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Evaluating Tennis Player Styles in Relation to Tour Averages

What is the website about?

The purpose is to create classifications of a professional player’s style and compare their relative strengths through point by point data. The data gives us a good way to confirm our intuitions of a player and can be used to connect what we see on the TV to what is actually happening.

Assigning Labels

  • The process I used to assign to labels to each player involves three categories: Points by rally length, points by shot frequency, and winner types

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  • If a player had a tendency to play shorter points, then they are categorized as “First-Strike”, meaning that they look to be aggressive and try to finish the point earlier

  • If a player had a tendency to play longer points, then they are categorized as “Grinder”, meaning that they tend to be more patient and are more willing to have longer rallies

 

  • If a player hit more forehands and less backhands relative to the rest of the tour, then they are categorized “Likes to hit Forehand”, and similarly if they hit more backhands and less forehands then they are categorized “Likes to hit Backhand”

  • And if a player hits more forehands and backhands relative to the tour, without a big difference from either, then the player would be classified as “Likes to hit groundstrokes”

 

  • Players are categorized as using variety if they are higher than the tour average in any of the two categories listed: FH slice, BH slice, dropshot, lob

  • Players are categorized as “Likes to go to net” given that they go to the net more often than the tour average

Further Commentary

  • Players with big forehands tend to have more unforced errors and winner rates alongside with lower unforced errors and winner rates on the backhand

  • Examples include Tsitsipas, Ruud, Berrettini, Rublev

 

  • Essentially, players with big forehands have trained their backhands to serve more as a wall, and to be more solid

 

  • Similarly, players who are known for their backhands also exhibit a somewhat similar pattern (not as strong though), with their forehands having less unforced errors and winner counts and their backhands have a higher winner and unforced error count

  • Examples include Gasquet, Nishikori, Coric

 

  • So at the professional level, one way to interpret levels of unforced error would be that of aggressiveness, and players who have a natural affinity for one side (forehand or backhand) will tend to be more aggressive with that stroke relative to other players, and therefore have a higher rate of unforced errors

 

  • This affinity for a specific side in tennis could be attributed to eye dominance, another topic that might be covered later…

Additional Notes and Acknowledgements

 

  • The relative difference percentages can be misleading at times. For example, a player with more aces,will probably have a lower relative percentage of winners on both the forehand and backhand side. Could that be a case of the player just having a stronger serve overall, or do they just let less winners than the tour in general, so the serve seems stronger by comparison? Either way, the player would be classified as having a big serve relative to the other aspects of their game.

 

  • Moreover, I used ace count as the sole measure for measuring a big server, when other unlisted factors, such as unreturned serves paint a more accurate picture of how effective one’s serve is

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  • Some players have more matches than others, making this analysis more accurate for some players than others

  • while also taking account into different surfaces

 

  • The match statistics are charted by humans so there is potential for human error when classifying shots as unforced errors, but this shouldn’t have a major impact on the data

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