2.3 Moneyball: The Power of Sports Analytics

2.3 Moneyball: The Power of Sports Analytics

Quick Question

 

If a baseball team’s OBP is 0.311 and SLG is 0.405, how many runs do we expect the team to score?

Using the linear regression model constructed during the lecture (the one that uses OBP and SLG as independent variables), enter the number of runs we expect the team to score:

Exercise 1

 Numerical Response 

 

Explanation

Our linear regression model was:

Runs Scored = -804.63 + 2737.77*(OBP) + 1584.91*(SLG)

Here, OBP is 0.311 and SLG is 0.405, so our prediction is:

Runs Scored = -804.63 + 2737.77*0.311 + 1584.91*0.405 = 689 runs

If a baseball team’s opponents OBP (OOBP) is 0.297 and oppenents SLG (OSLG) is 0.370, how many runs do we expect the team to allow?

Using the linear regression model discussed during the lecture (the one on the last slide of the previous video), enter the number of runs we expect the team to allow:

Exercise 2

 Numerical Response 

 

Explanation

Our linear regression model was:

Runs Allowed = -837.38 + 2913.60*(OOBP) + 1514.29*(OSLG)

Here, OOBP is 0.297 and OSLG is 0.370, so our prediction is:

Runs Scored = -837.38 + 2913.60*(.297) + 1514.29*(.370) = 588 runs

CheckShow Answer

 

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