Sports Analytics – Making Champions

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Most of the sports competitions today are more of financial competitions along with being the athletic one. More financial resources mean better infrastructure for training, which results in better performance on the field. But in one case, a group of undervalued underperforming players came together to convert themselves into one of the most successful major league basketball franchise. This proverbial underdog story has been converted into a much-celebrated movie with Brad Pitt donning the hat of Billy Beane, the male protagonist. The movie is “Moneyball”.

'Terminator' of Artificial IntelligenceHow did this underrated team with all the odds stacked against them win the battle against unfairness? The answer lies in the coach and manager Billy Beane thinking out-of-the-box and using statistics to identify players who were undervalued at that point of time, but showed promise to peak at the right time.
Moneyball story became a part of sports arena folklore in 2003. Since then, the importance of sports analytics has significantly increased. MIT sports analytics conference sells out every year. From 175 attendees in the year 2007 to more than 5000 attendees recently, it is one of the most widely attended conferences. Football has also followed the footsteps of cricket, tennis, baseball, American football, basketball, and rugby and introduced analytics into its decision-making process. Sports analytics operates in a very unique environment. In this particular industry, the payroll, performance, and pay of the players and the coaches is public domain knowledge. Revenue from media rights, brand endorsements etc. are also very important for putting together a bunch of high performers.

Sports Analytics – Data Points

Sports analytics is all about ascertaining which factors affect the results on the field and the starting point is measurement. The measurements can vary from on-field performance to training charts and the correlation between the two. The data points majorly comprise of play by play scores, offensive and defensive performance, team standings etc. to name a few. These performance measures are of great importance to the team coaches who can then deduce which methodology can work for the team. Analytics helps in augmenting the training efforts manifolds.
A major form of input for sports analytics comes in the form of videos. Along with all the video streams, performance data (available internally with the team and with external journalists and researchers), and the team rankings are also a key input in deciding the optimal team composition. Based on the weight attached to the performance indicators, every sportsman is given a score, and the optimal total score for a formidable team is computed.
Data is also collected from the other available mediums such as external news, social media feeds, videos, GPS data, etc. From there on, the critical points of the game are edited and compiled, like, in a game of football, all the clips of a foul are collected. All these data points are then statistically analyzed deploying mathematical analysis, classical & Bayesian Statistics, regression, data mining, machine learning, time series and then visualized using the various COTS tools available in the market to draw actionable insights.
The best example of this was England’s cricket team when Strauss took over the captaincy. With the vision to make England the number one test team, a SWOT analysis (both qualitative and quantitative) was carried out. With analytics in their arsenal, a plan for every possible scenario was etched out, following decision tree methodology. Hence, the team had a clear plan and a counter attack for every possible setback. This is now being applied to IPL in India wherein franchises are roping in big IT organizations like SAP to unlock the power of data.
Analytics have helped sports teams and franchises in knowing what the fans want and how to keep them engaged.

Identifying The Next Big Star

Over and above the process of improving the team performance, data is also being analyzed to spot who and from where the next big star will come. Data points like demography, socio-economic conditions are being studied to come to a logical inference. The major help has been in spotting the right player who is about to peak to build up a strong team. Trends and various other statistical analysis can point out which player is about to peak. Players who are still to reach their best performance can be acquired at a reasonable price in a team whereas; once they become performers, their price tags skyrocket. Hence, by catching them early and then grooming them to rise above the written script value for money seems to be the trend.
Even though Moneyball had an anti-climactic ending for the twins but since then the use of data in sports has only gone up north. Undoubtedly, in the coming years, the use of analytics in sports is only going to soar further. Analysis of data which showcases how an individual or a team will perform in different circumstance is already becoming mainstream and it is sure to play a pivotal role in making tomorrow’s champions.

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