Soccer analytics is an ever growing field of study as the game is becoming more and more popular as money flows into the sport in our tech crazy country. The focus on high level analytics is a trademark of all of the big clubs and federations across the globe, a highly secretive world of data collection that exists among the elites of the game. Yesterday afternoon I was fortunate enough to be invited to a presentation given by Iavor Bojinov. Bojinov is currently a candidate for his PHD from Harvard and had just finished presenting to the Philadelphia Union’s first team, as well as the u16 academy squad earlier that day. Iav produced an incredibly insightful presentation about how his advanced tracking of Premier League football matches can produce insights into where on the pitch a team may be struggling to retain possession of the ball, where they successfully retain the ball on the pitch, where they successfully disrupt play on the pitch, and where on the pitch they fail to disrupt opponents. He highlighted some very important topics that certain statistics fail to recognize, and also highlighted the areas where statistics are still advancing in the game, and what the data can help identify . I went into the presentation somewhat as a skeptic of analytics as I do not believe they truly have the ability to tell the entire picture of a football match, but I left with a new insight into how these logarithms can now help tell in what areas of the pitch a team may struggle to either disrupt the other team (tackle, intercept) or retain the ball (complete a pass, dribble, ect.).
So as I am nowhere nearly as intelligent as Iav is mathematically, I will not attempt to discuss his actual logarithm he used to find the data. From my very primitive understanding of the logic behind the data, the study used videos of the matches from the past three seasons to pinpoint exactly what happened at every instance of the match. Every time the ball was touched it was logged, with pinpoint accuracy in regards to time and most importantly where on the pitch it occurred. The spatial aspect of the study seems to be the most important aspect of the data as it can now tell where exactly the ball was either kept or given away and where a disruption either occurred or didn’t.
Burnley for example, who finished 19th in the 2014/2015 season, gave up the most goals in the league that season. The Clarets, according to Bojinov’s data, were far below the league average of ball retention in their attacking third. Burnley’s players ran further than any other team that season, but it mostly due to the fact that they rarely kept the ball in their attacking third of the pitch. Their disruption data told a bit of a different tail. The central midfielders were slightly above average in disrupting play, as well as their left wingers. This failed to make up for the fact that they could not keep possession of the ball long enough to keep the other team from overwhelming them, leading relegation from the top flight for the Clarets.
The data highlights Manchester City’s ability to retain possession very high up the pitch especially on the left side. The quality City bring to each match on the attacking side is proven by their much higher than average ball retention. The data was able to pinpoint “The Pochettino effect” on both Southampton and Spurs where the ball disruption of both sides were much higher than average due to the Argentines style of pressing all around the pitch. Southampton and Spurs under Pochettino had an average retention rate, but their disruption rate set them apart from the other sides substantially. Very obviously neither of these sides went on to win the league either season, but you can see his tactics being displayed by the data. It is then up to the coaching staff to interpret why they were unsuccessful both seasons in securing Champions League Football. Pochettino was only at Southampton for 18 months prior to moving to Tottenham. So possibly he was unable to reach the fitness levels necessary to play his prefered pressing system with the squad he inherited on the South Coast. Spurs disrupted play on the left side of the pitch in their defensive third below average during Poch’s first season. With Danny Rose injured most of the season and no real left back replacement available, as Spurs parted ways with Benoit Assou-Ekotto that season, the likes of Eric Dier, Vertonghen, and Vlad Chiriches were all deployed there as make shift replacements. This season Spurs purchased Ben Davies and have kept Danny Rose fit for almost the entire season leading to a much more stable left side. Possibly the analytical team at Spurs saw data similar to the maps displayed by Bojinov tonight in Philadelphia.
These analytics are much more advanced than the typical box score you may receive from the sports information employee at your college or high school. These ideas recognize where and when the events are occurring in the match and on the pitch, allowing you to pinpoint areas your team or other teams may be strong or weak. These statistics do fail to tell the entire picture, as they cannot account for effect of playing home and away, the variations of formations teams may play, or the exact reasons why the certain teams may see odd results. The data does recognize its failure to explain the luck involved in the sport as well. Bojinov mentioned during the presentation that studies have shown that close to 48% of goals which are classified as lucky, with either a favorable foul decision, unnatural deflection, own goals, or some other form of luck took place leading to the goal. So by no means is this the answer to all of your team’s problems, but it does make it easier to identify how you measure up to the league averages in both ball retention and disruption.
So this is all very good stuff but you may wonder how you can take these methods and use them in your coaching career. Without videotaping the game and highlighting the action taking place, it may be very hard to use these methods with your teams, but hopefully these ideas will allow you to possible take note when you are watching matches of where your team disrupts the play frequently, where they struggle to, where your team keeps the ball successfully, and where they struggle to. This may be a little bit high tech for most club soccer teams, but these methods of statistics can be used in their most basic forms to track both a team’s successes and failures in both categories in the different areas of the pitch. Once again I do believe there is much more to soccer than just numbers, but ignoring the statistics and data patterns collected can lead to a lack of recognition of the problem areas a team may possess. You can read Iavor’s entire report here and I would highly suggest looking into his studies and research if you are interested.
Sources : Bojinov, Iavor. "The Pressing Game: Optimal Defensive Disruption in Soccer."MIT Sloan Sports Analytics Conference (2016): 1-8. Web. 29 Apr. 2016.