Sports are often a powerful unifying force that not only brings people of all nations and ethnicities together, but also inspires and motivates generations of people across the globe. Over the course of history, eras are often defined by generations and the players that define it. As with all eras of sports, they begin with the rise of superstar athletes that dominate the game for several years, and end with the decline to make way for the next generation. In the tennis world, we are beginning to see the end of the “Big 3” era, one of the most dominant reigns not just in tennis, but in the history of sports. Roger Federer, who some consider to be the greatest of all time, retired last year after an illustrious, record-shattering career. Rafael Nadal, who had a twenty year reign as the king of clay, is expected to retire at the end of next season. Novak Djokovic, who just won his record 24th grand slam title in September, is approaching the end of his career at the age of 36. Over the past two decades, these three have combined to win 66 out of 80 grand slam tournaments and held the number one ranking for a whopping 909 weeks, which is roughly equivalent to 17 years. A dominance like this for two decades will likely never be seen ever again.

One of the unique things about Federer, Nadal, and Djokovic is that they each had a playing surface that they were associated with and famous for. For Federer, it was the grass court, where he currently holds the record for the most Wimbledon titles with 8 in his career. For Nadal, it was the clay court, where his 14 French Open titles is a record for most grand slams won at one tournament. Djokovic’s best playing surface was the hard court, where he has won the Australian Open 10 times. The unique thing about each playing surface is that they each have their own properties that distinguish them from each other, and there is a noticeable difference in style. For instance, the grass court is known to be the fastest surface on the tour, but the ball does not bounce that well. In contrast, the clay court is known to be a much slower surface, but the ball bounces higher than on a grass court. Finally, the hard court has a playing speed in between the grass and clay courts, but the ball bounces the highest out of the three surfaces. Because of the differences between the three surfaces, certain players on the tour perform better or worse than others on different surfaces because their strengths and weaknesses can be inflated depending on where they play. The playing surfaces also allow different strategies to be implemented tailored to the opponent or court style. 

On the side, this project will also evaluate playing styles in relation to surfaces. In tennis there are generally considered to be four main types of players. First, the offensive baseliner is a player that depends on hitting groundstrokes often and aggressively. Then, there's the counterpuncher, who is often very consistent and doesn’t give up too many errors. The serve and volley player is next, who is someone that serves very well and often finishes points at the net. Finally, there’s the all court player, who can do a little bit of everything well. There’s no true way to truly categorize players based off of those descriptions, but this project will associate statistics relevant to those playing styles. For the serve and volley player, net points and serve statistics will be a good indicator of how effective this player is. Counter punchers and offensive baseliners will both be evaluated by errors and return of serve stats, but winners will also be included for the offensive baseliner. Finally, the all-court player will be evaluated on everything. In the conclusion, these comparisons will be made to see what the most effective strategy is on which surface. 

The purpose of this machine learning project is to understand how playing surfaces in tennis contribute to the success of a tennis player. This project will analyze the statistics of tennis players on different surfaces to understand which metrics lead to success on which surfaces. Some of the statistics that will be used to answer this question will relate to aces, serve percentage, return percentage, points won, winners, unforced errors, forced errors, and net points. This project will utilize a server called Ultimate Tennis Statistics, which is a real time database that has recorded every single statistic of every single player in the Open Era. The cutoff will be this most recent US Open that concluded on September 10th. All data collected after that date will not be included in this study. The project will have data from active and former players, and will use performance data in grand slams to analyze the success rate on a playing surface. This project will utilize repeated performance metrics to analyze success; it will look for consistency of success over time as opposed to just winning straight up. Below are ten questions that this project will attempt to answer.

Finally, the methodology of the project will be various machine learning algorithms to analyze the data. After the data has been prepared, unsupervised learning algorithms will initially be used to perform analysis. Association rule mining will be used to analyze associations between the surfaces and their statistical groupings. Clustering will then be used to see if there are any groupings or connections between the surfaces. After that, supervised learning will be used in the form of support vector machines, naive bayes analysis, and decision trees. Those three methods will be grouped together to see if the surfaces can be classified based on the serving and rallying datasets. Finally, a linear regression will be implemented to predict the success of a tennis player in terms of victories on a surface. It will also be used to identify significant variables for each surface, and all surfaces. Please proceed to the data prep page to see how the data was prepared for analysis.