Calculating Sports Star Damages

  • Date14 December, 2015
  • Author Ephraim Stulberg, Matt Mulholland
  • Location Canada

The lawsuit of Canadian tennis star Eugenie Bouchard v. United States Tennis Association is still at an early stage. However, as forensic accountants who spend much of their time thinking about personal injury damages and playing tennis (albeit poorly), it is tempting to speculate on some of the interesting issues that the case may present as it progresses.

First, some background. Ms. Bouchard rose to national celebrity in 2014 following a series of strong results in the first three of tennis’ four “Grand Slam” events, rising to 5th in the world rankings, and earning over US$3 million in prize money.

Her 2015 season, however, was marred by a series of poor results. At the U.S. Open in September, Ms. Bouchard appeared to have regained her form, reaching the 4th round. However, prior to her match against eventual finalist Roberta Vinci, Bouchard allegedly slipped on a “foreign substance” in the locker room, leading to injuries that allegedly led to her withdrawal from the tournament.

Let us assume that her injuries prove to be short-term, and that her loss of income is related solely to a single tournament, the U.S. Open. She earned slightly over US$200,000 for reaching the quarter finals; by comparison, the eventual champion of that event won more than US$3 million. Clearly, that is a very large potential income loss for a very short period of time (a matter of days).

Analyzing Bouchard’s potential income loss can be viewed as sports betting in reverse: one “predicts,” retrospectively, how she would have fared had she not been injured. In predicting how much money Ms. Bouchard might have earned from the tournament, one would adopt a probability-weighted approach to predicting the odds of her reaching each successive round.

Various models have been built, based on empirical data, that can predict — with varying degrees of probability — the relative odds of success for each player in a professional tennis match, using variables such as rankings, age etc.

Let us assume a very simplified model in which the higher-ranked player has a 70 per cent chance of victory in any given match. Using the actual results and prize money from the 2015 U.S. Open, but assuming Bouchard had not suffered any injury, she would have stood a 70 per cent chance of winning her next match, but the cumulative odds of success diminish in each round; her odds of reaching the finals would have been 15 per cent, and her odds of winning the tournament would have been only around 10 per cent.

Although these odds are low, the payoff of reaching each successive round is quite high, and the model estimates Bouchard’s losses for the U.S. Open at slightly over US$600,000.

What readers may not realize is that this is precisely the same type of approach that is universally used to apply contingencies to future income losses in even the most run-of-the-mill of cases. The projected income in each year is equal to the predicted income level multiplied by the cumulative odds that the plaintiff would have survived and continued in the workforce for each successive year.

Bouchard’s slip-and-fall is still recent, and hopefully she will return to her form of 2014, but what if her injuries prove long lasting?

In such a case, predicting her results, but for the incident, in a single tournament becomes less important; we are more concerned with projecting her income over a longer period.

In many professions, there is a relatively predictable age-earnings curve. Income rises as individuals gain experience in their chosen fields, reaches a peak, and then declines as individuals cut back on their hours. For example, earnings for lawyers tend to peak in their late 40s and 50s, and although some lawyers may continue practicing well into their 60s or 70s, their earnings in those decades will be
significantly lower than their peak.

This is important to keep in mind when projecting income based on historic levels — people do not simply continue earning the same level of income until they retire.

Age earnings curves are occupation-specific. In professional golf, for example, earnings from tournaments tend to peak in a player’s 30s, decline in their 40s, but then to rise (sometimes significantly) in their early 50s once they become eligible for the “Champions” (i.e. seniors’) tour.

How about professional tennis players? According to data published by the USTA, the average professional tennis career lasts for seven years, and the average age for players in the top 60 in the world was roughly 24 years old. Lower ranked players tend to be younger on average; it would appear that players do not continue playing very long past their peak, and that lower ranked players are generally those who are still trying to prove themselves.

Conversely, the average career length for players who achieve the measure of success that Bouchard did in 2014 would, on average, play for a longer period of time. The top player, Serena Williams, has been playing professionally for 18 years.

Personal injury cases involving celebrities attract high levels of popular attention, and are played for high stakes. But the conceptual inputs into an income loss calculation for an injured professional tennis player are no different than those that go into calculating any other income loss.

By Ephraim Stulberg and Matt Mulholland.  Published in The Lawyers Weekly – December 18, 2015.

The statements or comments contained within this article are based on the author’s own knowledge and experience and do not necessarily represent those of the firm, other partners, our clients, or other business partners.