The National Football League will use Amazon Web Services as its standard machine learning and analytics provider to boost the performance of the league's player statistics platform. The announcement is just the latest customer win AWS has touted at its re:Invent conference this week, following similar cloud deals with Time Warner and Intuit. AWS also announced new cloud deals with the the Walt Disney Company and Expedia on Wednesday. Amazon said the NFL will use AWS' machine learning and data analytics services to improve the statistical capabilities and performance of the league's Next Gen Stats platform, which basically tags up players and tracks new stats like speed, rushes and passes. AWS will also become an "Official Technology Provider" of the NFL.
Oceania's esports industry just took a huge step forward. Australia has opened its very first Esports High Performance Centre in Sydney, a new home base for Oceania's leading League of Legends team, the LG Dire Wolves. Established in Sydney's city sporting precinct, sitting in the side of Allianz Stadium looking towards the Sydney Cricket Ground, the facility aims to drive growth and development in Australia's esports industry. The facility will be stocked with new technology in eye-tracking and performance analysis, as part of a partnership with the University of Technology Sydney. The Dire Wolves, alongside Australia's leading mixed-gender Counter-Strike team, Supa-Stellar, will train and develop surrounded by some of Sydney's traditional sports teams, also residents of the precinct, including the Sydney Swans, Sydney Sixers, Sydney Roosters, Sydney FC, Cricket NSW, and the NSW Waratahs.
We all know the robots are coming. That probably inspires some complicated feelings. So, it's comforting when a three-year development effort to make a robot that can set a speed record results in a human victory... by a wide margin. Yamaha and robotics developer SRI have been working on a humanoid that can ride an unmodified motorcycle. The goal was to beat the lap times of one of the most successful motorcycle racers of all time, Valentino Rossi.
Infosys, a global leader in technology services and consulting, is aiming to reinvent the way people consume sport using extensive player data. The Indian firm, which had revenues of $9.5 billion in its last financial year, demonstrated its'Infosys Information Platform (IIP)' during the recent ATP Tennis tournament in London, of which it was a headline sponsor. Speaking to Access AI, the firm's head of energy and services for Europe Mohamed Anis, who joined in 2000, said Infosys uses machine learning to analyse historical data on player performance, which in turn is able to predict behaviour, shot selection, and even a probabilistic outcome of the match itself. Anis (pictured) said the data is delivered in real time and can be used to help spectators view the game/match on an entirely different level – comparable to that of the coach. "Tennis has been around for a very long time," explained Anis.
For data prone to noise and anomalies (most data, if we're being honest), a Long Short Term Memory network (LSTM), preserves the long term memory capabilities of the RNN, while filtering out irrelevant data points that are not part of the pattern. Mechanically speaking, the LSTM adds an extra operation to nodes on the map, the outcome of which determines whether the data point will be remembered as part of a potential pattern, used to update the weight matrix, or forgotten and cast aside as noise. For example, to train the HR network, the first input to the network is the number of homers the player hit in his first game, the second input to the network is the number the player hit in his second game and so on. With a network to train and data to train it with, we can now look at a test case where the network attempted to learn Manny Machado's performance patterns and then made some predictions.
After hours and days of trial and error (and error and trial again) I feel confident enough to release the culmination of my two previous articles (part 1 & part 2) -- a Machine Learning / Artificial Intelligence fantasy football 2017 cheat sheet. Sometimes the best applications of artificial intelligence and machine learning simply uncover new insights or confirm insights already in hand. So we can infer from this that overall previous year rank is a fairly good indicator of where their rank will be the subsequent year but if we're looking to identify top point scorers or exclude the bottom rung scorers this may not be a great help. It wants us to exclude Quarterbacks to get there and anyone who has the name "ronnie":) Quarterbacks are few and far between and as we traverse the right side of the tree (the high rank number -- lower performance rank) quarterbacks are at the top.
Methods 2127 observations of competition best performances and mass spectrometry-measured serum androgen concentrations, obtained during the 2011 and 2013 International Association of Athletics Federations World Championships, were analysed in male and female elite track and field athletes. Results The type of athletic event did not influence fT concentration among elite women, whereas male sprinters showed higher values for fT than male athletes in other events. When compared with the lowest female fT tertile, women with the highest fT tertile performed significantly (p 0.05) better in 400 m, 400 m hurdles, 800 m, hammer throw, and pole vault with margins of 2.73%, 2.78%, 1.78%, 4.53%, and 2.94%, respectively. The paper correlates testosterone levels in athletes with their performance at a recent World Championship and makes causal claims about the affects of testosterone on female performance.
Computer scientists at University of Southampton are testing an artificially intelligent tool for predicting Premier League football results. The machine learning algorithm has managed to beat BBC football commentator Mark Lawrenson's predictions for two seasons in a row. Fantasy football is a game in which users assemble an imaginary team of real-life footballers and score points based on the players' actual statistical performance during the season. Fantasy managers can compete with Squadguru's AI in the Challenge the Squadguru league in the free Fantasy Premier League salary cap game by entering league code 2917382-677658.
I watched England's final Test against South Africa and really felt for Keaton Jennings. The lack of backing I received was because of my poor performances against New Zealand, but I reckon if I could have been allowed to move through that challenging learning experience of my first real dip in form as an England player, I would have been better for it. Moving through the difficult learning process of being a newcomer to Test cricket is not straightforward and, should a player receive the full support of selection when experiencing a dip in form, it offers a healthy return on the investment of your initial selection. In reality, as much as performance matters so much to you as an individual, there is so much more that matters than scoring runs and the England cricket team winning matches and series.
Real-time insights generated from connected stadiums and even connected athletes are changing coaching styles, training approaches and the way fans engage with what they are seeing. In preparation for last year's Olympic Games, the IBM jStart team designed a digital training solution for the USA Cycling team. The solution pulled together all data sources that were part of an IoT solution into a single dashboard, giving the team real-time, actionable insights that helped take them from fifth place in world competition to a gold medal in the 2016 World Championship and silver in the 2016 Olympics. This is just a quick glimpse into how the sporting IoT can enhance fan engagement and provide athletes and coaches with real-time, actionable insights from data.