Performance analysis in traditional sports has become a mature area of sports science, whereas its application in esports remains relatively limited. This study applies an interpretable machine learning framework combining SHAP analysis and five classic algorithms to evaluate the performance of professional Counter-Strike players. Among the models tested via five-fold cross-validation, XGBoost demonstrated the best performance. The dataset comprises 1468 professional players from the Counter-Strike: Global Offensive period (2012–2023) and 880 from the Counter-Strike 2 period (2023–2025), with each player evaluated using 39 advanced in-game behavioral metrics categorized into seven skill dimensions (e.g., firepower, entrying, trading, clutching, opening, sniping, utility). Results show that key performance indicators such as Kills Per Round (KPR), Opening Success (OS), Rounds With a Kill (RWK), Rounds With a Multi-Kill (RWMK), Support Rounds (SR), Pistol Round Rating (PRR), and Saves Per Round Loss (SPRL) consistently ranked among the top ten features in both periods. The top six influential features of the model reveal interactions between features and exhibit a positive correlation with SHAP values. The case diagnostics further enabled precise evaluation of individual strengths and weaknesses. This exploratory study provides empirical insights and an interpretable machine learning analytical paradigm for performance evaluation and talent identification in esports.
NovakARBennettKJMPlussMA, et al.Performance analysis in esports: modelling performance at the 2018 league of legends world championship. Int J Sports Sci Coa2020; 15: 809–817.
2.
MiglioreL. What is esports? The past, present, and future of competitive gaming. In: MiglioreLMcGeeCMooreMN (eds) Handbook of esports medicine: clinical aspects of competitive video gaming. Cham: Springer International Publishing, 2021, pp.1–16.
3.
TangTKucekJToepferS. Active within structures: predictors of esports gameplay and spectatorship. Commun Sport2020; 10: 195–215.
4.
CranmerEEHanD-IDvan GisbergenM, et al.Esports matrix: structuring the esports research agenda. Comput Hum Behav2021; 117: 106671.
5.
NagorskyEWiemeyerJ. The structure of performance and training in esports. Plos One2020; 15: e0237584.
6.
PostmaDBvan DeldenRWvan HilvoordeIM. “Dear IOC”: considerations for the governance, valuation, and evaluation of trends and developments in eSports. Front Sports Act Liv2022; 4: 899613.
7.
MolinaPGómez-GonzalvoFValenciano-ValcárcelJ. The sportization of esports and its implications in the near future of sport. Sport Ethics Philos2025; 19: 20–34.
8.
YoungS. Professional Counter-Strike: An Analysis of Media Objects, Esports Culture, and Gamer Representation. PHD Thesis, University of Southern Mississippi, 2021.
9.
HongHJ. Esports: the need for a structured support system for players. Eur Sport Manag Q2023; 23: 1430–1453.
10.
ParryJGiesbrechtJ. Esports, real sports and the Olympic virtual series. J Philos Sport2023; 50: 208–228.
11.
FormosaJO'donnellNHortonEM, et al.Definitions of esports: a systematic review and thematic analysis. Proc ACM Hum-Comput Interact2022; 6: 1–45.
12.
SongHLiYZouX, et al.Elite male table tennis matches diagnosis using SHAP and a hybrid LSTM–BPNN algorithm. Sci Rep-Uk2023; 13: 11533.
13.
HonglinSYutaoLPengyuP, et al.Analysis of technical-tactical performance in table tennis based on machine learning and evaluation theory. P I Mech Eng P-J SpoEpub ahead of print 10 May 2025. DOI: https://doi.org/10.1177/17543371251335408.
14.
YuanBZhuJPanP, et al.Winning determinants between top and second division in Chinese professional football leagues: an interpretable machine learning approach. Bmc Sports Sci Med R2025; 17: 86.
15.
LlvarinenT. Predicting round outcomes in counters-strike: global offensive by examining smoke grenade usage patterns. Thesis, Tilburg university, 2022.
16.
PassosPAraújoDVolossovitchA. Performance analysis in team sports. London, UK: Routledge, Taylor & Francis Group, 2017.
17.
MemmertDRaabeD. Data analytics in football: Positional data collection, modelling and analysis. London: Routledge, 2023.
18.
TyreA. A Current Comparison of e-Sports & Traditional Sports, “Plug Me in Coach!”. M.A., California State University, Fullerton, United States, California, 2018.
19.
Martin-NiedeckenALSchättinA. Let the body’n’brain games begin: toward innovative training approaches in esports athletes. Front Psychol2020; 11: 138.
20.
SummerleyR. The development of sports: a comparative analysis of the early institutionalization of traditional sports and E-sports. Games Cult2019; 15: 51–72.
21.
RejtharJKavříkD. The hot hand in esports. SN Comput Sci2025; 6: 303.
22.
SharpeBTBesombesNWelshMR, et al.Indexing esport performance. J Electron Gaming Esports2023; 1: jege.2022–0017.
23.
JennySEDouglasMRKMC, et al.Virtual(ly) athletes: where eSports fit within the definition of “sport”. Quest2017; 69: 1–18.
24.
XenopoulosPDoraiswamyHSilvaC. Valuing Player Actions in Counter-Strike: Global Offensive. In: 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 10-13 December 2020, pp.1283–1292. Piscataway, NJ, USA: IEEE.
25.
HirotaM. Predicting Win Conditions of Counter-Strike: Global Offensive for Analyzing Round Progression. In: 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), Kitakyushu, Japan, 29 October 2024 - 01 November 2024, pp.1287–1288. Piscataway, NJ, USA: IEEE.
26.
RejtharJKotrbaV. Confirmation of the validity of the HLTV ranking. Icga J2023; 45: 2–15.
27.
ShenkmanEMolodchikMParshakovP. Online versus offline eSports tournaments: prize structure and individual performance. J Sport Econ2024; 26: 115–132.
28.
MengQKeGWangT, et al.A communication-efficient parallel algorithm for decision tree. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, December 5–10, 2016, pp.1279–1287. Red Hook, NY, USA: Curran Associates Inc.
29.
KeGMengQFinleyT, et al.Lightgbm: a highly efficient gradient boosting decision tree. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4-9 December 2017, pp.3149–3157. Red Hook, NY, USA: Curran Associates, Inc.
30.
ShiYKeGChenZ, et al.Quantized training of gradient boosting decision trees. Adv Neural Inf Process Syst2022; 35: 18822–18833.
31.
SongHLiuT. Comment on “Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care”. Sports Med2025; 55: 2351–2353.
NalluriMPentelaMEluriNR. A scalable tree boosting system: xG boost. Int J Res Stud Sci Eng Technol2020; 7: 36–51.
34.
RudinC. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell2019; 1: 206–215.
35.
HuMZhangHWuB, et al.Interpretable predictive model for shield attitude control performance based on XGboost and SHAP. Sci Rep-Uk2022; 12: 18226.
36.
OuyangYLiXZhouW, et al.Integration of machine learning XGBoost and SHAP models for NBA game outcome prediction and quantitative analysis methodology. Plos One2024; 19: e0307478.
37.
ShmueliG. To explain or to predict?Stat Sci2010; 25: 289–310.
38.
GuyonIElisseeffA. An introduction to variable and feature selection. J Mach Learn Res2003; 3: 1157–1182.
39.
CuiZDongYYangH, et al.Machine learning prediction models for multidrug-resistant organism infections in ICU ventilator-associated pneumonia patients: analysis using the MIMIC-IV database. Comput Biol Med2025; 190: 110028.
40.
LuXQiuH. Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning. Bmc Med Inform Decis2023; 23: 59.
41.
UrbaniakKWątróbskiJSałabunW. Identification of Players Ranking in E-Sport. Appl Sci2020; 10: 1–35.
42.
VargaPScholzTMTanET. Esports player analyticsRoutledge handbook of esports. London: Routledge, 2024, pp.191–202.
43.
YalçınerSKilciK. Effect of pistol round and first kill on match outcome in the counter-strike: global offensive major esports championships. Aloma Rev Psic Cièn Educ Esport2023; 41: 21–25.
44.
XenopoulosPFreemanWRSilvaC. Analyzing the Differences between Professional and Amateur Esports through Win Probability. In: the ACM Web Conference 2022 (WWW '22), Lyon, France, April 25–29, 2022, pp.3418–3427. New York, NY, USA: Association for Computing Machinery.
45.
StåhlM. Who is possible online?Topos: J Philos Cult St2022; 1: 100–109.
46.
RogersRShefferML. Diversity and Inclusion in the Esports Industry. 1 ed. Lanham: Lexington Books, 2023.
47.
XiaXSalinasAMorstatterF. Precision Under Fire: Analysis and Predictive Modeling in Counter-Strike: Global Offensive. In: 2025 IEEE Conference on Games (CoG), Lisbon, Portugal, August 26–29, pp.1–8. Piscataway, NJ, USA: IEEE.
48.
Gisbert-PérezJGarcía-NaveiraAMartí-VilarM, et al.Key structure and processes in esports teams: a systematic review. Curr Psychol2024; 43: 20355–20374.
49.
JiSWangXLyuT, et al.Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: a non-linear and interaction effect analysis. J Transp Geogr2022; 103: 103414.
50.
KarkazisKFishmanJR. Tracking U.S. Professional athletes: the ethics of biometric technologies. Am J Bioeth2017; 17: 45–60.
51.
RogersMCrozierAJSchranzNK, et al.Player profiling and monitoring in basketball: a delphi study of the most important non-game performance indicators from the perspective of elite athlete coaches. Sports Med2022; 52: 1175–1187.
52.
LiuJHuangGHyyppäJ, et al.A survey on location and motion tracking technologies, methodologies and applications in precision sports. Expert Syst Appl2023; 229: 120492.
53.
Meng-LewisYDonnaWYupeiZ, et al.Understanding complexity and dynamics in the career development of eSports athletes. Sport Manag Rev2022; 25: 106–133.
54.
BubnaKTrotterMGWatsonM, et al.Coaching and talent development in esports: a theoretical framework and suggestions for future research. Front Psychol2023; 14: 1191801.
55.
ChangK-L. The Selection of Gamers for Professional Esports Teams. J Mult-Valued Log S2020; 35: 325.
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