Ok, I’m going to be honest… I’m not really happy with this post. I keep deleting it and re-writing it, but can’t get it in a form where it eloquently says what I want it to say. (Insert your own <like all of your other posts> joke here).
I’m trying to say the following things:
- Trading in sports – or any field – is about predicting what will happen in the future;
- Data are a summary of the past. If the future behaves like the past, then the data are likely to be useful; if it doesn’t, they’re likely to be less useful;
- There is often information about the way things are likely to change in the future that’s external to, and not included in, data;
- This means that predictions for sports trading based on statistical procedures will always be improved by the inclusion of additional knowledge and information that is provided by experts.
That’s what the rest of this post is trying to say. Unfortunately, it’s an admission of a poor post that I’m having to tell you this in advance, rather than letting you draw these conclusions yourself.
Anyway…
It’s often said that ‘with the benefit of hindsight, things could have been done better’. But since hindsight isn’t available when trading on sports, the best we can do is make optimal use of foresight.
This season has been a record-breaker for the NFL. Among other tumbling records, at 1371, the number of touchdowns in the regular season is the largest in the league’s 99-year history.
Of course, random variation means records will be broken from time to time just by chance, but if this sudden increase in points was actually predictable, then bets placed on NFL would have been improved if they had taken this into account.
Naturally, as statisticians, our primary source of evidence is contained in data, and we aim to exploit basic patterns and trends in data to help make predictions for the future. But data are by definition a snapshot of the past, and the models we develop will only work well if the future behaves like the past. Admittedly, if changes have already occurred, these will be encapsulated in data, and can be extrapolated into predictive models for the future. But data do not, in themselves, describe mechanisms of change. And it will always be essential to use additional sources of information and knowledge, not contained in data, to temper, inform and modify predictions from data-based statistical models.
With all that in mind, I found this article an interesting read. It provides a chronology of events connected to the NFL, all of which have contributed one way or another to the current attack-based tendency of play. The foresight to use this knowledge at the start of the season, to modify predictions to account for a likely increase in points due to a greater emphasis on attack, would almost certainly have led to better predictions than those provided by using data-based models only.
