Ian left Decision Technology in July 2012.
Ian Graham is the head of football research at Decision Technology, based in London. Since 2005 he has developed a set of statistical models for the prediction of football matches and the rating of players. He holds a PhD in theoretical physics.
Seems a silly question, right? The final will be at Bayern’s home stadium. But it won’t be full of Bayern’s fans, as it is when they usually play. So are they really going to enjoy the full effects of home advantage?
Home European Cup Finals
There are previous instances of European club finals being played at one of the teams’ home grounds: Madrid won the European Cup “at home” in 56/57, Inter won in 64/65 and Roma lost in 83/84. In the UEFA Cup, Feyenoord won in 01/02, Sporting Lisbon lost in 04/05 and finally in the European Cup Winners Cup Barcelona won in 81/82. Thanks Wikipedia!
So four wins and two losses, that’s exactly what you’d expect with full home advantage in effect and two teams of equal ability. But most of these matches are far in the past: maybe the home teams were strong favourites and maybe the stadia all those years ago really were full of home fans. Is there anything else that can help us?
Home Domestic European Cup Finals
I was surprised to find (thanks again, Wikipedia) that French, Norwegian, Danish and Swedish have been played at club grounds, and there have been many instances of cup finals “at home”.
What an exciting start to the season. At nearly three goals per game, there have been plenty of great matches. Outlandish score-lines such as 8-2, 1-6 and 3-5 have led commentators to scratch their heads.
The Guardian asks whether it’s money spent on strikers, and Yahoocites improved attacks and failures at the back. But is the goal glut real?
Short Term Variation
The power of statistics is that it accounts for short term variation. When information is limited, we should be wary about drawing conclusions from our observations.
So when we get an “unusual” result, such as 2.98 goals per game so far this season, we can use statistics to tell us if that really is a surprise, or if it’s to be expected – after all, we’ve only seen 99 games.
How to Predict Goals Scored
We use our team strength model to predict Premier League matches. Total goals is something we can predict. At the start of the season, for example, we predicted Liverpool to score 1.69 and concede 1.02 goals at home to average Premier League opposition.
How many times does a manager or a player claim “a draw was a good result?” Whether this is justifying the result after the fact (i.e. if you’ve been 3-0 down a draw is certainly a good result), or whether the team went out to play for a draw, I decided to see how often a draw really was a good result given the pre-match predictions.
Who’s Happy With a Draw?
A draw is better than a loss and the one league point it brings is some comfort to fans, but more often it is two points lost rather than one point gained. Consider a match between evenly matched sides. In that case the home team has about a 50% chance of victory, and the away team about a 25% chance. Continue reading →
We’ve been working with Castrol and MLS since March to deliver the MLS Castrol Index. Beyond player ratings, we’ll be predicting the MLS Cup Playoffs once the regular season is finished. So we’ve been taking a look at MLS teams and the structure of the league…
We applied our team strength model, using weighted historical full time scores to rate teams, to MLS. Who’s looking good this season? Seattle is the standout team in terms of goal scoring threat – we rate them as scoring 1.6 goals on neutral territory against the average MLS opponent. After Seattle, the next best teams are remarkably evenly matched, with Red Bull New York in second scoring only 0.2 more goals against the average opponent than Columbus Crew, ranked 12th. Continue reading →
We should cite some related work – Soccer By the Numbers used the variation in wins across teams to measure competitiveness, and 5 Added Minutes looked at points per game gained by top five and bottom five teams.
These analyses are worth a read. However, Spain doesn’t really stand out as an uncompetitive league using those rough measures since it’s only the top two teams who have extreme results (see our La Liga pre-season predictions). We used a different approach to highlight why Spain is different.
Arsenal have scored only three goals this season. Should they be worried about their inability to convert shots into goals?
Arsenal fan @Orbinho – mentor to @OptaJoe – tweeted that Man Utd have scored with 30% of their shots last season while Arsenal have only managed to score with 7% of theirs. The chart shows that they rank equal 14th, alongside West Brom and Norwich, in terms of shot conversion this season.
Last season Arsenal ranked fourth with a shot-to-goal conversion rate of 14%. Man Utd topped the conversion charts with 16.4% of their shots finding the back of the net.
Another neat way of looking at the transfers is by division and by country – where do the new players come from and where do the old players go?
Across the football league structure, transfers are still quite parochial. Of 760 total deals, 144 were free agent signings and 356 were loans and transfers from one of the 92 English League clubs. A lot of that is due to the national rather than international nature of the lower divisions; but even of 105 deals with players signing for Premier League clubs, 39 were from another Premier League club, 20 were from the Championship and 13 were free agents. Only 33 signings came from overseas leagues.
Here’s another way to visualise transfers – by date. Again, thanks to the fantastic BBC transfers resource for the data.
According to the BBC within-association transfers can begin as soon as domestic competitions finish and they go on (as anyone who watches Sky Sports News will know) until 11pm on 31st August. I was interested to find out what type of transfers happen when and whether “transfer deadline day” really is as exciting as Jim White makes it sound.
It’s been a busy summer, with the usual frantic end to the transfer window. I’ve been looking at the excellent data provided by BBC Football, and playing around with different visualisations of transfers involving English and Scottish clubs.
I grabbed the transfers for the entire window and it’s such a rich data set that one post is not really enough space to visualise it all properly. For this first post I thought I’d just display the basic patterns that appear by club.
So, the Champions League draw has taken place. Who is in a tough group and who has an easy ride?
Measuring Group Strength
We use ourEuropean Team Strength model to rank team across Europe and also to make Champions League predictions. Let’s take a look at how we rank the 32 Champions league qualifiers
We rank the teams by their chance of winning a match against an average European side. For example, Barcelona we rate as having a 91% chance of beating the average European league team, whereas Ajax only have a 62% chance.
Group A looks tough, with Man City and Bayern both ranked in the top 10 among Champions League Clubs. Group H is strong due to Barcelona, but the second to fourth teams in group H are weaker than average. Man United‘s group looks particularly low ranked.
Visualising group Strength
We can visualise group strength by plotting the goal difference that each team in each group is expected to achieve against an average European league team on a neutral venue. We find that group A is strongest, achieving an average goal difference of 1.7 goals, while group C attains on average a goal difference of only 0.9 goals against an average European league team. Continue reading →