Teoria e Efeito causal

Em fevereiro, Stefan Szymanski publicou um texto no site Soccernomics chamado "Soccer Analytics: Science or Alchemy?". Ele trata de muitos temas: previsão no futebol, aumento no número de dados, modelos com muitas variáveis, teoria a partir dos dados, etc. Vários dos aspectos foram tratados no blog nos últimos anos.

No final ele apresenta quatro pontos:

(1) Theories are useful because they help us to identify causal effects. Big data approaches in soccer seem largely to avoid theorizing, which risks reducing the analysis simply to the search for correlations. I don’t think any science can prosper if straightjacketed in this way.

(2) In natural science causal effects are usually tested using controlled experiments. Social science relies on observational data, which makes the identification of causality much more difficult. There have been huge advances in our understanding of how to identify causality through statistical methods over the last three decades, but as yet I’ve seen little recognition of the issue in soccer analytics papers.

(3)There is a financial profit to be made in soccer analytics. Mostly this is not about beating the bookies but advising clubs on identification of strategies, playing talent and so on. This world closely resembles the world of alchemy- it is secretive and given to obscure utterings. Results are announced but not explained, success is claimed but not proven. This is perhaps inevitable as long as the potential for profit exists. There are also such non-scientific analyses of the stock market, together with promises of untold returns. The only cure for this a healthy skepticism.

(4) In the end, I believe, soccer analytics will be judged on its capacity to predict. I have outlined some of the challenges to developing predictions in relation to game results, but there are potentially other areas where soccer analytics can contribute, involving outcome related to specific on-field events.