Football is perhaps the most popular sport in the world. It is a well-known fact that it has been the subject of deep analysis in recent years. So much so that some competitions are studied and discussed for days, months or even years.
In this article, I will investigate one of the matches that still holds its place in memory, the legendary 2005 Champions League Milan – Liverpool final.
Just as Solvia Risk Solutions brings together the data of companies processes with SAP Signavio Process Intelligence and quickly analyzes how the processes are progressing, what the key points are, and how improvements can be made; I want to show you how process mining can be applied in all processes with graphs, diagrams and various calculations using the detailed data of a final match, using Process Intelligence, the product designed by SAP Signavio for Process Mining.
So how was this data combined with the SAP Signavio Process Intelligence product used for business processes? It was simply defined as a business process until each attack made by the teams was terminated. When the ball passed to the other team, a new business process started. In other words, each attack had its own case IDs, and the entire match was turned into successive processes over this data.
Let’s start with an overview of this long final, played in May 2005. Liverpool had reached the final by surprising the authorities in their adventure in the Champions League, where they were second in the group and advanced to the next rounds. AC Milan, on the other hand, was the team that was seen as one of the best squads of its time and the favorite to win the final match.
Team Lineups
1st Half Analysis
The first danger in the match, which commenced with magnificent noise and atmosphere, started with the free kick that Milan won in the first minute. Set-piece master Andrea Pirlo found captain Maldini and Milan took the lead with an early goal.
The match continued with the high tempo brought by the early goal and Liverpool attacked to respond, while a controversial position took place in the Milan penalty area in the 38th minute. As the referee continued the match while the Liverpool players were waiting for a penalty, a quick attack opportunity arose for Milan. They doubled the lead with a perfect counterattack.
By easily finding the 2nd Goal scored by AC Milan among more than 300 cases defined during the match, we can analyze how it started, progressed, and concluded with the Process Discovery widget. Naturally, since this position is a position that the players quickly shape with single passes, we came across a simpler process analysis. That’s why I wanted to use the “Cycle Time” view, which calculates the duration of the transitions, not the number of transitions. In the following positions, this image will become a little different.
Going back to the match again, Liverpool was stunned after this goal. In the attack that started with Pirlo after Gerard’s passing mistake, Milan brought Crespo together with Kaka’s excellent pass. Milan increased the difference to 3 with a stylish chip goal.
In the first half, which was at a high tempo, Milan did not give any chances to the opposition. Most spectators thought that this match was over, that the winner of the cup was Milan. Now let’s look at the statistics of this one-sided first half.
First, let’s start by briefly summarizing how these widgets that you see above and that you will see later in the article are created. These widgets were created in two different ways. First, in Signavio’s ready-made widgets, by selecting and filtering the necessary fields from the data set we have. The second is with SIGNAL codes, which I can call the programming language of Signavio, which I use for more complex analyzes and calculations. (This language is also very simple to learn).
When we look at the number of shots and passes, it looks like the data of a balanced first half. But that will change, especially when we decide to look at shooting data in terms of quality rather than quantity.
XG analysis; This reflects the goal expectation brought by a team’s shots. It is also calculated over XG 1. For example, while the XG of a shot from 40 meters is close to 0, the XG of a shot from an empty goal is close to 1, meaning near certainty of scoring a goal.
This data was visualized with the correlation analysis techniques of SAP Signavio. As seen in the chart above, there were no clear goal opportunities until the 38th minute, including Milan’s surprise set-piece goal. However, in the attacks in the 38th and 42nd minutes, we see that Milan’s positions are clearer and more dangerous than any other positions in the first half.
2nd Half Analysis
At the beginning of the second half, Liverpool would show both the opposition and the audience that they would not give up. In fact, the events that developed between the 45th and 60th minutes are so striking that it will be necessary to analyze this data under a separate heading.
45′ – 60th Minute Analysis
The miracle would begin at the 53rd minute. Meeting the cross made by Riise from the left wing, Gerard fired the first bullet with a magnificent header and scored Liverpool’s first goal.
Two minutes after this goal, Liverpool returned to the center in the attack that started with Riise from the left wing and opened the covered Milan defense with a magnificent shot by Smicer from 25 meters that scored a second goal.
However, Liverpool would not stop until the score was even. Panicking Milan’s defense gave way again, and Gattuso brought down Liverpool Captain Gerrard in the penalty area. Xabi Alonso took the penalty kick. Even though Dida, one of the best goalkeepers of an era, saved this tense penalty, Xabi Alonso hit the rebound ball and equalized the score. (3-3)
So, what does the data of these “magical” 15 minutes tell us?
While Liverpool’s overwhelming dominance is evident in the number of passes, there is no significant difference in the number of shots. However, it should not be forgotten that the only period in which Liverpool is superior in these statistics is these 15 minutes.
XG (Goal Prospect) analysis reveals how much extra to expectation Liverpool’s first two goals were. Gerrard’s goal is 0.05 (5% chance to score) and Smicer’s goal is 0.02 (2% chance to score) as a result of XG. The high XG data we see on the far right of the graph are the consecutive penalty shoot-out by Xabi Alonso and a shot from the rebound ball that scored.
From the 60th minute, everyone agreed that this match would take its place among the legends. However, in the remainder of the second half, this high tempo was replaced by a half-hour where both sides were more controlled. Milan recovered and had two clear scoring chances but could not get past the Liverpool defense and the goalkeeper. The data for the second half is as follows:
In the continuation of that fantastic period, we see that the gap between the pass numbers is almost closed, and even Milan dominates in the number of shots.
As we can see in the XG analysis, Liverpool lost their rhythm on offense after leveling the match. After Milan got over the shock, they had clear goal opportunities, but could not find the finishing they were looking for.
Extra Time Analysis
Milan took the lead in performance again in extra time. Liverpool, on the other hand, felt the effects of fatigue more than their rivals.
The number of accurate passes also proves the situation I mentioned above. The number of shots seems close, but if we look at how dangerous these shots are:
Liverpool’s long and ineffective shots are evident on the chart. The position that Milan found with Shevchenko in the last minutes of extra time and could not pass goalkeeper Dudek in both tries makes the story of this match even more interesting. The teams go to the penalty shootout with the equality that does not break even in overtime.
Penalties
While Milan can only score 2 of their first 4 penalty shootouts, Liverpool finds their 3rd goal in the 4th penalty.
Milan’s star Shevchenko, who takes the ball while both teams are on the last penalty, cannot pass the goalkeeper Dudek again and the winner of this grand final is Liverpool.
If we consider the match as a business process, we have summarized how the process progresses section by section. Now, let’s travel from the part to the whole and look at the general data of the match.
Contrary to the result, we can see from the statistics that Milan is the team that has more possession of the ball, shoots more, and passes more accurately.
Pass Analysis
When we analyze the pass statistics on a player basis; Pirlo and Xabi Alonso, playmakers of the two teams, appear to be the players with the highest number of passes. However, in Liverpool, Gerrard helped Alonso with close statistics, while Pirlo stood out more in the team. Another remarkable detail is that Milan’s second most passing player, who made more passes throughout the match, is Marcos Evangelista (Cafu), the team’s right-back with 94 passes. In fact, Cafu made more passes than Liverpool’s playmakers Xabi Alonso (84) and Gerrard (75).
So, to whom and how did the quarterbacks of the two teams send the ball the most?
It is noteworthy that Pirlo gave the same number of passes to the 3 players to whom he sent the ball the most. We can talk about a homogeneous pass structure. On the Xabi Alonso side, there is Smicer, who is the owner of the 2nd goal at the top, although he was included in the game later. The most striking difference here is that while Pirlo was able to find the team’s forwards Shevchenko (7) and Crespo (2) a total of 9 times, Alonso was able to find Liverpool forwards Baros and Cisse only twice. Here we see that Pirlo is one of the most important factors in Milan’s superior game. Because both teams have built their game plans on Pirlo and Alonso. However, Pirlo’s ability to lead and move the game forward and the involvement of Liverpool forwards seems to make the difference.
The map of the attacks that started with Andrea Pirlo in the Process Discovery tool, which we used to explain the formation of goals before. Here, too, what draws my attention the most is the fact that Pirlo’s attacks somehow met with Kaka (Ricardo I. D. S. Leite), although he was less prominent in other statistics. For offense, we can mention that Pirlo’s biggest assistant is Kaka, the other star of the team.
In Xabi Alonso, on the other hand, the situation is slightly different, with a less diverse structure. The biggest difference between the two maps, when carefully examined, is that Alonso has no shooting pass. In fact, none of the attacks he started resulted in a shot. We can show this as the biggest difference between the match performances of the two playmakers. We can make a similar inference with the previous map for the Alonso – Gerrard connection here. The ball met with Gerrard in most of Alonso’s attacks.
There is one last point I would like to mention about the pass data. With Signavio Process Intelligence’s feature called Automated Insights, we can see Signavio’s insights about the process under review.
For example, according to the defined match process data, we can automatically see the analysis in which the attacks starting from the goalkeeper end in a shorter time than the other attack start types. From this, we can deduce that the goalkeepers of the teams try more long passes or quick starts, however, the attacks end quickly or the ball passes to the opposing team in a short time.
Shot Analysis
Another proof of the critical role Shevchenko played in the course of the match; He is at the top with 6 shot attempts, most of which are clear opportunities, but these shots have not yielded any results. On the Liverpool side, the graph is more balanced. At the top of the chart, Xabi Alonso’s penalty kick and the goal he found by completing the rebound ball carried him to the top in the number of shots. Here, let’s investigate the efficiency of the attackers in a heatmap graphic.
Liverpool strikers Baros and Cisse can take only 1 shot each in 120 minutes, while Milan strikers Shevchenko and Crespo have a total of 9 shots. In other words, contrary to expectations, we can say that Liverpool wrote this comeback story not with their attackers, but with surprise names.
Finally, I would like to talk about the Signavio Value Chain, which I use to collect and easily access deeper analyzes in one place.
A Signavio Value Chain model, which can be opened by clicking the icons (Ring Chart) next to the players, has been created, where the actions of each player in the match can be analyzed.
If we need to summarize the findings, we obtained with the Process Intelligence product:
- Milan was the superior side in every aspect of the game throughout the match, but when they left the control to Liverpool in a part of the match, they shared the match and had difficulty turning it in their favor again. We have seen again in numbers that stability can sometimes be more important than all game plans. Especially it must be said that Liverpool, who took almost all the opportunities they found, did a great job in finishing.
- The match was shaped by the leadership of two players; Xabi Alonso and Pirlo. According to the results, Pirlo was more effective in this role. We can say that Andrea Pirlo and the attacks that he shaped are the first point that the opponents to face the Milan squad of those times should take precautions about. In addition, it is a fact that one of the most important elements when analyzing teams in today’s football is the connections between the players. At AC Milan, the Pirlo – Kaka link could be the biggest threat to rivals.
- Cafu, one of Milan’s secret heroes, should also be mentioned. We can see from the graphs that activity could not be blocked for 120 minutes. Accordingly, while the strength of Milan’s right side emerges, the left side of Liverpool’s defense can be defined as a weakness. Of course, while making this inference, it should not be forgotten that both of Liverpool’s goals started with Riise playing on the left wing.
As a sports fan, there is much more data and scenarios to investigate and analyze, but let’s end here for now. I leave it to your imagination how SAP Signavio, which can generate these analyzes from only passing and shooting data of a football match, can help you proactively analyze, model, manage, monitor, and improve your business processes.
Watch our video now as we analyze the 2005 champions league with SAP Signavio.
