesis tennis ranking performance would be enhanced by improving a player’s stroke skills was correct. We were, however, somewhat surprised by the magnitude of that difference between foot taps and ranking.  

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

Although there were aspects of this study that had never been done before, there are limitations to our study. Even though there was a variety of athletes, the different levels of competitive play between athletes could have affected the results of the study. We also only tested tennis-specific movements and agility to ranking performance, so whether similar results would be found for other intermittent tests (i.e. 30-15) or other types of measures of performance such as lower or upper body power, remains to be seen. Furthermore, the PI underhand fed each ball to the athletes. This may have caused inconsistency in the spin and could have affected the velocities of the strokes. With the help of a ball machine, this would have provided greater accuracy and precision with ball feeding.

CONCLUSIONS

This study has shown a player’s serve and backhand velocity can be used to determine a UTR value. Strong correlations were found between the backhand and serve velocity corresponding to UTR. However, future research should aim at investigating a larger sample size of higher division ranked players (Division I or professional), specifically separating males and females, intermittent endurance capacity, or lower body power to further identify specific variables that may influence UTR. These results highlight the importance that tennis specific stroke skills (backhand and serve velocity) can be used as a practical performance test to precisely and individually prescribe training regimens.  

APPLICATIONS IN SPORT

Since tennis has progressed from a sport in which skill was the primary prerequisite for successful performance, into a sport that requires the complex interaction of several tennis-specific components, it is vital to identify the most influential factors on performance and ranking measures. Since the UTR is the highest tennis ranking worldwide, analysis shows it would be beneficial to predict a UTR ranking based off a tennis player’s sport specific metrics (serve, backhand, forehand velocity, agility, and footwork). The results of this present study underline the importance of tennis-specific characteristics. According to our findings, a player’s power (serve and backhand velocity) seem to be the most important components in collegiate athletes to predict a player’s UTR. Therefore, we would recommend using these tests in the framework of physical testing and training regimens. Additionally, the present results could be useful to compare the development of players and to create individual fitness programs. This would enable the identification of weaknesses in different parameters and facilitate the design of more efficient and optimize training programs. To date, no study has investigated the specific tennis variables that influence the UTR and to what extent.

ACKNOWLEDGMENTS

None

REFERENCES

  1. Aronson, A. (2017). TenisRank: A new ranking of tennis players based on PageRank. arXiv preprint arXiv:1711.11122.
  2. Birrer, R., Levine, R., Gallippi, L., & Tischler, H. (1986). The correlation of performance variables in preadolescent tennis players. The Journal of Sports Medicine and Physical Fitness, 26(2), 137-139.
  3. Bishop, P., Cureton, K., & Collins, M. (1987). Sex difference in muscular strength in equally-trained men and women. Ergonomics, 30(4), 675-687.
  4. Bragg, R., & Andriacchi, T. (2001). The lateral reaction step in tennis footwork. Paper presented at the ISBS-Conference Proceedings Archive.
  5. Brechbuhl, C., Girard, O., Millet, G. P., & Schmitt, L. (2018). Differences within elite female tennis players during an incremental field test. Medicine and science in sports and exercise, 50(12), 2465-2473.
  6. Brown, S. (2017). A PageRank Model for Player Performance Assessment in Basketball, Soccer and Hockey. arXiv preprint arXiv:1704.00583.
  7. Cohen, J. (1988). Statistical power analysis for the behaviors science. (2nd). New Jersey: Laurence Erlbaum Associates Inc. Hillsdale.
  8. Corporation, T. S. (2013). Coach’s Eye. In. Retrieved from https://www.coachseye.com.
  9. Dingle, N., Knottenbelt, W., & Spanias, D. (2012). On the (page) ranking of professional tennis players. In Computer Performance Engineering (pp. 237-247): Springer.
  10. Dobos, K., & Nagykáldi, C. (2016). Relationship between physical characteristics and competitive performance of under-12 and 14-year-old elite boy and girl tennis players. Trends in Sport Sciences, 23(2).
  11. Đurović, N., Dizdar, D., & Zagorac, N. (2015). Importance of hierarchical structure determining tennis performance for modern defensive baseliner. Collegium antropologicum, 39(Supplement 1), 103-108.
  12. Elliott, B., Fleisig, G., Nicholls, R., & Escamilia, R. (2003). Technique effects on upper limb loading in the tennis serve. Journal of Science and Medicine in Sport, 6(1), 76-87.\
  13. Fernandez-Fernandez, J., Sanz-Rivas, D., & Mendez-Villanueva, A. (2009). A review of the activity profile and physiological demands of tennis match play. Strength & Conditioning Journal, 31(4), 15-26.
  14. Fernandez-Fernandez, J., Ulbricht, A., & Ferrauti, A. (2014). Fitness testing of tennis players: How valuable is it? British Journal of Sports Med, 48(Suppl 1), i22-i31.
  15. Fett, J., Ulbricht, A., Wiewelhove, T., & Ferrauti, A. (2017). Athletic performance, training characteristics, and orthopedic indications in junior tennis Davis Cup players. International Journal of Sports Science & Coaching, 12(1), 119-129.
  16. Girard, O., & Millet, G. P. (2009). Physical determinants of tennis performance in competitive teenage players. The Journal of Strength & Conditioning Research, 23(6), 1867-1872.
  17. Hayes, M. J., Spits, D. R., Watts, D. G., & Kelly, V. G. (2018). The Relationship Between Tennis Serve Velocity and Select Performance Measures. Journal of strength and conditioning research.
  18. Holloway, J. B. (1998). A summary chart: Age related changes in women and men and their possible improvement with training. The Journal of Strength & Conditioning Research, 12(2), 126-128.
  19. Howell, D. E., & Cancado, A. L. (2013). Universal system, method and computer program product for determining a tennis player rating and ranking. Washington, DC: U.S. Patent and Trademark Office.
  20. Huggins, J., Jarvis, P., Brazier, J., Kyriacou, Y., & Bishop, C. (2017). Within-and between-session reliability of the spider drill test to assess change of direction speed in youth tennis athletes. International Journal of Sports and Exercise Medicine, 3.
  21. Jay, D., & Mark, R. (2011). Developing Agility and Quickness. Champaign, IL: Human Kinetics.
  22. Johnson, C., & McHugh, M. (2006). Performance demands of professional male tennis players. British Journal of Sports Medicine, 40(8), 696-699.
  23. Karnia, M., Garsztka, T., Rynkiewicz, M., Rynkiewicz, T., Zurek, P., Łuszczyk, M., & Ziemann, E. (2010). Physical performance, body composition and body balance in relation to national ranking positions in young Polish tennis players. Baltic Journal of Health and Physical Activity, 2(2), 113-123.
  24. Knottenbelt, W. J., Spanias, D., & Madurska, A. M. (2012). A common-opponent stochastic model for predicting the outcome of professional tennis matches. Computers & Mathematics with Applications, 64(12), 3820-3827.
  25. Kolman, N. S., Kramer, T., Elferink-Gemser, M. T., Huijgen, B. C., & Visscher, C. (2019). Technical and tactical skills related to performance levels in tennis: A systematic review. Journal of Sports Sciences, 37(1), 108-121.
  26. Kovacs, M. (2006). Applied physiology of tennis performance. British Journal of Sports Medicine, 40(5), 381-386.
  27. Kovacs, M., & Ellenbecker, T. (2011). An 8-stage model for evaluating the tennis serve: Implications for performance enhancement and injury prevention. Sports Health, 3(6), 504-513.
  28. Kramer, T., Huijgen, B. C., Elferink-Gemser, M. T., & Visscher, C. (2017). Prediction of tennis performance in junior elite tennis players. Journal of Sports Science & Medicine, 16(1), 14.
  29. Kuroda, Y., Suzuki, N., Dei, A., Umebayashi, K., Takizawa, K., & Mizuno, M. (2015). A comparison of the physical fitness, athletic performance and competitive achievements of junior and senior tennis players. Malaysian Journal of Movement, Health & Exercise, 4(1).
  30. Lambert, C. (2018). Universal Tennis Rating is a new System for grading tennis players. Retrieved from https://www.si.com/tennis/2015/10/06/universal-tennis-rating-boston-open.
  31. Laubach, L. L. (1976). Comparative muscular strength of men and women: A review of the literature. Aviation, Space, and Environmental Medicine, 47(5), 534-542.
  32. Lindeman, R. (1980). Introduction to bivariate and multivariate analysis. (No. 04; QA278, L553.).
  33. Meckel, Y., Hophy, A., Dunsky, A., & Eliakim, A. (2015). Relationships between physical characteristics and ranking of young tennis players. Central European Journal of Sport Sciences and Medicine, 2(10), 5-12.
  34. Motegi, S., & Masuda, N. (2012). A network-based dynamical ranking system for competitive sports. Scientific Reports, 2, 904.
  35. Oracle, U. (2017). The UTR 16-Level Chart. Retrieved from https://blog.universaltennis.com/category/guiding-principles/.
  36. Oracle, U. (2017). Top Tennis Nations, Based on Universal Tennis Ratings. Retrieved from https://blog.universaltennis.com/2017/06/14/top-tennis-nations-based-on-universal-tennis-ratings/.
  37. Oracle, U. (2018). UTR’s CEO Discuss the New Algorithm. Retrieved from https://blog.universaltennis.com/2018/01/04/utr-ceo-mark-leschly-live-on-tennis-channel/.
  38. Oracle, U. (2018). How UTR Works. Retrieved from https://blog.universaltennis.com/2018/07/03/how-utr-works/.
  39. Radicchi, F. (2011). Who is the best player ever? A complex network analysis of the history of professional tennis. PloS one, 6(2), e17249.
  40. Reid, M., Elliott, B., & Crespo, M. (2013). Mechanics and learning practices associated with the tennis forehand: A review. Journal of Sports Science & Medicine, 12(2), 225.
  41. Roetert, E. P., Garrett, G. E., Brown, S. W., & Camaione, D. N. (1992). Performance profiles of nationally ranked junior tennis players. The Journal of Strength & Conditioning Research, 6(4), 225-231.
  42. Slezak, A. (2016). The Reliability of the UTR System. Retrieved from https://www.smarthlete.com/article/76/the-reliability-of-the-utr-system.
  43. Smekal, G., Pokan, R., Von Duvillard, S., Baron, R., Tschan, H., & Bachl, N. (2000). Comparison of laboratory and on-court endurance testing in tennis. International journal of sports medicine, 21(04), 242-249.
  44. Spanias, A. D., & Knottenbelt, B. W. (2013). Tennis player ranking using quantitative models. Manuscript. http://www. doc. ic. ac. uk/~ wjk/publications/spanias-knottenbelt-mis-2013. pdf.
  45. Spanias, D. (2014). Professional Tennis: Quantitative Models and Ranking Algorithms. (Doctoral dissertation, Imperial College London).
  46. Stephen, B., & Tim, P. (2014). Developing an improved tennis ranking system. Journal of Quantitative Analysis in Sports, 10(2).
  47. Ulbricht, A., Fernandez-Fernandez, J., Mendez-Villanueva, A., & Ferrauti, A. (2016). Impact of fitness characteristics on tennis performance in elite junior tennis players. The Journal of Strength & Conditioning Research, 30(4), 989-998.
  48. Zingaro, R. E. (2008). A correlation between core strength and serve velocity in collegiate tennis players. California University of Pennsylvania.
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