The data We currently have available all Champion Ladder match data for seasons 1-51. This post will just be a quick rundown of what is in the dataset, so that I have a reference for when we start to do some real analysis. The spreadsheets contain one line for each game, so the number of rows gives us the number of games and the number of columns are the number of parameters recorded for each match.
Now that the boots are hung up and the blood washed from the uniforms, it’s that time you have all been waiting for. Time to take a look back at the season that was in all its glory. Firstly, congratulations to Mongloom for coming out on top and thanks to all of you for a really enjoyable season. Predictions For those of you wondering how my preseason predictions hold up in comparison with the actual results, the answer is pretty clear:
Since this is a game of dice and silliness, let’s try to predict the season’s results by throwing numbers around and seeing what sticks. Starting from the baseline of each team’s record to date, we will add in some information with a slightly larger sample size by using results from the Champions Ladder Season 2. So for each individual game, we estimate the home win/tie/away win probability by using the weighted average of
With round one nearly over it’s probably a bit late for this, but I thought it might be fun to try a preseason analysis of the division and see if we can come up with some wild guesses about how the season might play out. So, for those of you who can’t be bothered scouting out your opponents, here is a brief look at what everyone brings to the table leading up to season one.