Question

Is there a relationship between total team salary and team performance in a sport such as...

Is there a relationship between total team salary and team performance in a sport such as professional basketball? To answer this question we will examine the number of wins and total team payroll in a recent season for all the teams in the National Basketball Association (NBA). The data are in this Excel file.

Question 1. Let the x-variable be the team payroll and let the y-variable be the team wins. Find the intercept b0 and slope b1 of the least squares prediction line  = b0 + b1*Payroll.

  intercept (use 2 decimal places in your answer)
  slope (use 3 decimal places in your answer)

Question 2. If an NBA team spent an additional $9.2 million in team salary, how many more games could the team expect to win? (Use 1 decimal place in your answer).


Question 3. Suppose a basketball team spent $111 million on salaries and won 46 games.
Part a. Based on their payroll would they have done better or worse than predicted? (only 1 submission allowed).

Worse than predictedBetter than predicted     


Part b. What is the residual for this basketball team? (use 2 decimal places in your answer)

Question 4. All teams have a goal to make the postseason playoffs. A basketball team thinks that it can make the playoffs next year if it wins 12 more games. How much should the basketball team increase its payroll if it wants to make the playoffs next year?

Note: Use 2 decimal places. Your answer should be in units of $1 million; do NOT include a dollar sign in the answer; for example, if a team should increase its payroll by $5,250,000, then the answer is 5.25.

$

increase in team payroll (in $million) needed for basketball team to win 12 more games

Recent BASKETBALL Team Payrolls and Wins
Team Payroll Wins
Cleveland Cavaliers 137.363 50
Los Angeles Lakers 105.355 35
Boston Celtics 115.084 55
Orlando Magic 98.285 25
Denver Nuggets 107.889 46
Portland Trail Blazers 119.109 49
Houston Rockets 119.070 65
San Antonio Spurs 116.154 47
Dallas Mavericks 85.904 24
New Orleans Pelicans 119.800 48
Utah Jazz 107.613 48
Atlanta Hawks 100.414 24
Phoenix Suns 94.818 21
Miami Heat 131.223 44
Chicago Bulls 89.425 27
Philadelphia 76ers 100.794 52
Detroit Pistons 100.086 39
Indiana Pacers 94.430 48
Charlotte Hornets 117.228 36
Milwaukee Bucks 120.805 44
Brooklyn Nets 96.040 28
Toronto Raptors 116.575 59
New York Knicks   107.855 29
Golden State Warriors 137.495 58
Minnesota Timberwolves 117.469 47
Memphis Grizzlies 110.273 22
Oklahoma City Thunder 134.294 48
Washington Wizards 124.180 43
Los Angeles Clippers 118.907 42
Sacramento Kings 95.628 27
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Answer #1

Answer to 1st question :

where x = team payroll and y is the team wins and also a = y - intercept = b0

The excel output of the following data is given below -

So, Mean of x , = 111.32 and Mean of y , = 41

Standard deviation of x , S.D. (x) = 14.02 , and , Standard Deviation of y , S.D. (y) = 12.22

Number of Observations , n = 30

b1 = 0.541 = slope and b0 = y - intercept = -19.22

The equation is -

= -19.22 + 0.541x

Answer to 2nd question :
Given , x = $ 9.2 + $ 137.363 = $146.563 (The NBA team is Cleveland Cavaliers)

= -19.22 + (0.541 x 146.653) = 60.1 wins approximately

Originally they had 50 wins , So , the team could expect to win 10.1 extra games

Answer to 3rd question :

Given , x = $111

= -19.22 + (0.541 x 111) = 40.83

They had fared better than predicted...... (Answer a)

Originally the had won 46 games

The residual = Original - predicted = 46 - 40.83 = 5.17 (Answer b)

Answer to 4th question :
Let that basket ball team be Cleveland Cavaliers

They have originally 50 wins , If additional 12 wins are added to their tally their total wins would be 62

So , substituting these values in the prediction equation we solve for x ,

we get the value of x = $150.13

Increase in team payroll (in $million) needed for basketball team to win 12 more games = $150.13 - $137.363

= $12.77

(NOTE THAT : In Answers 2 & 4 I just took an example from the data , for other values the answer can be different)

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