The Pythagorean Expectation, introduced by Bill James in baseball analysis, is a statistical concept that can be applied to various sports to explain team success and predict future results. This project derives the Pythagorean Expectation for five different leagues in five different sports:

1. Major League Baseball (MLB)
2. English Premier League (soccer)
3. Indian Premier League (cricket)
4. National Basketball Association (NBA)
5. National Hockey League (NHL)

The project is structured into sections, each dedicated to applying the Pythagorean Expectation to a specific sports league.

This section focuses on deriving the Pythagorean Expectation for MLB teams during the 2018 season. The analysis includes considering runs scored and conceded, creating a regression model to establish the relationship between win percentage and the Pythagorean Expectation, and interpreting the results.

Here, the Pythagorean Expectation is applied to cricket, specifically the Indian Premier League (IPL) in 2018. The T20 format and unique characteristics of cricket are taken into account. The section discusses the code, results, and considerations, including the impact of a smaller number of teams and games in the IPL.

Similar analyses are conducted for the NBA and NHL, applying the Pythagorean Expectation to basketball and hockey, respectively. The results are compared to those of MLB, highlighting similarities and differences in the predictive power of the Pythagorean model across different sports.

This section explores whether the Pythagorean Expectation serves as an effective predictor for second-half win percentages in MLB. The analysis compares its forecasting accuracy with first-half win percentage, providing insights into the model's reliability over different time periods.

The Pythagorean Expectation, initially developed for baseball, demonstrates its applicability and predictive power across various sports leagues. While some variations and considerations arise, the overall effectiveness of this statistical model provides valuable insights into team performance and forecasting. Further exploration and application in sports analytics are encouraged.