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.