###### Abstract  
This study used a novel machine learning approach to uncover key service variables that maximize effectiveness in men's professional doubles tennis. A large dataset of 14,146 serves from 97 Davis Cup doubles matches played between 2010 and 2019 was analyzed using explainable AI techniques. The angle and distance from the bounce to the sidelines of the serves were found to best distinguish the points won with access from rallies lasting more than three strokes. Optimal serve angle ranges of 5.7-8.7 deg substantially increased the probability of serving an ace by over 80%, compared to around 30% when serving used more central angles. Lateral bounce distances of 0-28 cm from the sidelines also boosted the ace probability by over 50%. The serve speed was shown to have less influence on serve effectiveness as compared to singles tennis, with velocities above 187 km h\({}^{-1}\) only increasing the probability of serving an ace by 10%. These findings have important practical implications for the tactical decision-making and technical training of serves in men's professional doubles tennis. The data highlight that the angle and placement of serves are more important than velocity for attaining effective serves in doubles. Coaches and players can use this knowledge to pay special attention to the most important variables in the effectiveness of serves, such as the line distance and angle, in order to maximize the performance of the doubles serve. The novel methodology used in this study provides a valid and reliable way to calculate the efficiency of actions in various sport disciplines using tracking data and machine learning approaches.  
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in doubles, service tactics are also shaped by contextual information like the score and the returner position, which influence players' decision-making [11]. By leveraging such situational data, servers can increase serve variety, target specific opponent weaknesses, maintain consistency, and increase unpredictability [12]. Collectively, these findings underscore the serve as a critically decisive stroke with major tactical and strategic implications in high-level doubles tennis.  
The introduction of tracking technology like Hawk-Eye in tennis has enabled access to rich datasets, which researchers have utilized to predict game indicators and parameters especially related to the serve [6; 13]. For singles tennis, an analysis of serving patterns shows accuracy measures are more impactful than speed [11]. For doubles, tracking data reveals that the serving placement and direction strongly influence the effectiveness of and anticipation by returners [14; 15].  
Machine learning techniques have also been applied in recent tennis literature leveraging these large datasets [16]. Broad applications include the stroke classification [17], an automated recognition of movement patterns [18; 19], evaluating player performance [20; 21], and modeling shot variety and effectiveness [22]. Significant attention has focused on predicting serve characteristics and overall match outcomes. The different modeling approaches explored include regression, point-based comparisons, neural networks, random forests, and ensemble methods [23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34].  
In summary, ample research has investigated match prediction in tennis using diverse machine learning techniques on increasing volumes of tracking data. However, doubles tennis has received less specific focus, representing an open area for deeper investigation, particularly around service analytics. Therefore, the aim of this study was to uncover the variables associated with a greater serve effectiveness in men's professional doubles matches using a large dataset from Davis Cup doubles matches and machine learning techniques. The results obtained highlight the angle and the distance of the bounce of the ball from the line as the most important and decisive variables related to the effectiveness of serves.