Loss of Prize Earnings of a Tennis Player - Part 2

In Part 1, I looked at different insurance policies that an athlete can purchase. In this article, I have a closer look at how professional tennis player’s loss of earnings could be quantified in the event of, say, an accident.

Professional tennis players are self-employed and their earnings primarily depend on winnings from tournaments - some players also receive income from endorsement deals and exhibitions. The prize money from tournaments is usually directly linked to performance and is based on several variables including:

  • total prize money available for each tournament;
  • number of tournaments played in a season; and
  • performance at each tournament.

Another variable that affects a player’s earnings is the ranking. For instance, a player might not qualify for tournaments with the highest prize money due to a lower ranking and, therefore, must play lower category tournaments. Although the tournament’s prize money generally increases each year, a player’s performance at those events may differ.

In my case example, I use Petra Kvitova, a Czech tennis player who won Wimbledon in 2011 and 2014, to illustrate what issues I would look at when analysing her lost earnings.

Loss of prize earnings example

Ms Kvitova suffered an injury in December 2016 and was out of the competition from January to May 2017. The table below summarises her prize winnings for the last seven years.

tennis blog chart

The above shows that Ms Kvitova played on average 19 tournaments per year. It can also be seen that her average winnings, including the Wimbledon titles in 2011 and 2014, are higher (by USD 822k) and her ranking improved in both years.

On one hand, in a hypothetical scenario, if she did not win any of the four Grand Slam titles during the year but made only the quarter-finals at all of them, her average total earnings would be around USD 3.1m (based on the quarter-finals prize earnings for each Grand Slam between 2011 and 2016, in addition to her winnings from other tournaments). On the other hand, if she won two to four Grand Slam titles in the year, the value would have gone up to USD 4m and above.

To project the loss of earnings of an individual, I would look at the individual’s pre-incident level of earnings (e.g. salary, dividends, pension). For a tennis player, I would need to analyse the prize money earned, which depends on performance at each event. I’d expect to see more variability in earnings from one year to the next. Therefore, a potential loss and the basis of the above metrics could be calculated as:

  • average of all prize winnings from 2011 to 2016; or
  • average of prize winnings from 2011 to 2016 excluding Grand Slam titles; or
  • different scenarios based on the expected prize winnings such as achieving at least a 3rd round/4th round/quarter-final/semi-final in each Grand Slam based on a player’s average historical performance of each Grand Slam.

Other issues that I would consider in the quantification of my calculation are:

  • the likelihood of winning Grand Slams titles and overall performance in the season;
  • the average prize money of future tournaments;
  • the ability to qualify for high prize tournaments if the ranking drops down during the season;
  • any ongoing injuries that could affect the performance; and
  • the retirement age of a player.


It is likely that a part of my evaluation of lost earnings would be based on the findings of an independent tennis expert. Likewise, it would be necessary to review witness statements from coaches and medical expert evidence to assess any current and long-term injuries.


Many variables affect a tennis player’s performance. The players usually say that to win a Grand Slam title everything must click together – team chemistry, luck, being healthy, being in a good shape on the day, being happy in personal life or having enough support. The calculation of lost prize earnings is therefore inherently uncertain, perhaps more so than other personal injury income losses as a result of having many unpredictable variables that are directly linked to the player’s performance.


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