The Angry Statistician

Hofstadter mentions the following recursive relation in his great book "Gödel, Escher, Bach": \[ \begin{align} g(0) &= 0;\\ g(n) &= n-g(g(n-1)). \end{align} \] I claim that \( g(n) = \left\lfloor \phi\cdot (n+1) \right\rfloor \), where \( \phi = \frac{-1+\sqrt{5}}{2}\), and I'll show this using a technique that makes proving many identities of this type nearly automatic. Let \( \phi\cdot n = \left\lfloor \phi\cdot n \right\rfloor + e\), where \( 0 < e < 1\) as \( \phi \) is irrational, nor can \(e = 1-\phi\), and note that \(\phi\) satisfies \( {\phi}^2 + \phi - 1 = 0\). Some algebra gives \[ \begin{align} n-\left\lfloor \left( \left\lfloor \phi\cdot n \right\rfloor + 1 \right) \cdot \phi \right\rfloor &= n-\left\lfloor \left( n\cdot \phi - e + 1 \right) \cdot \phi \right\rfloor \\ &= n-\left\lfloor n\cdot {\phi}^2 - e\cdot \phi + \phi \right\rfloor \\ &= n-\left\lfloor n\cdot \left(1-\phi\right) - e\cdot \phi + \phi \right\rfloor \\ ...

Here's a nice little problem from the 13th All Soviet Union Mathematics Olympiad. Given a set of real numbers \(S\) containing 0 and 1 that's closed under finite means, show that it contains all rational numbers in the interval \(\left[0,1\right]\). This isn't a difficult problem, but there's a particularly nice solution. First observe that if \(x\in S\) then both \(\frac{x}{4}\) and \(\frac{3x}{4}\) are in \(S\); average \(\{0,x\}\) to get \(\frac{x}{2}\), average \(\{0, \frac{x}{2}\}\) to get \(\frac{x}{4}\), average \(\{\frac{x}{2}, x\}\) to get \(\frac{3x}{4}\). We could show any rational number \(\frac{m}{n}\) with \(1\leq m < n\) is in \(S\) if we had \(n\) distinct elements from \(S\) that summed to \(m\). Let's exhibit one. Start with an array with \(m\) 1s on the left, \(n-m\) 0s on the right. Repeatedly replace adjacent \(x,y\) values with \(\frac{3(x+y)}{4}, \frac{(x+y)}{4}\), where either \(x=1,y\neq1\) or \(x\neq 0, y=0\), until there is one...

A tribute to Martin Gardner . For which sets of positive integers does the sum equal the product? For example, when does \( x_1 + x_2 = x_1\cdot x_2\)? It's easy to see that this is only true when \(x_1 = x_2 = 2\). In the general case our equality is \(\sum_{i=1}^{n} x_i= \prod_{i=1}^{n} x_i \). We can rearrange terms to give \[x_1+x_2+\sum_{i=3}^{n} x_i= x_1\cdot x_2\cdot \prod_{i=3}^{n} x_i,\] and this in turn factors nicely to give us \[\left( x_1\cdot \prod_{i=3}^{n} x_i - 1\right)\cdot \left( x_2\cdot \prod_{i=3}^{n} x_i - 1\right) = \left( \prod_{i=3}^{n} x_i \right)\cdot \left(\sum_{i=3}^{n} x_i \right) + 1.\] How does this help? Consider the case \(n=5\), and without loss of generality assume \(x_i \ge x_{i+1}\) for all \(i\). If \(x_3=x_4=x_5=1\) our factorized equation becomes \[(x_1-1)\cdot(x_2-1)=4,\] with the obvious solutions \( x_1=5, x_2=2; x_1=3, x_2=3\). The only remaining case to consider is \(x_3=2\), as any other case forces  \( \sum_{i=1}^{n} x_...

My friend Jordan Ellenberg wrote an article for the Wall Street Journal recently describing a metric to roughly measure the least read books, which he calls the Hawking Index (HI). As he describes it, take the page numbers of a book's five top highlights, average them, and divide by the number of pages in the whole book. On a discussion thread on Facebook, this led to a proposal from me for measuring the general quality of a quote. Assume a user has some level of discrimination \(D\); the higher the value of \(D\), the more likely they are to quote a passage. Now assume each passage has some measure of quality \(Q\); the higher the value of \(Q\), the more likely a passage is to be quoted. Let's try a classic Bradley-Terry-Luce model - if a user with discrimination \(D\) quotes at least one passage from a particular work, the probability \(p\) that they'll quote a given passage with quality \(Q\) from that same work is roughly \[ p = \frac{Q}{Q+D}.\] Nice, but where ca...

Meredith Wills , Will Carroll and  myself  are donating four student two-day tickets, including lunch, for the upcoming baseball analytics  Saberseminar  run by Dan Brooks . This is a wonderful event, and 100% of the proceeds are donated to the Jimmy Fund . You must  be a current student. Meredith and myself will by choosing four students by the end of this week, Sunday April 13, 2014. Please note: These tickets are for both days, August 17-18, 2014 The event is in Boston, MA Lunch is included, but no other meals Transportation and lodging are not included If you would like to be considered for a donated ticket, please send: Your full name (first and last) If you're outside of the Boston area, how will you be getting to the event? Your school affiliation and whether high school or college Best contact email address (if different from reply-to address) A little about your baseball interests, analytical or otherwise Do you see yourself workin...

As I've mentioned previously , a common method used in sports for estimating game outcomes known as log5 can be written \[p = \frac{p_1 q_2}{p_1 q_2+q_1 p_2}\] where \(p_i\) is the fraction of games won by team \(i\) and \(q_i\) is the fraction of games lost by team \(i\). We're assuming that there are no ties. What's the easiest way to derive this estimate? Here's one argument. Assume team \(i\) has a probability \(p_i\) of beating an average team (a team that wins half its games). Now imagine that this means for any given game the team has some "strength" sampled from [0,1] with median \(p_i\) and that the stronger team always wins. Thus, the probability that team 1 beats team 2 is \[ p = \int_0^1 \int_0^1 \! \mathrm{Pr}(p_1 > p_2) \, \mathrm{d} p_1 \mathrm{d} p_2 .\] This looks complicated, but but with probability \(p_1\) team 1 is stronger than an average team and with probability \(p_2\) team 2 is stronger than an average team. From this perspecti...

It's time for a workstation upgrade; here's what I've assembled. The massive case has plenty of space for additional drives to store the extreme amount of data that nearly every sport is now collecting together with the associated video footage. The case, power supply and motherboard allow up to 3 additional video cards to use as GPU units as your analytical needs demand (and budget can handle). Cooler Master Cosmos II - Ultra Full Tower Computer Case An absolutely massive case - plenty of room for an E-ATX motherboard, large power supply, multiple video cards and hard drives. PC Power & Cooling 1200W Silencer MK III Series Modular Power Supply An exceptionally large power supply, but platinum rated and plenty of room to spare allows it to operate nearly silenty, plus it'll handle any additional video cards you'll add later for GPU computing. ASUS Rampage IV Black Edition LGA 2011 Extended ATX Intel Motherboard Space for 4 video cards,...