Colossl Victory

Expected Points Here’s a quick look at the expected points from Huddersfield Town’s first 8 games this season compared to the corresponding games last season (Cardiff for Stoke) as well as our first 8 games last season. (The expected points would be higher if I had not included the expected goal total from the penalty awarded on Danny Rose, as it was never a penalty, but for the sake of objectivity I have left it in). First let's look at this season’s expected points compared to the corresponding fixtures from last year. I think the first thing it shows is that we were right to be worried after the first 3 games, the performances were clearly worse this season (also worse compared to our opening 3 games last season) and we hadn’t even earnt a whole expected point in that time, the results and performances were bad. I also believe that this has had a knock on effect as to how people have viewed the games since then, as a lot of people have seen the results not improve and ...

Problems and things to correct I think the most glaring issue is that the most likely result calculators logic is not wholly sound, as I think it has some dependent variables being treated as independent and thus the probabilities are not quite correct. For example: goal difference and probability a team scores and concede the corresponding values - if GD is 1 then probability of Team A scoring 2 and conceding 0 is clearly 0 which is clearly not the same as P(A scores 2)*P(A concedes 0). The same goes as for one team to score and other team concedes, they are clearly related.  Result and score are very different, it’s much easier to say a team will win a game than to say by how much, and the current system doesn’t really reflect this at all. It gives a predicted GD but if say team A is 0.5 xG better than team B does this mean to expect team A to win or draw. It may be better to predict the result first and followed by a score based on predicted goals.  There is a pretty b...

The prediction algorithm has been through 2 main versions so far. I unfortunately don’t have the original anymore so can’t quantify how effective it was comparatively, all I can say is that it wasn’t as good as V2 but better than just predicting the most common score from the previous season every week. Version 1 A simple calculation of: total form diff * a + position diff * b + location form (home teams home form - away teams away form) * c + home advantage. The values of A, B, C and home advantage were all calculated by searching all values in a range to find the values that produced the highest total number of correct result over all previous games. A very slow process but it gave decent accuracy. Version 2 The predictive element was replaced with a system to predict the goal difference and another to convert that to a likelihood for each score line, so the one with highest probability can be picked. A linear regression algorithm is used for the predictive element, where a...

For the last 2 seasons, I have been developing and using a football prediction algorithm; mostly to beat my friends in prediction leagues but also due to my own curiosity about how much a result can be preset before the game and if shock results are as shocking as they seem. I have decided to start writing about it now because; I’m about to make some huge changes to not only the algorithm used but the attributes and accuracy methods. I’ve also been slowly looking at and analysing other stats and information to try and explain some of the less straightforward decision clubs (specifically my own team - Huddersfield Town) make, as well as why some teams are doing well while others struggle.   For me blogging about these changes allows me to start doing that more formally, while giving my friends and family a break from my constant “statistics and computer science nonsense”.