Know your Stats: The Key Pass

In my last piece, I started looking at the importance of properly contextualizing player performance in order to isolate what we --  as fans, managers, and coaches --truly care about: ability and value. In this piece, I'd like to show how it is that advanced metrics can help out in that difficult task.

I'm very partial to passes. Passing is my favorite part of the game. Nothing trumps a side that can pass the ball around fluidly, and aesthetically nothing beats a beautifully threaded through-ball to a put a forward in scoring position. Therefore, a metric that I'm very partial to is the "Key Pass". First, let's begin by defining the Key Pass. Per Opta Sports, the company that measures and tracks the metric, the definition of the Key Pass is:

The final pass or pass-cum-shot leading to the recipient of the ball having an attempt at goal without scoring.

So there we go. The definition provides a standard for the people at Opta to objectify events, and it makes the data they provide very reliable, moving away from subjectivity. Opta essentially has large number of people sitting in their viewing center (or working remotely, maybe?) counting Key Passes. Sounds like a fun job. Unless your assignment is to track Crystal Palace games or something. 

The Key Pass metric provides one big advantage over assists,  and that advantage can be found in the final clause of the definition: "without scoring". The Key Pass is a better way to unveil the true measure of what a player is actually doing on the field.  Let's view this through an example of an one of the best playmaykers in the world, Mesut Ozil, who has averaged around 4 Key Passes per 90 minutes over the past few seasons -- an impressive figure.

Say Mesut Ozil filters a beautiful ball from midfield in  between the two center backs. Last season, Cristiano Ronaldo would have been on the receiving end of such a pass, and Cristiano Ronaldo, being the monster that he is, probably would have buried the ball in the back of the net (the guy breaks hands from 30 meters – a goalkeeper a few meters away has no chance). Now, this season, Nicklas Bendtner could be on the receiving end of those passes, and it is just as likely that Bendtner will trip over his own feet as it is that he scores.

Observe Nicklas Bendtner in his natural habitat

So we have two situations where Mesut Ozil makes the exact same pass and gives his forward the same chance of scoring – let’s say an *85% chance. The 15% left to actually score the goal will be decided by 1) the scorer’s ability and 2) random chance (say a beach ball getting in the way). Neither 1 or 2 actually tell us anything about Mesut Ozil. And it is 1 and 2 that determines whether Ozil's pass gets tallied up as a an assist.

*Note: these percentages are mere abstractions to make my point.

Now, expand that situation to a much grander scale, where a creative midfielder plays with only Bedntners and no Ronaldos. I’m inclined to say his assist numbers would not be as high with Bendtners as they would be with Ronaldos. 

Now, this is not to say that the Key Pass Metric is a flawless measure. This article from Statsbomb does a fairly good job at evaluating the Key Pass, pointing to one big flaw: not all key passes are created the same. In the example given above, say instead of Ozil filtrating a perfect ball that leaves the forward on a 1 v 1 with the goalkeeper and with an 85% chance of finishing, he puts a little too much on the ball and instead leaves the forward at an awkward angle with the goalkeeper and only with a 70% chance of scoring. Also, not every final pass is equally as "key" -- there is a big difference between a  ball played from midfield to put a forward through and a short tap in pass in the keeper's box.

Over many different observations (instances), these differences are meaningful, and they are directly a result of a player's ability. However, the Key Pass is a great starting point and a vast improvement over the statistics we have typically had to contend with. Anyway, I strongly recommend the Statsbomb article; it actually attempts to normalize for the differences in the quality of the Key Pass using another metric called Expected Goals (ExpG). It shows the type of improvement that we can continue to make in soccer analytics.

My initial intent with this piece was to actually start engaging in analytics and to track Ozil's career based on the Key Pass. However, the availability of soccer statistics is very limited, and the availability of advanced soccer statistics is even more limited. I have put in a request to Opta to see if they will grant me access to their data, which they do grant to bloggers and writers as long as their projects merit it. Hopefully I get access to it, and the quality of my content on this space can go up a notch.

I'll leave you with some passing gems from Ozil:

And in case you were wondering about the significance of the picture at the beginning of the post, here are some gems from one of the masters of the Key Pass, Carlos Valderrama:

Around the Web:

• Richard Whittall with a short but thought provoking look at football finance and the possibilities of teams...GASP...actually spending their money wisely and not indiscriminately.
• Statsbomb with a similar piece, suggesting improvements over the decision making organization that most teams employ in an effort to improve that word we all love: efficiency.
• Over the weekend: Barcelona, Real Madrid, and Atletico Madrid combined for a 16-0 trouncing over their opponents. Gotta love the parity in La Liga. I'm just gonna keep hitting the snooze button, sadly.
• A World Cup Draw simulator! The most competitive field ever? 

Soccer Analytics: From Fantex to the Fundamentals

Fantex Brokerage Services recently announced that it will be offering shares in the stock of Houston Texas Running Back Arian Foster. That’s right – shares in an athlete. Essentially, Fantex paid Foster a cool $10 million for a 20 percent stake in Foster’s future income, which includes but is not limited to: contracts, endorsement deals, and public speaking. Fantex will begin to offer shares in that 20% stake as early as next month.

This is an incredibly interesting idea. Beyond the logistical limitations (will players want to do this? How will leagues respond?), I think it strikes the core of much of what analytics in sports is attempting to get at it: player value.

More specifically, I want to examine this story from the point of view of soccer analytics in an effort to start building an analytical framework – to start molding a mode of thinking through which we can analyze players and team performance. That all sounds rather abstract and wordy, but there really are some very concrete and real ideas behind what Fantex is trying to do. Caution: I started writing this article by talking about Fantex’s IPO of Arian Foster, but by the end it will have little to nothing to do with this. If you’re interested in the valuation of Arian Foster, check out NYU Stern Professor and valuation demi-God Aswath Damodaron’s blog post.

Let’s start by examining the most obvious parallel: an athlete has an intrinsic value, much like a company has an intrinsic value.

This intrinsic value can be measured by performance metrics – say goals allowed by a goalkeeper and revenues by a company. However, both of these statistical measures must be viewed in a proper context. Let’s take this very obvious example: Ford Motor Company rakes in revenues that are far larger than they were 100 years ago. A part of this revenue growth is simply because Ford produces more cars than they did 100 years since more people demand Ford cars . However, a major part of this large growth in revenues is due to inflation – Ford takes in more for every car for a reason that is really beyond their control.

Similarly, and this is another obvious example (but that’s why I want to use it – it elucidates the point quite well), the amount of goals a goalkeeper allows is highly dependent on the context within which he performs. A goalkeeper that plays for a team at the bottom of the league (which presumably allows a lot of goals, although it could be that they just don’t score many goals. But for now, let’s assume that they’re bad at both defending and attacking) will allow more goals than a goalkeeper that plays for a team at the top of the league.

Like I said, this is an obvious example that most fans intuitively get and wouldn’t make the mistake of overlooking. However, there are many other instances where the importance of context won’t be so clear.

And I feel like this is where most professional soccer analysis starts to fall apart – a failure to properly contextualize performance makes it hard to analyze what actually is due to player performance and what is simply a product of the context or even flukes.

It is also symptomatic of a larger problem within the community of soccer analysis, journalism, blogging, etc, and that is a problem of “irrationality”. Pick up a copy of any big soccer newspaper (Marca, Sport, La Gazzette…) if you’d like a quick survey of any and all logical fallacies: straw men arguments, inductive fallacies, the fallacy of false cause, etc. Much of soccer punditry in every part of the world consists of knee-jerk reactions to fluctuations in performance over small sample sizes of games or even a single game. Take, for example, the guy pictured above  -- public enemy #1 of rationality. The World Cup (and the Euro) is a perfect opportunity for Mr. Alexi Lalas to make outlandish claims on the basis of 45, 90, 135, or how ever many minutes (all small samples, no doubt) he's happened to watch this team play during the World Cup. Now, Lalas happens to say things that are easily recognizable as drivel  -- but what about your favorite pundits? Your favorite newspaper?

Take, for example, Lionel Messi’s current dip in performance – he hasn’t scored a goal in 4 games. Alarmists are already ringing up theories that Messi is taking the club season off in order to be at full peak for the World Cup, which is what he really wants to win. This is after about a month of mediocre performances from Messi.

I believe a major part of better decision making for soccer managers, owners, and executives and a major part of the discipline brought along with soccer analytics has nothing to do with data or statistical analysis. In fact, saying that is probably misattribution of the word “soccer analytics” on my part. Being a better soccer executive and being a smarter fan has nothing do with data; but it has everything to do with being a more rational observer and analyst. This is a point Professor Chris Anderson alludes to in his interview with our blog:

“General texts explaining decision making and analysis like Daniel Kahneman’s Thinking, Fast and Slow or Silver’s The Signal and the Noise are very useful for understanding how people think about and interpret information.”

In a game such as soccer, where research has shown (mainly by Professor Anderson) that luck plays a bigger role than in any other sport, a rigorous decision making and logical foundation is even more important – there is more “noise” that distorts our understanding of what is actually going on in the pitch.

Now, being irrational and succumbing to the emotions of being a fan is part of what makes soccer beautiful. I’m not suggesting that we need to be cold, emotionless fans. Rather, what I am suggesting is that it is important to detach ourselves from the irrationality of being a fan in order to see the game as what it is, not as what we may construct it to be based on biases and faulty reasoning. “Animal spirits” don’t just apply to the stock market.


Around the Web:

• StatsBomb examines the concept of managerial fingerprints through the profile of Andres Villas Boas
• Sam Borden of the NY Times has a nice little profile on Espanyol and what it's like to be "Barcelona's little brother"
• James Grayson explores how repeatable of a "skill" total shots are
• Richard Whittall with a piece on the more financial side of soccer as he looks into UEFA's Champions League media deals

Soccer Analytics: An Interview with Chris Anderson

The age of Big Data is upon us. In industries throughout the world, the collection and analysis of data is a focal point of decision making.  Sports are no exception --  not surprising given that professional sports are billion dollar industries.  In the past decade, sports such as baseball and basketball have started integrating statistical and objective analysis into player evaluation and team management. Soccer --futbol, football, the Beautiful Game -- is slowly beginning to accept analytics as a decision-making tool. 

Cornell's own Chris Anderson is one of the leading innovators in the burgeoning field of soccer analytics. On campus, he is a Professor of Government and Labor Relations whose work primarily converges the fields of  economics, politics, and sociology.

Outside of  the classroom, Professor Anderson's interests lie on the soccer pitch, where he played as a goalkeeper in the German lower divisions.  Along with David Sally, Professor Anderson authored The Numbers Game: Why Everything You Know About Soccer is Wrong, a book that breaks many established conceptions in soccer and counters them with an objective, analytical approach -- all supported with careful statistical analysis. It is a great read for any soccer fan looking to nuance his/her view of the game, but it can also serve as a great introduction to anyone that is just becoming interested in the sport.

I hope to review the book later in the semester, mostly as a basic introduction to the field of soccer analytics. Professor Anderson is also co-partner of Anderson Sally, a sports analytics consulting firm that works closely with professional teams.

Professor Anderson recently took time out of his busy schedule (check out his recent interview on CNN ) to answer some questions for the Cornell Sports Business Society. 

Professor Anderson, first I’d like to thank you on behalf of the Cornell ILR Sports Business Society for taking time out of your busy schedule to do this interview. It is very exciting to see that a member of the Cornell community is one of the leading figures in the growing field of soccer analytics. First, would you mind giving us your personal definition of “soccer analytics”?

It’s become kind of a catch-all term for all kinds of things. I think it’s basically one thing that’s applied to another: first of all, it’s analytics – which is about collecting and interpreting information, evidence, data, what-have-you. That information can be quantitative or qualitative in nature; and analytics is not just about having information, but also about deriving meaning from it, and doing so in a systematic way.  

Wikipedia defines analytics as “the discovery and communication of meaningful patterns in data”, and I think that sums it up nicely. Analytics is becoming a common tool across lots of industries, and soccer analytics is simply analytics ideas and practices applied to the game of soccer. Within soccer, we’re talking about analytics with regard to playing the game, recruiting players, or player fitness – the various areas that affect a team’s performance.

How did you first become interested in analytics in soccer, and how did you start getting involved in the field?

For me it started with a love of the game. I've always been interested in understanding soccer as a game played by 22 people who have to make decisions both in isolation (e.g., do I pass, dribble, or shoot?) and together (as part of a team). We tell a story in the book about how I took an analytical approach to soccer from an early age; more recently, Michael Lewis’ book Moneyball got me excited about the potential of applying similar ideas to soccer.

Then I started a soccer analysis blog on a lark, and attended the MIT Sloan Sports Analytics Conference (which was pretty inspiring). As the blogging and analysis became more serious, David Sally and I started talking about writing a book about soccer analytics. That book eventually became The Numbers Game. I guess the lesson for me was that it’s fun to start small and go from there – the key to any of it is to stick with it over time.

Do you think the growing economic disparity between the richer clubs and the poorer clubs in the European leagues will help the field of soccer analytics grow even faster than it already is? There was a similar context in the mainstream emergence of sabermetrics, where Billy Beane had to look for ways to compete with the big money teams in the Majors.

That’s a good question, and I’m not sure of the answer. In principle, the clubs with less money to spend on superstars should be willing to try new ways of winning or to get more bang out of the buck for money invested in analytics. A great example of a club that did some fairly basic but very effective things coming out of analysis were Bolton Wanderers under their then-manager Sam Allardyce (who now coaches West Ham United). Bolton was able to do much better than their wage budget would have suggested.  

But the reality at many of the lesser clubs is that money is really tight, and clubs find it difficult to justify spending money on people, software, data, and computers to ramp up their analytics operations. So ironically, the better-financed clubs like Manchester City or Liverpool are spending more money and resources on analytics, and they benefit from those investments. By the way, Billy Beane is a huge soccer fan, and I’d love to see him give advice to soccer teams (and you’d only have to hire one guy) – but I don’t think he’s available!

One of the bigger and most counter-intuitive points you make in your book “The Numbers Game” is the importance of luck in the game of soccer – significantly more than in any other sport. Do you think that observation should have any effect on the way teams and fans analyze on-field performance?

I would hope so, but I’m note sure. It should be pretty logical. More randomness and luck means more noise in the data, and that should make fans and clubs look longer term. In statistics language, what you want is a bigger sample before drawing any kinds of firm conclusions about performance because outcomes can be too much influenced by chance in the short term. But of course, telling a fan or a coach not to worry about the last 2-3 games is likely to encounter resistance. So we have to divorce our role as fans and the emotion that comes with that from the reality of what the data really do or don’t tell us.

If patience is hard to come by – and it always is – then another thing the role of luck and chance should teach us is that fans and coaches might be well-advised to focus more on those aspects of a team’s or player’s performance that are more controllable or have less chance. Shot conversion rates are an example of a performance indicator that is less replicable than, say, producing high quality chances in the first place. The former regress more quickly to the mean than the latter.

On a similar note, we have seen the emergence of analytics in other major sports, namely baseball, where analytics are firmly entrenched. However, soccer is a very different game than baseball – it is much more fluid with fewer fixed events. How does that limit the extent of the objective analysis that can be used to view the game?

It doesn't really; people working on basketball and hockey, for instance – two sports that are fluid and team-based – have already shown us that quite a lot of interesting insights can be produced about soccer’s “cousins”. At the same time, it’s probably naive to think you can simply apply ideas from one sport – especially one, like baseball, that’s very different – to another. So you have to be careful, and every sport has to find its best ways of using analysis. More fundamentally, the nature of the game makes soccer analytics simply a harder set of analytical problems. But that doesn't mean it can’t be done.

One could say that soccer has always been a mathematical game, but in a different way than baseball in that it is a very geometric game. Formations have been a big obsession from the very beginnings of the sport, shape – mostly defensive -- is always emphasized by youth coaches, and triangles are a big part of the ideology of FC Barcelona. Do you think the close connection between soccer and geometry opens up other objective frontiers within the game of soccer?

It’s only natural. Soccer is a game of space and a game of timing, so when it comes to the spatial aspects, and the team aspects of players having to coordinate, I think there is a lot of potential here. It’s also an area that is easier to explain to coaches (say, on a blackboard or a computer screen) than a set of numbers.

Would you mind sharing with us any soccer analytics research you are currently working? Any upcoming books or projects we can look forward to?

Not at the moment. Actually, I am busy working on various projects related to my job as a political scientist in the Government Department. That’s keeping me pretty busy.

Lastly, could you recommending some crucial readings for any readers looking to start learning about soccer analytics?

Going back to your first question, I think it would be important for any aspiring analyst to get a good handle on “analytics” – analytical thinking, analysis tools (econometrics, statistics, etc.) – as well as soccer as a game. There are a variety of sources out there, and many of them aren't very technical (which is nice). My personal favorites that I recommend with regularity are books like Jonathan Wilson’s Inverting the Pyramid: The History of Football Tactics and Kuper and Szymanski’s Soccernomics. 

More generally, I would say that becoming familiar with soccer analytics does not imply only learning about soccer. I would always recommend reading (not just watching) Michael Lewis’ Moneyball and Jona Keri’s The Extra 2%.  Scorecastingby Moskowitz and Wertheim and Basketball on Paper by Dean Oliver are excellent, too. General texts explaining decision making and analysis like Daniel Kahneman’s Thinking, Fast and Slow or Silver’s The Signal and the Noise are very useful for understanding how people think about and interpret information.

Finally, I would recommend reading all the great material that’s now available courtesy of various analytics-focused blogs. For soccer, I would recommend socceranalysts.com and statsbomb.com. But there are also lots of great analysis blogs on hockey and basketball, for instance.

Blogger Roundtable: The Best Commissioner in Sports

For the first Blogger Roundtable event of the semester, we asked some of our newest Sports Business Society bloggers the big question.  Who do they think is the"best" commissioner in sports today.  It was up to them to decide what "best" meant, and they came up with a variety of choices and explanations (along with one notable omission).

Read what they had to say after the jump:


Kevin Cole (CALS '15)- Roger Goodell, NFL

It is hardly uncommon to hear a negative sentiment about one of the commissioners of the four major pro sports leagues in America. Whether it is a function of media portrayal, individual personalities, the nature of the position, or some combination of factors, David Stern, Gary Bettman, Bud Selig, and Roger Goodell descriptions more often touch on words like 'arrogant', 'out of touch', or 'despot', than they do 'likeable'. However, setting popularity aside, the success that each has experienced with their respective league varies quite a lot- And in terms of league popularity, it's hard to argue with the job that Goodell has done for the NFL. 



Courtesy of www.businessinsider.com.

When Goodell began his reign atop the NFL in 2006, the league had already long been the standard for American professional sports. During his tenure, the NFL has remained at the top in the U.S., while furthering it's brand internationally. The global success is reflected by the influx of non-American NFL players (Ziggy Ansah, Margus Hunt, and Bjoern Werner all were taken in the first two rounds of this year's draft), games played on foreign soil (London, Toronto), foreign fan clubs, and the international coverage of the Super Bowl. 

 Goodell's rule has not been controversy free, however. His practice of being judge and jury for NFL suspensions and fines, along with the crackdown on excessive celebrations have been instrumental in earning the "No Fun League" moniker and simultaneously paint Goodell as a dictator. Needless to say, the replacement referees were nothing short of a disaster. However, while he has displayed iron-fisted tendencies, Goodell's has been relatively even with his punishments. His strict penalties not only hit players, but have extended to front offices and owners who have attempted to skirt the league's salary cap rules (see Redskins, Washington and Cowboys, Dallas, 2010). In summation, Goodell has held organizations, players, and coaches accountable for their actions under rules that he considers to be fair and just.

Matthew Hakimian (ILR '17)- Roger Goodell, NFL

Since taking the reins from longtime commissioner Paul Tagliabue in 2006, Roger Goodell has stood head and shoulders above every major American sports commissioner in large part due to his steadfast ways and persistent actions.   Various issues have arisen thus far throughout Goodell’s tenure that he has handled fairly well. Prior to the 2011 season, against all odds, Goodell remained cool in the face of pressure and was able to help orchestrate a deal between the NFL team owners and the NFL Player’s Association which saw the establishment of a new collective bargaining agreement, and effectively avoided any missed regular season game in which each team would have certainly lost its fair share of revenue.

Moreover, Goodells has been a major supporter of player safety, which can be seen by the disciplinary actions he has handed down. Whether its through weekly fines to players such as Ndamukong Suh or James Harrison for illegal hits, or dropping down steep penalties on an entire organization such as the New Orleans Saints, Goodell has made a firm effort to protect the welfare of the players. More recently, the National Football League was able to strike a $765 million deal with a group of ex-players over concussion-related brain injuries. The agreement certainly could not have been made without the headship of Commissioner Goodell, and it was immediately seen as a big win for the NFL to have finally gotten this situation resolved. Whether it has been through his efforts to relocate an NFL franchise overseas or awarding New York/New Jersey with the first cold weather Super Bowl in 2014, Goodell has continuously sought to innovate his league. 

Sebastian Perez-Vargas (ILR '17)- David Stern, NBA

It's easy to find faults in every commissioner, but that's the benefit of hindsight analysis. Focusing on the positives, though, you have to marvel at what David Stern has done with the game of basketball since his appointment in the mid-1980s. 

Out of all of the American sports, basketball is the one that has globalized to the greatest extent --  to the point that it is the 2nd most popular sport globally. If any sport is going to challenge soccer internationally, it's going to be basketball, and that all started with Stern, MJ, and Nike in the 1980s. It's not just because of MJ, though. Other stars such as Kobe Bryant and LeBron James are enormous figures around the world. Stern has been at the center of all of this, pushing the game of basketball beyond American borders through marketing and outreach campaigns. 

The effect has been not just American basketball players penetrating international markets, but international players impacting the NBA -- from Yao Ming to Dirk Nowitzki to Manu Ginobili. More generally, the fluid and selfless European style of play has impacted the way basketball in the US is being played. The reigning two-time NBA champions, for example, follow a very European "smallball" style, with no real post presence. The globalization of the game of basketball that has occurred under David Stern has had an impact on the NBA both on and off the court.  

Jon Levitan (ILR '17)- Bud Selig, MLB

Major League Baseball’s Bud Selig is the best commissioner in America sports. Selig is not without his flaws, the 1994 strike, which cancelled the entire postseason and World Series, was disastrous, and he was late to realize the massive PED problem on his hands. Despite these flaws, Selig has done a remarkable job keeping his players happy while at the same time creating a system of incentives that maintains a level of parity, even as teams like the Yankees and Dodgers payrolls’ expand. Baseball’s wage system comes far closer to a true free market than any of the other American sports, and its few restrictions on unhinged capitalism, like the revenue sharing program and the luxury tax, make it possible for poorer teams like the Rays and Athletics to experience consistent success. 

This wage system, which guarantees all contracts and doesn’t restrict their length or cost, allowed MLB to be the only major sport to survive the most recent wave of CBA expirations without a lockout. While his sport doesn’t have the worldwide appeal that basketball does, Major League Baseball is beginning to grow the sport in non-traditional baseball countries like Brazil and Italy, which will allow the talent pool to grow, further increasing the levels of parity in the game. Selig should not be judged on mistakes like his handling of the steroid issue, which caused short-term problems for the game, but rather on his salary system, that simultaneously rewards players fully for their performance, yet also makes it possible for poorer teams to stay competitive with the financial powerhouses.

Well that's what our bloggers think. Do you agree? Did they miss somebody? Leave your comments below and answer the poll at the top of the blog to show us what you think.