How To Analyze Pass Maps In Football & How They Help Analysts


How To Analyze Pass Maps In Football & How They Help Analysts

How To Analyze Pass Maps In Football & How They Help Analysts

The analysts working behind the scenes have access to a vast amount of data in the modern era. Millions of data points are generated per game to research, analyze, and analyze different player qualities and performance patterns. Football is a passing-based sport. Without the passes, there would be no prospect of scoring goals. Therefore, studying a player’s or team’s passes over time can show any underlying trends that may exist and aid in analyzing a player’s or team’s passing performance.

How can we evaluate a player's skill using pass analysis?

There isn’t a single approach to plot these pass maps because it all depends on the passing behavior of the player being studied. However, when analyzing passes, it is typically more accurate and clear to look at all of the passes made throughout a season or more. For instance, observing how a ball-playing center half-pings those long balls for the team’s attackers further up the field may be helpful. The chart below displays all of Ruben Dias’ successful long passes for Manchester City during the 2020–21 Premier League season.

This pass map demonstrates how effective Dias was at distributing those long passes to players like Raheem Sterling, Riyad Mahrez, Phil Foden, and Bernado Silva, primarily from inside his half. This demonstrates the player’s skill with the ball, which is what teams like Manchester City that play possession-based football would ideally desire from a center-half. As a result, analysts can gain valuable insights from these pass maps when scouting players or even studying opponents before a game or competition.

Why should passes be cluttered?

Another way to research a player’s passing style is by clustering passes. This is mostly done to determine the most typical types of passes a player makes over time. Again, this helps analysts determine the kinds of passes a player is most likely to make on the field. The two progressive passes that Varane made the most of during his final campaign with Real Madrid are depicted in the pass map below. The right half-space and right wing of the field are the destinations of the majority of his advancing passes.

Therefore, it can be concluded that Varane prefers to play his passes primarily to the right wing of the field when looking to advance the ball. Another personal choice is how many clusters to divide the passes into. The top two or three clusters are typically taken into account when examining the passes while building clusters. To avoid spotting patterns where none exist, this is done. The Serie A pass map up top shows how Luis Alberto moved the ball forward for Lazio. Luis Alberto of the Lazio team manoeuvred between the opposing defence and the midfield and played cutting passes forward. To retrieve the ball from his defence and advance it upfield, he also dove deep.

This is precisely highlighted by his two most common progressive pass clusters. The cluster that starts closer to the center circle from when he drops deep to aid his side advance the ball follows the left half-space pass that he frequently makes into the penalty area.

How can different pass types be analyzed to learn more about a player?

Passes into the penalty area or switches are some further examples of the types of passes that can be plotted based on the player’s profile and his position on the squad. Yacine Adli’s pass maps from his time at Bordeaux in 2021–2022 demonstrate his ability to pass from both deeper and higher up the field.

How can heat maps be used to evaluate a player's passing style?

We have now seen how analysts can use pass maps to analyse a player’s performance. However, these are not your only choices. Pass heat maps are a popular tool for analysing players’ passing skills. Let’s look at an example of how analysts can employ pass heat maps. There was a lot of discussion about Manchester United’s David De Gea’s passing skills throughout the previous season. The pass end locations of all successful passes made by David De Gea, Robert Sanchez, and David Raya are compared in the pass heat map above.

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De Gea’s passing range is far smaller than that of his Spanish contemporaries. In contrast to the other two goalkeepers, De Gea receives the majority of his successful passes close to his penalty area. This knowledge may be applied, for instance, to Manchester United’s manager’s plan to replace De Gea with a goalkeeper who is more at ease handling the ball.

Analysts can also utilize pass heat maps to research the places from which a player is making passes. The heat map below shows the locations on the field where Jack Grealish started his forward passes while playing for Manchester United in the Premier League the previous season. The manager could better allocate a player to a position on the squad by using this kind of information to assist them to comprehend the player’s characteristics.

Other techniques for examining passes

Reviewing player performance using scatter plots is another option. Based on two measurements that are plotted on each axis, these are typically used to compare players. When it comes to delivering those through-balls, Lionel Messi, a former Barcelona star, is well ahead of the competition. In contrast, Real Madrid’s Toni Kroos has the talents necessary to transfer the play from one side to the other. Both of these are vital abilities for a player to possess, and they serve as significant metrics for assessing player performance. 

The “xT” or Expected Threat matrices is another statistic that has gained significant importance recently. xT from passes uses the ball’s beginning and final positions to determine how dangerous a pass is. A more excellent value of xT indicates that the pass ended up in a risky region of the field, increasing the likelihood that the receiving team will have a scoring opportunity. xT heat maps, like the other heat maps, previously covered, display the regions from where players make passes with the highest xT. The areas from where the fullbacks of the top 6 Premier League teams made their most dangerous passes are shown in the image below. 

This heat map also shows the roles that these players are expected to play because, even though they are all fullbacks for their respective teams, each one of them occupies a different space when making their most dangerous passes close to the opponent’s penalty area, indicating the roles they play and how their managers intend to use them.

Key Takeaway…

We have learned via this blog why data analysts working with different football clubs evaluate passes using not only pass maps but also other graphics like a passing network, heat map, and scatterplots to learn more about various players. We have also seen how, depending on the needs of the manager and the squad, these analysts may use these data points and graphics to study particular facets of a player’s passing skill set and ability. Pass maps are a crucial part of the analytics that are being used in football more and more every day. Read more informative blogs on fantasy app developer.

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