Thank you! As I hinted in my response to Nebu above, I can see there are some personal predictions as well. I have a couple doubts about using it, though:

• I wouldn't want to publicly predict very personal events, even if it's pseudonymous, done through VPN and marked as non-public. Just knowing that such a sensitive information is sitting on a server somewhere would make me uncomfortable.
• When analysing predictions, grouping them by tags is a necessary feature for me. For example, I suspect I'm too pessimistic about my personal finance, but too optimistic about interpersonal relations. In fact, this impression is what got me started on this idea in the first place. I would consider implementing this feature in PredictionBook, but then I would want to do that through my regular github account, so my identity could be linked to my predictions even easier.
• There doesn't seem to be any way to export your predictions for a more advanced analysis.

Overall, though, this is a great project and very relevant. I'm just being very picky.

Thanks. This is all very relevant. And no, there is no backstory, at least not that I shared anywhere.

You might consider reading "Superforecasting: The Art and Science of Prediction," by Philip Tetlock. Or go to the Good Judgment Project web site and watch the 5-part Superforecasting master class.

Yes, I read "Superforcasting". I didn't know they had masterclass and will look it up. I suspect their teachings will be somewhat less useful for predictions of personal importance: different biases will be at play here. But it should be worthwhile to watch it anyway.

First, the question has to pass the clairvoyant test.

Actually, I think if you are going to assess your own predictions, you can afford the luxury of being a bit less specific, especially if the prediction is made in the short-term. For example, consider a made-up question:

"Will Adam be able to get back to cycling within a month [after a recent accident]?"

If Adam resumes cycling but it causes him considerable pain, I know that's not what I intended when asking the original question. On the other hand, if Adam recovers fully but starts playing rugby instead of cycling because he discovers he enjoys it more, I know the answer to the intended question is "yes". (The imprecise part of the question here is "be able to", but as long as I can reliably recall the intention when writing those words, they cause no loss of precision.)

Second, you might want to have some scheme for Bayesian updating your forecast.

Hmm. For now I was planning to make my predictions once and forget about them until the outcome is known. I'm not sure I want to spend more effort on them, at least not so early into the project.

And then you'll want to use Brier Scores (or something like them) to assess your accuracy.

If you know R, there's actually a Brier score function you can use. But I can't imagine it's very difficult to set up in Excel.

Brier score would be great at telling me how accurate I am, but not what mistakes I'm making, at least based on my very limited understanding of the metric. As a basic analysis method, I was planning to group my predictions by the forecast probability (e.g., 0%-10%, 10%-20%, ... ranges, or maybe ranges 1pp wide at the extremes and growing exponentially towards the centre, that would probably make more sense), and simply chart them grouped by tag. I must admit, my knowledge of statics has always been very poor, so I'm sure there is some better analysis/visualisation methodology.