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Innovative stats and high-tech court furniture feature at the 2018 Australian Open
As with most other sports, statistics are featuring more and more in the coverage of major tennis events as broadcasters seek to maximise audience engagement by giving viewers greater insight into the on-court action.
Typical statistics which tennis fans will be familiar with include number of winners, forced/unforced errors and first serve percentage. However, how helpful are these kinds of traditional statistics in explaining why a particular player wins a match? In matches that are particularly close, it is sometimes the case that the player who makes more serves, makes more returns and wins more points ends up losing the match. Such anomalies have led the Game Insight Group (GIG), a collaboration between Tennis Australia and Victoria University, to devise a new set of statistics in an attempt to provide a more accurate representation of what happens on the court by drilling down further into player performance.
Probably the most prevalent of such statistics at this year’s Australian Open is the “win prediction”, which ascribes a percentage chance to each player of winning the match in question. The most obvious consideration to take into account when predicting the winner of a professional tennis match is the respective players’ world rankings, which determine which events they can enter and what seedings (if any) they are given. However, rather than using the world rankings, GIG’s win predictor uses the Elo rating system instead. The Elo ratings change based on who a player has played against (and whether he or she won or lost) whereas the world rankings are based on a player’s tournament results (e.g. which round he or she reached) and don’t take account of the rankings of the players that he or she has played against. By way of example, under the Elo rating system, beating the number 10 in the world will be worth more than beating the number 100 in the world.
Tennis commentators love to talk about the ability of the top players to win the “big points”, but traditional statistics (with the exception of the break point conversion ratio) seldom reflect the extent to which a player succeeds or “chokes” at the critical moments of a match. US sports commentators and fans often refer to a player’s ability to perform under pressure as “clutch”, particularly in relation to basketball and baseball. GIG have therefore developed a “clutch index” for tennis players, which takes into account the weighting in terms of importance of every point in a match (with match point having the highest weighting and every other point having a relative weighting to that) and reflects the proportion of the particularly important points which were won by the player in question.
Another new GIG statistic, which perhaps could be most useful in the context of coaching and match preparation, is “work rate”. It’s derived from data comprising acceleration, speed, distance travelled and direction change, which is then converted to the amount of energy burned by the player in question. As opposed to the win predictor and clutch index, both of which are based on historical match/results data, work rate utilises the data gathered by Hawk-Eye’s cameras, which not only track the ball’s movement each millisecond, but also the players moving towards it.
Aside from innovation regarding data, this year’s Australian Open is also showcasing new high-tech umpires’ chairs on the three main show courts at Melbourne Park. The new chairs are fully automatic, with the umpires being able to elevate the chairs to the required height at the touch of a button, and feature built-in LEDs.
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