fa·nat·ic /fe-natik/ Marked by excessive enthusiasm and often intense uncritical devotion
Sports fans are some of the most passionate people on the planet. You live and die by each game, minute, and second. Athletes often say that their home town fans are the best in the world. As you know, not every team can have the “best” fans. In this series, we attempt to actually prove which teams have the best fans.
Fandom can be measured in two ways:
- Fans buying tickets to and showing up to every game. The quality of the opponent and the quality of their team should be irrelevant
- Having loud crowds at games, thus giving the home team an advantage
We will use the following framework to identify the teams that truly have the best fans.
Part 1 – Attendance Numbers
Here we will look at attendance numbers for each game over the past 3 seasons. Because we are looking at it on a game by game basis, we will be able to tell which teams have fans showing up to games that aren’t marquee games.
We will combine these numbers with Sagarin ratings. This will give us a general idea of how good each team was so we can identify which games we would expect to have low attendance.
Attendance numbers can be doctored a bit, but we will work with the data we have available. As noted in the article, attendance is usually reported as the number of tickets sold to a game rather than the actual number of people in the seats at a given game. We will work with this definition of attendance for the remainder of the series.
A team’s success in the previous season may have an effect on “attendance” for the following season due to advance season ticket sales, etc.
Part 2 – Home Court/Field/Ice Advantage
Home court/field/ice advantage will be determined by comparing a team’s home win % vs. away win %. This is a little more straight forward than the attendance analysis.
Remember that external factors that may play a part in home field advantage such as location, days of rest, and altitude (think Utah/Denver).
For more info on home field advantage in general, watch this video from the MIT Sloan Sports Analytics Conference.
So there it is, the framework for the analysis of the best fans in sports. Keep an eye out for the upcoming posts! What team do you think has the best fans in sports?
Here is the 2012-13 NBA schedule that includes days of rest for each team, home and away. It lists each game for the entire year, with days of rest being broken down into the following categories:
|B2B||Back to back games|
|3 in 4, B2B||3 games in the last 4 days, including back to back|
|3 in 4||3 games in the last 4 days|
|4 in 5||4 games in the last 5 days|
|1 Day Rest||1 day rest|
|2 Days Rest||2 days rest|
|3-4 Days Rest||3-4 days rest|
|5 Days Rest||5 days rest|
To download, click below:
As we continue our Returning Starters Series, we turn to the impact of returning starting quarterbacks. We have seen in Part I and Part II that neither returning starters nor experience predict success the next year. Since the quarterback has such a profound effect on the team, I decided to see if having a returning quarterback had a significant impact on team success.
THE QUARTERBACK POSITION
In football, the quarterback is the leader of the team. He demands respect from his entire team, and plays the most important position on the field.
Other positions substitute guys in and out without the crowd hardly noticing. When the quarterback goes out? Not only does everyone gasp, there is usually quite a reaction- either good or bad- to the situation. All eyes are on the quarterback, and for good reason.
A returning quarterback for a team is always a comforting thought. Breaking in a new one? Not so much. Quarterbacks receive a lot of attention because they have such an impact on the game.
Although on the field the quarterback only directly effects the offense, he can also psychologically motivate all players on the team- defense included. Everyone looks to the quarterback, and if he is pouting or has poor body language, it spreads across the team.
The quarterback also has other effects on the defense such as controlling the clock to give the defense a rest or not turning the ball over and putting the defense in a bad position.
I gathered the data using Phil Steele’s returning starters numbers, which includes whether or not teams return their starting quarterback. Pairing those numbers with each team’s ELO rating, I looked to see if a higher proportion of teams with returning starting quarterbacks improved compared to teams that didn’t have returning starting quarterbacks.
The ELO rating adjusts for strength of schedule and only considers wins and losses. It was a component in the now defunct BCS calculation.
Using data from 2008-2011 (4 seasons), I first discovered that teams with a returning quarterback improve their team rating compared to the year before by 2.58%, while those that didn’t have a returning quarterback regressed, having an average rating of -1.85%.
To see if this difference was significant, I performed a difference of proportions hypothesis test. Overall, we had 480 data points for the 4 years. Of the 480 teams, 69% had returning starting quarterbacks, while the other 31% didn’t.
I defined “improving” as a team increasing it’s ELO rating by 5% compared to the previous year. Although this is somewhat arbitrary, a 5% increase in ELO rating means a team improved 3-5 points, which appears to be pretty significant if you step back and look at the difference in the quality of teams with 5 point spreads.
I decided to use the 5% threshold rather than simply positive versus negative because I thought it was too subjective using a simple positive/negative. A team that decreased by 0.01% would be counted as a worse team while a team that increased by 0.01% would be counted as an improving team. I didn’t think that was a proper representation of whether a team truly had better success.
Of all teams that returned their quarterback, 38.96% of the teams improved their ELO rating by 5% the next year compared to the year before. On the flip side, only 27.92% of teams that didn’t have a returning starting quarterback improved their team ELO rating compared to the previous year.
There are a few details to consider about the methodology. One aspect is my definition of improving. I defined “improving” as a team increasing it’s ELO rating compared to the year before. The problem with this is that a team improving it’s ELO rating by 5% counts for the same as a team that improved it’s rating by 15%.
Another consideration is the validity of the ELO rating. Although it was used by the BCS, it is still debatable that one number can truly represent how good a team is.
One thing the model doesn’t account for are coaching and system changes. A returning starting quarterback with a new coach and system coming in doesn’t exactly have the same effect as a 4th year returning quarterback who has been in the same system his whole career.
After performing the hypothesis test, it appears that there isn’t enough evidence to unequivocally say that having a returning starter quarterback improves the team.
In this case, the p-value was 0.0965, which means that at the 10% significance level we fail to reject the hypothesis that the proportion of teams with a returning starting quarterback have the same effect on teams as a team that doesn’t have a returning starting quarterback.
Although we can’t say anything definitive statistically, I would still argue that returning a quarterback is generally a good thing. As described above in the considerations, there are a few variables that may change that statistical outcome of my analysis.
Do you think returning a starting quarterback is a crucial component of forecasting success?