I am thrilled to announce that I will be speaking at this year’s New England Symposium on Statistics in Sports (NESSIS) on September 24th. Earlier this year, StatDNA announced a Soccer Analytics research competition and my paper was selected as the winning entry. I’ll be giving a talk titled “A framework for tactical analysis and individual offensive production assessment in soccer using Markov chains”. Catchy, right? Well, if that didn’t grab your attention, Chris Stride from the University of Sheffield will be giving a talk called “Cheating in football: Team culture, player behavior,or question of circumstance?” and there are several soccer related posters as well. If you’re attending NESSIS, drop me a line at srudd@onfooty.com or come say hi after my talk. I’ll also be attending the post conference drink-up at Porter Square’s Tavern in the Square. For those that can’t attend the conference, below is my abstract. You can find the others here.
A FRAMEWORK FOR TACTICAL ANALYSIS AND INDIVIDUAL OFFENSIVE PRODUCTION ASSESSMENT IN SOCCER USING MARKOV CHAINS
Markov Chains are an effective way to model transitions between states. Assuming that the current state is independent from the previous state, Markov Chains can be used to model the set of state transitions that make up a possession in soccer. The transitions are used to determine the probability a possession ends in one of two final states; scoring a goal or relinquishing possession to the opposing team. Once the final probabilities are known foreach state, they can be used to determine game situations from which goals are more likely to develop, team strengths and weaknesses and metrics for assessing the offensive contributions of players.
Using this framework on the sample data set, we found that teams are more likely to score from taking long corners than short corners, with the notable exception of Tottenham Hotspur who excel at short corners. The top 3 teams most likely to score from a long corner are: Arsenal, Newcastle and Stoke. The top 3 teams most likely to concede from a long corners are: Everton, Arsenal and Newcastle. The framework can also be used to look at various game situations like building from the back, counter-attacks, free kicks, and entries into the final third, for example.
Additionally the transition probabilities can be used to determine which individuals are best at receiving the ball in situations with a high probability of scoring and which individuals are best at moving the ball to an improved state with a higher probability of scoring than their current state. The top 3 players for increasing the probability of scoring are Tim Cahill, Yaya Toure and Cesc Fabregas. The 3 most wasteful players who decrease their teams probability of scoring the most are Darren Bent, Peter Odemwingie and Gael Clichy. The top 3 players who receive the ball in the most advantageous states are Dimitar Berbatov, Nile Ranger and Benjani Mwauruwari.
