Predicting countries’ growth is an extremely challenging and important part of macro economy. Current models for GDP forecasting are complex and involve the combination of many different variables often gathered from different institution. The paradigm “more data more information” in the era of Big Data has to be carefully considered, in fact it is not always true. It is indeed unclear how pollution, transport, population etc, should be combined in a forecasting model and a high dimensional space often leads to results hard to understand. We propose a different approach. Using only export data, a new variable called Fitness based on them, and simple statistical mechanics’ tools we construct a forecasting model well grounded, easy to understand and able to compete with the International Monetary Fund forecasting. The advantages of using only export data are different. First, there is no need of complex combination involving regressions or others hard to understand fit. Second, starting with raw data, taken from COMTRADE, we can enhance its quality and control noise. Third, our model is easy to understand and to reproduce. Using export and a Hidden Markov Model description of production we can estimate the state of competitiveness of each country in each commodity. Using such states we can evaluate the Fitness of countries, i.e. their potential of growth. Fitness proved to be a new and useful variable in the description of GDP growth, and, combined with the GDP per capita, shows a non trivial dynamics in the bidimensional plane. Studying this dynamics we can predict countries’ growth for a 5 years horizon competing with IMF in terms of accuracy.