[Football] No crowds - advantage away side

[GOM]

Short term and small sample but early evidence from the Bundesliga is showing that playing in empty stadiums could favour the away team.

Now if this is true, how will this affect our run in?

https://www.theguardian.com/footbal...ue-restart-will-not-be-football-as-we-know-it

 

[southstandandy]

Win our 4 away games and we'll be safe - Happy days!

 

[Swansman]

Thats good. All the very difficult games are at home and almost all of the must-win games are away.

 

[bn1&bn3 Albion]

Having quickly skimmed through the results it looks like most the away wins came from teams that were favourites.. You'll need more than 4 rounds of games to make any judgement.

 

[Icy Gull]

Someone made the point that we groan quite a bit during home games when things aren’t going well so maybe an empty Amex will take away some of the pressure that moaning puts on players in home games. He had a point imo

 

[The Wizard]

I think no fans definitely is in our favour with the away games we have left.

 

[Harry Wilson's tackle]

People have been using this as an argument for playing at neutral grounds.

Nothing to see here.

It's a statistical quirk derived from a small sample size.

 

[LamieRobertson]

People have been using this as an argument for playing at neutral grounds.

Nothing to see here.

It's a statistical quirk derived from a small sample size.

Maybe we should have a poll

 

[Moshe Gariani]

Having quickly skimmed through the results it looks like most the away wins came from teams that were favourites.. You'll need more than 4 rounds of games to make any judgement.

This. Plenty of good theory and evidence about why home advantage exists without a noisy, partisan, crowd influence.

 

[The Wizard]

Having thought about it and perhaps a silly comparison but having played amateur football on Sunday/Saturday, home advantage still exists there and there’s no fans there, i guess a lot of the advantage just comes from being used to your surroundings and such.

That being said I still think there’s a small advantage in us playing our important away games without the crowd, could reduce the home advantage slightly.

 

[Harry Wilson's tackle]

<sigh>

Measuring the home-field advantage of a team (in a league with balanced schedule) requires a determination of the number of opponents for which the result at home-field was better ( k 1 {\displaystyle k_{1}} k_{1}), same ( k 0 {\displaystyle k_{0}} k_{0}), and worse ( k − 1 {\displaystyle k_{-1}} k_{{-1}}). Goals scored and conceded – in so called combined measure of home team advantage – are used to determine which results are better, same, and which are worse. Given two results between teams T 1 {\displaystyle T_{1}} T_{1} and T 2 {\displaystyle T_{2}} T_{2}, h T 1 : a T 2 {\displaystyle h_{T_{1}}:a_{T_{2}}} {\displaystyle h_{T_{1}}:a_{T_{2}}} played at T 1 {\displaystyle T_{1}} T_{1}'s field and h T 2 : a T 1 {\displaystyle h_{T_{2}}:a_{T_{1}}} {\displaystyle h_{T_{2}}:a_{T_{1}}}played at T 2 {\displaystyle T_{2}} T_{2}'s field, we can compute differences in scores (e.g. from T 1 {\displaystyle T_{1}} T_{1}'s point of view): d h , T 1 = h T 1 − a T 2 {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}} {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}}and d a , T 1 = a T 1 − h T 2 {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}} {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}}. Team T 1 {\displaystyle T_{1}} T_{1} played better at home field if d h , T 1 > d a , T 1 {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}}, and T 1 {\displaystyle T_{1}} T_{1} played better at away field if d h , T 1 < d a , T 1 {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} (for example, if Arsenal won 3–1 at home against Chelsea, i.e. d h , A r s e n a l = 2 {\displaystyle d_{h,Arsenal}=2} {\displaystyle d_{h,Arsenal}=2}, and Arsenal won 3–0 at Chelsea, i.e. d a , A r s e n a l = 3 {\displaystyle d_{a,Arsenal}=3} {\displaystyle d_{a,Arsenal}=3}, then the result for Arsenal at home was worse). Same approach has to be used for all opponents in one season to obtain k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}.

Values of k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}} are used to estimate probabilities as p ^ r = k r + 1 K + 3 , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1}, where K {\displaystyle K} K is total number of opponents in a league (this is Bayesian estimator). To test hypothesis that home-field advantage is statistically significant we can compute P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( k 1 + 1 , k − 1 + 1 ) {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)}, where I 1 / 2 ( ) {\displaystyle I_{1/2}()} {\displaystyle I_{1/2}()} is incomplete gamma function. For example, Newcastle in 2015/2016 English Premier League season recorded better result at home field for 13 opponents, same result with 4 opponents, and worse result for two opponents; therefore P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( 14 , 3 ) = 0.998 {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} and hypothesis about home team advantage can be accepted. This procedure was introduced and applied by Marek and Vávra (2017)[10] on English Premier League seasons 1992/1993 – 2015/2016.

Marek and Vávra (2018)[11] described procedure which allows to use observed counts of combined measure of home team advantage ( k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}) in two leagues to be compared by the test for homogeneity of parallel samples (for the test see Rao (2002)[12]). The second proposed approach is based on distance between estimated probability description of home team advantage in two leagues ( p ^ r = k r K , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1}) which can be measured by Jeffrey divergence (a symmetric version of Kullback–Leibler divergence). They tested five top level English football leagues and two top level Spanish leagues between 2007/2008 and 2016/2017 season. The main result is that home team advantage in Spain is stronger. Spanish La Liga has the strongest home team advantage and English football league two has the lowest home team advantage among analysed leagues.

 

[Is it PotG?]

Measuring the home-field advantage of a team (in a league with balanced schedule) requires a determination of the number of opponents for which the result at home-field was better ( k 1 {\displaystyle k_{1}} k_{1}), same ( k 0 {\displaystyle k_{0}} k_{0}), and worse ( k − 1 {\displaystyle k_{-1}} k_{{-1}}). Goals scored and conceded – in so called combined measure of home team advantage – are used to determine which results are better, same, and which are worse. Given two results between teams T 1 {\displaystyle T_{1}} T_{1} and T 2 {\displaystyle T_{2}} T_{2}, h T 1 : a T 2 {\displaystyle h_{T_{1}}:a_{T_{2}}} {\displaystyle h_{T_{1}}:a_{T_{2}}} played at T 1 {\displaystyle T_{1}} T_{1}'s field and h T 2 : a T 1 {\displaystyle h_{T_{2}}:a_{T_{1}}} {\displaystyle h_{T_{2}}:a_{T_{1}}}played at T 2 {\displaystyle T_{2}} T_{2}'s field, we can compute differences in scores (e.g. from T 1 {\displaystyle T_{1}} T_{1}'s point of view): d h , T 1 = h T 1 − a T 2 {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}} {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}}and d a , T 1 = a T 1 − h T 2 {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}} {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}}. Team T 1 {\displaystyle T_{1}} T_{1} played better at home field if d h , T 1 > d a , T 1 {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}}, and T 1 {\displaystyle T_{1}} T_{1} played better at away field if d h , T 1 < d a , T 1 {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} (for example, if Arsenal won 3–1 at home against Chelsea, i.e. d h , A r s e n a l = 2 {\displaystyle d_{h,Arsenal}=2} {\displaystyle d_{h,Arsenal}=2}, and Arsenal won 3–0 at Chelsea, i.e. d a , A r s e n a l = 3 {\displaystyle d_{a,Arsenal}=3} {\displaystyle d_{a,Arsenal}=3}, then the result for Arsenal at home was worse). Same approach has to be used for all opponents in one season to obtain k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}.

Values of k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}} are used to estimate probabilities as p ^ r = k r + 1 K + 3 , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1}, where K {\displaystyle K} K is total number of opponents in a league (this is Bayesian estimator). To test hypothesis that home-field advantage is statistically significant we can compute P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( k 1 + 1 , k − 1 + 1 ) {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)}, where I 1 / 2 ( ) {\displaystyle I_{1/2}()} {\displaystyle I_{1/2}()} is incomplete gamma function. For example, Newcastle in 2015/2016 English Premier League season recorded better result at home field for 13 opponents, same result with 4 opponents, and worse result for two opponents; therefore P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( 14 , 3 ) = 0.998 {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} and hypothesis about home team advantage can be accepted. This procedure was introduced and applied by Marek and Vávra (2017)[10] on English Premier League seasons 1992/1993 – 2015/2016.

Marek and Vávra (2018)[11] described procedure which allows to use observed counts of combined measure of home team advantage ( k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}) in two leagues to be compared by the test for homogeneity of parallel samples (for the test see Rao (2002)[12]). The second proposed approach is based on distance between estimated probability description of home team advantage in two leagues ( p ^ r = k r K , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1}) which can be measured by Jeffrey divergence (a symmetric version of Kullback–Leibler divergence). They tested five top level English football leagues and two top level Spanish leagues between 2007/2008 and 2016/2017 season. The main result is that home team advantage in Spain is stronger. Spanish La Liga has the strongest home team advantage and English football league two has the lowest home team advantage among analysed leagues.

Easy for you to say.

 

[Harry Wilson's tackle]

The rest of that article says that home advantage in footy has been declining over the last 100 years (presumably because malpractice against the opposition, and discomfort, have declined). It also says that home advantage (disparity between home and away outcomes) appears to be more important for the shitter treams. Arguing from the particular to the general is always folly though. The exception neither proves nor disproves the rule. Indeed this season the very excellent Liverpool have a better home than away record, while the tedious and shit Palarse are much better away.

 

[Deleted member 2719]

Someone made the point that we groan quite a bit during home games when things aren’t going well so maybe an empty Amex will take away some of the pressure that moaning puts on players in home games. He had a point imo

I agree we have some grumpy home fans for years, the only time I remember them being grumpy at the goldstone was over Clive Walker.
The away fans always seem to have a more positive attitude imo.

 

[banjo]

Still think it’s a waste of time playing these games without fans. Surely the players get more of a buzz home or away in front of 30000 plus crowds. So don’t really see an advantage either way.

 

[GOM]

I thought this part was quite interesting

'It has also come to light from the German experience that games played behind closed doors tend to proceed more efficiently, with the ball in play for a greater proportion of the game than is normally the case.
Without a crowd there is evidently no point playing to the gallery or putting pressure on the referee to change his mind. Players just get on with the game, .............................................. Even goals as exquisite as the one scored by Bayern Munich’s Joshua Kimmich against Dortmund are not celebrated or savoured for long; when the symbiosis between performers and spectators is missing so is the sense of theatre'

Must admit I only watched one game, but are the players still rolling around in agony when someone touches them ?

 

[Saladpack Seagull]

I agree we have some grumpy home fans for years, the only time I remember them being grumpy at the goldstone was over Clive Walker.
The away fans always seem to have a more positive attitude imo.

Good point, Mouldy. But the old West Stand used to moan about a lot more than CW!

 

[Tom Hark Preston Park]

<sigh>

Measuring the home-field advantage of a team (in a league with balanced schedule) requires a determination of the number of opponents for which the result at home-field was better ( k 1 {\displaystyle k_{1}} k_{1}), same ( k 0 {\displaystyle k_{0}} k_{0}), and worse ( k − 1 {\displaystyle k_{-1}} k_{{-1}}). Goals scored and conceded – in so called combined measure of home team advantage – are used to determine which results are better, same, and which are worse. Given two results between teams T 1 {\displaystyle T_{1}} T_{1} and T 2 {\displaystyle T_{2}} T_{2}, h T 1 : a T 2 {\displaystyle h_{T_{1}}:a_{T_{2}}} {\displaystyle h_{T_{1}}:a_{T_{2}}} played at T 1 {\displaystyle T_{1}} T_{1}'s field and h T 2 : a T 1 {\displaystyle h_{T_{2}}:a_{T_{1}}} {\displaystyle h_{T_{2}}:a_{T_{1}}}played at T 2 {\displaystyle T_{2}} T_{2}'s field, we can compute differences in scores (e.g. from T 1 {\displaystyle T_{1}} T_{1}'s point of view): d h , T 1 = h T 1 − a T 2 {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}} {\displaystyle d_{h,T_{1}}=h_{T_{1}}-a_{T_{2}}}and d a , T 1 = a T 1 − h T 2 {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}} {\displaystyle d_{a,T_{1}}=a_{T_{1}}-h_{T_{2}}}. Team T 1 {\displaystyle T_{1}} T_{1} played better at home field if d h , T 1 > d a , T 1 {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}>d_{a,T_{1}}}, and T 1 {\displaystyle T_{1}} T_{1} played better at away field if d h , T 1 < d a , T 1 {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} {\displaystyle d_{h,T_{1}}<d_{a,T_{1}}} (for example, if Arsenal won 3–1 at home against Chelsea, i.e. d h , A r s e n a l = 2 {\displaystyle d_{h,Arsenal}=2} {\displaystyle d_{h,Arsenal}=2}, and Arsenal won 3–0 at Chelsea, i.e. d a , A r s e n a l = 3 {\displaystyle d_{a,Arsenal}=3} {\displaystyle d_{a,Arsenal}=3}, then the result for Arsenal at home was worse). Same approach has to be used for all opponents in one season to obtain k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}.

Values of k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}} are used to estimate probabilities as p ^ r = k r + 1 K + 3 , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}+1}{K+3}},r=-1,0,1}, where K {\displaystyle K} K is total number of opponents in a league (this is Bayesian estimator). To test hypothesis that home-field advantage is statistically significant we can compute P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( k 1 + 1 , k − 1 + 1 ) {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(k_{1}+1,k_{-1}+1)}, where I 1 / 2 ( ) {\displaystyle I_{1/2}()} {\displaystyle I_{1/2}()} is incomplete gamma function. For example, Newcastle in 2015/2016 English Premier League season recorded better result at home field for 13 opponents, same result with 4 opponents, and worse result for two opponents; therefore P ( p 1 > p − 1 ) = 1 − I 1 / 2 ( 14 , 3 ) = 0.998 {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} {\displaystyle P(p_{1}>p_{-1})=1-I_{1/2}(14,3)=0.998} and hypothesis about home team advantage can be accepted. This procedure was introduced and applied by Marek and Vávra (2017)[10] on English Premier League seasons 1992/1993 – 2015/2016.

Marek and Vávra (2018)[11] described procedure which allows to use observed counts of combined measure of home team advantage ( k 1 {\displaystyle k_{1}} k_{1}, k 0 {\displaystyle k_{0}} k_{0}, and k − 1 {\displaystyle k_{-1}} k_{{-1}}) in two leagues to be compared by the test for homogeneity of parallel samples (for the test see Rao (2002)[12]). The second proposed approach is based on distance between estimated probability description of home team advantage in two leagues ( p ^ r = k r K , r = − 1 , 0 , 1 {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1} {\displaystyle {\hat {p}}_{r}={\frac {k_{r}}{K}},r=-1,0,1}) which can be measured by Jeffrey divergence (a symmetric version of Kullback–Leibler divergence). They tested five top level English football leagues and two top level Spanish leagues between 2007/2008 and 2016/2017 season. The main result is that home team advantage in Spain is stronger. Spanish La Liga has the strongest home team advantage and English football league two has the lowest home team advantage among analysed leagues.

TL;DR

 

[Taybha]

Quite a few players actually play to the crowd and either thrive on it or disappear off the pitch when a bad pass is met with a collective crowd groan , players like Knockaert , Bissouma , Schelotto , are good examples of crowd player interaction

We have enough experienced players to cope with no fans and it shouldn't be used as any reason for our results , good or bad , it's the same for every team .

 

[Seasidesage]

Short term and small sample but early evidence from the Bundesliga is showing that playing in empty stadiums could favour the away team.

Now if this is true, how will this affect our run in?

https://www.theguardian.com/footbal...ue-restart-will-not-be-football-as-we-know-it

I think you have kinda of answered your own question really? The sample size is so small currently as to be irrelevant. If anything it will probably help us as the Amex is not the most intimidating of grounds is it? I do wonder though if one of the eventually relegated teams tries to use it to argue their relegation is invalid?

Playing out the remainder of the season in these circumstances is not fair, but given the alternatives I think it is the best of a bad job. PPG weighted or otherwise would definitely have been challenged in court IMO.