Since the weekend was filled with barbecues, families, and time away from the pseudo grind of the world, we decided to skip out on our weekly podcast. But we all love our “Game of the Week” contest so much that we decided to still preview tonight’s game of the week between Seattle and LA. This is what we do for you, America. This is our service.
We’ve talked quite a bit about game states on the blog over the last few weeks, both linking certain articles as well as talking about it on the podcast. The ability to take specific events and associate context with them to provide a better understanding of the match results is helpful.
However, there are times when I think Game States need to be refined based upon the situation. Take for instance our “game of the week” selection, New York Red Bulls at home against the potent Los Angeles Galaxy. There is a lot I could say about leaving Mike Magee behind in LA and losing Juninho just 10 minutes into the match. Attempting to use the typical goal game state doesn’t really work simply because of the lone goal was scored at the 91 minute mark.
If we were looking at this in a season long context and we wanted to see how good a team was in the “even goal state,” or maybe how long they played in an even goal state, 90+ minutes of data this match would go towards that game state and presumably help speak to each team’s ability. The problem is that on an individual game basis sometimes there is a need for another way to really apply context to this game.
Naturally, with the injury to Juninho the first thought is to apply game states to substitutions rather than goals. The problem with that—omitting Juninho’s substitution—is that substitutions take place in bunches in the second at the end of the game. It’s becomes difficult to separate where exactly there was a specific difference maker.
So I kind of abandoned the thought of single game states in this scenario and instead looked more for another pattern.
Above is a bit from the MLS site chalkboard. Events on the timeline have been taken from each team, and each has a corresponding event associated with it on the map of the pitch. I specifically used offensive-associated filters to help give me an idea of the effectiveness of each team and how often it was involved.
The specific filters used were: Through balls, Crosses (both successful and unsuccessful), Key Passes, Shots on target, shots off target and lastly, blocked shots. These are all decisively aggressive methods that appreciate a teams ability to drive towards the opposing goal. I’m not exactly sure what to make of all it, there are almost distinctive time blocks that belong to each team as they would hold the ball and look for their own attempts on goal.
You can see that each team had a couple of chances in the last 10 minutes and it came down to a bit of luck in the circumstances of the lone goal. The timeline itself looks almost like heart beat rhythm between each team and their respective attempts towards the opposing goal. This is kind of the pattern I was looking to find, but I don’t exactly know what to do with it.
In summation of the actual game, you could make some Carlos Cudicini references—see: Matthew Doyle for snark—and put a nice little bow on it. Yes, I do agree that LA’s Italian keeper should have come out of his goal to clear the attempt, but I happen to also think that this single game came down to a rather random occurrence. A simple mistake from a goal keeper who has been in residence at some prestigious clubs.
The league average team finishes a shot roughly once every 10 attempts. The New York Red Bulls scored on what was their 10th attempt at goal. While LA was stuck at 9. I know it’s not popular but I believe that sometimes it’s not necessarily about strategy or anything deep tactically. Instead, maybe it’s about fighting for 90 minutes, putting up as many (good) shots as possible and hoping one of them goes in. That sounds a bit Charles Reepish… I know, but sometimes it’s true. Sometimes the ball just finds its way into the back of the net.
Humans make mistakes and even the best goal keepers do, too.
Here is episode 7, all ready to go. If you ever have any questions, suggestions or general comments use the text boxes below.
Everyone, here we are with American Soccer Analysis Podcast Episode 6! We talk about Juan Agudelo, shots and finishing (skill vs. “luck”), grass pitches vs. artificial turf, Kei Kamara and his return to KC, and then some about bowel movements. LISTEN NOW!!!
Consider every conversation ever had about soccer tactics. I would bet 99.9% of them touched on one specific subject: possession. Whether it’s the men’s league team you play for, or the club team you cheer for, isn’t more possession always a good thing? I can’t answer that question confidently, but I will explore it.
The first obstacle to analyzing and discussing possession in MLS is the data itself. We get our data from Opta, and this is what Opta defines as possession:
During the game, the passes for each team are totaled up, and then each team’s total is divided by the game total to produce a percentage figure which shows the percentage of the game that each team has accrued in possession of the ball.
“Possession” in Opta’s data is thus a measure of the proportion of completed passes in a match for each team, not a proportion of time. A lot of short, quick passes will accrue possession for a team that may only have the ball for a matter of seconds. This isn’t necessarily bad or good. It is what it is, and we’ll work with it.
First things first before I make fun of the Revolution (and I will). Their defense has been—excluding the New York outlier—borderline elite this season. That’s possibly one of the few reasons they’re still afloat and maybe the only reason to watch them (sorry, Lee Nguyen).
The Los Angeles Galaxy’s Landon Donovan blew a PK in the 25th minute, and the Houston Dynamo managed a goal in the 56th, stealing three points in LA. Definitely not what our expert panel of misfits projected on Saturday’s podcast. The Galaxy controlled possession (59.3%), won more duels (55%), and earned more attempts (19 to 14), but earned nothing in the standings for its work.
Here’s a chronological summary of Houston’s shots:
Hello loyal readers/listeners! My apologies for not having this up yesterday, but it’s here now. A 55-min. bit on Markov Chains, Game States, and a preview of the Galaxy vs. Dynamo, while also introducing a new member of the American Soccer Analysis team. Hope you enjoy!
I’m no mathematician. Matty maybe, but I am not. So when approaching something like Game States, I felt it good to attempt to introduce it with something, though it’s rather ominous and a bit intimidating. So most–if not all–of the information provided is taken from a source who is smarter than I am.
That’s really what this blog is all about, finding people who know and understand the principles we are trying to learn and centralize the material and keep it in tidy location where people that are new—not just to the sport, but also the concept of analytics—can go to find information and grow their knowledge.
I talked a bit about Big vs. Small data last week–in case you missed it, go back and check it out–and we kind of talked about how you don’t have to necessarily rely on the revolution of big data. There is a need to make do with what is currently available. However, while that big data is sometimes available, there are other encumbrances to deal with:
Beyond the complexity and time constraints placed on the analysis, another major obstacle faced in the job – like that faced by so many people entrusted with big data within an organisation, football club or otherwise – is to make data useful, accessible and engaging to colleagues who have little interest or experience in dealing with numbers.
This from a recent interview with Ben Smith of the development performance systems at Chelsea FC. A club that is often quoted as one of the “big-4” in the English Premier League. It’s important to understand that, while many of these clubs have information at their disposal, few (if any) know or understand the practicality of implementing the information into their planning and preparation phase.
If fact, reading back on the ‘Counter Attack’ blog by Richard Whittal, some clubs–i.e. most–don’t pay their club analysts. That should give you a brief, if not all together insulting, view of how much they respect the value of the service provided. I’m not saying they don’t see it as useful in “some capacity”, I just think that, in terms of how much they pay the rest of the staff, they could afford to have a full time analyst, especially for what an analyst has the potential to provide.
But it is not, of course, just the coaching and scouting staff that benefit from the big data analytics being carried out at the club, the players are also reaping the rewards of the work across the club. He says: “Every one of Chelsea’s Academy players from the age of nine has a personalised development programme.”
There is some interesting stuff here to think about. It sounds a lot like how Ravi Ramineni has started helping out David Tenney, Sounders FC fitness coach, over the past 6-8 months.
By the way, h/t goes to Ravi who linked the article from his twitter.