Dataset of the Day: Hockey, Getting Fans in the Seats

The 2008-2009 NHL Season has been a thrilling one and it continues to be with the start of the playoffs. The game’s popularity has been growing and a rise in attendance figures has been a direct result. The Total NHL Attendance figure was broken this year for the fourth consecutive year. This news made me want to take a closer look at the data.

I first went to espn.com and looked at attendance figures from the 2008-2009 season. After looking over the stats I saw that some teams had regular sellouts and other teams struggled to fill the seats. The map below shows the percentage of seats that were filled throughout the season for each team. (click on the map for a larger view)

Why did some teams sell out every game while others showed poor attendance? I decided to investigate by using Finder! and Maker! to run correlations to determine why a team could or could not get fans in the arena.

The first thing I wanted to correlate was a team’s finishing place in the league and their attendance capacity percentage for the season. This is because a common theme in sports is that fans only go to watch a team if that team is winning. I mean who wants to go see the last place team in the league play.

The correlation shows some interesting results. It appears that the place of your team does not always affect the amount of fans you put in the seats. The correlation between the two factors was only .48 (high correlations are values close to 1 or -1). For example, the Ottawa Senators were able to fill 105% of their seats during the year yet they finished 22nd out of thirty teams in the league. Also, the Carolina Hurricanes who finished 11th in the league out of thirty teams only filled 88.5% of their seats (rated 10th worst in the league).

Now I looked at running some other correlations to see if any other factors resulted in getting people into the seats. Below is what I tried.

- Number of Consecutive Playoff or Non-Playoff Seasons (shows if a team has been continuously successful or unsuccessful)

- Unemployment % for February 2009 (If you’re broke and without a job, you probably won’t be spending your money to go to a hockey game)

- Average Temperature During Hockey Season (Hockey is a sport that is heavily followed in colder climates)

None of the correlations faired much better. Surprisingly Average Temperature During Hockey Season was the closest (-.59) This led me to the conclusion that it is a combination of different factors that determine if a team is able to get people in the seats for their games. Now I took several factors and gave them specific values and combined these to come up with the “The Kev Score”. I am hoping that “The Kev Score” will show how certain factors combined will determine if an NHL team will achieve their maximum attendance capacity.

Here is how I computed “The Kev Score”

Factors:
- Finishing Place (if in 1st place = 30 points, 2nd = 29 points, and so on)
- Temperature (Coldest City = 30 points, 2nd Coldest City = 29 points, and so on)
- Canada Factor (if a Canadian team you get 15 points added to your score)
- USA Hockey IQ Factor – if a USA city is known as a town known for hockey
o Good IQ (10 points added)
o Poor IQ (No points)
- City Population (Highest City Population = 30 points, 2nd Highest City Population – 29 points)

The Formula:
Finishing Place Points + Temperature Points + Canada Factor + Good USA Hockey IQ Factor + City Population Points = “The Kev Score”

The correlation between the Arena Full Capacity Percentage and the “Kev Score” is reasonably high at a score of .81. So is the “Kev Score” a reliable way to predict how to get fans in the seats. I decided to use the formula again but to test it with statistics from the 2007-2008 season. Here is what happened.

At a much lower correlation of .60 it seems that the “Kev Score” does not prove itself to be a strong indicator of fan attendance for the 07-08 season.

Was “The Kev Score” a reliable way to judge if a team would or would not have a strong attendance? Well not really but it worked better than all the other things I tried. See if you are able to discover your own “Kev Score” and help Hockey Team owners around the NHL discover how to bring more fans to their games.