We’ve all heard the saying “Be nice to nerds. Chances are you’ll end up working for one.” Not only did I work for one but I asked to work for him. John isn’t a nerd in the traditional sense. He has enough social presence to navigate the world without the awkwardness generally associated with unusually high intelligence and he’s surprisingly athletic, you know, for a nerd. His natural intellect is off the charts. He has a way with numbers. He speaks so quickly that you don’t believe even he can digest all the numbers he throws out.
One thing I gained from working with him is that anyone can learn numbers. Don’t misunderstand me, learning numbers and math is one thing. Having a brain that can process it at light speed is something else. But if you can change the way you think and break things down into smaller more easily to understand chunks I found understanding John was possible.
In the same way understanding John required me to consciously change the way I think about the numbers he was tossing out sabermetrics requires us to rewire how our brains think about baseball statistics.
Bill James defined sabermetrics as the search for objective knowledge about baseball.
Whether a sabermetric is measuring the performance of a position player or pitcher they are almost always expressed in wins, runs, or relative to league average. This is because the objective of baseball is to win the game by scoring more runs than your opponent.
This is the way in which we need to retrain our brains. While evaluating players and their performances we need to think in terms of how many runs or wins is this player and his performance worth. Or we need to think in terms of how much better or worse was he compared to the league average. This is thinking sabermetrically.
The questions we’re asking have not changed. We still want to know what player is “most valuable “. We still want to know which player throughout history is the greatest. We still want to know if pitchers are as valuable as hitters. Instead of using conjecture sabermetrics has taken a mathematical approach to attempt to answer these questions.
This does not mean we dismiss or no longer care about home runs, rbi, stolen bases, innings pitched, strikeouts and other traditional stats. Those stats are raw data and are often referred to as counting stats. Without this raw data, sabermetrics are not possible. We need to know these in order to properly evaluate players. In and of themselves they no longer provide us answers to our questions. Instead, they are pieces of a complex puzzle that we’re trying to solve in order to answer these questions.
Sabermetrics is a continuously evolving science. Sabermetricians by nature are not satisfied. They seek to improve upon the existing and seek new ways to measure. As the amount of data available continues to grow the number of metrics and their accuracy will increase.
I don’t have an advanced degree in math. I am proof that a person with a limited mathematical background can have a working understanding of advanced statistical analysis. You simply have to want to learn it.
In this series of articles, we will discuss metrics that you may not be familiar with. I won’t attempt to explain each metric mentioned in each article. My idea is to chip away one larger metric at a time.
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