Basic sports analytics using Pythagorean-expectation that can be used to learn sports analytics. This includes code in python along with notebooks and some R code.
The name comes from its resembelance to the Pythagoras Theorem in math (One of the core fundamental prequisites of learning about statistics|probability|random variables along with linear algebra, trig etc.). The formula is as follows:
The Pythagorean expectation, developed by Bill James in the early 1980s, is a baseball statistic that attempts to measure how many runs teams should have scored based on their overall performance. This metric combines both offensive and defensive statistics, such as batting average and earned run average (ERA), to determine an expected number of runs scored or prevented. The Pythagorean premise is that a team’s run differential should equal the number of runs expected from its performance. In sports, this theorem can be applied to any sport where teams score points and has quickly become one of the most important metrics in sports analytics. The concept has also been adapted into other sports such as cricket, football/soccer, basketball, hockey, and football. By combining numerical data, sports analytics can provide a more comprehensive understanding of each team’s performance and the overall outcome of sports games. As sports evolve, so does sports analytics; understanding Pythagorean expectation is an essential part of today’s sports analytics landscape
You can read more about pythagorean expectation in sports here: https://en.wikipedia.org/wiki/Pythagorean_expectation
In sum, the Pythagorean expectation is a sports statistic that combines both offensive and defensive statistics to calculate how many runs/points/goals teams should have scored based on their performance. This metric is widely used in sports analytics in the modern sports world
Please note, however, that the Pythagorean expectation is just a statistic and it does not guarantee wins or losses. Teams may still win games with a lower expected run differential or lose with a higher one. Ultimately, the Pythagorean expectation is just one of many sports analytics tools available to sports teams and organizations. Ultimately, sports analytics is only a part of the overall sports strategy, and it should be used in combination with other strategies for ultimate success
- Python
To get a local copy up and running follow these simple example steps.
👤 Maya D. - child of God
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This project is MIT licensed.