It has been too long on this website with not a mention of cricket. To remedy that, here is essential reading by Cameron Ponsonby at ESPNCricinfo on how fast bowling speeds are only a portion of the feel of a fast bowler’s pace:

It is very easy to think of facing fast bowling as primarily a reactive skill. In fact, read any article on quick bowling and it will invariably say you only have 0.4 seconds to react to a 90mph delivery.

But what does that mean? No one can compute information in 0.4 seconds. It’s beyond our realm of thinking in the same way that looking out of an aeroplane window doesn’t give you vertigo because you’re simply too high up for your brain to process it.

However, the reason it’s possible is because, whilst you may only have 0.4 seconds to react, you have a lot longer than that to plan. And the best in the world plan exceptionally well.

When the ball arrives to you, as the batter, literally faster than you can react to the ball, how fast a ball feels has way more to do with diversity between bowlers than the raw pace on the ball.

Excellent and insightful read.

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Another interesting piece by Ponsonby talks about data analytics in cricket. As Ponsonby mentions in his fast bowling article, cricket only dabbles in data analytics when compared to, say, baseball, where the analytics have been taken to another level altogether.

I think I’m okay with the balance that cricket has with its data analytics: I would rather have the analytics being fascinating reads for the fan, and an influence on the coaches/players, without their becoming all that anyone cares or talks about. I sometimes feel like the innate skill and art of sport gets lost in baseball. Makes for great reading though!

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In this study, we continuously monitored Egyptian fruit bats for months, recording audio and video around-the-clock. We analyzed almost 15,000 vocalizations, which accompanied the everyday interactions of the bats, and were all directed toward specific individuals, rather than broadcast. We found that bat vocalizations carry ample information about the identity of the emitter, the context of the call, the behavioral response to the call, and even the call’s addressee. Our results underline the importance of studying the mundane, pairwise, directed, vocal interactions of animals.

This is brilliant. They were able to correlate their data analysis of the bats’ vocalizations with the behavior and responses that they observed… so now we know more about how bats communicate! Simply by listening to the vocalization, the context, addressee, and even “the outcome of the interaction can be predicted above chance level”. Fascinating.

From the discussion:

It is important to note that we used one set of acoustic features for classification. However, many other multi-dimensional spectro-temporal representations can be tested. The bat’s brain could thus be using some other representation that encapsulates much more information regarding different social aspects. The bat may be able to classify the context of an interaction with higher confidence, based on some acoustic feature which it evolved to use and is yet to be determined. Our analysis is thus probably only a lower bound on what a bat is capable of extracting from aggressive social vocalizations. For example, we did not include any temporal information in our analysis.

In any acoustic signal, and especially where communication is involved, the time parameter is usually crucial and will add rich layers of information. For example, just imagine taking a piece of human speech, and (a) only looking at the overal speech parameters, versus (b) observing how the speech parameters change during the speech. Case (b) will provide far more information than case (a). I think we will discover over time that bats have a pretty well-evolved communication scheme.

This is fascinating stuff.

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Bayesian inference is a way to get sharper predictions from your data. It’s particularly useful when you don’t have as much data as you would like and want to juice every last bit of predictive strength from it.

Although it is sometimes described with reverence, Bayesian inference isn’t magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.

An excellent, simple introduction to Bayesian inference. This uses practical examples and an abundance of visual guides: especially useful if you don’t have an extensive math background.

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In the US, automobile fuel economy is usually measured in miles per gallon, mpg. This works, but there is a better metric, especially for comparison between values. Gallons per 100 miles is the way to go!

This is very well known, and even I’ve talked about this before. There are numerous online tools to do the conversion from mpg to gallons per 100 miles… but there don’t appear to be any simple conversion or calibration charts for it.

Well, here you are — an easy to use chart to convert between mpg and gallons per 100 miles (or, equivalently, from km/l to liters per 100km).

What’s wrong with using miles per gallon, though? Well, there’s nothing wrong with using it, of course (we all use it, after all!). It’s that it’s just not a good metric when it comes to comparisons.

This is because the mpg metric is not linear. This means that even a consistent difference in mpg, say a “10 mpg difference”, means different things based on where the difference is calculated from. This makes it very hard to calculate and compare the benefits of better fuel efficiency!

Let’s take a couple of examples and use the chart below. Let’s say you’re planning to shift from owning a 15mpg vehicle to owning a 25mpg vehicle. What are your fuel savings? On the other hand, say you’re shifting from a 25mpg vehicle to a 35mpg vehicle. What about now?

Let’s look at the chart. The horizontal axis shows miles per gallon, as indicated. The vertical axis shows gallons per 100 miles, also as indicated. Let’s find approximate numbers for our cases above:

• 15mpg → ≈ 6.7 gallons per 100 miles
• 25mpg → 4 gallons per 100 miles
• 35mpg → ≈ 2.8 gallons per 100 miles

For every 100 miles you drive, a “10mpg improvement” from 15mpg saves you 2.5 gallons (≈ 40%) of fuel. On the other hand, over the same 100 miles and the same “10mpg improvement”, but from 25mpg, you save only 1.2 gallons (≈ 30%) of fuel. See how these numbers are different, even though the mpg metric difference between the two cases remains constant?

The mpg metric would have worked, if our baseline was different. But does anyone ever say: “Hey, I have 3 gallons of fuel; how far can I go with it?” Instead, our question is always: “I need to drive 500miles; how much fuel would I need?”

Go ahead and download the full size chart and keep with you. If you’re in the market for cars, this will come in handy! You know how much you drive; this chart gives you an easy way to measure your particular fuel requirements (or savings).

P.S.: The above chart works with any ratio of units; just keep the units the same between the horizontal and vertical axes. So, for example, the same chart applies for km/l vs. liters per 100km.

One of my pet peeves with scientific journalism is the propensity to confuse correlation with causation. The idea is that just because two things are observed to happen at the same time (or before, or after, one another), does not imply that one causes the other.

In the latest example of this, the link between chocolate and good health is revisited.

The article opens with:

People who eat chocolate regularly tend to be thinner, new research suggests.

… which implies that a causation has been observed. The article goes on to make the following points:

[…] those who ate chocolate a few times a week were, on average, slimmer than those who ate it occasionally.

The link remained even when other factors, like how much exercise individuals did, were taken into account.

[…] it is how often you eat chocolate that is important, rather than how much of it you eat. The study found no link with quantity consumed.

So… I’d still lose weight if I ate a tonne of chocolate very frequently? Really?!

The most important statement, however, comes a little later:

But the findings only suggest a link - not proof that one factor causes the other.

… and,

And if you are looking to change your diet, you are likely to benefit most from eating more fresh fruits and vegetables.

Now guess what the headline of this article, which itself says that it’s only a link, and talks about maintaining an overall good diet, reads.

Chocolate ‘may help keep people slim’

Perfect, isn’t it?