Startseite Mathematik Generation of threat: crediting football players for creating dangerous actions in an unbiased way
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Generation of threat: crediting football players for creating dangerous actions in an unbiased way

  • Ali Baouan EMAIL logo , Sébastien Coustou , Mathieu Lacome , Sergio Pulido und Mathieu Rosenbaum
Veröffentlicht/Copyright: 24. September 2025
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Abstract

We introduce an innovative methodology to identify football players at the origin of threatening actions in a team. In our framework, a threat is defined as entering the opposing team’s danger area. We investigate the timing of threat events and ball touches of players, and capture their correlation using Hawkes processes. Our model-based approach allows us to evaluate a player’s ability to create danger both directly and through interactions with teammates. We define a new index, called Generation of Threat (GoT), that measures in an unbiased way the contribution of a player to threat generation. For illustration, we present a detailed analysis of Chelsea’s 2016–2017 season, with a standout performance from Eden Hazard. We are able to credit each player for his involvement in danger creation and determine the main circuits leading to threat. In the same spirit, we investigate the danger generation process of Stade Rennais in the 2021–2022 season. Furthermore, we establish a comprehensive ranking of Ligue 1 players based on their generated threat in the 2021–2022 season. Our analysis reveals surprising results, with players such as Jason Berthomier, Moses Simon and Frederic Guilbert among the top performers in the GoT rankings.


Corresponding author: Ali Baouan, Centre de Mathématiques Appliquées, CMAP, Ecole Polytechnique, route de Saclay, 91128 Palaiseau Cedex, France, E-mail: 

Funding source: Machine Learning & Systematic Methods in Finance

Funding source: Deep Finance and Statistics

Acknowledgments

The authors thank Anna Bonnet for her help with the estimation of Hawkes processes in large dimensions. They are also grateful to Charlotte Dion and Céline Duval.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: The authors gratefully acknowledge financial support from the chairs “Machine Learning & Systematic Methods in Finance” and “Deep Finance and Statistics”.

  7. Data availability: The data are not publicly available.

Appendix

A: Stade Rennais

In this appendix, we present a detailed analysis of one of the Ligue 1 teams in the 2021–2022 season.

A.1: Selected games

In the same spirit as Section 4, we choose a collection of games where the formation is the same and starting lineup is as stable as possible. Table 8 shows the selected matches for Stade Rennais. The team plays in a 433 formation in all of these games but the starting 11 is not always exactly the same. In fact, some players are sometimes rotated for a game or two, but we assume that the substitute behaves approximately the same as the starting player. Stade Rennais line up as follows in the selected games, where the main player in each position is in bold:

  1. Gomis/Alemdar

  2. TraoreOmari/Bade – Aguerd/Bade/Santamaria – Truffert/Meling

  3. MajerSantamaria/Martin – Tait

  4. BourigeaudLaborde/Guirassy – Terrier

Table 8:

List of selected games for Stade Rennais F.C.

Date Opponent Home or away Competition
May 11, 2022 Nantes Away French Ligue 1
Apr 2, 2022 Nice Away French Ligue 1
May 14, 2022 Marseille Home French Ligue 1
Dec 22, 2021 Monaco Away French Ligue 1
Mar 20, 2022 Metz Home French Ligue 1
Apr 15, 2022 Monaco Home French Ligue 1
Apr 30, 2022 St Etienne Home French Ligue 1
Apr 24, 2022 Lorient Home French Ligue 1
Nov 20, 2021 Montpellier Home French Ligue 1
May 21, 2022 Lille Away French Ligue 1
Nov 7, 2021 Lyon Home French Ligue 1

We construct a 12-dimensional counting process from the selected Stade Rennais games regardless of the players starting. We use the data from each game as long as the 11 players on the pitch correspond to the scheme provided above. We then fit a 12-dimensional Hawkes process and associate the estimated metrics of each position with the main player occupying it.

Table 9:

Generated threat metrics for the players of Stade Rennais. The table is sorted by GoT 90 ( i n d ) .

Player name GoT(dir) GoT(ind) GoT 90 ( d i r ) GoT 90 ( i n d )
Benjamin Bourigeaud 0.14 0.16 11.8 12.6
Martin Terrier 0.13 0.17 8.6 9.5
Lovro Majer 0.08 0.11 6.9 8.1
Flavien Tait 0.06 0.09 5.5 7.2
Adrien Truffert 0.02 0.06 2.4 5.2
Hamari Traoré 0.01 0.05 1.5 4.6
Nayef Aguerd 0.00 0.04 0.0 4.5
Baptiste Santamaría 0.00 0.05 0.4 4.3
Warmed Omari 0.00 0.04 0.0 4.0
Gaëtan Laborde 0.04 0.08 2.0 3.0
Alfred Gomis 0.00 0.01 0.0 0.7
Figure 7: 
Graph summarizing the interactions between Stade Rennais players. The width of an arrow from player p1 to player p2 is proportional to the expected number of touches of player p2 generated by one touch from player p1. The size of the circle of player p is proportional to the sum of the arrow sizes received, indicating the involvement of the player in the considered games. The color of the circle represents the GoT
i
 index for each player.
Figure 7:

Graph summarizing the interactions between Stade Rennais players. The width of an arrow from player p1 to player p2 is proportional to the expected number of touches of player p2 generated by one touch from player p1. The size of the circle of player p is proportional to the sum of the arrow sizes received, indicating the involvement of the player in the considered games. The color of the circle represents the GoT i index for each player.

A.2: Results and discussion

In Table 9, we rank the Stade Rennais players with respect to generated threat metrics. Figure 7 graphically represents the direct interactions between them and Figure 8 displays the estimated branching matrix. We can see that the team adopts a 433 shape that progresses mainly through the wings. The danger creation is asymmetric with more combinations occurring on the right side, where Majer is the most creative midfielder. Interestingly, despite being a central midfielder, Flavien Tait delivers a large value of GoT 90 ( i n d ) , indicating that he is a significant contributor to the team’s offensive efforts. In contrast, although Santamaria has more possession, he has limited involvement in creating threats. This difference in their threat generation can be attributed to their distinct roles on the field. On one hand, Tait is a more box-to-box midfielder who frequently projects forward and has a considerable direct threat metric. On the other hand, Santamaria belongs to a class of defensive midfielders who act as anchor points. They participate in the buildup close to the center backs and have limited interactions with the forward positions.

The main threat sources are Bourigeaud, Majer, and Terrier. These three players are outstanding going forward. Terrier is the leader of the team in goalscoring and ranks third in Ligue 1 but seems to be involved in danger creation as well. Bourigeaud generating the most threat is not surprising since he is the creative force of the team. In fact, he ranks first in the league in terms of key passes with 3.2 per game, and first in accurate crosses with 104 in the season.

Figure 8: 
Estimated branching matrix for Stade Rennais.
Figure 8:

Estimated branching matrix for Stade Rennais.

As expected, the center backs have zero direct threat contribution. However, in terms of indirect threat per 90 min  GoT 90 ( i n d ) , Aguerd and Omari rank fourth and sixth in the team respectively. The pair generates danger through their involvement in team build-up and possession. In particular, Aguerd and Omari are comfortable with the ball at their feet and rank eighth and twentieth in the league, respectively, in the number of passes per game with high success rates.

Finally, we can observe from Figure 7 some remarkable circuits that lead to dangerous situations. These patterns of play should be taken into account by an opposing team when facing Stade Rennais:

  1. Aguerd → Truffert → Terrier → Threat.

  2. Terrier → Tait → Threat. Terrier is highly effective in generating direct threats, but he also frequently combines with Flavien Tait to create danger. Similarly, Bourigeaud often gives the ball to Lovro Majer to generate indirect threat.

  3. Omari → Traoré → Bourigeaud → Threat.

  4. Omari → Bourigeaud → Threat. This is a straightforward pattern from defense to attack that should be controlled. Omari is highly successful in progressing the ball, both through slow build-up play by passing the ball to the right-back Traoré, as well as through fast transitions with direct passes to Bourigeaud.

B: Top 100 ranking of Ligue 1 player in terms of GoT

See Tables 10 and 11.

Table 10:

Ranking of Ligue 1 players in terms of GoT(dir).

Rank Name Position Team Minutes GoT(dir)
1 Lionel Messi 10 Paris Saint-Germain 630 0.130
2 Ángel Di María 10 Paris Saint-Germain 1,171 0.128
3 Moses Simon 11 Nantes 1,222 0.120
4 Kylian Mbappé 9 Paris Saint-Germain 1,338 0.110
5 Lionel Messi 9 Paris Saint-Germain 675 0.109
6 Martin Terrier 11 Rennes 1,386 0.108
7 Kylian Mbappé 11 Paris Saint-Germain 1,066 0.107
8 Romain Faivre 7 Brest 630 0.106
8 Houssem Aouar 7 Lyon 810 0.106
10 Sofiane Boufal 9 Angers 771 0.100
11 Jonathan Ikoné 7 Lille 767 0.096
12 Wissam Ben Yedder 9 Monaco 1,625 0.094
13 Franck Honorat 11 Brest 838 0.093
13 Karl Toko-Ekambi 11 Lyon 1,855 0.093
15 Benjamin Bourigeaud 10 Rennes 1,719 0.092
16 Sofiane Boufal 11 Angers 665 0.091
17 Justin Kluivert 11 Nice 1,207 0.090
19 Dimitri Payet 9 Marseille 617 0.088
19 Kevin Gameiro 10 Strasbourg 673 0.088
19 Neymar 11 Paris Saint-Germain 1,258 0.088
21 Jodel Dossou 10 Clermont 1,762 0.086
22 Lucas Da Cunha 10 Clermont 606 0.084
22 Jim Allevinah 11 Clermont 858 0.084
24 Frédéric Guilbert 2 Strasbourg 2,428 0.082
25 Armand Laurienté 9 Lorient 842 0.080
26 Ludovic Blas 7 Nantes 810 0.078
27 Gaël Kakuta 9 Lens 976 0.077
28 Arnaud Kalimuendo-Muinga 11 Lens 724 0.075
29 Cengiz Ünder 10 Marseille 1,047 0.074
29 Florent Mollet 10 Montpellier 1,269 0.074
31 Téji Savanier 7 Montpellier 2,209 0.071
32 Ghislain Konan 3 Reims 1,007 0.070
33 Lovro Majer 7 Rennes 1,302 0.068
34 Kevin Volland 7 Monaco 1,131 0.067
34 Jonathan Clauss 2 Lens 1,940 0.067
36 Angelo Fulgini 8 Angers 630 0.066
36 Andy Delort 10 Nice 1,478 0.066
38 Javairô Dilrosun 9 Bordeaux 675 0.064
39 Vanderson 10 Monaco 619 0.061
40 Lucas Paquetá 7 Lyon 1,248 0.060
40 Burak Yilmaz 9 Lille 1,900 0.060
43 Jonathan Bamba 11 Lille 1,763 0.059
43 Thomas Foket 2 Reims 631 0.059
43 Jason Berthomier 7 Clermont 1,244 0.059
45 Amine Gouiri 9 Nice 1,749 0.058
46 Gaëtan Laborde 9 Rennes 1,305 0.057
47 Ibrahima Sissoko 7 Strasbourg 826 0.055
48 Jonathan David 10 Lille 2,072 0.054
49 Dimitri Lienard 3 Strasbourg 1,728 0.050
51 Flavien Tait 8 Rennes 1,129 0.046
51 Florian Sotoca 10 Lens 1,119 0.046
51 Jérémy Le Douaron 10 Brest 759 0.046
53 Andy Delort 9 Nice 795 0.045
55 Youcef Atal 2 Nice 1,032 0.044
55 Randal Kolo Muani 9 Nantes 876 0.044
55 Stephy Mavididi 11 Montpellier 1,585 0.044
55 Aleksandr Golovin 11 Monaco 607 0.044
58 Elbasan Rashani 11 Clermont 1,588 0.043
59 Mohamed Bayo 9 Clermont 2,331 0.042
60 Abdu Conté 3 Troyes 695 0.041
60 Sanjin Prcic 11 Strasbourg 652 0.041
64 Bruno Guimarães 4 Lyon 900 0.040
64 Anthony Caci 3 Strasbourg 1,400 0.040
64 Kevin Gameiro 9 Strasbourg 1,458 0.040
64 Hicham Boudaoui 7 Nice 1,304 0.040
64 Gerson 8 Marseille 704 0.040
67 Sofiane Diop 11 Monaco 631 0.039
68 Igor Silva 2 Lorient 1,197 0.038
69 Renato Sanches 8 Lille 951 0.037
69 Issa Kaboré 2 Troyes 1,679 0.037
71 Mohamed-Ali Cho 10 Angers 807 0.035
71 Angelo Fulgini 9 Angers 751 0.035
74 Akim Zedadka 2 Clermont 3,330 0.034
74 Pol Lirola 2 Marseille 863 0.034
74 Adrien Thomasson 7 Strasbourg 1,853 0.034
76 Habib Diallo 10 Strasbourg 859 0.033
76 Xavier Chavalerin 11 Troyes 949 0.033
78 Jean-Ricner Bellegarde 11 Strasbourg 1,454 0.032
78 Maxence Caqueret 8 Lyon 1,389 0.032
80 Vital N’Simba 3 Clermont 2,731 0.031
80 Ruben Aguilar 2 Monaco 1,205 0.031
83 Seko Fofana 8 Lens 1,861 0.030
83 Ismail Jakobs 3 Monaco 650 0.030
83 Terem Moffi 10 Lorient 911 0.030
87 Valère Germain 9 Montpellier 1,083 0.029
87 Stéphane Bahoken 10 Angers 657 0.029
87 Youssouf Fofana 8 Monaco 1,116 0.029
87 Mattéo Guendouzi 7 Marseille 1,350 0.029
87 Ludovic Ajorque 10 Strasbourg 1,334 0.029
90 Ludovic Ajorque 9 Strasbourg 1,243 0.028
90 Marco Verratti 4 Paris Saint-Germain 602 0.028
93 Baptiste Santamaría 8 Rennes 675 0.027
93 Vincent Le Goff 3 Lorient 1,440 0.027
93 Ricardo Mangas 3 Bordeaux 613 0.027
96 Caio Henrique 3 Monaco 1,454 0.026
96 Mihailo Ristic 3 Montpellier 1,150 0.026
96 Junior Sambia 2 Montpellier 732 0.026
98 Souleyman Doumbia 3 Angers 1,797 0.025
98 Przemyslaw Frankowski 3 Lens 1,191 0.025
100 Florian Tardieu 8 Troyes 1,530 0.024
Table 11:

Ranking of Ligue 1 players in terms of GoT 90 ( i n d ) .

Rank Name Position Team Minutes GoT 90 ( i n d )
1 Lionel Messi 10 Paris Saint-Germain 630 14.911
2 Ángel Di María 10 Paris Saint-Germain 1,171 13.218
3 Neymar 11 Paris Saint-Germain 1,258 12.724
4 Marco Verratti 4 Paris Saint-Germain 602 12.581
5 Lionel Messi 9 Paris Saint-Germain 675 12.353
6 Romain Faivre 7 Brest 630 10.402
7 Houssem Aouar 7 Lyon 810 10.077
8 Téji Savanier 7 Montpellier 2,209 9.608
9 Marco Verratti 8 Paris Saint-Germain 1,069 9.446
10 Jason Berthomier 7 Clermont 1,244 9.340
11 Benjamin Bourigeaud 10 Rennes 1,719 9.211
12 Sofiane Boufal 9 Angers 771 9.100
13 Bruno Guimarães 4 Lyon 900 8.817
14 Dimitri Payet 9 Marseille 617 8.815
15 Moses Simon 11 Nantes 1,222 8.790
16 Martin Terrier 11 Rennes 1,386 8.639
17 Kylian Mbappé 11 Paris Saint-Germain 1,066 8.577
18 Frédéric Guilbert 2 Strasbourg 2,428 8.421
19 Ruben Aguilar 2 Monaco 1,205 8.019
20 Lovro Majer 7 Rennes 1,302 7.927
21 Lucas Da Cunha 10 Clermont 606 7.882
22 Kylian Mbappé 9 Paris Saint-Germain 1,338 7.733
23 Ghislain Konan 3 Reims 1,007 7.721
24 Sanjin Prcic 11 Strasbourg 652 7.624
25 Sofiane Boufal 11 Angers 665 7.547
26 Lucas Paquetá 7 Lyon 1,248 7.450
27 Karl Toko-Ekambi 11 Lyon 1,855 7.365
28 Jonathan Ikoné 7 Lille 767 7.285
29 Franck Honorat 11 Brest 838 7.207
30 Achraf Hakimi 2 Paris Saint-Germain 1,781 7.125
31 Idrissa Gueye 8 Paris Saint-Germain 662 7.090
32 Dimitri Lienard 3 Strasbourg 1,728 7.050
33 Gerson 8 Marseille 704 7.016
34 Vanderson 10 Monaco 619 6.939
35 Ibrahima Sissoko 7 Strasbourg 826 6.892
36 Ludovic Blas 7 Nantes 810 6.816
37 Gaël Kakuta 9 Lens 976 6.802
38 Jonathan Clauss 2 Lens 1,940 6.757
39 Justin Kluivert 11 Nice 1,207 6.700
40 Flavien Tait 8 Rennes 1,129 6.671
41 Kevin Gameiro 10 Strasbourg 673 6.569
42 Florent Mollet 10 Montpellier 1,269 6.465
43 Angelo Fulgini 8 Angers 630 6.459
44 Renato Sanches 8 Lille 951 6.427
45 Henrique 3 Lyon 619 6.385
46 Danilo Pereira 4 Paris Saint-Germain 879 6.346
47 Pol Lirola 2 Marseille 863 6.327
48 Jim Allevinah 11 Clermont 858 6.303
49 Youcef Atal 2 Nice 1,032 6.258
50 Maxence Caqueret 8 Lyon 1,389 6.229
51 Vital N’Simba 3 Clermont 2,731 6.066
52 Anthony Caci 3 Strasbourg 1,400 6.023
53 Emerson 3 Lyon 1,848 6.019
54 Aleksandr Golovin 11 Monaco 607 5.973
55 Birger Meling 3 Rennes 776 5.936
56 Juan Bernat 3 Paris Saint-Germain 777 5.929
57 Jodel Dossou 10 Clermont 1,762 5.864
58 Caio Henrique 3 Monaco 1,454 5.855
59 Jonas Martin 4 Rennes 1,115 5.810
60 Thomas Foket 2 Reims 631 5.805
61 Jonathan Bamba 11 Lille 1,763 5.632
62 Marquinhos 5 Paris Saint-Germain 2,340 5.625
63 Florian Sotoca 10 Lens 1,119 5.598
64 Jordan Ferri 8 Montpellier 2,129 5.516
65 Aurélien Tchouaméni 4 Monaco 1,620 5.431
66 Malo Gusto 2 Lyon 1,369 5.382
67 Cheick Oumar Doucouré 7 Lens 1,350 5.372
68 Armand Laurienté 9 Lorient 842 5.345
69 Akim Zedadka 2 Clermont 3,330 5.281
70 Presnel Kimpembe 6 Paris Saint-Germain 1,840 5.230
71 Ismail Jakobs 3 Monaco 650 5.199
72 Fábio 3 Nantes 726 5.154
73 Thilo Kehrer 2 Paris Saint-Germain 632 5.144
74 Cengiz Ünder 10 Marseille 1,047 5.079
75 Léo Dubois 2 Lyon 1,246 5.060
76 Mattéo Guendouzi 7 Marseille 1,350 4.995
77 Facundo Medina 4 Lens 1,329 4.953
78 Adrien Thomasson 7 Strasbourg 1,853 4.921
79 Nayef Aguerd 6 Rennes 1,698 4.908
80 Abdu Conté 3 Troyes 695 4.891
81 Javairô Dilrosun 9 Bordeaux 675 4.845
82 Hamari Traoré 2 Rennes 1,878 4.836
83 Przemyslaw Frankowski 3 Lens 1,191 4.778
84 Wissam Ben Yedder 9 Monaco 1,625 4.685
85 Vincent Le Goff 3 Lorient 1,440 4.669
86 Valentin Rongier 2 Marseille 899 4.668
87 Angelo Fulgini 9 Angers 751 4.661
88 William Saliba 5 Marseille 1,800 4.652
89 Boubacar Kamara 4 Marseille 1,497 4.636
90 Nuno Mendes 3 Paris Saint-Germain 1,246 4.599
91 Jason Denayer 6 Lyon 630 4.591
92 Baptiste Santamaría 8 Rennes 675 4.536
93 Jonathan Gradit 6 Lens 1,710 4.535
94 Youssouf Fofana 8 Monaco 1,116 4.517
95 Florian Tardieu 8 Troyes 1,530 4.496
96 Jordan Lotomba 2 Nice 1,410 4.454
97 Mihailo Ristic 3 Montpellier 1,150 4.439
98 Warmed Omari 5 Rennes 1,710 4.407
99 Melvin Bard 3 Nice 2,470 4.350
100 Seko Fofana 8 Lens 1,861 4.345
Table 12:

The main formation clusters for each team in Ligue 1 in the 2021–2022 season.

Team Formation cluster
Angers 3
Bordeaux 3
Brest 2
Clermont 1
Lens 3
Lille 2
Lorient 3
Lyon 1
Marseille 1
Metz 3
Monaco 1
Montpellier 1
Nantes 1
Nice 2
Paris Saint-Germain 1
Reims 3
Rennes 1
St Etienne 4
Strasbourg 3
Troyes 4

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Received: 2024-07-23
Accepted: 2025-08-01
Published Online: 2025-09-24

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