My generator works
I’m quite proud of how my blogpost template generator is taking shape. While there are still some edge cases that I haven’t covered, and a few more improvements to be made, it’s coming along nicely.
If you want to take a look, I linked the repository in a previous post. Here’s the permalink to the latest version of the script.
Shifting gears, I’m finally working on something pretty interesting at my day job. It’s simple codewise, but the theory behind it has been engaging. I’ve been focusing on building predictors for young athletes.
While these predictors aren’t complex from a technical perspective, they are quite powerful in terms of the insights they provide about athletic potential.
I’m finally doing some interesting things at work. Codewise they’re simple but I’m working on some predictors that are for young athletes and the theory behind them is interesting.
One of the most fascinating methods I’ve encountered is the Khamis-Roche method, which estimates a child’s adult height based on their current height, age,
and their parents’ heights. This method is widely used and doesn’t require invasive procedures like X-rays, which makes it especially practical.
How the Khamis-Roche Method Works:
- Inputs: The method takes the child’s current height, weight, age, and the average of the parents’ heights (mid-parental height).
- Mid-parental Height: This is calculated by averaging the father’s and mother’s heights.
- Gender Adjustment: The formula adds 5 inches (about 13 cm) for boys and subtracts the same amount for girls to account for typical growth patterns.
- Growth Data: The method considers the child’s current growth percentiles for a more accurate prediction.
- Accuracy: It’s around 95% accurate, especially for children aged 4 and older.
- Non-Invasive: Unlike other methods that rely on X-rays to estimate bone age, the Khamis-Roche method is completely non-invasive.
- Gender Differences: The formula is slightly adjusted for boys and girls to reflect different growth trajectories. This method is straightforward and grounded in general growth trends, making it reliable and easy to apply.
I’ve also realized that predicting the potential of a young athlete involves much more than just their technical abilities. It’s been eye-opening to see how various predictors, like growth patterns, influence assessments of athletic potential. These small discoveries have made my work feel less routine and more meaningful in recent days.
Things I like - in random order
A photo a took this past summer from the top of Redipuglia War Memorial. I did like the contrast between the white concrete and the blue sky.
Today’s Links
How Telegram became the Underworld’s favourite app The Daily
The Real-World cost of AI Machines like us podcast
Browsing the Archive
Hacker’s Movie Guide