I recently completed a small project in which I analyzed the popularity of teams that compete in the four major sports. In the analysis, I looked at metropolitan areas that have at least one team in each of the four major sports. Using Google Trends, I gathered weekly data from 01/02/05 - 11/01/14 to assess the relative popularity of teams in cities that met the inclusion criteria. Each data point represents how frequently a team was queried on Google relative to the Miami Heat in the week of 06/16/13 – 06/22/13. This data point was set to 100. More importantly, higher numbers indicate a higher search frequency and, for all intents and purposes, greater popularity. Below, I’ve included a heat map that illustrates the contrasts in average popularity among city-sport combinations over the time period. Note that for cities home to more than one team in a given sport, I took the maximum team score each week. For instance, if the Yankees had a popularity score of 10 in one week and the Mets had a score of 5 in the same week, I would set the Yankees’ score to be the New York baseball score for that week.

popularity

R code I used to produce the heatmap (will require the gplots and RColorBrewer libraries):

row.names(all.data) <- all.data$City
all.data <- all.data[,-1]
scaleyellowred <- colorRampPalette(c("lightyellow", "red"), space = "rgb")(100)
heatmap.2(as.matrix(all.data), Rowv = NA, Colv = NA, col = scaleyellowred, density.info="none",
          trace="none", cexRow=0.8, cexCol=1, margins=c(6,6))

In my statistical analysis, I used three-way between-subjects ANOVA (with a season variable as a block) to assess whether certain cities and sports attract significantly greater levels of interest. I performed this analysis using data only from the year 2013. Furthermore, to account for violations of the normality assumption, I performed a log transformation on the data. This did not affect the interpretability of results because, as alluded to above, the numeric quantities themselves added little value to the analysis. Additionally, the ordinal relationships remained intact after the log transformation. Here are slides with results from the project and the raw data from Google Trends.