If I think about this website’s library, I invariably land on one particular topic that’s woefully underrepresented. It’s sort of like, when you got a fever, and there’s only one prescription.

It’s time, once again, for Pong analytics. Welcome to Nognarok.


The format for this spring’s beer pong tourney was different than prior events, which mimicked the World Cup’s group-stage-to-single-elimination format. This time, we split up traditional teammates (except C-Vrach, making their first appearance), and made sure no player competed with the same partner twice. This left us with a 10-game, every-man-for-himself free-for-all, where each of the 10 contestants were entered into four otherwise-randomized matches.

Here are your results:

Blogs like this, and our broader understanding of the game itself, were the primary drivers of this alternative format. The majority of our historical data paired the same partners together every game, giving us a reasonably accurate snapshot of team performance, but leaving us with an inherently-biased understanding of The Player. Switching up teammates, and doing away with the elimination format, helped isolate individual performance – and gave us four more games worth of data on all those individuals.

State of the Union

We’re getting close, folks, to having a truly impressive understanding of the sport of Pong, knowledge which is made easier by the inherent nature of the beast. If you think about it, it’s taken this long to get here only because it never occurred to us in college, when we held games on a daily basis, to capture data. The scarcity of information – rather than our capacity or desire to analyze it – has been the true obstacle for years.

Pong actually lends itself well to statistical analysis, because there is a fair volume and diversity of counting stats; compare that to something like Beirut, and you can see the difference. But pong also enables statistical analysis because it’s more similar to baseball – largely a series of isolated 1-on-1 interactions between a pitcher and a hitter – than it is to sports like basketball or soccer, which involve the actions of numerous players, all of which affect the actions of each other. Pong, at it’s core, relatively isn’t all that complex, and so after watching film of nearly 40 games, we’ve already uncovered most of our key data points, and conceptualized many of the possible ways to think about the game, statistically.

There are, of course, many more topics to explore, and betters ways (e.g., SportVu) to track gameplay that would generate more sophisticated information. At the very least, we need more data in categories we’ve already established to beef up our N. Given the game’s simplicity, though, I wonder if we eventually reach a stage when there are almost no new metrics of substance to look into.

If we were better enabled by both technology and the frequency of games played, would we be able, statistically, to “solve” pong?

I doubt it, but that general query is why I’d argue that we’re currently in the Golden Age of Pong Analytics: at the perfect medium between the known and the unknown. We understand so much more than we did as undergrads, but there are still so many questions, so many topics left to discuss and wonder about. In fact, they’re even more fun now that we do know so much more.

That’s why we’re in the Golden Age, and also why I’m so delighted for The Pong Pod – Bobcat Territory’s first foray into podcasting, in which fellow-blogcatters Ezra, Bullets and I discuss this crazy sport of ours in glorious long-form. If you missed it, check out our pilot episode:

Frankly, I don’t want to get to a point where we’ve hammered everything out, where we know whether or not, and to what extent, and who would you rather, for every single topic. I certainly don’t want to stop learning, either, though….

….or to have somebody get hot in one tournament, and come out statistically looking better than someone else (ahem: me). Fuck that. We need larger samples from everybody.

So let’s dive in.


When we were first discussing pong stats, there was a great deal of novelty in having data at all. Colored bar charts rank-ordering Hits were exciting. From there, a lot of work was done to establish more advanced statistical concepts: ideas like Shot Value (# of Points generated or lost every time a player shoots) had to have their origins and calculations mapped out before we could incorporate them into our lexicon.

Given that, to a certain extent, we’ve now “been here and done that” with a lot of these ideas, there’s not as much to explain with each individual item. The ponderer really just wants to cut to the chase, and know how everybody performed. Thus, rather than me once again carving up charts for each category, I figured I’d just consolidate the major statistics into an interactive table for you, the readers, to play around with:

PlayerWinsLossesSinksEventsUFEsPointsShot Value Shooting %Save %Net

YOY Comparison

Now let’s do some comparing to earlier royal rumbles:

My takeaways:

  • Slightly surprising that Shooting % isn’t higher, considering the quality of the field. My guess? Some players who were hot last year cooled down (e.g., Marver: 22.4% in 2016, 15.6% in 2017), and some players just had an off day (e.g., Bullets: 18.5% in 2015, 17.7% in 2016, 11.1% in 2017)
  • Sinking’s pretty consistent, which makes sense – while some players, given their shot trajectories (e.g., Vool), probably have a higher Sink Rate, there’s probably not much of a difference in that metric among the broader pool
  • What is surprising is how our Save Rate dipped; I would have put money on that swinging in the opposite direction. One possibility: the shots of average-or-above players are harder to save. The other theory: some top flight defenders (e.g., Tufts) weren’t in attendance.
  • This year’s games were much more dramatic, seeing the closest average margin of victory.
  • Nice to see UFEs at an all-time low.


Now to the really fun stuff, touching on some of the day’s best performances:

  • Both Nog (Game 1) and  I (Game 10) posted Net Ratings of 12, challenging but coming up short of the overall record (Kambour, 13)
  • Nog (Game 7) tied the Events record of 25, set by Tufts back in 2015
  • Conrad, validating his reputation as a stingy defender during his first post-grad competition, posted two monster games of 15 saves (on 23 opps) and 13 saves
  • Ezra, Bullets, Conrad, Nog and me each posted one 0 UFE game. No Flat Hats this year, though.
  • Nog (Game 1, 30.0%) came up 0.1% short of the shooting % record (Vrach, 30.1%), but became the 2nd person to enter the 30% club
  • Marver, Ezra, Nog and Bullets combined for the fewest total UFEs (2) we’ve seen from a foursome, ever. Nice work!

Of course, the most eye-catching record that was broken isn’t on that list above. Instead, it was Nog, surpassing Marver’s 7-sink paragon by dropping in 8 of his own…. across a stretch of just 23 shot attempts. Here they are, in all their glory:


Truly incredible.

Also, truly rough on RJL, who – as Ezra hilariously put it on the day itself – clearly won the award for “Roughest Tournament.” In that game alone, he got sunk on 10 times; over the course of the day, 23 times. Lambie, his partner in that game against Nog, was the next closest at 18. And to make matters worse, RJL went 0-for-16 on his rebuttal attempts!

And the Winner is….

While “Best Game by a Player” has been an award to debate in past years, Nog’s first game this year owned that category within the first hour – posting an 8 sink, 30.1% shooting, +12 Net, 21 Event, 62.5% Save performance. Absurd.

One might think that game alone propelled him to a second MVP trophy, and you’d be right to think his resume overall is strong; he placed:

  • 1st in Shooting % (22.3%)
  • 1st in Events (15.8/g)
  • 1st in Sinks (3.5/g)
  • T-1st in UFEs (1.3/g)
  • 2nd in Shot Value (.106)
  • 3rd in both Save Rate (50%) and Net Rating (7.8/g)

He also finished tied with the second best record at 3-1. I’d argue, though, that the award has to go to yours truly – my case just has a slight edge.

  • Only 4-0 record
  • 1st in Shot Value (.119)
  • 1st in Save % (63.4%)
  • T-1st in Net Rating (9.3/g)
  • T-1st in UFEs (1.3/g)
  • T-1st in Points (10.5/g)
  • 2nd in Shooting % (19.3%)
  • T-2nd in Sinks (2.3/g)



What I’m excited for this year is to look at another level of performance, one that we haven’t seen before. Previously, we’ve examined the Player, and the Team; on a per-game, a tournament-long, and even an overall basis. Yet we’ve never looked at the best individual plays of the day. Now, it’s finally time.

Behold: your Nogfest 2017 Highlights:


Many of these rallies make it clear: we’re good at this. We didn’t get lucky, happening to stumble into one rare throw-save. We’ve played before. We know what we’re doing on the table.

In fact, there’s one particular type of sequence that can prove one’s credibility, in and of itself. It’s when you post a solid save, and then sink on your very next shot:


Whether it’s done by one player or by a team back-to-back, it really serves as a definitive tell that you’ve been there before. It’s a microcosm for skill; a professional’s point. Because of that, I felt we needed a term for it, so after consulting my fellow Pong Pod hosts, we’ve come up with one: Rampant Lion.

All of our plays can’t be refrigerator material, though; RJL’s not the only one who has a rough go of it from time to time. In truth, there were more than a handful of mishaps that occurred throughout the day.

Here are the best of them, in your Pong version of the Not-Top-10:



How can you avoid being on that video, or at the bottom of the performance chart at the top? Frankly, it’s hard to advise. Given its aforementioned simplicity, pong doesn’t really lend itself all that much to strategy. There are minor tactics to consider or techniques to follow (Conrad’s “pro tip”? Keep you paddle up!), and we’ll plan to talk through those possibilities on a future podcast. At it’s core, though, the margin of victory really boils down to this: Pong is a make-or-miss game.

At least, in a vacuum. There does seem to be one way you could stack the odds in your favor:

<“Away” teams play with their backs to the garage door>

The disparity, looking just at this year, seems to indicate a clear advantage for the Home team, although the difference gets less marked over time. And with either time-frame, the data is not entirely conclusive – for instance, the Home side gets a bump at this sample size due to the sheer fact that Nog plays most of his games over there.

Still: there does seem to be a difference; you do have to change your shot to accommodate that ceiling. I think there’s something to that. That’s probably the cause of the 0.9 point edge I found for the Home side (average sc0re: 17.5) vs. the Away (16.6).


Gentleman’s Rules

**Apologies in advance: these next two sections are a bit esoteric**

Despite how long we’ve all been playing (my 10-year Pong-versary is this fall), every once in awhile we stumble across rules that need clarifying – they’re complicated, after all. One particular edict that tends to muddle things is the difference between two confusingly-similar terms:

  1. Save-is-a-Save
    1. A non-returned Save counts as a point, no matter what
  2. Save-of-a-Save
    1. If the return of a Save is so difficult to manage that the Saving team is subsequently unable to continue the rally, the point resets in a Doobie

The latter gets screwed up more often than the former, so readers, please keep that in mind for all future competitions.

The reason that “Save-of-a-Save” – as a regulation – is the correct way for us to rule on said action, is that technically, the Return of a Save is just a regular shot attempt. We even count it statistically as such. And so, when said shot attempt is basically a line drive instead of a standard lobbed attempt, it’s breaking the game’s core philosophy. Literally, it infringes on the first rule of how to play: Pong is a gentleman’s game.

That core philosophy is why, I would argue, this following point should have been scored differently – which, incorrectly (IMO) concluded that Vool had posted a UFE.


This exchange prompted us to go from Up 15-12, to Down 15-16 – a stretch which included two more UFEs. We got shook. And yes, Vool didn’t adhere to a “play until you hear the whistle” mindset, and that was a significant cause of his inability to successfully continue the rally. But:

  1. Marver’s throw-save – not something our team did – spilled our team’s beer.
  2. I switched play to the other side of the table, and can be heard urging Vool earlier to do the same, specifically because the original cup was unplayable.
  3. Vool’s shot was clearly affected by beer which had spilled on my side of the table. This, in addition to his lapse of attention, also influenced the UFE.

The Great “Called-Doobie” Debate

Arguing over points… I would argue… is generally ungentlemanly, and so I usually try and steer clear of that practice; it doesn’t feel gentlemanly to log a point your opponent doesn’t believe you earned. That, however, brings me to my favorite part of this year’s tournament – other than donning my Sorcerer’s Apprentice hat and completing the 4-0 sweep.

In fact, this next debate puts to shame any others I’ve seen in the past, considering:

  1. It focused on the Match-Winning Point of a 21-19 game
  2. It involved all 10 of the day’s participants, arguing passionately
  3. In classic 12 Angry Men fashion, all members of the jury – lest one brave, handsome, morally-steadfast soul – started on one side, and ultimately landed on the other

The debate? Whether someone calling Doobie – verbally – trumps the actions on the table. The answer? No way in hell.

To recap: Vool and I are up 20-19 against Marver and RJL. Mid-rally, the ball hits Vool and my Doobie rack, and since it’s our Serve, I call out “Doobie!,” advising Vool to catch it. Instead, he shoots it back, but doesn’t strike the ball cleanly. The ball ends up bouncing on the divider, before continuing on its trajectory and hitting the cup – ricocheting off the rim without being saved.

Don’t take my word for it, though. Let’s go to the tape:


The first thing that jumps out is, damn that’s a shitty way to lose. Doobie call or not, a poorly-struck ball bouncing off the net and into the side of cup is flukey as hell. Rough way for Dem Boyz to go down. What matters more than the taste in your mouth, though, is that the ball MUST override whatever verbal “call” is made.

This certainly applies when the opposition makes said call. For instance, had Marver called Doobie, it wouldn’t have mattered. In fact, one of my biggest pet peeves is when a player continues the rally off the Doobie rail, and the opposing player catches it to end play anyway. The opposing player does not have that right.

Moreover, even if Vool had called Doobie on his own shot, he’s still allowed to continue the rally. Maybe he’s just trying to avoid a future argument, flagging for everyone that it hit the rail; that way there’s no debate, should his subsequent shot sail off the table.

Really, calling Doobie is analogous to the referees throwing a flag on the defense for jumping offside, but letting the play continue – giving the likes of Aaron Rodgers a “free play.” When that ball hits the Doobie rail but leaves you a clean bounce, you should 100% shoot it with confidence. There are no repercussions for hitting it off the table; only benefits should you hit the cup. And to a man, every single player is more accurate mid-rally than he is Serving instead.

Let it fly, gentlemen.

For the Love of the Game

The main topic Ezra, Bullets and I discussed on our first Pong Pod is an interesting one: why do we love this game so much? We’ve each spent thousands of hours playing it, talking about it, analyzing it, etc. What’s really driving that obsession? Why are you still reading this, 2700 words deep? Isn’t is just ping pong with beer?

While it’s hard to define exactly, it’s easy to see that it goes beyond mere gameplay. That’s insanely fun, of course, but there’s also this entire culture we’ve built around the game itself. There’s nostalgia involved, hearkening back to our BBDAS roots. It’s competitive; bragging rights are at stake. There’s terminology, from Doobie to Flying Rho to 3-9 Low. The data analysis angle breeds intellectual curiosity.

All these little components create this overall titan of an activity, one that enables us to connect with our friends on a human level. That’s why we love the game. And that’s why, far more than the videos I shared before, these are the true highlights of the tournament:



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