Expected goals (or xG for short) put simply is the probability of a goal determined by a range of variables such as shot distance, angle, body part and defensive pressure. xG has been around for some time now and is beginning to get some traction in the mainstream sports media. The purpose of this article is to use xG as a probability in binomial distribution models, what we can learn from this. Part two will use hypothesis testing to evaluate the xG value for penalties of 0.78.
By Matt Allen (@mattallen001)
February is upon us and we say good bye to the January transfer market. Clubs from Europe’s top 5 leagues made a total of 533 deals at a cost of £802.8m. These figures don’t include other big spending leagues such as the championship and the Bundesliga 2 which also spend a considerable amount of money. With such high amounts of money flowing around the world, I ask the question: is it an efficient market?
Manchester United were unsurprisingly victorious against a Burton Albion side which from the outset looked to play expansive attacking football. The teams lined up as follows:
When a club gets relegated to the championship, the most hard-hitting impact is the loss of television revenue paid to clubs purely for turning up. So, in order to combat the effects of this loss, the Premier League gives clubs parachute payments which help ease the transition. These payments last three years following the season of relegation and fall in value before clubs are faced with supporting their own finances. If the club is fortunate enough to be promoted then the parachute payments will end.