From Data to Dominance: An Analysis for the Modern Tennis Coach
Author
Affiliation
Parsa Keyvani
United States Tennis Association
1. Introduction
In my quest to secure an internship at the United States Tennis Association (USTA), I was presented with a fascinating challenge: to analyze a dataset encapsulating one set of a tennis match, rich with details on games, points, and individual shots. This dataset, a snapshot of the intense duel between two players during their second set, offers a deep dive into the dynamics of professional tennis through variables ranging from the basic, such as set number, game, and point, to the more intricate, like shot coordinates and the outcome of each play. My objective was to derive a nuanced metric for sideline and baseline accuracy for both shots and serves, aiming to quantify player performance with precision.
However, my ambition was not limited to developing these metrics. I envisioned going further by extracting additional insights that could potentially aid coaching methodologies. I aimed to uncover patterns and trends that could offer coaches tailored advice to enhance player performance. The ultimate goal of this project was to distill complex data into actionable strategies and facilitating a more personalized and effective coaching approach. Through the lens of this dataset, I sought to not only demonstrate my analytical skills but also contribute meaningful insights to the world of tennis, emphasizing the potential of data-driven decision-making in elevating athletic performance.
2. Methodology
My methdology to measure the accuracy of the players shots is categorized into two different categories. One for shots and another for serves hit by players.
2.1 Rally Shots Accuracy Metrics
To assess the efficacy of rally shots during a set, I have devised four metrics that capture the precision of shots in challenging areas of the court. These metrics are advantageous as they represent shots that are difficult for the opponent to return, thereby increasing the likelihood of winning points. The four metrics are:
Sideline Accuracy (RSA): This metric assesses the precision of shots near the sidelines, which can stretch the opponent and potentially lead to errors or weaker returns.
Baseline Accuracy (RBA): This metric evaluates the accuracy of shots that land near the baseline, a strategic area that can limit the opponent’s options and force deep returns.
Combined Baseline and Sideline Accuracy (CBSA): This metric combines the accuracy of shots near both the baseline and the sideline, reflecting shots that are exceptionally challenging for opponents to counter.
Combined Service Line and Sideline Accuracy (CSLSA): This metric reflects the precision of shots that are both close to the service line and the sideline, maximizing the difficulty for the opponent to execute a successful return.
Each metric is derived as a binary outcome, where 1 indicates an accurate shot according to the criterion, and 0 indicates otherwise. The proportion of accurate shots is then computed by dividing the number of shots meeting the accuracy criterion by the total number of shots taken.
2.2 Serve Shots Accuracy Metrics
In assessing the serve shot accuracy of players, I have developed five distinct metrics that target specific areas on the court. These areas are strategically significant as shots placed here are typically harder for an opponent to return. Successfully serving to these areas often leads to winning points. The five metrics are:
Sideline Accuracy (SA): This metric measures the precision of serves placed within 1 meter of the sidelines. A serve close to the sideline can force the receiver to stretch and potentially make a weaker return.
Service Line Accuracy (SLA): This metric evaluates serves that land within 1 meter of the service line. These serves are beneficial as they reduce the reaction time of the opponent.
Center Service Line Accuracy (CSLA): Serves that target the center line can confuse opponents and force a backhand return, which is often a weaker shot.
Combined Sideline and Service Line Accuracy (SSLA Combined): A serve that is close to both the sideline and the service line is particularly difficult to return, making this combined metric a robust indicator of serve effectiveness.
Combined Center Service Line and Service Line Accuracy (CSLSLA Combined): This metric assesses serves that are accurate in both the center and the service lines, maximizing the difficulty for the opponent.
Each metric is calculated as a binary outcome, where 1 indicates that the serve meets the specific accuracy criteria, and 0 otherwise. The total accuracy for each player is then determined by the proportion of their serves that meet these criteria relative to all serves made.
3. Rally Shots Accuracy Analysis
In the analysis of rally shots accuracy, four pivotal metrics that were presented in the methodology section were implemented to assess the proficiency of each player in executing shots within critical court zones. These metrics are instrumental in evaluating a player’s capability to produce shots that are challenging for the opponent to return, thus enhancing their likelihood of securing points during play.
Code
######## Developing the accuracy metrics for shots ########## Baseline Accuracytennis_data <- tennis_data %>%mutate(Is_BaselineAccurate =case_when( (abs(Xposition) >=10.887& Result =='IN') ~1,TRUE~0 ))# Sideline Accuracytennis_data <- tennis_data %>%mutate(Is_SidelineAccurate =case_when( (abs(Yposition) >=3.115& Result =='IN') ~1,TRUE~0 ))# Combined Baseline and Sideline Accuracytennis_data <- tennis_data %>%mutate(Combined_Baseline_Sideline_Accuracy =case_when( (abs(Xposition) >=10.887& Result =='IN') & (abs(Yposition) >=3.115& Result =='IN') ~1,TRUE~0 ))# Combined Service Line and Sideline Accuracytennis_data <- tennis_data %>%mutate(Combined_ServiceLine_Sideline_Accuracy =case_when( (abs(Yposition) >=3.115& Result =='IN') & (abs(Xposition) >=5.401&abs(Xposition) <=6.401& Result =='IN') ~1,TRUE~0 ))
In the table above, the Baseline Accuracy (RBA) metric illustrates a pronounced disparity between the two players Player 33 demonstrates a baseline accuracy rate of 8.1%, markedly higher than player 41’s rate of 4.2%. This significant margin underscores player 33’s strategic plan in exploiting the depth of the court, thereby exerting additional pressure on the opponent.
Shifting focus to Sideline Accuracy (RSA), the analysis depicts a closely matched performance, with player 33 marginally outperforming player 41, registering accuracy rates of 16.129% and 16.102% respectively. This negligible difference indicates that both players possess a nearly equivalent skill level in directing play laterally, compelling their adversary to traverse a wider area.
In evaluating Combined Baseline and Sideline Accuracy (CBSA), player 41 slightly outshines player 33, achieving an accuracy rate of 1.695% compared to player 33’s 1.613%. Although these percentages are modest, they represent the players’ aptitude in targeting the critical intersection zones of the court’s primary axes, a tactic that can be highly advantageous in match play.
Finally, the Combined Service Line and Sideline Accuracy (CSLSA) metric showcases player 33’s superior performance, with an accuracy rate of 1.6%, whereas player 41 did not manage to attain any accuracy within this stringent metric. This suggests that player 33 is more adept at serving to strategically important court junctions, adding complexity to the shots and challenging the opponent’s return capabilities.
Upon aggregating the insights from all four accuracy metrics, player 33 emerges with a slight edge in overall performance. Their higher accuracy in baseline shots and the ability to successfully serve to more complex court zones provide them with a strategic advantage. Player 41, while exhibiting consistent lateral accuracy, falls short in the combined metrics, which measure the precision in more strategically demanding court areas.
4. Serve Shots Accuracy Analysis
The serve is a critical shot in tennis as it sets the tone for each point. By examining the serve shot accuracy of the two players, we can determine which player has a better command of serve placement within strategic court zones. The following analysis, supported by the below plots, synthesizes the serve shot performance based on the five key accuracy metrics discussed in the methodology.
Code
######## Developing the accuracy metrics for serves ########## Service line Accuracytennis_data <- tennis_data %>%mutate(Is_ServiceLineAccurate =case_when( (abs(Xposition) >=5.401&abs(Xposition) <=6.401& Result =='IN') ~1,TRUE~0 ))# Sideline Accuracy# The shot metric for this can be used, just need to filter isServe == 1 for it.# Center Service Line Accuracytennis_data <- tennis_data %>%mutate(Is_CenterServiceLineAccurate =case_when( (abs(Yposition) >=0&abs(Yposition) <=1& Result =='IN') ~1,TRUE~0 ))# Combined Sideline and Serviceline Accuracytennis_data <- tennis_data %>%mutate(Combined_Serviceline_Sideline_Accuracy =case_when( (abs(Xposition) >=5.401&abs(Xposition) <=6.401& Result =='IN') & (abs(Yposition) >=3.115& Result =='IN') ~1,TRUE~0 ))# Combined Center Service Line and Serviceline Accuracytennis_data <- tennis_data %>%mutate(Combined_ServiceLine_CenterServiceLine_Accuracy =case_when( Is_ServiceLineAccurate ==1& Is_CenterServiceLineAccurate ==1~1,TRUE~0 ))
Sideline Accuracy (SA) measures how well players can target their serves close to the sidelines. In the plot above, player 33 again shows superiority with a 31% accuracy rate, outperforming player 41’s 27.9%. Such precision from player 33 forces the opponent to stretch more, potentially leading to errors or less aggressive returns.
Service Line Accuracy (SLA) reflects the players’ ability to serve near the service line, increasing the difficulty of the return. Player 33 displays a commanding lead with a 24% accuracy, compared to player 41’s 7%. This suggests player 33’s serves are consistently deeper, reducing the opponent’s reaction time.
Center Service Line Accuracy (CSLA) evaluates serves directed closer to the center service line. Player 33’s accuracy stands at 37.93%, only marginally higher than player 41’s 37.21%. Both players seem to have nearly equal proficiency in targeting the center, a tactic that can limit the opponent’s angle for a return shot.
In the metric of Combined Service Line and Sideline Accuracy (SSLA Combined), player 33 holds an accuracy rate of 6.9%, while player 41 falls behind at 4.7%. This combined measure indicates the ability to serve at the intersection of the two lines, a particularly challenging spot for opponents to counter.
The final metric, Combined Center Service Line and Service Line Accuracy (CSLSLA Combined), shows player 33 with a 20.7% accuracy compared to player 41’s 16.3%. This metric highlights a serve that combines the complexity of both the center service line and the service line, a blend that enhances serve effectiveness.
Overall, player 33 emerges as the more accurate server, demonstrating a strategic advantage in serve placement.
5. Pressure Situations Analysis
A critical moment in any tennis match is the high-pressure, long-duration point that can sway momentum and impact the mental resilience of players. Set 2, Game 3, Point 4 of the match data provided shows such a moment, with a prolonged rally totaling 18 shots. This specific rally culminated in player 41 committing an unforced error by hitting the ball out, handing the point victory to player 33.
In this intense exchange, both players showed a remarkable level of consistency and shot selection, with the majority of the shots landing within bounds and a tendency to push the opponent laterally across the court, as shown by the alternating Xpositions. The data reveals that both players were not only able to maintain rally intensity but also attempted to outmaneuver each other by varying shot depth and angles, as indicated by the mix of Ypositions.
The players’ shot selection remained aggressive, with player 33 consistently aiming close to the baseline, as evidenced by shots landing near the 10.887 mark, and player 41 employing a strategy to keep the ball in play, aiming for deep, central shots. Despite the pressure, both players managed to maintain a high level of accuracy until the final shot.
However, the data suggests a noticeable difference in the effectiveness of the players as the rally progressed. Player 33 was able to maintain their baseline accuracy, potentially indicating better endurance or mental fortitude in longer points. Meanwhile, player 41’s final shot, which went out by a small margin (-0.11 meters from the sideline), could be indicative of the increasing pressure affecting precision.
In this case, player 33’s ability to stay composed and force an error from player 41 demonstrates effective play under pressure and could be a key factor in overall match performance.
6. Reflection
Reflecting on the analytical journey and the metrics developed throughout this project, it is evident that feature engineering has played a pivotal role in enhancing the narrative that the data unfolds. By devising metrics such as Rally Sideline Accuracy (RSA), Rally Baseline Accuracy (RBA), and their combined counterparts for serve shots, the analysis transcends beyond mere numbers to tell a story that is of substantial value to a tennis coach.
The implemented metrics provide a quantitative backbone to assess players’ performance with precision. However, a lingering question remains:
important lingering question
What is the intent behind each shot, and how often do players succeed in executing their strategic placement? Or in other words, how do we measure intent?
This question signals a more sophisticated metric that could account for the players’ intentions – a measure that differentiates between an error and a missed strategic shot. Although the current dataset does not explicitly offer insight into players’ intentions, future work could involve developing a proxy for intent based on the patterns of play, players’ positions, and the context of the match.
Adding to this, further feature engineering could consider the players’ movement data, recovery time between shots, and physiological data such as heart rate to provide a holistic picture of performance under stress. Such features would not only enrich the current dataset but could also lead to a more tailored and nuanced coaching approach.
Moreover, in the realm of pressure situations, our analysis has uncovered that player 33 demonstrates a commendable degree of resilience and precision, particularly in longer rallies. This might suggest a psychological edge or superior conditioning, which could be further explored through data on player fitness levels and mental toughness.
Overall, the data at hand has been sculpted into a tool that narrates the tale of each player’s game. Future work could focus on capturing the essence of a player’s strategy, the influence of psychological pressures, and the physical demands of the sport. These additional layers of analysis would enrich the narrative and potentially transform the way coaches and players approach the game of tennis.
7. Future Work
This section outlines potential areas of analysis to explore in future, with an emphasis on questions that may reveal deeper insights into player performance and strategies. Each subsection below represents a specific analytical focus, with pertinent questions aimed at guiding subsequent research efforts.
7.1 Point Duration Analysis
Question 1
What is the average duration of points won versus points lost for each player, and does the duration of a point correlate with the likelihood of winning it for either player?
7.2 In/ Out Analysis
Question 2
What is the average duration of points won versus points lost for each player, and does the duration of a point correlate with the likelihood of winning it for either player?
