Spurs Home Game Ticket Sales Analysis

Renkoh KatoGuang LiJia Yu

Master of StatisticsCornell University

May 4, 2011

Agenda

Client & Problem

Key Assumptions

Influencing FactorAnalysis

Introduction1 Data Description2 Models and Analysis3

Project Objective

4 Summary

Preliminary Analysis

Ticket Sales Prediction

Sold-out Games PredictionTimeline & Approach

Overview Interactive Excel Tool

Recommendations

Limitations

2

Agenda

Client & Problem

Key Assumptions

Variables

Introduction1 Data Description2 Models and Analysis3

Project Objective

4 Summary

Preliminary Analysis

Multivariate Linear Regression

Logistic RegressionTimeline & Approach

Overview Excel Demo

Recommendations

Limitations

3

Introduction-Client

Who are the Spurs?

• 4 NBA Championships

• Third highest winning percentage in NBA

history

• Moved to AT&T Center in 2002. Seats

approximately 18,000 people

4

Introduction-Client’s Problem & Project Objective

Client’s Problem

The Spurs wants to explore the factors driving the ticket sales so that they can better predict the ticket demand, maximize ticket revenue and improve operation efficiency

Project Objective

Study and identify the most significant factors which affect ticket demand

Forecast ticket sales for home games during the regular season

Deliver measurable results to support strategic pricing for the Spurs in today’s dynamic market

5

Introduction-General Approach

Periodical Review and Adjustment

Brainstormed influential factors from both micro and macro levels Communicated with client on a regular basis and adjusted project approach accordingly Collected data both from client and public resources

Verified the data with the client and within the team Conducted preliminary analysis and graphs to understand the data Identified and eliminated outliers Modeling: general trend, linear regression and logistic regression models

Excel interactive model based on results generated from SAS Business recommendations based on statistical analysis Presentation Written reports

6

Phase 1 Oct. 2010- Dec. 2010

Identify influential factors and data

collection

Phase 2 Jan. 2011- Mar. 2011

Data cleaning and model building

Phase 3Apr. 2011- May 2011Recommendations

and final deliverables

Project Objective:Ticket Sales Forecast

Agenda

Client & Problem

Key Assumptions

Variables

Introduction1 Data Description2 Models and Analysis3

Project Objective

4 Summary

Preliminary Analysis

Multivariate Linear Regression

Logistic RegressionTimeline & Approach

Overview Excel Demo

Recommendations

Limitations

7

Ticket Sales Data from the Spurs

Influential Factors

Data from the Spurs

Data from research

Date of the game Game number Time of the game Opponent Sold-out game Promotion TV Rankings

Opponent team performance Spurs team performance Economic condition Weather

Brain Storming Client Communication Desk Research Expert Interview

8

Data Description- Data Overview

Season ticket (~70%)

Advance ticket (~11%)

Day of game ticket (~2%)

Night walkup ticket (~1%)

Group ticket (~16%)

328 home games

02-03 season~ 09-

10 season

41 games in each

Data Description- Variable Details

Predictor Variables

Response Variables

Data Transformation

Assigned dummy variables to categorical variables such as weather conditions, time of the game and sold-out games

Clustered variables such as day of week and opponents, and then assigned dummy variables to each cluster

Data Analysis

Validated ticket sales data for each type

Plotted the data to explore ticket sales trend

9

Variable Name Variable Type Source Sample Data

Season ticket Numeric Spurs 10,469

Advance ticket Numeric Spurs 4,547

Day of Game ticket Numeric Spurs 140

Night walkup ticket Numeric Spurs 47

Group ticket Numeric Spurs 1,393

Variable Name Variable Type Source Sample Data

Time of game Character Spurs Afternoon

Day of the week Character Research 7

Weekend indicator Character Research 1

Game number Numeric Spurs 57

Soldout game indicator Character Spurs 1

Opponent Character Spurs Sacramento Kings

Opponent group Character Research 3

Spurs winning % last 10 Numeric Research 90%

Opponent winning % last 10 Numeric Research 70%

Spurs winning % Numeric Research 70%

Opponent winning % Numeric Research 68%

Average temperature °F Numeric Research 50

Weather condition Character Research 1

Unemployment rate % Numeric Research 6.3

32

Identify other outliers

Data Description- Identifying Outliers

Sold out game, when sales>16,000

161 sold-out games in total

Ticket sales reached the limit for such games and thus may mislead the models

Ticket Sales vs Opponent Winning Percentage

Ticket Sales vs Spurs Winning Percentage

Spurs Winning% vs Date

Opponent Winning% vs Date Date

10

Kick out first 4 games in each season

1

Kick out sold-out games

Final dataset for

multivariate linear

regression

Calculated Cook’s distance; cook’s distance >1?

Researched on such games; big promotion event, the first home game after a long time, etc. ?

Kick out these outliers

Agenda

Client & Problem

Key Assumptions

Variables

Introduction1 Data Description2 Models and Analysis3

Project Objective

4 Summary

Preliminary Analysis

Multivariate Linear Regression

Logistic RegressionGeneral Approach

Overview Excel Demo

Recommendations

Limitations

11

Modeling and Analysis- Preliminary Analysis

Advance Ticket

Total Paid Ticket Season Ticket

Day of Game Ticket

Time series of ticket sales by different ticket types

Preliminary Conclusion: Overall fluctuations of ticket sales for different types within each season seem to be decreasing Sales for day of game and advance tickets are decreasing from 02-03 season to 09-10 season Sales for season tickets, which accounts for ~70% of total tickets sales, varied significantly by year.

12

Modeling and Analysis- Preliminary Analysis

Total Paid Ticket

Time series of ticket sales by different ticket types

13

Preliminary Conclusion: Higher unemployment rate, lower saleSales for season tickets increased after 04-05 season, the Spur NBA championship year. Therefore, we anticipate that changes in season ticket sales are more sensitive to expectations on team performance

3.0%

3.5%

4.0%

4.5%

5.0%

5.5%

6.0%

6.5%

7.0%

7.5%

8,000

8,500

9,000

9,500

10,000

10,500

11,000

11,500

12,000

2002-2003 2003-2004 2004-2005 2005-2006 2006-2007 2007-2008 2008-2009 2009-2010

Unemployment Rate

SeasonTickets

Sold

Unemployment Rate vs. Season Tickets Sold

Average ticket sales trend by type from 02-03 season to 09-10 season

Season

Average of

Season Ticket

Average of

Group Ticket

Average of

Advance Ticket

Average of Day

of Game Ticket

Average of Night

Walkup Ticket

Average of

Total Tickets

2002-2003 10,341 3,209 1,963 469 252 14,629

2003-2004 9,898 4,500 2,510 413 160 15,231

2004-2005 9,828 5,149 2,452 423 166 15,444

2005-2006 11,257 5,432 2,008 272 86 16,339

2006-2007 11,134 5,836 1,734 278 117 16,181

2007-2008 11,799 5,249 1,307 246 104 16,081

2008-2009 11,205 5,295 1,302 217 120 15,491

2009-2010 10,086 5,906 1,258 182 148 14,627

Average 10,694 5,072 1,817 312 144 15,503

Preliminary Conclusion: Gate Tickets (advance, day of game, and night walkup) show a downward trend while group tickets have been showing an upward trend Percentage of group ticket has been rising from 10.97% (02-03 season) to 20.19% (09-10 season). Advance ticket sales have become less significant in total tickets sales, percentage decreasing from 13.42% (02-03 season) to 8.60% (09-10 season) Percentage of other types of tickets remains relatively constant in the past 8 seasons

14

Season

Average of

Seasonal Ticket

Average of

Group Ticket

Average of

Advance Ticket

Average of Day

of Game Ticket

Average of Night

Walkup Ticket

Average

of Total Tickets

2002-2003 70.69% 10.97% 13.42% 3.21% 1.72% 100%

2003-2004 64.99% 14.77% 16.48% 2.71% 1.05% 100%

2004-2005 63.64% 16.67% 15.88% 2.74% 1.07% 100%

2005-2006 68.90% 16.62% 12.29% 1.66% 0.52% 100%

2006-2007 68.81% 18.03% 10.72% 1.72% 0.72% 100%

2007-2008 73.37% 16.32% 8.13% 1.53% 0.65% 100%

2008-2009 72.33% 17.09% 8.41% 1.40% 0.77% 100%

2009-2010 68.96% 20.19% 8.60% 1.24% 1.01% 100%

Average 68.98% 16.36% 11.72% 2.02% 0.93% 100%

Modeling and Analysis- Preliminary Analysis

Analysis of ticket sales and corresponding day of week

Preliminary Conclusion: Most sold-out games are held on Fridays and Saturdays, which is in line with common sense Most home games happen on Wednesdays, Fridays and SaturdaysDay of the week has a significant influence on total tickets sales. As we can see more tickets sales occur during the weekend.We expect ticket sales to fluctuate with respect to different days of the week Group ticket sales change contributes to the major difference of total tickets sales for different days of the week One fact worth noting is that the Spurs have great ticket sales Thursdays. However, compared with 74 games scheduled on Wednesdays, only 25 games happened on Thursdays during the past 8 seasons

Day of the

week

Total sold-

out games

Number

of games

Average of

total ticket

Average of

season ticket

Average of

group ticket

Average of

advance ticket

Average of day

of game ticket

Average of

night ticket

Sunday 13 25 15,486 10,795 2,269 1,927 347 147

Monday 6 26 14,712 10,754 2,180 1,256 343 178

Tuesday 18 53 15,085 10,725 2,232 1,639 334 155

Wednesday 26 74 15,106 10,527 2,406 1,708 313 153

Thursday 13 25 15,809 10,855 2,067 2,486 283 118

Friday 42 67 15,959 10,808 2,909 1,840 268 133

Saturday 43 58 16,094 10,605 3,026 2,005 328 130

Average 23 47 15,503 10,694 2,536 1,817 312 144

15

Modeling and Analysis- Preliminary Analysis

Analysis of ticket sales and corresponding opponents

Preliminary Conclusion: The chart summarizes the distribution of average ticket sales and displays the opponents in decreasing order for total ticket sales. LA Lakers, Dallas Mavericks and Houston Rockets are the three teams with the highest average ticket sales Season ticket sales remain relatively constant for different opponents Advance ticket sales follow a similar distribution with total ticket sales, whereas other ticket types do not show a particular trend

16

Opponents

Number of sold-

out games

Number

of games

Average of

total ticket

StdDev of

total ticket

Average of

season ticket

Average of

group ticket

Average of

advance ticket

Average of day

of game ticket

Average of

night ticket

Los Angeles Lakers 14 15 16,470 611 10,821 2,233 3,050 265 101

Dallas Mavericks 12 16 16,401 706 10,800 2,416 2,915 199 71

Houston Rockets 11 16 16,170 678 10,824 2,572 2,356 290 130

Boston Celtics 5 8 15,894 1,297 10,764 2,547 2,184 245 154

Utah Jazz 10 15 15,857 1,019 10,611 2,597 2,019 449 181

Phoenix Suns 10 14 15,842 1,393 10,830 2,302 2,364 246 101

New Orleans Hornets 9 14 15,795 892 10,704 2,848 1,797 317 130

Cleveland Cavaliers 6 8 15,778 1,445 10,723 2,486 2,121 336 112

Minnesota Timberwolves 9 15 15,724 989 10,642 2,526 2,029 380 146

Detroit Pistons 4 8 15,593 1,195 10,812 2,329 2,009 295 148

Denver Nuggets 9 15 15,581 1,456 10,652 2,520 1,922 335 151

Orlando Magic 4 8 15,518 1,151 10,609 2,451 1,918 399 141

Sacramento Kings 6 15 15,517 1,062 10,639 2,354 1,966 410 148

Chicago Bulls 4 8 15,443 1,434 10,582 2,569 1,744 393 155

Indiana Pacers 4 8 15,410 1,189 10,710 2,360 1,857 348 135

Portland Trailblazers 4 14 15,395 1,115 10,855 2,565 1,457 361 158

Atlanta Hawks 4 8 15,333 1,120 10,677 3,212 977 256 212

New York Knicks 2 8 15,269 909 10,764 2,873 1,116 334 182

Philadelphia 76ers 4 8 15,268 1,396 10,742 2,406 1,770 232 118

Seattle Supersonics 4 11 15,241 1,416 10,626 2,655 1,428 345 187

Milwaukee Bucks 2 8 15,209 1,488 10,612 2,378 1,607 449 165

Washington Wizards 4 8 15,173 1,702 10,660 2,770 1,385 196 162

Miami Heat 3 8 15,141 1,575 10,532 2,063 2,162 267 118

Charlotte Bobcats 0 6 15,041 626 10,892 2,725 964 293 168

Memphis Grizzlies 6 16 15,031 1,759 10,614 2,722 1,275 269 151

Los Angeles Clippers 4 15 15,025 1,339 10,593 2,764 1,258 272 138

New Jersey Nets 2 8 14,790 1,206 10,602 2,447 1,290 291 161

Toronto Raptors 2 8 14,717 1,760 10,697 2,164 1,289 367 199

Golden State Warriors 3 15 14,693 1,256 10,568 2,548 1,142 287 148

Oklahoma City Thunder 0 4 14,434 553 10,566 2,833 705 171 160

Average 5 11 15,425 1,191 10,691 2,541 1,736 310 148

Modeling and Analysis- Preliminary Analysis

Sold out game analysis

Preliminary Conclusion: Ha lf of the games are sold out games, indicating outliers accounts for 50% of total data Sold out percentages in different week days and different seasons are consistent with the average ticket sales analysis With the season approaching the end, there is a larger possibility that it is a sold out game 93% of LA Lakers games are sold out! The sold out probability differs for different time periods of the season

Sold out game vs day of week

17

Opponent

Number of

games

Number of sold

out games

Sold out

percentage

Atlanta Hawks 8 4 50%

Boston Celtics 8 5 63%

Charlotte Bobcats 6 0 0%

Chicago Bulls 8 4 50%

Cleveland Cavaliers 8 6 75%

Dallas Mavericks 16 12 75%

Denver Nuggets 15 9 60%

Detroit Pistons 8 4 50%

Golden State Warriors 15 3 20%

Houston Rockets 16 11 69%

Indiana Pacers 8 4 50%

Los Angeles Clippers 15 4 27%

Los Angeles Lakers 15 14 93%

Memphis Grizzlies 16 6 38%

Miami Heat 8 3 38%

Milwaukee Bucks 8 2 25%

Minnesota Timberwolves 15 9 60%

New Jersey Nets 8 2 25%

New Orleans Hornets 14 9 64%

New York Knicks 8 2 25%

Oklahoma City Thunder 4 0 0%

Orlando Magic 8 4 50%

Philadelphia 76ers 8 4 50%

Phoenix Suns 14 10 71%

Portland Trailblazers 14 4 29%

Sacramento Kings 15 6 40%

Seattle Supersonics 11 4 36%

Toronto Raptors 8 2 25%

Utah Jazz 15 10 67%

Washington Wizards 8 4 50%

Total 328 161 49%

Day of the Week Number of games Number of sold out games Sold out percentage

Sunday 25 13 52%

Monday 26 6 23%

Tuesday 53 18 34%

Wednesday 74 26 35%

Thursday 25 13 52%

Friday 67 42 63%

Saturday 58 43 74%

Total 328 161 49%

Sold out game vs opponent

Sold out game vs game number

Modeling and Analysis- Preliminary Analysis

Modeling and Analysis- Multivariate Linear Regression

Influential factors and coefficients for 5 ticket types

Conclusion Unemployment rate, Spurs winning percentage and day of the week are the most significant variables in almost all 5 models for different ticket types Opponent team is not significant because we classified them as outliers and eliminated all sold out games in the linear regression models

18

Factor Effect Coefficients

Season Ticket Spurs WL Negative -2,063.48

Game Number Positive 6.36

Unemployment rate Negative -451.84

Advance Ticket Unemployment rate Positive 206.78

Spurs WL Positive 3,303.23

Opponent WL Positive 1,108.72

If weekend Positive 147.37

Day of Game Ticket Spurs WL Positive 1,070.57

Opponent WL L10 Positive 205.37

If weekend Positive 32.91

Night Walkup Ticket Unemployment rate Positive 18.90

Spurs WL Positive 278.81

Avg Temp Negative -1.83

Group Ticket Unemployment rate Negative -204.00

Spurs WL Negative -2,365.65

Opponent Group Positive

If weekend Positive 176.59

Logistic Regression for Sold Out Games

Conclusion: Logistic regression results line up with our preliminary analysis that variables day of week, game number and team performance are significant influential factors for sold out probability forecast

Modeling and Analysis- Logistics Regression

19

Factor Effect CoefficientsDay of week

Sunday Positive 0.150Monday Negative -1.410Tuesday Negative -0.650

Wednesday Negative -0.910Thursday Negative -0.610Friday Positive 1.220

Saturday Positive 2.210Game number Positive 0.024Opponent winning % last 10 Positive 2.370Spurs winning % Positive 5.960Opponent group

Opponent Group=1 Positive 1.920Opponent Group=2 Negative -0.002Opponent Group=3 Negative -0.404Opponent Group=4 Negative -1.514

Unemployment rate Negative -0.953

Agenda

Client & Problem

Key Assumptions

Variables

Introduction1 Data Description2 Models and Analysis3

Project Objective

4 Summary

Preliminary Analysis

Multivariate Linear Regression

Logistic RegressionGeneral Approach

Overview Excel Demo

Recommendations

Limitations

20

Prediction for the ticket sales Summary- Prediction in blind test data

Prediction for sold out game

Predict

Actual

Sold Out Non Sold Out

Sold Out 3 0

Non Sold Out 2 21

The overall accuracy rate is 92%

9,000

11,000

13,000

15,000

17,000

1 6 11 16 21 26

Game Number

Total Paid Tickets

21

Excel Tool for Interactive Analysis

We developed an interactive excel model with embedded prediction models.

It enables the client to input model parameters and get directly from this tool the prediction for sold out game probability, sales prediction for each ticket type and summary of ticket sales history

22

Click Here to Explore the Tool

Summary- Recommendations

Arrange more games on the weekends (Thursday, Friday and Saturday night) rather than Wednesdays

Hold more promotional events at the beginning of each season due to smaller sold-out probability for those games and more tickets available to sell

Season tickets sales account for the largest percentage (more than 60%) of total paid tickets sales and fluctuate most significantly. We advise the Spurs to focus on forecasting season ticket attendance for each game

23

Summary- Limitations

Limited access to ticket price data and current pricing strategies. Therefore, our team was not able to formulate suggestions on optimal price and revenue optimization

Kicking out all sold out games may bias the multivariate linear regression model

Inevitably, our team might miss some important factors for ticket sales due to time constraints and data availability (team roster, player transactions, detailed promotion campaign, competing games in other sport league etc.)

The sample size for training data is relatively small (328 games in total)

Prediction model is limited for long-term prediction due to possible changes on internal and external factors. Also, there are time lags when updating some predictor variables such as unemployment rate

24

Thank you!

Q&A

25

Renkoh KatoGuang LiJia Yu

Master of StatisticsCornell University