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No mercy

Progressive Forage Editor Lynn Jaynes Published on 30 August 2016

Nobody likes the end of a close basketball game because it’s nothing but a foul-fest; coaches routinely instruct players to foul the worst free-throw shooter on the opposite team. It’s so painful to watch.

And why is it the tallest, largest NBA players have the worst free-throw stats? NBA enthusiasts studied hand size as a factor in free-throw success – maybe larger hands make shooting more difficult? It’s an interesting argument but didn’t pan out under scrutiny.

A research guru then looked at another angle of the problem; perhaps taller players had higher shot trajectories, which translated to a faster rim approach (say a 20.4-feet-per-second drop as opposed to a 19.4-feet-per-second drop). If the shot was dead-on, it wouldn’t matter.

But if the ball nicked the rim, the chances of the slower ball bouncing through the hoop were greater – it was statistically measurable.

This theory did not turn out to be the greatest overall factor between good free-throw shooters and poor ones either, however. Nope. Under a mountain of data, they found it had everything to do with consistently low variations (in position, release point, velocity, angle, etc.), something that can be trained.

But unless someone had gone looking to measure different aspects of the problem to resolve the issue, the question wouldn’t have
been answered.

I don’t really give a pig’s tail as to why the NBA’s big men miss foul shots. What I do care about is taking apart the long-held traditions and beliefs about a thing and re-examining the issues minutely
– no mercy.

In Moneyball: The art of winning an unfair game, by Michael Lewis, the author re-examines statistical evidence in baseball, where the long-held practice of a player’s value was traditionally (and erroneously) based on batting average and runners batted in.

In the ’70s, Bill James started compiling data that drilled mercilessly into the game to try and win his fantasy baseball league. He minutely examined stats (sabermetrics) that previously had been deemed unimportant enough to not be measured, such as the distance of a fly ball, where a ground ball was picked up, who was pitching when a base was stolen, how many pitches a pitcher threw in a game and against which batters, and assigning values to outs, walks, steals, singles, doubles – all measured. How did a left-handed hitter perform against right-handed pitchers?

He took non-traditional stats, analyzed them and made correlations to find out which players were bringing more value to the game.

An aside – if you’ll permit me – such data gathering into basketball hand size and baseball fly specks of information ... really? Somebody has a whole lotta time on their hands and really should get out more.

And here’s the point: How much value is there to your production “game,” and what can you measure to evaluate your practices, which ultimately adds value? You may not be very far down the road with precision agriculture, or maybe you are and are overwhelmed with the mountain of data it generates, but precision agriculture allows us to see different angles of production problems. It makes us shed our long-held beliefs in a trade-off for practices of more value.

I was enlightened by an article in this issue (Precision nutrient management in forage systems) on precision ag and soil sampling that portrays exactly the same approach – going beyond just taking statistics to determine the value behind the numbers and make sure we’re looking at the right numbers and collecting the right data for the right reasons.

I think it’s dangerous to assume that we already have all the data we need to be good producers. We’ve got to continue measuring and collecting data and then mining it for correlations and value, even when it’s uncomfortable. I’m sure I heard some smart person say that … oh, wait, I know – it was that TV interview show I flipped to during the last two-minute foul-fest of an NBA game.  end mark

Lynn Jaynes
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