Give me a moment...

Doing the work for this blog and writing the posts is something I really enjoy and I often find myself thinking of new ideas at different times during the day (and night!). However, due to my professional and personal life, I find that ideas arrive faster than I am able to execute them. I do make a point of writing them all down, however, so provided here are a few ideas that I'm throwing around - the ideas that I will get around to and that I would have already got around to if I had time. The intentions of this post are threefold and two of them require feedback from the readership:

• See which ideas generate interest and debate - although I have my own opinions on the merits of each idea, these might not be aligned with those of the sports statistics community. This community is thriving, numerically (evidenced by the Sloan Sports Analytics Conference); however I think that the community aspect, sharing ideas and discussing to develop the field together, while progressing, could still improve further. I hope that by giving my ideas of areas of study, this will stimulate others to discuss and debate them.
• (Linked to the point above) I'd like to be pointed towards any existing work on any of these topics which I may not already be familiar with. Academic work includes literature reviews and the merits of these should not be lost on the analytics community. By acknowledging existing work, it is easier to see which areas could be added to and developed, as well as avoiding duplicating existing work.
• Finally, this list is a reminder to me of what I've said I want to do, so hopefully I'll be more likely to do it!

So, the list. In no particular order:

• A discussion of the principals of opponent-adjustment, with examples. Different methods exist for this and the nature of contrasting statistics and metrics necessitates these different methods. However, I think that by establishing some principals it is possible to develop a family of related methods which can be applied broadly to the wide array of numerical statistics across all sports.
• A look at form. Various sources now refer to "The Form Table", which seems to be a table of points accrued in the last 8 games. But why is 8 used? Is there any merit in this or is it, as it seems, arbitrary? Would a different number be better? Or a different way of measuring form altogether? I'd like to look at using different numbers of games, different weighting of games (in the 8 game model, the 8th most recent game has the same weighting as the most recent, but the 9th most recent is not considered at all) and possibly include opponent-adjustment mentioned above.
• A comparison of different tournament structures; league, knockout and other. How often does the team which is objectively best win? How can the probability of this occurring be improved (without everyone playing everyone else hundreds of times!)?
• A football win probability. Based on the NFL work by Brian Burke, develop a database of matches to allow the state of any game to be assessed objectively. Following this, the size of the impact of game events can be quantified. In the NFL, the game state can be defined by time, score, down, distance and field position. In football, although field position data is available, using that to split game states would lead to a very sparse database and limited sample size. My current plan is to use score and time along with, none, some or all of the following: yellow cards, red cards, substitutions made.
• (A lighter option). A look at fantasy football and whether it is possible to predict the weekly performance of players. Also, a look at possible strategies for choosing players to buy initially: which positions offer the best value for money? Is it best to go for a few star players with others that might be risky or look for a solid but unspectacular group?

These are the ideas that I've had. I would also like to delve more into the finer statistics offered by the MCFC Analytics datasets, but I am reaching the limits of what I can conceive and achieve in Excel. I am intending to teach myself R at some point, but that will probably be a slow process with the limited spare time that I have.

Please respond the suggestions I have made, either in the comments below or on Twitter.