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.
I’ll admit I didn’t have high hopes. How good could a free conference about big data and artificial intelligence be? Especially if the upgrade tickets, which I so frugally declined, were only $75? Well, I was pleasantly surprised.
Let’s deal with the negatives first. The morning registration line was long and it took some people 30 minutes o get through it. The exhibition hall was small with not nearly as many vendors as I am used to seeing at conferences. There was no free wifi in the main hall (um, admission was free so why do we deserve free wifi too?). Sometimes the sessions were so packed, there wasn’t even room to stand. And, some speakers didn’t even show up because, well, airplanes.
However, those negatives were completely washed aside with the positives. Some of the talks were quite good. Some of the speakers were quite good. The topics were quite good. They gave out free conference programs. And did I say free? Some free things are worth what you paid for them. This one was worth a lot more. I highly advise you to go and it’s definitely on my 2018 conference schedule as time well spent.
- Data science is often handled at the tail-end of a project. We only take the time to learn what happened after the fact and when it’s too late to do anything about the current situation. We need to do a better job of using our data for the future – for segmentation, targeting, to understand what our customers want, to uncover blind spots.
- Good data scientists care where the data came from, who created it, what it represents. They don’t just take the data and run it through stats programs and spit out reports. It’s not just about statistics and reporting. Data quality must come first.
- The real money is not in having the data but rather in knowing what questions to ask. Literally everyone has data but only the companies that hire the smart brains to ask the right questions will succeed with big data.
- You might think using artificial intelligence is very impersonal. On the contrary. It’s impossible for a human being to be personal with hundreds and thousands of people but AI allows you to be far MORE personal with thousands or millions of people.
- Computers and artificial intelligence need to learn the senses – for instance, they need to learn to see the types of moles on skin that will become cancer, learn to hear which wheels on a train are cracked and about to cause a train wreck.
- Algorithms are what make computer see and listen and as such algorithms are the future. Soon, companies will brag about their algorithms not their data.
- We need to let computers do the pattern recognition so that humans can do the strategizing and reasoning
- If you want to work with big data but can’t afford it, have no fears. So much software is free and open source. You can do anything you want with free tools so don’t let dollars hold you back from doing or learning.
- The danger with artificial intelligence is training it with bad, untrustworthy, biased data. We’ve all seen the reports of AI perpetuating racism because the training data contained racist data. You must choose good datasets that are clean and genuinely unbiased and only then will you find success.
Today’s walk took me into about six different Chinese bakeries. The funny thing is, for the most part, you can’t really tell which bakery you’re in. They generally carry the exact same items so I’m suspicious that many of them cart everything in from the same place either baked or ready to bake. But really, it doesn’t matter to me. I can tell that everything is pretty fresh. Indeed, some of the items are still warm and steaming when they’re put on the shelf.
What I like about nearly all of the Chinese bakeries in the Chinatown area of Toronto, is that they function on a self-serve basis. As you enter the store, you grab one of the trays and a set of tongs from the pile near the door. Then, as you wander down the one or two aisles, you open the little windows, pick out the items you want using the tongs, and place them on your tray. I quite like not having to point through glass to show someone which items I want. When you pay, each item is put into a tiny bag either by itself or with one other item. No food touching. Perfect.
Then, the other thing that simply cannot be beat is the price. Seriously, how can they keep this up? 50 cents or 80 cents per item, some of the larger items are still only $2 or $3. Sure, they have to compete with all the other bakeries in the area, but if the same item was available in a non-Chinese bakery, it would be priced at least twice as much. The problem is that the prices cause me to leave with far more goodies than I should. But is that really all that bad?
Anyways, today’s visit to Ding Dong netted an unreasonable number of items. The egg tart with the flaky crust disappeared almost instantly so I have no picture for you. For me, it wasn’t quite as good as a Greek egg tart but at one third the price, I was quite the happy camper. Yum. It was joined shortly thereafter by a beef bun, melon cookies, vanilla cake, cake in paper, the Chinese equivalent of a rice krispie square, a sugar bun, and a couple other things with names I can’t possibly pronounce.
I go to bakeries. A lot of bakeries. In fact, I never walk past bakeries without going in. And when the bakery is situated part way along 4 hour walk, it’s simply survival instinct to go inside even if it’s only for a quick smell. I have come to the realization that it is my duty to share my knowledge with you and ensure that you find and visit the best bakeries out there.
Among the five or so bakeries I visited today, the Tre Marie on St. Clair Avenue West was certainly a highlight. In fact, I think it’s one of the better bakeries I’ve visited in Toronto. It’s quite large and on one side, it includes a seating area for folks wanting lunch. Around to the other side, it has a huge section of bread behind which you can peak into the bakery and see everything happening. I’m a huge fan of bread and to feel the corn meal under my shoes was awesome. Bread that doesn’t spill its toppings all over the floor just isn’t real bread.
The best part for me, of course, was the sweet baking – fancy desserts, tons of pretty little cookies, one of a kind gorgeous cakes, and so much more. I ended up buying two things I’ve never seen before. One looked like a giant slice of calamari dipped in icing and it turned out to be a light crunchy (6 inch wide!) cookie. The second item turned out to be layers and layers of philo covering a yum cream filling.
Great selection, great service, home atmosphere. Yum. It’s on my visit again list!
- The Bakery Review: Brick Street Bakery (lovestats.wordpress.com)
Toronto is in the midst of a heated contest with two major mayoral candidates, Rob Ford and George Smitherman. Which means we’re going to hear a lot of numbers being thrown at us. So here is a quick and easy guide to what those numbers mean.
In this case, a poll means that a bunch of people have been asked who they will vote for, perhaps hundreds or thousands of people. Maybe you find out that 30% plan to vote for one person and 35% plan to vote for the other.
When you ask only a few hundred people who they will vote for, you have a bigger chance of making a mistake than if you ask thousands. Pollers call that chance of mistake the margin of error. I call it the jiggle factor.
So, the 30% and 35% are jiggly numbers. If you only ask a few hundred people in your poll, those numbers will jiggle a lot. If you ask thousands of people, the numbers won’t jiggle a lot.
With a margin of error, or jiggle factor, of 3 points, the 30% might jiggle as low as 27% or as high as 33%. And, the 35% might jiggle as high as 38% or as low as 32%.
See how we applied the jiggle factor(margin of error) to both numbers? It means that the low number is actually somewhere between 27 and 33 and the high number is actually somewhere between 32 and 38. Notice that those two sets of numbers overlap on 32 and 33. This tells you that the 30% and the 35% are not different from each other and that our two candidates are in a dead heat.
Even though the one number is 5 points larger than the other number, they really are equal to each other. The important part is to apply the jiggle to both numbers.
In the end, the only way to know if they are different is to vote. So vote!
(It’s a lot more complicated than this, but hopefully the general idea is clear.)
[tweetmeme source=”lovestats” only_single=false]To start on a sad note, I wish my schedule had let me attend all the days! Next year!
What did I like?
- Being in the same room as 650 like-minded researchers. It really did feel like I was with family. Going to the MRIA is a great chance to talk with researchers who are much more senior and experienced than I am.
- The exhibition room was great! So many vendors, so many candy dishes, so many great little treats and big give-aways! (If anyone has any spare treats, please send them my way. I would love to share them with the awesome @conversition team!)
- The food was stunning! Individual asian samplers. Much, much fun and interesting!
- The recognition of being green was great. No extra paper programs, no bottled water everywhere (jugs instead), no paper surveys everywhere.
- The voting system was great. Computerized and instant from dataonthespot.com.
- The Hotspex charging station! Finally! Next year, let’s get these in the presentation rooms.
- I loved that so many people were interested in hearing about social media research that my presentation was standing room only. Thank you for asking so many great questions. Thank you for making my day!
- I loved that a bunch of people kept me 45 minutes after the presentation throwing all kinds of questions at me. And then giving each of them a tshirt from conversition (send me photos of you in your shirt!). Thank you for making my day even better! 🙂
What didn’t I like?
- The lack of concrete research content. For my liking, there just wasn’t enough data or research methods or research results. I like to come home having learned something and I don’t know if I did this time.
- The onstage bickering during the morning presentation. I felt like I was watching children do a “My dad is better than your dad.” Thank goodness Michael Adams was smart enough to not join in the child’s play.
- I was disappointed that my personal mic didn’t work and I had to stand at the podium, and that there was no internet connection during my pres.
- The presentation evaluation questions were horrible. I hope a researcher didn’t write them as they made no sense and had confounds all over the place.
- The ‘green’ attempt failed with all the paper packaging at lunch.
And lastly, I hated not being there for days 2 and 3. There were so many great sessions coming up and so many new and interesting people to talk to. Next year, for sure!
As always, feel free to completely disagree with me!
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