You may or may not be a tennis fan, a Federer or Wozniacki fan, but no matter what sport you follow,  when we view the highlights of a sport chances are what you are viewing is the result of a lot of technology.

Photo  by Richard Fisher available under a Creative Commons Attribution-license.jpg

Photo  by Richard Fisher available under a Creative Commons Attribution-license


If you love tennis, then hopefully you enjoyed not only both the mens and womens singles final but some of the awesome matches that led up to the finals. Although it is the champion that gets most of the attention, let's not forget some of the amazing matches that led up to the final. This includes the Women's semi-final between Simona Halep and Angelique Kerber where they alternated match points. First to Halep, then Kerber, then back to Halep. Similarly seeing a new faces on the men's circuit with South Korean, Hyeon Chung and British Kyle Edmund who both made it to the semi-finals was refreshing.


Now that we got that out of the way, back to technology. I am not sure whose technology powered the Australian Open but just like the US Tennis Open in 2017, which was powered by IBM Watson, Machine Learning (ML) can provide almost real time updates that in the past would taken hours or days after the event. Now after a match is over within minutes it can assemble a highlights real. So what is ML basing it's decision on? It can be anything from the noise of the crowd and if they were cheering, booing, or maybe not reacting at all. It could be on the loudness of a players grunt, or reaction of the player or their oponent, their expressions and body language. You don't need a John McEnroe level of reaction to make a decision, "The ball was on the line." Infact it is interesting to see that even with an umpire present players can callenge a line call and will defer to the machine and playback for the final verdict. A classic example of this was on Federer vs Cilic at match point. Federer raised his hands, thinking that he had won the match, the crowd reacted a little different, there was a challenge, a very short delay, the slow-mo replay, and there you had it Federer with his 20th Grand Slam.


Machine Learning is obviously not just a feature that is limited to tennis it shows up in Golf, Football and for that matter any sport. Happy MLing at Superbowl 2018! If you want to learn more about Oracle's offering in Machine Learning check this out.