The Twelfth Man is not a myth. Fans really do improve the performance of their team

William Wagstaff & Michael Wagstaff • 3 December 2020

Home performance affected by lack of football crowds

If you don't want to know the score look away now

Fans have missed football and football has missed fans. The crowd is often referred to as the '12th man', helping the team home and roaring them to success. The crowd is thought to lift performance by getting behind the players. How can a team not be inspired when thousands of fans are singing and chanting your name?


The sound of the crowd has been missing since the first lockdown in March. When football restarted in the summer, no fans were allowed into the ground. Matches have been played against a background of coaches shouting from the touchline and the odd swear word on the pitch. Some clubs, such as Gillingham in League 1, have pumped fake crowd noise into the ground to improve the atmosphere and help the players. 


The big question is does the lack of a crowd affect performance and are some teams more likely to be affected than others?


To find out we analysed results for matches played without a crowd from June 2020 to end of November 2020 and compared them with matches with crowds from previous seasons. We found that:


  • In the Premier League, the percentage of home wins for all 190 matches played without a crowd is low but not the lowest in recent history.
  • The percentage of home losses, however, is historically high at 38%.
  • As a result, there is a significant difference in the percentage of points won at home without a crowd than with. The proportion of points gained at home without fans is 52% compared with 57% for the last completed season with fans. For the 2020/21 season so far, only 46% of points have been gained at home by Premier League teams.
  • The impact of the lack of crowds diminishes the further down the football league we go. Performances in Leagues 1 and 2 are the least affected by the lack of crowd as average attendances were lower to start with.


The pre-match build up

The Premier League and the EFL were both halted in March 2020. The Premier League restarted in June and completed 92 matches. The Championship also restarted in the summer but Leagues 1 and 2 did not complete the season with final placings being decided on a points per game system.


Much of our analysis is focused on the Premier League.  The average attendance in the Premier League is much higher than in the EFL which means that the impact of having no crowds is likely to be greatest.


To analyse the impact of having no crowd we have combined the 92 matches from the end of last season with the 98 matches played this season to 30th November 2020. In total there are 190 matches across two seasons where no crowd has been allowed.


Some analysis is included of the EFL but this is based on this season’s results and therefore contains fewer matches. It is, however, a useful indication of the impact on the EFL.


Kick off

We kick off by comparing the average number of wins by the home side in the Premier League with and without crowds. To do this we have used the average percentage of home wins in each season from 1993/4 to 2018/19 compared with the 190 Premier League matches for which no crowd was allowed (denoted by the red X).


Figure 1 shows that although the proportion of home wins is historically low without crowds it is not abnormally low. For the lockdown games 42% ended in a home win. In the last full season before lockdown the percentage was 48%. A lower percentage of home points was recorded in the 2015/16 season (41%).


Figure 1 : Percentage of home matches won, drawn and lost and points won at home 

It’s the same finding when we look at draws. The figure for the lockdown games is historically low but not abnormally so.

Where we see a big difference is with the percentage of losses. The 38% home losses for the crowdless games is historically high and this translates to an historically low percentage of points gained from home games. In the last full season before lockdown, teams, on average, achieved 57% of their points total at home. In the 190 games without fans this figure fell to 52%. This means that there is virtually no home advantage when no crowd is present.


At this point we have to ask how much of the drop off in home advantage is due to the form and relative strengths of the teams that have played at home this season? In other words, is this just a quirk of the fixture list pitching an out of form or weaker home team against stronger more in form opposition? The answer to this is no.


In 1997, Dixon and Coles presented a model for predicting football scores and results by modelling the number of goals scored in a match as a Poisson distribution (the probability for a team to score one goal, two goals, three goals and so on in a match).

 

Their model identified parameters to represent a team’s attacking strength, defensive strength and home advantage. Applying this model to Premier League seasons from 1993 and weighting more recent results to represent current form, we can obtain a measure of home advantage independent of the current attacking and defensive qualities of teams.



Figure 2 : The home advantage parameter

The model shows that the home advantage parameter has fallen to just over one (1.06) during the period of no crowds. 


A value of one shows that there is no home advantage and so the model verifies the diminished performance of teams at home with no crowds.

The second half

How does the lack of crowd affect the EFL compared with the Premier League? Using only data from the 2020/21 season up until 30th November 2020, we can see that the percentage of points won at home by Premier League teams falls to 46%. This is 12 percentage points down on the average points gained at home in the last five years.


The percentage of home games won in the Championship so far this season is three percentage points down on the average for the last five seasons. For League 1 and League 2 the drop off in points picked up at home is less than two percentage points down on the last five years.


Given that the lack of a home crowd diminishes performance, it is not surprising that there is negative correlation between crowd size and the percentage of points won at home, as shown in Figure 3. The higher the average crowd, the greater the drop off in home performance.


Figure 3 Relationship between average attendance and percentage drop off in points gained at home


Post-match analysis

Crowds play an important part in football and the lack of them has led to a diminishing of home advantage. With no ‘twelfth man’ to cheer on the team, the home side has lost its edge. Does this drop off in performance affect some teams more than others?

Figure 4 suggests that it does affect some teams differently. For this analysis we have calculated a home points per game (PPG) differential score. This is the number of points per game each team gained at home without the crowd minus the PPG with crowds.


For example, if a team gains 1.5 points per game at home with a crowd and 1 without a crowd, the difference is calculated as -0.5. The PPG with crowds is derived from match results from August to March of the 2019/20 season. The no crowd PPG is derived from results from the rest of the season plus the 2020/21 season to November 30th 2020. The chart excludes results for the three teams relegated at the end of last season and the three teams promoted.


Figure 4: Points per game differential (PPG without crowds minus PPG with crowds)

The chart shows that over half of Premier League sides gain fewer points per game at home without a crowd. It should be acknowledged that for some sides a loss of form might account for them gaining fewer points. Sheffield United for example have been poor at home and away this season. Liverpool is another. Although Liverpool is still very good they are not at the same heights as previously. Even so, it shows that some clubs have been hit worse than others by the exclusion of fans.


Our analysis shows that crowds really do make a difference to their team. This is most pronounced in the Premier League where crowd sizes are much higher. The return of home fans will be of considerable benefit to some teams.



Teams whose fans are not allowed back into the stadium might be at a disadvantage compared with those whose fans are allowed back in. Our research suggests that there might not be a level playing field for all teams as a result.


by Michael Wagstaff 8 April 2025
The huge volume of data available through consumer comments, reviews and surveys can make cutting through the noise difficult. In this article we discuss how text analytics combined with human expertise can uncover the insight.
by Michael Wagstaff 10 March 2025
Market research agencies are going all in on AI based models to generate next level consumer insight. But are these just more illusion than substance?
by Michael Wagstaff 3 March 2025
With the online survey already on the ropes due to poor quality, has data science finished it off?
by Michael Wagstaff 24 February 2025
Research agencies are pinning their futures on AI. Are they right to do so or are we missing trick by ditching the human?
by Michael Wagstaff 12 February 2025
Online surveys suffer from fake, ill considered and unrepresentative responses. What can be done to improve their reliability? Triangulation is the key.
by Michael Wagstaff 11 February 2025
With so many agency panels riddled with fake respondents resulting in poor quality data, are we witnessing the end of the online survey?
by Michael Wagstaff 6 February 2025
With the January transfer window closed, we run our predictive model to work out the probabilities of where each team will finish in the final Premier League table.
by Michael Wagstaff 5 February 2025
In this latest article in our series on the power of text analytics we look at how sentiment analysis can be used to really understand what customers think about product offerings.
by Michael Wagstaff 23 January 2025
The true value of review sites lies in going beyond the stars and analysing what reviewers are actually saying.
by Michael Wagstaff 17 January 2025
Start making sense of customer review and feedback data by using text analytics. In the first of a series of articles we discuss how it can help your business.
Show More