The National Collegiate Athletic Association’s Basketball Tournament, branded the March Madness has been raising a social media storm through last month.

One startup (amongst many peers), has been providing tools to gauge and provide actionable data to marketers throughout the event.

Cross-channel analytics provider Origami Logic found that although Twitter had the most number of posts about the tournament (71%) against Facebook’s 17%, the latter garnered 60% engagement as compared to the former’s 13% and Instagram’s 27%.

“Instagram posted the highest rate of engagement among all channels studied by Origami, with 1.71 interactions per post. Facebook finished right behind with 1.67, and Twitter produced 0.88,” reports DM News.

One way this study helps marketers is by making it clear as to the calendar pattern to be followed for advertising. Origami Logic, the SaaS based startup with the innovative marketing intelligence platform has now taken things further and “integrated creative assets and campaign performance data” in its latest Origami Logic Marketing Intelligence Platform, it revealed Tuesday.

In essence, with the platform marketers will be able to connect visuals with metrics, enabling an exhaustive angle of campaign performance across marketing channels. The platform also allows addition of creative elements such as artwork, videos and photos.

“Marketers work best when they can make decisions based on qualitative and quantitative insight. Numbers are not enough; the creative elements and emotional connections with consumers are just as critical in maximizing the business impact of marketing,” notes the co-founder and CEO of Origami Logic, Opher Kahane.

“The creative components driving consumer engagement and action are an important part of that story; the addition of creative-level insights helps marketers make better decisions,” he added.

The Origami Logic Marketing Intelligence Platform now automatically generates Content Cards, bringing together ad creatives, videos, social media posts and other visual assets with their individual performance data in real time.

Origami Logic’s clients include Visa, JCPenney, Cisco, Intel and Aeropostale.

Photo credit: M31. / Foter / CC BY

In lieu of the upcoming NCAA tournament office pools and pundit prognosis are starting to gain momentum. For the last few years, however the stakes are high; and the amount of predictive data available gets higher. Betting has been taken to an all new level with big data scientists using analytics to predict bids and sponsor competitions.

With millions fillings out brackets, coin flipping is no longer an option. The odds of making the correct predictions are exponentially increased with the correct analysis of data collected throughout the seaon, as well as previous years’ data which includes player statistics, tournament seeds, geographical factors and social media.

Kaggle HQ has yet again taken out their annual March Data Madness competition pits you against the millions of sports fans and office-pool bandwagoners who are hoping to win big by correctly predicting the outcome of the men’s NCAA basketball tournament. Presented by HP Software’s industry leading Big Data group and the HP Haven Big Data platform, this competition will test how well predictions based on data stack up against a (jump) shot in the dark.

The competition doesn’t just hone one’s analytical skills; Kaggle is also offering a hefty $15,000 cash prize to the team with the closest prediction. The cash prize and opportunity for glory is drawing an increasingly large field of competitors: 114 teams, 139 individual players, and 649 entries.

“The response is healthy so far and I’d expect many more to jump in, now that there’s a prize on offer,” says Will Cukierski, a Kaggle data scientist. “The make-or-break on our expectations will happen after the 2014 madness starts. We’re really excited to see if people can beat the traditional rankings and experts and seed-based predictions.”

In stage one of this two-stage competition, participants will build and test their models against the previous four tournaments. In the second stage, participants will predict the outcome of the 2015 tournament. While participation in the first stage is not necessary to enter the second, the first stage exists to incentivize model building and provide a means to score predictions. The real competition is forecasting the 2015 results, for which you’ll predict winning percentages for the likelihood of each possible matchup, not just a traditional bracket.

As well as sponsoring the event, HP are offering use of their Haven technology to fuel competitor’s algorithms. According to their blogpost:

You will have access to key HP Haven technologies, including HP Vertica Distributed R to accelerate your machine learning by running your R models across multiple nodes to vastly reduce execution time and analyze much larger data sets.

The competition started on Monday 2 February 2015 UTC and ends on Saturday 14 March 2015 UTC (40 total days).

This isn’t the first example of big data being used for prediction. More than a decade ago, professors Jay Coleman of the University of North Florida in Jacksonville, Allen Lynch of Mercer University in Macon, Georgia, and Mike DuMond of Charles River Associates and Florida State University in Tallahassee created the Dance Card  – a formula designed to predict which teams will receive at-large bids to the NCAA Tournament (aka the Big Dance). For the 2014 bids announced recently the dance card formula correctly predicted 35 of the 36 at-large bids. The model is a combined 108 of 110 over the last three years.

Big Data is also being used in a huge way by teams to improve performances. “Sports teams are using new analytical capabilities to improve their team personnel and on-court performance,” says Davis, vice president of Intel’s Data Center Group. “As an example, teams are using emerging technologies such as multi-view cameras that can measure the tendencies of players in very specific situations to improve performance.”

Full contest details can be found over at Kaggle.

Advanced data analytics has been used across the whole sporting spectrum to improve scouting, coaching and fan engagement. Baseball, football, and even ice hockey teams have got in on the act. The NBA themselves are no strangers to data analytics– but the latest tech in basketball talent scouting is a bit of an oddity.

In an attempt to pick up the team’s declining performance, the Milwaukee Bucks, hired facial coding expert Dan Hill last May. Dan’s job profile is to read facial expressions of prospective NBA players to pick out the ones with the right set of emotional attributes.

“We spend quite a bit of time evaluating the players as basketball players and analytically,” said David Morway, Milwaukee’s assistant general manager, explaining the context. “But the difficult piece of the puzzle is the psychological side of it, and not only psychological, character and personality issues, but also team chemistry issues.”

Psychologist Paul Ekman had developed the Facial Action Coding System (FACS) in the 70s which provided guidelines into reading a person’s emotional status through minute movements of 43 facial muscles, movements which reveal “intentions, decisions and actions.” Seven core emotions are identified: happiness, surprise, contempt, disgust, sadness, anger and fear.

As New York Times reports, Hill had spent 10 hours with Milwaukee’s team psychologist, Ramel Smith, watching video of various college prospects and picking apart the psyches of potential picks, prior to the 2014 draft.

Unsurprisingly, some have questioned the method and its efficiency. Martha Farah, a cognitive neuroscientist and director of the Center for Neuroscience & Society at the University of Pennsylvania, has her doubts about how well it works.

“It’s not easy to get good evidence, because a player’s performance and teamwork are complex outcomes, and the teams are not run like clinical trials, with coaches and managers blind to the facial coding findings and so forth,” she told NYTimes.

Read more here.

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(Image credit: V’ron, via Flickr)

This season sees Big Data hitting the basketball courts, as every NBA team has access to intricate data which tells them the position of the ball and every player, for every second, in every game of the season. The company making it happen are SportVU, founded in 2005 by technicians working on optical missile tracking systems for the Israeli government. When Stats took over SportVU in 2008, they adapted the missile technology to track movements around the basketball court.

“All sports are at that point where, like in a lot of businesses, they’re using a lot of (data) to make better decisions,” said Brian Kopp, senior vice president for sports solutions at Stats, a Chicago-based sports data company. “Basketball is pushing the front edge of that conversation.”

The system starts by recording the game from cameras in the rafters. It captures data 25 times a second on the two-dimensional location of each player, and three-dimensional location of the ball. The data is then uploaded to SportVU’s servers and stored in an Oracle database. Reports claim coaches have access to data as little as 60 seconds after the fact.

Although coaches have an intimate understanding of their teams, and the data collected on their own players usually just reinforces the coach’s prior knowledge, the SportVU system offers new insights into the strategies and strengths of rival players. It can also be used to identify which players are suffering from fatigue as the season goes on. Fans also have access to some of the data on the NBA website. They can see the spots players shoot most often from, and their success against different defence strategies by the opposing team.

Read more here.
(Photo credit: Silveira Neto)