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SPORTING THE ANALYTICS

2016-05-03/ Amarnath Mishra

In simple terms, Sports Analytics1  is all about being right in making a decision, both on and off field, mostly the latter. At times, such decisions may coincide with the gut feeling or human experience from the past and why not? The science is all about predicting based on the statistics from the past. This idea has been always there; with the advent of stronger efficiencies and computation speed in processing engines, the possibilities have hit the realities of today. Sports Analytics involves management of data, developing Analytics models and show up the results on the Information Systems for the end users to consume, who, more often than not, are decision makers 2. At least in the US, every major professional sports team either has an analytics department or analytics expert on staff 3.

Sports Analytics is also deployed for prediction of games, just like any other high-profile event. A company called Gracenote sports is hitting all the right notes in the same area. By acquiring sports data firms 4, Infostrada Sports and SportsDirect, they have been providing gold standard data, analytics and reporting tools to NOCs (National Olympic Committees), thus enabling these NOCs to take critical decisions such as selecting athletes to develop and choosing sport competitions to pursue. According to Gracenote 5, in 2016 Rio games, Asia will clinch one-third and Europe will account for 47% of all the medals. 

                                                                            

Sports fans, all around, are getting a sneak peek into information of their favorite team before a game begins. Fans are given prediction on matches, identifying how weaknesses and strengths of an opponent team will play out against your team’s strengths and weaknesses, how weather conditions can affect the course of the game etc 6 . FiveThirtyEight.com, which started as a polling aggregation website in 2008, evolved to become one such result predictor, which predicts before a game has been played, based on numbers as opposed to gut instincts.

As IoT (Internet of Things) is gaining popularity in everyday’s data-dependent decisions, Sports Analytics is not far behind in leveraging from the same. IoT started, majorly, with the FoB (fitness of beings) with Health app from Apple or Nike+ Running, Runtastic mobile apps etc 7. Increasingly, now, lots of wearables and streaming cameras, which are connected to internet and used to measure every move and make meaning out of it, are pushing IoT to the front seat of Sports Analytics movement. The accuracy is far from guts instincts; a scout uses such infrastructure to corroborate his decisions, a coach uses the same to understand and place players in vital positions of the game, a player using the same to improve his playing style and hence performance.

The History

Companies like Sportradar, keeper of play-by-play data and later delivering the same to companies in media, technology and sports, are pivotal in gathering game statistics and acting as the source engine for Sports Analytics. Sportradar, after building upon its success in Europe, in recent times has secured exclusive partnership with the NASCARNHL and NFL. The GSIS (Game Statistics Information System), used by NFL (National Football League), speaks for itself. It is a Windows-based tool to capture play-by-play game data 8. The chips used by NFL, on shoulder pads of players, provides real-time information of player stats like x-y-z coordinates, top speeds and acceleration that opens up a new array of possibilities in broadcasting, sponsorship and engaging with the fans 9.

                                                                               

Atlanta United FC, a Major League Soccer (MLC) expansion franchise, is in news these days as they use Sports Analytics to build an expansion roster, or in simple terms use technical scouting to build the team 10. The franchise, from the past experiences of its management & coaching staff and numerous visits by its front office to other MLC teams, is building up a style of play that the united experience deems fit – for e.g., how the right back wants to play, how they want their No.8 to play  11 and so on. The same is then fed into technical scouting, which tries to narrow down the targets that fit into the franchise’ budget. Around the same time, the General Manager shares the brainstorming process with the aid of technical side and the scouts start to look into the list of possible players and see who fit. To think, that Atlanta United FC is making their team from scratch, is a mountainous task but humbled in implementation due to the Soccer Analytics deployed by the management and used by the scouting team. Obviously, this useful infrastructure is not possible without the seamless connectivity, one avails around these days, between information islands.

When it comes to Sports Analytics there is one celebrity case – that of Moneyball. This is a story of the duo of Billy Beane (then General Manager of Oakland A’s baseball team) and Paul DePodesta (an analyst working for Billy Beane) who, in a bid to stand against bigger & wealthier baseball teams (like NewYork and Boston), were inspired and later adopted a school of baseball statistical analysis known as sabermetrics  12 (a reference to Society for American Baseball Research). DePodesta, in his recent speeches, has warned that a solo pursuit of causal relationship for making data driven decisions in Sports can lead to bad conclusions. According to him, emotional responses to data and player performance need to be separated wisely, i.e. stripping away biases of any type, in order to be successful at using Sports Analytics 13.

Beane had deployed two key procedures to select and retain his baseball players – developed regression models to predict performance of players and ensured the proper use of these models 14. The models used players past performance and current price as the predictors; the models were not to be revised based on opinions, but only when the changes occur in performance or price. Essentially, decisions to draft, play, retain and trade players were made based on regressive prediction models rather than experience from years of being in baseball. Such was the effect of these models that, between 2000 and 2006, Beane’s A’s were registering 95 wins a year, just 2 wins lesser than the mighty New York Yankees.

Similar prediction models are now used today in baseball, basketball, soccer, hockey and football. 15

                                                                                                 

As I write this blog, I got a pop-up from Google Alert about a piece of sports news 16 that outlines the recent win by Villanova Wildcats, over North Carolina Tar Heels in their latest encounter, at National Championship game [For many of us, who aren’t aware of these names, we are talking about a basketball game championship at University level in the US]. This news suggests how a last 5 seconds play saw Villanova went past their opponents with the last shot by Kris Jenkins at the NCAA’s Men Championship game. To many sports commentators, it looked as if Villanovan management was already up on their feet and celebrating as Jenkins had his knees parallel to the ground, after he received the pass from another Villanovian, standing prepared to score the winner. This has sent sports analyst into discussions that Villanovan had been reading their opponent’s defense all the time and knew exactly the person, to be juxtaposed just at the right place and moment, to score the winner – their leading 3 point shooter and hitting 39% of them before coming to the game, Kris Jenkins. Though North Carolinan defenders got mixed up in blocking Jenkins, many sports commentators believe that the winning shot was a move inspired by the statistics going into the game. The winning shot is present at this link for your reference.

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