Examining the impact of statistics on storytelling
Quidditch is still a new sport, and that newness shows itself in many different ways. Talking about quidditch still often elicits a question of how people fly, players aren’t given salaries, and there certainly isn’t a quidditch bubble being built for teams to play out the fall season amidst a pandemic. None of those will change anytime soon.
But there are ways for the sport to grow in the model of more established leagues like the NBA or the AUDL. The simplest way to do that: tracking statistics.
Why Statistics Are Important
Statistics can vary from the simple to complex, home runs to sabermetrics, but at their core they represent our ability to take what we’re seeing and make it quantifiable.
Tracking player statistics allows coaches to go into games knowing who the other team’s main scorers are, how the team has historically performed in close games, and even which weaknesses to exploit, depending on the extensiveness of the data.
Even on your own team, seeing possession data and individual plus-minus can help a coach or captain see the impact of their subbing and lineup decisions.
In addition, statistics can help enhance storylines and marketing materials, drumming up interest for players, teams, and conferences. All sports are, at their core, built on stories. Team standings and player profiles can help highlight underdogs, breakout stars, and MVPs.
Statistics can also help us market quidditch. Having publicly available stats lends legitimacy to quidditch. New fans can find their favorite teams and players by seeing which team plays the fastest or which player leads the league in assists.
Imagine the sport of quidditch is your favorite YA book. Statistics are the concrete, significant details that help ground you in a fictional world.
How Massachusetts Quidditch Conference Tracks Statistics
During the 2019–2020 USQ season, the Massachusetts Quidditch Conference (MQC) began tracking team and player statistics for the first time. Teams were sorted into D1/D2 based on their ELO rankings against other MQC teams. More importantly, a small group of dedicated volunteers attempted to dig into every possession of every game. It was a daunting task that, after a few bumps in the road, resulted in a concrete system that will be used for the 2020–2021 USQ season (vaccine pending).
Moving forward, every game will be notated to track lineup data (who is on the field) as well as a variety of counting stats like goals, assists, zero bludger drives forced, on a possession by possession basis. Through automated spreadsheets, this will create a “box score” for each team after the game. Those individual game stats will be kept in a database that helps us track statistics over the course of a season.They will also be used to create marketing materials like League Leaders and Players of the Week, highlighting the talented college players in the conference.
How You Can Track Stats
The biggest takeaway I personally took from tracking statistics this past year was that there is no shame in starting simple. Games and possessions can quickly add up, and whatever process a team or league chooses should be sustainable first and complex second. I highly recommend you take what MQC has done and adjust it to your needs. If you want something more complex, spruce it up. If it seems overwhelming, delete a few columns and take a step back. Even in the most popular sports in the world there is no one-size-fits-all approach to tracking statistics.
So let’s get started. Here is a template to track an individual game. MQC will be using playerIDs stylized as “team acronym, player initials” (ie. ecqJC), but on a smaller scale you could probably use a player’s full name or their number.
If you’re looking to see what a completed game looks like, here is a copy of an early season game between Boston University and Emerson College.
If you find yourself asking what the definition of a certain statistic means, I wouldn’t blame you. Much of the early conversation around tracking statistics centered around what constitutes a Turnover or a Beat Avoided. We created our own statistics glossary to use in order to standardize how stats are tracked game-to-game, and I’d recommend developing something similar (even if it’s just a mutual understanding) between all parties involved in tracking games.
After an individual game is completed, it’s inputted into a season database, which compiles all stats from the season and calculates per possession and per game stats.
Where Else You Can Go
I am not the first person who has tried (and succeeded!) at tracking statistics in quidditch.
Josh Mansfield, who recently stepped down as MLQ Gameplay Director, helped that organization experiment with different methods including tracking every beat thrown in a game and taking handwritten stats during the course of a game.
Andrew Axtell created QuidStats, a program that allows you to track stats while watching game film.
In fact, when working on these documents someone asked me what was stopping me from using Python to create a fully automated database to house all this information. My answer was simple, I don’t know Python. More specifically, I am only able to do what I am able to do.
There are probably many more members of the community who have put time into pet projects, hoping to advance the sport in their own ways. My hope is that these documents can help contribute to that development, and that they may inspire someone else to do it better than me!
In sports I have always been drawn to the analytical side of the game. There is something beautiful about digging into what a player is doing, why it is successful, and how I might look at the game in a new lens than before.
Statistics provide a longevity to sports, allowing them to exist long after the live game has ended. Statistics help cement player legacies, draw in new viewers, and give players a deeper understanding of the game they love.
To refer back to an earlier metaphor: imagine again that quidditch is your favorite YA book. Statistics are the concrete, significant details that help ground you in a fictional world. They get you to turn the page and read the next. When the book is done, they get you to read again.
In the Statistics Glossary there’s a specific definition I want to stress. A Turnover is described as an offensive player turning the quaffle over due to not scoring. We made a note to stress that if the quaffle is passed to a chaser and the receiving chaser does not have time to control the reception of the pass, the turnover is applied to the person who threw the ball. This was clarified to make sure that the recipients of a pass would not be penalized for being thrown a “hospital” pass. Too often female and gender non-conforming players are blamed for dropping uncatchable passes, and we want to make sure the data we collect doesn’t reinforce that misguided belief.
On the subject of equity within statistics, it can be easy to say that quantifiable information is equitable information. However, marginalized groups like BIPOC, female and gender non-confirming players, and queer people have historically been marginalized through seemingly “unbiased” data. Anyone using data to tell a story has a responsibility to make sure that the story is ethical and not influenced by implicit bias. I’m not sure exactly what that looks like, but I believe the conversation needs to happen before it becomes an issue, not after.