The Importance of Prior Experience in Sports
https://www.nytimes.com/2004/01/20/health/subconsciously-athletes-may-play-like-statisticians.html
This article discusses Bayesian statistics in the application of sports and others, and argues that the human brain subconsciously uses statistical approaches when it comes to making decisions involving uncertainty. Bayesian statistics require information on prior knowledge, and athletes use prior experience subconsciously when they make decisions such as what direction to hit the ball in, where to throw a pass too, whether to shoot the ball or pass to a teammate, etc. Yet some researchers may argue the opposite, suggesting that the brain may follow a different approach other than the Bayesian approach with prior experiences, since not everyone is capable of using statistical approaches in their brain. I agree with the author’s argument, since more experienced people are more capable of doing certain tasks. A rookie who first makes his debut in a professional game may have butterflies, as he has not experienced the atmosphere, the intensity, the jump from the college/high school ranks to the professional ranks. In contrast, experienced professional athletes have already gone through the prep-to-pro transition and have learned over time on how to take care of their bodies, how to refine their techniques, and pay especially close attention to the detail of doing things. Student athletes have to handle both studying and sports, while professional athletes focus on sports and pay close attention to detail with regards to on-court behavior and taking care of their bodies. In essence, being better at playing the sport requires more training, practice, and practical experience on the court. This is closely related to Bayesian statistics, since Bayesian statistics depends primarily on prior knowledge of individual events, with attention to detail.
This article relates to the course lecture portion about probability, specifically Bayes’ rule. Bayes rule states that given two events A and B, the probability of A happening given B is P(A|B) = P(B|A) * P(A) / P(B), where P(B) can be calculated using the law of total probability: P(B) = P(B | A) * P(A) + P(B | not A) * (1 – P(A)). In order to calculate P(A|B), we would have to know P(B | A), P(A), and P(B | not A). For example, let A be the event that a team wins a championship, and B be the event that a team selects a high school player. The probability of a team winning a championship given that they choose a high school draft pick P(A|B) is equal to the probability of that the team selects a high school player given that the team is already successful P(B|A) times the probability of the team winning a title P(A) divided by the probability of a high school player gets drafted P(B). For simplicity, let’s assume that the team is drafting a high school player who can legitimately help them win a championship, rather than just riding the end of the bench. If a team is looking to repeat a title, the management would rather seek out for players who could revamp the franchise (or keep the roster the same) rather than unproven high school draft picks, so P(B|A) is low. P(A) is high if the team decides to stick to their current roster or sign an all-star player who fits perfectly with their game plan. P(B) is usually high when a team is rebuilding, or have a losing record in recent seasons, not when a team has already won the last championship. So, a high school athlete can help his team win a state championship just because of his height and ability to jump over people and dunk, with little regards to his actual fundamentals. However, he may not succeed in the NBA since the NBA is much faster in tempo, and the players’ bodies are more built to intense contact.