The N.A.S.A. Roundtable … (Nerds Attack Soccer Analytics)

Landing a rover on Mars is CHILD’S PLAY compared to figuring out who’s really helping a soccer team win. … Yeah, we said that.


This piece originally ran in August of 2012

What happens when TSG reaches out to a bunch of nerds in high places who like numbers, triangle passes, Barcelona analogy misappropriations, Dax McCarty data, Roger Espinosa heat maps and deal in things called “machine learning” & “regression analyses?”

A massively data-erotic soccer column on game and player analysis that lies somewhere just left of Shelter Island and A Beautiful Mind, but well beyond Good Will Hunting.

Now our actors around the roundtable. NASA “Curiosity” seekers don’t have squat on these guys. They are:

» Steve Fenn: Steve is a mathematics lunatic (complimentary) and tweets at the poignant handle Optahunt. Steve also now writes for TSG & Big D Soccer … when Opta Chalkboards are not online. Welcome Steve, the John Nash of these proceedings. (Follow on twitter)

» Rui Xu: Rui is currently in his second season as the Performance Analyst with Sporting Kansas City. He graduated from the University of Southern California with a degree in Economics. Rui is like the Stu Ungar of this roundtable, pre-extracurriculars of course. (Follow on twitter)

» Alex Oshansky: Alex runs numbers for an investment fund in LA by day and makes soccer spreadsheets at night. This makes him the Bernie Madoff Blue Horseshoe (about to become the) Warren Buffet of the soccer world. He already is here in this column. Alex also writes for TSG and also on his own blog, Tempo Free Soccer(Follow on twitter)

“Them apples” ain’t about possession.

» Devin Pleuler: Devin is a computer science graduate from Wentworth Institute of Technology in Boston, where he played on the men’s varsity team as a goalkeeper. He’s writes the Central Winger analytics column for MLSsoccer.com. He’s a TSG alum and now a member of the MIT men’s soccer coaching staff. So he’s obviously Will Hunting here. (Follow on twitter)

TSG: Okay, what is the biggest misconception in what appears to be the growing market of data processing and statistical evaluation in soccer?

Pleuler: The biggest misconception is that soccer doesn’t lend itself to statistical analysis and that it’s free-flowing nature is the reason. Even with soccer-inclined geeks, this seems to be an argument that I am having constantly. I think the reason why this argument is so common is because our community doesn’t have well-defined (or perhaps, well-publicized) goals.
We may never be able to “break” the game like sabermetricians have to baseball, but that doesn’t mean that it is impossible to derive value from soccer statistics. Improving the efficiency of front office transfers by a few percentage points is a very reasonable goal – and that’s something with very tangible value.

Xu: I think the biggest one is that I don’t think we’re going to get the granularity that we get in baseball, and I don’t think it will surpass scouting in terms of importance when it comes to opponent scouting.

The BEST case scenario for soccer statistics is getting to where defensive statistics are in baseball., with Ultimate Zone Rating (UZR) and Defensive Runs Saved (DRS), which are known to take a long time to stabilize, and are very iffy to use in small sample sizes.

Olshansky: I don’t know if it it is a misconception exactly, but it seems like the analytical community is only just scratching the surface of what we can learn about soccer. The field is in a nascent stage, and I think some perspective regarding just how far there is to go is needed. Graham MacAree of SB Nation had a great treatise on the state of the field and what still must be accomplished.

For example, only recently have some analysts started to realize just how few crosses are actually completed (for some teams only 10%). What does this mean exactly? Is crossing now a bad strategy? It’s just one example of how little we really understand.

Fenn: Underestimating the value of context. Biggest culprit within that issue is pass completion without adjusting for pass difficulty or value. Devin’s recent Central Winger columns have been nice steps toward better understanding.

[Stoke City] Yes or…..

TSG: What is the one stat–in your expert opinion or through empirical review–that is the most mis-used stat in soccer?

Olshansky: I’m not the first one to say it, nor will I be the last, but possession % is still used far too often to denote who is outplaying whom. Devin has written about how it is often more a defensive statistic than an offensive one and is highly dependent on game situation.

Fenn: For me, the most misused is also the most used. Whether he actually said it or not, I agree with the Jonathan Wilson quote, “Goals are overrated.” Again, context is key, and Sam Green’s work on shot quality is a great way to gain better insight into scoring and goalkeeping.

Pleuler: Possession is undoubtably the most mis-used stat in soccer. Arguably, this is because it is significantly more nuanced and complicated than it is often made out to be. Since the number of possessions during a game is finite, it is in the best interest of the more efficient team to increase the rate of possessions. This is simply because there is less variation the larger the sample size. Conversely, after a team has scored, it’s in their best interest to decrease the number of remaining possessions in the game. The less possessions, the less chances there will be for your opponent to score.

No?

Barcelona does this by gaining and holding possession for long periods of time. Stoke does this by sitting deep in their own zone and becoming comfortable with their opponents possessing the ball for long periods of time. Barcelona and Stoke are attempting to do accomplish exactly the same thing and yet we view one style as artistic and the other as anti-soccer. One of these styles artificially inflates possession percentages, and one artificially deflates them. To talk about possession statistics without acknowledging this kind of context is really really bad.

Xu: By a GIGANTIC margin, possession percentage.  First of all, it doesn’t even describe what it claims to; it actually has nothing to do with ‘time;’ it’s calculated using team passes divided by total passes. The data providers are using pass volume as a proxy for time in possession because it’s easier to calculate. Graham Macaree has a great article on that here.

Secondly, nobody ever provides any context on possession percentage. Statements like “When the score is tied, the average team’s possession percentage is 50%. When they’re up by one, the average team’s possession is 40%. When they’re down by one, the average team’s possession is 60%” are actually pretty easy to find, and they tell WAY more of the story than just possession percentage by itself.

Finally, it’s unclear a) whether or not possession percentage is reflective of the scoreline at all, and b) whether possession percentage is reflective of the scoreline, or the scoreline is reflective of possession percentage.  Did the team lose 1-0 because they had a lower possession percentage, or did they have a lower possession percentage because they lost? There are just too many unknowns and ambiguities for it to be as ubiquitous as it is.

The best statistical proxy for winning? :>

TSG: What’s the one single stat that you would create–or has been created–that is the best proxy for winning.

Xu: Well, goal differential, but I’m guessing that’s not what you’re asking.

Essentially what you have to do is create an “expected goals” and “expected goals against” model, and compare that to your actual goal production to see if you’re overperforming or underperforming

You do that by finding inputs that create goals. That’s fairly simple: quantity of shots and quality of shots. Then you find inputs that create more shots and create better shots.  For example, crosses increase the number shots that a team takes, but those shots tend to be of poor quality.  1-on-1 vs the goalie have a very high probability of scoring, but they don’t happen that often. You find all of these, input them into an “expected goal differential model,” and you ‘ll get a very, very good proxy for how good your team is actually playing.

Olshansky: I think if someone could create a concise and logical way to rate the quality of chances, that would be a very useful statistic. Soccer is a game that often comes down to maybe five chances per team per game, so the ideal metric would assess a team’s chances of winning as: (average quality of chance X chances) – (opponent average quality of chance X chances). Of course there are a lot of factors that go into rating the quality of a chance, but I don’t think this hypothetical statistic is a pipe dream.

Fenn: Chances created adjusted for shot quality for offense. Quality of shots allowed on defense. Any of our answers would have to stand up to rigorous testing before claiming “best proxy for winning,” though.

Pleuler: This is a difficult question to answer because so many metrics are built directly on top of one another. Expected goals (and goals against) is perhaps the most obvious choice. But, you can’t calculate expected goals without quantifying the quality of each goal-scoring opportunity. You can’t accurately quantify the quality of a goal-scoring opportunity without quantifying the defensive pressure applied during that opportunity. You can’t quantify defensive pressure unless you quantify defensive shape, game context, and a handful of other complicated metrics.

Um, best line-up?

TSG: Ponder this:

Coaches tinker with line-ups all the time. If you employed a marketer or “business optimizer” in the role of coach, they might resort to a notion called A-B testing to improve the line-up. That is remove one player and replace him or her with another as a means of finding the best combination of players on the pitch. However there is a principle called multi-variate testing that allows for changing many variables at one time. Can this be applied to soccer in any way?

Pleuler: In Major League Soccer last year, the expected result of a game immediately following a loss declined significantly the more you changed your starting lineup. In other words, it is inefficient for a team to make wholesale changes to their lineup after a loss. I don’t think this bodes well for this kind of analysis.
Ford Bohrman of Soccer Statistically has taken a stab at adjusted plus-minus statistics for soccer – which is similar to what is being described here. Ford, like any good scientist, reported his findings even though he had less than steller results.

Fenn: Seems highly relevant to lineup selection and substitution patterns. Such analysis could shake up timing and nature of such decisions, which can be staid and predictable.

Xu: Yes, but with the caveat that you actually need to have a performance metric for the players in place.  Right now, we don’t even have a publicly available equivalent to batting average in soccer, let alone a true, accurate proxy for talent like WAR is in baseball.

Once you have that, you can start to do some pretty interesting things with multivariate analysis. I’ve mentioned a couple of times that I think synergy is an underrated aspect of soccer, so even if Player A is an absolute better player than Player B, it is possible that Player B has qualities that synergizes with his teammates, creating a better team overall.

Olshansky: Theoretically, yes. But I think we are still a long way off from having a model that could accurately assess multiple variables. Honestly, I don’t even think we are at the point where someone could tell me how many fewer goals the LA Galaxy would have conceded if Omar Gonzalez had been healthy the entire year.

TSG: What new metrics are important or would you create? Do any of these (link) make sense?

Not a good metric to get a +1 in….

Pleuler: Your metrics make a lot of intuitive sense, but they are mostly things that can be derived from base metrics that are already being collected. As a community, I think we are beyond the level of counting instances of particular on-the-ball events and should be looking at quantifying off-the-ball events. That’s where the future is.

Fenn: Using Smart Soccer technology I’d create a metric for off-the-ball positioning relative to opposition, teammates, and the ball. Very easy for observers to be unable to fully and acurately appreciate or criticize contributions in this area.

Olshansky: First of all, I like the SSF (Stupid Stupid Foul) idea. I still cringe thinking about Fabian Johnson bringing down Marco Pappa when he had no support and two other US defenders seemed to have him covered.

I have been tinkering with, and quite like, the idea of a Forced Turnover (FTO) statistic for defenders. It’s more than just winning tackles; it also includes clearances, recoveries, and interceptions. Would be interesting to see the MLS leaders in FTO.

Xu: Win/goal probability is the big one for me.  Every single action on the pitch is trying to accomplish one (or both) of two actions: scoring a goal or preventing a goal.  Therefore, every single action has a specific value to one of those two ends.  I want to know those values.  Unfortunately, that is an incredibly time-consuming endeavor because that would require watching every single MLS game and noting the position of the ball and every player on the pitch at every single moment of time, but it is certainly possible.

The benefit of a stat like WPA/GPA is that it automatically captures the entirety of a player’s work.  A lot of the concepts in your statistics are sound, but for me, I want to know all of a player’s value so I can determine if he’s overpaid/underpaid, etc.

Getting in early on the Apple Stock of soccer, advanced statistics….

TSG: One could argue that the real revolution in statistics is somewhat over now. Everyone–if they choose to spend the money–can dig deeper into “data.” The real revolution will be in the coach or manager or general manager who can combine statistical data into metrics and optimize on a per game or per player basis. Is this valid?

Olshansky: Somewhat this is true. I think the biggest issue is figuring out what to make of all the data. The real competition going on is among people like Rui, who are tinkering on spreadsheets trying to find wins amidst the avalanche of OPTA and Prozone data.

There is a certain game theory aspect to teams starting to incorporate statistical analysis more: how have the A’s done since their “Moneyball” halcyon days of the early 2000′s? How are the Rockets doing? The problem is that as soon as any metric becomes useful other teams will replicate it, rendering that initial advantage useless. This will be interesting to see played out going forward.

Fenn: Generaly agree, but I think a big part is analysts making their work accessable to coaches and players. A brilliant theory doesn’t help if it can’t be put into practice.

Pleuler: The revolution in soccer statistics will undoubtably take just as much social engineering as real engineering – and we’re very quickly reaching the threshold where the social barriers are just as daunting as the technical ones. But, this doesn’t mean that very significant progress still needs to be made on the technical side. The “revolution” may be over – and that depends on how we define that word – but the hard work is undoubtedly still going on.

Xu: I’m not as sure about this one as I was when I was first starting this job. When I first started, I was confident that, like, baseball, you’d be able to develop conclusions using statistics quicker than using your eye, but that’s not true in soccer at all.  It takes maybe a game to tell that Lionel Messi is the best player on the pitch, whereas statistically, it might take a few for all of the noise to cancel out. It’s actually pretty rare that I go to the coaches with something completely new about an upcoming team. Usually, I’m backing up an existing theory, or it’s something they already had an inkling about.

The real advantage of metrics is in player evaluation and roster decisions, in my eyes.  With performance analytics, you can put together a more efficient team, or you can evaluate if you’re making the correct draft choices, or making the right transfer moves.  Those are the macro-level decisions that really impact an organization.

TSG: Justifying it anyway you wish, who is your favorite player and/or what is your favorite stat about said player?

This wasn’t the booking…

Olshansky: Vinnie Jones, he of Lock, Stock, and Two Smoking Barrels and Snatch, holds the EPL record for quickest booking: 3 seconds. Art imitating life?

Xu: Aurelien Collin. Because, c’mon. Dude’s crazy, and he’s also an incredibly kind and generous person.

I don’t know if it counts as a ‘stat,’ but I absolutely love looking at Roger Espinoza’s heat maps for games. He worked really hard in the offseason on conditioning, and it shows in his work rate.

It’s all in the data…

Pleuler: I very much like Dax McCarty of the New York Red Bulls. I always seems to find him creeping toward the top of the different types of analysis that I do. As for a favorite statistic about a player – I don’t have one. It would probably be a bad idea for me to become attached to certain statistics because I want to be able to throw them away when I find something better. I do, however, think goalkeeper throw-ins are hilarious.

Fenn: Age is a stat, right? Good, then it’s @InfostradaLive after Messi set the Barcelona goal record: “At 27, Alexander The Great overlooked his empire and wept bitter tears because there were no more worlds left to conquer. Lionel Messi is only 24.” If this is hypocritical to my earlier context rant, I guess I’m like Private Joker’s peace sign next to “Born to Kill.” I think I was trying to say something about the duality of man. The Jungian thing.

TSG: Do you think there will ever be a single metric that accurately scores the full value of individual soccer players?

Value.

Pleuler: There already is a single metric that articulates the full value of a soccer player. It’s salary. The problem, of course, is that our method of calculating salary it is far too dependent on subjective opinion. What further complicates the measurement of player value is that players are worth differently to different teams.

For example, Peter Crouch is probably more valuable to Stoke than he is to any other team in the Premier League. Stoke’s pragmatic style of play may lead them to value him at $11m while any other team may value him at $8m. This is good for Stoke because they only need to pay more than the next highest bidder. On a side note – THIS is how MLS teams can gain a real advantage under the salary cap.

Fenn: The higher priority is lots of smaller metrics evaluating individual skills. With those a coach could target specific abilities needed for his team’s style of play. Weighing and summing individual metrices would be my preferred approach to an overall player rating. Would such a metric definitively say if one player is better than another? Probably not, but it could be good enough to revolutionize transfer and salary pricing.

Olshansky: Accurately? I don’t know. Probably some day, but the arc of time is not on the contemporary soccer analyst’s side. I think it will take many more years of data, experimenting, etc. before a truly accurate individual metric system emerges. I do think a good approximation is not far away. At this point, the best statistics are only useful for comparative purposes by position and are most useful for attacking players, but I am hopeful that defenders may still yet get their full due.

Xu: I do. I think WPA/GPA gets very close, and we’re not super far away from that.  It’ll be tough to break down in terms of a player’s individual characteristics (aerial ability, tackling ability, finishing ability, dribbling ability, etc.), but it captures a player’s totality, which is perfectly usable.

[end roundtable] Thanks guys.

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25 responses to this post.

  1. Posted by Matt Mathai on 2012/08/07 at 7:22 PM

    Fascinating stuff. I’ve always wondered how metrics can be used effectively to help our understanding of how a game is going (or went). I’m eager to see how things develop. Great article. Thanks.

    Reply

  2. This is wonderful stuff, guys. My meager scratchings at the Opta Chalkboard will be much enhanced by back-and-forth roundtables like these.

    Reply

  3. Posted by acoupstick on 2012/08/07 at 8:55 PM

    Hurray for nerds! Very interesting discussion….

    Reply

  4. I’d like to emphasize that none of us saw the others’ responses before offering our insights. That being the case I was pleased that some of my answers to open-ended questions mirrored that of professionals like Rui and Devin. However, side-by-side with the others’ insights, I feel like I was a little too into the whole brevity thing on a couple responses. Two more-verbose clarifications:
    – most mis-used stat in soccer? – While I absolutely agree that possession is massively overrated, I still stand by goals. Thinking about it, clearly goals are the only statistic that every soccer fan of all time has overvalued to some extent. This overvaluation skews salaries and transfer fees. We put too much weight on the player that touched the ball last, and not enough on everyone that put him in the position to succeed. Every year there are fluky goal-scoring freaks that disappear the next. Sustainability would be more predictable if we put scoring in better context.
    – new metrics? – quantifying which off-the-ball metrics are meaningful should be fascinating work. Better for a forward to constantly bluff defenders like Chris Wondolowski, or conserve energy and only attempt a couple excellent runs per match? When someone is constantly in space far from defenders, is it because he finds/creates that space or does the opposition deem him not a threat? Defensively, I want to have measures of which backlines are most in-sync when setting an offside trap, and who does best and worst job sticking with their assignment?

    I thoroughly enjoyed both writing my own observations and reading those of Rui, Devin, and Alex. Big thanks to Matthew for asking the questions and putting this together, as well.

    Reply

    • Posted by John Mosby on 2012/08/08 at 6:04 AM

      There is such a premieme on goals in this sport that it is sort of hard to over value, isn’t it? I guess a PK and a wondergoL count the same on the scoreboard, but do they have the same value? Maybe it depends on who is buying.

      Reply

      • PK vs golazo is a good illustration. I think we overvalue the thoroughly easy tap-in even more than PKs (admitting that sometimes real skill gets them in the position to do so). Also, you even see a goal that is purely accidental at times, but it shows up on the stat sheet equal to all other tallies.
        Players that are credited own goals are often over-blamed, too. Sometimes they make a huge error, but often those are little more than fluky.

        Reply

        • Posted by John Mosby on 2012/08/08 at 10:39 AM

          It’s funny when you mention own goals and it reminds me of the Dempsey strike against stoke city that was credited as an own goal. Wonderful shot from deuce, the keePer tips onto the bar, the ball bounces down, off the keeper and into the goal. You credit deuce with that goal and it might be enough to put him over the top, in getting that new contract from a contender. POV dictates salary as much as anything.

          Reply

  5. Posted by KMac on 2012/08/08 at 5:26 AM

    As a quant and a soccer enthusiast, I highly appreciated this article. Alexander the Great/Messi/The Full Metal Jacket-duality of man point was epic(to borrow a truly overused word)!

    Reply

    • Always glad to build off of other people’s work. @InfostradaLive’s bit on Alexander the Great and Messi is my all-time favorite tweet, bar none. After noting my own hypocrisy in this being completely devoid of context, I had no choice but to reference the crowning moment of Matthew Modine’s career.

      Reply

  6. Posted by GeorgeCross on 2012/08/08 at 6:38 AM

    This article is fantastic.

    Perhaps you can collectively come up with a better method of FIFA Ranking. While seeing England in 3rd place provides me with ample banter fodder, I obviously don’t believe it.

    How would you quantify how difficult an opportunity is? Because another stat you see often is shots on target / off target. Similarly with the possession stat it can be misleading – if a team like Stoke keeps a possession team like Arsenal at bay at leaves them taking a few low percentage shots from long distance that will never trouble the keeper how meaningful are they?

    Tangentially, one of my long standing pet peeves from commentators is when they say “he should have hit the target / frame” But the reality is that they probably could have got it on target but the keeper would have saved it. That’s why players aim for the corners, areas that are difficult for keepers to cover, but also runs the risk of the offensive player putting it the wrong side of the post

    In terms of running off the ball and taking defenders with you, do you have any idea how this will be measured? I am fascinated to hear your thoughts on some of the non tangibles.

    Thanks again for a great read on my commute.

    Reply

    • Although I do see where you’re coming from with “he should have hit the target”, in many circumstances, such as the common desperation shot that goes wide, I would much rather the player just hit the target with power. First of all, there’s the small chance that the keeper just misjudges it or it takes a deflection and either goes out for a corner or bounces into the back of the net. However the main goal when just lashing a shot at goal is trying to hit it so that the keeper can’t control it and a teammate pounces on the rebound.

      Reply

      • Posted by crow on 2012/08/08 at 9:35 AM

        One of my favorite things about dempsey is that he almost always hits his shots on frame even if there is a little less power on the shot. You don’t see him skying to many shots over the bar.

        Reply

    • - For an alternative to FIFA ratings, check out the ELO ratings:

      http://en.wikipedia.org/wiki/World_Football_Elo_Ratings

      Many consider these to be better, but they still have England 5th.
      – I included a hyperlink to Sam Green’s shot quality work in the article. It’s good enough to warrant dropping here, too:

      http://www.optasportspro.com/en/about/optapro-blog/posts/2012/blog-assessing-the-performance-of-premier-league-goalscorers.aspx

      Basically, Sam has broken the probability of shot outcomes based on shot location, then weighed how well strikers and keepers performed in the EPL relative to those expectations. Obviously, other factors we can’t track yet, like location of defenders and keepers, ball speed before struck, etc impact shot quality too, but Sam’s work is pretty amazing with the available resources.
      – Tracking off-the-ball play should be much easier to measure in MLS next year when everyone is outfitted with Smart Soccer technology. Sensors in every boot and the upper back of every shirt could make for revolutionary analysis. Doubtful that much of the most useful data will be made public, but it’s still exciting.

      Reply

  7. Posted by steve-o on 2012/08/08 at 7:11 AM

    I watch alot of soccer and the 10% completion stat for crosses was surprisingly low to me, but I suspect players like Beckham have a higher rate of completion and back when he played with Van Nistelrooy, I would bet it was even higher – would love to find out if I’m right (or just wrong). Maybe I’m biased but I loved watching them at Man U back in the day.

    Reply

    • Posted by Jackson on 2012/09/04 at 9:17 AM

      After reviewing a LOT of Opta data from MLS and the EPL, I can assure you that 10% number is low. Average cross success rate is about 18-22% for both leagues.

      Reply

  8. Posted by dth on 2012/08/08 at 11:13 AM

    I would like to see more work on how team shape shifts in concert in reaction to play. Something like a heat map only shows me what one player is doing–but sometimes I’d like to know something like, say: when Brek Shea cuts in, is the LB overlapping in response to the open space?

    Or, for another example: I noticed in the DCU vs COL game last Saturday that the dribbling threat of DeRo, Pontius and De Leon really unbalanced the COL defense, as they were trying to shade two defenders to each of them when an opportunity to dribble presented themselves. (Indeed, the one time they didn’t, De Leon destroyed Josh Williams off the dribble and created the goal.)

    Reply

  9. Posted by Chad on 2012/08/08 at 11:26 AM

    I’m definitely not a soccer analytics guy (which I will probably prove with this unintelligent post), but I’m surprised that possession is that stat that people are picking on. If I am understanding correctly (and I think Devin has mentioned this in the past), possession is measured by counting completed passes. That makes no sense to me unless the only reason for it is that completed passes are easy to count. How hard would it be to get someone with one of those chess game clocks to just toggle back and forth to measure when posession changes? Measuring possession in the current way (i.e completed passes… if that’s what is done) severely underrates the player that has the technique to slow the game down for his team and relieve pressure by staying on the ball with fakes and jukes and body positioning (Alonso at Sounders).

    To a non-stats person, I think it is strange that we don’t share a player’s top end speed and discuss it during games. We can all see who is fast, but there is a difference between fast and difference-making speed. Last, I would think that a loss of top end speed would be a significant factor in evaluating players as they age.

    Reply

    • Not all data collection companies determine possession percentage in this manner. And, I’m certainly not the only one to mention this.

      The “Chess Clock” way of measuring is probably equally misleading because possession isn’t binary. There are plenty of situations where neither Team A or Team B is in possession. With the chess clock, somebody unfairly gets credited with this time.

      Possession percentages themselves are pretty bad because they imply this binary definition. The third variable – time in which the ball is possessed – heavily skews the percentiles.

      For example, let’s create two hypothetical games. In game one the ball is possessed for 60/90 minutes. In the second game the ball is possessed 80/90 minutes.

      In game 1, Team A possesses the ball for 40 minutes to Team B’s 20 minutes. In game 2, Team A possesses the ball for 50 minutes to Team B’s 30 minutes. In both of these games team A had 20 minutes more possession than Team B – but the percentages are considerably different!

      Reply

      • Posted by Chad on 2012/08/08 at 3:01 PM

        Devin, thanks for taking the time to clarify. Hope to get to see you at a Revs game. I can see why possession percentage is misleading, but possession stats can be easily tweeked to adjust for the issues you mentioned. Instead of a binary comparison, you have 3 options: team A, team B or Neither team and then report the stat as total mins w ball instead of as a percentage.–call it Real Possession ™. minutes w the ball is crucial. Passes completed is a much worse measure.

        Reply

  10. Posted by Amy on 2012/08/08 at 2:35 PM

    Good job Cam!

    Reply

  11. [...] tips onto the bar, the ball bounces down, off the keeper and into the goal. … Link: The N.A.S.A. Roundtable … (Nerds Attack Soccer Analytics) « The … ← Epic Soccer Training Review – [...]

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  12. [...] MLS Stat Stuff In the course of filling in my database of player statistics, I started playing around with the data and have come up with the following tables(and added 2012). I think you’ll find it interesting, or I wouldn’t have bothered posting it. For me, I get a visual representation of the players that show consistency when on the offensive. Each of the tables are based on a top 20 points per 90 minutes basis with a minimum of 700 minutes played. The first table lists those players with current consecutive streaks of being in the top 20 of points per 90, including 2012 as of this past Sunday. You can generate your own Top 20 in Points, Goals, or Assists per 90 minutes for any season in league history on the MLS Player Statistics page of this site. Note that if you are into serious stats, this is a good read. [...]

    Reply

  13. Posted by Elliott on 2012/08/31 at 5:37 AM

    Liked the Crouch comment about relative value – even an immensely talented player, like Ibrahimovic, can be overpaid and too pricy if he doesn’t fit a team’s philosophy, like Barcelona.

    It’s interesting how most folks slam the basic (and too simple) possession statistic, yet Barcelona and Spain – the top teams of the current era – have shown that retaining the ball and success can go hand-in-hand. I’ve always thought in terms of chicken-and-egg: does possession equal dominance, or reflect it?

    Reply

    • The problem comes when lesser clubs think that possession itsef is the answer. For every Barca or Spain there are 5 teams that horde possession but don’t make much of it. Thus in the larger numbers possession doesn’t correlate to results.
      Part of the issue for me is that often a team wants possession much more than their opponent, and neither is making a tactical error by taking that stance.

      Reply

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