Ao menos na USP, sim.
Entrevista interessante no Estadão de hoje mas que está mais detalhada nesta outra do The Guardian. A matéria do Estadão vem com seis meses de atraso, como se vê, mas o mais engraçado é pensar no porquê da alta cúpula russa estar atenta a um estudo de big data com reconhecimento facial que pode dizer algo sobre alguém ser ou não gay.
Abstract: Balancing the allocation of games in sports competitions is an important organizational task that can have serious financial consequences. In this paper, we examine data from 9,930 soccer games played in the top German, Spanish, French, and English soccer leagues between 2007/2008 and 2016/2017. Using a machine learning technique for variable selection and applying a semi-parametric analysis of radius matching on the propensity score, we find that all four leagues have a lower attendance as the share of stadium capacity in games that take place on non-frequently played days compared to the frequently played days. In addition, we find that in all leagues except for the English Premier League, there is a significantly lower home advantage for the underdog teams on nonfrequent days. Our findings suggest that the current schedule favors underdog teams with fewer home games on non-frequent days. Therefore, to increase the fairness of the competitions, it is necessary to adjust the allocation of the home games on non-frequent days in a way that eliminates any advantage driven by the schedule. These findings have implications for the stakeholders of the leagues, as well as for coaches and players.
Agora pense nos calendários dos campeonatos estaduais e nacionais que temos…
Veja só este:
We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math fields. This ad was explicitly intended to be gender neutral in its delivery. Empirically, however, fewer women saw the ad than men. This happened because younger women are a prized demographic and are more expensive to show ads to. An algorithm that simply optimizes cost-effectiveness in ad delivery will deliver ads that were intended to be gender neutral in an apparently discriminatory way, because of crowding out. We show that this empirical regularity extends to other major digital platforms.
Imagino que vá gerar alguma polêmica, mas mostra como o alto valor de certos grupos (demográficos, etc) pode gerar resultados aparentemente contraintuitivos em termos de marketing.