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Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. (1402). Estimating Iranian Professional Football Players' Prices A Neural Networks Approach. جلوه هنر, 3(3), 13-28. doi: 10.22051/sbj.2023.42806.1069
Mehdi Salimi; Mohsen Tayebi; Amir Hossein Labbaf. "Estimating Iranian Professional Football Players' Prices A Neural Networks Approach". جلوه هنر, 3, 3, 1402, 13-28. doi: 10.22051/sbj.2023.42806.1069
Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. (1402). 'Estimating Iranian Professional Football Players' Prices A Neural Networks Approach', جلوه هنر, 3(3), pp. 13-28. doi: 10.22051/sbj.2023.42806.1069
Salimi, Mehdi, Tayebi, Mohsen, Labbaf, Amir Hossein. Estimating Iranian Professional Football Players' Prices A Neural Networks Approach. جلوه هنر, 1402; 3(3): 13-28. doi: 10.22051/sbj.2023.42806.1069

Estimating Iranian Professional Football Players' Prices A Neural Networks Approach

Sports Business Journal
مقاله 1، دوره 3، شماره 3 - شماره پیاپی 7، آذر 2023، صفحه 13-28    اصل مقاله (1.27 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22051/sbj.2023.42806.1069
نویسندگان
Mehdi Salimi* 1؛ Mohsen Tayebi2؛ Amir Hossein Labbaf3
1Associated Professor in Sport Management, Sport sciences Faculty, University of Isfahan, Isfahan, Iran.
2PhD in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
3MSc in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran.
چکیده
Purpose: The study aimed to estimate the price of Iranian professional football league players.
Methodology: The research method was mixed exploratory designs, qualitative and quantitative methods. The research participants in the qualitative section included managers, club coaches, and experts familiar with the football players' market. Therefore, fourteen participants were selected by snowball until we reached saturation. The research tool in the qualitative section included in-depth interviews, so the reliability of which the re-test method was 81%. In the quantitative section, the statistical population had all the football players in the Iranian Football Professional League during 2016-2020, and random sampling was done. So, 863 players were selected to use their data for analysis. The quantitative methods were also collected from valid sites and the Iranian Football League Organization. The model was also designed through radial neural networks using software SPSS and R.
Findings: Qualitative section showed that the player's performance, personal characteristics and abilities, club characteristics, and bubble-creating factors are influential in determining the price of football players. In the quantitative section, a model with three hidden layers was designed, which had a nominal error rate in predicting the price of players.
Originality: Today, one of the main problems in the field of transfer in football leagues is the small number of appropriate criteria for pricing players. This study aims to estimate the price of professional football players using artificial neural networks.
کلیدواژه‌ها
Football Clubs؛ Players Market؛ Players' Value؛ Pricing؛ Players Transfer
عنوان مقاله [English]
برآورد قیمت بازیکنان حرفه‌ای فوتبال ایران برمبنای استفاده از شبکه‌های عصبی
نویسندگان [English]
مهدی سلیمی1؛ محسن طیبی2؛ امیرحسین لباف3
1دانشیار مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
2دکتری گروه مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
3کارشناس ارشد مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران.
چکیده [English]
هدف: هدف از انجام این پژوهش برآورد قیمت بازیکنان لیگ برتر فوتبال ایران بود.
روش: روش تحقیق، آمیخته اکتشافی و ترکیبی از روش‌های کیفی و کمی بود. جامعه آماری پژوهش در بخش کیفی شامل مدیران، مربیان باشگاه‌ها و کارشناسان آشنا به حوزه بازار بازیکنان  فوتبال بودند که چهارده نفر از آنها به‌روش گلوله برفی تا رسیدن به اشباع نظری انتخاب شدند. ابزار تحقیق در بخش کیفی شامل مصاحبه عمیق بود که پایایی آن با روش باز‌آزمایی 81 درصد محاسبه شد. در بخش کمی، جامعه آماری کلیه بازیکنان لیگ حرفه‌ای فوتبال ایران طی سال‌های 99-1395 بودند که با روش نمونه‌گیری در دسترس، ۸۶۳ بازیکن انتخاب شدند. داده‌ها از سایت‌های معتبر و سازمان لیگ فوتبال ایران جمع آوری شد. همچنین این مدل از طریق شبکه‌های عصبی شعاعی با استفاده از نرم‌افزار SPSS و R طراحی شد.
یافته‌ها: بخش کیفی نشان داد که عملکرد بازیکن، ویژگی‌ها و توانایی‌های فردی، ویژگی‌های باشگاهی و عوامل حباب‌ساز در تعیین قیمت بازیکنان فوتبال مؤثر است. در قسمت کمی مدلی با سه لایه مخفی طراحی شد که کمترین میزان خطا را در پیش بینی قیمت بازیکنان داشت.
اصالت و ابتکار مقاله: امروزه یکی از مشکلات اساسی در زمینه نقل و انتقالات در لیگ های فوتبال کم بودن معیارهای مناسب برای قیمت گذاری بازیکنان است. هدف از این مطالعه برآورد قیمت بازیکنان حرفه ای فوتبال با استفاده از شبکه‌های عصبی مصنوعی است.
کلیدواژه‌ها [English]
ارزش بازیکنان, باشگاه‌های فوتبال, بازار بازیکنان, قیمت گذاری, نقل‌و‌انتقال بازیکنان
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آمار
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