Comparing Normal Distributions: Dodgers vs. Rockies Run Scoring Probability

  • Context: Undergrad 
  • Thread starter Thread starter dmlockwood
  • Start date Start date
  • Tags Tags
    Statistics
Click For Summary

Discussion Overview

The discussion revolves around comparing the run scoring probabilities of the Los Angeles Dodgers and the Colorado Rockies using normal distributions. Participants explore the statistical methods for determining the likelihood of one team scoring more runs than the other, focusing on the properties of normal distributions and the calculation of probabilities based on sample means and standard deviations.

Discussion Character

  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant presents the average runs scored and standard deviations for both teams, seeking assistance in comparing the two normal distributions.
  • Another participant explains the need to derive the distribution of the difference in scores (scoreR - scoreD) to find the probability of one team scoring more than the other.
  • A clarification is requested regarding the calculation of z scores and the necessity of knowing where the distributions intersect before subtracting results.
  • A subsequent reply emphasizes that z scores cannot be calculated first and then subtracted, detailing how to find the mean and variance of the difference and how to normalize the z score for the difference in scores.

Areas of Agreement / Disagreement

Participants generally agree on the approach of using the difference of normal distributions to find probabilities, but there is some uncertainty regarding the proper calculation of z scores and the interpretation of the distributions.

Contextual Notes

Participants express limitations in their understanding of the statistical methods involved, particularly in calculating z scores and the implications of the distributions' means and variances.

dmlockwood
Messages
2
Reaction score
0
Lets say, that the Los Angeles Dodgers have an average runs scored of 5.25 (the sample mean). The standard deviation across this sample is 3.15. The Colorado Rockies have an average runs scored of 4.45 and a standard deviation of 3.65. Assuming normal distributions, how would we determine the probability of the Rockies scoring more runs than the Dodgers, and vise versa.

I know that I am really generalizing things here...there is more to it. This example illustrates what I am trying to do though...compare two normal distrubutions. I have taken statistics courses, but it's been a while...any help on this would be greatly appreciated.
 
Physics news on Phys.org
You're looking for the probability P(scoreD < scoreR) or vice versa.

P(scoreD < scoreR) = P(0 < scoreR - scoreD)

which means you need to derive the distribution of the difference scoreR - scoreD.

Luckily, the difference of two normals is itself a normal distribution. See Properties. In your case, the difference is V = X - Y = scoreR - scoreD.
 
Last edited:
Clarification

I understand how you can find a z score for either of these distributions, but I don't know where to measure the score.

If I want to find the probability of the Dodgers scoring 4 runs...I could find the z score for that. Wouldn't I have to know where these two distributions meet though, before you can just subtract the results of the z score.

Thanks for your reply...if you could clarify this for me that would be great!
 
For this type of problem you cannot find the z scores first, then take their difference.

The mean of "R - D" is (mean of R) - (mean of D). Say the difference is M = 4.45 - 5.25.

The variance of "R - D" is (var. of R) + (var of D). Say the sum is V = (3.65)2 + (3.15)2.

Now normalize z = (x - M)/sqrt(V). This is your z score for "R - D".

Say you want P(0 < R - D) = 1 - P(R - D < 0). That is x = 0. The z score you want is then z = (0 - M)/sqrt(V) = -M/sqrt(V). Which will give you the probability that the Rockies will "beat" the Dodgers.
 
Last edited:

Similar threads

Replies
2
Views
2K
Replies
8
Views
3K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 1 ·
Replies
1
Views
8K
Replies
1
Views
2K
  • · Replies 6 ·
Replies
6
Views
7K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
Replies
4
Views
3K