Manchester City vs Liverpool: Passing network analysis

At the beginning of February, Manchester City drew 2-2 with Liverpool at the Etihad, so a football loving blog decided to take a look at the match from a network point of view, resulting in the following research. We have already reported about something similar regarding basketball.

The positions of the players are loosely based on the formations played by the two teams, although some creative license is employed for clarity. It is important to note that these are fixed positions, which will not always be representative of where a player passed/received the ball. Only the starting eleven is shown on the pitch, as the substitutes weren’t hugely interesting from a passing perspective in this instance. Only completed passes are shown. Darker and thicker arrows indicate more passes between each player. The player markers are sized according to their passing influence, the larger the marker, the greater their influence. The size and colour of the markers is relative to the players on their own team i.e. they are on different scales for each team.

In the reverse fixture, Yaya Touré and De Jong were very influential for City but Touré was away at the African Cup of Nations, while De Jong joined Milan shortly after that fixture. Their replacements in this game, Barry and Garcia, were less influential, although Barry had the strongest passing influence for City in this match, with Milner second. The central midfield two, Lucas and Gerrard, were very influential for Liverpool and strongly dictated the passing patterns of the team. They both linked well with the fullbacks and wider players, while Lucas also had strong links with Suárez and Sturridge. Certainly in this area of the pitch, Liverpool had the upper hand over City and this provided a solid base for Liverpool in the match.

Similarly to the Arsenal game, Liverpool showed less of an emphasis upon recycling the ball in deeper areas. Instead, they favoured moving the ball forward more directly, with Enrique often being an outlet for this via Reina and Agger. Liverpool’s fullbacks combined well with their respective wide-players, while also being strong options for Lucas and Gerrard. Strurridge was generally excellent in this match and was more influential in terms of passing than in his previous games against Norwich and Arsenal, combining well with Suárez, Lucas and Gerrard.

At least based on the past few games, Liverpool have shown the ability to alter their passing approach with a heavily possession orientated game against Norwich, followed up by more direct counter-attacking performances against Arsenal and Manchester City. The game against City was particularly impressive as this was mixed in with some good control in midfield via Lucas and Gerrard, which was absent against Arsenal. How this progresses during Liverpool’s next run of fixtures will be something to look out for.

Source: http://2plus2equals11.wordpress.com/2013/02/05/manchester-city-vs-liverpool-passing-network-analysis/

Basketball Isn’t a Sport. It’s a Statistical Network

Team sports and statistics are no strangers, take sabermetrics, that revolutionized game analysis for baseball, while making it more fun to watch. The story might sound familiar if you have seen Moneyball, where Brad Pitt took on the role of Billy Beane, who pumped up the game of the Oakland A’s.

Compared to baseball, though, basketball is much more dynamic, and ball movement becomes a key variable in success. Passing is one of the fundamentals of hoops, and in the upper ranks of the sport, turnovers — often the result of wayward passes — contribute to ticks in the win-loss column. Fast, agile passing can make or break a team. That’s why sabermetrics might not tell the entire story about what happens on the court. Researchers at Arizona State University, led by life science professor and basketball fan Jennifer Fewell and math professor Dieter Armbruster found an ideal model to explain the results of the 2010 NBA playoffs by simply keeping their eye on the ball. Their work opens the door to an entirely new line of sports analysis, from game-tape breakdown to highlight reels and augmented-reality visualizations.

Their method - not surprisingly – was network analysis, which turns teammates into nodes and exchanges — passes — into paths. From there, they created a flowchart of sorts that showed ball movement, mapping game progression pass by pass: Every time one player sent the ball to another, the flowchart lines accumulated, creating larger and larger and arrows. Using data from the 2010 playoffs, Fewell and Armbruster’s team mapped the ball movement of every play. Using the most frequent transactions — the inbound pass to shot-on-basket — they analyzed the typical paths the ball took around the court.

Network analysis of the Chicago Bulls, showing the majority of ball interaction remained with the point guard. Image: 

Network analysis of the Los Angeles Lakers shows the team is far more likely to distribute the ball among more players, using the “triangle offense.” 

For most teams, the inbound pass went primarily to the point guard, generally a team’s best ball handler. But point guard-centric, such as the Bulls, didn’t fare well in the 2010 playoffs, the researchers told Wired. On the other hand, the Los Angeles Lakers — which won the 2010 NBA championship — distributed the ball more evenly than their rivals, embracing what Phil Jackson calls the “triangle offense,” a technique pioneered by Hall of Fame coach Sam Barry. The basic idea is simple: Maintain balanced court spacing so any player can pass to another at any point.In their model, Fewell and Armbruster found a mathematical explanation for why the triangle offense works — the point guard was no longer the only player feeding passes to fellow players; his teammates were just as likely to take on that role. With more potential passers, there are more potential paths for the opposition to defend.

To quantify their results, published in the journal PLOS ONE, the researchers derived the entropy, or measure of system disorder, for each team during each game. In six of the eight first rounds, winners had higher team entropy, and therefore more randomness, than losers. Though the sample size of teams in the NBA playoffs may be small, the data suggest a possible relationship between quick, unpredictable ball movement and success in games.

While fans direct cheers that fill sports arenas toward athletic giants such as LeBron James or Kobe Bryant, bright statisticians still sit in the shadows. But when these mathematical stars begin helping LeBron improve his game, it’s certain they’ll hear more and more of the applause.

Hungry for more? Read the full article on Wired.