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Data Analytics Practice: Interpreting Event Data

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Data basis event data

We regularly produce and post match events dashboards from various leagues for our community. These reports are based on event data. The analysis and processing of this event data is more complex and time-consuming than the analysis of purely quantitative statistics data (stats). This is because several thousand events and hundreds of thousands of data points are recorded per game. Every single action like pass, dribble, shot, interception etc. is recorded including the x and y coordinates or the start and end coordinates (position). For each action it is also recorded whether it was successful or not. In addition, each action is also tagged with flags, such as Cross, ShotAssisst, Corner etc.

The collection of the events data is done live in realtime by the data providers in many leagues. The data is continuously available during the game. Thus, the view of the data and interpretation can already be included in the break speech. of the coach.

The collection is semi-automatic, supplemented by one person per team who tags/flags every single event in realtime including coordinates.

Today we want to show you our match event report based on the match Winterthur 1-1 Young Boys from March 4th. We will guide you through the report and explain the individual graphics in detail, including a short analysis and interpretation of the events. The match dataset consists of 1'845 events. Passes make up about 50% of the set.

Event data post match report

As example we take the Swiss Super League game Winterthur - Young Boys. First here the whole report. It is very extensive and contains 15 individual graphs.We have automated the creation of the report as much as possible and it is created within minutes.

It's exciting what you can learn with event data, even if you didn't see a second of the game. These kinds of reports are used for game and opponent analysis. From this, strategies, styles, strengths and weaknesses of the teams can be derived. Also the left/center/right distribution is an exciting point. As a club, event data can also be used to create individual logics and visualizations depending on the playing philosophy.

Wherever you see disharmonies and other patterns, interesting insights can be gained.

As with any of our outputs, this is a snapshot in time. We always start with a prototype, which we continue to develop iteratively. Just writing this blog has already generated more ideas for improvement.


Contains general information like teams, logos, result. League and date.

Starting Formations

The home team (Winterthur) is shown in blue and the away team (Young Boys) in red.

Bruno Berner used a 4-2-3-1 (89% of the time this season) and Raphael Wicky relied on a

a 4-3-1-2 with a midfield diamond, as in every Super League game so far.


With the help of heat maps, we determine in which zone of the pitch the team was particularly active and present. Darker areas indicate a high density of action. The thinner, the more presence the team had in that area

It can be seen that Winterthur had the main focus mainly in and near their own penalty area. They have had most of their defensive and offensive actions (mainly passes and duels) in the playmaking zone. Their left side seems a bit more played. While they have been able to show themselves sparingly in the opponent's half.

Quite different for the Young Boys. The focus is around the halfway line and 30 meters in front of the opponent's goal. This indicates that they tried to play around the opponent's defensive block with a lot of shifts until a good chance for space gain presented itself. Their right side was used a bit more on offense.

Passes into Final Third

This chart shows the passes into the final third. The successful ones in blue/red and the unsuccessful ones in gray. The number and success rate is noted in the corner.

YB has a much higher success rate and also much more passes and diagonal shifts. In this way, they tried to put the Winterthur defensive block in trouble.

Winterthur had trouble gaining space in the center and thus almost no successful progressive passes in the center. They were a bit more successful on their right side. Of course, this is always to the credit of the opponent as well. For both teams, the long balls from the very back, probably from the goalies, were very unsuccessful.

Passes into Penalty Area

This chart shows the passes or crosses into the opponent's penalty area. The successful ones again in blue/red and the unsuccessful ones in gray. The number and success rate is recorded in the bottom corner. In green are the shot assists. A shot assist is a pass followed by a shot. It can be assumed that the pass has put the teammate in a promising position. Shot assists are sometimes called key passes.

Again, the volume of YB's passes and shot assists is much higher. What stands out is that Young Boys had numerous successful diagonal passes or crosses into the penalty area. However, their success rate in the center was low, as the opposing block probably had good and condensed arguments against it.

Winterthur recorded a much higher success rate. This was probably favored by counterattacks with more space and fewer opponents between the ball and the goal. In addition, they were more successful and dangerous on the right.

Domination by Zone

Shows the dominance in each of the 30 zones of the pitch. Per zone, we compare the number of offensive and defensive actions executed. If the dominance is 55% or higher, we color the corresponding zone in blue/red. Contested zones with 45 to 55% share are colored in gray. This graph thus shows the territorial presence dominance of the teams.

Young Boys had a clear dominance in 16 zones. The further forward, the more difficult it was to "conquer" and dominate the zone. Winterthur had significantly more action than their opponents in the goalkeeper clearance zone, in the right corner and excitingly on their right wing.

Shot Map

In this graphic the shots of both teams are integrated. We distinguish between shot on goal (filled), shot next to goal (empty) and a shot on goal (star). Blocked shots are not included. The number of shots is shown in the corner.

The position of shots has changed a lot in recent years. Less and less teams take long shots from outside the penalty area. The exception is free kicks, where you can prepare in peace and without pressure from the opponent.

Winterthur took its far fewer shots (7) very selectively. They waited until they were in a favorable position, close to the goal. All shots inside the box came on goal. This is probably a clear strategy of the coach. Many other "low possession teams" shoot far more often from outside the penalty area. Again, a look over several games would be valuable. We also create a game analysis report over several games, where you can see strategy and trends better purely because of the larger sample size. The goal was scored by Roman Buess after winning the ball in offensive pressing and a quick switch with an outnumber situation.

Young Boys also finished predominantly from inside the penalty area. Their "shot on target" rate was 47%. Meschak Elia was responsible for the star, who after a long throw-in into the penalty area and a header extension, headed the ball sensitively into the corner. Goal by standard situation.

Dribblings in offensive half

This graph again combines the dribblings in the opponent's half of both teams.

We distinguish between successful dribblings (filled) and unsuccessful (empty).

The number of dribblings is shown in the corner. From this you can see from which position and how successful the dribblings were executed.

Young Boys had 26 dribbles and Winterthur 17. So did YB dribble more? You have to consider that Winterthur had only 33% possession for their 17 dribbles. Fairly compared and extrapolated or downscaled to 50% possession, the result is 26:19 for the Winterthur team.

Winterthur have thus effectively dribbled more than the Bernese! This is probably an integral part of Bruno Berner's counter football. After winning the ball, the team switches quickly and deliberately seeks the risk quickly with vertical passes or dribbles. The dribbling success rate is 35%:29% for YB.

Young Boys had an accumulation on their left side and tried more dribblings from a central position. Successful dribblings in the center are more dangerous than those close to the line, because after that you have options on both sides.

The adjustment of each volume-based number is particularly important in soccer data analysis. A pure number has a very limited added value.

Examples of how you still see too much. He scored 12 goals, dished out 7 assists, took 15 shots.... All this has a very limited value. Only with a fair adjustment and a fair view, we add context and are able to compare performances fairly and gain valuable insights. More on this topic in our article Comparing Players Fairly.

Recoveries & Interceptions

First to the definitions.

Recovery: "Any action that ends a possession of the opposition team."

Interception: "An act of player actively intercepting the ball by anticipating its movement when the opponent is shooting, passing or crossing."

Recoveries are far more common than interceptions because interceptions require a player to actively intercept a pass. Whereas with recoveries, any ball lost by the opponent counts.

This chart shows the number and positions of recoveries and interceptions in the opponent's half. From this, the pressing level and pressing intensity can be derived.

Winterthur won five balls in the opponent's half. Whereby one led to the equalizing goal by Buess. On the opposite side, the high aggressive pressing and the many ball wins of the Bernese can be seen. These were balanced by the L/M/R distribution.

Pressure Storyline

This graph is a kind of "emotion curve" or dramaturgy of the game. We count the number of passes in the attacking third per minute. We use a moving average over four minutes so that the curves are smoothed. This helps to smooth out short-term fluctuations in the data and better visualize long-term trends.

The attendance in the final third was very one-sided. Winterthur had only isolated low and short-lasting deflections, which nevertheless pointed to a few moments of orchestrated attacks.

Progressive Passes Allowed

The graph looks at the defense and shows where and how many progressive passes were allowed to the opponent in their own half. Only successful passes are shown. From this you can see how well the defensive filter worked in midfield or defense. But also the defensive strategy, whether a team directs its opponents to the outside.

Winterthur has allowed 33 passes (in red because from YB). With the L/C/R distribution percentages, we calculate the percentage from which zone this pass was allowed.

Here there was an imbalance with Winterthur, as they allowed 61% of the passes on their right side. The long YB diagonal balls from left to right also stand out positively. Also because there is less progression from right to left.

The defensive filter of the Bernese worked well in general and especially in the center. Only 12% of the passes were hit from there. But more importantly, not a single pass was able to gain space vertically from the center. Of course, it is also important to note here that both teams are always responsible for this and it is always a mix between quality and style.

The Bernese have allowed much less progressive passes in total. These predominantly via their left side (62%). It makes sense to look at such trends over several games, as exciting insights can be gained.

Patterns and novelty often remain hidden to the eye of the beholder, as perception is overloaded by the complexity and variability of the game and the many stimuli. To protect ourselves, our brain automatically filters certain events in the subconscious and thus have no chance to be recognized. .

Although you see them with your eyes, patterns and even tactical innovations often remain hidden. Because people often don't even see and recognize what they don't expect or can't explain to themselves.

Here, too, data analysis offers added value. You can always contrast it with your own perception. If the view is congruent, one gets additional confirmation. If not, it forces one to look more closely. This is an added value in both cases.


Producer, logo and data source.

Team Key Attacking Events A good view is also offensive half in large with the key events in the attack

Player level events & player coaching sessions

With the available data you can also produce events on player level. These help a team and player reflect on their own performance. We have included four player graphs for your analysis, which we leave uncommented.

More and more, data is also being used in individual player coaching. Like a mental coach, for example, the player gets external support from a specialist, for his individual development.

External #coaching for players by specialists is becoming more and more important to achieve excellence and to use all possibilities and resources. Whether #mental coach #tactics coach #athletics coach or #data analysis coach. In the end, it's not talent but #development that pays the rent.

We also offer individual tactics player coaching, in which perception is sharpened, also data-supported, and options for action are jointly reflected upon and further developed.

"True discovery consists not in finding new territory, but in seeing things with new eyes." Marcel Proust

"Progress and success come less from solving problems and much more from seizing opportunities." John Naisbitt

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As an exercise you get several games to analyze and interpret. Have fun!

Wherever disharmonies and other patterns can be seen in the data, exciting insights are ready to be discovered.

Event match analysis over several games

An analysis over several games is more meaningful when you want to analyze performances and trends. Here is an example over four games. We will discuss this report in detail at a later date.

footballytics - making better decisions with smart data

We combine the competencies of football tactics, scouting and data analytics and advise and support clubs in interpreting and using data to make better decisions in scouting and match analysis based on data validation.


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