## 7 Discussion  
To address the first research question of whether game days generate more foot traffic and revenue in our POI compared to non-game days, we conducted an analysis of the location data. Specifically, we performed [PERSON]'s statistical test to compare the number of people who were detected within our POI on game days versus the closest non-game days. The results of our analysis were consistent with prior research, indicating that individuals are more likely to visit our POI while games are taking place [61]. This finding suggests that game days may play an important role in driving both foot traffic and revenue for our POI. We found that people tend to disperse randomly before the game, but after the game, they were more likely to be clustered around the downtown area, which is closer to the stadium. Additionally, the spillover effects of the game were visually evident as they expanded outwards from the stadium and into various POI throughout the SLMA, particularly in the central region. The correlations between game days and increased economic activity are evident from our analysis, but pinpointing causality requires more advanced methods. Future research should consider employing synthetic controls or similar approaches to conclusively determine the causal impact of sporting events. Second, we utilized the Safe Graph spend data to investigate changes in revenue during game days compared to the closest non-game day, which showed a surge in revenue in our study area. To gain a deeper understanding of this phenomenon, we conducted a comparison of the revenue generated in our POI. Specifically, we compared the total revenue generated during the game days with the nearest non-game days within a week for comparison, choosing the nearest day to avoid conflicts of game days. Our analysis showed that restaurants and bars had a 38% increase in revenue, hotels had an 8% increase, grocery stores had a 37% increase, and liquor stores had a 34% increase in revenue (Table 3). To further investigate the trend in the downtown area, we focused on the 4 kilometers radius of the stadium. Our analysis revealed a 4% increase in revenue for restaurants and bars, a 3% increase for hotels, a 6% increase for grocery stores, and a significant 27% increase for liquor stores (Table 3). It is important to note that there may be other confounding factors, such as consumer behavior, marketing strategies, or seasonal trends, that may have influenced the observed increase in revenue at the various POI during game days. Additionally, it should be emphasized that we conducted a direct comparison of the total amount spent in our POI during a game and closest non-game days, rather than a day-to-day comparison. This distinction is important because it allows us to assess the overall spending patterns of participants rather than just their spending on individual days, which could be subject to fluctuations and outliers. It should also be noted that the 4 km radius surrounding the stadium had a significantly lower number of POI compared to the entire SLMA (Table 3). This may have contributed to the smaller increase in revenue observed in our POI compared to the Metro Area, as there are likely more opportunities for spending outside of the immediate vicinity of the stadium. Our study also compared revenue with respect to home games and teams playing to identify any significant differences (Figure 9). The results showed that the Milwaukee Brewers and Pittsburgh Pirates consistently generated higher revenue in our POI than other teams. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Area** & **Restaurants and Bars** & **Hotels** & **Grocery Stores** & **Liquor Stores** \\ \hline SLMA & +38\% & +8\% & +37\% & +34\% \\ \hline
4 KM radius & +4\% & +3\% & +6\% & +27\% \\ from stadium & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage change in revenue during game and non-game days.