Ticket Sales For Oakland Athletics Baseball Club Physical Education Essay

Oakland Athletics Baseball Club (A’s) was one of 28 professional teams divided into 4 leagues. A’s ranking in his own league was 2nd and Mark Nobel was one of his star player, who was arguing to raise his salary from $40,000 p.a to $600,000 p.a by taking the plea that in addition to his contribution to success of A’s in 1980, he had also been attraction at the box office. Further Nobel had won Gold Glove Award as best fielding pitcher and finished 2nd in the balloting for the Cy Young Award which reinforced his claims.

In order to verify the Nobel’s claims and to give a judicious advice to Mr. Steward Roddey, general manger A’s baseball team, various mathematical and statistical analysis have been performed. Further a model has been developed to forecast the future tickets sale based on certain parameters.

As per our analysis, Nobel claim regarding major attraction at box office was not right. Further he was not the major factor in success of A’s. No doubt, A’s success rate was high when Nobel played but Nobel played most of his matches against low ranking teams’ resulting in high success rate. Further, average tickets sale for the matches when Nobel played is high because of one outlier, Detroit match. Actually that was double header match and, A’s position, at that time, was one and Detroit team “game behind” position was also zero. This phenomenon lead to high average of Nobel matches.

Keeping in view all these points and arguments, it is recommended to convince the Nobel to review its stance and revised salary package may be negotiated accordingly.

Detailed Study and Analysis

Two separate worksheets have been created by filtering the data in to Nobel’s game and non Nobel’s games using the filtering command and then copying the data one by one to separate sheets for further analysis. Both sheets are available as exhibit 1 & exhibit 2 in the end of this report.

If we analyze the data available in exhibit 1 &2 using different Ms Excel techniques i.e sum, count, countif, and percentage, we come across the inferences mentioned in exhibit 3.

It is quite evident from exhibit-3, Nobel’s matches tickets sale is 8% higher (54%) as compare to no Nobel’s matches (46%). Nobel itself may be the one factor but there may be other factors contributing to this attraction as well. If we carefully examine exhibit 3, we can easily deduce, Nobel played most of the games in night time (56%) where it might be more chance of attendance. Further, only 22% matches were televised, so likely more chances of viewer in the ground. Moreover, noble played more matches at weekends which might also be a factor having more average tickets sold in Nobel games.

On the other hand, very less promos have been used for Nobel’s games, only (23%) and despite this Nobel attracts the crowed.

For two other important factors, team position and games behind, average is same for bother Nobel’s games and Non Nobel’s games.

Let us focus our analysis on two parameters for sales break down i.e on opposing teams and games in which Nobel played (exhibit 4). Luckily a very useful tool pivot table is available is Ms Excel. We notice again the same result i.e average tickets for Nobel played matches is higher but when closely probe the table, we witnesses that Nobel is not the only criteria. The major factor affecting the average of attendance is opponent team. For example, overall attendance average for Yankees is 40,445, Boston 13,023, Baltimore 11,753 and Kansas City 11,652 despite Noble played very few matches against these team i.e only two out of three matches against Yankees and two out of four against Boston. Further Nobel did’t play even single match against Baltimore and Kansas City despite their tickets sold are above average.

From the table, another thing which we figured out is the league against you play. If you play matches against opponent league i.e Eastern Division, that attract more spectators instead of teams of Western Division. There are three teams from Eastern Division league having tickets sale above average.

Even, decrease in tickets sale was witnessed for matches against Yankees, Milwaukee, Boston and Cleveland by 6%, 27%, 7% &33% respectively when Nobel plays. However, increase in tickets sale have been witnessed in the matches played against Seattle, Detroit, White Sox and Texas by 22%, 42%, 30% and 14% respectively. Actually these are low ranking teams and presence of Nobel might create attraction for the public (exhibit 5).

Let us apply the regression analysis to determine the importance of various factors in the attendance that happened, but before doing this exercise it is necessary to refine the data and eliminate / add the factors that are irrelevant / relevant to our analysis. During exploration of mess to find out the problem and opportunities, we stumble upon the inference, that opposing team number don’t have any sense and Ms excel would not be able to develop correlation with the opposing team numbers and tickets sold, rather it would be much better to allot ranking on the basis of percent of success for regression analysis. Because team success rate and spectators’ interest in the game might have some relationship.

There is team in the data “White Sox” whose ranking / percent of success is not available in the provided data. It is assumed that “White Sox” did not play in any league and hence least ranking has been allocated to “White Sox”

There are six double header entries in the 75 entries. It means, Oakland A’s played two consecutive games on the same day between the same teams. One ticket at the same price as a single game ticket, provided admission to both games. It is obvious that attendance in double header matches should be more because one can watch two games in one ticket. To add this factor, another column was added in the data with “double header” name.

Before searching for solution and to know the importance of various factors in the attendance, let us 1st comprehend the problem little bit more by drawing an influence chart.

Oakland Position

Avg. Temp

Game Behind

Weather

Day of Week

Promo

Double Header

Opponent Team

Tickets Sale

Day and Time

Time of day

Televised

TV & Promo

Precipitations

Nobel

Opp. Team Ranking Success

To understand the relationship, let us 1st determine the correlation of individual factor with the tickets sale.

Correlation of No. of Tickets Sold with the other Factors

Opp. Team Ranking.

Game Behind

Position

Day of Week

Avg. Temp.

Precip

Time of Game

Double Header

Televised

Promo

Nobel

-50.90%

7.48%

-11.49%

-0.70%

-6.08%

-9.74%

12.87%

20.63%

-9.79%

26.66%

7.65%

From above table, it is quite obvious that opposing team ranking on the basis of success percentage is most relevant factor, then promo, double header, time of game, televised, precipitations and then Nobel respectively.

Relationship among response variable (tickets sold) and explanatory variables can also be established through multiple regressions (exhibit 6):

If we look at the coefficients values & P values, it give us almost the same end results as we deuced from correlations i.e ranking of opponent team is the most relevant factor then d. header, promo, time of game, precipitations and then Nobel respectively. Over and above, P Values give us the probability that a & b we determined as coefficients were by luck i.e how likely the coefficients we calculated would be wrong. Hence lesser the P Value, greater will be surety of of each factor relationship with attendance.

R square and adjusted R square tells us that how well regression equation fits data. 41.44% R square means that probability of fitness of regression line is 41.44%. As we are using multiple regression, hence it is better to use adjusted R. Further significance of F tells us the chance that R square we got was luck. As our significance of F is very low (0.0171%) hence we are 99.98% confident that regression equation/ line is not through by just luck.

Keeping in view the above discussion, one can easily establish the opinion that most important factor influencing the ticket sale is opponent team. If Oakland A’s plays against strong team will attract more crowed and vice versa. No doubt, A’s own performance also matters, and A’s performance is not merely dependent on Mr. Nobel (exhibit 7). Nobel games win % is 67% (22/33%), it means, out of 16 home games played by Nobel, 11 would have been won by A’s. As A’s overall success rate is 51.2 %, hence out of total 75 home games, A’s would have won 38 games. 11 (67%) when Nobel was playing and 27 (46%) when Nobel was not playing). Hence we can say that A’s team does not win because of Nobel, however it is true that success rate increases when Nobel plays.

It is recommended that all the points mentioned in above paragraphs one (1) to three (3) may be explained to Mr. Nobel while negotiating the salary. Further, Roddey should keep the following things in mind while deciding the salary of Nobel.

Nobel played only 16 matches out of 75 home matches, hence he may affect only 21% matches.

If we normalize the outlier of Detroit match, Nobel average comes down to 11,988 instead of 12,664 leaving difference of only 1,129 per match. As Noble played 16 matches, total difference would be 18,058, multiplied by the average ticket price $3.66; total resulting figure will $ 66,093 instead of 105,650 as claimed by Nobel.

As Nobel correlation to attract the crowed is just 7.65%.

In team success, there is significant contribution of Billy Martin as evident from history.

Promotions also played vital role in attraction of spectators.

Part II.

Before developing a model for future forecasting, input parameters (explanatory) having high P value for their respective coefficients have been eliminated from data and revised multiple regression applied. Following parameters were eliminated:-

Position

Game behind

Average Temperatures

Televised

Revised regression chart is attached as exhibit 8

Trend chart on the basis of projected values and actual values is attached as exhibit 9

Model Snapshot is attached as exhibit 10. (Complete model is available in corresponding Ms Excel File)

Exhibit 1-non Nobel Games’ Data

Date

Tickets Sold

Opp. Team

Position

G. Behind

D.o. Week

Temp.

Precipitation

Time of Game

Televised

Promotions

Nobel

4/10

24,415

2

5

1

4

57

0

2

0

0

0

4/11

5,729

2

3

1

5

66

0

2

0

0

0

4/12

5,783

2

7

1

6

64

0

1

0

0

0

4/13

6,300

2

5

1

7

62

0

1

0

0

0

4/15

2,140

1

6

1

2

60

0

2

0

0

0

4/16

2,418

1

4

1

3

61

0

1

0

0

0

4/18

6,570

3

3

1

5

58

0

2

0

0

0

4/20

9,014

3

1

0

7

57

1

1

1

0

0

5/2

8,636

5

1

0

5

57

0

2

0

0

0

5/3

7,062

5

1

0

6

59

0

1

1

0

0

5/5

12,605

11

1

0

1

60

0

2

0

0

0

5/6

24,272

11

1

0

2

60

0

2

0

1

0

5/7

4,731

11

1

0

3

60

0

1

0

0

0

5/10

4,929

7

1

0

6

55

1

1

0

0

0

5/23

4,141

12

4

2

5

56

0

2

0

0

0

5/24

5,061

12

3

2

6

55

0

1

1

0

0

5/26

21,882

13

4

2

1

58

0

1

1

0

0

5/27

4,488

13

4

3

2

58

0

2

0

0

0

5/28

4,094

13

3

2

3

59

0

1

0

0

0

6/7

12,990

9

3

6

6

61

0

1

0

0

0

6/8

18,753

9

3

7

7

63

0

1

0

0

0

6/9

20,162

10

3

7

1

61

0

2

0

0

0

6/10

3,873

10

3

7

2

59

0

2

0

0

0

6/11

5,628

10

3

7

3

60

0

1

0

1

0

6/13

47,768

4

3

7

5

60

0

2

0

0

0

6/15

46,294

4

3

8

7

64

0

1

0

1

0

6/23

17,666

6

3

9

1

62

0

2

0

1

0

6/24

4,899

6

4

10

2

62

0

2

0

0

0

6/25

6,856

6

4

11

3

63

0

1

0

0

0

6/28

5,204

8

4

12

6

69

0

1

1

0

0

6/29

7,369

8

4

12

7

63

0

1

0

0

0

7/11

7,696

3

5

12

5

62

0

2

0

0

0

7/16

7,413

5

5

13

3

65

0

2

0

0

0

7/17

6,370

5

3

12

4

65

0

1

0

0

0

7/19

6,506

11

3

11

6

65

0

1

1

0

0

7/20

10,606

11

3

11

7

65

0

1

1

1

0

7/21

14,588

7

3

12

1

65

0

2

0

1

0

7/23

4,765

7

3

12

3

64

0

1

0

0

0

8/4

16,741

2

2

12

1

65

0

2

0

0

0

8/5

4,651

2

2

12

2

67

0

2

0

0

0

8/6

6,697

2

2

12

3

63

0

1

0

0

0

8/8

6,283

1

2

13

5

62

0

2

0

0

0

8/10

13,062

1

2

13

7

63

0

1

0

0

0

8/19

11,934

9

2

15

2

67

0

2

0

0

0

8/21

10,947

9

2

15

4

61

0

1

0

0

0

8/22

11,532

10

2

15

5

62

0

2

0

0

0

8/23

10,578

10

2

16

6

64

0

1

0

1

0

8/24

18,745

10

2

17

7

63

0

1

0

1

0

8/26

32,905

4

2

17

2

62

0

2

0

1

0

9/8

9,731

12

3

19

1

65

0

2

0

0

0

9/9

2,443

12

3

18

2

63

0

1

0

0

0

9/12

17,440

13

2

17

5

62

0

2

0

0

0

9/13

11,253

13

2

16

6

61

0

1

0

0

0

9/14

10,756

13

2

17

7

63

0

1

0

0

0

9/23

3,069

8

2

15

2

70

0

2

0

0

0

9/24

3,836

8

2

14

3

69

0

2

0

0

0

9/25

3,180

8

2

14

4

64

0

1

0

0

0

9/27

4,581

6

2

13

6

62

0

1

0

0

0

9/28

10,662

6

2

12

7

65

0

1

0

1

0

Exhibit 2- Nobel Games’ Data

Date

Tickets Sold

Opp. Team

Position

G. Behind

Day of Week

Temp.

Prec

Time of Game

Televised

Promotions

Nobel

4/14

5,260

1

7

2

1

60

0

2

0

1

1

4/19

5,239

3

2

1

6

59

0

1

1

0

1

5/4

18,217

5

1

0

7

58

0

1

0

0

1

5/11

7,839

7

1

0

7

57

0

1

0

0

1

5/25

10,549

12

5

3

7

57

0

1

0

0

1

6/6

15,947

9

3

6

5

59

0

2

0

0

1

6/14

27,312

4

3

7

6

63

0

1

0

0

1

6/27

8,482

8

4

11

5

69

0

2

0

1

1

7/10

11,337

3

5

12

4

66

0

2

0

0

1

7/18

5,949

11

3

12

5

60

1

2

0

0

1

7/22

8,645

7

3

12

2

63

0

2

0

0

1

8/9

13,629

1

2

12

6

63

0

1

0

1

1

8/20

7,569

9

2

15

3

65

0

2

1

0

1

8/25

47,946

4

2

17

1

62

0

2

0

0

1

9/10

3,598

12

2

17

3

64

0

1

0

0

1

9/26

5,099

6

2

14

5

64

0

2

0

0

1

Exhibit 3

Description

Nobel Game

%

No Nobel Games

%

Total

Total Tickets

202,617

24%

640,702

76%

843,319

Average Tickets

12,664

54%

10,859

46%

23,523

Games Count

16

21%

59

79%

75

Day Games

7

44%

32

54%

39

Night Games

9

56%

27

46%

36

Televised

2

22%

7

78%

9

Promotion

3

23%

10

77%

13

Precipitations

1

33%

2

67%

3

Avg. Temp

62

50%

62

50%

124

Avg. Game Behind

9

50%

9

50%

18

Average Position

3

51%

3

49%

6

Average Day of Week

5

52%

4

48%

9

Exhibit 4

Avg Sale Breakdown (Team no. as per original data)

Teams No./ Nobel

Count of Sold

Average of Sold

Sum of Sold

Total Count of Sold

Total Average of Sold

Total Sum of Sold

0

1

0

1

0

1

1

4

2

5,976

9,445

23,903

18,889

6

7,132

42,792

2

7

10,045

70,316

7

10,045

70,316

3

3

2

7,760

8,288

23,280

16,576

5

7,971

39,856

4

3

2

42,322

37,629

126,967

75,258

5

40,445

202,225

5

4

1

7,370

18,217

29,481

18,217

5

9,540

47,698

6

5

1

8,933

5,099

44,664

5,099

6

8,294

49,763

7

3

2

8,094

8,242

24,282

16,484

5

8,153

40,766

8

5

1

4,532

8,482

22,658

8,482

6

5,190

31,140

9

4

2

13,656

11,758

54,624

23,516

6

13,023

78,140

10

6

11,753

70,518

6

11,753

70,518

11

5

1

11,744

5,949

58,720

5,949

6

10,778

64,669

12

4

2

5,344

7,074

21,376

14,147

6

5,921

35,523

13

6

11,652

69,913

6

11,652

69,913

Grand Total

59

16

10,859

12,664

640,702

202,617

75

11,244

843,319

Exhibit 5

Ranking of Opponent team, Nobel Plays and Double Header Matches

Total Average of Sold

Teams as per new Ranking

No D. Header

D. Header

Total Sum of Sold 2

Average of Sold

Sum of Sold 2

Average of Sold

Sum of Sold 2

No Nobel

10,194

570,858

23,281

69,844

10,859

640,702

1

39,600

79,199

47,768

47,768

42,322

126,967

2

11,753

70,518

11,753

70,518

3

11,652

69,913

11,652

69,913

4

8,933

44,664

8,933

44,664

5

10,513

84,105

10,513

84,105

7

11,744

58,720

11,744

58,720

8

10,045

70,316

10,045

70,316

9

5,344

21,376

5,344

21,376

10

8,094

24,282

8,094

24,282

11

7,133

14,266

9,014

9,014

7,760

23,280

12

3,614

10,841

13,062

13,062

5,976

23,903

13

4,532

22,658

4,532

22,658

Nobel

12,648

164,418

12,733

38,199

12,664

202,617

1

37,629

75,258

37,629

75,258

4

5,099

5,099

5,099

5,099

5

11,758

23,516

18,217

18,217

13,911

41,733

7

5,949

5,949

5,949

5,949

9

7,074

14,147

7,074

14,147

10

7,839

7,839

8,645

8,645

8,242

16,484

11

5,239

5,239

11,337

11,337

8,288

16,576

12

9,445

18,889

9,445

18,889

13

8,482

8,482

8,482

8,482

Grand Total

10,656

735,276

18,007

108,043

11,244

843,319

Exhibit 6

Regression Statistics

Multiple R

64.37%

R Square

41.44%

Adjusted R Square

31.21%

 

Df

F

Significance F

Regression

11

4.0522964

0.0171%

 

Coefficients

P-value

Lower 95%

Upper 95%

Intercept

17,251

48.78%

(32,144)

66,646

Team Ranking.

(1,297)

0.00%

(1,863)

(731)

Position

(177)

81.51%

(1,685)

1,331

G. Behind

(9)

96.86%

(459)

441

D.o.Week

297

60.49%

(843)

1,436

Temp.

(68)

87.32%

(921)

785

Precip

(3,882)

46.69%

(14,481)

6,717

T.o.Game

3,111

19.02%

(1,583)

7,806

Tele

(350)

90.93%

(6,469)

5,769

D.Header

10,002

0.81%

2,699

17,306

Promo

6,119

1.97%

1,009

11,230

Nobel

1,421

55.28%

(3,336)

6,178

Exhibit 7

Nobel Success Rate vs. Total Success Rate

No. of Matches

%

Total Nobel Games (Home/Road)

33

Win

22

67%

Total Noble Games (Home)

16

Success (by applying success rate)

11

69%

Total Oakland As Home Games

75

Success rate of Oakland

51.2%

Total Wins (Oakland A’s Home)

38

No Noble Wins

 

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