نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
2 دانشیار مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
3 استادیار مدیریت ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
4 استادیار بیومکانیک ورزشی، دانشکده علوم ورزشی، دانشگاه اصفهان، اصفهان، ایران
چکیده
هدف از انجام‌شدن این پژوهش، علاوه‌بر تخمین قیمت بازیکنان لیگ حرفه ای فوتبال ایران، مقایسه روش های رگرسیون و شبکه های عصبی در پیش بینی آن بود. روش انجام‌شدن پژوهش با استفاده از طرح های آمیخته اکتشافی بود که تلفیقی از روش های کیفی و کمی است. جامعه آماری پژوهش در بخش کیفی، مدیران، مربیان باشگاه ها و کارشناسان خبره و آشنا با حوزه خرید و فروش بازیکنان بودند که 14 نفر تا رسیدن به نقطه اشباع به روش گلوله‌برفی انتخاب شدند. در بخش کمی نیز جامعه آماری، همۀ فوتبالیست های لیگ حرفه ای فوتبال خلیج فارس در سالهای 2019-2018 بودند که 226 نفر از آن‌ها با استفاده از فرمول کوکران به‌عنوان نمونه با روش تصادفی طبقه ای انتخاب شدند. ابزار پژوهش در روش کیفی، مصاحبه عمیق بود که پایایی آن از طریق روش بازآزمون 81/0 محاسبه شد. داده های مورد نیاز برای تجزیه و تحلیل روش های کمی نیز از سایت های معتبر و سازمان لیگ فوتبال ایران جمع آوری شدند. از ضریب همبستگی پیرسون، رگرسیون خطی و شبکه های عصبی شعاعی نیز با بهره گیری از نرم افزارهای اس‌پی‌اس‌اس نسخة 21 و آر نسخه 3.6.2 برای پیش بینی و طراحی مدل استفاده شد. یافته های پژوهش در بخش کیفی حاکی از این بود که عملکرد بازیکن، ویژگی های شخصی، توانایی های آن‌ها، ویژگی های باشگاه و عوامل ایجادکننده حباب، در تعیین قیمت بازیکنان فوتبال تأثیر دارند. همچنین نتایج پژوهش نشان داد که با وجود مزایای روش های رگرسیون در پیش بینی مسائل، به‌علت چندوجهی و پیچیده‌بودن قیمت گذاری بازیکنان فوتبال، مدل های شبکه مصنوعی متغیرهای بیشتری را پوشش می‌دهد و کاراتر و دقیقتر خواهند بود.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Comparison of Linear Regression and Artificial Neural Network Methods for Estimating the Price of Iranian Professional Football Players
نویسندگان [English]
- mohsen tayebi 1
- Mohamad Soltan Hoseini 2
- Mahdi Salimi 3
- Shahram Lenjannezhadian 4
1 Ph.D. Student in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
2 Associated Professor in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
3 Assistant Professor in Sport Management, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
4 Assistant Professor in Sport Biomechanics, Faculty of Sport Sciences, University of Isfahan, Isfahan, Iran
چکیده [English]
The purpose of this study was to estimate the prices of Iranian professional soccer league players, as well as to compare the regression methods and neural networks in predict that. The research method was mixed exploratory designs, which is a combination of qualitative and quantitative methods. The statistical population of the research in the qualitative sector consisted of managers, coaches of clubs and experts familiar with the field of player buying and selling that fourteen people were selected by snowball method until reaching the saturation point. The statistical population in the quantitative section also included all footballers present in the Persian Gulf Football Professionals League in the years 2017-2018, which 226 of them were selected by Cochran formula and selected by stratified random sampling. The research instrument in qualitative method included in-depth interview with statistical sample whose reliability was calculated 81% through test-retest method. The data needed for quantitative analysis were also collected from reputable sites and the Iranian Football League. Pearson correlation coefficient, linear regression and radial neural networks were used to predict and design the model using SPSS software version 21 and R version 3.6.2. Qualitative research findings showed that player performance, personal characteristics, abilities, club characteristics and bubble-generating factors are effective in determining the price of football players. The results indicated that, despite the benefits of regression methods in predicting problems, because of the multifaceted and complex pricing of soccer players, artificial neural network models would cover more variables and be more efficient and accurate.
کلیدواژه‌ها [English]
- Pricing football players
- Persian Gulf League
- neural networks
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