Understanding a Fan Base Beyond the Ballpark: A statistical analysis of the transportation to major sporting events in NYC

Transcription

1 Understanding a Fan Base Beyond the Ballpark: A statistical analysis of the transportation to major sporting events in NYC 1. Abstract Paper Track: Business of Sports Paper ID: 1562 The sports industry has a distinct advantage over many other B2C industries in that its customers, or fans, routinely make irrational decisions fueled by passion. As sports organizations increasingly adopt loyalty programs, apps, and new technologies in an effort to understand their fans interaction, involvement, and habits inside the stadium, we extend the focus of our analysis outside of stadium walls. Our analysis takes a look at openly available data in the NYC region in order to better understand event-related transportation decisions. Understanding these decisions and incorporating a data driven approach could enable sports organizations to tailor outreach, and predict engagement levels for the fan base and improve the overall fan experience. For our analysis, we analyzed five open source data sets: 2014 NYC Yellow and Green Cab Trip Information, NYC Uber data, NYC demographic data, NYC weather data, and relevant baseball game information for the New York Mets and the New York Yankees 2014 season. From there, we conducted exploratory data analysis, using geolocation and time data to observe taxi and transportation flow. We applied both unsupervised and supervised machine learning algorithms to determine clusters of activity to and from some of NYC s largest observed sporting events and also to gain a better understanding of fan behavior leading up to and after the game. Based on the lessons learned from these machine learned clusters, we employed a set of statistical models to effectively determine feature importance and to estimate potential origins and destinations for future events given the observable variables. 2. Introduction Throughout the 2014 baseball season, over 66,000 fans took private yellow or green taxis as a means of transportation to New York Yankees and New York Mets home games. Even more interesting, these fans came from over 200 unique zip codes spanning multiple boroughs, illustrating the reach of both teams throughout the New York City area. Given that New York City is world renowned for the extent of its public transportation system, the fact that fans would view a taxi as a viable alternative illustrates the consumer s willingness to take alternative private transportation to attend sporting events. Delving deeper into yellow and green NYC taxi data, Exhibit 1 illustrates an increased willingness to pay demonstrated by fans attending baseball games in New York. As shown, on average, fans of both the New York Yankees and the New York Mets are willing to pay increased fares to arrive at the ballpark. Interestingly, as we look at cab fares terminating in the vicinity of Citi Field, we observe multiple peaks in terms of fare paid. This distribution warranted further investigation to 1

2 determine its root cause. Figure 1: Illustration of Cab Fares by Classified Destination We observed a median total Yankees game fare of approximately $16.50, for Mets games, $ This all compares to the median general NYC cab total fare of $ To us, this represents the premium that fans put on going to sporting events. This willingness to spend represents a clear opportunity for teams to further own the fan experience beyond the stadium. 3. Data and Preparation Initially, we used exploratory data analysis in order to better understand baseball fans taxi originations and the demographics of those areas. New York City provides the perfect platform for this analysis, given its two fan bases with similar means to both arrive at and leave baseball games. The raw dataset was obtained from the NYC cab website[1]. We specifically obtained data from March-September of 2014, as we were looking at the 2014 seasons for both teams.. When combined into one, the raw dataset had over 81 million rows with the following relevant columns: pickup_datetime, dropoff_datetime, passenger_count, trip_distance, pickup_longitude, pickup_latitude, dropoff_longitude, dropoff_latitude, fare_amount, total_amount. In order to filter this dataset, we took the longitude and latitude with a 0.5 mile radius around Yankee Stadium ( , ) and Citi Field ( , ). Next, we went to Baseball Reference[2] and Retrosheet[3] to obtain all relevant baseball information, including game start times and end times. Using start times, we retained all cabs arriving two hours or less before start, and one hour after, as we assume some fans arrive late. Conversely, we considered taxis that left a game 90 minutes before the end of the game and up to two 2

3 hours after, as many fans remain in the area immediately following a game. This greatly reduced the raw dataset to the following value counts: Next, we obtain respect zip codes and boroughs from latitude and longitude data, which allowed us to consider Census demographic information, which is parsed by zip code. We retained zip codes with n>100 rides, in order to avoid a few rides skewing data. Interestingly, there were plenty of taxis coming straight from major transit hubs such as LaGuardia and JFK airports. This left us with a total of 66 unique zip codes. Next, we applied various unsupervised learning algorithms to the data. This is useful to determine clusters without target variables. The most effective algorithm that we found was k-means, which is widely used across many different sectors. Within k-means, the Euclidean distance metric is calculated, and from there, it becomes an optimization problem with the goal of minimizing the cluster sum of squared errors (also called cluster inertia). One of the main criticisms of k-means is that the number of clusters must be specified beforehand. We initially used an x-means algorithm which automatically selects the number of clusters (the k) by optimization; however, similar to marketing functions that often run campaigns of different complexity, we decided to use the elbow method with k-means. Generally, elbow methods mitigate some drawbacks of clustering algorithms and give a visual representation of the number of clusters and their corresponding distortion. Figure 2: k-means cluster analysis 3

4 In Exhibit 2, there are two distinct areas where we observe elbows - at 3 and at 6. Next, we analyzed each of the clusters, as shown in Exhibits 3 and 4. Figure 3: Income, Ethnicity, Marital Status by Cluster Figure 4: Education by Cluster Next, we visually analyzed the arrival patterns of each cluster. In Exhibits 5 and 6, we observe New York Yankees and New York Mets traffic by departure location for each taxi that arrived near the stadium. The points are color coded to represent the cluster group that they belong to. 4. Data Visualization Mets: 4

5 Figure 5: Departure Location by Cluster for Mets Games Yankees: Figure 6: Departure Location by Cluster for Yankees Games Based on these visualizations, we can clearly see the battleground areas for Mets and Yankees Fans. While Yankees fans show a greater density, especially in expected areas such as the Bronx, there are similar concentrations of the second cluster in Mid-town and Long Island City. 5. Predicting traffic flow While understanding the types of fans through the aggregate season taxi amount is important, it is also of significance to understand and predict the amount of taxis coming to any individual game. By predicting the traffic flow, a team can potentially extend the game experience to hours before the game when fans are beginning their journey to the stadium. An interesting aspect of fan experience, is that the simple act of arriving at the stadium can greatly impact the likelihood that they will return. Based on this assumption, we employed different machine learning algorithms to best determine how a team can predict taxi flow to a baseball game, and the best performing machine learning algorithm was a random forest. We used an input dataset with the following columns: team power ranking, opponent power ranking, month of season, wind speed, game promotion, team rank, day or night, attendance, day of week, number of game streak, start time hour, games behind, weather summary, precipitation intensity, and temperature. 5

6 All of the columns were numeric values except for Day of Week (M/T/W/R/F/Sat/Sun), Day/Night, weather summary (clear/rain), and game promotion (Y/N). In order to get our target variable of taxi rides, we took raw taxi data set and grouped by the date and whether it was a pickup and dropoff; subsequently, a count was applied and merged with the baseball in dataset, leaving us with the total amount of taxis coming to the respective stadiums. We trained a random forest of exactly 10,000 trees for both teams and calculated the mean absolute value. Here were the following results: Mets Train/Test Random Forest mean absolute value: Yankees Train/Test mean absolute value: Mets Out of Bag mean absolute value: Yankees Out of Bag mean absolute value: One of the drawbacks is that a random forest is tough to interpret, as it is implicitly a black box machine learning algorithm. Nonetheless the increase in accuracy is enough to justify its use. Below is a histogram of showing the residuals from an out-of-the-bag random forest regression. While the train and test with a 25% split performed better, due to the size of dataset, with only one season, the more unbiased prediction is obtained using outof-the-bag random forests. Overall, this points to confidence that given a large taxi flow, by using open source and attainable data, teams can both estimate taxi flow, understand where taxis are coming from, and understand the type of fans that ride them to games. Figure 6: Histogram of random forest residuals 6

7 One of the benefits of using a random forest is to gain an understanding of the feature importance of the input variables. Feature importance can be calculated by looking at the average impurity that is decreased from all the decision trees in the process. The average feature importance between both teams is shown in the data visualization below. Here, we explore the top 12 features, as everything else proved to be of minimal importance. Figure 7: Feature Importance of Random Forest Model A cursory review of feature importance reveals some obvious conclusions better attended games are more important, due to volume effects, for example. However, we found it interesting that the temperature was viewed as very important, but other potentially inclement weather variables, such as rain, did not seem to have an impact. Below we compare two of these variables (rain and temperature), temperature having high predictive value, and rain having low. Visualizing cabs by team, we notice distinct differences between their distribution, despite similar n of taxi (rainy-day cabs had about 10% more pickups). In particular, for Mets fans, there is a thick concentration of Midtown pickups that is absent for hot days absent from rainy days: 7

8 Figure 8: Total Cabs by type of weather (NYM cabs in blue, NYY cabs in orange) Interestingly, the same trends do not apply to the Yankees fan base. Nonetheless, the difference between these two graphs can hold considerable value for an organization for further discussion, see Section 6 below. These nuances can potentially be explained one might conclude, for instance that when it is heavily raining, it is often hard to get a cab in general; however, when the weather is hotter, consumers may feel willing to spend the extra money to avoid the Manhattan heat, but competition for taxis would not be as fierce. Also of interest is that the opponent s power ranking proved to be more influential than the actual home team; having a grasp of the opponent clearly helps to determine how many fans will choose to take taxis to arrive at the ballpark. 6. Takeaways Throughout this paper, we have noted salient trends and made specific predictions about cab flow, using demographics, baseball, and weather data to buttress our analyses. Nonetheless, it is important to note what organizations can do with this type of information. 8

9 Not only can teams use this type of information to predict cab flow, they can also connect these insights to custom promotions for example, noting the surfeit of cab rides coming out of Midtown on hot days, the New York Mets might offer an air-conditioned party-bus that picks up at known or predicted cab hotspots, leveraging their data to better connect with their fans. In addition, they can make use of the battleground areas, such as Long Island City, in order to focus their marketing and cultivate deeper fan relationships in these areas. Finally, organizations can hone in on demographic-tinged clusters to predict not only where certain fans will travel from, but also what type of fan (and what purchasing power they may be likely to have) comes from what area, in order to provide those areas with custom messages and marketing. 7. Conclusion The rise of analytics and data collection has led to new business opportunities in sports that never existed. While teams have historically looked to control the fan experience in the stadium, we believe the next natural step is to utilize analytics to extend the fan experience outside the stadium and before the game. Through a deeper understanding of these transport-related decisions, organizations can begin to understand the passion that fans exhibit and provide unparalleled access, experiences, and offerings that take advantage of this data. Organizations can ensure a memorable experience from the moment a fan begins to make their way to the ballpark and again as they continue with their day after the game. The potential to use analytics to control the fan experience is a new and exciting step in the evolving landscape of sports business analytics; teams at the forefront of these advances will reap considerable benefits as they connect further with their fan-base in all aspects of the game day experience. 9

10 References [1] [2] [3] 10

11 Appendix An appendix is not required, but if you have one please include it here. 11