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Found 3 results

  1. I've been experimenting with artificial intelligence engines in an attempt to formulate a profitable strategy in the correct scores markets. Beating the market odds though is never going to be an easy task. The current stage of this research has reached a point where I'm combining three models to take a consensus of them all to indicate highly rated home wins, draws or aways. One model looks at the home team's record and ranks the chances of H,D or A, a second performs the same task with the away side's record, and a final model that looks at both records to reach a conclusion. Because of
  2. Many football rating methods are compiled on a match-by-match basis in real time, inasmuch a ratings is assessed for a game at hand, then using the result of said game, the rating is adjusted accordingly. The most recent match therefore has perhaps too much of an influence on how the new rating moves. This approach presented here also uses past results to calculate the outcome, but it looks at ALL previous game results and treats them as equally important. Trends therefore are not picked up, whichever way a team's form is heading is totally ignored. A good or bad thing? . . . let's find o
  3. Hello, I have been playing around with a new prediction model for all the Leagues that BBC posts match reports on (all professional English and Scottish Leagues). The number of variables you can derive from the text commentary is quite good, especially for a lower leagues. It is still early phase, and I'm still tweaking some things. The final goal is to have a fully automated workflow that: 1) scrapes every new game from BBC, derives events from text (Shots, Bookings, Goals, Substitutions, Cards, Offside), 2) Aggregates the statistics for each game and applies Expected G
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