Moneyball is a book about how statistical analysis allowed a small market baseball team to compete with the large market teams. Moneyball created the blue print for measuring what mattered in baseball and being hyper-focused on the undervalued baseball player.
We live in a measured, quantitative world. Everything is being measured, re-measured, counted and double checked to make sure we've got the right data. How the data is applied? Well, that's another story. What we're doing with the data sometimes run into moral hazard – (Hello banks!) However, fundamentally, the last fifteen years have been about processing information.
What if we started applying statistical analysis to our friends? Could we keep stats on our friends behaviors? What would the ideal stats be? Which stats would correlate best with true great friends? Is it different for everyone? When do you need a certain friend for a certain thing?
Here are a few stats it would be funny to see:
How many times they are late?
How many times you have to call them to get them somewhere?
Do they answer when you call them?
Number of times they were too drunk and you had to take them home?
Number of times out and sat in the corner?
Number of times out and they immediately interacted with new people?
Number of times laughed at jokes?
Number of times they didn't laugh?
Number of times invited to a party and showed up?
Time spent being annoying?
Number of topics they can competently discuss?
Times they got offended?
And on and on….
I guess what I am driving at is, is there a match.com or eHarmony.com for friends? Can we algorithmicly determine who are good people to be friends with?
Originally appeared https://laurent-courtines.com/moneyball-applied-metrics-to-friends from http://laurent-courtines.com