American Soccer Analysis has been in the analytics game since 2013, and, early on in this project, we noticed something that’s always troubled us when it comes to taking the seminal analytics studies and concepts developed in Europe and applying it to an MLS data-set. To put it frankly, they don’t work as well.
Read MoreDo you ever find yourself yelling “JUST SHOOT THE BALL!” at the TV screen? Of course you do, you watch soccer! Sometimes it can be maddening to see your star striker make his/her way into the box, only to futz around with a pass or dribble. At times it doesn’t even matter whether that pass or dribble was successful. Does it seem like your team does it particularly bad? You’re probably not alone.
Psychologists will be quick to point out a thing called negativity bias. Basically, we probably all think our team dilly-dallies in the box more than others because we remember it better. The existence of this bias, by the way, is supported by a convincing amount of experimental evidence. But it begs the question, who is empirically more likely to shoot when they can?
Read MoreThe 2017 MLS season began with a bang over the weekend! During this time, I had a look in the archive room on shots taken (2011-2016) and thought it would be a nice time to examine shot placement in MLS. This analysis will use some of the ideas from Colin Trainor’s article from Statsbomb a couple of years ago (using one season data from Europe’s Top five leagues (2012/13), while also building upon his piece and examining shot locations and placement in further detail.
At the start of Colin’s piece, he straight out stated that one thing has to be reiterated time and time again: “you can never just take the first metric at face value as further analysis can be undertaken, and inevitably this second level of analysis can provide insights that are missed at the higher end of data review”. Now that is not to say that my piece will be anything better, that was actually Colin’s second analysis on the topic (the first you can access when you read his post above). I will try and build upon his analysis by using MLS shot data to look at more ‘specific zones’ in greater detail and how these end up in placements/areas (in the goal). Before I do that, let’s look at the placement conversion rates in MLS.
Read MoreAbout two weeks ago James Yorke of Statsbomb wrote an end of season review for the 2015/2016 Premier League where he outlined a few shot and conversion figures. I found these figures intriguing and decided to use the same process to evaluate the MLS and more specifically if there are any differences between the Eastern and Western conferences. Before we examine any differences between the two MLS conferences, let’s have a look at the league as a whole.
From 2011 to the current season, the figures match up as follows. Keep in mind that the 2016 season is currently in a busy schedule (I have only been able to factor in games up until and including Sporting Kansas City vs Orlando City on May 15th 2016).
Table after the jump.
Read MoreOn Saturday the U.S.A. cruised to a 6-0 victory over Cuba in the Gold Cup quarterfinals and advanced to play Jamaica in the semifinals. When a country with the population and the financial resources of the U.S. pounds on a country whose players are much more intrigued with the idea of leaving the team, it’s hard to get too excited about the victory. It’s an even harder match to break down statistically. How do you analyze a drubbing? Let’s just all feel good, right? Believe it or not I’ve found some statistics that will sober you right up.
Read MoreHopefully most of you read Part I of my series on Expected Wins in Major League Soccer. As a quick reminder the Expected Wins analysis is my internal data quality review on the seven data points I use to support my quantitative Possession with Purpose analysis; the stronger the correlation these data points have the more confidence I have in the overall Indices that are created to assess team performance.
For your benefit, in case you forgot, here are the seven data points I continue to analyze as we reach the 92 game point in MLS; which equals 184 events:
All data points, at this time, have equal weight.
What is interesting is that over the week to week course of the season 40% (20/50) of the weekly top five teams, in Attacking PWP, have averaged less than 50% possession in their matches.
For me that's pretty cool as it indicates this analysis is not really biased towards teams that use a shorter-passing scheme in attack. Week 5, 3 of 5 teams were under 50% and the other two were both under 51% possession.
Some of those teams are possession based teams like DC United, Portland and Seattle but in that week the margin of possession did not have as much effect as the ability of those teams to finish quality chances - the top three teams that week all scored goals equal to their shots on goal.
The five teams that week who exceeded 80% in Passing Accuracy; usually a good indicator of ground based attacking all finished outside the top 5.
Moving on after that tidbit, here's the averages for overall (blue bar), teams that win (green bar), teams that draw (orange bar) and teams that lose (red bar).
Facts as they exist today after 184 Events in 2014:
Like after XpW 1 (102 Events - 51 games) losing teams shoot the ball more often, on average, but are less accurate when it comes to putting those shots on target and into the net. Patience in creating quality continues to outweigh quantity...
Overall, the averages for Shots on Goal for winning teams has increased from XpW 1 (4.90) to XpW 2 (5.36); basically the better teams have gotten better and the losing teams have gotten worse (3.84 now) versus (4.10 in XpW 1).
I wonder how that trend will continue through the rest of this year?
Tthe 2% gap in Passing Accuracy between winning teams and losing teams has held from XpW 1 to XpW 2.
The gap in Shots on Goal has increased in losing teams to 10% as opposed to 9% (XpW 1).
The gap in Goals scored has remained near steady at roughly ~30%; though slightly smaller in XpW 2.
Losing teams still continue to take more Shots than winning teams; 12.74 (winning teams) to 12.80 (losing teams) but... that gap has dropped since XpW 1 - perhaps losing teams are looking to be more patient in their shot selection?
So how does the overall data relate in an Exponential Relationship?
Observations:
The light shaded lines are the lines of data as in XpW 1 - and the trend-line colors remain the same.
This time the R2 has dropped just a tad.98 to .95 - all things considered most would consider that correlation Rock Solid... I do - and the correlation of these data points, viewed as a whole, have a higher correlation together than Goal Differential (R2 = .88) to Points in the League Table.
Goal differential is usually a great indicator but it also remains a qualitative statistical indicator not a quantitative indicator.
Like last time there remains a difference in the R2 between winning teams, teams that draw, and losing teams; with draws now having greater correlation than wins. Why? I'm not sure - but as noted by the closeness of all the data points there still remains a fine line between winning, losing and drawing.
Last time I felt that helped explain the difference between mistakes or unlucky breaks - I continue to sense that is the main difference. So might this be an indicator of luck - I don't know - what do you think?
I have seen discussions of late, on Telly, and in some articles written elsewhere, that focus more on 'space available' as opposed to just Shots Taken... hopefully that trend continues!
I also remain hopeful that OPTA and other statistical web sites will offer up more critical events taking place in the Final Third... One other article written since XpW 1 is my analysis (as promised in Xpw 1) on defensive indicators; here's a link to Hurried Passes and those details.
In closing:
I still don't have enough data, in my opinion, to offer additional thoughts on individual team performance relative to home and away games; that probably won't have statistical reliability until the midpoint of the season (game 323 - events # 646).
There are trends but I'll save that for another article, enough for now.
Best, Chris
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