Saturday, June 6, 2015

Insights Before the French Open Men's Final
The Roland Garros Men's Final between Novak Djokovic and Stan Wawrinka is an especially important matchup. Djokovic seeks to become only the eighth man in the Open Era to win a career grand slam, while Wawrinka seeks to cement his legacy in the record books by becoming a multiple grand slam winner. In light of this final, DataBucket presents key insights from three different perspective to keep you up-to-date with the tennis world.

From a Head-to-Head Perspective

The following infographic reveals some interesting facts regarding this blockbuster match-up.
From a Surface Perspective

DataBucket also examined detailed statistics across grand slams, and found that Roland Garros matches are more tipsy-turvy in terms of change in momentum, but tend to have a more decisive winner at the end. The following graphs and insights support our conclusions:

  1. Roland Garros matches tend to be more straightforward affairs than other Grand Slams. In Australian Open and Wimbledon, 48% and 52% of matches go beyond three sets, compared to 44% for Roland Garros. As a result, don’t expect a tediously long match.
  2. Most people think that clay court matches will have more errors due to the surface's tendency to slow down and lengthen rallies. However, statistics show that the winner/unforced error ratio in Roland Garros is comparable to Australian and US Opens. This means viewers can still expect an entertaining final.
  3. The audience should expect more shifts in momentum. Sets will be won more decisively; as shown, only 13.2% of sets go to tiebreaks, less than in any other grand slam. However, there are also more breaks of serves - return games are won 23% of the time, more than in any other grand slam.

From a Historical Perspective

We also wanted to analyze tennis information independent of the upcoming French Open final. We sought to quantify how consistent players are in this current era compared to top players in 2000. Rankings out of the top 100 are excluded to disregard players' rankings when they first became professional, since it isn't indicative of the performance they are known for.

The following two graphs plot the average rankings of such players with their ranking standard deviation, over their entire careers:

  1.  Roger Federer, Pete Sampras, and Andre Agassi all have lower standard deviations than the other players. This is because these players spent a lot of time at certain ranks - Sampras and Federer were consistently #1 for a long time, as was Andre Agassi for the top 5.
  2. Top players today, such as Federer, Nadal, and Murray, have lower overall standard deviations compared to Sampras and Agassi. This is understandable, because of the consistency of the players today (Federer most often at 1, Nadal most often at 2, and Murray most often at 4).
  3. In 2000 and now, players with higher average rank have a higher standard deviation. This is likely due to the fact that players that are on average, worse, tend to be less consistent as well. Also, top players can only have leeway to move down the ranks, whereas other players have room to move up or down the ranks.