First, some background. Bouchard rose to national celebrity
in 2014 following a series of strong results in the first three of tennis found
“Grand Slam” events, reaching at least the semifinals of each the Australian
and French Opens and Wimbledon, rising to 5th in the world rankings,
and earning over $3M (USD) in prize money. Her 2015 season, however, was marred
by a series of poor results. At the U.S. Open in September, 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.
Setting aside the various issues related to liability, it is
instructive to consider the potential claims for loss of income both in the
short term and the long term.
A Probabilistic
Approach
Let us assume that Ms. Bouchard’s injuries prove to be
short-term, and that her loss of income is related solely to a single
tournament, the U.S. Open. Ms. Bouchard earned slightly over $200,000 for
reaching the quarterfinals; by comparison, the eventual champion of that event
won over $3M. Clearly, that is a very large potential income loss for a very
short period of time (a matter of days).
Analyzing Ms. 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 Ms. Bouchard 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% chance of victory in any given match. Using the actual
results and prize money from the 2015 US Open, but assuming Ms. Bouchard had
not suffered any injury, one possible income loss model would look something like
this. Ms. Bouchard would have stood a 70% chance of winning her next match, but
the cumulative odds of success diminish in each round.
Ms. Bouchard’s slip-and-fall is still recent, and hopefully
she will return to her form of 2014, - her results so far this year have been promising - 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.
How does one do this? In a typical personal injury case,
there are two main inputs: predicted income level, and predicted retirement age. It is important to understand the linkage
between these two.
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. The following table shows the age earnings profile for Canadian lawyers,
based on data from Statistics Canada’s 2011 National Household Survey.
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 earnings. 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 “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 Ms. 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. Again, a more detailed
study would be necessary.
Conclusion
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.
An abbreviated version of this post appeared in the December 18, 2015 edition of Lawyers Weekly.
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