Search

Background Advanced footwear technology improves average running economy compared with racing flats in sub-elite athletes. However, not all athletes benefit as performance changes vary from a 10% drawback to a 14% improvement. The main beneficiaries from such technologies, world-class athletes, have only been analyzed using race times. Objective The aim of this study was to measure running economy on a laboratory treadmill in advanced footwear technology compared to a traditional racing flat in world-class Kenyan (mean half-marathon time: 59:30 min:s) versus European amateur runners. Methods Seven world-class Kenyan and seven amateur European male runners completed a maximal oxygen uptake assessment and submaximal steady-state running economy trials in three different models of advanced footwear technology and a racing flat. To confirm our results and better understand the overall effect of new technology in running shoes, we conducted a systematic search and meta-analysis. Results Laboratory results revealed large variability in both world-class Kenyan road runners, which ranged from a 11.3% drawback to a 11.4% benefit, and amateur Europeans, which ranged from a 9.7% benefit to a 1.1% drawback in running economy of advanced footwear technology compared to a flat. The post-hoc meta-analysis revealed an overall significant medium benefit of advanced footwear technology on running economy compared with traditional flats. Conclusions Variability of advanced footwear technology performance appears in both world-class and amateur runners, suggesting further testing should examine such variability to ensure validity of results and explain the cause as a more personalized approach to shoe selection might be necessary for optimal benefit.

Introduction: In soccer, the video assistant referee (VAR), which refers to a match official who reviews video footage and communicates with the head referee, has relatively recently been introduced to the game. While previous research has examined the descriptive statistics of the impact of VAR on soccer, so far, no research has examined fans’ reaction to its implementation. One way to study how fans feel about its introduction to soccer is to use Twitter analysis collecting input from many different fans all around the world. Therefore, this report examines the use of Twitter as a technology to obtain data from fans in real time coordinated through various time zones in response to live events as its happening during a broadcasted sports event. In more detail, the aim of this study is to analyze how sports fans on Twitter react during soccer games in response to VAR used in the English Premier League via sentiment analysis to examine the emotions demonstrated in the published tweets. Methods: For this study, the Twitter data, made up of 643,251 tweets posted during the examined games of the 2019-2020 English Premier League season, was collected and analyzed. Two different text mining techniques were applied for both event detection and sentiment analysis. The first method consisted of a frequency analysis of the content of tweets to determine if and when the VAR was involved and a lexicon-based system to calculate the corresponding sentiment. The second method attempted to do both event detection of VAR incidents and sentiment analysis with the help of a supervised machine learning algorithm. Since the machine learning method performed better, this was used to categorize or label each tweet accordingly. Results: The results showed VAR tweets have a significantly lower sentiment value than the collected non-VAR tweets. Furthermore, when games were split into quantiles and examined, it was found that VAR tweets were overrepresented in quantiles with low average sent

Introduction: In soccer, the video assistant referee (VAR), which refers to a match official who reviews video footage and communicates with the head referee, has relatively recently been introduced to the game. While previous research has examined the descriptive statistics of the impact of VAR on soccer, so far, no research has examined fans’ reaction to its implementation. One way to study how fans feel about its introduction to soccer is to use Twitter analysis collecting input from many different fans all around the world. Therefore, this report examines the use of Twitter as a technology to obtain data from fans in real time coordinated through various time zones in response to live events as its happening during a broadcasted sports event. In more detail, the aim of this study is to analyze how sports fans on Twitter react during soccer games in response to VAR used in the English Premier League via sentiment analysis to examine the emotions demonstrated in the published tweets. Methods: For this study, the Twitter data, made up of 643,251 tweets posted during the examined games of the 2019-2020 English Premier League season, was collected and analyzed. Two different text mining techniques were applied for both event detection and sentiment analysis. The first method consisted of a frequency analysis of the content of tweets to determine if and when the VAR was involved and a lexicon-based system to calculate the corresponding sentiment. The second method attempted to do both event detection of VAR incidents and sentiment analysis with the help of a supervised machine learning algorithm. Since the machine learning method performed better, this was used to categorize or label each tweet accordingly. Results: The results showed VAR tweets have a significantly lower sentiment value than the collected non-VAR tweets. Furthermore, when games were split into quantiles and examined, it was found that VAR tweets were overrepresented in quantiles with low average sent