Sports industry is undoubtedly one of the biggest industries in the world. In order to put it in numbers, the sports market reached the amount of 458.8 billion in 2019 but it declined to 388.3 billion in 2020. Sports market is expected to reach a value of 599.9 billion by 2025 and 826 billion by 2030 (The Business Research Company, 2021).
As many of the other industries, the sports industry was largely affected by the COVID-19 outbreak. As mentioned also above, the sports market suffered a 15.4% decline in its value (The Business Research Company, 2021). The main reason why this happened is that the main source of revenue of almost all of the sports associations is their fans. With COVID-19 regulations, fans were not only disallowed from following their favourite teams but they were somehow distanced from the whole concept of supporting and keeping up with the team that they support. Cancellation of games, abstention from action, ban of fans in stadiums, poor athlete performance to abstention or psychological state after consecutive quarantines were some of the most outstanding issues that arose and had the result of making fans more distanced from the teams that they support.
All of the issues mentioned above, stress out the importance of having fans pleased and offer them the best quality of spectacle. Fans that are pleased with their team’s performance will tend to bring more revenue by buying sports equipment related to the team, by paying high prices for getting seats or season tickets for games, by paying to visit the stadium itself or the team’s facilities or by even buying products that are closely related to their teams. Also, most of the time, a bigger fan base will bring greater sponsorship deals to teams and associations. It is not a secret that the more consistently spectaculous a team is, the bigger fan base that it will gain throughout the years. But how can a team be always on top of their performance and attract as many fans as they can, who will eventually lead in bigger revenue levels?
The answer to the previous question is by keeping the performance of the whole team and their players as individuals to the highest levels possible. Data Analytics is a technology which has been established for many years in various industries. Sports is one of the industries that are reportedly really slow in digesting it and applying it to their processes but lately it seems there are multiple improvements at accepting it. Its adoption is in a really immature state yet but tremendous efforts of implementing and establishing it specially in the team’s sports sector are happening.
Let’s take the example of the National Basketball Association (NBA), one of the world’s most marketed sports products. Historically, in basketball there were people who were responsible for noting statistics about players in order to either scout or either improve their in-game performance through coaching. These statistics were mainly held in paper and only classical statistics such as points, attempts, assists etc could be noted by these people. The recent installment of cameras all around the court gives the opportunity for teams to keep much more detailed statistics which can be further analysed by business analytics. These analyses can later be used by machine learning models in order to design winning strategies. Strategies such as planning the most effective defensive plan for top-notch teams or players or the most efficient offensive plays according to the roster’s characteristics and capabilities are some of the examples that can provide a competitive advantage to teams who use such innovative technologies (HBS Digital Initiative, 2020).
Stephen Curry’s shot selection analysis by data analytics. Source: https://digital.hbs.edu/platform-digit/submission/how-data-analytics-is-revolutionizing-the-nba/
One of the key concerns of all the teams is keeping their players well rested so that they can avoid potential injuries. Lately, teams have been selecting data from their players by offering them wearable equipment, by monitoring their sleep patterns or even collecting biological samples from them in order to track their fitness level, their rest levels and even predict their future performance. By analyzing these data, teams are trying to design the most appropriate rest strategies for each of their players. The more tired a player is, the more prone to injury he/she will be. In order to always keep them on top of their fitness so that they can contribute at most during the games, they are applying all these monitoring techniques for collecting relevant data (HBS Digital Initiative, 2020).
Last but not least, data analytics play a major role nowadays in scouting players. Back in the days, scouts would rely their decisions for players in statistics held in paper, by watching them in live action or by highlights found online. With data analytics, statistics can be analyzed in depth, thus providing a clearer and more detailed report about a player, reducing the risk of incorrect decision making. Transfers and contracts offered to players are costing a lot of money to teams and these are two of the most important expenses for a team or association, so taking right decisions in order to attract the most suitable plates for the team in order to achieve consistent performance levels is vital for them (HBS Digital Initiative, 2020).
NFL (American Football) Players Performance Analysis through Microsoft Power BI. Source: https://sqldusty.com/2017/07/28/power-bi-nfl-football-stats-comparisons-and-analysis-report-is-now-available/
Of course, the data models that are being analysed are far from perfect at the moment. One parameter that is really hard to take into account and that is difficult to be applied to the data models is athlete’s psychology, not only during a game but also his psychological state throughout the whole time of him/her being a member of a team. Data analysis has changed the way that teams operate but in order to take right decisions they still have to consider the human factor. What can be predicted, though, is that teams who invest and use further new technologies in order to analyse the tremendous amount of data that they can get from their athletes can be ahead of their opponents and build consistency in terms of performance so that they can always be at the top, which naturally attracts more and more fans, thus revenue, as explained in this post.
References
HBS Digital Initiative. (2020). How data analytics is revolutionizing the NBA. Available at: <https://digital.hbs.edu/platform-digit/submission/how-data-analytics-is-revolutionizing-the-nba/>
Ryan D. (2017). Power BI NFL Football stats comparison and analysis report is now available! Available at: <https://sqldusty.com/2017/07/28/power-bi-nfl-football-stats-comparisons-and-analysis-report-is-now-available/>
The Business Research Company. (2021). Global Sports Market Report Opportunities And Strategies. Available at: <https://www.thebusinessresearchcompany.com/report/sports-market>
Interesting article Georgios, thank you for sharing it! I have recently read another argument about all the teams in NBA shooting excessive three-pointers, not only the guards and forwards but also centers(the tallest players on the field not usually known for their shooting skills). So in very simple terms, these analytics tools were recommending the teams either to go for 2 point dunks or 3-pointers. The logic behind it is that 2 point dunks are the shot attempts with the highest probability, and 3-pointers being more valuable and almost the same probability with the shots made 1-2 steps inside the line. As a result of these recommendations, every team started to shoot excessive number of 3-pointers(started with Houston Rockets). Even the centers started practicing and taking 3-pointer attempts. The number of 3 point attempts per game have 10-folded in 2018 from what the numbers were in 70s and beginning of 80s.
However one of the teams that did not use these recommendations, San Antonio Spurs, not shot excessive threes and had a phenomenal season in 2018. Their star player, Damar DeRozan, was relying heavily on his mid-range shot, the exact opposite of what the algorithms were recommending. They had a great season and made to the playoffs easily. The reason why they were successful later explained by an ESPN analyst as Damar DeRozan’s mid-range shot threat being so strong for the defending teams and the need of a double-team, defending a player with two players, when he fakes a mid-range and drives in. The defending teams were not ready for Damar DeRozan’s drives and that was making their defenses collapse and letting the Spurs to find the “most available man” and go for an easy shot, what their head coach Popovich loved doing for ages.
The main point that I wanted to highlight is that the use of data analytics and algorithms by professional sports teams is a very exciting and challenging area, but using them without a proper understanding not only makes the teams spend lots of effort in order to change their fundamental approaches to the games and play the same boring game with the whole league but also, can make them even less successful than they possibly can be without following every recommendation of the algorithm. I believe as time passes and the use of algorithms become mainstream, each and every team should pay attention to understand their’s better and only take decisions that compliments to their own strategy.
Very interesting post! I watch a lot of football myself, and I believe advanced statistics will become more and more important in scouting and rating players. For example, xA and xG statistics, based on a database of all historically recorded situations on the pitch, are highly valuable to judge a players quality in my opinion. However, there is a large section of traditional football fans who disregard the use of data and statistics, and they prefer the so-called “eye test”. Their argument is that advanced statistics do not take into account the value of how beautiful a player plays. I personally think this is not true, as you have statistics of dribbles p90, take-ons p90, key passes, attempted passes into the final third etc. You can find out almost everything from just the stats, and I believe nostalgia is one of the main reasons the use of statistics is being held back in sports. What are your views on this?