Sports Analytics: Remember, Statistics aren’t Perfect!
We came across an interesting article in the Wharton Magazine blog titled “The Dangerous Data Fetishes of Sports Analytics” by Ian Cooper. The main point of the article is that some sports statistics do not add value. The main example Ian cites is the “PDO” variable which is used in hockey.
Take the example of PDO, a meaningless acronym that’s simply the sum of a team’s overall shooting percentage (the number of shots that result in goals) and its save percentage (the percentage of opponents’ shots that its goalie prevents from going in the net).
Overall, PDO can add some value, but it is not a perfect measure. Ian points out one of the flaws in the measure: the assumption is that hitting the net (scoring) from any distance is a repeatable task. So it doesn’t matter how far away from the net you are when you shoot. That doesn’t seem to make sense — but this is assumed in the PDO variable! As Ian states:
I dug a bit further and what I found was really interesting (although not perhaps entirely surprising): those who shot from “in-close” did a better job of hitting the net than those who shot from far away. (For more, read “The Relationship Between Shooting Distance and Shot Percentage on Net.”)
That seems pretty straight-forward! I have played hockey my entire life, and I agree with the study’s assessment — it is easier to score when being close to the net!!
Here is Ian’s main point:
But what’s increasingly clear in hockey, as in all areas that are being transformed by big data, is that data don’t apply themselves. Concepts like repeatability, sustainability and sample size are useful, but only if you understand how and when to apply them. To do that, it takes a good qualitative understanding of whatever subject you’re studying (in my case hockey), constant attention to whether the data are relevant to the hypothesis you’re testing, and the creativity and analytical chops to interpret whatever your scatter plot, histogram or regression model might be telling you.
This is similar in stock selection and other sports — you need to understand why a variable works in order for it to be useful. Otherwise, it is most likely just noise.
As an avid NBA fan, I am aware of Daryl Morey, who is famous for his “sports analytics” capabilities. He is the General Manager of the Houston Rockets, who have had a successful season by all accounts. Part of his success has been driven through a combination of good draft picks such as Terrence Jones and Donatas Motiejunas, as well as picking up a lesser-known player in Patrick Beverly in free agency. However, he was also able bring in some franchise players such as Dwight Howard (free agency) and James Harden (via trade), as well as pick up Josh Smith at pennies on the dollar (bad fallout from the Pistons).
Is the success of the Houston Rockets driven by sports analytics? Some say yes — the 76ers hired Sam Hinkie from Houston to employ similar advanced analytics. Sam has, for better or worse, embraced the tanking philosophy. However, others disagree.
I leave the last word to Charles Barkley for his views on sports analytics (and Daryl Morey):
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Definitions of common statistics used in our analysis are available here (towards the bottom)