📈🏀Thursday Theory: Regression to the Mean. While the nature of many phenomena is not constant or linear, analysts can graph a line like the blue one which shows the general trend of an occurrence to represent the mean or 'average' of the data. In any given scenario, some data will be above the average (green) and some will be below it (red). Regression or reversion to the mean posits that in a series of two events, if the first one is an extreme let's say above the mean, the second will likely be closer to the mean so as to preserve the average. If the second piece of data is an extreme, then the first can be seen as closer to the mean.
People and natural occurrences tend to perform towards their average and if they severely outperform or underperform, the probability says that the next piece of data will be corrective, and reside closer to the mean. 🏀🏀LeBron James averages 27 points per game in his career. If he blows up for a 50 point game, the chances of him performing closer to his average in his next game are very likely. As is the nature of probability, though, he may very well have a strong game but on average the game after a huge performance will be closer to his mean points per game of 27. If his first game as a Laker is way above average (wouldn't that be sooo LeBron?), His 2nd game will most likely be lower than his first game and closer to his average of 27 points.
Again, there is never a guarantee with short term probability, but over the long term most pieces of extreme data are followed by data that regresses or reverts back to the mean. 🤓➕➖✖️➗
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