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# Nivedhan QM PPT.pptx

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# Nivedhan QM PPT.pptx

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### Nivedhan QM PPT.pptx

1. 1. Application of Poisson Distribution By Nivedhan K
2. 2. Poisson Distribution? 2 The Poisson Distribution is a probability distribution that is used to model the probability that a certain number of events occur during a fixed time interval when the events are known to occur independently and with a constant mean rate.
3. 3. The history of the Poisson Distribution 3 Like many statistical tools and probability metrics, the Poisson Distribution was originally applied to the world of gambling. In 1830, French mathematician Siméon Denis Poisson developed the distribution to indicate the low to the high spread of the probable number of times that a gambler would win at a gambling game – such as baccarat – within a large number of times that the game was played. The wide range of possible applications of Poisson’s statistical tool became evident several years later, during World War II when a British statistician used it to analyze bomb hits in the city of London. R.D. Clarke refined the Poisson Distribution is a statistical model and worked to reassure the British government that the German bombs fell randomly, or purely by chance and that its enemies lacked sufficient information to be targeting certain areas of the city. Since then, the Poisson Distribution’s been applied across a wide range of fields of study, including medicine, astronomy, business, and sports.
4. 4. Formula 4 Where: • e is Euler's number (e = 2.71828...) • x is the number of occurrences • x! is the factorial of x • λ is equal to the expected value (EV) of x when that is also equal to its variance
5. 5. Application used is sports 5
6. 6. Example 6 In our example, we will use the data from the 2018-2019 English Premier League to calculate a hypothetical match between Manchester City and Liverpool. Manchester is the home team, while Liverpool is the away team.
7. 7. 7 Before calculating these, we need to know: •The total home goals scored by all EPL teams •The total away goals scored by all EPL teams •The average number of home goals and away goals per match for the whole league We need to calculate Manchester City’s & Liverpool’s: •Home goal average •Average goals allowed per home match
8. 8. 8 Calculating Attack Strength With these results, we can easily calculate attack strength for the home and away teams. Attack Strength is the team’s average number of goals, divided by the league’s Average number of goals. Home Manchester City’s Attack Strength: 3.00 ÷ 1.53 = 1.96 Away Liverpool’s Attack Strength: 1.78 ÷ 1.147 = 1.55 Calculating Defense Strength Calculating Defense Strength is just as easy. Simply divide the team’s average number of goals allowed by the league’s average number of goals allowed. Home Manchester City’s Defense Strength: 0.63 ÷ 1.147 = 0.55 Away Liverpool’s Defense Strength: 0.63 ÷ 1.532 = 0.41
9. 9. 9 Goal expectancy Now that we have determined each team’s Attack Strength and Defense Strength, we can calculate each team’s likely score. Manchester City goal expectancy To determine how many goals Manchester City will likely score, we need to multiply Manchester City’s Attack Strength by Liverpool’s Defense Strength and the league’s average number of home goals. That gives us: 1.96 × 0.41 × 1.532 = 1.23 Liverpool goal expectancy To determine how many goals Liverpool will likely score, we need to multiply Liverpool’s Attack Strength by Manchester City’s Defense Strength and the league’s average number of away goals. That gives us: 1.55 × 0.55 × 1.147 = 0.997 Average goals scored in the match Manchester City: 1.23 Liverpool: 0.997
10. 10. 10 Now that we have each team’s home and away from defense and attack strengths, we can easily use them with the Poisson formula to calculate the probability of any possible outcome. The Poisson Formula is: P = (λx e –λ) / x! Using this formula, you can calculate the probability for any number of goals. (limit yourself from 1 to 5) separately in the top in “Event occurrences”, and the expected average goals scored per match in the bottom, in “Expected event occurrences”. That gives us the following probability for Manchester City Goals: That gives us the following probability for Liverpool City Goals:
11. 11. 11 Predicting the match outcome based on these probabilities To get each possible score, simply multiply the probability of each possible score by each team by the probability of each possible score by the other team. This gives you the following distribution: So, the most likely score is 1 – 1 or 1 – 0 followed by 0 – 0 or 0 – 1. Given the defense averages of both teams, it is easy to see how these would be very likely scores.
12. 12. Thank You!! 12