  Sports betting's recent federal legalisation in the USA coincides with the
golden age of machine learning. If bettors can leverage data to reliably
predict the probability of an outcome, they can recognise when the bookmaker's
odds are in their favour. As sports betting is a multi-billion dollar industry
in the USA alone, identifying such opportunities could be extremely lucrative.
Many researchers have applied machine learning to the sports outcome prediction
problem, generally using accuracy to evaluate the performance of predictive
models. We hypothesise that for the sports betting problem, model calibration
is more important than accuracy. To test this hypothesis, we train models on
NBA data over several seasons and run betting experiments on a single season,
using published odds. We show that using calibration, rather than accuracy, as
the basis for model selection leads to greater returns, on average (return on
investment of $+34.69\%$ versus $-35.17\%$) and in the best case ($+36.93\%$
versus $+5.56\%$). These findings suggest that for sports betting (or any
probabilistic decision-making problem), calibration is a more important metric
than accuracy. Sports bettors who wish to increase profits should therefore
select their predictive model based on calibration, rather than accuracy.
