Seth Partnow ‘s mention of Charles Goodhart’s Law sent me off looking for Norman Scotch.
The line that prompted this search was Seth’s observation:
we might be seeing the basketball version of an economic concept known as Goodhart’s Law, which holds that when a measure becomes a target, it ceases to be a good measure
Norman wrote about magic, sorcery, and football among urban Zulu in 1961.
The connection I made was been Charles Goodhart and these lines from Norman’s paper:
when we view the football team we clearly see how the winning of matches is almost always explained by references to magic. In usual cases it is magic of the inyanga …
An inyanga is a Zulu doctor. Norman reports:
It is common knowledge, and not surprising, that in an effort to produce winning teams each of these football teams employs an inyanga, or Zulu doctor, who serves the dual purpose of strengthening his own team by magic and ritual, and of forestalling the sorcery directed at his team by rival inyangas. Although no inyanga with whom I talked would admit that he employed sorcery against opposing teams, each was convinced that this was the practice of rival inyangas.
Norman’s paper presents urban Zulu football as a case study of “how innovations can only be built on previous cultural patterns”.
Seth explores the relationship between analytics and NBA basketball in a way that has a connection with Norman and urban football. Seth suggests:
Misused and misinterpreted, new information can easily make a particular team worse off if the wrong lessons are drawn—and across basketball, new orthodoxies based on partially understood data could end up swapping old counterproductive biases for new ones.
I wondered if a new era of sport analytics requires science and magic.
A great deal of time is spent analysing opponents’ performances to search for patterns that are threats and weaknesses. I see a parallel between the IF… THEN approaches of contemporary analysis and the forestalling behaviours of inyangas.
There is a fascinating learning journey for each of us as we move from the machineries of our analysis to an understanding of the dynamics of performance. Charles Goodhart’s paradox (law) is that as we use the machineries of analysis we must be sensitive to the fallibility of our analysis as it iterates in performance contexts.
Norman advises that “when a team consistently loses it is the inyanga who is replaced, not the player”. Seth concludes his discussion of basketball analytics:
Ultimately, the goal is to score the most points on a per possession basis, not to hit benchmarks for the sake of doing so. It’s good to play the right way, but better to play the best way for your own personnel. Striking that balance isn’t easy, but when hit, it takes analytics from the realm of the spreadsheet and puts it back on the court, where it ultimately belongs.
The support for performance on the court is a discovery of magic as well as the application of science. There is a strong tradition of sharing the science of analytics.
It would be great to share stories of magic too.
UNC Women’s Basketball (Kevin813, CC BY-NC-ND 2.0)
Vuvuzela (Moe Moosa, CC BY-NC-ND 2.0)