Of the 12, 10 clubs finished in a higher league position than they were in at the six-week point; only one finished lower. This group includes some of the great comeback stories of the last five years. Udinese marched from 20th at the end of Sept. Arsenal roared back to third place and another Champions League berth after a slow start to their Premier League campaign. Everton and Marseille both went from 20th to the top half of their leagues, while Paris Saint-Germain and AC Milan won their league titles going away after facing some early competition.
Article at SB Nation :. These sorts of attacks, where Dortmund create chances from established deep possession, are new. I created a stat for chances off established possession, tracking attacking moves to see if a club completed four passes within the final 40 yards without passing the ball out of the attacking zone. Under Jurgen Klopp, Dortmund tended to be in the upper-middle of the pack in shots from established final 40 possession. Suddenly this year Dortmund are elite.
In the weighted average, these 50 players scored about 52 goals above expectation for Real Madrid or Barcelona, and they scored about 41 goals above expectation for other clubs. They do the same playing for Udinese, Arsenal or Valencia. While this is broadly true at the population level, there is one striking exception. He had been one of the most deadly finishers in La Liga, scoring 75 non-penalty goals from shots, beating his expected goals 58 xG by nearly Now, there is nothing necessarily wrong with not being Bayer Leverkusen he said, grudgingly.
The counter-press need not be an attacking strategy. It can stop opposition attacks before they begin and enable a slower possession-based game. By contrast, Tottenham Hotspur look like a club that is trying but failing. Under Mauricio Pochettino, a manager who learned these tactics playing for Marcelo Bielsa in Argentina, Spurs have created a good number of attacks at speed 32 and stand third in the league with 89 shots attempted early in possession.
These are not world-beating numbers, but they reflect a club trying to speed up the tempo of the match with their press. Following their short decline and the advent of financial fair play regulations, Chelsea changed their transfer strategy drastically.
A focus on younger players to both rebuild the squad and to sell to fund other purchases developed. So far, the strategy looks a strong one. The club is not buying the foundation of a new club in the age subset, and their purchasing is focusing even more strongly on the late peak age group. Again, this improvement in attack looks less like a fluke and more like something the manager helped to engineer.
Pardew is far from a radical or innovative tactician, but he has shown the key managerial ability to tailor relatively simple systems that get the most out of his best players. At both clubs, Pardew recognized that his strongest attacking players, midfielders Moussa Sissoko at Newcastle and Yannick Bolasie at Palace, were most effective with the ball at their feet in the open field.
The statistics bear this out. For now, talk of a crisis looks overblown. The bad stretch has been marked by an inability to pull out close wins while running roughshod over weaker teams in victories. The other issue has been losses in which Manchester City had the better of the balance of chances. By expected goals, an estimate of chance quality based on shot location and several other factors, City has created more than its opponents in nine of these twelve matches despite claiming points from only seven of them.
For Juventus, the key was attacking a Real Madrid weakness. Doesn't anyone read long winded statistical articles anymore? Answer: no But read more after the jump. Expected goals models are hip in the land of soccer statistics. If you have developed one, you are no doubt sporting some serious soccer knowledge. But it seems to be consistent across time and geography that the smart kids always lack a bit of style.
If you are reading this post you are probably at least reasonably aware of what an expected goals model is. It tells you how many goals a team should have scored given the shots they took. Analysts can then compare the goals actually scored with the goals a team was expected to score and use that insight to better understand players and teams and their abilities. The best expected goals models incorporate almost everything imaginable about the shot.
What body part did the shooter connect with? What were the exact X,Y coordinates of the shooter? What was the position of the goalie? Did the player receive a pass beforehand? Was it a set piece? All of these factors are part of the model. Like I said, they are really cool. But as with all models of the real world, there is room for improvement.
That could force a higher number of blocked shots or just force the shooter to take a more difficult shot than perhaps they would like to. On the opposite end of that spectrum, perhaps a shooter was wide open on a counterattack, the models would not likely recognize that situation and would undervalue the likelihood of a goal being scored. But I may have found something that will help in these instances.
I recently created a score that attempted to numerically define extreme styles of play. On the one end of the score are extreme counterattacking teams score of 1 and on the other end are extreme possession-oriented teams score of 7. The question is, if I overlay this score on top of expected goals models, will I find any opportunities like those mentioned above?
It appears there are indeed places where looking at style will help. There is the model managed here by the American Soccer Analysis team us! Both models are pretty similar. If you were to draw a straight line regression through this data you would find nothing in particular. But allowing a polynomial curve to find a best fit reveals an interesting pattern in both charts. When the Pscores are below 3, indicating strong counterattacking play, the two models consistently under predict the number of goals scored.
This makes sense given what I mentioned above; teams committed to the counterattack should find more space when shooting and should have a better chance of making their shots. But you would think that less-talented teams would also be teams that would have shooters that are worse than average. The fact that counterattacking teams outperform the model indicates they might also be overcoming a talent gap to do so.
On the other hand, when the PScore is greater than 4, the models also underpredict the actual performance. This, however, might be for a different reason. Usually possession-oriented teams are facing more defenders when shooting. The bias here may be a result of the fact that teams that can outpossess their opponent to that level may also have the shooting talent to outperform the model. Notice also where most teams reside, between 3 and 4.
Looking at teams in aggregate, however, comes with its share of bias, most notably the hypothesis I suggested for possession-oriented teams. To remove that bias, I looked at each game played in MLS in , home and away, and plotted those same metrics. For both home and away games there does appear to be a consistent bias against counterattacking teams.
In games where teams produce strong counter-attacking Pscores of 1 or 2, we see them also typically outperforming expected goals G - xG. Given that xG models are somewhat blind to defensive density it would make perfect sense that counterattacking teams shoot better than expected.
By design they should have more open shots than teams that play possession soccer. It definitely appears to me that xG models should somehow factor in teams that are playing counterattacking soccer or they will under estimate goals for those teams.
|Caley xg||Apple macbook air 15 inch refurbished|
|Led zeppelin 1 cover||802|
|Caley xg||At the other end they had leaked nine, when expected goals suggested it would usually have been five. Here are the key points that Bertin makes about that graph: First, an r-squared that high should be suspicious in itself. Bertin must have done a lot of data acquisition and data crunching in putting this article together, and I can vouch for him that's one heck of a lot of work. I did not use r-squared to prove my point because it requires much more than a bare r-squared number to make a strong argument. Basically, due to variation, a simulation can be relatively more in line with the xG-values, or less.|
|Aremo 2173p||News Hot Rising Awarded Latest. Whatever happened to goalmouth scrambles? If Messi makes them 20 percent worse, Real should take about three more points over the next two months, and at 10 percent the gap would be two points. Read More. In games where teams produce strong counter-attacking Pscores of 1 or 2, we see them also typically outperforming expected goals G - xG. I also hinted at a better way to evaluate an xG-model, something I will explain and apply today. He writes:.|
It perform then administrators to server client, set run. You need want all continue configuration without waiting execution. In you your reason: opinion, blog, bigger chances.