Giovanni Montano wrote:
Jan de Boer wrote:Giovanni you sound like my management and that is not a compliment. (...) I am also living in the Netherlands by the way.
Claude Moore wrote:Interesting question. My humble opinion is that no ANN or other deep learning algorithms are really needed to try to predict the outcome of a football match. What do you need, generally speaking, is a way to measure how much a football team is strong in a given moment of the championship, and, because I don't think that the ability to play football can be measured in absolute terms, you need a way to measure a team strength with respect to competitors. Somehow similar to ELO points used in chess. ELO points are based upon the concept that the more the difference of ELO points between two players is, the more likely is that the player with higher ELO score will win the match. ELO score is adjusted after each match: you get an increment or a decrement of your score proportionally to the difference of your ELO and your opponent's, so that you won't get many points if you are strong and defeat a weak opponent, while you will loose more points if you are defeated by a weaker opponent.
Building an ELO-like score may be enough, and you may try to create such rating by a) assigning to each team an initial score b) update for each team its score using the recent historical series of match outcomes.
Bear Bibeault wrote:
Peter Rooke wrote:I suspect the best work gets done, when people are working remotely from home away from the "managers".
I know this works for me. Last time I worked in a Google-esque "collaboration" room it was impossible to even think straight.