Fansided

College basketball takeaways: Alabama has to play a little undermanned

By Joseph Nardone

EUGENE, OR - DECEMBER 11: Head coach Avery Johnson of the Alabama Crimson Tide signals to his bench during the first half of the game against the Oregon Ducks at Matthew Knight Arena on December 11, 2016 in Eugene, Oregon. (Photo by Steve Dykes/Getty Images)
EUGENE, OR - DECEMBER 11: Head coach Avery Johnson of the Alabama Crimson Tide signals to his bench during the first half of the game against the Oregon Ducks at Matthew Knight Arena on December 11, 2016 in Eugene, Oregon. (Photo by Steve Dykes/Getty Images) /
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A video board display’s the closing numbers after the closing bell of the Dow Industrial Average at the New York Stock Exchange on November 22, 2017 in New York. / AFP PHOTO / Bryan R. Smith (Photo credit should read BRYAN R. SMITH/AFP/Getty Images)
A video board display’s the closing numbers after the closing bell of the Dow Industrial Average at the New York Stock Exchange on November 22, 2017 in New York. / AFP PHOTO / Bryan R. Smith (Photo credit should read BRYAN R. SMITH/AFP/Getty Images) /

Data. Data. Data.

This isn’t about the eyeball test being more valuable than advanced-stats. The two should live together in the same world, providing context for each other, while helping to paint the most accurate picture as humanly possible for consumers of college hoops.

That being said, and deep breathes here … all the data people are using at the moment happen to be corrupt. Near gosh slam worthless, really.

As a bunch of teams are devouring on cupcakes, what good are the numbers on Wendell Carter Jr.’s blocks per outing if the majority of them came against the ho-hum competition?

A logical question then emerges: When, exactly, is the data good for use?

The correct answer is actually at the end of the season, with a full sample-size available, but in terms of it being less misleading, we can go with at some point in January.

But wait! I have a few more issues with how we use numbers in the realm of unpaid laborers trotting about the hardwood for our entertainment.

People will often want to quantify a player’s data in terms relative to games against top-25 or top-50 competition. That is fine. It is far better than wanting to use data from games against Utah Valley State Northwestern Southern Most District (not a real school, for what it is worth), but there should be a caveat attached to those numbers, too.

Simply because Player-X from Power Program-Y is also putting up huge numbers against another power team, it doesn’t inherently mean he is playing against someone to be considered near his equal at his position.

More bluntly put: It is obviously better for a stud player to be playing well against stud teams, but the latter might not be so studly opposite the position of the player.

As a theoretical example: Pretend a highly touted center for Duke is about to play a big game against a top-10 team in the country. Now pretend that the top-10 team in the country is really good at every position but center. Is that fictitious center’s numbers all that valuable then?

And we haven’t even mentioned scheme, opposing schemes against, and so on. There are so many variables that flat-line numbers can often do an injustice to players and teams.

Again, noting this has as strongly possible — data is good. The more information we have, the better. Ignoring data is dumb. It would be like not wanting the inspector’s report on a house you want to buy just because it is too much information.

Nevertheless, let’s just be careful when spewing these numbers at this point in the season. Provide the context to go around them. Without that, the digits next to a player’s name are just a grouping of numbers minus proper attribution of other non-numerical data.