Abstract:The paper proposes an alternative way to observe and extract the multiple matches games of sports, i.e.: tennis tournament in the Athlete's Historical Relative Performance Index and its representation as graph. The finding of the small world topology is elaborated along with further statistical patterns in the fashion of the weighted and directed network. The explanation of the sport tournament system as a highly optimized system is hypothetically proposed. Finally, some elaborations regarding to further direc… Show more
“…The power-law and the Lévy distributions are the two models performing better on the raw data, thus the considered network exhibits many properties typical of scalefree networks. Assuming the network as power-law, we can measure the scale-free parameter γ: it is found to be γ in ≈ 1.66 for indegrees, γ out ≈ 2.12 for out-degrees and γ undirect ≈ 1.38 for the undirect network, values which are consistent with the ones found in literature in [14] and [13].…”
Section: E Considerations On Network Naturesupporting
confidence: 87%
“…Most of the players have a small number of matches and then quit playing the major tournaments, on the contrary, there is a small group of top-players who perform many matches against weaker players and among themselves. This phenomenon is an observation of the rich get richer effect driven by the attractiveness of the high connected nodes as opponent for new-comers; an interpretation of the richness that the players achieve could be their gain of some sort of "experience" during the matches of their carreer, as already pointed out in [13].…”
Section: Hubsmentioning
confidence: 60%
“…In case of multiple links the weights are just summed. Similar representations were adopted in [14] considering data up to 2010, in [13] considering data between 90s and 00s of male and female matches of Grand Slams only with different weights function, and in [15] with data of top-100 players only and different weights function. The obtained graph is not symmetric, not even if the respective unweighted version is considered.…”
Section: Generation Of Dataset and Networkmentioning
confidence: 99%
“…In professional tennis as well, there are few studies examining how to map matches into complex networks and then developing new ranking methods alternative to the ATP (Association of Tennis Professionals) official one. The first work of this kind is represented by [13], where the authors explained the network generation and then they performed some simple analysis on single Grand Slams tournaments matches only (i.e. four tournaments each year: respectively Australian Open, Roland Garros, Wimbledon and U.S. Open).…”
Who are the most significant players in the history of men tennis? Is the official ATP ranking system fair in evaluating players scores? Which players deserved the most contemplation looking at their match records? Which players have never faced yet and are likely to play against in the future? Those are just some of the questions developed in this paper supported by data updated at April 2018 1 . In order to give an answer to the aforementioned questions, complex network science techniques have been applied to some representations of the network of men singles tennis matches. Additionally, a new predictive algorithm is proposed in order to forecast the winner of a match.
“…The power-law and the Lévy distributions are the two models performing better on the raw data, thus the considered network exhibits many properties typical of scalefree networks. Assuming the network as power-law, we can measure the scale-free parameter γ: it is found to be γ in ≈ 1.66 for indegrees, γ out ≈ 2.12 for out-degrees and γ undirect ≈ 1.38 for the undirect network, values which are consistent with the ones found in literature in [14] and [13].…”
Section: E Considerations On Network Naturesupporting
confidence: 87%
“…Most of the players have a small number of matches and then quit playing the major tournaments, on the contrary, there is a small group of top-players who perform many matches against weaker players and among themselves. This phenomenon is an observation of the rich get richer effect driven by the attractiveness of the high connected nodes as opponent for new-comers; an interpretation of the richness that the players achieve could be their gain of some sort of "experience" during the matches of their carreer, as already pointed out in [13].…”
Section: Hubsmentioning
confidence: 60%
“…In case of multiple links the weights are just summed. Similar representations were adopted in [14] considering data up to 2010, in [13] considering data between 90s and 00s of male and female matches of Grand Slams only with different weights function, and in [15] with data of top-100 players only and different weights function. The obtained graph is not symmetric, not even if the respective unweighted version is considered.…”
Section: Generation Of Dataset and Networkmentioning
confidence: 99%
“…In professional tennis as well, there are few studies examining how to map matches into complex networks and then developing new ranking methods alternative to the ATP (Association of Tennis Professionals) official one. The first work of this kind is represented by [13], where the authors explained the network generation and then they performed some simple analysis on single Grand Slams tournaments matches only (i.e. four tournaments each year: respectively Australian Open, Roland Garros, Wimbledon and U.S. Open).…”
Who are the most significant players in the history of men tennis? Is the official ATP ranking system fair in evaluating players scores? Which players deserved the most contemplation looking at their match records? Which players have never faced yet and are likely to play against in the future? Those are just some of the questions developed in this paper supported by data updated at April 2018 1 . In order to give an answer to the aforementioned questions, complex network science techniques have been applied to some representations of the network of men singles tennis matches. Additionally, a new predictive algorithm is proposed in order to forecast the winner of a match.
“…It was originally developed in computer science. It has been used in various fields of study including chemistry [4], medicine [5] and [6], sociology [7] and [8], finance [9], [10], [11], [12], and [13] transportation [14] and as well as sports [15] and [16].…”
Identifying the root causes of an out-of-control signal is a crucial part in statistical process control. In this paper we propose a network topology approach to do such analysis in multivariate process variability monitoring. A real example will be presented to illustrate the analysis of the proposed method.
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