My Answer For Who’s The Most Improved Pinball Player

Okay, so writing “this next essay right away” didn’t come to pass, because all sorts of other things got in the way. But to get back to where we had been: we hoped to figure out which of the players at the local pinball league had most improved over the season. The data I had available. But data is always imperfect. We try to learn anyway.

What data I had was this. Each league night we selected five pinball games. Each player there played those five tables. We recorded their scores. Each player’s standing was based on, for each table, how many other players they beat. If you beat everyone on a particular table, you got 100 points. If you beat all but three people, you got 96 points. If ten people beat you, you got 90 points. And so on. Add together the points earned for all five games of that night. We didn’t play the same games week to week. And not everyone played every single week. These are some of the limits of the data.

My first approach was to look at a linear regression. That is, take a plot where the independent variable is the league night number and the dependent variable is player’s nightly scores. This will almost certainly not be a straight line. There’s an excellent chance it will never touch any of the data points. But there is some line that comes closer than any other line to touching all these data points. What is that line, and what is its slope? And that’s easy to calculate. Well, it’s tedious to calculate. But the formula for it is easy enough to make a computer do. And then it’s easy to look at the slope of the line approximating each player’s performance. The highest slope of their performance line obviously belongs to the best player.

And the answer gotten was that the most improved player — the one whose score increased most, week to week — was a player I’ll call T. The thing is T was already a good player. A great one, really. He’d just been unable to join the league until partway through. So nights that he didn’t play, and so was retroactively given a minimal score for, counted as “terrible early nights”. This made his play look like it was getting better than it was. It’s not just a problem of one person, either. I had missed a night, early on, and that weird outlier case made my league performance look, to this regression, like it was improving pretty well. If we removed the missed nights, my apparent improvement changed to a slight decline. If we pretend that my second-week absence happened on week eight instead, I had a calamitous fall over the season.

And that felt wrong, so I went back to re-think. This is dangerous stuff, by the way. You can fool yourself if you go back and change your methods because your answer looked wrong. But. An important part of finding answers is validating your answer. Getting a wrong-looking answer can be a warning that your method was wrong. This is especially so if you started out unsure how to find what you were looking for.

So what did that first answer, that I didn’t believe, tell me? It told me I needed some better way to handle noisy data. I should tell apart a person who’s steadily doing better week to week and a person who’s just had one lousy night. Or two lousy nights. Or someone who just had a lousy season, but enjoyed one outstanding night where they couldn’t be beaten. Is there a measure of consistency?

And there — well, there kind of is. I’m looking at Pearson’s Correlation Coefficient, also known as Pearson’s r, or r. Karl Pearson is a name you will know if you learn statistics, because he invented just about all of them except the Student T test. Or you will not know if you learn statistics, because we don’t talk much about the history of statistics. (A lot of the development of statistical ideas was done in the late 19th and early 20th century, often by people — like Pearson — who were eugenicists. When we talk about mathematics history we’re more likely to talk about, oh, this fellow published what he learned trying to do quality control at Guinness breweries. We move with embarrassed coughing past oh, this fellow was interested in showing which nationalities were dragging the average down.) I hope you’ll allow me to move on with just some embarrassed coughing about this.

Anyway, Pearson’s ‘r’ is a number between -1 and 1. It reflects how well a line actually describes your data. The closer this ‘r’ is to zero, the less like a line your data really is. And the square of this, r2, has a great, easy physical interpretation. It tells you how much of the variations in your dependent variable — the rankings, here — can be explained by a linear function of the independent variable — the league night, here. The bigger r2 is, the more line-like the original data is. The less its result depends on fluke events.

This is another tedious calculation, yes. Computers. They do great things for statistical study. These told me something unsurprising: r2 for our putative best player, T, was about 0.313. That is, about 31 percent of his score’s change could be attributed to improvement; 69 percent of it was noise, reflecting the missed nights. For me, r2 was about 0.105. That is, 90 percent of the variation in my standing was noise. This suggests by the way that I was playing pretty consistently, week to week, which matched how I felt about my season. And yes, we did have one player whose r2 was 0.000. So he was consistent and about all the change in his week-to-week score reflected noise. (I only looked at three digits past the decimal. That’s more precision than the data could support, though. I wouldn’t be willing to say whether he played more consistently than the person with r2 of 0.005 or the one with 0.012.)

Now, looking at that — ah, here’s something much better. Here’s a player, L, with a Pearson’s r of 0.803. r2 was about 0.645, the highest of anyone. The most nearly linear performance in the league. Only about 35 percent of L’s performance change could be attributed to random noise rather than to a linear change, week-to-week. And that change was the second-highest in the league, too. L’s standing improved by about 5.21 points per league night. Better than anyone but T.

This, then, was my nomination for the most improved player. L had a large positive slope, in looking at ranking-over-time. L also also a high correlation coefficient. This makes the argument that the improvement was consistent and due to something besides L getting luckier later in the season.

Yes, I am fortunate that I didn’t have to decide between someone with a high r2 and mediocre slope versus someone with a mediocre r2 and high slope. Maybe this season. I’ll let you know how it turns out.

Who We Just Know Is Not The Most Improved Pinball Player

Back before suddenly everything got complicated I was working on the question of who’s the most improved pinball player? This was specifically for our local league. The league meets, normally, twice a month for a four-month season. Everyone plays the same five pinball tables for the night. They get league points for each of the five tables. The points are based on how many of their fellow players their score on that table beat that night. (Most leagues don’t keep standings this way. It’s one that harmonizes well with the vengue and the league’s history.) The highest score on a game earns its player 100 league points. Second-highest earns its scorer 99 league points. Third-highest earns 98, and so on. Setting the highest score to a 100 and counting down makes the race for the top less dependent on how many people show up each night. A fantastic night when 20 people attended is as good as a fantastic night when only 12 could make it out.

Last season had a large number of new players join the league. The natural question this inspired was, who was most improved? One answer is to use linear regression. That is, look at the scores each player had each of the eight nights of the season. This will be a bunch of points — eight, in this league’s case — with x-coordinates from 1 through 8 and y-coordinates from between about 400 to 500. There is some straight line which comes the nearest to describing each player’s performance that a straight line possibly can. Finding that straight line is the “linear regression”.

A straight line has a slope. This describes stuff about the x- and y-coordinates that match points on the line. Particularly, if you start from a point on the line, and change the x-coordinate a tiny bit, how much does the y-coordinate change? A positive slope means the y-coordinate changes as the x-coordinate changes. So a positive slope implies that each successive league night (increase in the x-coordinate) we expect an increase in the nightly score (the y-coordinate).

For me, I had a slope of about 2.48. That’s a positive number, so apparently I was on average getting better all season. Good to know. And with the data on each player and their nightly scores on hand, it was easy to calculate the slopes of all their performances. This is because I did not do it. I had the computer do it. Finding the slopes of these linear regressions is not hard; it’s just tedious. It takes these multiplications and additions and divisions and you know? This is what we have computing machines for. Setting up the problem and interpreting the results is what we have people for.

And with that work done we found the most improved player in the league was … ah-huh. No, that’s not right. The person with the highest slope, T, finished the season a quite good player, yes. Thing is he started the season that way too. He’d been playing pinball for years. Playing competitively very well, too, at least when he could. Work often kept him away from chances. Now that he’s retired, he’s a plausible candidate to make the state championship contest, even if his winning would be rather a surprise. Still. It’s possible he improved over the course of our eight meetings. But more than everyone else in the league, including people who came in as complete novices and finished as competent players?

So what happened?

T joined the league late, is what happened. After the first week. So he was proleptically scored at the bottom of the league that first meeting. He also had to miss one of the league’s first several meetings after joining. The result is that he had two boat-anchor scores in the first half of the season, and then basically middle-to-good scores for the latter half. Numerically, yeah, T started the season lousy and ended great. That’s improvement. Improved the standings by about 6.79 points per league meeting, by this standard. That’s just not so.

This approach for measuring how a competitor improved is flawed. But then every scheme for measuring things is flawed. Anything actually interesting is complicated and multifaceted; measurements of it are, at least, a couple of discrete values. We hope that this tiny measurement can tell us something about a complicated system. To do that, we have to understand in what ways we know the measurements to be flawed.

So treating a missed night as a bottomed-out score is bad. Also the bottomed-out scores are a bit flaky. If you miss a night when ten people were at league, you get a score of 450. Miss a night when twenty people were at league, you get a score of 400. It’s daft to get fifty points for something that doesn’t reflect anything you did except spread false information about what day league was.

Still, this is something we can compensate for. We can re-run the linear regression, for example, taking out the scores that represent missed nights. This done, T’s slope drops to 2.57. Still quite the improvement. T was getting used to the games, apparently. But it’s no longer a slope that dominates the league while feeling illogical. I’m not happy with this decision, though, not least because the same change for me drops my slope to -0.50. That is, that I got appreciably worse over the season. But that’s sentiment. Someone looking at the plot of my scores, that anomalous second week aside, would probably say that yeah, my scores were probably dropping night-to-night. Ouch.

Or does it drop to -0.50? If we count league nights as the x-coordinate and league points as the y-coordinate, then yeah, omitting night two altogether gives me a slope of -0.50. What if the x-coordinate is instead the number of league nights I’ve been to, to get to that score? That is, if for night 2 I record, not a blank score, but the 472 points I got on league night number three? And for night 3 I record the 473 I got on league night number four? If I count by my improvement over the seven nights I played? … Then my slope is -0.68. I got worse even faster. I had a poor last night, and a lousy league night number six. They sank me.

And what if we pretend that for night two I got an average-for-me score? There are a couple kinds of averages, yes. The arithmetic mean for my other nights was a score of 468.57. The arithmetic mean is what normal people intend when they say average. Fill that in as a provisional night two score. My weekly decline in standing itself declines, to only -0.41. The other average that anyone might find convincing is my median score. For the rest of the season that was 472; I put in as many scores lower than that as I did higher. Using this average makes my decline worse again. Then my slope is -0.62.

You see where I’m getting more dissatisfied. What was my performance like over the season? Depending on how you address how to handle a missed night, I either got noticeably better, with a slope of 2.48. Or I got noticeably worse, with a slope of -0.68. Or maybe -0.61. Or I got modestly worse, with a slope of -0.41.

There’s something unsatisfying with a study of some data if handling one or two bad entries throws our answers this far off. More thought is needed. I’ll come back to this, but I mean to write this next essay right away so that I actually do.

Can We Tell Whether A Pinball Player Is Improving?

The question posed for the pinball league was: can we say which of the players most improved over the season? I had data. I had the rankings of each of the players over the course of eight league nights. I had tools. I’ve taken statistics classes.

Could I say what a “most improved” pinball player looks like? Well, I can give a rough idea. A player’s improving if their rankings increase over the the season. The most-improved person would show the biggest improvement. This definition might go awry; maybe there’s some important factor I overlooked. But it was a place to start looking.

So here’s the first problem. It’s the plot of my own data, my league scores over the season. Yes, league night 2 is dismal. I’d had to miss the night and so got the lowest score possible.

Is this getting better? Or worse? The obvious thing to do is to look for a curve that goes through these points. Then look at what that curve is doing. The thing is, it’s always possible to draw a curve through a bunch of data points. As long as there’s not something crazy like there’s four data points for the same league night. As long as there’s one data point for each measurement you can always connect those points to some curve. Worse, you can always fit more than one curve through those points. We need to think harder.

Here’s the thing about pinball league night results. Or any other data that comes from the real world. It’s got noise in it. There’s some amount of it that’s just random. We don’t need to look for a curve that matches every data point. Or any data point particularly. What if the actual data is “some easy-to-understand curve, plus some random noise”?

It’s a good thought. It’s a dangerous thought. You need to have an idea of what the “real” curve should be. There’s infinitely many possibilities. You can bias your answer by choosing what curve you think the data ought to represent. Or by not thinking before you make a choice. As ever, the hard part is not in doing a calculation. It’s choosing what calculation to do.

That said there’s a couple safe bets. One of them is straight lines. Why? … Well, they’re easy to work with. But we have deeper reasons. Lots of stuff, when it changes, looks like it’s changing in a straight line. Take any curve that hasn’t got a corner or a jump or a break in it. There’s a straight line that looks close enough to it. Maybe not for long, but at least for some stretch. In the absence of a better idea of what ought to be right, a line is at least a starting point. You might learn something even if a line doesn’t fit well, and get ideas for why to look at particular other shapes.

So there’s good, steady mathematics business to be found in doing “linear regression”. That is, find the line that best fits a set of data points. What do we mean by “best fits”?

The mathematical community has an answer. I agree with it, surely to the comfort of the mathematical community. Here’s the premise. You have a bunch of data points, with a dependent variable ‘x’ and an independent variable ‘y’. So the data points are a bunch of points, $\left(x_j, y_j\right)$ for a couple values of j. You want the line that “best” matches that. Fine. In my pinball league case here, j is the whole numbers from 1 to 8. $x_j$ is … just j again. All right, as happens, this is more mechanism than we need for this problem. But there’s problems where it would be useful anyway. And for $y_j$, well, here:

j yj
1 467
2 420
3 472
4 473
5 472
6 455
7 479
8 462

For the linear regression, propose a line described by the equation $y = m\cdot x + b$. No idea what ‘m’ and ‘b’ are just yet. But. Calculate for each of the $x_j$ values what the projection would be, that is, what $m\cdot x_j + b$. How far are those from the actual $y_j$ data?

Are there choices for ‘m’ and ‘b’ that make the difference smaller? It’s easy to convince yourself there are. Suppose we started out with ‘m’ equal to 0 and ‘b’ equal to 472. That’s an okay fit. Suppose we started out with ‘m’ equal to 100,000,000 and ‘b’ equal to -2,038. That’s a crazy bad fit. So there must be some ‘m’ and ‘b’ that make for better fits.

Is there a best fit? If you don’t think much about mathematics the answer is obvious: of course there’s a best fit. If there’s some poor, some decent, some good fits there must be a best. If you’re a bit better-learned and have thought more about mathematics you might grow suspicious. That term ‘best’ is dangerous. Maybe there’s several fits that are all different but equally good. Maybe there’s an endless series of ever-better fits but no one best. (If you’re not clear how this could work, ponder: what’s the largest negative real number?)

Good suspicions. If you learn a bit more mathematics you learn the calculus of variations. This is the study of how small changes in one quantity change something that depends on it; and it’s all about finding the maxima or minima of stuff. And that tells us that there is, indeed, a best choice for ‘m’ and ‘b’.

(Here I’m going to hedge. I’ve learned a bit more mathematics than that. I don’t think there’s some freaky set of data that will turn up multiple best-fit curves. But my gut won’t let me just declare that. There’s all kinds of crazy, intuition-busting stuff out there. But if there exists some data set that breaks linear regression you aren’t going to run into it by accident.)

So. How to find the best ‘m’ and ‘b’ for this? You’ve got choices. You can open up DuckDuckGo and search for ‘matlab linear regression’ and follow the instructions. Or ‘excel linear regression’, if you have an easier time entering data into spreadsheets. If you’re on the Mac, maybe ‘apple numbers linear regression’. Follow the directions on the second or third link returned. Oh, you can do the calculation yourself. It’s not hard. It’s just tedious. It’s a lot of multiplication and addition and you know what? We’ve already built tools that know how to do this. Use them. Not if your homework assignment is to do this by hand, but, for stuff you care about yes. (In Octave, an open-source clone of Matlab, you can do it by an admirably slick formula that might even be memorizable.)

If you suspect that some shape other than a line is best, okay. Then you’ll want to look up and understand the formulas for these linear regression coefficients. That’ll guide you to finding a best-fit for these other shapes. Or you can do a quick, dirty hack. Like, if you think it should be an exponential curve, then try fitting a line to x and the logarithm of y. And then don’t listen to those doubts about whether this would be the best-fit exponential curve. It’s a calculation, it’s done, isn’t that enough?

Back to lines, back to my data. I’ll spare you the calculations and show you the results.

Done. For me, this season, I ended up with a slope ‘m’ of about 2.48 and a ‘b’ of about 451.3. That is, the slightly diagonal black line here. The red circles are what my scores would have been if my performance exactly matched the line.

That seems like a claim that I’m improving over the season. Maybe not a compelling case. That missed night certainly dragged me down. But everybody had some outlier bad night, surely. Why not find the line that best fits everyone’s season, and declare the most-improved person to be the one with the largest positive slope?

Who’s The Most Improved Pinball Player?

My love just completed a season as head of a competitive pinball league. People find this an enchanting fact. People find competitive pinball at all enchanting. Many didn’t know pinball was still around, much less big enough to have regular competitions.

Pinball’s in great shape compared to, say, the early 2000s. There’s one major manufacturer. There’s a couple of small manufacturers who are well-organized enough to make a string of games without (yet) collapsing from not knowing how to finance game-building. Many games go right to private collections. But the “barcade” model of a hipster bar with a bunch of pinball machines and, often, video games is working quite well right now. We’re fortunate to live in Michigan. All the major cities in the lower part of the state have pretty good venues and leagues in or near them. We’re especially fortunate to live in Lansing, so that most of these spots are within an hour’s drive, and all of them are within two hours’ drive.

Ah, but how do they work? Many ways, but there are a couple of popular ones. My love’s league uses a scheme that surely has a name. In this scheme everybody plays their own turn on a set of games. Then they get ranked for each game. So the person who puts up the highest score on the game Junkyard earns 100 league points. The person who puts up the second-highest score on Junkyard earns 99 league points. The person with the third-highest score on Junkyard earns 98 league points. And so on, like this. If 20 people showed up for the day, then the poor person who bottoms out earns a mere 81 league points for the game.

This is a relative ranking, yes. I don’t know any competitive-pinball scheme that uses more than one game that doesn’t rank players relative to each other. I’m not sure how an alternative could work. Different games have different scoring schemes. Some games try to dazzle with blazingly high numbers. Some hoard their points as if giving them away cost them anything. A score of 50 million points? If you had that on Attack From Mars you would earn sympathetic hugs and the promise that life will not always be like that. (I’m not sure it’s possible to get a score that low without tilting your game away.) 50 million points on Lord of the Rings would earn a bunch of nods that yeah, that’s doing respectably, but there’s other people yet to play. 50 million points on Scared Stiff would earn applause for the best game anyone had seen all year. 50 million points on The Wizard of Oz would get you named the Lord Mayor of Pinball, your every whim to be rapidly done.

And each individual manifestation of a table is different. It’s part of the fun of pinball. Each game is a real, physical thing, with its own idiosyncrasies. The flippers are a little different in strength. The rubber bands that guard most things are a little harder or softer. The table is a little more or less worn. The sensors are a little more or less sensitive. The tilt detector a little more forgiving, or a little more brutal. Really the least unfair way to rate play is comparing people to each other on a particular table played at approximately the same time.

It’s not perfectly fair. How could any real thing be? It’s maddening to put up the best game of your life on some table, and come in the middle of the pack because everybody else was having great games too. It’s some compensation that there’ll be times you have a mediocre game but everybody else has a lousy one so you’re third-place for the night.

Back to league. Players earn these points for every game played. So whoever has the highest score of all on, say, Attack From Mars gets 100 league points for that regardless of whatever they did on Junkyard. Whoever has the best score on Iron Maiden (a game so new we haven’t actually played it during league yet, and that somehow hasn’t got an entry on the Internet Pinball Database; give it time) gets their 100 points. And so on. A player’s standings for the night are based on all the league points earned on all the tables played. For us that’s usually five games. Five or six games seems about standard; that’s enough time playing and hanging out to feel worthwhile without seeming too long.

So each league night all the players earn between (about) 420 and 500 points. We have eight league nights. Add the scores up over those league nights and there we go. (Well, we drop the lowest nightly total for each player. This lets them miss a night for some responsibility, like work or travel or recovering from sickness or something, without penalizing them.)

As we got to the end of the season my love asked: is it possible to figure out which player showed the best improvement over time?

Well. I had everybody’s scores from every night played. And I’ve taken multiple classes in statistics. Why would I not be able to?

Reading the Comics, April 19, 2018: Late Because Of Pinball Edition

Hi, all. I apologize for being late in posting this, but my Friday and Saturday were eaten up by pinball competition. Pinball At The Zoo, particularly, in Kalamazoo, Michigan. There, Friday, I stepped up first thing and put in four games on the Classics, pre-1985, tournament bank and based on my entry scores was ranked the second-best player there. And then over the day my scores dwindled lower and lower on the list of what people had entered until, in the last five minutes of qualifying, they dropped off the roster altogether and I was knocked out. Meanwhile in the main tournament, I was never even close to making playoffs. But I did have a fantastic game of Bally/Midway’s World Cup Soccer, a game based on how much the United States went crazy for soccer that time we hosted the World Cup for some reason. The game was interrupted by one of the rubber straps around one of the kickers (the little triangular table just past the flippers that you would think would be called the bumpers) breaking, and then by the drain breaking in a way that later knocked the game entirely out of the competition. So anyway besides that glory I’ve been very busy trying to figure out what’s gone wrong and stepping outside to berate the fox squirrels out back, and that’s why I’m late with all this. I’m sure you relate.

Bill Holbrook’s Kevin and Kell rerun for the 15th is the anthropomorphic numerals strip for the week. Also the first of the anthropomorphic strips for the week. Calculating taxes has always been one of the compelling social needs for mathematics, arithmetic especially. If we consider the topic to be “accounting” then that might be the biggest use of mathematics in society. At least by humans; I’m not sure how to rate the arithmetic that computers do even for not explicitly mathematical tasks like sending messages back and forth. New comic strip tag for around here, too.

Bill Schorr’s The Grizzwells for the 17th sees Fauna not liking trigonometry class. I’m sympathetic. I remember it as seeming to be a lot of strange new definitions put to vague purposes. On the bright side, when you get into calculus trigonometry starts solving more problems than it creates. On the dim side, at least when I took it they tried to pass off “trigonometric substitution” as a thing we might need. (OK, it’s come in useful sometimes, but not as often as the presentation made it look.) Also a new comic strip tag.

Eric the Circle for the 18th, this one by sdhardie, is a joke in the Venn Diagram mode. The strip’s a little unusual for not having one of the circles be named Eric. Not a new comic strip tag.

Ham’s Life on Earth for the 19th leaves me feeling faintly threatened. Maybe it’s just me. Also not a new comic strip tag, somehow.

Lord Birthday’s Dumbwitch Castle for the 19th is a small sketch and mostly a list of jokes. This is the normal format for this strip, which tests the idea of what makes something a comic strip. I grant it’s a marginal inclusion, but I am tickled by the idea of a math slap so here you go. This one’s another new comic strip tag.

Reading the Comics, September 19, 2017: Visualization Edition

Comic Strip Master Command apparently doesn’t want me talking about the chances of Friday’s Showcase Showdown. They sent me enough of a flood of mathematically-themed strips that I don’t know when I’ll have the time to talk about the probability of that episode. (The three contestants spinning the wheel all tied, each spinning $1.00. And then in the spin-off, two of the three contestants also spun$1.00. And this after what was already a perfect show, in which the contestants won all six of the pricing games.) Well, I’ll do what comic strips I can this time, and carry on the last week of the Summer 2017 A To Z project, and we’ll see if I can say anything timely for Thursday or Saturday or so.

Jim Scancarelli’s Gasoline Alley for the 17th is a joke about the student embarrassing the teacher. It uses mathematics vocabulary for the specifics. And it does depict one of those moments that never stops, as you learn mathematics. There’s always more vocabulary. There’s good reasons to have so much vocabulary. Having names for things seems to make them easier to work with. We can bundle together ideas about what a thing is like, and what it may do, under a name. I suppose the trouble is that we’ve accepted a convention that we should define terms before we use them. It’s nice, like having the dramatis personae listed at the start of the play. But having that list isn’t the same as saying why anyone should care. I don’t know how to balance the need to make clear up front what one means and the need to not bury someone under a heap of similar-sounding names.

Mac King and Bill King’s Magic in a Minute for the 17th is another puzzle drawn from arithmetic. Look at it now if you want to have the fun of working it out, as I can’t think of anything to say about it that doesn’t spoil how the trick is done. The top commenter does have a suggestion about how to do the problem by breaking one of the unstated assumptions in the problem. This is the kind of puzzle created for people who want to motivate talking about parity or equivalence classes. It’s neat when you can say something of substance about a problem using simple information, though.

Terri Libenson’s Pajama Diaries for the 18th uses trigonometry as the marker for deep thinking. It comes complete with a coherent equation, too. It gives the area of a triangle with two legs that meet at a 45 degree angle. I admit I am uncomfortable with promoting the idea that people who are autistic have some super-reasoning powers. (Also with the pop-culture idea that someone who spots things others don’t is probably at least a bit autistic.) I understand wanting to think someone’s troubles have some compensation. But people are who they are; it’s not like they need to observe some “balance”.

Lee Falk and Wilson McCoy’s The Phantom for the 10th of August, 1950 was rerun Monday. It’s a side bit of joking about between stories. And it uses knowledge of mathematics — and an interest in relativity — as signifier of civilization. I can only hope King Hano does better learning tensors on his own than I do.

Mike Thompson’s Grand Avenue for the 18th goes back to classrooms and stuff for clever answers that subvert the teacher. And I notice, per the title given this edition, that the teacher’s trying to make the abstractness of three minus two tangible, by giving it an example. Which pairs it with …

Will Henry’s Wallace the Brace for the 18th, wherein Wallace asserts that arithmetic is easier if you visualize real things. I agree it seems to help with stuff like basic arithmetic. I wouldn’t want to try taking the cosine of an apple, though. Separating the quantity of a thing from the kind of thing measured is one of those subtle breakthroughs. It’s one of the ways that, for example, modern calculations differ from those of the Ancient Greeks. But it does mean thinking of numbers in, we’d say, a more abstract way than they did, and in a way that seems to tax us more.

Wallace the Brave recently had a book collection published, by the way. I mention because this is one of a handful of comics with a character who likes pinball, and more, who really really loves the Williams game FunHouse. This is an utterly correct choice for favorite pinball game. It’s one of the games that made me a pinball enthusiast.

Ryan North’s Dinosaur Comics rerun for the 19th I mention on loose grounds. In it T-Rex suggests trying out an alternate model for how gravity works. The idea, of what seems to be gravity “really” being the shade cast by massive objects in a particle storm, was explored in the late 17th and early 18th century. It avoids the problem of not being able to quite say what propagates gravitational attraction. But it also doesn’t work, analytically. We would see the planets orbit differently if this were how gravity worked. And there’s the problem about mass and energy absorption, as pointed out in the comic. But it can often be interesting or productive to play with models that don’t work. You might learn something about models that do, or that could.

How Much Might I Have Lost At Pinball?

After the state pinball championship last month there was a second, side tournament. It was a sort-of marathon event in which I played sixteen games in short order. I won three of them and lost thirteen, a disheartening record. The question I can draw from this: was I hopelessly outclassed in the side tournament? Is it plausible that I could do so awfully?

The answer would be “of course not”. I was playing against, mostly, the same people who were in the state finals. (A few who didn’t qualify for the finals joined the side tournament.) In that I had done well enough, winning seven games in all out of fifteen played. It’s implausible that I got significantly worse at pinball between the main and the side tournament. But can I make a logically sound argument about this?

In full, probably not. It’s too hard. The question is, did I win way too few games compared to what I should have expected? But what should I have expected? I haven’t got any information on how likely it should have been that I’d win any of the games, especially not when I faced something like a dozen different opponents. (I played several opponents twice.)

But we can make a model. Suppose that I had a fifty percent chance of winning each match. This is a lie in detail. The model contains lies; all models do. The lies might let us learn something interesting. Some people there I could only beat with a stroke of luck on my side. Some people there I could fairly often expect to beat. If we pretend I had the same chance against everyone, though, we get something that we can model. It might tell us something about what really happened.

If I play 16 matches, and have a 50 percent chance of winning each of them, then I should expect to win eight matches. But there’s no reason I might not win seven instead, or nine. Might win six, or ten, without that being too implausible. It’s even possible I might not win a single match, or that I might win all sixteen matches. How likely?

This calls for a creature from the field of probability that we call the binomial distribution. It’s “binomial” because it’s about stuff for which there are exactly two possible outcomes. This fits. Each match I can win or I can lose. (If we tie, or if the match is interrupted, we replay it, so there’s not another case.) It’s a “distribution” because we describe, for a set of some number of attempted matches, how the possible outcomes are distributed. The outcomes are: I win none of them. I win exactly one of them. I win exactly two of them. And so on, all the way up to “I win exactly all but one of them” and “I win all of them”.

To answer the question of whether it’s plausible I should have done so badly I need to know more than just how likely it is I would win only three games. I need to also know the chance I’d have done worse. If I had won only two games, or only one, or none at all. Why?

Here I admit: I’m not sure I can give a compelling reason, at least not in English. I’ve been reworking it all week without being happy at the results. Let me try pieces.

One part is that as I put the question — is it plausible that I could do so awfully? — isn’t answered just by checking how likely it is I would win only three games out of sixteen. If that’s awful, then doing even worse must also be awful. I can’t rule out even-worse results from awfulness without losing a sense of what the word “awful” means. Fair enough, to answer that question. But I made up the question. Why did I make up that one? Why not just “is it plausible I’d get only three out of sixteen games”?

Habit, largely. Experience shows me that the probability of any particular result turns out to be implausibly low. It isn’t quite that case here; there’s only seventeen possible noticeably different outcomes of playing sixteen games. But there can be so many possible outcomes that even the most likely one isn’t.

Take an extreme case. (Extreme cases are often good ways to build an intuitive understanding of things.) Imagine I played 16,000 games, with a 50-50 chance of winning each one of them. It is most likely that I would win 8,000 of the games. But the probability of winning exactly 8,000 games is small: only about 0.6 percent. What’s going on there is that there’s almost the same chance of winning exactly 8,001 or 8,002 games. As the number of games increases the number of possible different outcomes increases. If there are 16,000 games there are 16,001 possible outcomes. It’s less likely that any of them will stand out. What saves our ability to predict the results of things is that the number of plausible outcomes increases more slowly. It’s plausible someone would win exactly three games out of sixteen. It’s impossible that someone would win exactly three thousand games out of sixteen thousand, even though that’s the same ratio of won games.

Card games offer another way to get comfortable with this idea. A bridge hand, for example, is thirteen cards drawn out of fifty-two. But the chance that you were dealt the hand you just got? Impossibly low. Should we conclude from this all bridge hands are hoaxes? No, but ask my mother sometime about the bridge class she took that one cruise. “Three of sixteen” is too particular; “at best three of sixteen” is a class I can study.

Unconvinced? I don’t blame you. I’m not sure I would be convinced of that, but I might allow the argument to continue. I hope you will. So here are the specifics. These are the chance of each count of wins, and the chance of having exactly that many wins, for sixteen matches:

Wins Percentage
0 0.002 %
1 0.024 %
2 0.183 %
3 0.854 %
4 2.777 %
5 6.665 %
6 12.219 %
7 17.456 %
8 19.638 %
9 17.456 %
10 12.219 %
11 6.665 %
12 2.777 %
13 0.854 %
14 0.183 %
15 0.024 %
16 0.002 %

So the chance of doing as awfully as I had — winning zero or one or two or three games — is pretty dire. It’s a little above one percent.

Is that implausibly low? Is there so small a chance that I’d do so badly that we have to figure I didn’t have a 50-50 chance of winning each game?

I hate to think that. I didn’t think I was outclassed. But here’s a problem. We need some standard for what is “it’s implausibly unlikely that this happened by chance alone”. If there were only one chance in a trillion that someone with a 50-50 chance of winning any game would put in the performance I did, we could suppose that I didn’t actually have a 50-50 chance of winning any game. If there were only one chance in a million of that performance, we might also suppose I didn’t actually have a 50-50 chance of winning any game. But here there was only one chance in a hundred? Is that too unlikely?

It depends. We should have set a threshold for “too implausibly unlikely” before we started research. It’s bad form to decide afterward. There are some thresholds that are commonly taken. Five percent is often useful for stuff where it’s hard to do bigger experiments and the harm of guessing wrong (dismissing the idea I had a 50-50 chance of winning any given game, for example) isn’t so serious. One percent is another common threshold, again common in stuff like psychological studies where it’s hard to get more and more data. In a field like physics, where experiments are relatively cheap to keep running, you can gather enough data to insist on fractions of a percent as your threshold. Setting the threshold after is bad form.

In my defense, I thought (without doing the work) that I probably had something like a five percent chance of doing that badly by luck alone. It suggests that I did have a much worse than 50 percent chance of winning any given game.

Is that credible? Well, yeah; I may have been in the top sixteen players in the state. But a lot of those people are incredibly good. Maybe I had only one chance in three, or something like that. That would make the chance I did that poorly something like one in six, likely enough.

And it’s also plausible that games are not independent, that whether I win one game depends in some way on whether I won or lost the previous. But it does feel like it’s easier to win after a win, or after a close loss. And it feels harder to win a game after a string of losses. I don’t know that this can be proved, not on the meager evidence I have available. And you can almost always question the independence of a string of events like this. It’s the safe bet.

What Pinball Games Are Turing Machines?

I got to thinking about Turing machines. This is the conceptual model for basically all computers. The classic concept is to imagine a string of cells. In each cell is some symbol. It’s gone over by some device that follows some rule about whether and how to change the symbol. We have other rules that let us move the machine from one cell to the next. This doesn’t sound like much. But it’s enough. We can imagine all software to be some sufficiently involved bit of work on a string of cells and changing (or not) the symbols in those cells.

We don’t normally do this, because it’s too much tedious work. But we know we could go back to this if we truly must. A proper Turing machine has infinitely many cells, which no actual computer does, owing to the high cost of memory chips and the limited electricity budget. We can pretend that “a large enough number of cells” is good enough; it often is. And it turns out any one Turing machine can be used to simulate another Turing machine. This requires us to not care about how long it takes to do something, but that’s all right. Conceptually, we don’t care.

And I specifically got wondering what was the first pinball machine to be a Turing machine. I’m sure that modern pinball machines are, since there have been computers of some kind in pinball machines since the mid-1970s. So that’s a boring question. My question is: were there earlier pinball machines that satisfy the requirements of a Turing machine?

My gut tells me there must be. This is mostly because it’s surprisingly hard not to create a Turing machine. If you hang around near mathematics or computer science people you’ll occasionally run across things like where someone created a computer inside a game like Minecraft. It’s possible to create a Turing machine using the elements of the game. The number of things that are Turing-complete, as they say, is surprising. CSS version 3, a rule system for how to dress up content on a web site, turns out to be Turing-complete (if you make some reasonable extra suppositions). Magic: The Gathering cards are, too. So you could set up a game of Magic: the Gathering which simulated a game of Minecraft which itself simulated the styling rules of a web page. Note the “you” in that sentence.

That’s not proof, though. But I feel pretty good about supposing that some must be. Pinball machines consist, at heart, of a bunch of switches which are activated or not by whether a ball rolls over them. They can store a bit of information: a ball can be locked in a scoop, or kicked out of the scoop as need be. Points can be tallied on the scoring reel. The number of balls a player gets to plunge can be increased — or decreased — based on things that happen on the playfield. This feels to me like it’s got to be a Turing-complete scheme.

So I suspect that the layout of a pinball game, and the various ways to store a bit of information, with (presumably) perfect ball-flipping and table-nudging skills, should make it possible to make a Turing machine. (There ought not be a human in the loop, but I’m supposing that we could replace the person with a mechanism that flips or nudges at the right times or when the ball is in the right place.) I’m wanting for proof, though, and I leave the question here to tease people who’re better than I am at this field of mathematics and computer science.

And I’m curious when the first game that was so capable was made. The very earliest games were like large tabletop versions of those disappointing car toys, the tiny transparent-plastic things with a ball bearing you shoot into one of a series of scoops. Eventually, tilt mechanisms were added, and scoring reels, and then flippers, and then the chance to lock balls. Each changed what the games could do. Did it reach the level of complexity I think it did? I’d like to know.

Yes, this means that I believe it would be theoretically possible to play a pinball game that itself simulated the Pinball Arcade program simulating another pinball game. If this prospect does not delight you then I do not know that we can hope to ever understand one another.

How Much I Did Lose In Pinball

A follow-up for people curious how much I lost at the state pinball championships Saturday: I lost at the state pinball championships Saturday. As I expected I lost in the first round. I did beat my expectations, though. I’d figured I would win one, maybe two games in our best-of-seven contest. As it happened I won three games and I had a fighting chance in game seven.

I’d mentioned in the previous essay about how much contingency there is especially in a short series like this one. My opponent picked the game I expected she would to start out. And she got an awful bounce on the first ball, while I got a very lucky bounce that started multiball on the last. So I won, but not because I was playing better. The seventh game was one that I had figured she might pick if she needed to crush me, and if I had gotten a better bounce on the first ball I’d still have had an uphill struggle. Just less of one.

After the first round I got into a set of three “tie-breaking” rounds, used to sort out which of the sixteen players ranked as number 11 versus number 10. Each of those were a best-of-three series. I did win one series and lost two others, dropping me into 12th place. Over the three series I had four wins and four losses, so I can’t say that I mismatched there.

Where I might have been mismatched is the side tournament. This was a two-hour marathon of playing a lot of games one after the other. I finished with three wins and 13 losses, enough to make me wonder whether I somehow went from competent to incompetent in the hour or so between the main and the side tournament. Of course not, based on a record like that, but — can I prove it?

Meanwhile a friend pointed out The New York Times covering the New York State pinball championship:

The article is (at least for now) at https://www.nytimes.com/2017/02/12/nyregion/pinball-state-championship.html. What my friend couldn’t have known, and what shows how networked people are, is that I know one of the people featured in the article, Sean “The Storm” Grant. Well, I knew him, back in college. He was an awesome pinball player even then. And he’s only got more awesome since.

How awesome? Let me give you some background. The International Flipper Pinball Association (IFPA) gives players ranking points. These points are gathered by playing in leagues and tournaments. Each league or tournament has a certain point value. That point value is divided up among the players, in descending order from how they finish. How many points do the events have? That depends on how many people play and what their ranking is. So, yes, how much someone’s IFPA score increases depends on the events they go to, and the events they go to depend on their score. This might sound to you like there’s a differential equation describing all this. You’re close: it’s a difference equation, because these rankings change with the discrete number of events players go to. But there’s an interesting and iterative system at work there.

(Points only expire with time. The system is designed to encourage people to play a lot of things and keep playing them. You can’t lose ranking points by playing, although it might hurt your player-versus-player rating. That’s calculated by a formula I don’t understand at all.)

Anyway, Sean Grant plays in the New York Superleague, a crime-fighting band of pinball players who figured out how to game the IFPA rankings system. They figured out how to turn the large number of people who might visit a Manhattan bar and casually play one or two games into a source of ranking points for the serious players. The IFPA, combatting this scheme, just this week recalculated the Superleague values and the rankings of everyone involved in it. It’s fascinating stuff, in that way a heated debate over an issue you aren’t emotionally invested in can be.

Anyway. Grant is such a skilled player that he lost more points in this nerfing than I have gathered in my whole competitive-pinball-playing career.

So while I knew I’d be knocked out in the first round of the Michigan State Championships I’ll admit I had fantasies of having an impossibly lucky run. In that case, I’d have gone to the nationals and been turned into a pale, silverball-covered paste by people like Grant.

Thanks again for all your good wishes, kind readers. Now we start the long road to the 2017 State Championships, to be held in February of next year. I’m already in 63rd place in the state for the year! (There haven’t been many events for the year yet, and the championship and side tournament haven’t posted their ranking scores yet.)

How Much Can I Expect To Lose In Pinball?

This weekend, all going well, I’ll be going to the Michigan state pinball championship contest. There, I will lose in the first round.

I’m not trying to run myself down. But I know who I’m scheduled to play in the first round, and she’s quite a good player. She’s the state’s highest-ranked woman playing competitive pinball. So she starts off being better than me. And then the venue is one she gets to play in more than I do. Pinball, a physical thing, is idiosyncratic. The reflexes you build practicing on one table can betray you on a strange machine. She’s had more chance to practice on the games we have and that pretty well settles the question. I’m still showing up, of course, and doing my best. Stranger things have happened than my winning a game. But I’m going in with I hope realistic expectations.

That bit about having realistic expectations, though, makes me ask what are realistic expectations. The first round is a best-of-seven match. How many games should I expect to win? And that becomes a probability question. It’s a great question to learn on, too. Our match is straightforward to model: we play up to seven times. Each time we play one or the other wins.

So we can start calculating. There’s some probability I have of winning any particular game. Call that number ‘p’. It’s at least zero (I’m not sure to lose) but it’s less than one (I’m not sure to win). Let’s suppose the probability of my winning never changes over the course of seven games. I will come back to the card I palmed there. If we’re playing 7 games, and I have a chance ‘p’ of winning any one of them, then the number of games I can expect to win is 7 times ‘p’. This is the number of wins you might expect if you were called on in class and had no idea and bluffed the first thing that came to mind. Sometimes that works.

7 times p isn’t very enlightening. What number is ‘p’, after all? And I don’t know exactly. The International Flipper Pinball Association tracks how many times I’ve finished a tournament or league above her and vice-versa. We’ve played in 54 recorded events together, and I’ve won 23 and lost 29 of them. (We’ve tied twice.) But that isn’t all head-to-head play. It counts matches where I’m beaten by someone she goes on to beat as her beating me, and vice-versa. And it includes a lot of playing not at the venue. I lack statistics and must go with my feelings. I’d estimate my chance of beating her at about one in three. Let’s say ‘p’ is 1/3 until we get evidence to the contrary. It is “Flipper Pinball” because the earliest pinball machines had no flippers. You plunged the ball into play and nudged the machine a little to keep it going somewhere you wanted. (The game Simpsons Pinball Party has a moment where Grampa Simpson says, “back in my day we didn’t have flippers”. It’s the best kind of joke, the one that is factually correct.)

Seven times one-third is not a difficult problem. It comes out to two and a third, raising the question of how one wins one-third of a pinball game. Most games involve playing three rounds, called balls, is the obvious observation. But this one-third of a game is an average. Imagine the two of us playing three thousand seven-game matches, without either of us getting the least bit better or worse or collapsing of exhaustion. I would expect to win seven thousand of the games, or two and a third games per seven-game match.

Ah, but … that’s too high. I would expect to win two and a third games out of seven. But we probably won’t play seven. We’ll stop when she or I gets to four wins. This makes the problem hard. Hard is the wrong word. It makes the problem tedious. At least it threatens to. Things will get easy enough, but we have to go through some difficult parts first.

There are eight different ways that our best-of-seven match can end. She can win in four games. I can win in four games. She can win in five games. I can win in five games. She can win in six games. I can win in six games. She can win in seven games. I can win in seven games. There is some chance of each of those eight outcomes happening. And exactly one of those will happen; it’s not possible that she’ll win in four games and in five games, unless we lose track of how many games we’d played. They give us index cards to write results down. We won’t lose track.

It’s easy to calculate the probability that I win in four games, if the chance of my winning a game is the number ‘p’. The probability is p4. Similarly it’s easy to calculate the probability that she wins in four games. If I have the chance ‘p’ of winning, then she has the chance ‘1 – p’ of winning. So her probability of winning in four games is (1 – p)4.

The probability of my winning in five games is more tedious to work out. It’s going to be p4 times (1 – p) times 4. The 4 here is the number of different ways that she can win one of the first four games. Turns out there’s four ways to do that. She could win the first game, or the second, or the third, or the fourth. And in the same way the probability she wins in five games is p times (1 – p)4 times 4.

The probability of my winning in six games is going to be p4 times (1 – p)2 times 10. There are ten ways to scatter four wins by her among the first five games. The probability of her winning in six games is the strikingly parallel p2 times (1 – p)4 times 10.

The probability of my winning in seven games is going to be p4 times (1 – p)3 times 20, because there are 20 ways to scatter three wins among the first six games. And the probability of her winning in seven games is p3 times (1 – p)4 times 20.

Add all those probabilities up, no matter what ‘p’ is, and you should get 1. Exactly one of those four outcomes has to happen. And we can work out the probability that the series will end after four games: it’s the chance she wins in four games plus the chance I win in four games. The probability that the series goes to five games is the probability that she wins in five games plus the probability that I win in five games. And so on for six and for seven games.

So that’s neat. We can figure out the probability of the match ending after four games, after five, after six, or after seven. And from that we can figure out the expected length of the match. This is the expectation value. Take the product of ‘4’ and the chance the match ends at four games. Take the product of ‘5’ and the chance the match ends at five games. Take the product of ‘6’ and the chance the match ends at six games. Take the product of ‘7’ and the chance the match ends at seven games. Add all those up. That’ll be, wonder of wonders, the number of games a match like this can be expected to run.

Now it’s a matter of adding together all these combinations of all these different outcomes and you know what? I’m not doing that. I don’t know what the chance is I’d do all this arithmetic correctly is, but I know there’s no chance I’d do all this arithmetic correctly. This is the stuff we pirate Mathematica to do. (Mathematica is supernaturally good at working out mathematical expressions. A personal license costs all the money you will ever have in your life plus ten percent, which it will calculate for you.)

Happily I won’t have to work it out. A person appearing to be a high school teacher named B Kiggins has worked it out already. Kiggins put it and a bunch of other interesting worksheets on the web. (Look for the Voronoi Diagramas!)

There’s a lot of arithmetic involved. But it all simplifies out, somehow. Per Kiggins’ work, the expected number of games in a best-of-seven match, if one of the competitors has the chance ‘p’ of winning any given game, is:

$E(p) = 4 + 4\cdot p + 4\cdot p^2 + 4\cdot p^3 - 52\cdot p^4 + 60\cdot p^5 - 20\cdot p^6$

Whatever you want to say about that, it’s a polynomial. And it’s easy enough to evaluate it, especially if you let the computer evaluate it. Oh, I would say it seems like a shame all those coefficients of ‘4’ drop off and we get weird numbers like ’52’ after that. But there’s something beautiful in there being four 4’s, isn’t there? Good enough.

So. If the chance of my winning a game, ‘p’, is one-third, then we’d expect the series to go 5.5 games. This accords well with my intuition. I thought I would be likely to win one game. Winning two would be a moral victory akin to championship.

Let me go back to my palmed card. This whole analysis is based on the idea that I have some fixed probability of winning and that it isn’t going to change from one game to the next. If the probability of winning is entirely based on my and my opponents’ abilities this is fair enough. Neither of us is likely to get significantly more or less skilled over the course of even seven matches. We won’t even play long enough to get fatigued. But ability isn’t everything.

But our abilities aren’t everything. We’re going to be playing up to seven different tables. How each table reacts to our play is going to vary. Some tables may treat me better, some tables my opponent. Luck of the draw. And there’s an important psychological component. It’s easy to get thrown and to let a bad ball wreck the rest of one’s game. It’s hard to resist feeling nervous if you go into the last ball from way behind your opponent. And it seems as if a pinball knows you’re nervous and races out of play to help you calm down. (The best pinball players tend to have outstanding last balls, though. They don’t get rattled. And they spend the first several balls building up to high-value shots they can collect later on.) And there will be freak events. Last weekend I was saved from elimination in a tournament by the pinball machine spontaneously resetting. We had to replay the game. I did well in the tournament, but it was the freak event that kept me from being knocked out in the first round.

That’s some complicated stuff to fit together. I suppose with enough data we could possibly model how much the differences between pinball machines affects the outcome. That’s what sabermetrics is all about. Representing how severely I’ll build a little bad luck into a lot of bad luck? Oh, that’s hard.

Too hard to deal with, at least not without much more sports psychology and modelling of pinball players than we have data to do. The supposition that my chance of winning is fixed for the duration of the match may not be true. But we won’t be playing enough games to be able to tell the difference. The assumption that my chance of winning doesn’t change over the course of the match may be false. But it’s near enough, and it gets us some useful information. We have to know not to demand too much precision from our model.

And seven games isn’t statistically significant. Not when players are as closely matched as we are. I could be worse and still get a couple wins in when they count; I could play better than my average and still get creamed four games straight. I’ll be trying my best, of course. But I expect my best is one or two wins, then getting to the snack room and waiting for the side tournament to start. Shall let you know if something interesting happens.

Finally, What I Learned Doing Theorem Thursdays

The biggest thing I learned from my Theorem Thursdays project was: don’t do this for Thursdays. The appeal is obvious. If things were a little different I’d have no problem with Thursdays. But besides being a slightly-read pop-mathematics blogger I’m also a slightly-read humor blogger. And I try to have a major piece, about seven hundred words that are more than simply commentary on how a comic strip’s gone wrong, ready for Thursday evenings my time.

That’s all my doing. It’s a relic of my thinking that the humor blog should run at least a bit like a professional syndicated columnist’s, with a fixed deadline for bigger pieces. While I should be writing more ahead of deadline than this, what I would do is get to Wednesday realizing I have two major things to write in a day. I’d have an idea for one of them, the mathematics thing, since I would pick a topic the previous Thursday. And once I’ve picked an idea the rest is easy. (Part of the process of picking is realizing whether there’s any way to make seven hundred words about something.) But that’s a lot of work for something that’s supposed to be recreational. Plus Wednesdays are, two weeks a month, a pinball league night.

So Thursday is right out, unless I get better about having first drafts of stuff done Monday night. So Thursday is right out. This has problems for future appearances of the gimmick. The alliterative pull is strong. The only remotely compelling alternative is Theorems on the Threes, maybe one the 3rd, 13th, and 23rd of the month. That leaves the 30th and 31st unaccounted for, and room for a good squabble about whether they count in an “on the threes” scheme.

There’s a lot of good stuff to say about the project otherwise. The biggest is that I had fun with it. The Theorem Thursday pieces sprawled into for-me extreme lengths, two to three thousand words. I had space to be chatty and silly and autobiographic in ways that even the A To Z projects don’t allow. Somehow those essays didn’t get nearly as long, possibly because I was writing three of them a week. I didn’t actually write fewer things in July than I did in, say, May. But it was fewer kinds of things; postings were mostly Theorem Thursdays and Reading the Comics posts. Still, overall readership didn’t drop and people seemed to quite like what I did write. It may be fewer but longer-form essays are the way I should go.

Also I found that people like stranger stuff. There’s an understandable temptation in doing pop-mathematics to look for topics that are automatically more accessible. People are afraid enough of mathematics. They have good reason to be terrified of some topic even mathematics majors don’t encounter until their fourth year. So there’s a drive to simpler topics, or topics that have fewer prerequisites, and that’s why every mathematics blogger has an essay about how the square root of two is irrational and how there’s different sizes to infinitely large sets. And that’s produced some excellent writing about topics like those, which are great topics. They have got the power to inspire awe without requiring any warming up. That’s special.

But it also means they’re hard to write anything new or compelling about if you’re like me, and in somewhere like the second hundred billion of mathematics bloggers. I can’t write anything better than what’s already gone about that. Liouville’s Theorem? That’s something I can be a good writer about. With that, I can have a blog personality. It’s like having a real personality but less work.

As I did with the Leap Day 2016 A To Z project, I threw the topics open to requests. I didn’t get many. Possibly the form gave too much freedom. Picking something to match a letter, as in the A to Z, gives a useful structure for choosing something specific. Pick a theorem from anywhere in mathematics? Something from algebra class? Something mentioned in a news report about a major breakthrough the reporter doesn’t understand but had an interesting picture? Something that you overheard the name of once without any context? How should people know what the scope of it is, before they’ve even seen a sample? And possibly people don’t actually remember the names of theorems unless they stay in mathematics or mathematics-related fields. Those folks hardly need explained theorems with names they remember. This is a hard problem to imagine people having, but it’s something I must consider.

So this is what I take away from the two-month project. There’s a lot of fun digging into the higher-level mathematics stuff. There’s an interest in it, even if it means I write longer and therefore fewer pieces. Take requests, but have a structure for taking them that makes it easy to tell what requests should look like. Definitely don’t commit to doing big things for Thursday, not without a better scheme for getting the humor blog pieces done. Free up some time Wednesday and don’t put up an awful score on Demolition Man like I did last time again. Seriously, I had a better score on The Simpsons Pinball Party than I did on Demolition Man and while you personally might not find this amusing there’s at least two people really into pinball who know how hilarious that is. (The games have wildly different point scorings. This like having a basketball score be lower than a hockey score.) That isn’t so important to mathematics blogging but it’s a good lesson to remember anyway.

How Pinball Leagues and Chemistry Work: The Mathematics

My love and I play in several pinball leagues. I need to explain something of how they work.

Most of them organize league nights by making groups of three or four players and having them play five games each on a variety of pinball tables. The groupings are made by order. The 1st through 4th highest-ranked players who’re present are the first group, the 5th through 8th the second group, the 9th through 12th the third group, and so on. For each table the player with the highest score gets some number of league points. The second-highest score earns a lesser number of league points, third-highest gets fewer points yet, and the lowest score earns the player comments about how the table was not being fair. The total number of points goes into the player’s season score, which gives her ranking.

You might see the bootstrapping problem here. Where do the rankings come from? And what happens if someone joins the league mid-season? What if someone misses a competition day? (Some leagues give a fraction of points based on the player’s season average. Other leagues award no points.) How does a player get correctly ranked?

Last weekend I visited the Vintage Flipper World pinball museum just outside Ann Arbor, Michigan. Among the games there was Gottleib’s 1955 table Sweet Add-A-Line. It’s a peculiar table by modern standards, since nearly all the playfield is a bunch of lanes, channels through which the pinball might roll. But …

Each of the lanes is numbered. Rolling one down lights up that number in the backglass, as above. And if you roll all the numbers in one of the eight strips of tape, the game opens up bonus opportunities. It’s a fun game and certainly one of the top adding-machine-themed pinball machines I’ve ever played. I grant this is of marginal mathematical content, but, heck, I smiled.

The Internet Pinball Database has a scan of the game’s advertising flyer, which I like if nothing else for its defensive “Amusement Pinballs: as American as Baseball and Hot Dogs!” slogan.

At The Pinball Tables

A neat coincidence happened as our local pinball league got plans under way for tonight. There are thirteen pinball machines in the local venue, and normally four of them get picked for the night’s competition. The league president’s gone to a randoom number generator to pick the machines, since this way he doesn’t have to take off his hat and draw pinball table names from it. This week, though, he reported that the random number generator had picked the same four tables as it had last session.

There’s a decent little probability quiz to be built around that fact: how many ways there are to get four tables out of the thirteen available, obviously, and from that what the chance is of repeating the selection of tables from the last session. And there are subtler ones, like, what’s the chance of the same tables being drawn two weeks in a row over the course of the season (which is eight meetings long, and one postseason tournament), or what’s the chance of any week’s selection of tables being repeated over the course of a season, or of a year (which has two seasons). And I leave some space below for people who want to work out these problems or figure out similar related ones.

It’s also a reminder that just because something is randomly drawn doesn’t mean that coincidences and patterns won’t appear. It would be a touch suspicious, in fact, if the random number generator never picked the same table (or several tables) in successive weeks. But it’s still a rare enough event that it’s interesting to see it happen.

Pinball and Large Numbers

I had another little occasion to reflect on the ways of representing numbers, as well as the chance to feel a bit foolish, this past weekend so I’m naturally driven to share it. This came about on visiting the Silverball Museum, a pinball museum, or arcade, in Asbury Park, New Jersey. (I’m not sure the exact difference between a museum in which games are playable by visitors and an arcade, except for the signs affixed to nearly all the games.) Naturally I failed to bring my camera, so I can’t easily show what I had in mind; too bad.

Pinballs, at least once they got around to having electricity installed, need to show the scores. Since about the mid-1990s these have been shown by dot matrix displays, which are pretty easy to read — the current player’s score can be shown extremely large, for example — and make it easy for the game to go into different modes, where the scoring and objectives of play vary for a time. From about the mid-1970s to the mid-1990s eight-segment light-emitting diodes were preferred, for that “small alarm clock” look. And going before that were rotating number wheels, which are probably the iconic look to pinball score boards, to the extent anyone thinks of a classic pinball machine in that detail.

But there’s another score display, which I must admit offends my sense of order. In this, which I noticed mostly in the machines from the 1950s, with a few outliers in the early 60s (often used in conjunction with the rotating wheels), the parts of the number are broken apart, and the score is read by adding up the parts which are lit up. The machine I was looking at had one column of digits for the millions, another for hundreds of thousands, and then another with two-digit numbers.