Changing the game: Sports Tech with the Toronto Argonauts and the Blue Jays, #BigDataTO #BigData #AI
Notes from the #BigDataTO conference inToronto
Panel: Mark Silver, @silveratola, Stadium Digital; Michael Copeland, @Mike_G_copeland, Toronto Argonauts; Jonathan Carrigan, @J_carrigan, MLSE; Andrew Miller, @BlueJays, Toronto Blue Jays
- There is a diverse fan base across all Toronto teams, and their preferences and values are diverse in terms of who are they and what drives them to watch and attend games. There are many segments of people not just ‘fans.’
- Fandom takes many shapes and sizes and you always need to grow and rebuild the fan base. You can’t appeal to only avid fans. You must also appear to casual fans. You need to go beyond the narrow focus of superfans.
- The strategy of loyalty programs is that they are an engagement tool to gather data for mining, generate in-game activation, let people win prizes by participating, help partners better understand the fans, and this creates wins across the board – for the team, the partners, and the fans.
- The teams want to learn what people are doing during the game as opposed to guessing. Which benefits do they use their points for, what do they choose at the concession stand, are they watching road games. And this is not just for season ticket holders but people across North America watching games. We need to use the data to learn how to scale beyond ticket holders.
- People want more meaningful and personal relationships with their sports teams. We need to learn what food they want, what environment they want in the venue, what relationships they want outside of the game. And we need to filter out the noise and deliver.
- We’ve all done the analogue research. It’s been done for 100 years and it’s not unique to sport. How do we use technology to do it better now. WHO – we need to stop guessing and start using more efficient research. This massive data we have will tell you many things like WHAT do they want. They might want NEW THINGS that you didn’t offer before, an app, an emoji. The data will also ASSIST your team with player recruitment and roster management. We’ve been doing all this for ages and now we want to do it better, more efficiently, most cost effectively.
- Big data is not free though. Not all stadiums have wifi to do wifi research and it’s expensive to invest in putting wifi in a stadium. We need to spread the cost among multiple agencies.
- This isn’t a technology project. Rather it’s a people project. For instance, a chef can do vastly more with ten ingredients than I can. We need to change the way we engage with fans and leverage partner relationships. Yes, we’re investing in technology but the focus is people. We need to translate tools for each part of the business, reimagine how we engage with fans, and how we make a profit. You can buy a beautiful car but you need to learn to drive to take full advantage of that new car.
[tweetmeme source=”lovestats” only_single=false]The normal curve is an enigma for many people. We speak of good luck and bad luck, hope that we always have good and then curse when it turns out bad. Like when Cinnabon is closed on the same day you forgot to eat breakfast.
So far, Paul the Octopus has had a lot of good luck in predicting World Cup match winners. Perhaps he always goes for the food that is closest to him or the food that is in the best light or the food that moved most live-like or the food next to his preferred tentacle. I’m assuming, of course, that like humans who prefer left or right, Paul too has his own tentacle preferences. I’m also assuming that he isn’t juiced up or taking bribes.
Wouldn’t it be great fun if someone could collect up all the relevant variables and run some predictive modeling? Time of day, day of week, feeding schedule, lightness, location, direction, colour, and who knows what other selection criteria are of supreme importance to our eight legged friend. What kind of r square do you think we would get? 0.3? 0.8? Woah… too far into geeky stats there.
As fun as it is to listen to the Oracle of Paul, he won’t defy the odds. He’ll just take his rightful place on the normal distribution whether it’s on the extreme right or just slightly to the left of right. But I know we’re all hoping for the extreme right.
In Paul we trust.
Read these too