A Dimension Reduction Approach to Player Rankings in European Football

Player performance evaluation is a challenging problem with multiple dimensions. Football (soccer) is the largest sports industry in terms of monetary value and it is paramount that teams can assess the performance of players for both financial and operational reasons. However, this is a difficult task, not only because performance differs from position to position, but also it is based on competition, time played and team play-styles. Because of this, raw player statistics are not comparable across players and must be processed to facilitate a fair performance evaluation. Furthermore, teams may have different requirements and a generic player performance evaluation does not directly serve the particular expectations of different clubs. In this study, we provide a generic framework for estimating player performance and performing player-fit-to-criteria assessment, under different objectives, for left and right backs from competitions worldwide. The results show that the players who have ranked high have increased their transfer values and they have moved to suitable teams. Global nature of the proposed methodology expands the analyzed player pool, facilitating the search for outstanding players from all available competitions

SPOR ANALİTİĞİ İÇİN VERİ GÜDÜMLÜ PERFORMANS ANALİZ ÇERÇEVESİ

Performance evaluation is a challenging, multidimensional and multi-criteria assessment problem. One application area is the player transfers in football (soccer), where player performance must be evaluated in-line with their responsibilities on the field. In this area of study, raw player performance statistics are not representative because of the external factors impacting the performance such as time-played, injuries, competition difficulty and characteristics, strength of the opponent, impact of actions in the game as well as the positions played. In addition, transfer market has unique financial dynamics in terms of transfer fees and player valuation. Some of the factors that affect transfer fees are athletic performance, properties of clubs and competitions and player popularity. The rich set of factors makes modelling transfer fees a challenging machine learning problem. This thesis provides a dynamic, context-dependent, probabilistic and hierarchical bottom-up approach for evaluating performance under uncertainty for custom requirements. Furthermore, the proposed framework links the performance metrics and various data sources to model transfer fees using machine learning ensembling methods. The proposed framework is generic and it can be adapted to other team sports.Performans değerlendirmesi çok boyutlu ve çok kriterli olmasının yanı sıra zorlu bir değerlendirme problemdir. Bu problemin uygulama alanlarından birisi olan futbolda oyuncu performansları oyuncuların sahadaki görevlerini ve istatistiklerini etkileyen diğer faktörleri (oyun süresi, rakibin gücü, müsabakanın stili) göz önüne alarak birçok değişken üzerinden değerlendirilmelidir. Bu alanda, oyuncuların saf istatistikleri oyuncu performansına yönelik bilgi verme konusunda yetersiz kalmaktadır. Bunun sebebi oyuncunun pozisyonunun yansıra, bu istatistikleri etkileyen birçok dış etmen bulunmasıdır. Bu etmenlerden bazıları, oyun suresi, sakatlıklar, müsabaka zorluğu ve karakteristikleri, rakibin gücü, oyundaki aksiyonların önemi, oyuncu pozisyonu vs. gibi faktörlerdir. Buna ek olarak futbolda oyuncu transferleri transfer ücretleri ve oyuncu değerlemesi bakımından genelden farklı finansal dinamiklere sahiptir. Oyuncuların transfer ücretleri oyuncu performansına ek olarak takım ve müsabaka özellikleri ile oyuncunun popülaritesi gibi faktörler bu dinamikleri etkileyen etkenlerden bazılarıdır. Bu çoklu etken seti, transfer ücretlerinin tespitini zorlayıcı bir makine öğrenmesi problemine dönüştürmektedir. Bu tez dinamik, olasılıksal, bağlama bağlı, hiyerarşik ve tabandan-tepeye bir yaklaşımla bilinmezlik altında performans analizini veri-tabanlı, gereksinim değişimlerine uyum sağlayan uyarlanır bir çerçeveye oturtmaktadır. Buna ek olarak, sunulan çerçeve performans değerlendirmeleri ve farklı veri kaynaklarını birleştirerek transfer ücretlerini makine öğrenmesi topluluk yöntemleriyle modellemektedir. Önerilen çerçeve genellenebilir olup diğer takım sporlarına da uyarlanabilir niteliktedir.Ph.D. - Doctoral Progra

The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples

Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These examples are generated by adding non-random noise to the testing samples in order to make classifier misclassify the given data. Adversarial attacks use these intentionally generated examples and they pose a security risk to the machine learning based systems. To be immune to such attacks, it is desirable to have a pre-processing mechanism which removes these effects causing misclassification while keeping the content of the image. JPEG and JPEG2000 are well-known image compression techniques which suppress the high-frequency content taking the human visual system into account. JPEG has been also shown to be an effective method for reducing adversarial noise. In this paper, we propose applying JPEG2000 compression as an alternative and systematically compare the classification performance of adversarial images compressed using JPEG and JPEG2000 at different target PSNR values and maximum compression levels. Our experiments show that JPEG2000 is more effective in reducing adversarial noise as it allows higher compression rates with less distortion and it does not introduce blocking artifacts