Modern sport is a complex business machine. Performances on the field affect the amount of people sitting in your stadium, numbers of sponsors and then the amount of money you have to spend across the entire business. The amount of money that the franchise makes then directly contributes to the success of the team.
To take a more holistic look at sport is incredibly important in the current economic climate. Just having players on the pitch performing well is not the only indicator of success.
Therefore clubs today are using business analytics to extrapolate data from fans and their businesses in order to achieve success in their profits as well as in their sporting performance. Using algorithms to predict injuries and improve performances are important, but not as important as making sure that club employees are paid.
This is particularly important in sports like european footall where the financial fair play rules are coming into effect. This means that even if a team is winning competitions and performing well on the field, if they are making significant losses then they could have competition winnings docked or taken away, in addition to being banned from elite continental competitions.
This kind of threat is especially daunting to clubs who may be bankrolled by rich owners or who are leveraging excessive credit. As such fan engagement and finding new revenue streams has become increasingly important to these teams to allow sustained success and important to other teams to help them maximise their earning potential.
So what kinds of analytics are teams and franchises leveraging to make the most of their fans and sponsorship income potential?
In The Stadium
How did fans get to the stadium? How did this affected their experience?
Are people all entering through their allotted gates and if not why? Knowing this kind of thing allows franchises to not only streamline the entry and exit, but also strategically place food and merchandise to make the experience as pleasant as possible for their fans whilst maximising the potential to make money from them.
Are different areas of the crowd expecting different experiences and are clubs acting accordingly? Season ticket holders for instance are likely to act differentlyto those who have bought tickets as a one off luxury. Many analytical models currently being used, especially across Europe, are based on the amount of season tickets being sold, changing this to a foundation of current data will help clubs to make more informed and profitable decisions.
When people are exiting the game, what are they doing? Are they likely to linger in the stadium? It could be anything from the weather to the result of the team. For instance in the English FA Cup final the victorious team’s fans stayed in the stadium for a considerably longer time than those on the losing side. This means that these fans are more likely to contribute more to the club than if they are outside the stadium. This may be a one off given the importance of the match, but is there a way of attempting to replicate this during normal games?
If you are a major franchise, the majority of your fans won’t be in the stadium to watch a game. They will instead be watching at home on their TV, where additional analytics can be gathered.
After all, one of the contentious issues that is currently affecting the English football league is the difference in TV rights between the Premier League and lower leagues in the country. Being able to use analytics to track what viewers are likely to be interested in can mean that advertisements during commercial breaks or even those being shown around the stadium can be targeted.
With new technologies such as Tivo, viewers not only have the freedom to make the viewing experience their own through recording or skipping through certain ad breaks or programs, but also gives companies additional insight into their viewing habits.
Having statistics driven results for advertisers is a fantastic way to develop targeted campaigns,which is likely to see happier advertisers and an increase in advertising revenues.
Regardless of whether a fan is watching the match from home or a pitch/court side seat, the likelihood is that you will be making a purchase based on your team support at some point.
Analytics allow people to make quicker decisions and put the items that they are likely to buy in front of them when they want to buy. Through analysis of buying habits a team can see when somebody is likely to buy. People are more likely to buy after searching for an item online for instance. If you make the purchasing process easier based on this insight then sales are easier to come by.
This form of analytics not only allows teams to benefit from their fans, but also means that it is easier for the fans to make a purchase.
Although these kinds of analytics would not normally be something that coaches and performance managers would be looking at on a daily basis, in today’s world these are equally, if not more important, than the analytics used to measure on field performance. Taking these into consideration is increasingly important for the end results.
long term team perform often comes down to making sure that the rest of the business is performing. The fall from grace that several teams have seen, especially in British football is testament to this. The concentration on team performance rather than overall company performance has seen teams like Portsmouth and Leeds United plummet from strong sporting positions due to weak business platforms.
Creating models where you can measure what your fans want and how you can give it to them is vital. Sports analytics goes further than simply asking how an athlete can run faster or make better decisions. It needs to have just as much focus on how the business can run smoother and make informed financial decisions.