An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance
Abstract
1. Introduction
2. Materials and Methods
2.1. Sample
2.2. Design and Ethics Committee
2.3. Methodology
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- Goals is the total number of goals scored by the team per season.
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- Possession value (PV) measures the probability of a team scoring from their possession.
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- Expected goals (xG) is the estimation of the probability of any given shot being converted to a goal based on various different factors describing the shot. Shot location is the most common factor, which depends on two variables: the distance and the angle toward the goal when the shot was taken.
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- Expected goals on target (xGOT) measures the post-shot quality of on-target efforts at goal. It is an indicator of how well a player is shooting.
2.4. Statistical Analysis
3. Results
4. Discussion
4.1. Limitation Section
4.2. Practical Application
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Pollard, R. Charles Reep (1904–2002): Pioneer of notational and performance analysis in football. J. Sports Sci. 2002, 20, 853–855. [Google Scholar] [CrossRef]
- Lames, M.; Hermann, S.; Prüßner, R.; Meth, H. Football Match Dynamics Explored by Recurrence Analysis. Front. Psychol. 2021, 12, 747058. [Google Scholar] [CrossRef] [PubMed]
- Rein, R.; Memmert, D. Big data and tactical analysis in elite soccer: Future challenges and opportunities for sports science. SpringerPlus 2016, 5, 1410. [Google Scholar] [CrossRef] [PubMed]
- Lucey, P.; Bialkowski, A.; Monfort, M.; Carr, P.; Matthews, I. “Quality vs Quantity”: Improved shot prediction in soccer using strategic features from spatiotemporal data. In Proceedings of the 9th Annual MIT Sloan Sports Analytics Conference, Boston, MA, USA, 27–28 February 2015; pp. 1–9. [Google Scholar]
- Tenga, A.; Ronglan, L.T.; Bahr, R. Measuring the effectiveness of offensive match-play in professional soccer. Eur. J. Sport Sci. 2010, 10, 269–277. [Google Scholar] [CrossRef]
- Fernández, J.; Bornn, L.; Cervone, D. A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. Mach. Learn. 2021, 110, 1389–1427. [Google Scholar] [CrossRef]
- Power, P.; Ruiz, H.; Wei, X.; Lucey, P. Not all Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, 13–17 August 2017; pp. 1605–1613. [Google Scholar]
- Decroos, T.; Brasen, L.; Van Haaren, J.; Davis, J. Actions speak louder than goals: Valuing players actions in soccer. In Proceedings of the 25rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 4–8 August 2019; pp. 1851–1861. [Google Scholar]
- Brasen, L.; Van Haaren, J. Measuring Football players on the-ball contributions from passes during games. In Proceedings of the International Workshop on Machine Learning and Data Mining for Sports Analytics, Dublin, Ireland, 10 September 2018; Springer: Cham, Switzerland, 2018; pp. 3–15. [Google Scholar]
- Spearman, W.; Basye, A.; Dick, G.; Hotovy, R.; Pop, P. Physics based modeling of pass probabilities in soccer. In Proceedings of the MIT Sloan Sports Analytics Conference, Boston, MA, USA, 3–4 March 2017; pp. 1–14. [Google Scholar]
- Anzer, G.; Bauer, P. A Goal Scoring Probability Model for shots based on synchronized positional and event data in football (Soccer). Front. Sports Act. Living 2021, 3, 624475. [Google Scholar] [CrossRef]
- Davis, J.; Robberechts, P. How data availability affects the ability to learn good xG models. In Proceedings of the 7th International Workshop of Machine Learning and Data Mining for Sports Analytics, Ghent, Belgium, 14–18 September 2020; pp. 17–27. [Google Scholar]
- Brechot, M.; Flepp, R. Dealing with Randomness in Match Outcomes: How to Rethink Performance Evaluation in European Club Football Using Expected Goals. J. Sports Econ. 2020, 21, 335–362. [Google Scholar] [CrossRef]
- Noordman, R. Improving the Estimation of Outcome Probabilities of Football Matches Using In-Game Information; Technical Report; Amsterdam School of Economics: Amsterdam, The Netherlands, 2019; Available online: https://www.scisports.com/wp-content/uploads/2019/10/Noordman-Rogier-12366315-MSc-ETRICS.pdf (accessed on 27 June 2024).
- Mead, J.; O’Hare, A.; McMenemy, P. Expected goals in football: Improving model performance and demonstrating value. PLoS ONE 2023, 18, e0282295. [Google Scholar] [CrossRef]
- Goodman, M. A New Way to Measure Keepers’ Shot Stopping: Post-Shot Expected Goals; StatsBomb: Singapore, 2018; Available online: https://statsbomb.com/articles/soccer/a-new-way-to-measure-keepers-shot-stopping-post-shot-expected-goals/ (accessed on 28 June 2024).
- Baron, E.; Sandholtz, N.; Pleuler, D.; Chan, T.C.Y. Miss it like Messi: Extracting value from off-target shots in soccer. J. Quant. Anal. Sports 2024, 20, 37–50. [Google Scholar] [CrossRef]
- Liu, H.; Hopkins, W.; Gómez-Ruano, M.A.; Molinuevo, J.S. Inter-operator reliability of live football match statistics from OPTA Sportsdata. Int. J. Perform. Anal. Sport 2013, 13, 803–821. [Google Scholar] [CrossRef]
- Linke, D.; Link, D.; Lames, M. Football-specific validity of TRACAB’s optical video tracking systems. PLoS ONE 2020, 15, e0230179. [Google Scholar] [CrossRef] [PubMed]
- Madrero, P.; Fernández, J.; Arias, M. Creating a Model for Expected Goals in Football Using Qualitative Player Information; Technical Report; Universitat Politècnica de Catalunya (UPC): Barcelona, Spain, 2020; Available online: https://upcommons.upc.edu/bitstream/handle/2117/328922/147841.pdf (accessed on 10 August 2024).
- Pappalardo, L.; Cintia, P.; Rossi, A.; Massucco, E.; Ferragina, P.; Pedreschi, D.; Giannotti, F. A public data set of spatio-temporal match events in soccer competitions. Sci. Data 2019, 6, 236. [Google Scholar] [CrossRef] [PubMed]
- Errekagorri, I.; Fernandez-Navarro, J.; López-Del Campo, R.; Resta, R.; Castellano, J. An eight-season analysis of the teams’ performance in the Spanish LaLiga according to the final league ranking. PLoS ONE 2024, 19, e0299242. [Google Scholar] [CrossRef] [PubMed]
- Errekagorri, I.; López-Del-Campo, R.; Resta, R.; Castellano, J. Performance Analysis of the Spanish Men’s Top and Second Professional Football Division Teams during Eight Consecutive Seasons. Sensors 2023, 23, 9115. [Google Scholar] [CrossRef]
- González-Rodenas, J.; Ferrandis, J.; Moreno-Pérez, V.; López-Del-Campo, R.; Resta, R.; Del-Coso, J. Differences in playing style and technical performance according to the team ranking in the Spanish football LaLiga. A thirteen seasons study. PLoS ONE 2023, 18, e0293095. [Google Scholar] [CrossRef]
- Link, D.; Hoernig, M. Individual ball possession in soccer. PLoS ONE 2017, 12, e0179953. [Google Scholar] [CrossRef]
- Wang, S.H.; Qin, Y.; Jia, Y.; Igor, K.E. A systematic review about the performance indicators related to ball possession. PLoS ONE 2022, 17, e0265540. [Google Scholar] [CrossRef]
- Iván-Baragaño, I.; Maneiro, R.; Losada, J.; Ardá, A. Technical-tactical evolution of women’s football: A comparative analysis of ball possessions in the FIFA Women’s World Cup France 2019 and Australia & New Zealand 2023. Biol. Sport 2024, 42, 11–20. [Google Scholar]
- Maneiro, R.; Losada, J.L.; Casal, C.A.; Ardá, A. Identification of explanatory variables in possession of the ball in High-Performance Women’s Football. Int. J. Environ. Res. Public Health 2021, 18, 5922. [Google Scholar] [CrossRef]
- García-Calvo, T.; Ponce-Bordón, J.C.; Leo, F.M.; López-Del-Campo, R.; Nevado-Garrosa, F.; Pulido, J.J. How Does Ball Possession Affect the Physical Demands in Spanish LaLiga? A Multilevel Approach. Res. Q. Exerc. Sport 2023, 94, 931–939. [Google Scholar] [CrossRef]
- Jerome, B.W.C.; Stoeckl, M.; Mackriell, B.; Dawson, C.W.; Fong, D.T.P.; Folland, J.P. Evidence for a new model of the complex interrelationship of ball possession, physical intensity and performance in elite soccer. Scand. J. Med. Sci. Sports 2024, 34, e14546. [Google Scholar] [CrossRef] [PubMed]
- Pappalardo, L.; Rossi, A.; Natilli, M.; Cintia, P. Explaining the difference between men’s and women’s football. PLoS ONE 2021, 16, e0255407. [Google Scholar] [CrossRef] [PubMed]
- Plakias, S.; Tsatalas, T.; Armatas, V.; Tsaopoulos, D.; Giakas, G. Tactical Situations and Playing Styles as Key Performance Indicators in Soccer. J. Funct. Morphol. Kinesiol. 2024, 9, 88. [Google Scholar] [CrossRef] [PubMed]
- Pratas, J.M.; Volossovitch, A.; Carita, A.I. Goal scoring in elite male football: A systematic review. J. Hum. Sport Exerc. 2018, 13, 218–230. [Google Scholar] [CrossRef]
- Prieto, J.; Gómez, M.-Á.; Sampaio, J. From a static to a dynamic perspective in handball match analysis: A systematic review. Open Sports Sci. J. 2015, 8, 24–34. [Google Scholar] [CrossRef]
- Garganta, J. Trends of tactical performance analysis in team sports: Bridging the gap between research, training and competition. Rev. Port. Cien. Desporto 2009, 9, 81–89. [Google Scholar]
Variable | Mean | SD | Lowest | Highest | ||||
---|---|---|---|---|---|---|---|---|
LigaF | LaLiga | LigaF | LaLiga | LigaF | LaLiga | LigaF | LaLiga | |
Goals/90 | 1.57 | 1.32 | 0.94 | 0.48 | 0.67 | 0.66 | 4.57 | 2.29 |
PV/90 | 1.68 | 1.32 | 0.34 | 0.16 | 1.18 | 1.10 | 2.50 | 1.69 |
xG/90 | 1.39 | 1.30 | 0.73 | 0.31 | 0.65 | 0.87 | 3.74 | 2.04 |
xGOT/90 | 1.36 | 1.32 | 0.69 | 0.35 | 0.58 | 0.86 | 3.42 | 2.04 |
Percentile | ||||
---|---|---|---|---|
Variable | LigaF | LaLiga | ||
Weakness Index (20th) | Strength Index (80th) | Weakness Index (30th) | Strength Index (70th) | |
Goals/90 | 0.93 | 2.01 | 1.02 | 1.53 |
PV/90 | 1.36 | 1.93 | 1.20 | 1.33 |
xG790 | 0.83 | 1.68 | 1.12 | 1.37 |
xGOT/90 | 0.85 | 1.70 | 1.12 | 1.50 |
Goals/90 | LigaF | LaLiga | ||
---|---|---|---|---|
r | R2 | r | R2 | |
PV/90 | 0.9188 | 0.8442 | 0.6760 | 0.4570 |
xG/90 | 0.9910 | 0.9820 | 0.9326 | 0.8698 |
xGOT/90 | 0.9785 | 0.9574 | 0.9616 | 0.9248 |
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Ruiz-de-Alarcón-Quintero, A.; De-la-Cruz-Torres, B. An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance. Data 2024, 9, 102. https://doi.org/10.3390/data9090102
Ruiz-de-Alarcón-Quintero A, De-la-Cruz-Torres B. An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance. Data. 2024; 9(9):102. https://doi.org/10.3390/data9090102
Chicago/Turabian StyleRuiz-de-Alarcón-Quintero, Anselmo, and Blanca De-la-Cruz-Torres. 2024. "An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance" Data 9, no. 9: 102. https://doi.org/10.3390/data9090102
APA StyleRuiz-de-Alarcón-Quintero, A., & De-la-Cruz-Torres, B. (2024). An Expected Goals on Target (xGOT) Metric as a New Metric for Analyzing Elite Soccer Player Performance. Data, 9(9), 102. https://doi.org/10.3390/data9090102