Since 2007, Big Data has been increasingly used in the sports industry. Data tracking has become the norm in all disciplines. Thus, since 2013, the NBA has installed tracking systems in each of its 29 stadiums. However, it remains to be determined for what purpose this data can be used and how to create the alchemy between sport and Big Data.
Thanks to Big Data, it is now possible for coaches to accurately analyze the performance of professional athletes in order to determine their strengths and weaknesses and fully exploit their potential. Discover the many possibilities offered by the association between sport and Big Data.
Why Is Sports Analytics Changing The World?
Basically, sports analysis is carried out either for sports teams that directly participate in games or for sports betting companies.
Sports analytics can be explained as the use of data related to any sport or game. Like player stats, weather conditions, the recent team wins/losses, etc. With this data, you can build predictive machine-learning models to make informed decisions on behalf of management. The main goal of sports analysis is to improve the team’s performance and improve the chances of winning the game.
The value of winning speaks volumes and takes many forms, such as trickling down for fans to fill stadium seats, television contracts, fan shop merchandise, parking, concessions, sponsorship, registration, retention, and local pride.
Sports analytics
1. Predictive Analysis
The main use case is predictive analysis, which can give an idea of how a team should be on matchday. Which trainee gives better team performance and increases the team’s chance of winning.
Using machine learning models, predicting which player performs best in which position on matchday is possible. Such a model will be based on a player’s statistics as a base, how well they performed against an opponent in matches like playing home or away, etc. So, it is possible to predict which players fit which position, given the conditions of the game and the adversaries you face.
2. Player Analysis
It allows for improving each player’s on-pitch performance and fitness level by analyzing their training pattern and diet chart and then revising those based on our analysis.
3. Fan management analysis
With the help of social processing data, patterns, and form clusters/groups using clustering algorithms in the fan base and campaign in target groups can be found. Knowing the factors that attract fans the most, team management can focus on improving this aspect, which will lead to the acquisition of new fans and the retention of old ones.
4. Using advanced visualization to provide insights
Data visualization is a critical tool in today’s data-driven world, and the field of sports is never an exception. Thus, presenting data in a graphical format allows management to see analytics presented visually with charts and graphs, allowing them to grasp complex concepts or discover new ideas.
The next step towards graphical representation is interactive visualization. You can take the concept a step further by using technologies such as spreadsheet applications to drill down into charts and graphs for more detail and zone-level understanding, interactively changing the depth of the data you see and how it is processed.
5. Team Manager Dashboard
Player performance statistics will be presented in an interactive dashboard format to better understand the game.
6. Fans panel
Fans can get their favorite player’s statistics in a match and can compare their gameplay with other players of opponents or the same team.
7. Understanding the fan network
The responsive dashboards allow them to engage with fans one-on-one, create targeted advertising campaigns, and use the data they collect to track and analyze fan behavior. So the management knows what makes their fans go crazy over their command and work harder on that part.
8. Finding common interest
Using data obtained from social networks such as Facebook, Twitter, and Instagram, you can analyze the factors that attract the most passionate fans of the team, and with this, it is possible to carry out promotions.
3 Ways Big Data Will Revolutionize Sports
1. Predict fan preferences
Analytics technology can improve the experience for sports fans. The more ticket sellers and teams know about fans’ preferences, the more they can cater to them. Fans today bring their smartphones to stadiums and want technology to enhance their experience. In response to meet the increasing demand, organizers of significant sporting events and stadium owners are utilizing cloud, mobile, and analytics technologies to provide unparalleled experiences.
Many changes are expected in the near future. A mobile application can assist spectators in locating the closest available parking spot upon their arrival at the stadium without the need for any human intervention, thereby streamlining the process. In the field, you can access instant replays, alternate viewpoints, and close-ups. Fans can use their mobile devices to order food and drinks and have them delivered to their seats without missing a beat. Your smartphone can also show you the nearest bathrooms. Finally, after the game, the app can provide traffic directions and suggest the fastest route home.
2. Measuring the physical condition of athletes using wearables
Data from connected objects such as connected wristbands, AR glasses, smartwatches, and wearables provide real-time insights about each player. Heart rate, cadence, or acceleration are all data that these devices can measure. Likewise, wearable devices reduce the number of injuries. Rugby, for example, has seen a decrease in injuries due to the use of wearable devices. Sensors record the impact of collisions and the intensity of the activity and compare it to historical data in a database to determine if a player is at risk of injury.
3. Influencing coach decisions
Data can help coaches and players make decisions that can affect the outcome of a game. Coaches can choose the best players, build optimal teams, and make smarter decisions on the pitch.
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Conclusion
Many experts fear that Big Data will ruin sports fun by replacing hearts with numbers. Analytical technologies also risk distorting sports. For example, to prevent spectators from getting bored during a game, Major League Baseball has taken several initiatives. Between each pitch, hitters must keep one foot in the batsman’s box to eliminate wasted time. Changes like this make more money and keep fans coming back. In the end, the focus could be on fan satisfaction rather than the sport itself.
Another problem to be feared is the misuse of data and the refusal of athletes to contribute to the collection of data. Analysts will most likely want to know the exact number of steps taken by players or draw a parallel between what they eat during the week and their performance on the pitch, but those concerned will certainly want to keep their privacy.
However, the benefits brought by Big Data should be much greater. Fan retention and fan base development are some of the biggest benefits of data analytics. Thus, IBM and SAS plan to improve the experience of spectators thanks to Big Data. Likewise, collecting data could allow games to be better planned or open access to more information for fans.
