student from 01.01.2020 to 01.01.2024
Moskva, Moscow, Russian Federation
VAC 5.8.4 Физическая культура и профессиональная физическая подготовка
VAC 1.1.10 Биомеханика и биоинженерия
VAC 1.2.1 Искусственный интеллект и машинное обучение
VAC 1.2.2 Математическое моделирование, численные методы и комплексы программ
VAC 1.2.3 Теоретическая информатика, кибернетика
VAC 1.5.2 Биофизика
VAC 1.5.5 Физиология человека и животных
VAC 1.5.8 Математическая биология, биоинформатика
VAC 3.3.9 Медицинская информатика
VAC 3.1.33 Восстановительная медицина, спортивная медицина, лечебная физкультура, курортология и физиотерапия
VAC 5.2.3 Региональная и отраслевая экономика
VAC 5.2.6 Менеджмент
VAC 5.3.2 Психофизиология
VAC 5.3.4 Педагогическая психология, психодиагностика цифровых образовательных сред
VAC 5.8.6 Оздоровительная и адаптивная физическая культура
VAC 5.8.7 Методология и технология профессионального образования
VAC 5.6.6 История науки и техники
VAC 5.12.1 Междисциплинарные исследования когнитивных процессов
VAC 5.12.4 Когнитивное моделирование
UDK 796.052.224 при защите
UDK 796.052.16 Обсуждение тактики
UDK 796.058.4 Анализ соревнований
UDK 347.514.3 В спорте
UDK 796.05 Направления в спорте. Спортивные средства. Стратегия. Тактика
UDK 355.233.22 Физическое воспитание. Физические упражнения. Плавание. Фехтование. Спорт
UDK 351.855.3 Игры. Спорт. Увеселения
UDK 00 Наука в целом (информационные технологии - 004)
UDK 31 Демография. Социология. Статистика
GRNTI 77.31 Спортивные соревнования
GRNTI 77.03 Теория физической культуры и спорта
GRNTI 77.00 ФИЗИЧЕСКАЯ КУЛЬТУРА И СПОРТ
GRNTI 83.00 СТАТИСТИКА
GRNTI 14.00 НАРОДНОЕ ОБРАЗОВАНИЕ. ПЕДАГОГИКА
GRNTI 20.00 ИНФОРМАТИКА
OKSO 49.04.03 Спорт
OKSO 49.06.01 Физическая культура и спорт
OKSO 02.00.00 Компьютерные и информационные науки
OKSO 06.00.00 Биологические науки
OKSO 09.00.00 Информатика и вычислительная техника
OKSO 32.00.00 Науки о здоровье и профилактическая медицина
OKSO 39.00.00 Социология и социальная работа
OKSO 44.00.00 Образование и педагогические науки
BBK 75 Физическая культура и спорт
BBK 1 ОБЩЕЕ И МЕЖДИСЦИПЛИНАРНОЕ ЗНАНИЕ
BBK 22 Физико-математические науки
BBK 28 Биологические науки
BBK 3 ТЕХНИКА. ТЕХНИЧЕСКИЕ НАУКИ
BBK 58 Прикладные отрасли медицины
BBK 73 Научно-информационная деятельность
BBK 74 Образование. Педагогическая наука
TBK 5 ПРИКЛАДНЫЕ НАУКИ. ТЕХНИКА. МЕДИЦИНА
TBK 7 ОБЩЕСТВЕННЫЕ НАУКИ. ЭКОНОМИКА. ПРАВО.
BISAC SPO061030 Coaching / Soccer
BISAC SPO040000 Soccer
BISAC SEL SELF-HELP
BISAC COM014000 Computer Science
BISAC COM072000 Computer Simulation
BISAC COM017000 Cybernetics
BISAC COM018000 Data Processing
BISAC COM062000 Data Modeling & Design
BISAC COM089000 Data Visualization
BISAC COM021000 Databases / General
BISAC COM021030 Databases / Data Mining
BISAC COM023000 Educational Software
BISAC COM025000 Expert Systems
BISAC COM074000 Hardware / Mobile Devices
BISAC COM080000 History
BISAC COM079010 Human-Computer Interaction (HCI)
BISAC COM032000 Information Technology
BISAC COM031000 Information Theory
BISAC COM004000 Intelligence (AI) & Semantics
BISAC COM060000 Internet / General
BISAC COM039000 Management Information Systems
BISAC COM077000 Mathematical & Statistical Software
BISAC COM051000 Programming / General
BISAC COM051300 Programming / Algorithms
BISAC COM012040 Programming / Games
BISAC COM060170 Web / Content Management Systems
In order to compare the defensive effectiveness of football teams using tactical systems with different quantitative composition of the defensive line, the defensive effectiveness of teams from five European leagues and the Russian Premier League for 8 full seasons 2014-2022 was compared on a number of indicators (goals conceded, shots allowed on their own goal, expected goals conceded) based on electronic data resource understat.com. According to statistical analysis, tactical systems with two central defenders are more defensively effective than tactical systems with three central defenders, according to the xGa indicator (expected goals conceded). Significant differences in xGa were obtained for the English Premier League, the Spanish La Liga; differences at the trend level in xGa were obtained for the French Ligue 1 and all the championships considered in general. Further research is needed, taking into account not only the type of tactical system of the team, but also the type of the opposing team and other factors affecting the defensive effectiveness of the team.
football team, tactics, defensive efficiency, tactical system
1. Timofeev A.I. On the problem of organizing the game in defense with various tactical schemes in football. Actual problems of theory and practice of physical culture, sports and tourism: Materials of the VII All-Russian scientific and practical conference of young scientists, graduate students, undergraduates and students with international participation, Kazan, April 26, 2019, Volume 2, Kazan: Volga Region State Academy of Physical Culture, Sports and Tourism, 2019. – pp. 332-333 (in Russian). EDN: VUHCCE
2. Shved M.A. Modern features of tactical structures and principles of professional teams / League of Researchers of the Moscow State Pedagogical University: collection of articles of the student open conference. At 4 t., Moscow, November 21-25, 2022. Volume 1, Moscow: Moscow City Pedagogical University, 2022, pp. 149-156 (in Russian). EDN: KIZIPR
3. Memmert D., Raabe D., Schwab S., Rein R. A tactical comparison of the 4-2-3-1 and 3-5-2 formation in soccer: A theory-oriented, experimental approach based on positional data in an 11 vs. 11 game set-up. PLoS One, 2019, 14 (1), Article e0210191. DOI: https://doi.org/10.1371/journal.pone.0210191
4. Forcher L., Forcher L., Waesche H., Jekauc D., Woll A., Altmann S. The Influence of Tactical Formation on Physical and Technical Match Performance in Male Soccer: A Systematic Review. International Journal of Sports Science and Coaching, 2022, 18 (5), pp. 1820-1849. DOI: https://doi.org/10.1371/journal.pone.0265501
5. Zhukov M.N., Osetrov I.A., Mikhailov P.V., Alayev S.V. Statistical methods for competition activity evaluation in football. Vestnik sportivnoy nauki [Sports Science Bulletin], 2012, 4, pp. 6-9 (in Russian). EDN: RRTXSZ
6. Titovets S.V., Labzo S.A. Modern technologies for determining the effectiveness of competitive activity in football. Promising directions in the field of physical culture, sports and tourism: Materials of the VIII All-Russian scientific and practical conference with international participation, Nizhnevartovsk, March 23-24, 2018. Nizhnevartovsk: Nizhnevartovsk State University, 2018, pp. 530-532 (in Russian). EDN: XYWRAl
7. Moldagereev M.M. X-guide to football analytics. Vestnik Moskovskoy mezhdunarodnoy akademii [Bulletin of the Moscow International Academy], 2019 (2), pp. 54-63 (in Russian). EDN: MGGFRM
8. Mead J., O’Hare A., McMenemy P. Expected goals in football: Improving model performance and demonstrating value. PLoS One, 2023, 18 (4), Article e0282295. DOI: https://doi.org/10.1371/journal.pone.0282295
9. Umami I., Gautama D.H., Hatta H.R. Implementing the Expected Goal (xG) model to predict scores in soccer matches. International Journal of Informatics and Information System, 2021, 4 (1), pp. 38-54. DOI: https://doi.org/10.47738/ijiis.v4i1.76
