For those that don’t know, the default data type for a .csv is not an array or list, its a data table, which takes up far more memory than it should and converts all string to factors for easier use in regression. This is dumb and in my experience will 5x-10x the time it takes to read in the file and memory it takes up. For a quick fix, set stringsAsFactors=F. If you don’t have column headings, which I normally don’t, set header=F as well:

data = read.csv(“datafile.csv”,header=F,stringsAsFactors = F)

First, as implied by the title of the article, games are decreasing in popularity on Android as a share of total downloads, while that same share is increasing on iOS. In December, games were 36.1% of iTunes downloads and 22% of Android downloads.

Next, I wanted to tackle some misconceptions about app pricing on the two platforms. As a proportion, paid apps are an almost negligible proportion of downloads on Android (where they hover around 3-4%). Consequently, average “app purchase price” (shown below) is quite low compared to iOS, where the proportion of paid app downloads is between 6 and 10 times as high.

The above plot is misleading because it hides two important facts:

• $.99 apps are a VERY large proportion of iOS app downloads
• a relatively larger proportion of app downloads on Android are at “premium” price points due to this relative lack of apps at price points less than $1

As a result, average app price, conditional on non-free apps, is actually higher on Android.

The point of this article isn’t to steer developers of apps (premium or otherwise) to or away from either platform, each of which has its strengths. You can read up on monetization of platforms here and here (one article is very pro-iOS, the other very pro-Android). Rather, I wanted to reinforce a basic lesson from Stat 101: averages can be very misleading.

“We endear ourselves to the teams and even use terms such as ‘we’ and ‘us’ when talking about their most recent triumph or failure. It’s natural that something which creates such a deep emotional reaction yields so much conversation both in person and in social networks and social apps.”

and another one on why athletes and particularly fans engage with social networks:

“Rather than simply reading about players online, collecting cards, or wearing their jerseys, fans can follow their favorite players on Twitter, subscribe to their Facebook feed, or even see where they check-in on Foursquare, I see this trend not only continuing, but accelerating in the coming year as social media continues to become more ubiquitous.”

Just thought it was cool and would make my mom proud.

• search traffic for the terms shopping and discounts spiked more than 1300% and 3000%, respectively on Black Friday and returned to normal on Cyber Monday
• I highlight tons of great Holiday Apps and Games to keep you busy through the season
• paid downloads were up 7% on iPhone

More details can be found in the report below:

Chomp Charts November

I do most of my heavy computation in Python, leaving R for primarily making pretty plots, exploratory data analysis (EDA) when I first get my hands on a data set, and using my favorite R packages/functions that I’ll never implement on my own (ie Random Forests, CART, SVM). Below is an example plot, with two sets of numbers, a legend, and dates on the axis. Hopefully this is more helpful than the R documentation.

The full ingredient list is:

• 1 pkg egg noodles.
• cook, drain, put in large pot and add
• 1/2 cup sugar
• 1 cup sour cream
• 1 container cottage cheese
• 3 eggs beaten
• 1 sm can crushed pineapple with juice
• 1 stick melted margarine (butter)
• 1 apple grated (thick)
• 1 teaspoon vanilla
• 1.5 teaspoon cinnamon

Beat eggs then add all other ingredients, cottage cheese last. When fully mixed the ingredients should smell like kugel and have roughly this consistency:

Mix well put in large baking pan sprinkle with crushed cornflake crumbs and dot with butter. Bake 1 hour at 350 and it should look like this:

Last, but certainly not least, here’s a pic of my grandmother (right) and me as its her recipe:

I’ll try to motivate this in as general a setting as possible. Suppose you have some metric (batting average, earnings, engagement) for a population of individuals (baseball players, businesses, users of your product) over several time periods. A traditional Random effects model estimates an intercept term for every individual. In some situations the assumption is unrealistic.

Often populations contain individuals who are indistinguishable from average, meaning its better to estimate their value with the overall mean rather than with their own data. This implies the metric is not predictive for that player. By definition, those not in the previous group are systematically high or low relative to the average. Examples of the second group include Barry Bonds, who always hit more home runs and took more steroids than average, Warren Buffet and Berkshire Hathaway, who always made better investments than average, or my Google friends’ use of Facebook since the release of Google+, which is systematically lower than average. This is best visualized by two distributions, the black spike with all its probability at the overall mean (average individuals), and the red distribution with most of its probability far above or below this value (non-average individuals).

Once we find the probability each player is a member of the two categories for each metric, we can tell if a metric is predictive if: a) most individuals are systematically different from the average and b) most of the metric’s variance is explained by the model. Finally, the obligatory plot showing our method performs at least as well on a holdout sample as other methods for the 50 metrics tested:

For those interested (which should be everyone but in practice is almost no one), our method also automatically controls for multiple testing, as we perform 1,575 tests in our analysis.

This paper was co-authored with Blake McShane, James Piette, and Shane Jensen, and can be viewed as a more technical companion piece to our previous paper “A Point-Mass Mixture Random Effects Model for Pitching Metrics” which can be downloaded here. The poorly commented python code for the MCMC sampler can be found here. If you’re interested in implementing or tweaking our methodology, feel free to send me an email or reach out on Twitter.

It’s exactly the same graphic as before, but with two decimal places. While my 8th grade Chemistry teacher may be glad that they are using more significant digits, I honestly don’t care. They were there before, just not displayed. Lame.

In their blog post, they claim: “This project represents the biggest step forward in accuracy, transparency and our technology in Klout’s history.” They support this vague claim with the histogram below, showing the differences in Klout scores, before and after the change:

This histogram leaves tons of open questions. Is this different than your normal daily shift in scores? The histogram reminds me of a t-distribution with a fatter positive tail. If more people are signing up for Klout than are leaving, thats probably what it should look like anyways as users hookup more networks and gradually become more active online. The graphic doesn’t show that your score is any better, just that it changed. That’s not impressive at all.

My beef with Klout remains simply that the service provides us with no real validation or explanation of our scores. They don’t show us how many times we have been RT’ed, mentioned, etc. On Google, you can look up your page rank, on app stores you can see your average rating and number of ratings, on Klout, you are told that your true reach has increased, but not told what that implies or how you can verify it.

Klout is still the social influence measurement leader, but with Peerindex rapidly improving (and better in many ways in my opinion), and new competitors such as Proskore and Kred popping up, Klout should be worried. I’ll have a review of both Proskore and Kred up shortly as well so you can easily compare them for yourself.

Ingredients:

• 3 1/2 cups all-purpose flour
• 1 tablespoon baking powder
• 1 teaspoon baking soda
• 1/2 teaspoon kosher salt
• 4 teaspoons ground cinnamon
• 1/2 teaspoon ground cloves
• 1/2 teaspoon ground allspice
• 1 cup vegetable oil
• 1 cup honey
• 1 1/2 cups granulated sugar
• 1/2 cup brown sugar
• 3 large eggs at room temperature
• 1 teaspoon vanilla extract
• 1 cup coffee
• 1/2 cup fresh orange juice
• 1/4 cup bourbon

Preparation Instructions:

1. In a large bowl, whisk together the flour, baking powder, baking soda, salt, cinnamon, cloves and allspice
2. Make a well in the center, and add oil, honey, granulated sugar, brown sugar, eggs, vanilla, coffee, orange juice and bourbon
3. Mix well and then portion into tins. Do not overbeat! If you are using an electric mixer, do so at a low setting.

This is a huge recipe and its into FIVE 7.75 Inch x 3.75 Inch x 2.75 loaf pans. I normally just pick up a pack of 6 disposable aluminum tins at the grocery store.

The batter should look like this once combined:

The cakes keep for up to 5 days and honestly are better after sitting for a day, just cover with foil or cling wrap in the tin to store. Enjoy!

The paper describing their methodology can be found here. The files are available here. In the extracted directory simply run “make all”.

I ran into two compilation errors:

I changed uint to int on line 1272 and combined the cout onlines 1273 and 1274 into one line, which fixed the complaints. Then the build complained:

Simply comment out “LINKFLAGS = -static” and everything should happily compile.