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Yayın Prediction of final football league standings by dynamic frontier estimation(Işık Üniversitesi, 2017-07-13) Yılmaz, Melike; Atan, Sabri Tankut; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans ProgramıData Envelopment Analysis (DEA) is a commonly used method for efficiency assessment of teams in many sports including football. In this work, we investigate how estimations of final league standings with DEA efficiency measures evolve as the season progresses using Turkish first division football league data for five seasons. After the conclusion of each week, a DEA analysis is run using available data until then, and computed efficiencies are used to estimate the final table standings. While the estimates fluctuate early in the season, they tend to stabilize after several weeks. Tracking the weekly progress of the results may give the teams a chance to finish in a better position by focusing on their inefficiencies. Furthermore, the choice of the DEA linear programming model makes a difference in the quality of the results. Estimations are improved by using a model that incorporates expert knowledge about football.Yayın Dynamic frontier estimation for monitoring team performances: A case on Turkish first division football league(Emerald Group Publishing Limited, 2019-06-10) Yılmaz, Melike; Aksezer, Sezgin Çağlar; Atan, Sabri TankutPurpose: This paper aims to investigate how predictions of football league standings and efficiency measures of teams, obtained through frontier estimation technique, evolve compared to actual results. Design/methodology/approach: The study is based on data from the Turkish first division football league. Historical data for five seasons, from 2011 to 2016, are used to compare weekly estimates to de facto results. Data envelopment analysis efficiency measures are used to estimate team performances. After each week, a data envelopment analysis is run using available data until then, and final team standings are estimated via computed efficiencies. Estimations are improved by using a data envelopment analysis model that incorporates expert knowledge about football. Findings: Results indicate that deductions can be made about the league’s future progress. Model incorporating expert knowledge tends to estimate the performance better. Although the prediction accuracy starts out low in early stages, it improves as the season advances. Scatter of individual teams’ performances show fluxional behaviour, which attracts studying the impact of uncontrollable factors such as refereeing. Originality/value: While all previous studies focus on season performance, this study handles the problem as a combination of weekly performance and how it converges to reality. By tracking weekly performance, managers get a chance to confront their weak performance indicators and achieve higher ranking by improving on these inefficiencies.
