Using numbers in sports isn't new.
In the early days of organized sports in the United States, fans would get their newspaper and immediately read box scores to get sports updates. They read about Ted Williams who went 2-for-4 with an RBI, and they read about Jerry West who scored 25 points with six rebounds and six assists.
That has remained largely unchanged throughout the years. It’s a beautiful, basic reminder of the simplicity of sports.
But in recent years, there has been a revolution about just how much numbers can tell us about the sports we love.
Famed by the book, Moneyball, advanced analytics in all sports have begun to go mainstream in recent years. New statistics have been introduced to try and further quantify the previously unqualifiable to see just how far we can analyze what we watch with our eyes and comprehend with our brain.
It’s quite simple: Analytics are the future, if they’re not already the present.
They’re the way we can find out that a player who hits .290 and has an on base percentage of .320 maybe isn’t as “valuable” as the player who hits .272 but gets on base at a clip of .350. They're the way we can find the value of two free agent quarterbacks to decide which deserves a contract.
There are obviously people who will disagree with the legitimacy of advanced statistics, for a wide variety of reasons. They’ll probably try to say “they know what they saw,” which is, in fact, sometimes a legitimate argument. Sometimes, statistics can lie.
But almost 100 percent of the time, there is a statistical explanation for everything.
For a simple exercise, try and explain the difference between a .275 and .300 hitter in Major League Baseball without using those numbers. How can you accurately tell that .025 of a difference in batting average is enough to put someone in the Hall of Fame or to just say that he was a good player?
When you’re done with that, try and talk your way through the difference between a 20-point per game player in the NBA and a 17-point per game player, without using those numbers or those statistics. Eventually, you have to mention that maybe the 20-point scorer took more shots, or is a more natural scorer. Maybe the 17-point scorer is a more natural passer.
In essence, you need as many data points as you can collect to more accurately breakdown what players are doing at the highest levels of play. That’s something that even the most hardened “stats are for dummies and nerds” fan can buy in to.
It’s difficult to dissect the miniscule differences between some of the world’s best athletes, even if you watch every game of that team’s season, to decide if one player is “better” than another. Which is where analytics come in.
Analytics are unbiased, they spare no expense at telling us which player is a good player or a bad player, no matter our preconceived notions. They tell us, upfront, who is more valuable to a team in that particular category.
To demonstrate this, let’s take use an example from Game 4 of the Stanley Cup Final, where the Predators defeated the Penguins 4-1. The Predators have dominated the series in shot attempts (known as Corsi), and probably should be ahead, or maybe even could have swept the series if not for unsustainably horrific goaltending.
Player A was on the ice for 16 shot attempts for (on net, missed and blocked shots) and just seven against. Player B was on the ice for 16 shot attempts for, but 24 against. Player B played four more minutes in the game.
Player B scored a goal, Player A had an assist. The box score would show that Player B, with a goal, was the better player. That makes some sense.
But the other was clearly more valuable to his team, especially in his defensive end, as he had the puck more time than he was defending. His line also scored a goal.
Player A is Mike Fisher of the Predators. Player B is Sidney Crosby of the Penguins, perhaps the best player in the league this year and maybe the best since Wayne Gretzky.
Does this mean that Fisher is a better player? Absolutely not. Does this mean that Fisher had a better game? Probably.
Fisher had the puck more than his opponents at five-on-five play, as illustrated by he and his teammates’ shot attempts while on the ice. It may seem as basic as hockey can get, that you want to have the puck more than the other team. But there are still people who feel that Corsi and other statistics are irrelevant and show no correlation to winning.
They can bring their criticisms to the Chicago Blackhawks, who turned to an outside analytics company in 2009 to breakdown their numbers for the team's usage. From 2010 to 2015, the Blackhawks won three Stanley Cups.
Or they can yell “leadership wins games!” at the San Antonio Spurs, one of the first users of SportVU, who’ve won two titles and played for three since 2005 when SportVU was started.
Of course, analytics aren’t everything. Teams get dominated in advanced statistics all the time, yet find a way to win. That doesn’t make them “winners” or even the statistical departments wrong. It makes that team lucky. Or at the very least, there will be a need for more data points.
It may seem simple to say “the team with the puck in hockey the most will win the most,” or that “the team that gets the most people on base in baseball will win the most games,” but there are still detractors.
In reality, the game hasn’t changed. There’s just numbers to quantify what you saw with your eyes. For whatever reason, that scares people.
Statisticians aren’t making the game less fun, they’re trying to give statistical reasons as to why things are happening.
There are so many other stats that I have neither the ability nor space to speak on, but know that they’re out there. Don’t talk about “what you just know,” research a player’s win shares in the NBA. Talk about how a third baseman's batting average on balls in play (BABIP) is higher than another’s, and debate that.
You’ll still be talking about player’s numbers and you’ll still mention how good you think they are. You’ll just be looking at those numbers through a different prism. And definitely a smarter prism.
