Xing Player

[Rozella Dibley]

Gordonfounded Xing on the basis of a simple JPEG decoding library that he had developed. It attracted the attention of Chris Eddy, who had developed a technique for processing Discrete cosine transforms (DCT) efficiently through software. Eddy's technique helped create the first Xing MPEG video player, a very simple MS-DOS app that could play an I-frame-only MPEG video stream encoded with constant quantization, at 160x120 resolution.

Over the next years, Xing expanded in several directions: Windows support for the XingMPEG player, a software MPEG audio decoder, a real-time ISA 160x120 MPEG capture board (XingIt!), a JPEG management system (Picture Prowler), and networking. Xing released a handful of network products before StreamWorks, the first streaming audio and video system for the Internet, with support for both live and pre-encoded sources. RealVideo appeared in 1997 (just before StreamWorks), but at the time, the company behind the technology (Progressive Networks) had only published RealAudio and its flagship technology was primary for broadcasting audio.

After the launch of StreamWorks, the company raised $5M in venture capital, but Progressive Networks (which was renamed "RealNetworks") raised considerably more in its initial public offering and acquired many of Xing's competitors (e.g. Vivo Software). Despite that, Xing experienced a period of expansion through its "Audio Catalyst" MP3 software and "MP3 Grabber".

In 1998, Xing partnered with SimplyTV to launch a service to offer near-broadcast quality video on demand. This service would require a 200 kilobits/s broadband connection, which was not popular at that time. Forrester Research and RealNetwork were skeptical about its success.[1]

XingMPEG Player is a versatile media player designed to handle MPEG files with ease. This software offers a user-friendly interface combined with essential features for a seamless media playback experience. Whether you are watching videos or listening to audio files, XingMPEG Player provides a reliable solution for your multimedia needs.

Copyright: 2016 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All player transfer records and league game results are available from the online football database IFFHS CWR points are available from Harvard Dataverse Repository. DOI:

Many natural and man-made systems composed of connected components can be modeled by networks, whose properties are proven to be associated with the functionalities of the systems or their components. Scale-free structure is a common macroscopic property found in social networks. It has been shown that this property could be the cause of the unpredictability of epidemic spreading [13]. Degree-degree mixing is another macroscopic measure which affects the ability of an evolving system to achieve consensus [14]. Microscopic network properties refer to the topological properties of individual nodes and edges and are widely used to measure the functionalities of system components. They were used successfully to characterize the importance of web pages [15], to identify influential spreaders in social networks [16], to find drug targets [17], to characterize dynamic behavior in gas-liquid two-phase flow [18, 19] and to explain the roles taken by research institutions in applying for science funding [20].

Professional football is famous for its scarcity of talents. Therefore, wealthy clubs are willing to pay millions of euros in exchange for one qualified player. Moreover, the average annual expense, revenue and volume, i.e., the sum of expense and revenue as a measure of the flow of transfer fees through a club, of clubs show an increasing trend from 2011 to 2015 (Fig 1). The top 10 clubs in terms of annual expense, revenue and volume are summarized in Table A in S1 File. The average expense of the clubs was over 5 million euros and the average transfer fee volume exceeded 10 million euros.

However, the financial resources of the elite clubs are not evenly distributed. Fig 2A shows the distribution of average annual expenses of the clubs in 5 years. The distribution is heavy-tailed, in which top spending clubs have the ability of raising an amount of money 10 times larger than most of the clubs. Fig 2B shows the proportion of clubs and their cumulative expenses in the transfer market in a percent scale. It is shown that 80% of the total transfer fee is spent by less than 20% of the clubs. Fig 2C shows the Gini coefficient [21] of transfer expenses of all clubs from 2011 to 2015. An increasing trend indicates that the inequality in financial ability of professional football clubs is gradually magnifying. Overall, we have observed substantial and increasing inequality in the financial ability of professional clubs.

A: Distribution of annual club expenses in euros. B: Cumulative distribution of annual club expenses. Clubs are sorted in descending order by the transfer expense, and the percentage of total expense was plotted against the corresponding percentage of clubs. The dashed line denotes the situation where all clubs spend the same amount of money in the player transfer market. C: Gini coefficient of annual transfer expense, revenue and volume of the clubs.

A: The in-degree and out-degree distributions of the network. B: The in-degree/out-degree relation for each club in the transfer network. C: The distribution of excess degree kex of all nodes in the transfer network. The standard deviation of kex is 5.7.

In this section, we will explore the relationship between the functionalities of a club and its network properties. The ultimate measure of the success of a commercial organization is its profitability, which also applies to a professional football club. Generally speaking, clubs with the highest achievements in prestigious competitions are also the ones that generate the largest revenue from various commercial activities [1]. Meanwhile, a club could also profit directly from the transfer market, by receiving more compensation from the players transferred out than it pays to acquire new players. Therefore, the club functionalities can be described either by its match performance or its transfer profit. Match performance includes the domestic and international match results. We quantify domestic performance of a club by the average game points in its domestic league matches from 2011 to 2015. On the other hand, the five year aggregate IFFHS Club World Ranking (CWR) point is employed to quantify the overall performance of a club in both domestic and international competition [25]. Table B in S1 File shows the top 10 clubs in terms of their match performance. Fig 5 shows that although the distributions of average league game points and aggregate IFFHS CWR points exhibit different characteristics, the two performance measures are positively correlated. The ability of profiting from player transfers are defined by two measures, i.e., the average annual transfer balance and the cumulative price overflow from player transits. If a player has transferred from club A to club B then to club C, we define that the player has transited through club B. The price overflow of this player in club B is the difference between the transfer fees payed by club C to club B and by club B to club A. Table C in S1 File shows the top 10 clubs in terms of profitability in the transfer market. Table 1 shows that the two categories of club functionalities are generally weakly or not correlated. Overall, match performance and transfer profitability can be considered as independent indicators of the functionalities of a professional football club.

International sports labor migration shows characteristics different from those of domestic sports labor movements [11, 12]. In this paper, the football player transfer network can also be separated into two subnetworks accordingly. The domestic transfer network contains only transfers within a same league and the international transfer network contains only international transfers between different leagues. Table 4 shows the correlation of network properties and club functionalities in both international and domestic transfer networks. It is shown that the node properties taking account of global network connections, i.e., eigenvector centrality, PageRank centrality, betweenness centrality and closeness centrality, are better indicators of both domestic and international match performance, while the node property taking account of local network connections, i.e., effective size, in the domestic transfer network is a better indicator of match performance.

The movement of a player can take two different forms, i.e., transfer and loan. In a transfer, the player terminates his contract with the former club and signs a contract with the new club, while in a loan, the player is allowed to temporarily play for a club other than the one he is currently contracted to. The player loan network can be constructed similarly to the player transfer network, in which the nodes are clubs and directed edges are player loans. However, loan network and transfer network show different characteristics. In the 24 leagues, only 394 out of 410 clubs are involved in player loans. The number of edges is 2509, with totally 3586 player loan records. All the nodes in the loan network are weakly connected but the network has 129 strongly connected components. The clustering coefficient in the loan network is 0.11 compared to 0.21 in transfer network. The average path length is 4.1, compared to 2.8 in the transfer network. Therefore, the loan network is a sparsely and weakly connected network with multiple weakly disconnected subnetworks. Moreover, the properties of nodes in the two networks are loosely related. Although the correlations of eigenvector centrality, effective size and betweenness centrality of the same nodes in the two networks are positive and strong (0.46, 0.75 and 0.51, respectively), those of the PageRank centrality and closeness centrality of the same nodes in both networks are weak (-0.02 and 0.08, respectively).

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