And the crowd goes wild! [*crowd noises, ahhhh!!!*]
Your fantasy soccer team is victorious once again.
Your friends in your fantasy league are starting to think either you’re psychic or you’ve got some
sort of insider information. Your secret? Data.
When you drafted your goalie you looked for one with a high saves per shots on goal ratio.
And when you named your team captain, you made sure their successful pass
rate and shots on goal stats were in the top ten percent of all the other players.
You made sure to steer clear of hot-tempered players who have high foul counts.
Plus, your secret weapon is your ace penalty kicker.
How did you know how to choose players? It’s all there already recorded in the
game history. All you had to do was search for winning patterns.
Owners of sports teams aren’t the only ones using sports data to make decisions.
The stakes are high for cities trying to appease both fans and players while planning stadiums,
journalists reporting on the sports industry and last night’s game, and fantasy sports teams too.
Let’s hustle for those bragging rights! I’m Jessica Pucci and welcome to Study Hall:
Data Literacy presented by Arizona State University and Crash Course.
Data is everywhere in sports. The most straightforward data involves scores,
win counts, and a variety of rankings. But we can also collect data on individual
players, entire teams, and even stadiums. With more sophisticated data collection
technology, we can even track the movement of players and the ball,
[or puck, or I dunno… oars] in real time. And if you’re one of those people whose
eyes glaze over when a game is on, this episode is still for you!
The sports industry itself has plenty of data drama, and we know from last episode
that following the money can turn up some pretty interesting power moves.
People with authority, like team coaches and owners, use data to make draft,
lineup, and salary decisions. Data can help an American football
coach decide to go for the 2-point conversion or stick with kicking.
Same goes for a cricket scout deciding whether to draft the inexperienced rookie on a hot streak or
the player that has a solid career overall but whose last season was pretty rough.
Take the true story of the 2002 Oakland A’s, made famous by the movie Moneyball.
Inspired by the sabermetrics pioneer Bill James, general manager Billy Beane,
and his assistant Paul DePodesta, wanted to use statistics to help them make smart
decisions and stretch their budget. Before it was all about gut instinct
and the subjective opinions of talent scouts. But the A’s new approach recruited players not
based on a one-time tryout, but on numbers like on-base percentage --
or how many times they got on base regardless of how well they could hit.
They didn’t go all the way to the World Series, but data took them awfully close and helped them
win more games with a much smaller budget. Now the game has fundamentally changed,
and many more teams are hiring data analysts. Let’s see what the day-to-day
[and play-by-play] life of a sports data scientist looks like in the Data Point.
Today our to-do list includes using past data to help us make decisions about both the present
(tonight’s baseball game) and the future (next year’s team).
Tonight we’re playing a team with a superstar hitter.
Our goal is to prevent them from scoring, and we want to use data to help us.
We know every player has a range of how they’ve hit and scored.
So to get the edge in tonight’s game, we should try to replicate scenarios where
the superstar did...less than stellar. Like checking out pitch-hit combinations
from this season’s game data and seeing what kind of pitch they just can’t… hit.
The data reveals their kryptonite, the sinker pitch.
They foul out almost every time. A quick call to the pitching coach makes
sure tonight’s pitchers throw some sinker pitches during warm ups.
Now, we can think a little further into the future.
It’s our job to set up for next year too. Our team has a first base player who is retiring,
so we’ll need someone to fill their shoes. We’ve been using a popular statistic to
measure our players’ skills called “wins above replacement” or WAR for short.
Calculating it is pretty complicated, but the idea is fairly simple.
WAR measures how much a player contributes to a team’s wins compared to some generic replacement.
A player with a large WAR score contributes way more to the team than an average player.
To keep our current team strength we need to find a player whose WAR score
matches our retiring player. Even better if we can sign
someone with a higher score for the same price as our current player’s salary.
We pull five potential candidates from our favorite baseball database
to pass along to the talent scouts and call it a day.
On-base percentages or WAR might not cause wins, but correlation is good enough as long as we still
end up winning more games on average. It’s actually secretive stuff.
If others catch wind of the stats we’re using to win, we’ll lose our advantage.
Like on-base percentage isn’t nearly as hot as when the A’s first used it.
Competition is everywhere, even in spreadsheets. And there are definitely a lot of spreadsheets.
Sports data can quickly get complicated and, like... large across both space and time.
Luckily the experts know how to wrangle this complex data.
All those fancy graphics we see onscreen are actually nicely packaged data visualizations.
Like shot charts in tennis that show us where on the court a
player’s shots have landed so far in the match. But we fans aren’t just sitting on the sidelines.
You might not think about your passion for sports as a passion for data,
but they actually go hand in hand. All of those careful tallies of your
favorite hockey player’s shots paved the way for you to be a data collector.
And all those debates in chat rooms about who’s a stronger offensive player prepared you to be
a data analyst, making arguments based on data. Fans use data to strategize about fantasy sports
teams, reason out bets, and even enhance their viewing experience.
But before you place a bet or use statistics to smack talk your competition you should know of
some data traps to look out for. Don’t let your enthusiasm
lead you into a gambler’s fallacy. Say you bet your friend that your favorite soccer
team will win today’s game. [Loser buys pizza!] The sports analysts say that the odds,
or relative probabilities, are 1:3 that they win. All that means is that in a hypothetical world
where we could replay this same game over and over again your team is expected to win
once for every three times they lose. Hey, who doesn’t love an underdog?
But what if they’ve already lost three games in a row?
It might be tempting to think they’re “due” for a win.
Unfortunately for you and your team, that isn’t how odds work.
Remember the odds we're talking about are in a hypothetical world
where we play this same game over and over. So, during the season in the really real world,
how many times in a row they’ve won or lost doesn’t affect whatever happens in the next
game, and the odds change each game. But what about the effect of morale?
You may have heard sports commentators remark on various “hot streaks” of players or
teams who keep scoring or winning. Like if a basketball player makes
a bunch of free throws in a row, you might think they have a “hot hand” and it’s more
likely they’ll make the next one. But actually, the numbers have been
crunched for a variety of sports and there isn’t much evidence that actually happens.
But don’t all of these cold calculations take the fun away from the game?
Don’t worry, there’s still plenty of randomness left over to keep things unexpected and exciting.
If we could predict the outcome of every game, we’d be rich and soaking
up the sun on our private island! Numbers and fun facts can actually
enhance the viewing experience. Quick! The viewers want answers
to a barrage of questions in real time. When was the last time a tennis player lost
the first set but ended up winning the match? How often have these two volleyball players
faced off against one another? Sports commentators are supported
by teams of behind-the-scenes researchers so that they can provide historical tidbits in
real time and add context to the current game. Real-time data also helps when a referee’s call
is questioned or when we look away from the TV screen at exactly the wrong moment.
Let’s replay it in sloooo-mooooo, because remember videos count as data.
Journalists use historical data in their sports reporting for context too, like history on
players and teams leading up to a big game. Readers may not remember which quarterback
is new to the Super Bowl and which already has a championship ring.
If the journalist is feeling lucky, they may even make a data-driven
prediction on who’s going to win. After a game, journalists can dig
into key turning points and see if there are any historical precedents or
if anything could have foreshadowed the outcome. Maybe a basketball game is lost by a free throw.
But maybe we shouldn’t be that surprised since the player’s free throw
track record under pressure is way worse than their usual free throw performance.
To get data on our favorite team or sport if we don’t want to tally it ourselves
we’re dedicated to our fantasy league, but maybe we are not that dedicated,
some educational institutions provide a library database specifically for sports statistics resources.
There are also open source datasets and software packages to access and parse
sports data from various websites and databases. Thanks to dedicated, data-savvy sports fans,
we actually have a lot of information going back pretty far in time.
But let’s zoom out for a moment. Beyond a particular game, sports
data can also be used to assess structural patterns in the sports industry as a whole.
Data can be a powerful lie-detector. Are you seeing a suspicious
uptick in some athletes’ performance? If no other player’s data history has ever
improved so much and so suddenly, you may have a performance-enhancing drug scandal on your hands.
Data can also make it easier to plan the sports events themselves.
How fans engage with a team can help decide where to place stadiums and how large to make them.
If a city only has fair-weather fans, it may not make sense to invest in a bigger stadium.
But if a team’s popularity is on the rise we might make up the cost of the new stadium more easily.
Deciding to move a team’s stadium can have huge unintended
consequences on team performance though. Just ask the American football team the Chargers.
They moved to Los Angeles from San Diego after over 50 years; their fans didn’t.
When they first moved in 2017 they played in a soccer stadium while waiting for
their new stadium to be built, and in the meantime, lost their home team advantage.
Attendance data and the sound from the stands shows that the home team
crowd was actually outnumbered by the opposing team’s fans a lot.
Data-driven decisions impact the sports world from who is chosen to play for
a team to what plays are used in a game. Plus we collect data ourselves and spout off
stats for fun as a way to show our team spirit. So today we learned how managers, coaches,
and fans use data to push toward victory and make sports more fun to watch.
Even though our personal use of sports data can be lighthearted,
we keep our data literacy tools at the ready, learning to be careful of relying on hot
hands and wishing for the end of a losing streak based on the odds.
Next time we’ll talk about a different type of team that still wants to win. That’s right,
we’ll cover data in elections. It’ll be a party! Thanks for watching Study Hall: Data Literacy,
which is produced by Arizona State University and the Crash Course team at Complexly.
If you liked this video and want to keep learning with us here in Study Hall, be sure to subscribe.
You can learn more about ASU and the videos produced by Crash Course in the
links in the description. See you next time!